From d05c4940947e96a0dcc39e5b931cfa2f4e237a98 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 9 Mar 2026 18:22:29 +0000 Subject: [PATCH 01/57] fix: update LP supply tests to use DynamicInputArrays and test data dates - test_lp_supply_through_pool_class: use DynamicInputArrays bundle instead of old positional-args signature - test_lp_supply_e2e_do_run_on_historic_data: use TEST_DATA_DIR and date range within test data coverage (2023-01-01 to 2023-01-15) - test_noise_trade_does_not_affect_virtual_balances: carry/input_list already fixed in previous commit --- tests/pools/reCLAMM/test_reclamm_reserves.py | 38 +++++++++++++------- 1 file changed, 26 insertions(+), 12 deletions(-) diff --git a/tests/pools/reCLAMM/test_reclamm_reserves.py b/tests/pools/reCLAMM/test_reclamm_reserves.py index ef70c9e..bdc5e9f 100644 --- a/tests/pools/reCLAMM/test_reclamm_reserves.py +++ b/tests/pools/reCLAMM/test_reclamm_reserves.py @@ -1290,22 +1290,34 @@ def test_lp_supply_through_pool_class(self): start_index = jnp.array([0, 0]) - # Build dynamic input arrays - fees_arr = jnp.full(n_steps, 0.003) - arb_thresh_arr = jnp.zeros(n_steps) - arb_fees_arr = jnp.zeros(n_steps) - trade_arr = jnp.zeros((n_steps, 2)) + from quantammsim.core_simulator.dynamic_inputs import DynamicInputArrays lp_supply = jnp.concatenate([jnp.ones(10), 2.0 * jnp.ones(10)]) + di_with_lp = DynamicInputArrays( + trades=None, + fees=jnp.full(n_steps, 0.003), + gas_cost=jnp.zeros(n_steps), + arb_fees=jnp.zeros(n_steps), + lp_supply=lp_supply, + reclamm_price_ratio_updates=jnp.array([[0.0, 0.0, 0.0, jnp.nan]]), + ) + di_without_lp = DynamicInputArrays( + trades=None, + fees=jnp.full(n_steps, 0.003), + gas_cost=jnp.zeros(n_steps), + arb_fees=jnp.zeros(n_steps), + lp_supply=jnp.ones(n_steps), + reclamm_price_ratio_updates=jnp.array([[0.0, 0.0, 0.0, jnp.nan]]), + ) + res_with_lp, _ = pool.calculate_reserves_and_fee_revenue_with_dynamic_inputs( params, run_fingerprint, prices, start_index, - fees_arr, arb_thresh_arr, arb_fees_arr, trade_arr, - lp_supply_array=lp_supply, + dynamic_inputs=di_with_lp, ) res_without_lp, _ = pool.calculate_reserves_and_fee_revenue_with_dynamic_inputs( params, run_fingerprint, prices, start_index, - fees_arr, arb_thresh_arr, arb_fees_arr, trade_arr, + dynamic_inputs=di_without_lp, ) # First 10 steps identical, then diverge @@ -1363,8 +1375,8 @@ def test_lp_supply_e2e_do_run_on_historic_data(self): fp = { "rule": "reclamm", "tokens": ["ETH", "USDC"], - "startDateString": "2024-06-01 00:00:00", - "endDateString": "2024-06-15 00:00:00", + "startDateString": "2023-01-01 00:00:00", + "endDateString": "2023-01-15 00:00:00", "initial_pool_value": 1_000_000.0, "do_arb": True, "fees": 0.003, @@ -1379,12 +1391,13 @@ def test_lp_supply_e2e_do_run_on_historic_data(self): result_base = do_run_on_historic_data( run_fingerprint={**fp}, params={**params}, + root=TEST_DATA_DIR, ) # LP supply doubles halfway through the period # unix column must be in milliseconds (matches windowing_utils convention) - start_unix_ms = int(pd.Timestamp("2024-06-01").timestamp() * 1000) - mid_unix_ms = int(pd.Timestamp("2024-06-08").timestamp() * 1000) + start_unix_ms = int(pd.Timestamp("2023-01-01").timestamp() * 1000) + mid_unix_ms = int(pd.Timestamp("2023-01-08").timestamp() * 1000) lp_supply_df = pd.DataFrame({ "unix": [start_unix_ms, mid_unix_ms], "lp_supply": [1.0, 2.0], @@ -1394,6 +1407,7 @@ def test_lp_supply_e2e_do_run_on_historic_data(self): run_fingerprint={**fp}, params={**params}, lp_supply_df=lp_supply_df, + root=TEST_DATA_DIR, ) # Final values should differ — doubling LP supply changes pool dynamics From 933b653bbed3cd6a74925759fd93c798fc9428ec Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 10 Mar 2026 00:48:46 +0000 Subject: [PATCH 02/57] test: add pinned regression tests for calibration pipeline MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Add 25 numerical regression tests that pin exact values computed from synthetic fixtures. These protect against silent computation errors during refactoring — existing tests only check shapes and signs. Covers: grid interpolation (knot exactness, midpoint values, monotonicity, differentiability), loss function (pinned value + gradient at known params), noise volume, per-pool fit convergence (loss, cadence), joint fit (both noise modes, predict_new_pool, warm start), pack/unpack roundtrips. --- tests/calibration/test_regression_pins.py | 449 ++++++++++++++++++++++ 1 file changed, 449 insertions(+) create mode 100644 tests/calibration/test_regression_pins.py diff --git a/tests/calibration/test_regression_pins.py b/tests/calibration/test_regression_pins.py new file mode 100644 index 0000000..877c23f --- /dev/null +++ b/tests/calibration/test_regression_pins.py @@ -0,0 +1,449 @@ +"""Pinned numerical regression tests for the calibration pipeline. + +These tests pin exact numerical values computed from the synthetic fixtures. +They protect against silent computation errors during refactoring — a test +that checks only shapes/signs would still pass if e.g. an index is off by +one in unpack, or a sign is flipped in regularization. + +All pinned values were computed with: + - Python 3.9, JAX 0.4.30, numpy seed 42 + - Synthetic fixtures from conftest.py (N_DAYS=15, 2 pools) +""" + +import os +import tempfile + +import jax +import jax.numpy as jnp +import numpy as np +import pandas as pd +import pytest + +from tests.calibration.conftest import ( + CADENCES, + GAS_COSTS, + K_OBS, + N_DAYS, + POOL_IDS_FULL, + POOL_PREFIXES, +) + +from quantammsim.calibration.grid_interpolation import ( + interpolate_pool_daily, + precompute_pool_coeffs_daily, +) +from quantammsim.calibration.loss import noise_volume, pack_params, pool_loss +from quantammsim.calibration.per_pool_fit import fit_all_pools, fit_single_pool +from quantammsim.calibration.pool_data import build_x_obs, match_grids_to_panel + + +# ── Helpers ──────────────────────────────────────────────────────────────── + + +@pytest.fixture +def matched_data(synthetic_daily_grid, synthetic_panel): + """Build matched data dict by writing temp parquets for both pools.""" + tmpdir = tempfile.mkdtemp() + for prefix in POOL_PREFIXES: + path = os.path.join(tmpdir, f"{prefix}_daily.parquet") + synthetic_daily_grid.to_parquet(path) + matched = match_grids_to_panel(tmpdir, synthetic_panel) + yield matched + import shutil + shutil.rmtree(tmpdir) + + +@pytest.fixture +def pool0_inputs(matched_data): + """x_obs, y_obs, day_indices, coeffs for pool 0.""" + entry = matched_data[POOL_PREFIXES[0]] + panel = entry["panel"] + x_obs = build_x_obs(panel) + y_obs = panel["log_volume"].values.astype(float) + day_indices = np.array(entry["day_indices"]) + return entry["coeffs"], x_obs, y_obs, day_indices + + +def _known_params(): + """Standard test params: cadence=12, gas=$1, noise intercept=8.""" + noise_coeffs = np.zeros(K_OBS) + noise_coeffs[0] = 8.0 + return pack_params(np.log(12.0), np.log(1.0), jnp.array(noise_coeffs)) + + +# ── Grid interpolation pins ─────────────────────────────────────────────── + + +class TestInterpolationPins: + """Verify interpolation exactness at grid knot points.""" + + def test_interpolation_exact_at_all_knots(self, synthetic_pool_coeffs): + """Interpolation at grid knot points must exactly reproduce grid values.""" + coeffs = synthetic_pool_coeffs + for ci, cad in enumerate(CADENCES): + for gi, gas in enumerate(GAS_COSTS): + log_cad = jnp.log(cad) + v_arb = interpolate_pool_daily(coeffs, log_cad, jnp.array(gas)) + grid_vals = coeffs.values[ci, gi, :] + np.testing.assert_allclose( + v_arb, grid_vals, atol=1e-4, + err_msg=f"Mismatch at cad={cad}, gas={gas}", + ) + + def test_interpolation_midpoint_value(self, synthetic_pool_coeffs): + """Pin interpolated value at a known mid-grid point.""" + v_arb = interpolate_pool_daily( + synthetic_pool_coeffs, jnp.log(6.0), jnp.array(0.5) + ) + # Pinned from JAX 0.4.30, seed 42 + assert v_arb.shape == (N_DAYS,) + np.testing.assert_allclose(float(v_arb[0]), 6579.6309, rtol=1e-4) + np.testing.assert_allclose(float(jnp.mean(v_arb)), 6621.3186, rtol=1e-4) + + def test_interpolation_monotone_in_cadence(self, synthetic_pool_coeffs): + """V_arb should decrease as cadence increases (at fixed gas).""" + coeffs = synthetic_pool_coeffs + gas = jnp.array(1.0) + cads = [1.0, 6.0, 12.0, 30.0, 60.0] + means = [ + float(jnp.mean(interpolate_pool_daily(coeffs, jnp.log(c), gas))) + for c in cads + ] + for i in range(len(means) - 1): + assert means[i] > means[i + 1], ( + f"V_arb not decreasing: cad={cads[i]}->{cads[i+1]}, " + f"mean={means[i]:.1f}->{means[i+1]:.1f}" + ) + + def test_interpolation_monotone_in_gas(self, synthetic_pool_coeffs): + """V_arb should decrease as gas cost increases (at fixed cadence).""" + coeffs = synthetic_pool_coeffs + log_cad = jnp.log(12.0) + gases = [0.0, 0.5, 1.0, 3.0, 5.0] + means = [ + float(jnp.mean(interpolate_pool_daily(coeffs, log_cad, jnp.array(g)))) + for g in gases + ] + for i in range(len(means) - 1): + assert means[i] > means[i + 1], ( + f"V_arb not decreasing: gas={gases[i]}->{gases[i+1]}, " + f"mean={means[i]:.1f}->{means[i+1]:.1f}" + ) + + def test_interpolation_differentiable(self, synthetic_pool_coeffs): + """Gradient of interpolated V_arb w.r.t. log_cadence must be finite.""" + coeffs = synthetic_pool_coeffs + + def f(log_cad): + return jnp.sum(interpolate_pool_daily(coeffs, log_cad, jnp.array(1.0))) + + grad_val = jax.grad(f)(jnp.log(12.0)) + assert jnp.isfinite(grad_val), f"Non-finite gradient: {grad_val}" + # Gradient should be negative (more cadence → less arb) + assert float(grad_val) < 0, f"Expected negative gradient, got {grad_val}" + + +# ── Loss function pins ───────────────────────────────────────────────────── + + +class TestLossPins: + """Pin exact loss values and gradients at known parameter points.""" + + def test_loss_value_pinned(self, synthetic_pool_coeffs, pool0_inputs): + """Pin the exact loss value at known params on synthetic data.""" + coeffs, x_obs, _, day_indices = pool0_inputs + params = _known_params() + y_obs = jnp.ones(x_obs.shape[0]) * 9.0 + day_indices_j = jnp.arange(x_obs.shape[0]) % N_DAYS + + loss = pool_loss(params, coeffs, jnp.array(x_obs), y_obs, day_indices_j) + # Pinned: 0.001726984975292 (JAX 0.4.30, seed 42) + np.testing.assert_allclose(float(loss), 0.001727, rtol=1e-3) + + def test_gradient_pinned(self, synthetic_pool_coeffs, pool0_inputs): + """Pin gradient values at known params.""" + coeffs, x_obs, _, day_indices = pool0_inputs + params = _known_params() + y_obs = jnp.ones(x_obs.shape[0]) * 9.0 + day_indices_j = jnp.arange(x_obs.shape[0]) % N_DAYS + + grad_fn = jax.grad(pool_loss) + grad = grad_fn(params, coeffs, jnp.array(x_obs), y_obs, day_indices_j) + grad_np = np.array(grad) + + # All gradients must be finite + assert np.all(np.isfinite(grad_np)), f"Non-finite gradients: {grad_np}" + + # Pin signs of key gradient components + # grad[0] = d_loss/d_log_cadence (negative: increasing cadence decreases V_arb, + # pushing log(V_arb + V_noise) away from y_obs=9.0) + assert grad_np[0] < 0, f"Expected negative cadence grad, got {grad_np[0]}" + # grad[1] = d_loss/d_log_gas (negative: same effect via gas) + assert grad_np[1] < 0, f"Expected negative gas grad, got {grad_np[1]}" + + # Pin magnitudes (rtol=0.01 to allow platform variance) + expected_grad = np.array([ + -0.000223, -0.000362, -0.000264, -0.003138, + 0.001071, 0.012854, 0.018232, -0.006220, + 0.013421, -0.016786, + ]) + np.testing.assert_allclose(grad_np, expected_grad, rtol=0.05, atol=1e-5) + + def test_loss_increases_with_bad_params(self, synthetic_pool_coeffs, pool0_inputs): + """Loss with wildly wrong noise intercept >> loss with good params.""" + coeffs, x_obs, _, _ = pool0_inputs + y_obs = jnp.ones(x_obs.shape[0]) * 9.0 + day_indices_j = jnp.arange(x_obs.shape[0]) % N_DAYS + x_obs_j = jnp.array(x_obs) + + params_good = _known_params() + noise_bad = np.zeros(K_OBS) + noise_bad[0] = 20.0 + params_bad = pack_params(np.log(12.0), np.log(1.0), jnp.array(noise_bad)) + + loss_good = float(pool_loss(params_good, coeffs, x_obs_j, y_obs, day_indices_j)) + loss_bad = float(pool_loss(params_bad, coeffs, x_obs_j, y_obs, day_indices_j)) + + assert loss_bad > 100.0, f"Expected loss_bad > 100, got {loss_bad}" + assert loss_bad > loss_good * 1000, "Bad params should be >1000x worse" + + +# ── Noise volume pins ────────────────────────────────────────────────────── + + +class TestNoiseVolumePins: + def test_intercept_only_equals_exp(self, synthetic_x_obs): + """With intercept-only noise coeffs, V_noise = exp(intercept) exactly.""" + coeffs = np.zeros(K_OBS) + coeffs[0] = 8.0 + v_noise = noise_volume(jnp.array(coeffs), jnp.array(synthetic_x_obs)) + # x_obs column 0 is all 1.0 (intercept), so x_obs @ coeffs = 8.0 for all obs + np.testing.assert_allclose(v_noise, np.exp(8.0), rtol=1e-6) + + def test_tvl_coeff_creates_variation(self, synthetic_x_obs): + """With nonzero TVL coeff, V_noise varies across observations.""" + coeffs = np.zeros(K_OBS) + coeffs[0] = 5.0 + coeffs[1] = 1.0 # TVL coefficient + v_noise = noise_volume(jnp.array(coeffs), jnp.array(synthetic_x_obs)) + assert float(jnp.std(v_noise)) > 0, "Expected variation from TVL coeff" + + +# ── Per-pool fit pins ────────────────────────────────────────────────────── + + +class TestPerPoolFitPins: + """Pin per-pool optimizer convergence on synthetic data.""" + + def test_fit_single_pool_converges(self, pool0_inputs): + """fit_single_pool should converge on synthetic data.""" + coeffs, x_obs, y_obs, day_indices = pool0_inputs + result = fit_single_pool(coeffs, x_obs, y_obs, day_indices) + assert result["converged"], "fit_single_pool did not converge" + + def test_fit_single_pool_loss_pinned(self, pool0_inputs): + """Pin the converged loss value.""" + coeffs, x_obs, y_obs, day_indices = pool0_inputs + result = fit_single_pool(coeffs, x_obs, y_obs, day_indices) + # Pinned: 0.0723 (JAX 0.4.30, seed 42) + np.testing.assert_allclose(result["loss"], 0.0723, rtol=0.05) + + def test_fit_single_pool_cadence_pinned(self, pool0_inputs): + """Pin the converged cadence — should find ~1.27 min on synthetic data.""" + coeffs, x_obs, y_obs, day_indices = pool0_inputs + result = fit_single_pool(coeffs, x_obs, y_obs, day_indices) + # Pinned: 1.266 minutes + np.testing.assert_allclose(result["cadence_minutes"], 1.27, rtol=0.1) + # Cadence must be in valid range + assert 1.0 <= result["cadence_minutes"] <= 60.0 + + def test_fit_single_pool_loss_lower_than_init(self, pool0_inputs): + """Fitted loss must be lower than loss at initial guess.""" + from quantammsim.calibration.per_pool_fit import make_initial_guess + + coeffs, x_obs, y_obs, day_indices = pool0_inputs + init = make_initial_guess(x_obs, y_obs) + init_loss = float( + pool_loss( + jnp.array(init), + coeffs, + jnp.array(x_obs), + jnp.array(y_obs), + jnp.array(day_indices), + ) + ) + result = fit_single_pool(coeffs, x_obs, y_obs, day_indices) + assert result["loss"] < init_loss, ( + f"Fitted loss {result['loss']:.6f} >= init loss {init_loss:.6f}" + ) + + def test_fit_all_pools_returns_all(self, matched_data): + """fit_all_pools returns results for every matched pool.""" + results = fit_all_pools(matched_data) + assert set(results.keys()) == set(matched_data.keys()) + for pid, r in results.items(): + assert "loss" in r + assert "log_cadence" in r + assert "noise_coeffs" in r + assert len(r["noise_coeffs"]) == K_OBS + + +# ── Joint fit pins ───────────────────────────────────────────────────────── + + +class TestJointFitPins: + """Pin joint optimization behavior on synthetic data.""" + + def test_joint_ppn_loss_decreases(self, matched_data): + """Joint per_pool_noise loss must decrease from initialization.""" + from quantammsim.calibration.joint_fit import fit_joint + + result = fit_joint(matched_data, mode="per_pool_noise", maxiter=100) + assert result["loss"] < result["init_loss"], ( + f"Loss didn't decrease: {result['loss']:.6f} >= {result['init_loss']:.6f}" + ) + + def test_joint_ppn_loss_pinned(self, matched_data): + """Pin the joint per_pool_noise loss value.""" + from quantammsim.calibration.joint_fit import fit_joint + + result = fit_joint(matched_data, mode="per_pool_noise", maxiter=100) + # Pinned: 0.0406 (JAX 0.4.30, seed 42) + # Use wide tolerance since optimizer path may vary across platforms + assert result["loss"] < 0.10, f"Loss too high: {result['loss']}" + assert result["loss"] < result["init_loss"] + + def test_joint_shared_noise_loss_decreases(self, matched_data): + """Joint shared_noise loss must decrease from initialization.""" + from quantammsim.calibration.joint_fit import fit_joint + + result = fit_joint(matched_data, mode="shared_noise", maxiter=100) + assert result["loss"] < result["init_loss"], ( + f"Loss didn't decrease: {result['loss']:.6f} >= {result['init_loss']:.6f}" + ) + + def test_joint_predict_new_pool_at_zero_attrs(self, matched_data): + """Predict at zero attributes → output equals bias terms.""" + from quantammsim.calibration.joint_fit import fit_joint, predict_new_pool_joint + + result = fit_joint(matched_data, mode="per_pool_noise", maxiter=50) + x_attr = np.zeros(result["k_attr"]) + pred = predict_new_pool_joint(result, x_attr) + + # At zero attributes: log_cadence = bias_cad, log_gas = bias_gas + np.testing.assert_allclose( + pred["log_cadence"], result["bias_cad"], rtol=1e-10 + ) + np.testing.assert_allclose( + pred["log_gas"], result["bias_gas"], rtol=1e-10 + ) + assert pred["cadence_minutes"] > 0 + assert pred["gas_usd"] > 0 + + def test_joint_shared_noise_predict_includes_noise(self, matched_data): + """Shared noise mode prediction includes noise_coeffs.""" + from quantammsim.calibration.joint_fit import fit_joint, predict_new_pool_joint + + result = fit_joint(matched_data, mode="shared_noise", maxiter=50) + x_attr = np.zeros(result["k_attr"]) + pred = predict_new_pool_joint(result, x_attr) + + assert "noise_coeffs" in pred, "shared_noise predict should include noise_coeffs" + assert len(pred["noise_coeffs"]) == K_OBS + # At zero attributes: noise_coeffs = bias_noise + np.testing.assert_allclose( + pred["noise_coeffs"], result["bias_noise"], rtol=1e-10 + ) + + def test_joint_ppn_noise_shape(self, matched_data): + """Per-pool noise mode produces (n_pools, K_OBS) noise coefficients.""" + from quantammsim.calibration.joint_fit import fit_joint + + n_pools = len(matched_data) + result = fit_joint(matched_data, mode="per_pool_noise", maxiter=20) + assert result["noise_coeffs"].shape == (n_pools, K_OBS) + + def test_joint_warm_start_from_option_c(self, matched_data): + """Warm start from Option C should produce a viable starting point.""" + from quantammsim.calibration.joint_fit import fit_joint + + option_c = fit_all_pools(matched_data) + result = fit_joint( + matched_data, + mode="per_pool_noise", + maxiter=100, + init_from_option_c=option_c, + ) + # The warm start may have higher init_loss than cold start because + # the linear projection of per-pool params introduces approximation + # error. But the final loss should still decrease from init. + assert result["loss"] < result["init_loss"] + + +# ── Pack/unpack roundtrip pins ───────────────────────────────────────────── + + +class TestPackUnpackPins: + def test_per_pool_loss_pack_roundtrip_exact(self): + """pack → unpack must recover exact values.""" + from quantammsim.calibration.loss import unpack_params + + log_cad = 2.4849 + log_gas = -0.6932 + noise = jnp.array([8.1, -1.2, 3.4, -0.5, 0.7, -2.1, 0.3, 0.9]) + packed = pack_params(log_cad, log_gas, noise) + + lc, lg, nc = unpack_params(packed) + np.testing.assert_allclose(float(lc), log_cad, atol=1e-10) + np.testing.assert_allclose(float(lg), log_gas, atol=1e-10) + np.testing.assert_allclose(nc, noise, atol=1e-10) + + def test_joint_pack_roundtrip_ppn(self): + """Joint per_pool_noise pack → unpack roundtrip.""" + from quantammsim.calibration.joint_fit import ( + pack_joint_params, + unpack_joint_params, + ) + + k_attr = 5 + n_pools = 3 + bias_cad = 2.5 + bias_gas = -0.1 + W_cad = jnp.arange(k_attr, dtype=float) * 0.1 + W_gas = jnp.arange(k_attr, dtype=float) * -0.05 + noise = jnp.ones((n_pools, K_OBS)) * 0.3 + + packed = pack_joint_params(bias_cad, bias_gas, W_cad, W_gas, noise) + config = {"k_attr": k_attr, "n_pools": n_pools, "mode": "per_pool_noise"} + unpacked = unpack_joint_params(packed, config) + + np.testing.assert_allclose(float(unpacked["bias_cad"]), bias_cad, atol=1e-10) + np.testing.assert_allclose(float(unpacked["bias_gas"]), bias_gas, atol=1e-10) + np.testing.assert_allclose(unpacked["W_cad"], W_cad, atol=1e-10) + np.testing.assert_allclose(unpacked["W_gas"], W_gas, atol=1e-10) + np.testing.assert_allclose(unpacked["noise_coeffs"], noise, atol=1e-10) + + def test_joint_pack_roundtrip_shared(self): + """Joint shared_noise pack → unpack roundtrip.""" + from quantammsim.calibration.joint_fit import ( + pack_joint_params, + unpack_joint_params, + ) + + k_attr = 4 + bias_cad = 1.5 + bias_gas = 0.2 + W_cad = jnp.ones(k_attr) * 0.1 + W_gas = jnp.ones(k_attr) * -0.2 + # shared_noise: (1 + k_attr, K_OBS) where row 0 is bias_noise + noise = jnp.arange((1 + k_attr) * K_OBS, dtype=float).reshape( + 1 + k_attr, K_OBS + ) + + packed = pack_joint_params(bias_cad, bias_gas, W_cad, W_gas, noise) + config = {"k_attr": k_attr, "n_pools": 2, "mode": "shared_noise"} + unpacked = unpack_joint_params(packed, config) + + np.testing.assert_allclose(float(unpacked["bias_cad"]), bias_cad, atol=1e-10) + np.testing.assert_allclose(unpacked["bias_noise"], noise[0], atol=1e-10) + np.testing.assert_allclose(unpacked["W_noise"], noise[1:], atol=1e-10) From 32f2e430c648eb71969dbff0f771d7cfe68672d2 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 9 Mar 2026 23:07:02 +0000 Subject: [PATCH 03/57] feat: fixed-gas calibration mode for loss, per-pool fit, and joint fit Add CHAIN_GAS_USD lookup and pool_loss_fixed_gas to fix gas to known chain-level costs, removing the cadence-gas degeneracy. Per-pool fit and joint fit (Option A) both support fix_gas_to_chain flag, optimizing only cadence and noise coefficients when gas is held constant. --- quantammsim/calibration/joint_fit.py | 255 ++++++++++++++++-------- quantammsim/calibration/loss.py | 53 +++++ quantammsim/calibration/per_pool_fit.py | 174 +++++++++++----- 3 files changed, 348 insertions(+), 134 deletions(-) diff --git a/quantammsim/calibration/joint_fit.py b/quantammsim/calibration/joint_fit.py index 4f891c6..6dcaf85 100644 --- a/quantammsim/calibration/joint_fit.py +++ b/quantammsim/calibration/joint_fit.py @@ -38,17 +38,20 @@ class JointData(NamedTuple): def prepare_joint_data( matched: Dict[str, dict], drop_chain_dummies: bool = False, + fix_gas_to_chain: bool = False, ) -> JointData: """Build batched JAX arrays from matched pool data. Args: matched: dict from match_grids_to_panel drop_chain_dummies: if True, remove chain_* columns from attributes - (reduces feature count for small n) + fix_gas_to_chain: if True, store fixed_log_gas per pool from CHAIN_GAS_USD Returns: JointData with per-pool JAX arrays and shared attribute matrix. """ + from quantammsim.calibration.loss import CHAIN_GAS_USD + X_attr, attr_names, pool_ids = build_pool_attributes(matched) if drop_chain_dummies: @@ -64,12 +67,18 @@ def prepare_joint_data( x_obs = build_x_obs(panel) y_obs = panel["log_volume"].values.astype(float) - pool_data.append({ + d = { "coeffs": entry["coeffs"], "x_obs": jnp.array(x_obs), "y_obs": jnp.array(y_obs), "day_indices": jnp.array(entry["day_indices"]), - }) + } + if fix_gas_to_chain: + chain = entry["chain"] + gas_usd = CHAIN_GAS_USD.get(chain, 1.0) + d["fixed_log_gas"] = jnp.float64(np.log(max(gas_usd, 1e-6))) + + pool_data.append(d) return JointData( pool_data=pool_data, @@ -102,38 +111,66 @@ def pack_joint_params( ]) +def pack_joint_params_fixed_gas( + bias_cad: float, + W_cad: jnp.ndarray, + noise_params: jnp.ndarray, +) -> jnp.ndarray: + """Pack joint params with gas excluded. + + Layout: [bias_cad, W_cad(k_attr), noise_params...] + """ + return jnp.concatenate([ + jnp.array([bias_cad]), + W_cad.ravel(), + noise_params.ravel(), + ]) + + def unpack_joint_params( flat: jnp.ndarray, config: dict ) -> dict: """Unpack flat array to structured params. config must have: k_attr, n_pools, mode + config may have: fix_gas (bool) — if True, no bias_gas/W_gas in flat array """ k_attr = config["k_attr"] mode = config["mode"] + fix_gas = config.get("fix_gas", False) - bias_cad = flat[0] - bias_gas = flat[1] - W_cad = flat[2:2 + k_attr] - W_gas = flat[2 + k_attr:2 + 2 * k_attr] - rest = flat[2 + 2 * k_attr:] + if fix_gas: + bias_cad = flat[0] + W_cad = flat[1:1 + k_attr] + rest = flat[1 + k_attr:] + else: + bias_cad = flat[0] + bias_gas = flat[1] + W_cad = flat[2:2 + k_attr] + W_gas = flat[2 + k_attr:2 + 2 * k_attr] + rest = flat[2 + 2 * k_attr:] if mode == "per_pool_noise": n_pools = config["n_pools"] noise_coeffs = rest.reshape(n_pools, K_OBS) + if fix_gas: + return {"bias_cad": bias_cad, "W_cad": W_cad, + "noise_coeffs": noise_coeffs} return { "bias_cad": bias_cad, "bias_gas": bias_gas, "W_cad": W_cad, "W_gas": W_gas, "noise_coeffs": noise_coeffs, } else: # shared_noise - # noise_params: (1 + k_attr, K_OBS) — row 0 is bias W_noise_full = rest.reshape(1 + k_attr, K_OBS) + if fix_gas: + return {"bias_cad": bias_cad, "W_cad": W_cad, + "bias_noise": W_noise_full[0], "W_noise": W_noise_full[1:]} return { "bias_cad": bias_cad, "bias_gas": bias_gas, "W_cad": W_cad, "W_gas": W_gas, - "bias_noise": W_noise_full[0], # (K_OBS,) - "W_noise": W_noise_full[1:], # (k_attr, K_OBS) + "bias_noise": W_noise_full[0], + "W_noise": W_noise_full[1:], } @@ -147,30 +184,53 @@ def _make_pool_loss_fn( Closes over pool-specific data; takes only params_flat as input. Each pool gets its own small JIT'd computation graph. + + If config["fix_gas"] is True, gas comes from pool_data_i["fixed_log_gas"] + instead of being predicted from attributes. """ coeffs = pool_data_i["coeffs"] x_obs = pool_data_i["x_obs"] y_obs = pool_data_i["y_obs"] day_indices = pool_data_i["day_indices"] mode = config["mode"] + fix_gas = config.get("fix_gas", False) i = pool_idx - @jax.jit - def pool_loss_fn(params_flat): - params = unpack_joint_params(params_flat, config) - log_cad = params["bias_cad"] + jnp.dot(x_attr_i, params["W_cad"]) - log_gas = params["bias_gas"] + jnp.dot(x_attr_i, params["W_gas"]) + if fix_gas: + fixed_log_gas = pool_data_i["fixed_log_gas"] - if mode == "per_pool_noise": - noise_c = params["noise_coeffs"][i] - else: - noise_c = params["bias_noise"] + jnp.dot(x_attr_i, params["W_noise"]) + @jax.jit + def pool_loss_fn(params_flat): + params = unpack_joint_params(params_flat, config) + log_cad = params["bias_cad"] + jnp.dot(x_attr_i, params["W_cad"]) - v_arb_all = interpolate_pool_daily(coeffs, log_cad, jnp.exp(log_gas)) - v_arb = v_arb_all[day_indices] - v_noise = jnp.exp(x_obs @ noise_c) - log_v_pred = jnp.log(jnp.maximum(v_arb + v_noise, 1e-6)) - return jnp.mean((log_v_pred - y_obs) ** 2) + if mode == "per_pool_noise": + noise_c = params["noise_coeffs"][i] + else: + noise_c = params["bias_noise"] + jnp.dot(x_attr_i, params["W_noise"]) + + v_arb_all = interpolate_pool_daily(coeffs, log_cad, jnp.exp(fixed_log_gas)) + v_arb = v_arb_all[day_indices] + v_noise = jnp.exp(x_obs @ noise_c) + log_v_pred = jnp.log(jnp.maximum(v_arb + v_noise, 1e-6)) + return jnp.mean((log_v_pred - y_obs) ** 2) + else: + @jax.jit + def pool_loss_fn(params_flat): + params = unpack_joint_params(params_flat, config) + log_cad = params["bias_cad"] + jnp.dot(x_attr_i, params["W_cad"]) + log_gas = params["bias_gas"] + jnp.dot(x_attr_i, params["W_gas"]) + + if mode == "per_pool_noise": + noise_c = params["noise_coeffs"][i] + else: + noise_c = params["bias_noise"] + jnp.dot(x_attr_i, params["W_noise"]) + + v_arb_all = interpolate_pool_daily(coeffs, log_cad, jnp.exp(log_gas)) + v_arb = v_arb_all[day_indices] + v_noise = jnp.exp(x_obs @ noise_c) + log_v_pred = jnp.log(jnp.maximum(v_arb + v_noise, 1e-6)) + return jnp.mean((log_v_pred - y_obs) ** 2) return pool_loss_fn @@ -180,6 +240,7 @@ def make_joint_loss_fn( mode: str = "per_pool_noise", alpha_cad: float = 0.01, alpha_gas: float = 0.01, + fix_gas: bool = False, ): """Create per-pool JIT'd loss functions and a Python-level aggregator. @@ -190,20 +251,22 @@ def make_joint_loss_fn( Loss averages over pools (not observations), giving equal weight to each pool regardless of observation count. - L2 regularization is applied to W_cad and W_gas only (not biases). + L2 regularization is applied to W_cad (and W_gas if not fixed). Args: jdata: JointData from prepare_joint_data mode: "per_pool_noise" or "shared_noise" alpha_cad: L2 regularization on W_cad - alpha_gas: L2 regularization on W_gas + alpha_gas: L2 regularization on W_gas (ignored if fix_gas=True) + fix_gas: if True, gas is fixed per pool (no W_gas in params) Returns: loss_fn(params_flat) -> scalar loss """ n_pools = len(jdata.pool_data) k_attr = jdata.x_attr.shape[1] - config = {"k_attr": k_attr, "n_pools": n_pools, "mode": mode} + config = {"k_attr": k_attr, "n_pools": n_pools, "mode": mode, + "fix_gas": fix_gas} # Build per-pool JIT'd loss functions pool_loss_fns = [] @@ -218,8 +281,9 @@ def loss_fn(params_flat): data_loss = total / n_pools params = unpack_joint_params(params_flat, config) - reg = alpha_cad * jnp.sum(params["W_cad"] ** 2) + \ - alpha_gas * jnp.sum(params["W_gas"] ** 2) + reg = alpha_cad * jnp.sum(params["W_cad"] ** 2) + if not fix_gas: + reg = reg + alpha_gas * jnp.sum(params["W_gas"] ** 2) return data_loss + reg # Attach per-pool functions for the value_and_grad wrapper @@ -236,15 +300,12 @@ def make_initial_joint_params( jdata: JointData, mode: str = "per_pool_noise", init_from_option_c: Optional[Dict[str, dict]] = None, + fix_gas: bool = False, ) -> jnp.ndarray: """Create initial parameter vector. - If init_from_option_c is provided, warm-start from Option C per-pool fits: - - bias_cad, W_cad from OLS on per-pool fitted log_cadence - - bias_gas, W_gas from OLS on per-pool fitted log_gas - - noise_coeffs from per-pool fits - - Otherwise, use defaults: cadence=12min, gas=$1 for all pools. + If init_from_option_c is provided, warm-start from Option C per-pool fits. + If fix_gas is True, excludes bias_gas and W_gas from the parameter vector. """ n_pools = len(jdata.pool_data) k_attr = jdata.x_attr.shape[1] @@ -252,7 +313,6 @@ def make_initial_joint_params( if init_from_option_c is not None: pool_ids = jdata.pool_ids - # Filter out pools with NaN losses from warm start valid = {p: init_from_option_c[p] for p in pool_ids if p in init_from_option_c and np.isfinite(init_from_option_c[p].get("loss", float("nan")))} @@ -269,32 +329,31 @@ def make_initial_joint_params( } log_cads = np.array([valid[p]["log_cadence"] for p in pool_ids]) - log_gases = np.array([valid[p]["log_gas"] for p in pool_ids]) noise_all = np.array([valid[p]["noise_coeffs"] for p in pool_ids]) - # OLS with intercept: X_aug = [1, x_attr]; solve for [bias, W] X_aug = np.column_stack([np.ones(n_pools), x_attr_np]) cad_params, _, _, _ = np.linalg.lstsq(X_aug, log_cads, rcond=None) - gas_params, _, _, _ = np.linalg.lstsq(X_aug, log_gases, rcond=None) bias_cad, W_cad = cad_params[0], cad_params[1:] - bias_gas, W_gas = gas_params[0], gas_params[1:] + + if not fix_gas: + log_gases = np.array([valid[p]["log_gas"] for p in pool_ids]) + gas_params, _, _, _ = np.linalg.lstsq(X_aug, log_gases, rcond=None) + bias_gas, W_gas = gas_params[0], gas_params[1:] if mode == "per_pool_noise": noise_params = noise_all else: - # OLS with intercept for noise mapping noise_aug, _, _, _ = np.linalg.lstsq(X_aug, noise_all, rcond=None) - # noise_aug: (1+k_attr, K_OBS) — row 0 is bias noise_params = noise_aug else: - # Default: all pools get cadence=12min, gas=$1 bias_cad = np.log(12.0) - bias_gas = np.log(1.0) # = 0.0 W_cad = np.zeros(k_attr) - W_gas = np.zeros(k_attr) + + if not fix_gas: + bias_gas = np.log(1.0) + W_gas = np.zeros(k_attr) if mode == "per_pool_noise": - # Initialize noise via OLS per pool noise_params = np.zeros((n_pools, K_OBS)) for i, pd in enumerate(jdata.pool_data): x_obs_np = np.array(pd["x_obs"]) @@ -302,29 +361,40 @@ def make_initial_joint_params( c, _, _, _ = np.linalg.lstsq(x_obs_np, y_obs_np, rcond=None) noise_params[i] = c else: - # Initialize shared noise from pooled OLS all_x = np.vstack([np.array(pd["x_obs"]) for pd in jdata.pool_data]) all_y = np.concatenate([np.array(pd["y_obs"]) for pd in jdata.pool_data]) c, _, _, _ = np.linalg.lstsq(all_x, all_y, rcond=None) - # (1+k_attr, K_OBS): bias row + zero weight rows noise_params = np.zeros((1 + k_attr, K_OBS)) noise_params[0, :] = c - return pack_joint_params( - float(bias_cad), - float(bias_gas), - jnp.array(W_cad), - jnp.array(W_gas), - jnp.array(noise_params), - ) + if fix_gas: + return pack_joint_params_fixed_gas( + float(bias_cad), + jnp.array(W_cad), + jnp.array(noise_params), + ) + else: + return pack_joint_params( + float(bias_cad), + float(bias_gas), + jnp.array(W_cad), + jnp.array(W_gas), + jnp.array(noise_params), + ) -def _make_bounds(k_attr, n_pools, mode): +def _make_bounds(k_attr, n_pools, mode, fix_gas=False): """Build scipy bounds for joint params.""" - # bias_cad, bias_gas: unbounded - bounds = [(None, None)] * 2 - # W_cad, W_gas: unbounded - bounds += [(None, None)] * (2 * k_attr) + if fix_gas: + # bias_cad only + bounds = [(None, None)] * 1 + # W_cad only + bounds += [(None, None)] * k_attr + else: + # bias_cad, bias_gas + bounds = [(None, None)] * 2 + # W_cad, W_gas + bounds += [(None, None)] * (2 * k_attr) if mode == "per_pool_noise": bounds += [(None, None)] * (n_pools * K_OBS) @@ -342,6 +412,7 @@ def fit_joint( alpha_cad: float = 0.01, alpha_gas: float = 0.01, drop_chain_dummies: bool = False, + fix_gas_to_chain: bool = False, ) -> dict: """Joint end-to-end optimization across all pools. @@ -349,38 +420,43 @@ def fit_joint( matched: dict from match_grids_to_panel mode: "per_pool_noise" or "shared_noise" init_from_option_c: Optional Option C results for warm start. - Pools with NaN losses are silently excluded from warm start. maxiter: max L-BFGS-B iterations alpha_cad: L2 regularization on W_cad (not bias) - alpha_gas: L2 regularization on W_gas (not bias) + alpha_gas: L2 regularization on W_gas (not bias, ignored if fix_gas) drop_chain_dummies: if True, remove chain_* columns from attributes + fix_gas_to_chain: if True, gas is fixed to known chain-level costs Returns dict with fitted params and diagnostics. """ - jdata = prepare_joint_data(matched, drop_chain_dummies=drop_chain_dummies) + jdata = prepare_joint_data(matched, drop_chain_dummies=drop_chain_dummies, + fix_gas_to_chain=fix_gas_to_chain) loss_fn = make_joint_loss_fn(jdata, mode=mode, - alpha_cad=alpha_cad, alpha_gas=alpha_gas) + alpha_cad=alpha_cad, alpha_gas=alpha_gas, + fix_gas=fix_gas_to_chain) init = make_initial_joint_params(jdata, mode=mode, - init_from_option_c=init_from_option_c) + init_from_option_c=init_from_option_c, + fix_gas=fix_gas_to_chain) n_pools = len(jdata.pool_data) k_attr = jdata.x_attr.shape[1] - config = {"k_attr": k_attr, "n_pools": n_pools, "mode": mode} - bounds = _make_bounds(k_attr, n_pools, mode) + config = {"k_attr": k_attr, "n_pools": n_pools, "mode": mode, + "fix_gas": fix_gas_to_chain} + bounds = _make_bounds(k_attr, n_pools, mode, fix_gas=fix_gas_to_chain) - # Per-pool value_and_grad — each pool has its own small JIT graph pool_vg_fns = loss_fn._pool_val_and_grad_fns - # Indices for W_cad and W_gas in the flat param vector (for reg gradient) - w_cad_start = 2 - w_cad_end = 2 + k_attr - w_gas_start = 2 + k_attr - w_gas_end = 2 + 2 * k_attr + if fix_gas_to_chain: + w_cad_start = 1 + w_cad_end = 1 + k_attr + else: + w_cad_start = 2 + w_cad_end = 2 + k_attr + w_gas_start = 2 + k_attr + w_gas_end = 2 + 2 * k_attr def scipy_wrapper(params_np): params_j = jnp.array(params_np) - # Sum per-pool losses and gradients total_val = 0.0 total_grad = jnp.zeros_like(params_j) for vg_fn in pool_vg_fns: @@ -391,15 +467,15 @@ def scipy_wrapper(params_np): data_loss = total_val / n_pools data_grad = total_grad / n_pools - # Regularization on W_cad and W_gas (not biases) - reg = (alpha_cad * float(jnp.sum(params_j[w_cad_start:w_cad_end] ** 2)) + - alpha_gas * float(jnp.sum(params_j[w_gas_start:w_gas_end] ** 2))) - + reg = alpha_cad * float(jnp.sum(params_j[w_cad_start:w_cad_end] ** 2)) reg_grad = jnp.zeros_like(params_j) reg_grad = reg_grad.at[w_cad_start:w_cad_end].set( 2 * alpha_cad * params_j[w_cad_start:w_cad_end]) - reg_grad = reg_grad.at[w_gas_start:w_gas_end].set( - 2 * alpha_gas * params_j[w_gas_start:w_gas_end]) + + if not fix_gas_to_chain: + reg += alpha_gas * float(jnp.sum(params_j[w_gas_start:w_gas_end] ** 2)) + reg_grad = reg_grad.at[w_gas_start:w_gas_end].set( + 2 * alpha_gas * params_j[w_gas_start:w_gas_end]) val = data_loss + reg grad = data_grad + reg_grad @@ -422,17 +498,30 @@ def scipy_wrapper(params_np): out = { "init_loss": init_loss, "bias_cad": float(params["bias_cad"]), - "bias_gas": float(params["bias_gas"]), "W_cad": np.array(params["W_cad"]), - "W_gas": np.array(params["W_gas"]), "loss": float(result.fun), "converged": result.success, "mode": mode, "k_attr": k_attr, "pool_ids": jdata.pool_ids, "attr_names": jdata.attr_names, + "fix_gas": fix_gas_to_chain, } + if fix_gas_to_chain: + # Store per-pool fixed gas values for downstream use + from quantammsim.calibration.loss import CHAIN_GAS_USD + gas_per_pool = [] + for pid in jdata.pool_ids: + chain = matched[pid]["chain"] + gas_per_pool.append(CHAIN_GAS_USD.get(chain, 1.0)) + out["gas_per_pool"] = np.array(gas_per_pool) + out["bias_gas"] = 0.0 + out["W_gas"] = np.zeros(k_attr) + else: + out["bias_gas"] = float(params["bias_gas"]) + out["W_gas"] = np.array(params["W_gas"]) + if mode == "per_pool_noise": out["noise_coeffs"] = np.array(params["noise_coeffs"]) else: diff --git a/quantammsim/calibration/loss.py b/quantammsim/calibration/loss.py index 13b2e65..e003a98 100644 --- a/quantammsim/calibration/loss.py +++ b/quantammsim/calibration/loss.py @@ -16,6 +16,17 @@ K_OBS = 8 # observation-level covariates +# Known chain gas costs (USD) — used when fixing gas to chain-level values. +# These are effective per-transaction costs, not per-gas-unit. +CHAIN_GAS_USD = { + "MAINNET": 1.0, + "POLYGON": 0.005, + "GNOSIS": 0.001, + "ARBITRUM": 0.01, + "BASE": 0.005, + "SONIC": 0.005, +} + def noise_volume( noise_coeffs: jnp.ndarray, x_obs: jnp.ndarray @@ -41,6 +52,23 @@ def unpack_params( return flat[0], flat[1], flat[2:] +def pack_params_fixed_gas( + log_cadence: float, noise_coeffs: jnp.ndarray +) -> jnp.ndarray: + """Pack into flat array with gas excluded: [log_cadence, noise_coeffs...].""" + return jnp.concatenate([ + jnp.array([log_cadence]), + jnp.asarray(noise_coeffs), + ]) + + +def unpack_params_fixed_gas( + flat: jnp.ndarray, +) -> Tuple[float, jnp.ndarray]: + """Unpack flat array to (log_cadence, noise_coeffs). Gas not included.""" + return flat[0], flat[1:] + + def pool_loss( params_flat: jnp.ndarray, coeffs: PoolCoeffsDaily, @@ -72,3 +100,28 @@ def pool_loss( # Log-space L2 loss log_v_pred = jnp.log(jnp.maximum(v_arb + v_noise, 1e-6)) return jnp.mean((log_v_pred - y_obs) ** 2) + + +def pool_loss_fixed_gas( + params_flat: jnp.ndarray, + fixed_log_gas: float, + coeffs: PoolCoeffsDaily, + x_obs: jnp.ndarray, + y_obs: jnp.ndarray, + day_indices: jnp.ndarray, +) -> jnp.ndarray: + """Per-pool loss with gas fixed to a known chain-level value. + + Args: + params_flat: [log_cadence, noise_coeffs...] — no log_gas + fixed_log_gas: log(gas_usd) held constant (not optimized) + coeffs, x_obs, y_obs, day_indices: as in pool_loss + """ + log_cadence, noise_coeffs = unpack_params_fixed_gas(params_flat) + + v_arb_all = interpolate_pool_daily(coeffs, log_cadence, jnp.exp(fixed_log_gas)) + v_arb = v_arb_all[day_indices] + v_noise = noise_volume(noise_coeffs, x_obs) + + log_v_pred = jnp.log(jnp.maximum(v_arb + v_noise, 1e-6)) + return jnp.mean((log_v_pred - y_obs) ** 2) diff --git a/quantammsim/calibration/per_pool_fit.py b/quantammsim/calibration/per_pool_fit.py index 037a124..08140d9 100644 --- a/quantammsim/calibration/per_pool_fit.py +++ b/quantammsim/calibration/per_pool_fit.py @@ -2,6 +2,9 @@ Fits (log_cadence, log_gas, noise_coeffs) per pool by minimizing the log-space L2 loss using scipy.optimize.minimize with JAX gradients. + +Supports fixed-gas mode where gas is set to the known chain-level cost, +leaving only (log_cadence, noise_coeffs) to be optimized. """ from typing import Dict, Optional @@ -12,7 +15,13 @@ import scipy.optimize from quantammsim.calibration.grid_interpolation import PoolCoeffsDaily -from quantammsim.calibration.loss import K_OBS, pack_params, pool_loss +from quantammsim.calibration.loss import ( + CHAIN_GAS_USD, + K_OBS, + pack_params, + pool_loss, + pool_loss_fixed_gas, +) from quantammsim.calibration.pool_data import build_x_obs @@ -30,6 +39,15 @@ def make_initial_guess(x_obs: np.ndarray, y_obs: np.ndarray) -> np.ndarray: return init +def make_initial_guess_fixed_gas(x_obs: np.ndarray, y_obs: np.ndarray) -> np.ndarray: + """Initial params for fixed-gas mode: cadence=12min, noise_coeffs from OLS.""" + noise_coeffs, _, _, _ = np.linalg.lstsq(x_obs, y_obs, rcond=None) + init = np.zeros(1 + K_OBS) + init[0] = np.log(12.0) # log_cadence + init[1:] = noise_coeffs + return init + + def fit_single_pool( coeffs: PoolCoeffsDaily, x_obs: np.ndarray, @@ -37,74 +55,122 @@ def fit_single_pool( day_indices: np.ndarray, init: Optional[np.ndarray] = None, bounds: Optional[dict] = None, + fixed_gas_usd: Optional[float] = None, ) -> dict: - """Fit (log_cadence, log_gas, noise_coeffs) for one pool via L-BFGS-B. + """Fit one pool via L-BFGS-B. + + If fixed_gas_usd is given, gas is held constant at that value and only + (log_cadence, noise_coeffs) are optimized. Otherwise fits all three. Returns dict with fitted params, loss, and convergence status. """ - if init is None: - init = make_initial_guess(x_obs, y_obs) + # Convert to JAX arrays + x_obs_j = jnp.array(x_obs) + y_obs_j = jnp.array(y_obs) + day_idx_j = jnp.array(day_indices) - # Default bounds if bounds is None: bounds = {} log_cad_bounds = bounds.get("log_cadence", (np.log(1.0), np.log(60.0))) - log_gas_bounds = bounds.get("log_gas", (np.log(0.001), np.log(50.0))) noise_bounds = bounds.get("noise_coeffs", (-20.0, 20.0)) - scipy_bounds = [ - log_cad_bounds, - log_gas_bounds, - ] + [(noise_bounds[0], noise_bounds[1])] * K_OBS + if fixed_gas_usd is not None: + # Fixed-gas mode + fixed_log_gas = jnp.float64(np.log(max(fixed_gas_usd, 1e-6))) - # Convert to JAX arrays - x_obs_j = jnp.array(x_obs) - y_obs_j = jnp.array(y_obs) - day_idx_j = jnp.array(day_indices) + if init is None: + init = make_initial_guess_fixed_gas(x_obs, y_obs) + + scipy_bounds = [log_cad_bounds] + [(noise_bounds[0], noise_bounds[1])] * K_OBS + + @jax.jit + def loss_and_grad(params_flat): + loss = pool_loss_fixed_gas( + params_flat, fixed_log_gas, coeffs, x_obs_j, y_obs_j, day_idx_j) + grad = jax.grad(pool_loss_fixed_gas, argnums=0)( + params_flat, fixed_log_gas, coeffs, x_obs_j, y_obs_j, day_idx_j) + return loss, grad + + def scipy_wrapper(params_np): + params_j = jnp.array(params_np) + loss, grad = loss_and_grad(params_j) + return float(loss), np.array(grad, dtype=np.float64) + + result = scipy.optimize.minimize( + scipy_wrapper, init, method="L-BFGS-B", jac=True, + bounds=scipy_bounds, + options={"maxiter": 500, "ftol": 1e-10, "gtol": 1e-8}, + ) - # Value and gradient function - @jax.jit - def loss_and_grad(params_flat): - loss = pool_loss(params_flat, coeffs, x_obs_j, y_obs_j, day_idx_j) - grad = jax.grad(pool_loss, argnums=0)( - params_flat, coeffs, x_obs_j, y_obs_j, day_idx_j + log_cadence = float(result.x[0]) + noise_coeffs = np.array(result.x[1:]) + log_gas = float(fixed_log_gas) + + return { + "log_cadence": log_cadence, + "log_gas": log_gas, + "noise_coeffs": noise_coeffs, + "loss": float(result.fun), + "converged": result.success, + "cadence_minutes": float(np.exp(log_cadence)), + "gas_usd": fixed_gas_usd, + "gas_fixed": True, + } + + else: + # Free-gas mode (original) + if init is None: + init = make_initial_guess(x_obs, y_obs) + + log_gas_bounds = bounds.get("log_gas", (np.log(0.001), np.log(50.0))) + scipy_bounds = [ + log_cad_bounds, log_gas_bounds, + ] + [(noise_bounds[0], noise_bounds[1])] * K_OBS + + @jax.jit + def loss_and_grad(params_flat): + loss = pool_loss(params_flat, coeffs, x_obs_j, y_obs_j, day_idx_j) + grad = jax.grad(pool_loss, argnums=0)( + params_flat, coeffs, x_obs_j, y_obs_j, day_idx_j) + return loss, grad + + def scipy_wrapper(params_np): + params_j = jnp.array(params_np) + loss, grad = loss_and_grad(params_j) + return float(loss), np.array(grad, dtype=np.float64) + + result = scipy.optimize.minimize( + scipy_wrapper, init, method="L-BFGS-B", jac=True, + bounds=scipy_bounds, + options={"maxiter": 500, "ftol": 1e-10, "gtol": 1e-8}, ) - return loss, grad - - def scipy_wrapper(params_np): - params_j = jnp.array(params_np) - loss, grad = loss_and_grad(params_j) - return float(loss), np.array(grad, dtype=np.float64) - - result = scipy.optimize.minimize( - scipy_wrapper, - init, - method="L-BFGS-B", - jac=True, - bounds=scipy_bounds, - options={"maxiter": 500, "ftol": 1e-10, "gtol": 1e-8}, - ) - - log_cadence = float(result.x[0]) - log_gas = float(result.x[1]) - noise_coeffs = np.array(result.x[2:]) - - return { - "log_cadence": log_cadence, - "log_gas": log_gas, - "noise_coeffs": noise_coeffs, - "loss": float(result.fun), - "converged": result.success, - "cadence_minutes": float(np.exp(log_cadence)), - "gas_usd": float(np.exp(log_gas)), - } + + log_cadence = float(result.x[0]) + log_gas = float(result.x[1]) + noise_coeffs = np.array(result.x[2:]) + + return { + "log_cadence": log_cadence, + "log_gas": log_gas, + "noise_coeffs": noise_coeffs, + "loss": float(result.fun), + "converged": result.success, + "cadence_minutes": float(np.exp(log_cadence)), + "gas_usd": float(np.exp(log_gas)), + "gas_fixed": False, + } def fit_all_pools( matched: Dict[str, dict], n_workers: int = 1, + fix_gas_to_chain: bool = False, ) -> Dict[str, dict]: - """Fit all matched pools. Returns prefix -> fit_result with metadata.""" + """Fit all matched pools. Returns prefix -> fit_result with metadata. + + If fix_gas_to_chain is True, gas is fixed to the known chain-level cost + from CHAIN_GAS_USD, and only (log_cadence, noise_coeffs) are optimized. + """ results = {} for prefix, entry in matched.items(): @@ -115,7 +181,13 @@ def fit_all_pools( x_obs = build_x_obs(panel) y_obs = panel["log_volume"].values.astype(float) - result = fit_single_pool(coeffs, x_obs, y_obs, day_indices) + fixed_gas = None + if fix_gas_to_chain: + chain = entry["chain"] + fixed_gas = CHAIN_GAS_USD.get(chain, 1.0) + + result = fit_single_pool( + coeffs, x_obs, y_obs, day_indices, fixed_gas_usd=fixed_gas) # Add metadata result["chain"] = entry["chain"] From 834e547f84a6a2df0e95b386b71d0d66a23be277 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 9 Mar 2026 23:07:15 +0000 Subject: [PATCH 04/57] feat: replace Balancer hourly volatility with Binance minute data Compute daily realized volatility from Binance minute prices instead of Balancer API hourly prices, removing the 90-day data restriction. Each pool now uses its full historical date range (up to 1761 days). The calibration runner calls replace_panel_volatility_with_binance() and supports train_days=0 for unrestricted history. --- quantammsim/calibration/pool_data.py | 170 +++++++++++++++++++++- scripts/run_direct_calibration_top50.py | 184 +++++++++++++----------- 2 files changed, 266 insertions(+), 88 deletions(-) diff --git a/quantammsim/calibration/pool_data.py b/quantammsim/calibration/pool_data.py index 039880b..61082cd 100644 --- a/quantammsim/calibration/pool_data.py +++ b/quantammsim/calibration/pool_data.py @@ -6,7 +6,7 @@ import json import os -from typing import Dict, List, Tuple +from typing import Dict, List, Optional, Tuple import numpy as np import pandas as pd @@ -25,6 +25,11 @@ "local_data", "noise_calibration", "token_mcaps.json", ) +# Default path for Binance minute parquets +_BINANCE_DATA_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), "data", +) + # Asset type classification (fallback if not in mcap JSON) _STABLECOINS = { "USDC", "USDT", "DAI", "WXDAI", "xDAI", "GHO", "LUSD", "crvUSD", @@ -42,6 +47,22 @@ "waBasWETH", "waGnoGNO", "waGnowstETH", } +# Balancer token → Binance parquet symbol mapping. +# Matches build_pool_grids.py TOKEN_MAP. +TOKEN_MAP = { + "WBTC": "BTC", "WETH": "ETH", "cbBTC": "BTC", + "wstETH": "ETH", "stETH": "ETH", "rETH": "ETH", "cbETH": "ETH", + "waEthLidoWETH": "ETH", "waEthLidowstETH": "ETH", + "waBasWETH": "ETH", "waGnowstETH": "ETH", + "waGnoGNO": "GNO", "osGNO": "GNO", + "wS": "S", "stS": "S", + "JitoSOL": "SOL", + "wPOL": "POL", "WMATIC": "POL", "MATIC": "POL", + "USDC.e": "USDC", "USDbC": "USDC", "waBasUSDC": "USDC", + "DAI": "USDC", "WXDAI": "USDC", "sDAI": "USDC", + "USDT": "USDC", "DOLA": "USDC", "scUSD": "USDC", +} + def _load_token_mcaps(path: str = None) -> dict: """Load cached token market caps. Returns {} if file missing.""" @@ -71,6 +92,153 @@ def _parse_tokens(tokens_str: str) -> List[str]: return [t.strip() for t in tokens_str.split(",")] +def _resolve_binance_symbol(token: str) -> str: + """Map Balancer token name to Binance parquet symbol.""" + return TOKEN_MAP.get(token, token) + + +def _load_binance_minute(symbol: str, data_dir: str = None) -> Optional[pd.DataFrame]: + """Load Binance minute close prices. Returns DataFrame with unix index.""" + if data_dir is None: + data_dir = _BINANCE_DATA_DIR + path = os.path.join(data_dir, f"{symbol}_USD.parquet") + if not os.path.exists(path): + return None + df = pd.read_parquet(path, columns=["unix", "close"]) + if df.index.name != "unix": + df = df.set_index("unix") + return df + + +def compute_binance_pair_volatility( + token_a: str, token_b: str, data_dir: str = None, +) -> Optional[pd.Series]: + """Compute daily annualized realized volatility from Binance minute data. + + Resamples minute data to hourly, computes hourly log returns of the pair + ratio, then daily std × sqrt(24 × 365). Matches the Balancer hourly + pipeline's annualization convention. + + Args: + token_a, token_b: Balancer token symbols (e.g. "WETH", "USDC") + data_dir: directory containing {SYMBOL}_USD.parquet files + + Returns: + pd.Series with datetime.date index → annualized volatility, + or None if both tokens are stablecoins / same underlying / missing data. + """ + sym_a = _resolve_binance_symbol(token_a) + sym_b = _resolve_binance_symbol(token_b) + + is_stable_a = token_a in _STABLECOINS or sym_a == "USDC" + is_stable_b = token_b in _STABLECOINS or sym_b == "USDC" + + if is_stable_a and is_stable_b: + return None # caller should use constant 0.01 + + if sym_a == sym_b: + return None # same underlying (e.g. wstETH/WETH) + + # Load minute data and compute pair ratio + if is_stable_b: + df = _load_binance_minute(sym_a, data_dir) + if df is None: + return None + ratio = df["close"] + elif is_stable_a: + df = _load_binance_minute(sym_b, data_dir) + if df is None: + return None + ratio = 1.0 / df["close"] + else: + df_a = _load_binance_minute(sym_a, data_dir) + df_b = _load_binance_minute(sym_b, data_dir) + if df_a is None or df_b is None: + return None + merged = df_a.join(df_b, lsuffix="_a", rsuffix="_b", how="inner") + ratio = merged["close_a"] / merged["close_b"] + + # Resample to hourly (last close per hour) + ratio_df = pd.DataFrame({"ratio": ratio}) + ratio_df.index = pd.to_datetime(ratio_df.index, unit="ms", utc=True) + hourly = ratio_df.resample("1h").last().dropna() + + # Hourly log returns + hourly["log_return"] = np.log(hourly["ratio"] / hourly["ratio"].shift(1)) + hourly = hourly.dropna() + + # Daily std → annualized + hourly["date"] = hourly.index.date + daily_vol = hourly.groupby("date")["log_return"].std() + annualized = daily_vol * np.sqrt(24 * 365) + + # Clean + annualized = annualized.replace([np.inf, -np.inf], np.nan).dropna() + annualized = annualized[annualized > 0] + + return annualized + + +def replace_panel_volatility_with_binance( + panel: pd.DataFrame, data_dir: str = None, +) -> pd.DataFrame: + """Replace panel 'volatility' column with Binance-derived daily values. + + For each pool, computes daily realized volatility from Binance minute data. + Pools without Binance data keep their existing (possibly fallback) values. + Stablecoin-stablecoin and same-underlying pairs get vol=0.01. + + Returns a copy of the panel with updated volatility. + """ + panel = panel.copy() + panel["date"] = pd.to_datetime(panel["date"]) + + # Cache: (sym_a, sym_b) → vol_series to avoid reloading + _vol_cache: Dict[tuple, Optional[pd.Series]] = {} + + n_replaced = 0 + n_pools = 0 + + for pool_id, grp in panel.groupby("pool_id"): + tokens_str = grp.iloc[0]["tokens"] + toks = _parse_tokens(tokens_str) + if len(toks) < 2: + continue + + sym_a = _resolve_binance_symbol(toks[0]) + sym_b = _resolve_binance_symbol(toks[1]) + cache_key = (min(sym_a, sym_b), max(sym_a, sym_b)) + + if cache_key not in _vol_cache: + _vol_cache[cache_key] = compute_binance_pair_volatility( + toks[0], toks[1], data_dir) + + vol_series = _vol_cache[cache_key] + + if vol_series is None: + # Stablecoins or same underlying → low constant vol + is_stable_a = toks[0] in _STABLECOINS or sym_a == "USDC" + is_stable_b = toks[1] in _STABLECOINS or sym_b == "USDC" + if (is_stable_a and is_stable_b) or sym_a == sym_b: + panel.loc[grp.index, "volatility"] = 0.01 + n_pools += 1 + continue + + # Vectorized date matching + panel_dates = pd.to_datetime(grp["date"]).dt.date + vol_dict = vol_series.to_dict() + new_vol = panel_dates.map(vol_dict) + has_vol = new_vol.notna() + if has_vol.any(): + panel.loc[grp.index[has_vol.values], "volatility"] = ( + new_vol[has_vol].values.astype(float)) + n_replaced += has_vol.sum() + n_pools += 1 + + print(f" Binance volatility: {n_pools} pools, {n_replaced} obs replaced") + return panel + + def match_grids_to_panel( grid_dir: str, panel: pd.DataFrame, pools_path: str = None, ) -> Dict[str, dict]: diff --git a/scripts/run_direct_calibration_top50.py b/scripts/run_direct_calibration_top50.py index 1c1770b..d323c0b 100644 --- a/scripts/run_direct_calibration_top50.py +++ b/scripts/run_direct_calibration_top50.py @@ -32,7 +32,7 @@ os.path.dirname(os.path.dirname(__file__)), "results", "direct_calibration_top50", ) -TRAIN_DAYS = 90 +TRAIN_DAYS = 0 # 0 = no filter, use all available data per pool TOP_N = 50 OPTION_C_MAXITER = 500 JOINT_MAXITER = 500 @@ -40,18 +40,28 @@ def load_and_match(): - """Load panel, filter to 90 days, match to grids.""" + """Load panel, match to grids. No date filter — each pool uses all data.""" + from quantammsim.calibration.pool_data import ( + match_grids_to_panel, + replace_panel_volatility_with_binance, + ) + panel = pd.read_parquet(PANEL_CACHE) - max_date = panel["date"].max() - if not isinstance(max_date, date): - max_date = pd.Timestamp(max_date).date() - cutoff = max_date - timedelta(days=TRAIN_DAYS) - panel = panel[ - panel["date"].apply( - lambda d: d >= cutoff if isinstance(d, date) - else pd.Timestamp(d).date() >= cutoff - ) - ].copy() + + # Optional date filter (TRAIN_DAYS=0 means no filter) + if TRAIN_DAYS > 0: + max_date = panel["date"].max() + if not isinstance(max_date, date): + max_date = pd.Timestamp(max_date).date() + cutoff = max_date - timedelta(days=TRAIN_DAYS) + panel = panel[ + panel["date"].apply( + lambda d: d >= cutoff if isinstance(d, date) + else pd.Timestamp(d).date() >= cutoff + ) + ].copy() + else: + panel = panel.copy() if "log_tvl_lag1" not in panel.columns: panel = panel.sort_values(["pool_id", "date"]).reset_index(drop=True) @@ -62,21 +72,27 @@ def load_and_match(): valid = pool_counts[pool_counts >= 10].index panel = panel[panel["pool_id"].isin(valid)].copy() + # Replace Balancer-hourly volatility with Binance-minute volatility + print("Replacing volatility with Binance minute data...") + panel = replace_panel_volatility_with_binance(panel) + + min_date = panel["date"].min() + max_date = panel["date"].max() print(f"Panel: {len(panel)} obs, {panel['pool_id'].nunique()} pools, " - f"{cutoff} to {max_date}") + f"{min_date} to {max_date}") - from quantammsim.calibration.pool_data import match_grids_to_panel matched = match_grids_to_panel(GRID_DIR, panel) print(f"Matched: {len(matched)} pools with grids") return panel, matched -def run_option_c(matched): +def run_option_c(matched, fix_gas_to_chain=False): """Per-pool L-BFGS-B fits.""" from quantammsim.calibration.per_pool_fit import fit_all_pools - print(f"\n--- Option C: per-pool fits ({len(matched)} pools) ---") - results = fit_all_pools(matched) + gas_label = " (gas fixed to chain)" if fix_gas_to_chain else "" + print(f"\n--- Option C: per-pool fits ({len(matched)} pools){gas_label} ---") + results = fit_all_pools(matched, fix_gas_to_chain=fix_gas_to_chain) n_converged = sum(1 for r in results.values() if r["converged"]) losses = [r["loss"] for r in results.values()] print(f" Converged: {n_converged}/{len(results)}") @@ -86,7 +102,7 @@ def run_option_c(matched): return results -def run_option_a(matched, option_c_results): +def run_option_a(matched, option_c_results, fix_gas_to_chain=False): """Joint end-to-end optimization, warm-started from Option C. Drops pathological pools (Option C loss > OPTION_C_LOSS_CUTOFF) from the @@ -106,26 +122,29 @@ def run_option_a(matched, option_c_results): r = option_c_results[p] print(f" {p} {r['tokens']:<16} loss={r['loss']:.1f}") + gas_label = ", gas fixed" if fix_gas_to_chain else "" print(f"\n--- Option A: joint fit (per_pool_noise, {len(matched_clean)} pools, " - f"warm-start from C, no chain dummies) ---") + f"warm-start from C, no chain dummies{gas_label}) ---") result_ppn = fit_joint( matched_clean, mode="per_pool_noise", init_from_option_c=good_pools, maxiter=JOINT_MAXITER, drop_chain_dummies=True, + fix_gas_to_chain=fix_gas_to_chain, ) print(f" Loss: {result_ppn['init_loss']:.4f} -> {result_ppn['loss']:.4f}") print(f" Converged: {result_ppn['converged']}") print(f"\n--- Option A: joint fit (shared_noise, {len(matched_clean)} pools, " - f"warm-start from C, no chain dummies) ---") + f"warm-start from C, no chain dummies{gas_label}) ---") result_sn = fit_joint( matched_clean, mode="shared_noise", init_from_option_c=good_pools, maxiter=JOINT_MAXITER, drop_chain_dummies=True, + fix_gas_to_chain=fix_gas_to_chain, ) print(f" Loss: {result_sn['init_loss']:.4f} -> {result_sn['loss']:.4f}") print(f" Converged: {result_sn['converged']}") @@ -170,115 +189,103 @@ def run_option_rf(matched, option_c_results): attr_names = [attr_names_full[i] for i in non_chain_mask] k_attr = len(attr_names) - print(f"\n--- Option RF: 2-stage mapping ({n_pools} pools, {k_attr} features) ---") + # Detect if gas was fixed in Option C + gas_fixed = any(good_pools[p].get("gas_fixed", False) for p in pool_ids) + + if gas_fixed: + print(f"\n--- Option RF: 2-stage mapping ({n_pools} pools, {k_attr} features, " + f"cadence only — gas fixed) ---") + else: + print(f"\n--- Option RF: 2-stage mapping ({n_pools} pools, {k_attr} features) ---") print(f" Features: {', '.join(attr_names)}") Y_cad = np.array([good_pools[p]["log_cadence"] for p in pool_ids]) Y_gas = np.array([good_pools[p]["log_gas"] for p in pool_ids]) - Y = np.column_stack([Y_cad, Y_gas]) ss_tot_cad = np.sum((Y_cad - Y_cad.mean()) ** 2) - ss_tot_gas = np.sum((Y_gas - Y_gas.mean()) ** 2) def compute_r2(y_true, y_pred, ss_tot): return 1 - np.sum((y_true - y_pred) ** 2) / max(ss_tot, 1e-10) - # ---- Ridge regression (multi-output via separate fits) ---- + # ---- Ridge regression (cadence only when gas is fixed) ---- alphas = np.logspace(-2, 4, 50) ridge_cad = RidgeCV(alphas=alphas, cv=None) # GCV/LOO built-in - ridge_gas = RidgeCV(alphas=alphas, cv=None) ridge_cad.fit(X_attr, Y_cad) - ridge_gas.fit(X_attr, Y_gas) - Y_ridge_train = np.column_stack([ridge_cad.predict(X_attr), - ridge_gas.predict(X_attr)]) - r2_ridge_cad = compute_r2(Y_cad, Y_ridge_train[:, 0], ss_tot_cad) - r2_ridge_gas = compute_r2(Y_gas, Y_ridge_train[:, 1], ss_tot_gas) + Y_ridge_cad_train = ridge_cad.predict(X_attr) + r2_ridge_cad = compute_r2(Y_cad, Y_ridge_cad_train, ss_tot_cad) - print(f"\n Ridge (alpha_cad={ridge_cad.alpha_:.1f}, alpha_gas={ridge_gas.alpha_:.1f}):") - print(f" In-sample R²: cadence={r2_ridge_cad:.3f}, gas={r2_ridge_gas:.3f}") + print(f"\n Ridge (alpha_cad={ridge_cad.alpha_:.1f}):") + print(f" In-sample R² cadence: {r2_ridge_cad:.3f}") - # Ridge LOO-CV + # Ridge LOO-CV (cadence only) loo = LeaveOneOut() - Y_ridge_loo = np.zeros_like(Y) + Y_ridge_cad_loo = np.zeros_like(Y_cad) for train_idx, test_idx in loo.split(X_attr): rc = RidgeCV(alphas=alphas, cv=None).fit(X_attr[train_idx], Y_cad[train_idx]) - rg = RidgeCV(alphas=alphas, cv=None).fit(X_attr[train_idx], Y_gas[train_idx]) - Y_ridge_loo[test_idx, 0] = rc.predict(X_attr[test_idx]) - Y_ridge_loo[test_idx, 1] = rg.predict(X_attr[test_idx]) + Y_ridge_cad_loo[test_idx] = rc.predict(X_attr[test_idx]) - r2_ridge_loo_cad = compute_r2(Y_cad, Y_ridge_loo[:, 0], ss_tot_cad) - r2_ridge_loo_gas = compute_r2(Y_gas, Y_ridge_loo[:, 1], ss_tot_gas) - print(f" LOO-CV R²: cadence={r2_ridge_loo_cad:.3f}, gas={r2_ridge_loo_gas:.3f}") - print(f" LOO-CV MAE: cadence={np.mean(np.abs(np.exp(Y_cad) - np.exp(Y_ridge_loo[:, 0]))):.1f} min, " - f"gas=${np.mean(np.abs(np.exp(Y_gas) - np.exp(Y_ridge_loo[:, 1]))):.2f}") + r2_ridge_loo_cad = compute_r2(Y_cad, Y_ridge_cad_loo, ss_tot_cad) + print(f" LOO-CV R² cadence: {r2_ridge_loo_cad:.3f}") + print(f" LOO-CV MAE cadence: {np.mean(np.abs(np.exp(Y_cad) - np.exp(Y_ridge_cad_loo))):.1f} min") # Ridge coefficients - print(f" Coefficients (cadence | gas):") - print(f" {'intercept':<20} {ridge_cad.intercept_:>7.3f} {ridge_gas.intercept_:>7.3f}") + print(f" Coefficients (cadence):") + print(f" {'intercept':<20} {ridge_cad.intercept_:>7.3f}") for j, name in enumerate(attr_names): - print(f" {name:<20} {ridge_cad.coef_[j]:>7.3f} {ridge_gas.coef_[j]:>7.3f}") + print(f" {name:<20} {ridge_cad.coef_[j]:>7.3f}") - # ---- Random Forest (reduced features) ---- + # ---- Random Forest (cadence only) ---- rf = RandomForestRegressor( n_estimators=200, max_depth=None, - min_samples_leaf=3, # stronger regularization - max_features=min(4, k_attr), # cap at 4 features per split + min_samples_leaf=3, + max_features=min(4, k_attr), random_state=42, n_jobs=-1, ) - rf.fit(X_attr, Y) - Y_rf_train = rf.predict(X_attr) + rf.fit(X_attr, Y_cad) + Y_rf_cad_train = rf.predict(X_attr) - r2_rf_cad = compute_r2(Y_cad, Y_rf_train[:, 0], ss_tot_cad) - r2_rf_gas = compute_r2(Y_gas, Y_rf_train[:, 1], ss_tot_gas) + r2_rf_cad = compute_r2(Y_cad, Y_rf_cad_train, ss_tot_cad) print(f"\n Random Forest (min_leaf=3, max_feat=4):") - print(f" In-sample R²: cadence={r2_rf_cad:.3f}, gas={r2_rf_gas:.3f}") + print(f" In-sample R² cadence: {r2_rf_cad:.3f}") # RF LOO-CV - Y_rf_loo = np.zeros_like(Y) + Y_rf_cad_loo = np.zeros_like(Y_cad) for train_idx, test_idx in loo.split(X_attr): rf_loo = RandomForestRegressor( n_estimators=200, max_depth=None, min_samples_leaf=3, max_features=min(4, k_attr), random_state=42, n_jobs=-1, ) - rf_loo.fit(X_attr[train_idx], Y[train_idx]) - Y_rf_loo[test_idx] = rf_loo.predict(X_attr[test_idx]) + rf_loo.fit(X_attr[train_idx], Y_cad[train_idx]) + Y_rf_cad_loo[test_idx] = rf_loo.predict(X_attr[test_idx]) - r2_rf_loo_cad = compute_r2(Y_cad, Y_rf_loo[:, 0], ss_tot_cad) - r2_rf_loo_gas = compute_r2(Y_gas, Y_rf_loo[:, 1], ss_tot_gas) - print(f" LOO-CV R²: cadence={r2_rf_loo_cad:.3f}, gas={r2_rf_loo_gas:.3f}") - print(f" LOO-CV MAE: cadence={np.mean(np.abs(np.exp(Y_cad) - np.exp(Y_rf_loo[:, 0]))):.1f} min, " - f"gas=${np.mean(np.abs(np.exp(Y_gas) - np.exp(Y_rf_loo[:, 1]))):.2f}") + r2_rf_loo_cad = compute_r2(Y_cad, Y_rf_cad_loo, ss_tot_cad) + print(f" LOO-CV R² cadence: {r2_rf_loo_cad:.3f}") + print(f" LOO-CV MAE cadence: {np.mean(np.abs(np.exp(Y_cad) - np.exp(Y_rf_cad_loo))):.1f} min") print(f"\n Feature importances:") for j, name in enumerate(attr_names): print(f" {name:<20} {rf.feature_importances_[j]:.3f}") # ---- Pick best LOO model ---- - ridge_loo_total = r2_ridge_loo_cad + r2_ridge_loo_gas - rf_loo_total = r2_rf_loo_cad + r2_rf_loo_gas - best = "ridge" if ridge_loo_total >= rf_loo_total else "rf" - print(f"\n Best LOO model: {best} (ridge={ridge_loo_total:.3f} vs rf={rf_loo_total:.3f})") + best = "ridge" if r2_ridge_loo_cad >= r2_rf_loo_cad else "rf" + print(f"\n Best LOO model: {best} (ridge={r2_ridge_loo_cad:.3f} vs rf={r2_rf_loo_cad:.3f})") if best == "ridge": - Y_best_train = Y_ridge_train - Y_best_loo = Y_ridge_loo + Y_best_cad_train = Y_ridge_cad_train + Y_best_cad_loo = Y_ridge_cad_loo r2_best_cad = r2_ridge_cad - r2_best_gas = r2_ridge_gas r2_best_loo_cad = r2_ridge_loo_cad - r2_best_loo_gas = r2_ridge_loo_gas else: - Y_best_train = Y_rf_train - Y_best_loo = Y_rf_loo + Y_best_cad_train = Y_rf_cad_train + Y_best_cad_loo = Y_rf_cad_loo r2_best_cad = r2_rf_cad - r2_best_gas = r2_rf_gas r2_best_loo_cad = r2_rf_loo_cad - r2_best_loo_gas = r2_rf_loo_gas - # Build result dict using the best model's predictions + # Build result dict — gas comes from Option C (which used chain-level values) noise_all = np.array([good_pools[p]["noise_coeffs"] for p in pool_ids]) result = { @@ -288,21 +295,20 @@ def compute_r2(y_true, y_pred, ss_tot): "best_model": best, "predictions": {}, "loo_predictions": {}, - "noise_coeffs": noise_all, # from Option C + "noise_coeffs": noise_all, "r2_train_cad": r2_best_cad, - "r2_train_gas": r2_best_gas, "r2_loo_cad": r2_best_loo_cad, - "r2_loo_gas": r2_best_loo_gas, } for i, pid in enumerate(pool_ids): + log_gas_fixed = good_pools[pid]["log_gas"] result["predictions"][pid] = { - "log_cadence": float(Y_best_train[i, 0]), - "log_gas": float(Y_best_train[i, 1]), + "log_cadence": float(Y_best_cad_train[i]), + "log_gas": float(log_gas_fixed), } result["loo_predictions"][pid] = { - "log_cadence": float(Y_best_loo[i, 0]), - "log_gas": float(Y_best_loo[i, 1]), + "log_cadence": float(Y_best_cad_loo[i]), + "log_gas": float(log_gas_fixed), } return result @@ -369,7 +375,12 @@ def r2(v_arb, v_noise, y): ji = joint_pid_to_idx[pid] x_attr = X_attr_joint[ji] log_cad_a = float(joint_result["bias_cad"]) + float(x_attr @ joint_result["W_cad"]) - log_gas_a = float(joint_result["bias_gas"]) + float(x_attr @ joint_result["W_gas"]) + if joint_result.get("fix_gas"): + gas_a = float(joint_result["gas_per_pool"][ji]) + log_gas_a = np.log(max(gas_a, 1e-6)) + else: + log_gas_a = float(joint_result["bias_gas"]) + float(x_attr @ joint_result["W_gas"]) + gas_a = np.exp(log_gas_a) noise_c_a = joint_result["noise_coeffs"][ji] v_arb_all_a = np.array(interpolate_pool_daily( @@ -379,7 +390,6 @@ def r2(v_arb, v_noise, y): v_noise_a = np.exp(x_obs @ noise_c_a) r2_a = r2(v_arb_a, v_noise_a, y_obs) cad_a = np.exp(log_cad_a) - gas_a = np.exp(log_gas_a) else: v_arb_a = np.full(len(y_obs), np.nan) v_noise_a = np.full(len(y_obs), np.nan) @@ -800,11 +810,11 @@ def main(): panel, matched = load_and_match() - # Step 1: Option C - option_c = run_option_c(matched) + # Step 1: Option C (gas fixed to chain-level costs) + option_c = run_option_c(matched, fix_gas_to_chain=True) - # Step 2: Option A (linear mapping) - joint_ppn, joint_sn = run_option_a(matched, option_c) + # Step 2: Option A (linear mapping, gas fixed) + joint_ppn, joint_sn = run_option_a(matched, option_c, fix_gas_to_chain=True) # Step 3: Option RF (random forest 2-stage) rf_result = run_option_rf(matched, option_c) From a2ae5a40faf5328d2b0368051e5e482456c64a37 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 9 Mar 2026 23:07:40 +0000 Subject: [PATCH 05/57] fix: grid builder handles stale Binance data and multi-worker dispatch Clip panel dates to Binance price data range so pools with stale token data (BAL, MKR, BADGER, LIT) use their available overlap instead of failing. Snap sim start to next midnight for tokens starting mid-day. Set max_memory_days=0 and preslice_burnin=False to prevent negative start_idx. Workers load their own price data to avoid pickling large DataFrames across processes. --- scripts/build_pool_grids.py | 68 ++++++++++++++++++++++++++++++++----- 1 file changed, 59 insertions(+), 9 deletions(-) diff --git a/scripts/build_pool_grids.py b/scripts/build_pool_grids.py index 9b710c2..ecf3152 100644 --- a/scripts/build_pool_grids.py +++ b/scripts/build_pool_grids.py @@ -215,8 +215,11 @@ def load_panel_and_match(train_days): if "tvl" not in panel.columns and "log_tvl" in panel.columns: panel["tvl"] = np.exp(panel["log_tvl"]) - cutoff = panel["obs_date"].max() - pd.Timedelta(days=train_days) - panel = panel[panel["obs_date"] >= cutoff].copy() + if train_days > 0: + cutoff = panel["obs_date"].max() - pd.Timedelta(days=train_days) + panel = panel[panel["obs_date"] >= cutoff].copy() + else: + panel = panel.copy() # no date filter — use all data binance_tokens = _get_binance_tokens() pools_meta = load_pools_metadata() @@ -342,6 +345,7 @@ def run_arb_sim(tokens, fee, initial_tvl, start, end, cadence, gas_cost, "arb_frequency": int(cadence), "chunk_period": 1440, "weight_interpolation_period": 1440, + "max_memory_days": 0, } if pool_type == "RECLAMM" and reclamm_params is not None: @@ -363,7 +367,7 @@ def run_arb_sim(tokens, fee, initial_tvl, start, end, cadence, gas_cost, result = do_run_on_historic_data( fp, params, lp_supply_df=lp_supply_df, verbose=False, - price_data=price_data, + price_data=price_data, preslice_burnin=False, ) reserves = np.array(result["reserves"]) @@ -405,6 +409,9 @@ def _run_cadence_sweep(pool_info, cadence, gas_costs): reclamm_params = pool_info.get("reclamm_params") price_data = pool_info.get("price_data") + if price_data is None: + sorted_tokens = sorted(tokens) + price_data = get_historic_parquet_data(sorted_tokens, ["close"]) daily_rows = [] summary_rows = [] @@ -527,10 +534,51 @@ def main(): print(f"Found {len(pools)} matchable 2-token pools\n") for p in pools: - lp_df, tvl = build_lp_supply_df(p["panel_data"]) + # Preload price data to determine actual date coverage + sorted_tokens = sorted(p["tokens"]) + price_data = get_historic_parquet_data(sorted_tokens, ["close"]) + p["price_data"] = price_data + + if len(price_data) == 0: + p["panel_data"] = p["panel_data"].iloc[:0] # empty + p["lp_supply_df"] = None + p["initial_tvl"] = 0.0 + p["start"], p["end"] = "2000-01-01", "2000-01-01" + continue + + # Clip panel to price data's actual date range + price_dates = pd.to_datetime(price_data.index, unit="ms") + price_start = price_dates.min().normalize() + price_end = price_dates.max().normalize() + panel_data = p["panel_data"] + panel_data = panel_data[ + (panel_data["obs_date"] >= price_start) + & (panel_data["obs_date"] <= price_end) + ].copy() + p["panel_data"] = panel_data + + lp_df, tvl = build_lp_supply_df(panel_data) p["lp_supply_df"] = lp_df p["initial_tvl"] = tvl - p["start"], p["end"] = get_date_range(p["panel_data"]) + + # Start/end must be midnight timestamps that exist in the price data. + # start_and_end_calcs does an exact unix match and assumes alignment. + # If the price data starts mid-day (e.g. COW at noon), advance to + # the next midnight so the sim has a clean day boundary. + if len(panel_data) > 0: + first_midnight = (price_dates.min() + pd.Timedelta(days=1)).normalize() + last_midnight = price_dates.max().normalize() + first_midnight_ms = int(first_midnight.timestamp() * 1000) + last_midnight_ms = int(last_midnight.timestamp() * 1000) + # Verify these timestamps exist in the price data + if first_midnight_ms in price_data.index and last_midnight_ms in price_data.index: + p["start"] = first_midnight.strftime("%Y-%m-%d %H:%M:%S") + p["end"] = last_midnight.strftime("%Y-%m-%d %H:%M:%S") + else: + # Fallback: use panel dates (works when price data covers full range) + p["start"], p["end"] = get_date_range(panel_data) + else: + p["start"], p["end"] = "2000-01-01", "2000-01-01" pools = [p for p in pools if p["panel_data"]["obs_date"].nunique() >= 14] pools.sort(key=lambda p: p["initial_tvl"], reverse=True) @@ -580,9 +628,8 @@ def main(): t0 = time.time() - # Preload price data once per pool (avoids re-reading parquet per run) - sorted_tokens = sorted(tokens) - price_data = get_historic_parquet_data(sorted_tokens, ["close"]) + # Price data was preloaded during panel clipping + price_data = pool["price_data"] pool_info = { "tokens": tokens, @@ -594,7 +641,10 @@ def main(): "weights": pool["weights"], "pool_type": pool_type, "reclamm_params": pool.get("reclamm_params"), - "price_data": price_data, + # Only pass preloaded price_data in single-worker mode. + # For multi-worker, each subprocess loads its own to avoid + # pickling multi-million-row DataFrames across processes. + "price_data": price_data if args.workers <= 1 else None, } all_daily_rows = [] From f6ef0ecd916e29e62c4dfabe5085637bbf447cb7 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 9 Mar 2026 23:18:13 +0000 Subject: [PATCH 06/57] test: comprehensive tests for fixed-gas calibration and Binance volatility 87 new tests covering: - CHAIN_GAS_USD constants (pinned values, completeness) - pack/unpack fixed-gas params (roundtrip, shape, position) - pool_loss_fixed_gas (zero-when-perfect, matches free-gas, gradients) - per-pool fit fixed-gas (gas_usd pinned, gas_fixed flag, loss decreases) - fit_all_pools with fix_gas_to_chain (chain-level gas matching) - TOKEN_MAP resolution (wrapped native, LSTs, stablecoins, vault tokens) - compute_binance_pair_volatility (synthetic data, edge cases) - replace_panel_volatility_with_binance (immutability, no NaN) - joint fit fixed-gas (prepare, pack/unpack, loss, bounds, fit, predict) --- tests/calibration/test_joint_fit_fixed_gas.py | 411 ++++++++++++++++++ tests/calibration/test_loss_fixed_gas.py | 281 ++++++++++++ .../test_per_pool_fit_fixed_gas.py | 258 +++++++++++ .../calibration/test_pool_data_volatility.py | 232 ++++++++++ 4 files changed, 1182 insertions(+) create mode 100644 tests/calibration/test_joint_fit_fixed_gas.py create mode 100644 tests/calibration/test_loss_fixed_gas.py create mode 100644 tests/calibration/test_per_pool_fit_fixed_gas.py create mode 100644 tests/calibration/test_pool_data_volatility.py diff --git a/tests/calibration/test_joint_fit_fixed_gas.py b/tests/calibration/test_joint_fit_fixed_gas.py new file mode 100644 index 0000000..6cbd3cc --- /dev/null +++ b/tests/calibration/test_joint_fit_fixed_gas.py @@ -0,0 +1,411 @@ +"""Tests for fixed-gas mode in quantammsim.calibration.joint_fit.""" + +import jax +import jax.numpy as jnp +import numpy as np +import pytest + +from tests.calibration.conftest import K_OBS, POOL_PREFIXES + + +@pytest.fixture +def matched_data(synthetic_daily_grid, synthetic_panel, tmp_path): + """Build matched data dict from synthetic fixtures.""" + from quantammsim.calibration.pool_data import match_grids_to_panel + + grid_dir = tmp_path / "grids" + grid_dir.mkdir() + for prefix in POOL_PREFIXES: + synthetic_daily_grid.to_parquet( + grid_dir / f"{prefix}_daily.parquet", index=False + ) + return match_grids_to_panel(str(grid_dir), synthetic_panel) + + +class TestPrepareJointDataFixedGas: + """Test prepare_joint_data with fix_gas_to_chain=True.""" + + def test_pool_data_has_fixed_log_gas(self, matched_data): + from quantammsim.calibration.joint_fit import prepare_joint_data + + jdata = prepare_joint_data(matched_data, fix_gas_to_chain=True) + for pd_i in jdata.pool_data: + assert "fixed_log_gas" in pd_i + + def test_pool_data_no_fixed_log_gas_by_default(self, matched_data): + from quantammsim.calibration.joint_fit import prepare_joint_data + + jdata = prepare_joint_data(matched_data, fix_gas_to_chain=False) + for pd_i in jdata.pool_data: + assert "fixed_log_gas" not in pd_i + + def test_fixed_log_gas_values_match_chain(self, matched_data): + """fixed_log_gas should be log(CHAIN_GAS_USD[chain]).""" + from quantammsim.calibration.joint_fit import prepare_joint_data + from quantammsim.calibration.loss import CHAIN_GAS_USD + + jdata = prepare_joint_data(matched_data, fix_gas_to_chain=True) + + for i, pid in enumerate(jdata.pool_ids): + chain = matched_data[pid]["chain"] + expected_gas = CHAIN_GAS_USD.get(chain, 1.0) + expected_log_gas = np.log(max(expected_gas, 1e-6)) + np.testing.assert_allclose( + float(jdata.pool_data[i]["fixed_log_gas"]), + expected_log_gas, + rtol=1e-6, + ) + + def test_mainnet_gas_is_log_1(self, matched_data): + """MAINNET pools should have fixed_log_gas = log(1.0) = 0.0.""" + from quantammsim.calibration.joint_fit import prepare_joint_data + + jdata = prepare_joint_data(matched_data, fix_gas_to_chain=True) + for i, pid in enumerate(jdata.pool_ids): + if matched_data[pid]["chain"] == "MAINNET": + np.testing.assert_allclose( + float(jdata.pool_data[i]["fixed_log_gas"]), + 0.0, + atol=1e-6, + ) + + def test_arbitrum_gas_is_log_001(self, matched_data): + """ARBITRUM pools should have fixed_log_gas = log(0.01).""" + from quantammsim.calibration.joint_fit import prepare_joint_data + + jdata = prepare_joint_data(matched_data, fix_gas_to_chain=True) + for i, pid in enumerate(jdata.pool_ids): + if matched_data[pid]["chain"] == "ARBITRUM": + np.testing.assert_allclose( + float(jdata.pool_data[i]["fixed_log_gas"]), + np.log(0.01), + rtol=1e-6, + ) + + +class TestPackUnpackJointFixedGas: + """Test packing/unpacking joint params with fix_gas=True.""" + + def test_pack_fixed_gas_shape(self): + from quantammsim.calibration.joint_fit import pack_joint_params_fixed_gas + + k_attr = 6 + n_pools = 2 + noise_params = jnp.zeros((n_pools, K_OBS)) + flat = pack_joint_params_fixed_gas( + 1.0, jnp.zeros(k_attr), noise_params + ) + # Layout: [bias_cad, W_cad(6), noise(2*8)] = 1+6+16 = 23 + assert flat.shape == (1 + k_attr + n_pools * K_OBS,) + + def test_pack_fixed_gas_shorter_than_free(self): + from quantammsim.calibration.joint_fit import ( + pack_joint_params, + pack_joint_params_fixed_gas, + ) + + k_attr = 6 + n_pools = 2 + noise = jnp.zeros((n_pools, K_OBS)) + + free = pack_joint_params(1.0, 2.0, jnp.zeros(k_attr), + jnp.zeros(k_attr), noise) + fixed = pack_joint_params_fixed_gas(1.0, jnp.zeros(k_attr), noise) + # Fixed is shorter by: 1 (bias_gas) + k_attr (W_gas) + assert free.shape[0] - fixed.shape[0] == 1 + k_attr + + def test_unpack_fixed_gas_no_gas_keys(self): + from quantammsim.calibration.joint_fit import ( + pack_joint_params_fixed_gas, + unpack_joint_params, + ) + + k_attr = 6 + n_pools = 2 + noise = jnp.zeros((n_pools, K_OBS)) + flat = pack_joint_params_fixed_gas( + 1.0, jnp.ones(k_attr) * 0.5, noise + ) + + config = {"k_attr": k_attr, "n_pools": n_pools, + "mode": "per_pool_noise", "fix_gas": True} + params = unpack_joint_params(flat, config) + + assert "bias_cad" in params + assert "W_cad" in params + assert "noise_coeffs" in params + assert "bias_gas" not in params + assert "W_gas" not in params + + def test_unpack_roundtrip_per_pool_noise(self): + from quantammsim.calibration.joint_fit import ( + pack_joint_params_fixed_gas, + unpack_joint_params, + ) + + k_attr = 4 + n_pools = 3 + bias_cad = 2.5 + W_cad = jnp.array([0.1, -0.2, 0.3, -0.4]) + noise = jnp.arange(n_pools * K_OBS, dtype=float).reshape(n_pools, K_OBS) + + flat = pack_joint_params_fixed_gas(bias_cad, W_cad, noise) + config = {"k_attr": k_attr, "n_pools": n_pools, + "mode": "per_pool_noise", "fix_gas": True} + params = unpack_joint_params(flat, config) + + np.testing.assert_allclose(params["bias_cad"], bias_cad) + np.testing.assert_allclose(params["W_cad"], W_cad) + np.testing.assert_allclose(params["noise_coeffs"], noise) + + def test_unpack_roundtrip_shared_noise(self): + from quantammsim.calibration.joint_fit import ( + pack_joint_params_fixed_gas, + unpack_joint_params, + ) + + k_attr = 4 + bias_cad = 1.5 + W_cad = jnp.array([0.5, -0.5, 0.1, -0.1]) + # shared_noise: (1+k_attr, K_OBS) = (5, 8) + noise = jnp.arange((1 + k_attr) * K_OBS, dtype=float).reshape( + 1 + k_attr, K_OBS + ) + + flat = pack_joint_params_fixed_gas(bias_cad, W_cad, noise) + config = {"k_attr": k_attr, "n_pools": 99, + "mode": "shared_noise", "fix_gas": True} + params = unpack_joint_params(flat, config) + + np.testing.assert_allclose(params["bias_cad"], bias_cad) + np.testing.assert_allclose(params["W_cad"], W_cad) + np.testing.assert_allclose(params["bias_noise"], noise[0]) + np.testing.assert_allclose(params["W_noise"], noise[1:]) + + +class TestJointLossFixedGas: + """Test joint loss function with fix_gas=True.""" + + def _make_loss_fn(self, matched_data, mode="per_pool_noise"): + from quantammsim.calibration.joint_fit import ( + make_initial_joint_params, + make_joint_loss_fn, + prepare_joint_data, + ) + + jdata = prepare_joint_data( + matched_data, fix_gas_to_chain=True + ) + init = make_initial_joint_params( + jdata, mode=mode, fix_gas=True + ) + loss_fn = make_joint_loss_fn( + jdata, mode=mode, fix_gas=True + ) + return loss_fn, init, jdata + + def test_loss_scalar(self, matched_data): + loss_fn, init, _ = self._make_loss_fn(matched_data) + loss = loss_fn(init) + assert loss.shape == () + + def test_loss_positive(self, matched_data): + loss_fn, init, _ = self._make_loss_fn(matched_data) + loss = loss_fn(init) + assert float(loss) >= 0 + + def test_loss_differentiable(self, matched_data): + loss_fn, init, _ = self._make_loss_fn(matched_data) + grad = jax.grad(loss_fn)(init) + assert grad.shape == init.shape + assert jnp.all(jnp.isfinite(grad)) + + def test_loss_grad_nonzero(self, matched_data): + loss_fn, init, _ = self._make_loss_fn(matched_data) + grad = jax.grad(loss_fn)(init) + assert float(jnp.sum(jnp.abs(grad))) > 0 + + def test_no_gas_regularization(self, matched_data): + """With fix_gas=True, alpha_gas should have no effect.""" + from quantammsim.calibration.joint_fit import ( + make_initial_joint_params, + make_joint_loss_fn, + prepare_joint_data, + ) + + jdata = prepare_joint_data(matched_data, fix_gas_to_chain=True) + init = make_initial_joint_params(jdata, mode="per_pool_noise", fix_gas=True) + + loss_fn_a = make_joint_loss_fn( + jdata, mode="per_pool_noise", fix_gas=True, alpha_gas=0.0 + ) + loss_fn_b = make_joint_loss_fn( + jdata, mode="per_pool_noise", fix_gas=True, alpha_gas=100.0 + ) + + np.testing.assert_allclose( + float(loss_fn_a(init)), float(loss_fn_b(init)), rtol=1e-6 + ) + + def test_shared_noise_mode(self, matched_data): + loss_fn, init, _ = self._make_loss_fn( + matched_data, mode="shared_noise" + ) + loss = loss_fn(init) + assert loss.shape == () + assert float(loss) >= 0 + + def test_init_param_count_per_pool_noise(self, matched_data): + """Verify parameter count: 1(bias_cad) + k_attr(W_cad) + n_pools*K_OBS.""" + _, init, jdata = self._make_loss_fn(matched_data) + k_attr = jdata.x_attr.shape[1] + n_pools = len(jdata.pool_data) + expected = 1 + k_attr + n_pools * K_OBS + assert init.shape[0] == expected + + def test_init_param_count_shared_noise(self, matched_data): + """Verify: 1(bias_cad) + k_attr(W_cad) + (1+k_attr)*K_OBS.""" + _, init, jdata = self._make_loss_fn( + matched_data, mode="shared_noise" + ) + k_attr = jdata.x_attr.shape[1] + expected = 1 + k_attr + (1 + k_attr) * K_OBS + assert init.shape[0] == expected + + +class TestFitJointFixedGas: + """Test fit_joint with fix_gas_to_chain=True.""" + + def test_returns_result(self, matched_data): + from quantammsim.calibration.joint_fit import fit_joint + + result = fit_joint( + matched_data, mode="per_pool_noise", + fix_gas_to_chain=True, maxiter=20, + ) + assert isinstance(result, dict) + for key in ["bias_cad", "W_cad", "loss", "converged", "fix_gas"]: + assert key in result, f"Missing key: {key}" + + def test_fix_gas_flag_stored(self, matched_data): + from quantammsim.calibration.joint_fit import fit_joint + + result = fit_joint( + matched_data, mode="per_pool_noise", + fix_gas_to_chain=True, maxiter=10, + ) + assert result["fix_gas"] is True + + def test_gas_per_pool_stored(self, matched_data): + """Result should contain gas_per_pool with chain-level values.""" + from quantammsim.calibration.joint_fit import fit_joint + from quantammsim.calibration.loss import CHAIN_GAS_USD + + result = fit_joint( + matched_data, mode="per_pool_noise", + fix_gas_to_chain=True, maxiter=10, + ) + assert "gas_per_pool" in result + for i, pid in enumerate(result["pool_ids"]): + chain = matched_data[pid]["chain"] + expected = CHAIN_GAS_USD.get(chain, 1.0) + assert result["gas_per_pool"][i] == expected + + def test_loss_decreases(self, matched_data): + from quantammsim.calibration.joint_fit import fit_joint + + result = fit_joint( + matched_data, mode="per_pool_noise", + fix_gas_to_chain=True, maxiter=50, + ) + assert result["loss"] <= result["init_loss"] + + def test_w_gas_is_zeros(self, matched_data): + """With fixed gas, W_gas should be zeros (placeholder).""" + from quantammsim.calibration.joint_fit import fit_joint + + result = fit_joint( + matched_data, mode="per_pool_noise", + fix_gas_to_chain=True, maxiter=10, + ) + np.testing.assert_allclose(result["W_gas"], 0.0) + + def test_bias_gas_is_zero(self, matched_data): + from quantammsim.calibration.joint_fit import fit_joint + + result = fit_joint( + matched_data, mode="per_pool_noise", + fix_gas_to_chain=True, maxiter=10, + ) + assert result["bias_gas"] == 0.0 + + def test_warm_start_from_option_c(self, matched_data): + from quantammsim.calibration.joint_fit import fit_joint + from quantammsim.calibration.per_pool_fit import fit_all_pools + + option_c = fit_all_pools(matched_data, fix_gas_to_chain=True) + result = fit_joint( + matched_data, mode="per_pool_noise", + fix_gas_to_chain=True, + init_from_option_c=option_c, + maxiter=20, + ) + assert result["loss"] >= 0 + + def test_shared_noise_fixed_gas(self, matched_data): + from quantammsim.calibration.joint_fit import fit_joint + + result = fit_joint( + matched_data, mode="shared_noise", + fix_gas_to_chain=True, maxiter=20, + ) + assert "W_noise" in result + assert result["fix_gas"] is True + + def test_predict_new_pool_fixed_gas(self, matched_data): + """predict_new_pool_joint should still work with fixed-gas results.""" + from quantammsim.calibration.joint_fit import fit_joint, predict_new_pool_joint + + result = fit_joint( + matched_data, mode="shared_noise", + fix_gas_to_chain=True, maxiter=20, + ) + k_attr = result["W_cad"].shape[0] + x_attr_new = np.zeros(k_attr) + pred = predict_new_pool_joint(result, x_attr_new) + assert "cadence_minutes" in pred + assert pred["cadence_minutes"] > 0 + # gas_usd comes from bias_gas=0 + W_gas=0 → exp(0)=1.0 + assert pred["gas_usd"] > 0 + + +class TestMakeBoundsFixedGas: + """Test _make_bounds with fix_gas=True.""" + + def test_bounds_count_per_pool_noise(self): + from quantammsim.calibration.joint_fit import _make_bounds + + k_attr = 6 + n_pools = 3 + bounds = _make_bounds(k_attr, n_pools, "per_pool_noise", fix_gas=True) + # 1(bias_cad) + 6(W_cad) + 3*8(noise) = 31 + assert len(bounds) == 1 + k_attr + n_pools * K_OBS + + def test_bounds_count_shared_noise(self): + from quantammsim.calibration.joint_fit import _make_bounds + + k_attr = 6 + n_pools = 3 + bounds = _make_bounds(k_attr, n_pools, "shared_noise", fix_gas=True) + # 1(bias_cad) + 6(W_cad) + (1+6)*8(noise) = 63 + assert len(bounds) == 1 + k_attr + (1 + k_attr) * K_OBS + + def test_bounds_fewer_with_fixed_gas(self): + from quantammsim.calibration.joint_fit import _make_bounds + + k_attr = 6 + n_pools = 3 + free = _make_bounds(k_attr, n_pools, "per_pool_noise", fix_gas=False) + fixed = _make_bounds(k_attr, n_pools, "per_pool_noise", fix_gas=True) + # Difference: 1(bias_gas) + k_attr(W_gas) + assert len(free) - len(fixed) == 1 + k_attr diff --git a/tests/calibration/test_loss_fixed_gas.py b/tests/calibration/test_loss_fixed_gas.py new file mode 100644 index 0000000..f3cbf70 --- /dev/null +++ b/tests/calibration/test_loss_fixed_gas.py @@ -0,0 +1,281 @@ +"""Tests for fixed-gas extensions in quantammsim.calibration.loss.""" + +import jax +import jax.numpy as jnp +import numpy as np +import pytest + +from tests.calibration.conftest import K_OBS, N_DAYS + + +class TestChainGasUSD: + """Test CHAIN_GAS_USD constants are correct and complete.""" + + def test_known_chains(self): + from quantammsim.calibration.loss import CHAIN_GAS_USD + + assert CHAIN_GAS_USD["MAINNET"] == 1.0 + assert CHAIN_GAS_USD["POLYGON"] == 0.005 + assert CHAIN_GAS_USD["GNOSIS"] == 0.001 + assert CHAIN_GAS_USD["ARBITRUM"] == 0.01 + assert CHAIN_GAS_USD["BASE"] == 0.005 + assert CHAIN_GAS_USD["SONIC"] == 0.005 + + def test_all_values_positive(self): + from quantammsim.calibration.loss import CHAIN_GAS_USD + + for chain, cost in CHAIN_GAS_USD.items(): + assert cost > 0, f"{chain} gas cost must be positive" + + def test_mainnet_most_expensive(self): + from quantammsim.calibration.loss import CHAIN_GAS_USD + + mainnet = CHAIN_GAS_USD["MAINNET"] + for chain, cost in CHAIN_GAS_USD.items(): + if chain != "MAINNET": + assert cost < mainnet, f"{chain} should be cheaper than MAINNET" + + def test_six_chains(self): + from quantammsim.calibration.loss import CHAIN_GAS_USD + + assert len(CHAIN_GAS_USD) == 6 + + +class TestPackUnpackFixedGas: + """Test pack/unpack for fixed-gas param vectors.""" + + def test_pack_shape(self): + from quantammsim.calibration.loss import pack_params_fixed_gas + + flat = pack_params_fixed_gas(2.5, jnp.zeros(K_OBS)) + assert flat.shape == (1 + K_OBS,) + + def test_pack_shape_is_one_shorter_than_free(self): + from quantammsim.calibration.loss import pack_params, pack_params_fixed_gas + + free = pack_params(2.5, 0.0, jnp.zeros(K_OBS)) + fixed = pack_params_fixed_gas(2.5, jnp.zeros(K_OBS)) + assert free.shape[0] == fixed.shape[0] + 1 + + def test_roundtrip(self): + from quantammsim.calibration.loss import ( + pack_params_fixed_gas, + unpack_params_fixed_gas, + ) + + log_cad = 2.5 + noise_coeffs = jnp.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]) + + flat = pack_params_fixed_gas(log_cad, noise_coeffs) + lc, nc = unpack_params_fixed_gas(flat) + + np.testing.assert_allclose(lc, log_cad) + np.testing.assert_allclose(nc, noise_coeffs) + + def test_unpack_log_cadence_position(self): + """log_cadence is the first element.""" + from quantammsim.calibration.loss import pack_params_fixed_gas + + flat = pack_params_fixed_gas(3.14, jnp.ones(K_OBS) * 99.0) + np.testing.assert_allclose(flat[0], 3.14) + + def test_unpack_noise_coeffs_position(self): + """noise_coeffs are elements [1:].""" + from quantammsim.calibration.loss import pack_params_fixed_gas + + nc = jnp.arange(1, K_OBS + 1, dtype=float) + flat = pack_params_fixed_gas(0.0, nc) + np.testing.assert_allclose(flat[1:], nc) + + +class TestPoolLossFixedGas: + """Test pool_loss_fixed_gas with pinned numerical values.""" + + def _make_params(self, log_cad=None, noise_coeffs=None): + from quantammsim.calibration.loss import pack_params_fixed_gas + + if log_cad is None: + log_cad = float(jnp.log(jnp.array(12.0))) + if noise_coeffs is None: + noise_coeffs = jnp.zeros(K_OBS).at[0].set(8.0) + return pack_params_fixed_gas(log_cad, noise_coeffs) + + def _make_inputs(self, synthetic_pool_coeffs, synthetic_x_obs): + n_obs = synthetic_x_obs.shape[0] + n_days = int(synthetic_pool_coeffs.values.shape[2]) + day_indices = jnp.array(np.arange(n_obs) % n_days) + y_obs = jnp.ones(n_obs) * 9.0 + return jnp.array(synthetic_x_obs), y_obs, day_indices + + def test_scalar_output(self, synthetic_pool_coeffs, synthetic_x_obs): + from quantammsim.calibration.loss import pool_loss_fixed_gas + + params = self._make_params() + x_obs, y_obs, day_indices = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + fixed_log_gas = jnp.log(jnp.array(1.0)) + loss = pool_loss_fixed_gas( + params, fixed_log_gas, synthetic_pool_coeffs, x_obs, y_obs, day_indices + ) + assert loss.shape == () + + def test_positive(self, synthetic_pool_coeffs, synthetic_x_obs): + from quantammsim.calibration.loss import pool_loss_fixed_gas + + params = self._make_params() + x_obs, y_obs, day_indices = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + fixed_log_gas = jnp.log(jnp.array(1.0)) + loss = pool_loss_fixed_gas( + params, fixed_log_gas, synthetic_pool_coeffs, x_obs, y_obs, day_indices + ) + assert float(loss) >= 0 + + def test_zero_when_perfect(self, synthetic_pool_coeffs, synthetic_x_obs): + """Construct y_obs = log(V_arb + V_noise) exactly, verify loss ≈ 0.""" + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + from quantammsim.calibration.loss import ( + noise_volume, + pack_params_fixed_gas, + pool_loss_fixed_gas, + ) + + log_cad = jnp.log(jnp.array(12.0)) + fixed_log_gas = jnp.log(jnp.array(1.0)) + noise_coeffs = jnp.zeros(K_OBS).at[0].set(8.0) + + v_arb_all = interpolate_pool_daily( + synthetic_pool_coeffs, log_cad, jnp.exp(fixed_log_gas) + ) + n_obs = synthetic_x_obs.shape[0] + n_days = int(synthetic_pool_coeffs.values.shape[2]) + day_indices = jnp.array(np.arange(n_obs) % n_days) + v_arb = v_arb_all[day_indices] + v_noise = noise_volume(noise_coeffs, jnp.array(synthetic_x_obs)) + y_obs = jnp.log(jnp.maximum(v_arb + v_noise, 1e-6)) + + params = pack_params_fixed_gas(float(log_cad), noise_coeffs) + loss = pool_loss_fixed_gas( + params, fixed_log_gas, synthetic_pool_coeffs, + jnp.array(synthetic_x_obs), y_obs, day_indices, + ) + assert float(loss) < 1e-6 + + def test_matches_free_gas_at_same_value( + self, synthetic_pool_coeffs, synthetic_x_obs + ): + """Fixed-gas loss should equal free-gas loss when gas matches.""" + from quantammsim.calibration.loss import ( + pack_params, + pack_params_fixed_gas, + pool_loss, + pool_loss_fixed_gas, + ) + + log_cad = float(jnp.log(jnp.array(12.0))) + log_gas = float(jnp.log(jnp.array(1.0))) + noise_coeffs = jnp.zeros(K_OBS).at[0].set(8.0) + + x_obs, y_obs, day_indices = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + + free_params = pack_params(log_cad, log_gas, noise_coeffs) + fixed_params = pack_params_fixed_gas(log_cad, noise_coeffs) + + loss_free = pool_loss( + free_params, synthetic_pool_coeffs, x_obs, y_obs, day_indices + ) + loss_fixed = pool_loss_fixed_gas( + fixed_params, jnp.array(log_gas), synthetic_pool_coeffs, + x_obs, y_obs, day_indices, + ) + np.testing.assert_allclose(float(loss_free), float(loss_fixed), rtol=1e-6) + + def test_different_fixed_gas_different_loss( + self, synthetic_pool_coeffs, synthetic_x_obs + ): + """Different gas values should give different losses.""" + from quantammsim.calibration.loss import pool_loss_fixed_gas + + params = self._make_params() + x_obs, y_obs, day_indices = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + + loss_low = pool_loss_fixed_gas( + params, jnp.log(jnp.array(0.01)), + synthetic_pool_coeffs, x_obs, y_obs, day_indices, + ) + loss_high = pool_loss_fixed_gas( + params, jnp.log(jnp.array(10.0)), + synthetic_pool_coeffs, x_obs, y_obs, day_indices, + ) + assert float(loss_low) != float(loss_high) + + def test_grad_wrt_params_only(self, synthetic_pool_coeffs, synthetic_x_obs): + """Gradient is only w.r.t. params_flat (argnums=0), not fixed_log_gas.""" + from quantammsim.calibration.loss import pool_loss_fixed_gas + + params = self._make_params() + x_obs, y_obs, day_indices = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + fixed_log_gas = jnp.log(jnp.array(1.0)) + + grad = jax.grad(pool_loss_fixed_gas, argnums=0)( + params, fixed_log_gas, synthetic_pool_coeffs, x_obs, y_obs, day_indices, + ) + assert grad.shape == (1 + K_OBS,) + assert jnp.all(jnp.isfinite(grad)) + + def test_no_grad_wrt_fixed_gas(self, synthetic_pool_coeffs, synthetic_x_obs): + """fixed_log_gas should not be in the gradient (it's a constant).""" + from quantammsim.calibration.loss import pool_loss_fixed_gas + + params = self._make_params() + x_obs, y_obs, day_indices = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + fixed_log_gas = jnp.log(jnp.array(1.0)) + + # Gradient w.r.t. argnums=0 has shape (1+K_OBS,) — no gas element + grad = jax.grad(pool_loss_fixed_gas, argnums=0)( + params, fixed_log_gas, synthetic_pool_coeffs, x_obs, y_obs, day_indices, + ) + # If there were a gas gradient, shape would be (2+K_OBS,) + assert grad.shape[0] == 1 + K_OBS + + def test_grad_wrt_log_cadence_finite( + self, synthetic_pool_coeffs, synthetic_x_obs + ): + from quantammsim.calibration.loss import pool_loss_fixed_gas + + params = self._make_params() + x_obs, y_obs, day_indices = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + fixed_log_gas = jnp.log(jnp.array(1.0)) + + grad = jax.grad(pool_loss_fixed_gas, argnums=0)( + params, fixed_log_gas, synthetic_pool_coeffs, x_obs, y_obs, day_indices, + ) + assert jnp.isfinite(grad[0]) # log_cadence gradient + + def test_grad_wrt_noise_coeffs_finite( + self, synthetic_pool_coeffs, synthetic_x_obs + ): + from quantammsim.calibration.loss import pool_loss_fixed_gas + + params = self._make_params() + x_obs, y_obs, day_indices = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + fixed_log_gas = jnp.log(jnp.array(1.0)) + + grad = jax.grad(pool_loss_fixed_gas, argnums=0)( + params, fixed_log_gas, synthetic_pool_coeffs, x_obs, y_obs, day_indices, + ) + assert jnp.all(jnp.isfinite(grad[1:])) # noise_coeffs gradients diff --git a/tests/calibration/test_per_pool_fit_fixed_gas.py b/tests/calibration/test_per_pool_fit_fixed_gas.py new file mode 100644 index 0000000..f0a7d7f --- /dev/null +++ b/tests/calibration/test_per_pool_fit_fixed_gas.py @@ -0,0 +1,258 @@ +"""Tests for fixed-gas mode in quantammsim.calibration.per_pool_fit.""" + +import jax.numpy as jnp +import numpy as np +import pytest + +from tests.calibration.conftest import K_OBS, N_DAYS, POOL_IDS_FULL, POOL_PREFIXES + + +class TestInitialGuessFixedGas: + """Test make_initial_guess_fixed_gas.""" + + def test_shape(self, synthetic_x_obs): + from quantammsim.calibration.per_pool_fit import make_initial_guess_fixed_gas + + n_obs = synthetic_x_obs.shape[0] + y_obs = np.ones(n_obs) * 9.0 + init = make_initial_guess_fixed_gas(synthetic_x_obs, y_obs) + assert init.shape == (1 + K_OBS,) + + def test_one_shorter_than_free(self, synthetic_x_obs): + from quantammsim.calibration.per_pool_fit import ( + make_initial_guess, + make_initial_guess_fixed_gas, + ) + + n_obs = synthetic_x_obs.shape[0] + y_obs = np.ones(n_obs) * 9.0 + free = make_initial_guess(synthetic_x_obs, y_obs) + fixed = make_initial_guess_fixed_gas(synthetic_x_obs, y_obs) + assert free.shape[0] == fixed.shape[0] + 1 + + def test_log_cadence_default(self, synthetic_x_obs): + from quantammsim.calibration.per_pool_fit import make_initial_guess_fixed_gas + + n_obs = synthetic_x_obs.shape[0] + y_obs = np.ones(n_obs) * 9.0 + init = make_initial_guess_fixed_gas(synthetic_x_obs, y_obs) + np.testing.assert_allclose(init[0], np.log(12.0), atol=0.01) + + def test_noise_from_ols(self, synthetic_x_obs): + from quantammsim.calibration.per_pool_fit import make_initial_guess_fixed_gas + + n_obs = synthetic_x_obs.shape[0] + y_obs = np.ones(n_obs) * 9.0 + init = make_initial_guess_fixed_gas(synthetic_x_obs, y_obs) + noise_coeffs = init[1:] + assert len(noise_coeffs) == K_OBS + assert np.all(np.isfinite(noise_coeffs)) + + def test_noise_matches_free_gas_noise(self, synthetic_x_obs): + """OLS noise coeffs should be identical for free and fixed-gas init.""" + from quantammsim.calibration.per_pool_fit import ( + make_initial_guess, + make_initial_guess_fixed_gas, + ) + + n_obs = synthetic_x_obs.shape[0] + y_obs = np.ones(n_obs) * 9.0 + free = make_initial_guess(synthetic_x_obs, y_obs) + fixed = make_initial_guess_fixed_gas(synthetic_x_obs, y_obs) + np.testing.assert_allclose(free[2:], fixed[1:]) + + +class TestFitSinglePoolFixedGas: + """Test fit_single_pool with fixed_gas_usd.""" + + def _make_inputs(self, synthetic_pool_coeffs, synthetic_x_obs): + n_obs = synthetic_x_obs.shape[0] + n_days = int(synthetic_pool_coeffs.values.shape[2]) + day_indices = np.arange(n_obs) % n_days + y_obs = np.ones(n_obs) * 9.0 + return synthetic_x_obs, y_obs, day_indices + + def test_returns_result_dict(self, synthetic_pool_coeffs, synthetic_x_obs): + from quantammsim.calibration.per_pool_fit import fit_single_pool + + x_obs, y_obs, day_idx = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + result = fit_single_pool( + synthetic_pool_coeffs, x_obs, y_obs, day_idx, fixed_gas_usd=1.0 + ) + for key in [ + "log_cadence", "log_gas", "noise_coeffs", "loss", + "converged", "gas_fixed", + ]: + assert key in result, f"Missing key: {key}" + + def test_gas_fixed_flag(self, synthetic_pool_coeffs, synthetic_x_obs): + from quantammsim.calibration.per_pool_fit import fit_single_pool + + x_obs, y_obs, day_idx = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + result = fit_single_pool( + synthetic_pool_coeffs, x_obs, y_obs, day_idx, fixed_gas_usd=1.0 + ) + assert result["gas_fixed"] is True + + def test_free_gas_flag(self, synthetic_pool_coeffs, synthetic_x_obs): + from quantammsim.calibration.per_pool_fit import fit_single_pool + + x_obs, y_obs, day_idx = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + result = fit_single_pool( + synthetic_pool_coeffs, x_obs, y_obs, day_idx + ) + assert result["gas_fixed"] is False + + def test_gas_usd_pinned(self, synthetic_pool_coeffs, synthetic_x_obs): + """gas_usd in result must exactly match the fixed value.""" + from quantammsim.calibration.per_pool_fit import fit_single_pool + + x_obs, y_obs, day_idx = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + for gas_val in [0.001, 0.01, 0.5, 1.0, 5.0]: + result = fit_single_pool( + synthetic_pool_coeffs, x_obs, y_obs, day_idx, + fixed_gas_usd=gas_val, + ) + assert result["gas_usd"] == gas_val + + def test_log_gas_pinned(self, synthetic_pool_coeffs, synthetic_x_obs): + """log_gas must equal log(fixed_gas_usd).""" + from quantammsim.calibration.per_pool_fit import fit_single_pool + + x_obs, y_obs, day_idx = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + result = fit_single_pool( + synthetic_pool_coeffs, x_obs, y_obs, day_idx, fixed_gas_usd=2.5, + ) + np.testing.assert_allclose( + result["log_gas"], np.log(2.5), rtol=1e-6, + ) + + def test_cadence_in_range(self, synthetic_pool_coeffs, synthetic_x_obs): + from quantammsim.calibration.per_pool_fit import fit_single_pool + + x_obs, y_obs, day_idx = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + result = fit_single_pool( + synthetic_pool_coeffs, x_obs, y_obs, day_idx, fixed_gas_usd=1.0, + ) + cadence = result["cadence_minutes"] + assert 1.0 <= cadence <= 60.0 + + def test_noise_coeffs_length(self, synthetic_pool_coeffs, synthetic_x_obs): + from quantammsim.calibration.per_pool_fit import fit_single_pool + + x_obs, y_obs, day_idx = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + result = fit_single_pool( + synthetic_pool_coeffs, x_obs, y_obs, day_idx, fixed_gas_usd=1.0, + ) + assert len(result["noise_coeffs"]) == K_OBS + + def test_loss_decreases_from_init(self, synthetic_pool_coeffs, synthetic_x_obs): + from quantammsim.calibration.loss import pack_params_fixed_gas, pool_loss_fixed_gas + from quantammsim.calibration.per_pool_fit import ( + fit_single_pool, + make_initial_guess_fixed_gas, + ) + + x_obs, y_obs, day_idx = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + init = make_initial_guess_fixed_gas(x_obs, y_obs) + fixed_log_gas = jnp.float64(np.log(1.0)) + init_loss = float(pool_loss_fixed_gas( + jnp.array(init), fixed_log_gas, synthetic_pool_coeffs, + jnp.array(x_obs), jnp.array(y_obs), jnp.array(day_idx), + )) + + result = fit_single_pool( + synthetic_pool_coeffs, x_obs, y_obs, day_idx, fixed_gas_usd=1.0, + ) + assert result["loss"] <= init_loss + + def test_different_fixed_gas_different_cadence( + self, synthetic_pool_coeffs, synthetic_x_obs + ): + """Different gas values should (generally) lead to different fitted cadences.""" + from quantammsim.calibration.per_pool_fit import fit_single_pool + + x_obs, y_obs, day_idx = self._make_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + r_low = fit_single_pool( + synthetic_pool_coeffs, x_obs, y_obs, day_idx, fixed_gas_usd=0.001, + ) + r_high = fit_single_pool( + synthetic_pool_coeffs, x_obs, y_obs, day_idx, fixed_gas_usd=5.0, + ) + # Cadences should differ (gas-cadence tradeoff) + assert abs(r_low["log_cadence"] - r_high["log_cadence"]) > 0.01 + + +class TestFitAllPoolsFixedGas: + """Test fit_all_pools with fix_gas_to_chain=True.""" + + def _make_matched(self, synthetic_daily_grid, synthetic_panel, tmp_path): + from quantammsim.calibration.pool_data import match_grids_to_panel + + grid_dir = tmp_path / "grids" + grid_dir.mkdir() + for prefix in POOL_PREFIXES: + synthetic_daily_grid.to_parquet( + grid_dir / f"{prefix}_daily.parquet", index=False + ) + return match_grids_to_panel(str(grid_dir), synthetic_panel) + + def test_all_gas_fixed( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + from quantammsim.calibration.per_pool_fit import fit_all_pools + + matched = self._make_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + results = fit_all_pools(matched, fix_gas_to_chain=True) + for prefix, res in results.items(): + assert res["gas_fixed"] is True + + def test_gas_matches_chain( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + """Each pool's gas_usd should match CHAIN_GAS_USD[chain].""" + from quantammsim.calibration.loss import CHAIN_GAS_USD + from quantammsim.calibration.per_pool_fit import fit_all_pools + + matched = self._make_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + results = fit_all_pools(matched, fix_gas_to_chain=True) + for prefix, res in results.items(): + chain = res["chain"] + expected = CHAIN_GAS_USD.get(chain, 1.0) + assert res["gas_usd"] == expected, ( + f"{prefix} ({chain}): gas_usd={res['gas_usd']} != {expected}" + ) + + def test_free_gas_not_fixed( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + from quantammsim.calibration.per_pool_fit import fit_all_pools + + matched = self._make_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + results = fit_all_pools(matched, fix_gas_to_chain=False) + for prefix, res in results.items(): + assert res["gas_fixed"] is False diff --git a/tests/calibration/test_pool_data_volatility.py b/tests/calibration/test_pool_data_volatility.py new file mode 100644 index 0000000..6c5792e --- /dev/null +++ b/tests/calibration/test_pool_data_volatility.py @@ -0,0 +1,232 @@ +"""Tests for Binance volatility and TOKEN_MAP in quantammsim.calibration.pool_data.""" + +import numpy as np +import pandas as pd +import pytest +import os + +from tests.calibration.conftest import POOL_IDS_FULL + + +class TestTokenMap: + """Test TOKEN_MAP resolves Balancer tokens to Binance symbols correctly.""" + + def test_wrapped_native(self): + from quantammsim.calibration.pool_data import _resolve_binance_symbol + + assert _resolve_binance_symbol("WBTC") == "BTC" + assert _resolve_binance_symbol("WETH") == "ETH" + assert _resolve_binance_symbol("cbBTC") == "BTC" + + def test_lst_to_underlying(self): + from quantammsim.calibration.pool_data import _resolve_binance_symbol + + assert _resolve_binance_symbol("wstETH") == "ETH" + assert _resolve_binance_symbol("stETH") == "ETH" + assert _resolve_binance_symbol("rETH") == "ETH" + assert _resolve_binance_symbol("cbETH") == "ETH" + + def test_vault_tokens(self): + from quantammsim.calibration.pool_data import _resolve_binance_symbol + + assert _resolve_binance_symbol("waEthLidoWETH") == "ETH" + assert _resolve_binance_symbol("waEthLidowstETH") == "ETH" + assert _resolve_binance_symbol("waBasWETH") == "ETH" + assert _resolve_binance_symbol("waGnowstETH") == "ETH" + assert _resolve_binance_symbol("waGnoGNO") == "GNO" + + def test_stablecoins_map_to_usdc(self): + from quantammsim.calibration.pool_data import _resolve_binance_symbol + + for stable in ["DAI", "WXDAI", "sDAI", "USDT", "DOLA", "scUSD", + "USDC.e", "USDbC", "waBasUSDC"]: + assert _resolve_binance_symbol(stable) == "USDC", ( + f"{stable} should map to USDC" + ) + + def test_matic_variants(self): + from quantammsim.calibration.pool_data import _resolve_binance_symbol + + assert _resolve_binance_symbol("wPOL") == "POL" + assert _resolve_binance_symbol("WMATIC") == "POL" + assert _resolve_binance_symbol("MATIC") == "POL" + + def test_sonic_variants(self): + from quantammsim.calibration.pool_data import _resolve_binance_symbol + + assert _resolve_binance_symbol("wS") == "S" + assert _resolve_binance_symbol("stS") == "S" + + def test_passthrough_unknown(self): + from quantammsim.calibration.pool_data import _resolve_binance_symbol + + assert _resolve_binance_symbol("AAVE") == "AAVE" + assert _resolve_binance_symbol("LINK") == "LINK" + assert _resolve_binance_symbol("SNX") == "SNX" + + def test_jitosol(self): + from quantammsim.calibration.pool_data import _resolve_binance_symbol + + assert _resolve_binance_symbol("JitoSOL") == "SOL" + + +class TestComputeBinancePairVolatility: + """Test compute_binance_pair_volatility with synthetic Binance-like data.""" + + @pytest.fixture + def fake_binance_dir(self, tmp_path): + """Create fake Binance minute parquets for ETH and AAVE.""" + np.random.seed(42) + n_minutes = 24 * 60 * 7 # 7 days of minute data + base_ts = int(pd.Timestamp("2025-01-01").timestamp() * 1000) + unix = base_ts + np.arange(n_minutes) * 60_000 + + # ETH: geometric brownian motion starting at 3000 + eth_log_returns = np.random.normal(0, 0.0005, n_minutes) + eth_prices = 3000.0 * np.exp(np.cumsum(eth_log_returns)) + eth_df = pd.DataFrame({"unix": unix, "close": eth_prices}) + eth_df.to_parquet(tmp_path / "ETH_USD.parquet", index=False) + + # AAVE: correlated with ETH but with higher vol + aave_log_returns = 0.6 * eth_log_returns + 0.4 * np.random.normal( + 0, 0.001, n_minutes + ) + aave_prices = 200.0 * np.exp(np.cumsum(aave_log_returns)) + aave_df = pd.DataFrame({"unix": unix, "close": aave_prices}) + aave_df.to_parquet(tmp_path / "AAVE_USD.parquet", index=False) + + # USDC: constant at $1 (stablecoin proxy) + usdc_df = pd.DataFrame({ + "unix": unix, "close": np.ones(n_minutes), + }) + usdc_df.to_parquet(tmp_path / "USDC_USD.parquet", index=False) + + return str(tmp_path) + + def test_returns_series(self, fake_binance_dir): + from quantammsim.calibration.pool_data import compute_binance_pair_volatility + + vol = compute_binance_pair_volatility("WETH", "AAVE", fake_binance_dir) + assert isinstance(vol, pd.Series) + assert len(vol) > 0 + + def test_values_positive(self, fake_binance_dir): + from quantammsim.calibration.pool_data import compute_binance_pair_volatility + + vol = compute_binance_pair_volatility("WETH", "AAVE", fake_binance_dir) + assert (vol > 0).all() + + def test_annualized_magnitude(self, fake_binance_dir): + """Annualized vol should be in [0.01, 10.0] range for typical assets.""" + from quantammsim.calibration.pool_data import compute_binance_pair_volatility + + vol = compute_binance_pair_volatility("WETH", "AAVE", fake_binance_dir) + assert vol.median() > 0.01 + assert vol.median() < 10.0 + + def test_stable_vs_volatile(self, fake_binance_dir): + """ETH/USDC should just use ETH price (one-sided).""" + from quantammsim.calibration.pool_data import compute_binance_pair_volatility + + vol = compute_binance_pair_volatility("WETH", "USDC", fake_binance_dir) + assert isinstance(vol, pd.Series) + assert len(vol) > 0 + + def test_stable_stable_returns_none(self, fake_binance_dir): + from quantammsim.calibration.pool_data import compute_binance_pair_volatility + + vol = compute_binance_pair_volatility("USDC", "DAI", fake_binance_dir) + assert vol is None + + def test_same_underlying_returns_none(self, fake_binance_dir): + from quantammsim.calibration.pool_data import compute_binance_pair_volatility + + # WETH and wstETH both map to ETH + vol = compute_binance_pair_volatility("WETH", "wstETH", fake_binance_dir) + assert vol is None + + def test_missing_data_returns_none(self, fake_binance_dir): + from quantammsim.calibration.pool_data import compute_binance_pair_volatility + + vol = compute_binance_pair_volatility("WETH", "MAGIC", fake_binance_dir) + assert vol is None + + def test_daily_index_type(self, fake_binance_dir): + """Index should be datetime.date objects.""" + from quantammsim.calibration.pool_data import compute_binance_pair_volatility + import datetime + + vol = compute_binance_pair_volatility("WETH", "AAVE", fake_binance_dir) + for d in vol.index: + assert isinstance(d, datetime.date) + + def test_seven_days_of_data(self, fake_binance_dir): + """7 days of minute data → ~6 days of vol (first day partial).""" + from quantammsim.calibration.pool_data import compute_binance_pair_volatility + + vol = compute_binance_pair_volatility("WETH", "AAVE", fake_binance_dir) + assert 5 <= len(vol) <= 7 + + def test_stable_a_volatile_b(self, fake_binance_dir): + """When token_a is stable, should use 1/price_b.""" + from quantammsim.calibration.pool_data import compute_binance_pair_volatility + + vol = compute_binance_pair_volatility("DAI", "WETH", fake_binance_dir) + assert isinstance(vol, pd.Series) + assert len(vol) > 0 + + +class TestReplacePanelVolatility: + """Test replace_panel_volatility_with_binance.""" + + @pytest.fixture + def fake_binance_dir(self, tmp_path): + """Minimal fake Binance data for 3 days.""" + np.random.seed(42) + n_minutes = 24 * 60 * 3 + base_ts = int(pd.Timestamp("2025-12-01").timestamp() * 1000) + unix = base_ts + np.arange(n_minutes) * 60_000 + + eth_prices = 3000.0 + np.cumsum(np.random.normal(0, 1.0, n_minutes)) + eth_df = pd.DataFrame({"unix": unix, "close": eth_prices}) + eth_df.to_parquet(tmp_path / "ETH_USD.parquet", index=False) + + btc_prices = 60000.0 + np.cumsum(np.random.normal(0, 5.0, n_minutes)) + btc_df = pd.DataFrame({"unix": unix, "close": btc_prices}) + btc_df.to_parquet(tmp_path / "BTC_USD.parquet", index=False) + + return str(tmp_path) + + def test_returns_dataframe(self, synthetic_panel, fake_binance_dir): + from quantammsim.calibration.pool_data import replace_panel_volatility_with_binance + + result = replace_panel_volatility_with_binance( + synthetic_panel, fake_binance_dir + ) + assert isinstance(result, pd.DataFrame) + + def test_does_not_modify_input(self, synthetic_panel, fake_binance_dir): + from quantammsim.calibration.pool_data import replace_panel_volatility_with_binance + + original_vol = synthetic_panel["volatility"].copy() + replace_panel_volatility_with_binance( + synthetic_panel, fake_binance_dir + ) + pd.testing.assert_series_equal(synthetic_panel["volatility"], original_vol) + + def test_volatility_column_exists(self, synthetic_panel, fake_binance_dir): + from quantammsim.calibration.pool_data import replace_panel_volatility_with_binance + + result = replace_panel_volatility_with_binance( + synthetic_panel, fake_binance_dir + ) + assert "volatility" in result.columns + + def test_no_nans_introduced(self, synthetic_panel, fake_binance_dir): + """Pools without Binance data should keep original volatility, not NaN.""" + from quantammsim.calibration.pool_data import replace_panel_volatility_with_binance + + result = replace_panel_volatility_with_binance( + synthetic_panel, fake_binance_dir + ) + assert result["volatility"].notna().all() From 6f98236b4a799682c4c66361dcb9e5177a3c697a Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 10 Mar 2026 00:52:55 +0000 Subject: [PATCH 07/57] =?UTF-8?q?test:=20strengthen=20calibration=20tests?= =?UTF-8?q?=20=E2=80=94=20fix=20vacuous=20assertions=20and=20add=20missing?= =?UTF-8?q?=20coverage?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replace near-vacuous tests with substantive ones: - test_loss_with_heterogeneous_y (trivial !=) → test_day_indices_affect_loss - test_predict_with_nonzero_attrs (conditional guard) → test_predict_matches_linear_model - test_stable_vs_volatile_uses_single_asset (20x range) → hand-computed ground truth Add missing coverage: - PCHIP boundary clamping (cadence below min, gas above max) - replace_panel_volatility correctness (replaced values match compute_binance_pair_volatility) - Ground truth recovery, pinned loss values, OLS coefficient pinning Fix misleading name: test_grad_invariant_to_fixed_gas_perturbation → test_grad_changes_with_gas --- tests/calibration/test_joint_fit_fixed_gas.py | 138 ++++++--- tests/calibration/test_loss_fixed_gas.py | 169 +++++++--- .../test_per_pool_fit_fixed_gas.py | 157 +++++++++- .../calibration/test_pool_data_volatility.py | 293 +++++++++++++++--- 4 files changed, 621 insertions(+), 136 deletions(-) diff --git a/tests/calibration/test_joint_fit_fixed_gas.py b/tests/calibration/test_joint_fit_fixed_gas.py index 6cbd3cc..7981ff4 100644 --- a/tests/calibration/test_joint_fit_fixed_gas.py +++ b/tests/calibration/test_joint_fit_fixed_gas.py @@ -61,6 +61,7 @@ def test_mainnet_gas_is_log_1(self, matched_data): from quantammsim.calibration.joint_fit import prepare_joint_data jdata = prepare_joint_data(matched_data, fix_gas_to_chain=True) + found = False for i, pid in enumerate(jdata.pool_ids): if matched_data[pid]["chain"] == "MAINNET": np.testing.assert_allclose( @@ -68,12 +69,15 @@ def test_mainnet_gas_is_log_1(self, matched_data): 0.0, atol=1e-6, ) + found = True + assert found, "No MAINNET pool found in test data" def test_arbitrum_gas_is_log_001(self, matched_data): """ARBITRUM pools should have fixed_log_gas = log(0.01).""" from quantammsim.calibration.joint_fit import prepare_joint_data jdata = prepare_joint_data(matched_data, fix_gas_to_chain=True) + found = False for i, pid in enumerate(jdata.pool_ids): if matched_data[pid]["chain"] == "ARBITRUM": np.testing.assert_allclose( @@ -81,6 +85,25 @@ def test_arbitrum_gas_is_log_001(self, matched_data): np.log(0.01), rtol=1e-6, ) + found = True + assert found, "No ARBITRUM pool found in test data" + + def test_x_attr_has_correct_shape(self, matched_data): + from quantammsim.calibration.joint_fit import prepare_joint_data + + jdata = prepare_joint_data(matched_data, fix_gas_to_chain=True) + assert jdata.x_attr.shape[0] == len(jdata.pool_ids) + assert jdata.x_attr.shape[1] == len(jdata.attr_names) + + def test_pool_data_has_obs_arrays(self, matched_data): + from quantammsim.calibration.joint_fit import prepare_joint_data + + jdata = prepare_joint_data(matched_data, fix_gas_to_chain=True) + for pd_i in jdata.pool_data: + assert pd_i["x_obs"].ndim == 2 + assert pd_i["y_obs"].ndim == 1 + assert pd_i["day_indices"].ndim == 1 + assert pd_i["x_obs"].shape[0] == pd_i["y_obs"].shape[0] class TestPackUnpackJointFixedGas: @@ -204,29 +227,15 @@ def _make_loss_fn(self, matched_data, mode="per_pool_noise"): ) return loss_fn, init, jdata - def test_loss_scalar(self, matched_data): - loss_fn, init, _ = self._make_loss_fn(matched_data) - loss = loss_fn(init) - assert loss.shape == () - - def test_loss_positive(self, matched_data): - loss_fn, init, _ = self._make_loss_fn(matched_data) - loss = loss_fn(init) - assert float(loss) >= 0 - - def test_loss_differentiable(self, matched_data): + def test_loss_differentiable_and_nonzero_grad(self, matched_data): loss_fn, init, _ = self._make_loss_fn(matched_data) grad = jax.grad(loss_fn)(init) assert grad.shape == init.shape assert jnp.all(jnp.isfinite(grad)) + assert float(jnp.sum(jnp.abs(grad))) > 1e-10 - def test_loss_grad_nonzero(self, matched_data): - loss_fn, init, _ = self._make_loss_fn(matched_data) - grad = jax.grad(loss_fn)(init) - assert float(jnp.sum(jnp.abs(grad))) > 0 - - def test_no_gas_regularization(self, matched_data): - """With fix_gas=True, alpha_gas should have no effect.""" + def test_no_gas_regularization_but_cad_regularization_works(self, matched_data): + """alpha_gas has no effect, but alpha_cad DOES.""" from quantammsim.calibration.joint_fit import ( make_initial_joint_params, make_joint_loss_fn, @@ -236,17 +245,31 @@ def test_no_gas_regularization(self, matched_data): jdata = prepare_joint_data(matched_data, fix_gas_to_chain=True) init = make_initial_joint_params(jdata, mode="per_pool_noise", fix_gas=True) + # alpha_gas shouldn't matter loss_fn_a = make_joint_loss_fn( jdata, mode="per_pool_noise", fix_gas=True, alpha_gas=0.0 ) loss_fn_b = make_joint_loss_fn( jdata, mode="per_pool_noise", fix_gas=True, alpha_gas=100.0 ) - np.testing.assert_allclose( float(loss_fn_a(init)), float(loss_fn_b(init)), rtol=1e-6 ) + # alpha_cad SHOULD matter (positive control) + loss_fn_no_reg = make_joint_loss_fn( + jdata, mode="per_pool_noise", fix_gas=True, alpha_cad=0.0 + ) + loss_fn_big_reg = make_joint_loss_fn( + jdata, mode="per_pool_noise", fix_gas=True, alpha_cad=100.0 + ) + # With W_cad initialized to non-zero by warm start, these should differ. + # Even with default init (W_cad=0), perturbation test: + init_perturbed = init.at[1].set(1.0) # perturb first W_cad element + loss_no = float(loss_fn_no_reg(init_perturbed)) + loss_big = float(loss_fn_big_reg(init_perturbed)) + assert loss_big > loss_no, "alpha_cad regularization has no effect" + def test_shared_noise_mode(self, matched_data): loss_fn, init, _ = self._make_loss_fn( matched_data, mode="shared_noise" @@ -254,6 +277,9 @@ def test_shared_noise_mode(self, matched_data): loss = loss_fn(init) assert loss.shape == () assert float(loss) >= 0 + # Verify gradient works for shared_noise too + grad = jax.grad(loss_fn)(init) + assert jnp.all(jnp.isfinite(grad)) def test_init_param_count_per_pool_noise(self, matched_data): """Verify parameter count: 1(bias_cad) + k_attr(W_cad) + n_pools*K_OBS.""" @@ -296,8 +322,8 @@ def test_fix_gas_flag_stored(self, matched_data): ) assert result["fix_gas"] is True - def test_gas_per_pool_stored(self, matched_data): - """Result should contain gas_per_pool with chain-level values.""" + def test_gas_per_pool_stored_with_correct_values(self, matched_data): + """gas_per_pool has right length and chain-level values.""" from quantammsim.calibration.joint_fit import fit_joint from quantammsim.calibration.loss import CHAIN_GAS_USD @@ -306,22 +332,24 @@ def test_gas_per_pool_stored(self, matched_data): fix_gas_to_chain=True, maxiter=10, ) assert "gas_per_pool" in result + assert len(result["gas_per_pool"]) == len(result["pool_ids"]) for i, pid in enumerate(result["pool_ids"]): chain = matched_data[pid]["chain"] expected = CHAIN_GAS_USD.get(chain, 1.0) assert result["gas_per_pool"][i] == expected - def test_loss_decreases(self, matched_data): + def test_loss_decreases_substantially(self, matched_data): from quantammsim.calibration.joint_fit import fit_joint result = fit_joint( matched_data, mode="per_pool_noise", fix_gas_to_chain=True, maxiter=50, ) - assert result["loss"] <= result["init_loss"] + # Must decrease, not just by epsilon + assert result["loss"] < result["init_loss"] * 0.999 - def test_w_gas_is_zeros(self, matched_data): - """With fixed gas, W_gas should be zeros (placeholder).""" + def test_w_gas_and_bias_gas_are_zeros(self, matched_data): + """With fixed gas, W_gas and bias_gas should be zero placeholders.""" from quantammsim.calibration.joint_fit import fit_joint result = fit_joint( @@ -329,28 +357,23 @@ def test_w_gas_is_zeros(self, matched_data): fix_gas_to_chain=True, maxiter=10, ) np.testing.assert_allclose(result["W_gas"], 0.0) - - def test_bias_gas_is_zero(self, matched_data): - from quantammsim.calibration.joint_fit import fit_joint - - result = fit_joint( - matched_data, mode="per_pool_noise", - fix_gas_to_chain=True, maxiter=10, - ) assert result["bias_gas"] == 0.0 - def test_warm_start_from_option_c(self, matched_data): + def test_warm_start_from_option_c_runs(self, matched_data): + """Warm start from Option C should run without error and reduce loss.""" from quantammsim.calibration.joint_fit import fit_joint from quantammsim.calibration.per_pool_fit import fit_all_pools option_c = fit_all_pools(matched_data, fix_gas_to_chain=True) - result = fit_joint( + result_warm = fit_joint( matched_data, mode="per_pool_noise", fix_gas_to_chain=True, init_from_option_c=option_c, - maxiter=20, + maxiter=50, ) - assert result["loss"] >= 0 + # Should at least decrease from its own init + assert result_warm["loss"] <= result_warm["init_loss"] + assert result_warm["loss"] >= 0 def test_shared_noise_fixed_gas(self, matched_data): from quantammsim.calibration.joint_fit import fit_joint @@ -360,10 +383,12 @@ def test_shared_noise_fixed_gas(self, matched_data): fix_gas_to_chain=True, maxiter=20, ) assert "W_noise" in result + assert "bias_noise" in result assert result["fix_gas"] is True + assert result["loss"] >= 0 - def test_predict_new_pool_fixed_gas(self, matched_data): - """predict_new_pool_joint should still work with fixed-gas results.""" + def test_predict_new_pool_fixed_gas_pinned(self, matched_data): + """predict_new_pool_joint with zero attrs → gas_usd=1.0 exactly.""" from quantammsim.calibration.joint_fit import fit_joint, predict_new_pool_joint result = fit_joint( @@ -373,10 +398,41 @@ def test_predict_new_pool_fixed_gas(self, matched_data): k_attr = result["W_cad"].shape[0] x_attr_new = np.zeros(k_attr) pred = predict_new_pool_joint(result, x_attr_new) + assert "cadence_minutes" in pred assert pred["cadence_minutes"] > 0 - # gas_usd comes from bias_gas=0 + W_gas=0 → exp(0)=1.0 - assert pred["gas_usd"] > 0 + # bias_gas=0, W_gas=zeros → log_gas=0 → gas_usd=exp(0)=1.0 + np.testing.assert_allclose(pred["gas_usd"], 1.0, rtol=1e-6) + # cadence = exp(bias_cad + 0) = exp(bias_cad) + np.testing.assert_allclose( + pred["cadence_minutes"], np.exp(result["bias_cad"]), rtol=1e-6 + ) + # shared_noise mode should include noise_coeffs + assert "noise_coeffs" in pred + assert len(pred["noise_coeffs"]) == K_OBS + + def test_predict_matches_linear_model(self, matched_data): + """predict_new_pool_joint computes cadence = exp(bias_cad + W_cad @ x).""" + from quantammsim.calibration.joint_fit import fit_joint, predict_new_pool_joint + + result = fit_joint( + matched_data, mode="shared_noise", + fix_gas_to_chain=True, maxiter=20, + ) + k_attr = result["W_cad"].shape[0] + x_test = np.random.RandomState(42).randn(k_attr) + + pred = predict_new_pool_joint(result, x_test) + + # Verify cadence prediction directly against the linear model + expected_cadence = float(np.exp( + result["bias_cad"] + result["W_cad"] @ x_test + )) + np.testing.assert_allclose( + pred["cadence_minutes"], expected_cadence, rtol=1e-6, + ) + # Gas is fixed → always exp(0) = 1.0 + np.testing.assert_allclose(pred["gas_usd"], 1.0, rtol=1e-6) class TestMakeBoundsFixedGas: diff --git a/tests/calibration/test_loss_fixed_gas.py b/tests/calibration/test_loss_fixed_gas.py index f3cbf70..50a5122 100644 --- a/tests/calibration/test_loss_fixed_gas.py +++ b/tests/calibration/test_loss_fixed_gas.py @@ -107,7 +107,8 @@ def _make_inputs(self, synthetic_pool_coeffs, synthetic_x_obs): y_obs = jnp.ones(n_obs) * 9.0 return jnp.array(synthetic_x_obs), y_obs, day_indices - def test_scalar_output(self, synthetic_pool_coeffs, synthetic_x_obs): + def test_pinned_loss_value(self, synthetic_pool_coeffs, synthetic_x_obs): + """Loss at known params must match precomputed value.""" from quantammsim.calibration.loss import pool_loss_fixed_gas params = self._make_params() @@ -118,20 +119,28 @@ def test_scalar_output(self, synthetic_pool_coeffs, synthetic_x_obs): loss = pool_loss_fixed_gas( params, fixed_log_gas, synthetic_pool_coeffs, x_obs, y_obs, day_indices ) - assert loss.shape == () + # cadence=12, gas=1.0, noise=[8,0..0], y=9.0 → pinned + np.testing.assert_allclose(float(loss), 0.001727, atol=1e-4) - def test_positive(self, synthetic_pool_coeffs, synthetic_x_obs): + def test_pinned_loss_at_multiple_gas_values( + self, synthetic_pool_coeffs, synthetic_x_obs + ): + """Pinned loss values at gas=0.01, 1.0, 5.0.""" from quantammsim.calibration.loss import pool_loss_fixed_gas params = self._make_params() x_obs, y_obs, day_indices = self._make_inputs( synthetic_pool_coeffs, synthetic_x_obs ) - fixed_log_gas = jnp.log(jnp.array(1.0)) - loss = pool_loss_fixed_gas( - params, fixed_log_gas, synthetic_pool_coeffs, x_obs, y_obs, day_indices - ) - assert float(loss) >= 0 + # Precomputed with _make_params defaults: log_cad=log(12), noise=[8,0..0] + expected = {0.01: 0.0763, 1.0: 0.00173, 5.0: 0.0313} + for gas_val, exp_loss in expected.items(): + lg = jnp.log(jnp.array(gas_val)) + loss = float(pool_loss_fixed_gas( + params, lg, synthetic_pool_coeffs, x_obs, y_obs, day_indices + )) + np.testing.assert_allclose(loss, exp_loss, atol=1e-3, + err_msg=f"gas={gas_val}") def test_zero_when_perfect(self, synthetic_pool_coeffs, synthetic_x_obs): """Construct y_obs = log(V_arb + V_noise) exactly, verify loss ≈ 0.""" @@ -161,7 +170,7 @@ def test_zero_when_perfect(self, synthetic_pool_coeffs, synthetic_x_obs): params, fixed_log_gas, synthetic_pool_coeffs, jnp.array(synthetic_x_obs), y_obs, day_indices, ) - assert float(loss) < 1e-6 + assert float(loss) < 1e-10 def test_matches_free_gas_at_same_value( self, synthetic_pool_coeffs, synthetic_x_obs @@ -194,29 +203,49 @@ def test_matches_free_gas_at_same_value( ) np.testing.assert_allclose(float(loss_free), float(loss_fixed), rtol=1e-6) - def test_different_fixed_gas_different_loss( - self, synthetic_pool_coeffs, synthetic_x_obs - ): - """Different gas values should give different losses.""" + def test_loss_varies_with_gas_within_grid(self, synthetic_pool_coeffs, synthetic_x_obs): + """Gas values within grid range [0, 5] should produce distinct losses.""" from quantammsim.calibration.loss import pool_loss_fixed_gas params = self._make_params() x_obs, y_obs, day_indices = self._make_inputs( synthetic_pool_coeffs, synthetic_x_obs ) + # Stay within grid range (gas_costs=[0, 1, 5]) to avoid extrapolation plateau + losses = [] + for gas in [0.01, 0.1, 1.0, 3.0]: + lg = jnp.log(jnp.array(gas)) + loss = float(pool_loss_fixed_gas( + params, lg, synthetic_pool_coeffs, x_obs, y_obs, day_indices + )) + losses.append(loss) + # All 4 within-grid gas values should give distinct losses + assert len(set(f"{l:.8f}" for l in losses)) == 4 + + def test_day_indices_affect_loss(self, synthetic_pool_coeffs, synthetic_x_obs): + """Different day_indices must produce different loss — verifies per-day V_arb is used.""" + from quantammsim.calibration.loss import pool_loss_fixed_gas - loss_low = pool_loss_fixed_gas( - params, jnp.log(jnp.array(0.01)), - synthetic_pool_coeffs, x_obs, y_obs, day_indices, + params = self._make_params() + n_obs = synthetic_x_obs.shape[0] + n_days = int(synthetic_pool_coeffs.values.shape[2]) + x_obs = jnp.array(synthetic_x_obs) + y_obs = jnp.ones(n_obs) * 9.0 + fixed_log_gas = jnp.log(jnp.array(1.0)) + + day_idx_all_zero = jnp.zeros(n_obs, dtype=jnp.int32) + day_idx_varying = jnp.array(np.arange(n_obs) % n_days) + + loss_same = pool_loss_fixed_gas( + params, fixed_log_gas, synthetic_pool_coeffs, x_obs, y_obs, day_idx_all_zero ) - loss_high = pool_loss_fixed_gas( - params, jnp.log(jnp.array(10.0)), - synthetic_pool_coeffs, x_obs, y_obs, day_indices, + loss_vary = pool_loss_fixed_gas( + params, fixed_log_gas, synthetic_pool_coeffs, x_obs, y_obs, day_idx_varying ) - assert float(loss_low) != float(loss_high) + assert float(loss_same) != float(loss_vary) - def test_grad_wrt_params_only(self, synthetic_pool_coeffs, synthetic_x_obs): - """Gradient is only w.r.t. params_flat (argnums=0), not fixed_log_gas.""" + def test_grad_wrt_params(self, synthetic_pool_coeffs, synthetic_x_obs): + """Gradient w.r.t. params_flat has correct shape and is finite.""" from quantammsim.calibration.loss import pool_loss_fixed_gas params = self._make_params() @@ -230,52 +259,106 @@ def test_grad_wrt_params_only(self, synthetic_pool_coeffs, synthetic_x_obs): ) assert grad.shape == (1 + K_OBS,) assert jnp.all(jnp.isfinite(grad)) + # Gradient should be nonzero (we're not at the optimum) + assert float(jnp.sum(jnp.abs(grad))) > 1e-10 - def test_no_grad_wrt_fixed_gas(self, synthetic_pool_coeffs, synthetic_x_obs): - """fixed_log_gas should not be in the gradient (it's a constant).""" + def test_grad_changes_with_gas( + self, synthetic_pool_coeffs, synthetic_x_obs + ): + """Gradient w.r.t. params should differ at different fixed gas values. + + fixed_log_gas affects V_arb through grid interpolation, which shifts + the loss landscape and thus the gradient. + """ from quantammsim.calibration.loss import pool_loss_fixed_gas params = self._make_params() x_obs, y_obs, day_indices = self._make_inputs( synthetic_pool_coeffs, synthetic_x_obs ) + + grad_fn = jax.grad(pool_loss_fixed_gas, argnums=0) + grad_low = grad_fn( + params, jnp.log(jnp.array(0.01)), + synthetic_pool_coeffs, x_obs, y_obs, day_indices + ) + grad_high = grad_fn( + params, jnp.log(jnp.array(10.0)), + synthetic_pool_coeffs, x_obs, y_obs, day_indices + ) + # Gradients should differ because V_arb differs + assert not jnp.allclose(grad_low, grad_high, atol=1e-6) + + def test_extreme_negative_noise_finite(self, synthetic_pool_coeffs, synthetic_x_obs): + """Loss should remain finite with very negative noise intercept.""" + from quantammsim.calibration.loss import pool_loss_fixed_gas, pack_params_fixed_gas + + # Very negative noise intercept → V_noise ≈ 0, but V_arb still positive + nc = jnp.zeros(K_OBS).at[0].set(-100.0) + params = pack_params_fixed_gas(float(jnp.log(jnp.array(12.0))), nc) + + n_obs = synthetic_x_obs.shape[0] + n_days = int(synthetic_pool_coeffs.values.shape[2]) + day_indices = jnp.array(np.arange(n_obs) % n_days) + x_obs = jnp.array(synthetic_x_obs) + y_obs = jnp.ones(n_obs) * 9.0 fixed_log_gas = jnp.log(jnp.array(1.0)) - # Gradient w.r.t. argnums=0 has shape (1+K_OBS,) — no gas element + loss = pool_loss_fixed_gas( + params, fixed_log_gas, synthetic_pool_coeffs, x_obs, y_obs, day_indices + ) + assert jnp.isfinite(loss) + # V_noise ≈ 0, so log(V_arb) ≈ 8.5 vs y=9.0 → nonzero loss + assert float(loss) > 0.01 + # Gradient should also be finite grad = jax.grad(pool_loss_fixed_gas, argnums=0)( - params, fixed_log_gas, synthetic_pool_coeffs, x_obs, y_obs, day_indices, + params, fixed_log_gas, synthetic_pool_coeffs, x_obs, y_obs, day_indices ) - # If there were a gas gradient, shape would be (2+K_OBS,) - assert grad.shape[0] == 1 + K_OBS + assert jnp.all(jnp.isfinite(grad)) - def test_grad_wrt_log_cadence_finite( - self, synthetic_pool_coeffs, synthetic_x_obs - ): - from quantammsim.calibration.loss import pool_loss_fixed_gas + def test_boundary_clamp_cadence(self, synthetic_pool_coeffs, synthetic_x_obs): + """Cadence below grid min should clamp — loss at cad=0.5 equals cad=1.0.""" + from quantammsim.calibration.loss import pack_params_fixed_gas, pool_loss_fixed_gas - params = self._make_params() x_obs, y_obs, day_indices = self._make_inputs( synthetic_pool_coeffs, synthetic_x_obs ) + nc = jnp.zeros(K_OBS).at[0].set(8.0) fixed_log_gas = jnp.log(jnp.array(1.0)) - grad = jax.grad(pool_loss_fixed_gas, argnums=0)( - params, fixed_log_gas, synthetic_pool_coeffs, x_obs, y_obs, day_indices, + params_below = pack_params_fixed_gas(float(jnp.log(jnp.array(0.5))), nc) + params_at_min = pack_params_fixed_gas(float(jnp.log(jnp.array(1.0))), nc) + + loss_below = pool_loss_fixed_gas( + params_below, fixed_log_gas, synthetic_pool_coeffs, + x_obs, y_obs, day_indices, ) - assert jnp.isfinite(grad[0]) # log_cadence gradient + loss_at_min = pool_loss_fixed_gas( + params_at_min, fixed_log_gas, synthetic_pool_coeffs, + x_obs, y_obs, day_indices, + ) + np.testing.assert_allclose(float(loss_below), float(loss_at_min), rtol=1e-6) - def test_grad_wrt_noise_coeffs_finite( - self, synthetic_pool_coeffs, synthetic_x_obs - ): + def test_boundary_clamp_gas(self, synthetic_pool_coeffs, synthetic_x_obs): + """Gas above grid max should clamp — loss at gas=10 equals gas=5.""" from quantammsim.calibration.loss import pool_loss_fixed_gas params = self._make_params() x_obs, y_obs, day_indices = self._make_inputs( synthetic_pool_coeffs, synthetic_x_obs ) - fixed_log_gas = jnp.log(jnp.array(1.0)) - grad = jax.grad(pool_loss_fixed_gas, argnums=0)( - params, fixed_log_gas, synthetic_pool_coeffs, x_obs, y_obs, day_indices, + loss_above = pool_loss_fixed_gas( + params, jnp.log(jnp.array(10.0)), synthetic_pool_coeffs, + x_obs, y_obs, day_indices, ) - assert jnp.all(jnp.isfinite(grad[1:])) # noise_coeffs gradients + loss_at_max = pool_loss_fixed_gas( + params, jnp.log(jnp.array(5.0)), synthetic_pool_coeffs, + x_obs, y_obs, day_indices, + ) + np.testing.assert_allclose(float(loss_above), float(loss_at_max), rtol=1e-6) + + def test_k_obs_matches_loss_module(self): + """K_OBS in conftest must match K_OBS in loss.py.""" + from quantammsim.calibration.loss import K_OBS as K_OBS_IMPL + assert K_OBS == K_OBS_IMPL diff --git a/tests/calibration/test_per_pool_fit_fixed_gas.py b/tests/calibration/test_per_pool_fit_fixed_gas.py index f0a7d7f..df281c7 100644 --- a/tests/calibration/test_per_pool_fit_fixed_gas.py +++ b/tests/calibration/test_per_pool_fit_fixed_gas.py @@ -38,15 +38,17 @@ def test_log_cadence_default(self, synthetic_x_obs): init = make_initial_guess_fixed_gas(synthetic_x_obs, y_obs) np.testing.assert_allclose(init[0], np.log(12.0), atol=0.01) - def test_noise_from_ols(self, synthetic_x_obs): + def test_pinned_ols_coefficients(self, synthetic_x_obs): + """OLS on constant y=9.0: intercept should dominate, others near zero.""" from quantammsim.calibration.per_pool_fit import make_initial_guess_fixed_gas n_obs = synthetic_x_obs.shape[0] y_obs = np.ones(n_obs) * 9.0 init = make_initial_guess_fixed_gas(synthetic_x_obs, y_obs) - noise_coeffs = init[1:] - assert len(noise_coeffs) == K_OBS - assert np.all(np.isfinite(noise_coeffs)) + # Intercept should be close to 9.0 (y is constant) + np.testing.assert_allclose(init[1], 9.0, atol=0.01) + # Other noise coeffs should be near zero for constant y + assert np.all(np.abs(init[2:]) < 0.1) def test_noise_matches_free_gas_noise(self, synthetic_x_obs): """OLS noise coeffs should be identical for free and fixed-gas init.""" @@ -61,6 +63,20 @@ def test_noise_matches_free_gas_noise(self, synthetic_x_obs): fixed = make_initial_guess_fixed_gas(synthetic_x_obs, y_obs) np.testing.assert_allclose(free[2:], fixed[1:]) + def test_ols_with_heterogeneous_y(self, synthetic_x_obs): + """OLS on heterogeneous y should produce different coeffs than constant y.""" + from quantammsim.calibration.per_pool_fit import make_initial_guess_fixed_gas + + # y correlated with TVL (column 1) + y_het = synthetic_x_obs[:, 1] * 0.5 + 5.0 + np.random.RandomState(42).randn( + synthetic_x_obs.shape[0]) * 0.01 + init_het = make_initial_guess_fixed_gas(synthetic_x_obs, y_het) + init_const = make_initial_guess_fixed_gas( + synthetic_x_obs, np.ones(synthetic_x_obs.shape[0]) * 9.0 + ) + # Noise coefficients should differ substantially + assert np.max(np.abs(init_het[1:] - init_const[1:])) > 0.1 + class TestFitSinglePoolFixedGas: """Test fit_single_pool with fixed_gas_usd.""" @@ -72,6 +88,28 @@ def _make_inputs(self, synthetic_pool_coeffs, synthetic_x_obs): y_obs = np.ones(n_obs) * 9.0 return synthetic_x_obs, y_obs, day_indices + def _make_gt_inputs(self, synthetic_pool_coeffs, synthetic_x_obs): + """Make ground-truth y_obs from known params for recovery test.""" + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + from quantammsim.calibration.loss import noise_volume + + n_obs = synthetic_x_obs.shape[0] + n_days = int(synthetic_pool_coeffs.values.shape[2]) + day_indices = np.arange(n_obs) % n_days + + TRUE_LOG_CAD = float(np.log(10.0)) + TRUE_GAS = 0.5 + TRUE_NC = np.zeros(K_OBS) + TRUE_NC[0] = 7.0 + TRUE_NC[1] = 0.3 + + v_arb = np.array(interpolate_pool_daily( + synthetic_pool_coeffs, jnp.array(TRUE_LOG_CAD), jnp.array(TRUE_GAS) + ))[day_indices] + v_noise = np.array(noise_volume(jnp.array(TRUE_NC), jnp.array(synthetic_x_obs))) + y_obs = np.log(np.maximum(v_arb + v_noise, 1e-6)) + return synthetic_x_obs, y_obs, day_indices, TRUE_LOG_CAD, TRUE_GAS, TRUE_NC + def test_returns_result_dict(self, synthetic_pool_coeffs, synthetic_x_obs): from quantammsim.calibration.per_pool_fit import fit_single_pool @@ -137,7 +175,8 @@ def test_log_gas_pinned(self, synthetic_pool_coeffs, synthetic_x_obs): result["log_gas"], np.log(2.5), rtol=1e-6, ) - def test_cadence_in_range(self, synthetic_pool_coeffs, synthetic_x_obs): + def test_pinned_fit_on_constant_y(self, synthetic_pool_coeffs, synthetic_x_obs): + """Pinned fitted values for fixed_gas=1.0, y=9.0.""" from quantammsim.calibration.per_pool_fit import fit_single_pool x_obs, y_obs, day_idx = self._make_inputs( @@ -146,22 +185,45 @@ def test_cadence_in_range(self, synthetic_pool_coeffs, synthetic_x_obs): result = fit_single_pool( synthetic_pool_coeffs, x_obs, y_obs, day_idx, fixed_gas_usd=1.0, ) - cadence = result["cadence_minutes"] - assert 1.0 <= cadence <= 60.0 + assert result["converged"] + np.testing.assert_allclose(result["loss"], 1.30e-5, atol=5e-5) + np.testing.assert_allclose( + result["noise_coeffs"][0], 8.989, atol=0.05, + ) + assert 5.0 <= result["cadence_minutes"] <= 15.0 - def test_noise_coeffs_length(self, synthetic_pool_coeffs, synthetic_x_obs): + def test_ground_truth_recovery_fixed_gas( + self, synthetic_pool_coeffs, synthetic_x_obs + ): + """Fit on ground-truth y with correct gas → near-zero loss, correct cadence.""" from quantammsim.calibration.per_pool_fit import fit_single_pool - x_obs, y_obs, day_idx = self._make_inputs( + x_obs, y_obs, day_idx, true_lc, true_gas, true_nc = self._make_gt_inputs( synthetic_pool_coeffs, synthetic_x_obs ) result = fit_single_pool( - synthetic_pool_coeffs, x_obs, y_obs, day_idx, fixed_gas_usd=1.0, + synthetic_pool_coeffs, x_obs, y_obs, day_idx, fixed_gas_usd=true_gas, ) - assert len(result["noise_coeffs"]) == K_OBS + assert result["converged"] + assert result["loss"] < 1e-5 + + def test_ground_truth_recovery_free_gas( + self, synthetic_pool_coeffs, synthetic_x_obs + ): + """Fit on ground-truth y with free gas → near-zero loss.""" + from quantammsim.calibration.per_pool_fit import fit_single_pool + + x_obs, y_obs, day_idx, _, _, _ = self._make_gt_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + result = fit_single_pool( + synthetic_pool_coeffs, x_obs, y_obs, day_idx, + ) + assert result["converged"] + assert result["loss"] < 1e-5 def test_loss_decreases_from_init(self, synthetic_pool_coeffs, synthetic_x_obs): - from quantammsim.calibration.loss import pack_params_fixed_gas, pool_loss_fixed_gas + from quantammsim.calibration.loss import pool_loss_fixed_gas from quantammsim.calibration.per_pool_fit import ( fit_single_pool, make_initial_guess_fixed_gas, @@ -180,12 +242,12 @@ def test_loss_decreases_from_init(self, synthetic_pool_coeffs, synthetic_x_obs): result = fit_single_pool( synthetic_pool_coeffs, x_obs, y_obs, day_idx, fixed_gas_usd=1.0, ) - assert result["loss"] <= init_loss + assert result["loss"] < init_loss * 0.99 # at least 1% improvement def test_different_fixed_gas_different_cadence( self, synthetic_pool_coeffs, synthetic_x_obs ): - """Different gas values should (generally) lead to different fitted cadences.""" + """Gas values spanning 5000x should produce substantially different cadences.""" from quantammsim.calibration.per_pool_fit import fit_single_pool x_obs, y_obs, day_idx = self._make_inputs( @@ -197,8 +259,26 @@ def test_different_fixed_gas_different_cadence( r_high = fit_single_pool( synthetic_pool_coeffs, x_obs, y_obs, day_idx, fixed_gas_usd=5.0, ) - # Cadences should differ (gas-cadence tradeoff) - assert abs(r_low["log_cadence"] - r_high["log_cadence"]) > 0.01 + # 5000x gas range → cadences should differ substantially + assert abs(r_low["log_cadence"] - r_high["log_cadence"]) > 0.1 + + def test_fixed_vs_free_gas_on_gt_data( + self, synthetic_pool_coeffs, synthetic_x_obs + ): + """Free gas should achieve loss ≤ fixed gas (more degrees of freedom).""" + from quantammsim.calibration.per_pool_fit import fit_single_pool + + x_obs, y_obs, day_idx, _, _, _ = self._make_gt_inputs( + synthetic_pool_coeffs, synthetic_x_obs + ) + r_free = fit_single_pool( + synthetic_pool_coeffs, x_obs, y_obs, day_idx, + ) + r_fixed = fit_single_pool( + synthetic_pool_coeffs, x_obs, y_obs, day_idx, fixed_gas_usd=1.0, + ) + # Free gas has strictly more freedom → should do at least as well + assert r_free["loss"] <= r_fixed["loss"] * 1.01 class TestFitAllPoolsFixedGas: @@ -256,3 +336,48 @@ def test_free_gas_not_fixed( results = fit_all_pools(matched, fix_gas_to_chain=False) for prefix, res in results.items(): assert res["gas_fixed"] is False + + def test_both_pools_have_results( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + from quantammsim.calibration.per_pool_fit import fit_all_pools + + matched = self._make_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + results = fit_all_pools(matched, fix_gas_to_chain=True) + assert len(results) == len(matched) + for prefix in matched: + assert prefix in results + assert results[prefix]["converged"] + + def test_metadata_preserved( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + """Each result should carry chain, fee, tokens from the matched data.""" + from quantammsim.calibration.per_pool_fit import fit_all_pools + + matched = self._make_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + results = fit_all_pools(matched, fix_gas_to_chain=True) + for prefix, res in results.items(): + assert res["chain"] == matched[prefix]["chain"] + assert res["tokens"] == matched[prefix]["tokens"] + assert np.isfinite(res["fee"]) + + def test_mainnet_gas_1_arbitrum_gas_001( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + """Pin: MAINNET pool gets gas=1.0, ARBITRUM pool gets gas=0.01.""" + from quantammsim.calibration.per_pool_fit import fit_all_pools + + matched = self._make_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + results = fit_all_pools(matched, fix_gas_to_chain=True) + for prefix, res in results.items(): + if res["chain"] == "MAINNET": + assert res["gas_usd"] == 1.0 + elif res["chain"] == "ARBITRUM": + assert res["gas_usd"] == 0.01 diff --git a/tests/calibration/test_pool_data_volatility.py b/tests/calibration/test_pool_data_volatility.py index 6c5792e..d6e2dd1 100644 --- a/tests/calibration/test_pool_data_volatility.py +++ b/tests/calibration/test_pool_data_volatility.py @@ -3,7 +3,6 @@ import numpy as np import pandas as pd import pytest -import os from tests.calibration.conftest import POOL_IDS_FULL @@ -70,6 +69,34 @@ def test_jitosol(self): assert _resolve_binance_symbol("JitoSOL") == "SOL" +class TestGetAssetType: + """Test _get_asset_type classification.""" + + def test_stablecoins(self): + from quantammsim.calibration.pool_data import _get_asset_type + + for tok in ["USDC", "USDT", "DAI", "WXDAI", "sDAI", "DOLA", "scUSD"]: + assert _get_asset_type(tok, {}) == 0, f"{tok} should be stable (0)" + + def test_native_lst(self): + from quantammsim.calibration.pool_data import _get_asset_type + + for tok in ["WETH", "ETH", "wstETH", "WBTC", "BTC", "GNO", "S", "wS"]: + assert _get_asset_type(tok, {}) == 1, f"{tok} should be native/LST (1)" + + def test_volatile(self): + from quantammsim.calibration.pool_data import _get_asset_type + + for tok in ["AAVE", "LINK", "SNX", "CRV", "COMP"]: + assert _get_asset_type(tok, {}) == 2, f"{tok} should be volatile (2)" + + def test_mcap_override(self): + from quantammsim.calibration.pool_data import _get_asset_type + + mcaps = {"AAVE": {"asset_type": "stable", "mcap_usd": 1e9}} + assert _get_asset_type("AAVE", mcaps) == 0 # overridden to stable + + class TestComputeBinancePairVolatility: """Test compute_binance_pair_volatility with synthetic Binance-like data.""" @@ -103,12 +130,22 @@ def fake_binance_dir(self, tmp_path): return str(tmp_path) - def test_returns_series(self, fake_binance_dir): + def test_pinned_volatility_values(self, fake_binance_dir): + """Exact pinned values with seed(42).""" from quantammsim.calibration.pool_data import compute_binance_pair_volatility vol = compute_binance_pair_volatility("WETH", "AAVE", fake_binance_dir) - assert isinstance(vol, pd.Series) - assert len(vol) > 0 + expected = np.array([ + 0.27103676, 0.23068148, 0.43763073, 0.35174542, + 0.26827274, 0.35256874, 0.27833725, + ]) + np.testing.assert_allclose(vol.values, expected, rtol=1e-4) + + def test_exactly_seven_days(self, fake_binance_dir): + from quantammsim.calibration.pool_data import compute_binance_pair_volatility + + vol = compute_binance_pair_volatility("WETH", "AAVE", fake_binance_dir) + assert len(vol) == 7 def test_values_positive(self, fake_binance_dir): from quantammsim.calibration.pool_data import compute_binance_pair_volatility @@ -116,21 +153,43 @@ def test_values_positive(self, fake_binance_dir): vol = compute_binance_pair_volatility("WETH", "AAVE", fake_binance_dir) assert (vol > 0).all() - def test_annualized_magnitude(self, fake_binance_dir): - """Annualized vol should be in [0.01, 10.0] range for typical assets.""" + def test_pinned_median(self, fake_binance_dir): from quantammsim.calibration.pool_data import compute_binance_pair_volatility vol = compute_binance_pair_volatility("WETH", "AAVE", fake_binance_dir) - assert vol.median() > 0.01 - assert vol.median() < 10.0 + np.testing.assert_allclose(vol.median(), 0.2783, atol=0.001) + + def test_token_order_invariance(self, fake_binance_dir): + """vol(A,B) should equal vol(B,A) — log returns of reciprocal have same std.""" + from quantammsim.calibration.pool_data import compute_binance_pair_volatility + + vol_ab = compute_binance_pair_volatility("WETH", "AAVE", fake_binance_dir) + vol_ba = compute_binance_pair_volatility("AAVE", "WETH", fake_binance_dir) + np.testing.assert_allclose(vol_ab.values, vol_ba.values, rtol=1e-5) + + def test_stable_vs_volatile_uses_single_asset(self, fake_binance_dir): + """ETH/USDC should use just ETH price — verify against hand-computed ETH vol.""" + import os - def test_stable_vs_volatile(self, fake_binance_dir): - """ETH/USDC should just use ETH price (one-sided).""" from quantammsim.calibration.pool_data import compute_binance_pair_volatility - vol = compute_binance_pair_volatility("WETH", "USDC", fake_binance_dir) - assert isinstance(vol, pd.Series) - assert len(vol) > 0 + vol_pair = compute_binance_pair_volatility("WETH", "USDC", fake_binance_dir) + + # Hand-compute ETH-only vol for ground-truth comparison + eth = pd.read_parquet(os.path.join(fake_binance_dir, "ETH_USD.parquet")) + eth_ts = pd.DataFrame( + {"ratio": eth["close"].values}, + index=pd.to_datetime(eth["unix"].values, unit="ms", utc=True), + ) + hourly = eth_ts.resample("1h").last().dropna() + hourly["log_return"] = np.log(hourly["ratio"] / hourly["ratio"].shift(1)) + hourly = hourly.dropna() + hourly["date"] = hourly.index.date + daily_std = hourly.groupby("date")["log_return"].std() + expected = (daily_std * np.sqrt(24 * 365)).dropna() + expected = expected[expected > 0] + + np.testing.assert_allclose(vol_pair.values, expected.values, rtol=1e-5) def test_stable_stable_returns_none(self, fake_binance_dir): from quantammsim.calibration.pool_data import compute_binance_pair_volatility @@ -160,20 +219,16 @@ def test_daily_index_type(self, fake_binance_dir): for d in vol.index: assert isinstance(d, datetime.date) - def test_seven_days_of_data(self, fake_binance_dir): - """7 days of minute data → ~6 days of vol (first day partial).""" + def test_stable_a_volatile_b_uses_reciprocal(self, fake_binance_dir): + """DAI/WETH should use 1/ETH, giving same vol as WETH/DAI.""" from quantammsim.calibration.pool_data import compute_binance_pair_volatility - vol = compute_binance_pair_volatility("WETH", "AAVE", fake_binance_dir) - assert 5 <= len(vol) <= 7 - - def test_stable_a_volatile_b(self, fake_binance_dir): - """When token_a is stable, should use 1/price_b.""" - from quantammsim.calibration.pool_data import compute_binance_pair_volatility - - vol = compute_binance_pair_volatility("DAI", "WETH", fake_binance_dir) - assert isinstance(vol, pd.Series) - assert len(vol) > 0 + vol_forward = compute_binance_pair_volatility("WETH", "USDC", fake_binance_dir) + vol_reverse = compute_binance_pair_volatility("DAI", "WETH", fake_binance_dir) + # Both should be ETH vol (log returns of X and 1/X have same std) + np.testing.assert_allclose( + vol_forward.values, vol_reverse.values, rtol=1e-5, + ) class TestReplacePanelVolatility: @@ -195,38 +250,204 @@ def fake_binance_dir(self, tmp_path): btc_df = pd.DataFrame({"unix": unix, "close": btc_prices}) btc_df.to_parquet(tmp_path / "BTC_USD.parquet", index=False) + aave_prices = 200.0 + np.cumsum(np.random.normal(0, 0.5, n_minutes)) + aave_df = pd.DataFrame({"unix": unix, "close": aave_prices}) + aave_df.to_parquet(tmp_path / "AAVE_USD.parquet", index=False) + return str(tmp_path) - def test_returns_dataframe(self, synthetic_panel, fake_binance_dir): + def test_does_not_modify_input(self, synthetic_panel, fake_binance_dir): + from quantammsim.calibration.pool_data import replace_panel_volatility_with_binance + + original_vol = synthetic_panel["volatility"].copy() + replace_panel_volatility_with_binance( + synthetic_panel, fake_binance_dir + ) + pd.testing.assert_series_equal(synthetic_panel["volatility"], original_vol) + + def test_no_nans_introduced(self, synthetic_panel, fake_binance_dir): + """Pools without Binance data should keep original volatility, not NaN.""" from quantammsim.calibration.pool_data import replace_panel_volatility_with_binance result = replace_panel_volatility_with_binance( synthetic_panel, fake_binance_dir ) - assert isinstance(result, pd.DataFrame) + assert result["volatility"].notna().all() - def test_does_not_modify_input(self, synthetic_panel, fake_binance_dir): + def test_volatility_actually_changes(self, synthetic_panel, fake_binance_dir): + """At least some volatility values should differ after replacement.""" from quantammsim.calibration.pool_data import replace_panel_volatility_with_binance - original_vol = synthetic_panel["volatility"].copy() - replace_panel_volatility_with_binance( + original_vol = synthetic_panel["volatility"].values.copy() + result = replace_panel_volatility_with_binance( synthetic_panel, fake_binance_dir ) - pd.testing.assert_series_equal(synthetic_panel["volatility"], original_vol) + # At least one pool has BTC,ETH or AAVE,ETH — both have Binance data, + # and dates overlap (panel starts 2025-12-01, fake data starts 2025-12-01). + n_changed = (result["volatility"].values != original_vol).sum() + assert n_changed > 0, "No volatility values were replaced" - def test_volatility_column_exists(self, synthetic_panel, fake_binance_dir): + def test_replaced_values_are_positive(self, synthetic_panel, fake_binance_dir): + """Replaced volatility values must be positive.""" from quantammsim.calibration.pool_data import replace_panel_volatility_with_binance result = replace_panel_volatility_with_binance( synthetic_panel, fake_binance_dir ) - assert "volatility" in result.columns + assert (result["volatility"] > 0).all() - def test_no_nans_introduced(self, synthetic_panel, fake_binance_dir): - """Pools without Binance data should keep original volatility, not NaN.""" + def test_all_columns_preserved(self, synthetic_panel, fake_binance_dir): + """Output should have all original columns.""" from quantammsim.calibration.pool_data import replace_panel_volatility_with_binance result = replace_panel_volatility_with_binance( synthetic_panel, fake_binance_dir ) - assert result["volatility"].notna().all() + for col in synthetic_panel.columns: + assert col in result.columns + + def test_row_count_preserved(self, synthetic_panel, fake_binance_dir): + """Output should have the same number of rows.""" + from quantammsim.calibration.pool_data import replace_panel_volatility_with_binance + + result = replace_panel_volatility_with_binance( + synthetic_panel, fake_binance_dir + ) + assert len(result) == len(synthetic_panel) + + def test_replaced_values_match_binance_computation( + self, synthetic_panel, fake_binance_dir + ): + """Replaced vol values must equal compute_binance_pair_volatility exactly.""" + from quantammsim.calibration.pool_data import ( + compute_binance_pair_volatility, + replace_panel_volatility_with_binance, + ) + + result = replace_panel_volatility_with_binance( + synthetic_panel, fake_binance_dir + ) + + # BTC/ETH pool — compute expected vol independently + vol_btc_eth = compute_binance_pair_volatility("BTC", "ETH", fake_binance_dir) + assert vol_btc_eth is not None, "BTC/ETH vol should be computable" + vol_dict = vol_btc_eth.to_dict() + + pool0 = result[result["tokens"] == "BTC,ETH"].copy() + pool0_dates = pd.to_datetime(pool0["date"]).dt.date + matched_mask = pool0_dates.isin(vol_dict.keys()).values + matched = pool0[matched_mask] + assert len(matched) > 0, "No date overlap between panel and Binance data" + + for _, row in matched.iterrows(): + d = pd.to_datetime(row["date"]).date() + np.testing.assert_allclose( + row["volatility"], vol_dict[d], rtol=1e-6, + err_msg=f"BTC/ETH vol mismatch on {d}", + ) + + +class TestBuildPoolAttributeValues: + """Test build_pool_attributes returns correct numerical values, not just names.""" + + def _make_matched(self, synthetic_daily_grid, synthetic_panel, tmp_path): + from quantammsim.calibration.pool_data import match_grids_to_panel + from tests.calibration.conftest import POOL_PREFIXES + + grid_dir = tmp_path / "grids" + grid_dir.mkdir() + for prefix in POOL_PREFIXES: + synthetic_daily_grid.to_parquet( + grid_dir / f"{prefix}_daily.parquet", index=False + ) + return match_grids_to_panel(str(grid_dir), synthetic_panel) + + def test_chain_dummy_values( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + """MAINNET pool has chain_MAINNET=1, ARBITRUM pool has chain_MAINNET=0.""" + from quantammsim.calibration.pool_data import build_pool_attributes + + matched = self._make_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + X_attr, attr_names, pool_ids = build_pool_attributes(matched) + + # ARBITRUM is reference (alphabetically first), MAINNET gets a dummy + chain_idx = attr_names.index("chain_MAINNET") + for i, pid in enumerate(pool_ids): + if matched[pid]["chain"] == "MAINNET": + assert X_attr[i, chain_idx] == 1.0 + else: + assert X_attr[i, chain_idx] == 0.0 + + def test_log_fee_values( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + """log_fee should match panel values.""" + from quantammsim.calibration.pool_data import build_pool_attributes + + matched = self._make_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + X_attr, attr_names, pool_ids = build_pool_attributes(matched) + + fee_idx = attr_names.index("log_fee") + for i, pid in enumerate(pool_ids): + expected = np.log(matched[pid]["fee"]) + np.testing.assert_allclose(X_attr[i, fee_idx], expected, rtol=1e-3) + + def test_same_asset_type_for_btc_eth( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + """BTC,ETH pool: both native/LST → same_asset_type=1.""" + from quantammsim.calibration.pool_data import build_pool_attributes + + matched = self._make_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + X_attr, attr_names, pool_ids = build_pool_attributes(matched) + + sat_idx = attr_names.index("same_asset_type") + for i, pid in enumerate(pool_ids): + if matched[pid]["tokens"] == "BTC,ETH": + assert X_attr[i, sat_idx] == 1.0 + elif matched[pid]["tokens"] == "AAVE,ETH": + # AAVE=volatile(2), ETH=native(1) → different + assert X_attr[i, sat_idx] == 0.0 + + def test_pinned_attribute_values( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + """Pinned X_attr values for the two synthetic pools.""" + from quantammsim.calibration.pool_data import build_pool_attributes + + matched = self._make_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + X_attr, attr_names, pool_ids = build_pool_attributes(matched) + + # Pool 0 (0xaaaa = MAINNET, BTC/ETH, fee=0.003) + p0_idx = pool_ids.index("0xaaaa11112222aa") + np.testing.assert_allclose(X_attr[p0_idx, 0], 1.0) # chain_MAINNET + np.testing.assert_allclose( + X_attr[p0_idx, attr_names.index("log_fee")], np.log(0.003), rtol=1e-3 + ) + + # Pool 1 (0xbbbb = ARBITRUM, AAVE/ETH, fee=0.01) + p1_idx = pool_ids.index("0xbbbb33334444bb") + np.testing.assert_allclose(X_attr[p1_idx, 0], 0.0) # chain_MAINNET=0 + np.testing.assert_allclose( + X_attr[p1_idx, attr_names.index("log_fee")], np.log(0.01), rtol=1e-3 + ) + + def test_no_nans( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + from quantammsim.calibration.pool_data import build_pool_attributes + + matched = self._make_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + X_attr, _, _ = build_pool_attributes(matched) + assert not np.any(np.isnan(X_attr)) From a42a961641614dbb4b22c76fa0d546d1c294bf1e Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 10 Mar 2026 01:51:03 +0000 Subject: [PATCH 08/57] feat: composable CalibrationModel with pluggable Head components Add CalibrationModel coordinator and 5 Head implementations (PerPoolHead, FixedHead, LinearHead, PerPoolNoiseHead, SharedLinearNoiseHead) so that new model variants (MLP, delta heads, Huber loss) require only a new Head + tests, not edits across the codebase. All 207 existing tests pass unchanged; 69 new tests added (276 total). --- quantammsim/calibration/__init__.py | 10 + quantammsim/calibration/calibration_model.py | 348 +++++++++++++++ quantammsim/calibration/heads.py | 377 +++++++++++++++++ quantammsim/calibration/loss.py | 12 + tests/calibration/test_calibration_model.py | 421 +++++++++++++++++++ tests/calibration/test_heads.py | 379 +++++++++++++++++ 6 files changed, 1547 insertions(+) create mode 100644 quantammsim/calibration/calibration_model.py create mode 100644 quantammsim/calibration/heads.py create mode 100644 tests/calibration/test_calibration_model.py create mode 100644 tests/calibration/test_heads.py diff --git a/quantammsim/calibration/__init__.py b/quantammsim/calibration/__init__.py index a888758..1af09da 100644 --- a/quantammsim/calibration/__init__.py +++ b/quantammsim/calibration/__init__.py @@ -39,3 +39,13 @@ build_x_obs, match_grids_to_panel, ) +from quantammsim.calibration.calibration_model import CalibrationModel +from quantammsim.calibration.heads import ( + FixedHead, + Head, + LinearHead, + PerPoolHead, + PerPoolNoiseHead, + SharedLinearNoiseHead, +) +from quantammsim.calibration.loss import _compute_loss_huber diff --git a/quantammsim/calibration/calibration_model.py b/quantammsim/calibration/calibration_model.py new file mode 100644 index 0000000..88e940c --- /dev/null +++ b/quantammsim/calibration/calibration_model.py @@ -0,0 +1,348 @@ +"""Composable CalibrationModel with pluggable Head components. + +The CalibrationModel coordinates three heads (cadence, gas, noise) and +provides: + - Parameter packing/unpacking across all heads + - Per-pool JIT-compiled loss closures (same pattern as existing code) + - Joint loss aggregation with head regularization + - scipy L-BFGS-B fitting for both per-pool and joint modes + - Prediction for new pools + +All heads are concatenated in order [cadence | gas | noise] in the flat +parameter vector. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Callable, Dict, Optional + +import jax +import jax.numpy as jnp +import numpy as np +import scipy.optimize + +from quantammsim.calibration.grid_interpolation import interpolate_pool_daily +from quantammsim.calibration.heads import Head +from quantammsim.calibration.loss import K_OBS, noise_volume + + +@dataclass +class CalibrationModel: + """Composable calibration model with pluggable heads. + + Coordinates cadence_head, gas_head, and noise_head to build a single + flat parameter vector and produce per-pool JIT-compiled loss functions. + """ + + cadence_head: Head + gas_head: Head + noise_head: Head + loss_type: str = "l2" + huber_delta: float = 1.5 + + # ── Parameter geometry ───────────────────────────────────────────── + + def n_params(self, n_pools: int, k_attr: int) -> int: + """Total parameter count across all heads.""" + return ( + self.cadence_head.n_params(n_pools, k_attr) + + self.gas_head.n_params(n_pools, k_attr) + + self.noise_head.n_params(n_pools, k_attr) + ) + + def _head_slices(self, n_pools: int, k_attr: int): + """Return (start, end) index pairs for each head's param slice.""" + n_cad = self.cadence_head.n_params(n_pools, k_attr) + n_gas = self.gas_head.n_params(n_pools, k_attr) + n_noise = self.noise_head.n_params(n_pools, k_attr) + cad_end = n_cad + gas_end = cad_end + n_gas + noise_end = gas_end + n_noise + return (0, cad_end), (cad_end, gas_end), (gas_end, noise_end) + + # ── Initialization ───────────────────────────────────────────────── + + def pack_init(self, jdata, warm_start=None) -> np.ndarray: + """Concatenate head inits into a single flat NumPy vector.""" + cad_init = self.cadence_head.init(jdata, warm_start) + gas_init = self.gas_head.init(jdata, warm_start) + noise_init = self.noise_head.init(jdata, warm_start) + return np.concatenate([cad_init, gas_init, noise_init]) + + # ── Bounds ───────────────────────────────────────────────────────── + + def make_bounds(self, n_pools: int, k_attr: int) -> list: + """Concatenate per-head scipy bounds.""" + return ( + self.cadence_head.make_bounds(n_pools, k_attr) + + self.gas_head.make_bounds(n_pools, k_attr) + + self.noise_head.make_bounds(n_pools, k_attr) + ) + + # ── Loss functions ───────────────────────────────────────────────── + + def _compute_loss(self, residuals: jnp.ndarray) -> jnp.ndarray: + """Compute loss from residuals based on loss_type.""" + if self.loss_type == "huber": + delta = self.huber_delta + abs_r = jnp.abs(residuals) + huber = jnp.where( + abs_r <= delta, + 0.5 * residuals ** 2, + delta * (abs_r - 0.5 * delta), + ) + return jnp.mean(huber) + return jnp.mean(residuals ** 2) + + def make_pool_loss_fn( + self, + pool_idx: int, + pool_data_i: dict, + x_attr_i: jnp.ndarray, + n_pools: int, + k_attr: int, + ) -> Callable: + """Create a JIT-compiled loss function for a single pool. + + Closes over pool-specific data. Takes params_flat as sole argument. + Returns scalar loss (no regularization — that's added at aggregate level). + """ + coeffs = pool_data_i["coeffs"] + x_obs = pool_data_i["x_obs"] + y_obs = pool_data_i["y_obs"] + day_indices = pool_data_i["day_indices"] + + (cad_s, cad_e), (gas_s, gas_e), (noise_s, noise_e) = \ + self._head_slices(n_pools, k_attr) + + cad_head = self.cadence_head + gas_head = self.gas_head + noise_head = self.noise_head + compute_loss = self._compute_loss + i = pool_idx + + @jax.jit + def pool_loss_fn(params_flat): + cad_slice = params_flat[cad_s:cad_e] + gas_slice = params_flat[gas_s:gas_e] + noise_slice = params_flat[noise_s:noise_e] + + log_cad = cad_head.predict(cad_slice, i, x_attr_i) + log_gas = gas_head.predict(gas_slice, i, x_attr_i) + noise_c = noise_head.predict(noise_slice, i, x_attr_i) + + v_arb_all = interpolate_pool_daily( + coeffs, log_cad, jnp.exp(log_gas) + ) + v_arb = v_arb_all[day_indices] + v_noise = jnp.exp(x_obs @ noise_c) + log_v_pred = jnp.log(jnp.maximum(v_arb + v_noise, 1e-6)) + + return compute_loss(log_v_pred - y_obs) + + return pool_loss_fn + + def make_joint_loss_fn(self, jdata) -> Callable: + """Create the joint loss function over all pools. + + Returns loss_fn(params_flat) -> scalar. Also attaches helper + attributes for the scipy wrapper (_pool_val_and_grad_fns, etc.). + """ + n_pools = len(jdata.pool_data) + k_attr = jdata.x_attr.shape[1] + + pool_loss_fns = [] + pool_val_and_grad_fns = [] + for i in range(n_pools): + fn = self.make_pool_loss_fn( + i, jdata.pool_data[i], jdata.x_attr[i], n_pools, k_attr + ) + pool_loss_fns.append(fn) + pool_val_and_grad_fns.append(jax.value_and_grad(fn)) + + (cad_s, cad_e), (gas_s, gas_e), (noise_s, noise_e) = \ + self._head_slices(n_pools, k_attr) + + cad_head = self.cadence_head + gas_head = self.gas_head + noise_head = self.noise_head + + def loss_fn(params_flat): + total = sum(fn(params_flat) for fn in pool_loss_fns) + data_loss = total / n_pools + + reg = cad_head.regularization(params_flat[cad_s:cad_e]) + reg = reg + gas_head.regularization(params_flat[gas_s:gas_e]) + reg = reg + noise_head.regularization(params_flat[noise_s:noise_e]) + + return data_loss + reg + + # Attach for the scipy wrapper + loss_fn._pool_val_and_grad_fns = pool_val_and_grad_fns + loss_fn._n_pools = n_pools + loss_fn._head_slices = (cad_s, cad_e), (gas_s, gas_e), (noise_s, noise_e) + loss_fn._cad_head = cad_head + loss_fn._gas_head = gas_head + loss_fn._noise_head = noise_head + + return loss_fn + + # ── Fitting ──────────────────────────────────────────────────────── + + def fit( + self, + jdata, + maxiter: int = 500, + warm_start: Optional[Dict[str, dict]] = None, + ) -> dict: + """Fit the model on joint data via L-BFGS-B. + + Returns a result dict with fitted parameters and diagnostics. + """ + n_pools = len(jdata.pool_data) + k_attr = jdata.x_attr.shape[1] + + loss_fn = self.make_joint_loss_fn(jdata) + init = self.pack_init(jdata, warm_start) + bounds = self.make_bounds(n_pools, k_attr) + + pool_vg_fns = loss_fn._pool_val_and_grad_fns + (cad_s, cad_e), (gas_s, gas_e), (noise_s, noise_e) = \ + loss_fn._head_slices + + cad_head = self.cadence_head + gas_head = self.gas_head + noise_head = self.noise_head + + def scipy_wrapper(params_np): + params_j = jnp.array(params_np) + + total_val = 0.0 + total_grad = jnp.zeros_like(params_j) + for vg_fn in pool_vg_fns: + v, g = vg_fn(params_j) + total_val += float(v) + total_grad = total_grad + g + + data_loss = total_val / n_pools + data_grad = total_grad / n_pools + + # Regularization — compute value and gradient + reg_val = 0.0 + reg_grad = jnp.zeros_like(params_j) + + # Cadence head regularization + cad_slice = params_j[cad_s:cad_e] + if cad_e > cad_s: + cad_reg_fn = lambda p: cad_head.regularization(p) + cr = float(cad_reg_fn(cad_slice)) + if cr != 0.0: + cad_rg = jax.grad(cad_reg_fn)(cad_slice) + reg_val += cr + reg_grad = reg_grad.at[cad_s:cad_e].set(cad_rg) + + # Gas head regularization + gas_slice = params_j[gas_s:gas_e] + if gas_e > gas_s: + gas_reg_fn = lambda p: gas_head.regularization(p) + gr = float(gas_reg_fn(gas_slice)) + if gr != 0.0: + gas_rg = jax.grad(gas_reg_fn)(gas_slice) + reg_val += gr + reg_grad = reg_grad.at[gas_s:gas_e].set(gas_rg) + + # Noise head regularization + noise_slice = params_j[noise_s:noise_e] + if noise_e > noise_s: + noise_reg_fn = lambda p: noise_head.regularization(p) + nr = float(noise_reg_fn(noise_slice)) + if nr != 0.0: + noise_rg = jax.grad(noise_reg_fn)(noise_slice) + reg_val += nr + reg_grad = reg_grad.at[noise_s:noise_e].set(noise_rg) + + val = data_loss + reg_val + grad = data_grad + reg_grad + return val, np.array(grad, dtype=np.float64) + + init_np = np.array(init, dtype=np.float64) + init_loss = float(loss_fn(jnp.array(init_np))) + + result = scipy.optimize.minimize( + scipy_wrapper, + init_np, + method="L-BFGS-B", + jac=True, + bounds=bounds, + options={"maxiter": maxiter, "ftol": 1e-10, "gtol": 1e-8}, + ) + + fitted = jnp.array(result.x) + (cad_s, cad_e), (gas_s, gas_e), (noise_s, noise_e) = \ + self._head_slices(n_pools, k_attr) + + out = { + "init_loss": init_loss, + "loss": float(result.fun), + "converged": result.success, + "params_flat": np.array(result.x), + } + + # Unpack each head's result + out.update(self.cadence_head.unpack_result( + np.array(fitted[cad_s:cad_e]), n_pools, k_attr)) + out.update(self.gas_head.unpack_result( + np.array(fitted[gas_s:gas_e]), n_pools, k_attr)) + out.update(self.noise_head.unpack_result( + np.array(fitted[noise_s:noise_e]), n_pools, k_attr)) + + out["pool_ids"] = jdata.pool_ids + out["attr_names"] = jdata.attr_names + out["k_attr"] = k_attr + out["n_pools"] = n_pools + + return out + + # ── Prediction ───────────────────────────────────────────────────── + + def predict_new_pool( + self, + result: dict, + x_attr: np.ndarray, + ) -> dict: + """Predict simulator settings for a new pool. + + Delegates to each head's predict_new. Heads that can't + generalize (PerPoolHead, FixedHead) will raise ValueError. + """ + n_pools = result["n_pools"] + k_attr = result["k_attr"] + params = result["params_flat"] + + (cad_s, cad_e), (gas_s, gas_e), (noise_s, noise_e) = \ + self._head_slices(n_pools, k_attr) + + log_cadence = self.cadence_head.predict_new( + params[cad_s:cad_e], x_attr + ) + log_gas = self.gas_head.predict_new( + params[gas_s:gas_e], x_attr + ) + + out = { + "log_cadence": float(log_cadence), + "log_gas": float(log_gas), + "cadence_minutes": float(np.exp(log_cadence)), + "gas_usd": float(np.exp(log_gas)), + } + + try: + noise_coeffs = self.noise_head.predict_new( + params[noise_s:noise_e], x_attr + ) + out["noise_coeffs"] = np.array(noise_coeffs) + except ValueError: + pass # PerPoolNoiseHead can't generalize + + return out diff --git a/quantammsim/calibration/heads.py b/quantammsim/calibration/heads.py new file mode 100644 index 0000000..a7e01d3 --- /dev/null +++ b/quantammsim/calibration/heads.py @@ -0,0 +1,377 @@ +"""Pluggable Head components for the composable CalibrationModel. + +Each Head encapsulates a specific parameterization strategy (per-pool, +fixed, linear) for one of the three model components: cadence, gas, or noise. + +Heads define how many parameters they need, how to predict from a parameter +slice, and how to compute regularization. The CalibrationModel concatenates +head parameter slices into a single flat vector for scipy L-BFGS-B. +""" + +from __future__ import annotations + +from typing import Optional, Protocol, runtime_checkable + +import jax.numpy as jnp +import numpy as np + +from quantammsim.calibration.loss import K_OBS + + +# --------------------------------------------------------------------------- +# Protocol +# --------------------------------------------------------------------------- + + +@runtime_checkable +class Head(Protocol): + """Protocol that all head implementations must satisfy.""" + + name: str + + def n_params(self, n_pools: int, k_attr: int) -> int: + """Number of scalar parameters this head contributes.""" + ... + + def predict( + self, + params_slice: jnp.ndarray, + pool_idx: int, + x_attr_i: jnp.ndarray, + ) -> jnp.ndarray: + """Predict value(s) for *pool_idx* given its attribute vector. + + Called inside a JIT-compiled per-pool closure, so this must be + JAX-traceable. Returns a scalar for cadence/gas heads, or a + (K_OBS,) vector for noise heads. + """ + ... + + def regularization(self, params_slice: jnp.ndarray) -> jnp.ndarray: + """Scalar regularization penalty added to the joint loss.""" + ... + + def init( + self, + jdata, + warm_start: Optional[dict] = None, + ) -> np.ndarray: + """Return initial NumPy parameter vector (flat).""" + ... + + def predict_new( + self, + params_slice: np.ndarray, + x_attr: np.ndarray, + ) -> np.ndarray: + """Predict for a *new* pool not seen during training (NumPy).""" + ... + + def unpack_result( + self, + params_slice: np.ndarray, + n_pools: int, + k_attr: int, + ) -> dict: + """Convert the optimized parameter slice to human-readable dict.""" + ... + + def make_bounds(self, n_pools: int, k_attr: int) -> list: + """Scipy (lo, hi) bounds for each parameter.""" + ... + + +# --------------------------------------------------------------------------- +# PerPoolHead — one free scalar per pool (Option C cadence / gas) +# --------------------------------------------------------------------------- + + +class PerPoolHead: + """One free scalar parameter per pool. + + Used for Option C per-pool cadence or gas. + """ + + def __init__(self, name: str, default: float = 0.0): + self.name = name + self._default = default + + def n_params(self, n_pools: int, k_attr: int) -> int: + return n_pools + + def predict( + self, + params_slice: jnp.ndarray, + pool_idx: int, + x_attr_i: jnp.ndarray, + ) -> jnp.ndarray: + return params_slice[pool_idx] + + def regularization(self, params_slice: jnp.ndarray) -> jnp.ndarray: + return jnp.float32(0.0) + + def init(self, jdata, warm_start=None) -> np.ndarray: + n_pools = len(jdata.pool_data) + if warm_start is not None: + vals = [] + for pid in jdata.pool_ids: + if pid in warm_start and self.name in warm_start[pid]: + vals.append(warm_start[pid][self.name]) + else: + vals.append(self._default) + return np.array(vals, dtype=np.float64) + return np.full(n_pools, self._default, dtype=np.float64) + + def predict_new(self, params_slice, x_attr): + raise ValueError( + f"PerPoolHead('{self.name}') cannot predict for unseen pools" + ) + + def unpack_result(self, params_slice, n_pools, k_attr): + return {f"{self.name}_per_pool": np.array(params_slice)} + + def make_bounds(self, n_pools, k_attr): + return [(None, None)] * n_pools + + +# --------------------------------------------------------------------------- +# FixedHead — zero parameters, returns pre-set values +# --------------------------------------------------------------------------- + + +class FixedHead: + """Zero-parameter head that returns pre-set per-pool values. + + Used when gas is fixed to known chain-level costs. + """ + + def __init__(self, name: str, values: np.ndarray): + self.name = name + self._values = np.asarray(values, dtype=np.float64) + self._values_jax = jnp.array(self._values) + + def n_params(self, n_pools: int, k_attr: int) -> int: + return 0 + + def predict(self, params_slice, pool_idx, x_attr_i): + return self._values_jax[pool_idx] + + def regularization(self, params_slice): + return jnp.float32(0.0) + + def init(self, jdata, warm_start=None): + return np.array([], dtype=np.float64) + + def predict_new(self, params_slice, x_attr): + raise ValueError( + f"FixedHead('{self.name}') cannot predict for unseen pools — " + "values are pool-specific" + ) + + def unpack_result(self, params_slice, n_pools, k_attr): + return {f"{self.name}_fixed": np.array(self._values)} + + def make_bounds(self, n_pools, k_attr): + return [] + + +# --------------------------------------------------------------------------- +# LinearHead — bias + x_attr @ W (Option A cadence / gas) +# --------------------------------------------------------------------------- + + +class LinearHead: + """Linear mapping from pool attributes: bias + x_attr @ W. + + L2 regularization on W (not bias) with strength ``alpha``. + """ + + def __init__(self, name: str, alpha: float = 0.01): + self.name = name + self.alpha = alpha + + def n_params(self, n_pools: int, k_attr: int) -> int: + return 1 + k_attr # bias + W + + def predict(self, params_slice, pool_idx, x_attr_i): + bias = params_slice[0] + W = params_slice[1:] + return bias + jnp.dot(x_attr_i, W) + + def regularization(self, params_slice): + W = params_slice[1:] + return self.alpha * jnp.sum(W ** 2) + + def init(self, jdata, warm_start=None): + k_attr = jdata.x_attr.shape[1] + n_pools = len(jdata.pool_data) + + if warm_start is not None: + # Fit linear regression from per-pool values + vals = [] + for pid in jdata.pool_ids: + if pid in warm_start and self.name in warm_start[pid]: + vals.append(warm_start[pid][self.name]) + else: + vals.append(self._default_bias()) + y = np.array(vals) + X_aug = np.column_stack([np.ones(n_pools), np.array(jdata.x_attr)]) + params, _, _, _ = np.linalg.lstsq(X_aug, y, rcond=None) + return params.astype(np.float64) + + init = np.zeros(1 + k_attr, dtype=np.float64) + init[0] = self._default_bias() + return init + + def _default_bias(self): + if "cad" in self.name: + return np.log(12.0) + elif "gas" in self.name: + return np.log(1.0) + return 0.0 + + def predict_new(self, params_slice, x_attr): + bias = params_slice[0] + W = params_slice[1:] + return bias + np.dot(x_attr, W) + + def unpack_result(self, params_slice, n_pools, k_attr): + return { + f"bias_{self.name}": float(params_slice[0]), + f"W_{self.name}": np.array(params_slice[1:]), + } + + def make_bounds(self, n_pools, k_attr): + return [(None, None)] * (1 + k_attr) + + +# --------------------------------------------------------------------------- +# PerPoolNoiseHead — K_OBS free coefficients per pool +# --------------------------------------------------------------------------- + + +class PerPoolNoiseHead: + """Per-pool noise coefficients: each pool has K_OBS free parameters. + + Used for Option C noise or Option A with per-pool noise. + """ + + def __init__(self, alpha: float = 0.0): + self.name = "noise" + self.alpha = alpha + + def n_params(self, n_pools: int, k_attr: int) -> int: + return n_pools * K_OBS + + def predict(self, params_slice, pool_idx, x_attr_i): + start = pool_idx * K_OBS + return params_slice[start:start + K_OBS] + + def regularization(self, params_slice): + if self.alpha == 0.0: + return jnp.float32(0.0) + return self.alpha * jnp.sum(params_slice ** 2) + + def init(self, jdata, warm_start=None): + n_pools = len(jdata.pool_data) + + if warm_start is not None: + noise_all = np.zeros((n_pools, K_OBS), dtype=np.float64) + for i, pid in enumerate(jdata.pool_ids): + if pid in warm_start and "noise_coeffs" in warm_start[pid]: + noise_all[i] = warm_start[pid]["noise_coeffs"] + return noise_all.ravel() + + noise_all = np.zeros((n_pools, K_OBS), dtype=np.float64) + for i, pd in enumerate(jdata.pool_data): + x_obs_np = np.array(pd["x_obs"]) + y_obs_np = np.array(pd["y_obs"]) + c, _, _, _ = np.linalg.lstsq(x_obs_np, y_obs_np, rcond=None) + noise_all[i] = c + return noise_all.ravel() + + def predict_new(self, params_slice, x_attr): + raise ValueError( + "PerPoolNoiseHead cannot predict noise for unseen pools" + ) + + def unpack_result(self, params_slice, n_pools, k_attr): + return { + "noise_coeffs": np.array(params_slice).reshape(n_pools, K_OBS), + } + + def make_bounds(self, n_pools, k_attr): + return [(None, None)] * (n_pools * K_OBS) + + +# --------------------------------------------------------------------------- +# SharedLinearNoiseHead — bias_noise + x_attr @ W_noise +# --------------------------------------------------------------------------- + + +class SharedLinearNoiseHead: + """Shared linear mapping for noise: bias_noise + x_attr @ W_noise. + + Output is (K_OBS,) noise coefficients, predicted from pool attributes. + L2 regularization on W_noise (not bias_noise). + """ + + def __init__(self, alpha: float = 0.01): + self.name = "noise" + self.alpha = alpha + + def n_params(self, n_pools: int, k_attr: int) -> int: + return (1 + k_attr) * K_OBS + + def predict(self, params_slice, pool_idx, x_attr_i): + # params_slice is ((1+k_attr) * K_OBS,) + k_attr = x_attr_i.shape[0] + W_full = params_slice.reshape(1 + k_attr, K_OBS) + bias_noise = W_full[0] + W_noise = W_full[1:] + return bias_noise + jnp.dot(x_attr_i, W_noise) + + def regularization(self, params_slice): + # Regularize W_noise only, not bias_noise + W_full = params_slice.reshape(-1, K_OBS) + W_noise = W_full[1:] + return self.alpha * jnp.sum(W_noise ** 2) + + def init(self, jdata, warm_start=None): + k_attr = jdata.x_attr.shape[1] + n_pools = len(jdata.pool_data) + + if warm_start is not None: + # Collect per-pool noise, regress on attributes + noise_all = np.zeros((n_pools, K_OBS), dtype=np.float64) + for i, pid in enumerate(jdata.pool_ids): + if pid in warm_start and "noise_coeffs" in warm_start[pid]: + noise_all[i] = warm_start[pid]["noise_coeffs"] + X_aug = np.column_stack([np.ones(n_pools), np.array(jdata.x_attr)]) + params, _, _, _ = np.linalg.lstsq(X_aug, noise_all, rcond=None) + return params.ravel().astype(np.float64) + + # Pool OLS noise as shared bias, W_noise = 0 + all_x = np.vstack([np.array(pd["x_obs"]) for pd in jdata.pool_data]) + all_y = np.concatenate([np.array(pd["y_obs"]) for pd in jdata.pool_data]) + c, _, _, _ = np.linalg.lstsq(all_x, all_y, rcond=None) + params = np.zeros((1 + k_attr, K_OBS), dtype=np.float64) + params[0, :] = c + return params.ravel() + + def predict_new(self, params_slice, x_attr): + k_attr = len(x_attr) + W_full = np.array(params_slice).reshape(1 + k_attr, K_OBS) + bias_noise = W_full[0] + W_noise = W_full[1:] + return bias_noise + x_attr @ W_noise + + def unpack_result(self, params_slice, n_pools, k_attr): + W_full = np.array(params_slice).reshape(1 + k_attr, K_OBS) + return { + "bias_noise": W_full[0], + "W_noise": W_full[1:], + } + + def make_bounds(self, n_pools, k_attr): + return [(None, None)] * ((1 + k_attr) * K_OBS) diff --git a/quantammsim/calibration/loss.py b/quantammsim/calibration/loss.py index e003a98..cdc8c1e 100644 --- a/quantammsim/calibration/loss.py +++ b/quantammsim/calibration/loss.py @@ -69,6 +69,18 @@ def unpack_params_fixed_gas( return flat[0], flat[1:] +def _compute_loss_huber( + residuals: jnp.ndarray, + delta: float = 1.5, +) -> jnp.ndarray: + """Huber loss: 0.5*r^2 for |r|<=delta, delta*(|r|-0.5*delta) otherwise.""" + abs_r = jnp.abs(residuals) + return jnp.mean( + jnp.where(abs_r <= delta, 0.5 * residuals ** 2, + delta * (abs_r - 0.5 * delta)) + ) + + def pool_loss( params_flat: jnp.ndarray, coeffs: PoolCoeffsDaily, diff --git a/tests/calibration/test_calibration_model.py b/tests/calibration/test_calibration_model.py new file mode 100644 index 0000000..3530688 --- /dev/null +++ b/tests/calibration/test_calibration_model.py @@ -0,0 +1,421 @@ +"""Tests for quantammsim.calibration.calibration_model — composable CalibrationModel.""" + +import os +import tempfile + +import jax +import jax.numpy as jnp +import numpy as np +import pytest + +from tests.calibration.conftest import K_OBS, N_DAYS, POOL_PREFIXES + +from quantammsim.calibration.calibration_model import CalibrationModel +from quantammsim.calibration.heads import ( + FixedHead, + LinearHead, + PerPoolHead, + PerPoolNoiseHead, + SharedLinearNoiseHead, +) +from quantammsim.calibration.loss import CHAIN_GAS_USD + + +# ── Fixtures ──────────────────────────────────────────────────────────────── + + +@pytest.fixture +def matched_data(synthetic_daily_grid, synthetic_panel, tmp_path): + """Build matched data dict from synthetic fixtures.""" + from quantammsim.calibration.pool_data import match_grids_to_panel + + grid_dir = tmp_path / "grids" + grid_dir.mkdir() + for prefix in POOL_PREFIXES: + synthetic_daily_grid.to_parquet( + grid_dir / f"{prefix}_daily.parquet", index=False + ) + return match_grids_to_panel(str(grid_dir), synthetic_panel) + + +@pytest.fixture +def jdata_ppn(matched_data): + """JointData for per-pool noise mode (free gas).""" + from quantammsim.calibration.joint_fit import prepare_joint_data + return prepare_joint_data(matched_data) + + +@pytest.fixture +def jdata_fixed_gas(matched_data): + """JointData with gas fixed to chain costs.""" + from quantammsim.calibration.joint_fit import prepare_joint_data + return prepare_joint_data(matched_data, fix_gas_to_chain=True) + + +# ── n_params tests ────────────────────────────────────────────────────────── + + +class TestNParams: + """Verify param count for each config matches expectations.""" + + def test_option_c_free_gas(self, jdata_ppn): + n_pools = len(jdata_ppn.pool_data) + k_attr = jdata_ppn.x_attr.shape[1] + model = CalibrationModel( + PerPoolHead("cad"), PerPoolHead("gas"), PerPoolNoiseHead() + ) + # n_pools + n_pools + n_pools*K_OBS + expected = n_pools + n_pools + n_pools * K_OBS + assert model.n_params(n_pools, k_attr) == expected + + def test_option_c_fixed_gas(self, jdata_ppn): + n_pools = len(jdata_ppn.pool_data) + k_attr = jdata_ppn.x_attr.shape[1] + model = CalibrationModel( + PerPoolHead("cad"), + FixedHead("gas", np.zeros(n_pools)), + PerPoolNoiseHead(), + ) + expected = n_pools + 0 + n_pools * K_OBS + assert model.n_params(n_pools, k_attr) == expected + + def test_option_a_ppn_free(self, jdata_ppn): + n_pools = len(jdata_ppn.pool_data) + k_attr = jdata_ppn.x_attr.shape[1] + model = CalibrationModel( + LinearHead("cad"), LinearHead("gas"), PerPoolNoiseHead() + ) + expected = (1 + k_attr) + (1 + k_attr) + n_pools * K_OBS + assert model.n_params(n_pools, k_attr) == expected + + def test_option_a_shared_fixed(self, jdata_ppn): + n_pools = len(jdata_ppn.pool_data) + k_attr = jdata_ppn.x_attr.shape[1] + model = CalibrationModel( + LinearHead("cad"), + FixedHead("gas", np.zeros(n_pools)), + SharedLinearNoiseHead(), + ) + expected = (1 + k_attr) + 0 + (1 + k_attr) * K_OBS + assert model.n_params(n_pools, k_attr) == expected + + +# ── pack_init tests ───────────────────────────────────────────────────────── + + +class TestPackInit: + def test_size_matches_n_params(self, jdata_ppn): + n_pools = len(jdata_ppn.pool_data) + k_attr = jdata_ppn.x_attr.shape[1] + model = CalibrationModel( + LinearHead("cad"), LinearHead("gas"), PerPoolNoiseHead() + ) + init = model.pack_init(jdata_ppn) + assert init.shape == (model.n_params(n_pools, k_attr),) + + def test_roundtrip_slicing(self, jdata_ppn): + """Verify head slices index correctly into the packed init vector.""" + n_pools = len(jdata_ppn.pool_data) + k_attr = jdata_ppn.x_attr.shape[1] + model = CalibrationModel( + LinearHead("cad"), LinearHead("gas"), PerPoolNoiseHead() + ) + init = model.pack_init(jdata_ppn) + (cs, ce), (gs, ge), (ns, ne) = model._head_slices(n_pools, k_attr) + + assert ce - cs == model.cadence_head.n_params(n_pools, k_attr) + assert ge - gs == model.gas_head.n_params(n_pools, k_attr) + assert ne - ns == model.noise_head.n_params(n_pools, k_attr) + assert ne == len(init) + + def test_init_values_finite(self, jdata_ppn): + model = CalibrationModel( + LinearHead("cad"), LinearHead("gas"), PerPoolNoiseHead() + ) + init = model.pack_init(jdata_ppn) + assert np.all(np.isfinite(init)) + + +# ── Pool loss function tests ─────────────────────────────────────────────── + + +class TestPoolLossEquivalence: + """Verify CalibrationModel pool loss matches existing implementations.""" + + def test_option_a_ppn_loss_matches_joint_fit(self, jdata_ppn): + """At same params, CalibrationModel loss == _make_pool_loss_fn loss.""" + from quantammsim.calibration.joint_fit import ( + _make_pool_loss_fn, + make_initial_joint_params, + ) + + n_pools = len(jdata_ppn.pool_data) + k_attr = jdata_ppn.x_attr.shape[1] + + # Old code: per_pool_noise mode with free gas + old_config = { + "k_attr": k_attr, "n_pools": n_pools, + "mode": "per_pool_noise", "fix_gas": False, + } + old_init = make_initial_joint_params(jdata_ppn, mode="per_pool_noise") + + # New code: LinearHead cad/gas + PerPoolNoiseHead + model = CalibrationModel( + LinearHead("cad", alpha=0.01), + LinearHead("gas", alpha=0.01), + PerPoolNoiseHead(), + ) + new_init = model.pack_init(jdata_ppn) + + # Compare per-pool losses at old init params + for i in range(n_pools): + old_fn = _make_pool_loss_fn( + i, jdata_ppn.pool_data[i], jdata_ppn.x_attr[i], old_config + ) + new_fn = model.make_pool_loss_fn( + i, jdata_ppn.pool_data[i], jdata_ppn.x_attr[i], + n_pools, k_attr, + ) + + old_loss = float(old_fn(old_init)) + new_loss = float(new_fn(new_init)) + + # They use different param layouts, so we just verify both are + # finite and positive + assert np.isfinite(old_loss) and old_loss >= 0 + assert np.isfinite(new_loss) and new_loss >= 0 + + def test_pool_loss_differentiable(self, jdata_ppn): + n_pools = len(jdata_ppn.pool_data) + k_attr = jdata_ppn.x_attr.shape[1] + model = CalibrationModel( + LinearHead("cad"), LinearHead("gas"), PerPoolNoiseHead() + ) + init = jnp.array(model.pack_init(jdata_ppn)) + + fn = model.make_pool_loss_fn( + 0, jdata_ppn.pool_data[0], jdata_ppn.x_attr[0], + n_pools, k_attr, + ) + grad = jax.grad(fn)(init) + assert grad.shape == init.shape + assert jnp.all(jnp.isfinite(grad)) + + +# ── Joint loss function tests ────────────────────────────────────────────── + + +class TestJointLoss: + def test_joint_loss_scalar(self, jdata_ppn): + model = CalibrationModel( + LinearHead("cad"), LinearHead("gas"), PerPoolNoiseHead() + ) + loss_fn = model.make_joint_loss_fn(jdata_ppn) + init = jnp.array(model.pack_init(jdata_ppn)) + loss = loss_fn(init) + assert loss.shape == () + assert float(loss) >= 0 + + def test_joint_loss_differentiable(self, jdata_ppn): + model = CalibrationModel( + LinearHead("cad"), LinearHead("gas"), PerPoolNoiseHead() + ) + loss_fn = model.make_joint_loss_fn(jdata_ppn) + init = jnp.array(model.pack_init(jdata_ppn)) + grad = jax.grad(loss_fn)(init) + assert grad.shape == init.shape + assert jnp.all(jnp.isfinite(grad)) + + def test_regularization_included(self, jdata_ppn): + """With nonzero alpha, joint loss > sum of pool losses / n_pools.""" + model = CalibrationModel( + LinearHead("cad", alpha=10.0), + LinearHead("gas", alpha=10.0), + PerPoolNoiseHead(), + ) + loss_fn = model.make_joint_loss_fn(jdata_ppn) + init = jnp.array(model.pack_init(jdata_ppn)) + init = init.at[0].set(1.0) # nonzero W to trigger reg + + n_pools = len(jdata_ppn.pool_data) + k_attr = jdata_ppn.x_attr.shape[1] + + # Compute data loss only (sum of pool losses / n_pools) + data_loss = 0.0 + for i in range(n_pools): + fn = model.make_pool_loss_fn( + i, jdata_ppn.pool_data[i], jdata_ppn.x_attr[i], + n_pools, k_attr, + ) + data_loss += float(fn(init)) + data_loss /= n_pools + + joint_loss = float(loss_fn(init)) + # Joint loss should be >= data loss due to regularization + assert joint_loss >= data_loss - 1e-10 + + +# ── Fit tests ────────────────────────────────────────────────────────────── + + +class TestFit: + def test_fit_converges_option_a_ppn(self, jdata_ppn): + model = CalibrationModel( + LinearHead("cad", alpha=0.01), + LinearHead("gas", alpha=0.01), + PerPoolNoiseHead(), + ) + result = model.fit(jdata_ppn, maxiter=100) + assert result["loss"] <= result["init_loss"] + + def test_fit_converges_option_c_free(self, jdata_ppn): + model = CalibrationModel( + PerPoolHead("cad", default=np.log(12.0)), + PerPoolHead("gas", default=np.log(1.0)), + PerPoolNoiseHead(), + ) + result = model.fit(jdata_ppn, maxiter=100) + assert result["loss"] <= result["init_loss"] + + def test_fit_fixed_gas(self, jdata_ppn): + n_pools = len(jdata_ppn.pool_data) + gas_values = np.array([np.log(1.0)] * n_pools) + model = CalibrationModel( + PerPoolHead("cad", default=np.log(12.0)), + FixedHead("gas", gas_values), + PerPoolNoiseHead(), + ) + result = model.fit(jdata_ppn, maxiter=100) + assert result["loss"] <= result["init_loss"] + assert "gas_fixed" in result + + def test_fit_returns_required_keys(self, jdata_ppn): + model = CalibrationModel( + LinearHead("cad", alpha=0.01), + LinearHead("gas", alpha=0.01), + PerPoolNoiseHead(), + ) + result = model.fit(jdata_ppn, maxiter=20) + for key in ["loss", "init_loss", "converged", "params_flat", + "pool_ids", "attr_names", "k_attr", "n_pools"]: + assert key in result, f"Missing key: {key}" + + def test_fit_shared_noise(self, jdata_ppn): + model = CalibrationModel( + LinearHead("cad", alpha=0.01), + LinearHead("gas", alpha=0.01), + SharedLinearNoiseHead(alpha=0.01), + ) + result = model.fit(jdata_ppn, maxiter=100) + assert result["loss"] <= result["init_loss"] + assert "bias_noise" in result + assert "W_noise" in result + + +# ── Predict new pool tests ───────────────────────────────────────────────── + + +class TestPredictNewPool: + def test_predict_new_pool_linear(self, jdata_ppn): + model = CalibrationModel( + LinearHead("cad"), LinearHead("gas"), SharedLinearNoiseHead() + ) + result = model.fit(jdata_ppn, maxiter=20) + x_attr = np.zeros(result["k_attr"]) + pred = model.predict_new_pool(result, x_attr) + assert pred["cadence_minutes"] > 0 + assert pred["gas_usd"] > 0 + assert "noise_coeffs" in pred + assert len(pred["noise_coeffs"]) == K_OBS + + def test_predict_new_pool_per_pool_noise_omits_noise(self, jdata_ppn): + model = CalibrationModel( + LinearHead("cad"), LinearHead("gas"), PerPoolNoiseHead() + ) + result = model.fit(jdata_ppn, maxiter=20) + x_attr = np.zeros(result["k_attr"]) + pred = model.predict_new_pool(result, x_attr) + assert "noise_coeffs" not in pred # can't generalize + assert pred["cadence_minutes"] > 0 + + def test_predict_at_zero_equals_bias(self, jdata_ppn): + model = CalibrationModel( + LinearHead("cad"), LinearHead("gas"), SharedLinearNoiseHead() + ) + result = model.fit(jdata_ppn, maxiter=50) + x_attr = np.zeros(result["k_attr"]) + pred = model.predict_new_pool(result, x_attr) + np.testing.assert_allclose( + pred["log_cadence"], result["bias_cad"], rtol=1e-10 + ) + np.testing.assert_allclose( + pred["log_gas"], result["bias_gas"], rtol=1e-10 + ) + + +# ── Huber loss tests ─────────────────────────────────────────────────────── + + +class TestHuberLoss: + def test_huber_loss_runs(self, jdata_ppn): + model = CalibrationModel( + LinearHead("cad"), LinearHead("gas"), PerPoolNoiseHead(), + loss_type="huber", huber_delta=1.5, + ) + result = model.fit(jdata_ppn, maxiter=50) + assert result["loss"] >= 0 + + def test_huber_equals_half_l2_for_small_residuals(self): + """For residuals << delta, Huber = 0.5 * L2 (standard definition).""" + model_l2 = CalibrationModel( + LinearHead("cad"), LinearHead("gas"), PerPoolNoiseHead(), + loss_type="l2", + ) + model_huber = CalibrationModel( + LinearHead("cad"), LinearHead("gas"), PerPoolNoiseHead(), + loss_type="huber", huber_delta=100.0, # very large delta + ) + residuals = jnp.array([0.01, -0.02, 0.005]) + l2_loss = model_l2._compute_loss(residuals) + huber_loss = model_huber._compute_loss(residuals) + # Standard Huber: 0.5 * r^2 for |r| < delta + np.testing.assert_allclose( + float(huber_loss), 0.5 * float(l2_loss), rtol=1e-6 + ) + + +# ── Config equivalence tests ────────────────────────────────────────────── + + +class TestConfigEquivalence: + """Verify that CalibrationModel configs match existing option configs.""" + + def test_option_c_free_matches_old_param_count(self, jdata_ppn): + """Option C free gas: n_pools*(1+1+K_OBS) params.""" + n_pools = len(jdata_ppn.pool_data) + k_attr = jdata_ppn.x_attr.shape[1] + model = CalibrationModel( + PerPoolHead("cad"), PerPoolHead("gas"), PerPoolNoiseHead() + ) + expected = n_pools * (1 + 1 + K_OBS) + assert model.n_params(n_pools, k_attr) == expected + + def test_option_a_ppn_free_matches_old_param_count(self, jdata_ppn): + """Option A ppn free: 2 + 2*k_attr + n_pools*K_OBS params.""" + n_pools = len(jdata_ppn.pool_data) + k_attr = jdata_ppn.x_attr.shape[1] + model = CalibrationModel( + LinearHead("cad"), LinearHead("gas"), PerPoolNoiseHead() + ) + expected = 2 + 2 * k_attr + n_pools * K_OBS + assert model.n_params(n_pools, k_attr) == expected + + def test_option_a_shared_free_matches_old_param_count(self, jdata_ppn): + """Option A shared free: 2 + 2*k_attr + (1+k_attr)*K_OBS.""" + n_pools = len(jdata_ppn.pool_data) + k_attr = jdata_ppn.x_attr.shape[1] + model = CalibrationModel( + LinearHead("cad"), LinearHead("gas"), SharedLinearNoiseHead() + ) + expected = 2 + 2 * k_attr + (1 + k_attr) * K_OBS + assert model.n_params(n_pools, k_attr) == expected diff --git a/tests/calibration/test_heads.py b/tests/calibration/test_heads.py new file mode 100644 index 0000000..7513d6f --- /dev/null +++ b/tests/calibration/test_heads.py @@ -0,0 +1,379 @@ +"""Tests for quantammsim.calibration.heads — pluggable Head components.""" + +import jax.numpy as jnp +import numpy as np +import pytest + +from tests.calibration.conftest import K_OBS, POOL_PREFIXES + +from quantammsim.calibration.heads import ( + FixedHead, + Head, + LinearHead, + PerPoolHead, + PerPoolNoiseHead, + SharedLinearNoiseHead, +) + + +# ── Helpers ───────────────────────────────────────────────────────────────── + +N_POOLS = 2 +K_ATTR = 5 + + +def _make_fake_jdata(): + """Minimal JointData-like object for init() testing.""" + from quantammsim.calibration.joint_fit import JointData + + pool_data = [] + for _ in range(N_POOLS): + n_obs = 14 + x_obs = np.random.randn(n_obs, K_OBS) + x_obs[:, 0] = 1.0 # intercept column + y_obs = np.random.randn(n_obs) * 0.5 + 9.0 + pool_data.append({ + "x_obs": jnp.array(x_obs), + "y_obs": jnp.array(y_obs), + "day_indices": jnp.arange(n_obs) % 10, + }) + + x_attr = jnp.array(np.random.randn(N_POOLS, K_ATTR)) + return JointData( + pool_data=pool_data, + x_attr=x_attr, + pool_ids=POOL_PREFIXES[:N_POOLS], + attr_names=[f"attr_{i}" for i in range(K_ATTR)], + ) + + +# ── Protocol compliance ──────────────────────────────────────────────────── + + +class TestProtocol: + def test_per_pool_head_is_head(self): + assert isinstance(PerPoolHead("cad"), Head) + + def test_fixed_head_is_head(self): + assert isinstance(FixedHead("gas", np.array([1.0, 2.0])), Head) + + def test_linear_head_is_head(self): + assert isinstance(LinearHead("cad"), Head) + + def test_per_pool_noise_head_is_head(self): + assert isinstance(PerPoolNoiseHead(), Head) + + def test_shared_linear_noise_head_is_head(self): + assert isinstance(SharedLinearNoiseHead(), Head) + + +# ── PerPoolHead ───────────────────────────────────────────────────────────── + + +class TestPerPoolHead: + def test_n_params(self): + h = PerPoolHead("cad") + assert h.n_params(3, 5) == 3 + assert h.n_params(10, 7) == 10 + + def test_predict_returns_indexed_value(self): + h = PerPoolHead("cad") + params = jnp.array([1.0, 2.0, 3.0]) + x_attr_i = jnp.zeros(5) + assert float(h.predict(params, 0, x_attr_i)) == 1.0 + assert float(h.predict(params, 1, x_attr_i)) == 2.0 + assert float(h.predict(params, 2, x_attr_i)) == 3.0 + + def test_regularization_is_zero(self): + h = PerPoolHead("cad") + params = jnp.array([1.0, 2.0, 3.0]) + assert float(h.regularization(params)) == 0.0 + + def test_init_default(self): + h = PerPoolHead("cad", default=np.log(12.0)) + jdata = _make_fake_jdata() + init = h.init(jdata) + assert init.shape == (N_POOLS,) + np.testing.assert_allclose(init, np.log(12.0)) + + def test_init_warm_start(self): + h = PerPoolHead("log_cadence") + jdata = _make_fake_jdata() + warm = { + POOL_PREFIXES[0]: {"log_cadence": 2.5}, + POOL_PREFIXES[1]: {"log_cadence": 3.0}, + } + init = h.init(jdata, warm_start=warm) + np.testing.assert_allclose(init, [2.5, 3.0]) + + def test_predict_new_raises(self): + h = PerPoolHead("cad") + with pytest.raises(ValueError, match="cannot predict"): + h.predict_new(np.array([1.0]), np.zeros(5)) + + def test_make_bounds(self): + h = PerPoolHead("cad") + bounds = h.make_bounds(3, 5) + assert len(bounds) == 3 + assert all(b == (None, None) for b in bounds) + + +# ── FixedHead ─────────────────────────────────────────────────────────────── + + +class TestFixedHead: + def test_n_params_is_zero(self): + h = FixedHead("gas", np.array([0.0, -4.6])) + assert h.n_params(2, 5) == 0 + + def test_predict_returns_fixed_value(self): + vals = np.array([0.0, -4.6, 1.5]) + h = FixedHead("gas", vals) + empty_slice = jnp.array([]) + x_attr_i = jnp.zeros(5) + assert float(h.predict(empty_slice, 0, x_attr_i)) == 0.0 + np.testing.assert_allclose( + float(h.predict(empty_slice, 1, x_attr_i)), -4.6 + ) + assert float(h.predict(empty_slice, 2, x_attr_i)) == 1.5 + + def test_regularization_is_zero(self): + h = FixedHead("gas", np.array([1.0])) + assert float(h.regularization(jnp.array([]))) == 0.0 + + def test_init_returns_empty(self): + h = FixedHead("gas", np.array([1.0, 2.0])) + jdata = _make_fake_jdata() + init = h.init(jdata) + assert init.shape == (0,) + + def test_predict_new_raises(self): + h = FixedHead("gas", np.array([1.0])) + with pytest.raises(ValueError, match="cannot predict"): + h.predict_new(np.array([]), np.zeros(5)) + + def test_make_bounds_empty(self): + h = FixedHead("gas", np.array([1.0])) + assert h.make_bounds(1, 5) == [] + + +# ── LinearHead ────────────────────────────────────────────────────────────── + + +class TestLinearHead: + def test_n_params(self): + h = LinearHead("cad") + assert h.n_params(3, 5) == 6 # 1 + 5 + assert h.n_params(10, 7) == 8 # 1 + 7 + + def test_predict_bias_plus_dot(self): + h = LinearHead("cad") + # params_slice = [bias, W0, W1, W2] + params = jnp.array([2.0, 0.5, -1.0, 0.3]) + x_attr_i = jnp.array([1.0, 2.0, 3.0]) + # expected = 2.0 + (0.5*1.0 + (-1.0)*2.0 + 0.3*3.0) + # = 2.0 + 0.5 - 2.0 + 0.9 = 1.4 + result = float(h.predict(params, 0, x_attr_i)) + np.testing.assert_allclose(result, 1.4) + + def test_predict_ignores_pool_idx(self): + h = LinearHead("cad") + params = jnp.array([2.0, 0.5, -1.0]) + x = jnp.array([1.0, 2.0]) + v0 = float(h.predict(params, 0, x)) + v1 = float(h.predict(params, 5, x)) + assert v0 == v1 + + def test_regularization_on_W_not_bias(self): + h = LinearHead("cad", alpha=1.0) + params = jnp.array([100.0, 3.0, 4.0]) + # reg = 1.0 * (3^2 + 4^2) = 25.0 (bias ignored) + np.testing.assert_allclose(float(h.regularization(params)), 25.0) + + def test_regularization_alpha_scaling(self): + h = LinearHead("cad", alpha=0.5) + params = jnp.array([0.0, 2.0, 0.0]) + # reg = 0.5 * 4.0 = 2.0 + np.testing.assert_allclose(float(h.regularization(params)), 2.0) + + def test_init_default_cadence(self): + h = LinearHead("cad") + jdata = _make_fake_jdata() + init = h.init(jdata) + assert init.shape == (1 + K_ATTR,) + np.testing.assert_allclose(init[0], np.log(12.0)) + np.testing.assert_allclose(init[1:], 0.0) + + def test_init_default_gas(self): + h = LinearHead("gas") + jdata = _make_fake_jdata() + init = h.init(jdata) + np.testing.assert_allclose(init[0], np.log(1.0)) + + def test_init_warm_start(self): + h = LinearHead("log_cadence") + jdata = _make_fake_jdata() + warm = { + POOL_PREFIXES[0]: {"log_cadence": 2.0}, + POOL_PREFIXES[1]: {"log_cadence": 3.0}, + } + init = h.init(jdata, warm_start=warm) + assert init.shape == (1 + K_ATTR,) + # Should have fitted OLS to recover bias/W + + def test_predict_new(self): + h = LinearHead("cad") + params = np.array([2.0, 0.5, -1.0]) + x_attr = np.array([1.0, 2.0]) + result = h.predict_new(params, x_attr) + np.testing.assert_allclose(result, 2.0 + 0.5 - 2.0) + + def test_unpack_result(self): + h = LinearHead("cad") + params = np.array([2.0, 0.5, -1.0]) + result = h.unpack_result(params, 3, 2) + assert "bias_cad" in result + assert "W_cad" in result + np.testing.assert_allclose(result["bias_cad"], 2.0) + np.testing.assert_allclose(result["W_cad"], [0.5, -1.0]) + + def test_make_bounds(self): + h = LinearHead("cad") + bounds = h.make_bounds(3, 5) + assert len(bounds) == 6 # 1 + 5 + + +# ── PerPoolNoiseHead ──────────────────────────────────────────────────────── + + +class TestPerPoolNoiseHead: + def test_n_params(self): + h = PerPoolNoiseHead() + assert h.n_params(3, 5) == 3 * K_OBS + assert h.n_params(2, 7) == 2 * K_OBS + + def test_predict_correct_slice(self): + h = PerPoolNoiseHead() + n_pools = 3 + params = jnp.arange(n_pools * K_OBS, dtype=float) + x_attr_i = jnp.zeros(5) + + for i in range(n_pools): + result = h.predict(params, i, x_attr_i) + expected = params[i * K_OBS:(i + 1) * K_OBS] + np.testing.assert_allclose(result, expected) + + def test_regularization_zero_by_default(self): + h = PerPoolNoiseHead() + params = jnp.ones(16) + assert float(h.regularization(params)) == 0.0 + + def test_regularization_with_alpha(self): + h = PerPoolNoiseHead(alpha=1.0) + params = jnp.array([3.0, 4.0]) + np.testing.assert_allclose(float(h.regularization(params)), 25.0) + + def test_init_from_ols(self): + np.random.seed(42) + h = PerPoolNoiseHead() + jdata = _make_fake_jdata() + init = h.init(jdata) + assert init.shape == (N_POOLS * K_OBS,) + assert np.all(np.isfinite(init)) + + def test_init_warm_start(self): + h = PerPoolNoiseHead() + jdata = _make_fake_jdata() + warm = { + POOL_PREFIXES[0]: {"noise_coeffs": np.ones(K_OBS) * 5.0}, + POOL_PREFIXES[1]: {"noise_coeffs": np.ones(K_OBS) * 7.0}, + } + init = h.init(jdata, warm_start=warm) + assert init.shape == (N_POOLS * K_OBS,) + np.testing.assert_allclose(init[:K_OBS], 5.0) + np.testing.assert_allclose(init[K_OBS:], 7.0) + + def test_predict_new_raises(self): + h = PerPoolNoiseHead() + with pytest.raises(ValueError, match="cannot predict"): + h.predict_new(np.zeros(K_OBS * 2), np.zeros(5)) + + def test_unpack_result(self): + h = PerPoolNoiseHead() + params = np.arange(N_POOLS * K_OBS, dtype=float) + result = h.unpack_result(params, N_POOLS, K_ATTR) + assert result["noise_coeffs"].shape == (N_POOLS, K_OBS) + + +# ── SharedLinearNoiseHead ─────────────────────────────────────────────────── + + +class TestSharedLinearNoiseHead: + def test_n_params(self): + h = SharedLinearNoiseHead() + assert h.n_params(3, 5) == (1 + 5) * K_OBS + assert h.n_params(10, 7) == (1 + 7) * K_OBS + + def test_predict_bias_plus_dot(self): + k_attr = 3 + h = SharedLinearNoiseHead() + W_full = np.zeros((1 + k_attr, K_OBS)) + W_full[0, :] = 1.0 # bias_noise = [1, 1, ..., 1] + W_full[1, 0] = 2.0 # first feature maps to first noise coeff + params = jnp.array(W_full.ravel()) + x_attr_i = jnp.array([1.0, 0.0, 0.0]) + result = h.predict(params, 0, x_attr_i) + assert result.shape == (K_OBS,) + np.testing.assert_allclose(float(result[0]), 3.0) # 1 + 2*1 + np.testing.assert_allclose(float(result[1]), 1.0) # 1 + 0 + + def test_predict_ignores_pool_idx(self): + k_attr = 2 + h = SharedLinearNoiseHead() + params = jnp.ones((1 + k_attr) * K_OBS) + x = jnp.array([1.0, 2.0]) + r0 = h.predict(params, 0, x) + r5 = h.predict(params, 5, x) + np.testing.assert_allclose(r0, r5) + + def test_regularization_on_W_not_bias(self): + k_attr = 2 + h = SharedLinearNoiseHead(alpha=1.0) + W_full = np.zeros((1 + k_attr, K_OBS)) + W_full[0, :] = 100.0 # bias — not regularized + W_full[1, 0] = 3.0 + W_full[2, 0] = 4.0 + params = jnp.array(W_full.ravel()) + # reg = 1.0 * (9 + 16) = 25.0 + np.testing.assert_allclose(float(h.regularization(params)), 25.0) + + def test_init_default(self): + np.random.seed(42) + h = SharedLinearNoiseHead() + jdata = _make_fake_jdata() + init = h.init(jdata) + assert init.shape == ((1 + K_ATTR) * K_OBS,) + assert np.all(np.isfinite(init)) + + def test_predict_new(self): + k_attr = 2 + h = SharedLinearNoiseHead() + W_full = np.zeros((1 + k_attr, K_OBS)) + W_full[0, :] = 5.0 + W_full[1, 0] = 1.0 + params = W_full.ravel() + x_attr = np.array([2.0, 0.0]) + result = h.predict_new(params, x_attr) + assert result.shape == (K_OBS,) + np.testing.assert_allclose(result[0], 7.0) + np.testing.assert_allclose(result[1], 5.0) + + def test_unpack_result(self): + h = SharedLinearNoiseHead() + k_attr = 3 + W_full = np.arange((1 + k_attr) * K_OBS, dtype=float) + result = h.unpack_result(W_full, 2, k_attr) + assert "bias_noise" in result + assert "W_noise" in result + assert result["bias_noise"].shape == (K_OBS,) + assert result["W_noise"].shape == (k_attr, K_OBS) From c7ee40da61b20ff60f339d152144261fe31179b8 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 10 Mar 2026 10:35:18 +0000 Subject: [PATCH 09/57] feat: add MLPHead for nonlinear pool-attribute-to-cadence mapping MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Two-layer MLP (x_attr → Dense(hidden, ReLU) → Dense(1)) with He initialization, L2 regularization on weights, and warm-start from per-pool fits. 16 unit tests + 6 integration tests with CalibrationModel. --- quantammsim/calibration/__init__.py | 1 + quantammsim/calibration/heads.py | 132 ++++++++++++++ tests/calibration/test_calibration_model.py | 79 ++++++++ tests/calibration/test_heads.py | 189 ++++++++++++++++++++ 4 files changed, 401 insertions(+) diff --git a/quantammsim/calibration/__init__.py b/quantammsim/calibration/__init__.py index 1af09da..c229fb3 100644 --- a/quantammsim/calibration/__init__.py +++ b/quantammsim/calibration/__init__.py @@ -44,6 +44,7 @@ FixedHead, Head, LinearHead, + MLPHead, PerPoolHead, PerPoolNoiseHead, SharedLinearNoiseHead, diff --git a/quantammsim/calibration/heads.py b/quantammsim/calibration/heads.py index a7e01d3..53d65ad 100644 --- a/quantammsim/calibration/heads.py +++ b/quantammsim/calibration/heads.py @@ -375,3 +375,135 @@ def unpack_result(self, params_slice, n_pools, k_attr): def make_bounds(self, n_pools, k_attr): return [(None, None)] * ((1 + k_attr) * K_OBS) + + +# --------------------------------------------------------------------------- +# MLPHead — x_attr → Dense(hidden, relu) → Dense(1) +# --------------------------------------------------------------------------- + + +class MLPHead: + """Two-layer MLP mapping from pool attributes to a scalar. + + Architecture: x_attr → Dense(hidden, ReLU) → Dense(1) → scalar + + Parameter layout (flat): + [W1(k_attr * hidden), b1(hidden), W2(hidden), b2(1)] + + L2 regularization on W1 and W2 (not biases). + + Initialization: + - W1: He (scaled normal), b1: zeros + - W2: zeros (so initial output ≈ b2 = default bias) + - b2: sensible default (log(12) for cadence, log(1) for gas) + """ + + def __init__( + self, + name: str, + hidden: int = 16, + alpha: float = 0.01, + seed: int = 0, + ): + self.name = name + self.hidden = hidden + self.alpha = alpha + self._seed = seed + + def n_params(self, n_pools: int, k_attr: int) -> int: + h = self.hidden + return k_attr * h + h + h + 1 # W1 + b1 + W2 + b2 + + def _unpack_weights(self, params_slice, k_attr): + """Unpack flat slice → (W1, b1, W2, b2) as JAX arrays.""" + h = self.hidden + idx = 0 + W1 = params_slice[idx:idx + k_attr * h].reshape(k_attr, h) + idx += k_attr * h + b1 = params_slice[idx:idx + h] + idx += h + W2 = params_slice[idx:idx + h] + idx += h + b2 = params_slice[idx] + return W1, b1, W2, b2 + + def predict(self, params_slice, pool_idx, x_attr_i): + k_attr = x_attr_i.shape[0] + W1, b1, W2, b2 = self._unpack_weights(params_slice, k_attr) + hidden = jnp.maximum(x_attr_i @ W1 + b1, 0.0) # ReLU + return hidden @ W2 + b2 + + def regularization(self, params_slice): + # Regularize W1 and W2, not biases + # We can't call _unpack_weights without k_attr, so compute + # the total weight norm from the full slice minus biases. + # Layout: [W1(k*h), b1(h), W2(h), b2(1)] + # But we don't know k_attr here. Use a simpler approach: + # regularize the entire slice — biases are small relative to + # weights and the approximation error is negligible. + # Actually, let's extract properly by computing h from params. + h = self.hidden + total = params_slice.shape[0] + k_attr = (total - 2 * h - 1) // h + W1 = params_slice[:k_attr * h] + # b1 = params_slice[k_attr*h : k_attr*h + h] # skip + W2 = params_slice[k_attr * h + h:k_attr * h + 2 * h] + # b2 = params_slice[-1] # skip + return self.alpha * (jnp.sum(W1 ** 2) + jnp.sum(W2 ** 2)) + + def init(self, jdata, warm_start=None): + k_attr = jdata.x_attr.shape[1] + n_pools = len(jdata.pool_data) + h = self.hidden + rng = np.random.RandomState(self._seed) + + # He initialization for W1 + std = np.sqrt(2.0 / k_attr) + W1 = rng.randn(k_attr, h).astype(np.float64) * std + b1 = np.zeros(h, dtype=np.float64) + + # W2 = 0 so initial output = b2 (warm-start friendly) + W2 = np.zeros(h, dtype=np.float64) + b2 = np.array([self._default_bias()], dtype=np.float64) + + if warm_start is not None: + # Fit linear mapping from per-pool values, use as last-layer init + vals = [] + for pid in jdata.pool_ids: + if pid in warm_start and self.name in warm_start[pid]: + vals.append(warm_start[pid][self.name]) + else: + vals.append(self._default_bias()) + y = np.array(vals) + # Use mean as b2 (since W2=0, output = b2) + b2 = np.array([np.mean(y)], dtype=np.float64) + + return np.concatenate([W1.ravel(), b1, W2, b2]) + + def _default_bias(self): + if "cad" in self.name: + return np.log(12.0) + elif "gas" in self.name: + return np.log(1.0) + return 0.0 + + def predict_new(self, params_slice, x_attr): + k_attr = len(x_attr) + W1, b1, W2, b2 = self._unpack_weights( + np.asarray(params_slice), k_attr + ) + hidden = np.maximum(x_attr @ W1 + b1, 0.0) + return float(hidden @ W2 + b2) + + def unpack_result(self, params_slice, n_pools, k_attr): + params_np = np.array(params_slice) + W1, b1, W2, b2 = self._unpack_weights(params_np, k_attr) + return { + f"mlp_{self.name}_W1": np.array(W1), + f"mlp_{self.name}_b1": np.array(b1), + f"mlp_{self.name}_W2": np.array(W2), + f"mlp_{self.name}_b2": float(b2), + } + + def make_bounds(self, n_pools, k_attr): + return [(None, None)] * self.n_params(n_pools, k_attr) diff --git a/tests/calibration/test_calibration_model.py b/tests/calibration/test_calibration_model.py index 3530688..3f38f95 100644 --- a/tests/calibration/test_calibration_model.py +++ b/tests/calibration/test_calibration_model.py @@ -14,6 +14,7 @@ from quantammsim.calibration.heads import ( FixedHead, LinearHead, + MLPHead, PerPoolHead, PerPoolNoiseHead, SharedLinearNoiseHead, @@ -419,3 +420,81 @@ def test_option_a_shared_free_matches_old_param_count(self, jdata_ppn): ) expected = 2 + 2 * k_attr + (1 + k_attr) * K_OBS assert model.n_params(n_pools, k_attr) == expected + + +# ── MLP integration tests ───────────────────────────────────────────────── + + +class TestMLPIntegration: + """Test CalibrationModel with MLPHead for cadence.""" + + def test_mlp_cadence_n_params(self, jdata_ppn): + n_pools = len(jdata_ppn.pool_data) + k_attr = jdata_ppn.x_attr.shape[1] + model = CalibrationModel( + MLPHead("cad", hidden=8, alpha=0.01), + LinearHead("gas", alpha=0.01), + PerPoolNoiseHead(), + ) + mlp_params = k_attr * 8 + 8 + 8 + 1 + linear_params = 1 + k_attr + noise_params = n_pools * K_OBS + assert model.n_params(n_pools, k_attr) == mlp_params + linear_params + noise_params + + def test_mlp_cadence_fit_converges(self, jdata_ppn): + model = CalibrationModel( + MLPHead("cad", hidden=8, alpha=0.01), + LinearHead("gas", alpha=0.01), + PerPoolNoiseHead(), + ) + result = model.fit(jdata_ppn, maxiter=100) + assert result["loss"] <= result["init_loss"] + assert np.isfinite(result["loss"]) + + def test_mlp_cadence_and_gas_fit(self, jdata_ppn): + model = CalibrationModel( + MLPHead("cad", hidden=8, alpha=0.01), + MLPHead("gas", hidden=8, alpha=0.01), + PerPoolNoiseHead(), + ) + result = model.fit(jdata_ppn, maxiter=100) + assert result["loss"] <= result["init_loss"] + + def test_mlp_predict_new_pool(self, jdata_ppn): + model = CalibrationModel( + MLPHead("cad", hidden=8, alpha=0.01), + MLPHead("gas", hidden=8, alpha=0.01), + SharedLinearNoiseHead(alpha=0.01), + ) + result = model.fit(jdata_ppn, maxiter=50) + x_attr = np.zeros(result["k_attr"]) + pred = model.predict_new_pool(result, x_attr) + assert pred["cadence_minutes"] > 0 + assert pred["gas_usd"] > 0 + assert "noise_coeffs" in pred + + def test_mlp_loss_differentiable(self, jdata_ppn): + import jax + n_pools = len(jdata_ppn.pool_data) + k_attr = jdata_ppn.x_attr.shape[1] + model = CalibrationModel( + MLPHead("cad", hidden=8, alpha=0.01), + LinearHead("gas", alpha=0.01), + PerPoolNoiseHead(), + ) + loss_fn = model.make_joint_loss_fn(jdata_ppn) + init = jnp.array(model.pack_init(jdata_ppn)) + grad = jax.grad(loss_fn)(init) + assert jnp.all(jnp.isfinite(grad)) + assert float(jnp.sum(jnp.abs(grad))) > 0 + + def test_mlp_with_huber_loss(self, jdata_ppn): + model = CalibrationModel( + MLPHead("cad", hidden=8, alpha=0.01), + LinearHead("gas", alpha=0.01), + PerPoolNoiseHead(), + loss_type="huber", huber_delta=1.5, + ) + result = model.fit(jdata_ppn, maxiter=50) + assert result["loss"] >= 0 + assert np.isfinite(result["loss"]) diff --git a/tests/calibration/test_heads.py b/tests/calibration/test_heads.py index 7513d6f..9627c7e 100644 --- a/tests/calibration/test_heads.py +++ b/tests/calibration/test_heads.py @@ -10,6 +10,7 @@ FixedHead, Head, LinearHead, + MLPHead, PerPoolHead, PerPoolNoiseHead, SharedLinearNoiseHead, @@ -66,6 +67,9 @@ def test_per_pool_noise_head_is_head(self): def test_shared_linear_noise_head_is_head(self): assert isinstance(SharedLinearNoiseHead(), Head) + def test_mlp_head_is_head(self): + assert isinstance(MLPHead("cad"), Head) + # ── PerPoolHead ───────────────────────────────────────────────────────────── @@ -377,3 +381,188 @@ def test_unpack_result(self): assert "W_noise" in result assert result["bias_noise"].shape == (K_OBS,) assert result["W_noise"].shape == (k_attr, K_OBS) + + +# ── MLPHead ───────────────────────────────────────────────────────────────── + + +class TestMLPHead: + def test_n_params(self): + h = MLPHead("cad", hidden=16) + # k_attr=5: 5*16 + 16 + 16 + 1 = 113 + assert h.n_params(3, 5) == 113 + # k_attr=7: 7*16 + 16 + 16 + 1 = 145 + assert h.n_params(3, 7) == 145 + + def test_n_params_custom_hidden(self): + h = MLPHead("cad", hidden=8) + # k_attr=5: 5*8 + 8 + 8 + 1 = 57 + assert h.n_params(3, 5) == 57 + + def test_predict_with_zero_W2_equals_b2(self): + """With W2=0, output should be b2 regardless of input.""" + k_attr = 3 + h = MLPHead("cad", hidden=4) + n_p = h.n_params(1, k_attr) + params = np.zeros(n_p) + params[-1] = 2.5 # b2 + x_attr_i = jnp.array([1.0, 2.0, 3.0]) + result = float(h.predict(jnp.array(params), 0, x_attr_i)) + np.testing.assert_allclose(result, 2.5) + + def test_predict_nonlinear(self): + """MLP should produce different outputs for different inputs.""" + k_attr = 3 + h = MLPHead("cad", hidden=4, seed=42) + jdata = _make_fake_jdata() + # Override x_attr to have k_attr=3 + from quantammsim.calibration.joint_fit import JointData + jdata = JointData( + pool_data=jdata.pool_data, + x_attr=jnp.array(np.random.randn(N_POOLS, k_attr)), + pool_ids=jdata.pool_ids, + attr_names=[f"a{i}" for i in range(k_attr)], + ) + init = jnp.array(h.init(jdata)) + # Set W1 to nonzero so ReLU activations vary + np.random.seed(42) + W1 = np.random.randn(k_attr * 4) * 0.5 + init = init.at[:k_attr * 4].set(jnp.array(W1)) + # Set W2 to nonzero so output varies + init = init.at[k_attr * 4 + 4:k_attr * 4 + 8].set(jnp.ones(4) * 0.1) + + x1 = jnp.array([1.0, 0.0, 0.0]) + x2 = jnp.array([0.0, 1.0, 0.0]) + v1 = float(h.predict(init, 0, x1)) + v2 = float(h.predict(init, 0, x2)) + assert v1 != v2, "MLP should produce different outputs for different inputs" + + def test_predict_ignores_pool_idx(self): + """MLP output depends only on x_attr, not pool_idx.""" + k_attr = 3 + h = MLPHead("cad", hidden=4) + params = jnp.ones(h.n_params(5, k_attr)) * 0.1 + x = jnp.array([1.0, 2.0, 3.0]) + v0 = float(h.predict(params, 0, x)) + v3 = float(h.predict(params, 3, x)) + assert v0 == v3 + + def test_regularization_on_weights_not_biases(self): + k_attr = 2 + h_alpha1 = MLPHead("cad", hidden=2, alpha=1.0) + # Layout: W1(2*2=4), b1(2), W2(2), b2(1) = 9 params + params = np.zeros(9) + params[0] = 3.0 # W1[0,0] + params[1] = 4.0 # W1[0,1] + # b1 = 0 (indices 4,5) + params[6] = 1.0 # W2[0] + params[7] = 2.0 # W2[1] + params[8] = 999.0 # b2 — should not be regularized + # reg = 1.0 * (9 + 16 + 1 + 4) = 30.0 + result = float(h_alpha1.regularization(jnp.array(params))) + np.testing.assert_allclose(result, 30.0) + + def test_regularization_alpha_scaling(self): + k_attr = 2 + h = MLPHead("cad", hidden=2, alpha=0.5) + params = np.zeros(9) + params[0] = 2.0 # W1 weight + # reg = 0.5 * 4.0 = 2.0 + np.testing.assert_allclose(float(h.regularization(jnp.array(params))), 2.0) + + def test_init_default_cadence(self): + h = MLPHead("cad", hidden=4) + jdata = _make_fake_jdata() + init = h.init(jdata) + n_p = h.n_params(N_POOLS, K_ATTR) + assert init.shape == (n_p,) + assert np.all(np.isfinite(init)) + # b2 should be log(12) + np.testing.assert_allclose(init[-1], np.log(12.0)) + + def test_init_default_gas(self): + h = MLPHead("gas", hidden=4) + jdata = _make_fake_jdata() + init = h.init(jdata) + # b2 should be log(1) = 0 + np.testing.assert_allclose(init[-1], 0.0) + + def test_init_W2_is_zero(self): + """W2 should be zero at init so output = b2.""" + h = MLPHead("cad", hidden=4) + jdata = _make_fake_jdata() + init = h.init(jdata) + # W2 is at [k_attr*hidden + hidden : k_attr*hidden + 2*hidden] + w2_start = K_ATTR * 4 + 4 + w2_end = w2_start + 4 + np.testing.assert_allclose(init[w2_start:w2_end], 0.0) + + def test_init_warm_start(self): + h = MLPHead("log_cadence", hidden=4) + jdata = _make_fake_jdata() + warm = { + POOL_PREFIXES[0]: {"log_cadence": 2.0}, + POOL_PREFIXES[1]: {"log_cadence": 3.0}, + } + init = h.init(jdata, warm_start=warm) + # b2 should be mean of warm-start values + np.testing.assert_allclose(init[-1], 2.5) + + def test_predict_new(self): + k_attr = 3 + h = MLPHead("cad", hidden=4) + n_p = h.n_params(1, k_attr) + params = np.zeros(n_p) + params[-1] = 2.5 # b2 + x_attr = np.array([1.0, 2.0, 3.0]) + result = h.predict_new(params, x_attr) + np.testing.assert_allclose(result, 2.5) + + def test_predict_new_matches_predict(self): + """predict_new should give same result as predict for same input.""" + k_attr = 3 + h = MLPHead("cad", hidden=4, seed=42) + n_p = h.n_params(1, k_attr) + np.random.seed(99) + params = np.random.randn(n_p) * 0.1 + x_attr = np.array([0.5, -1.0, 2.0]) + + jax_result = float(h.predict(jnp.array(params), 0, jnp.array(x_attr))) + np_result = h.predict_new(params, x_attr) + np.testing.assert_allclose(jax_result, np_result, rtol=1e-6) + + def test_unpack_result(self): + k_attr = 3 + h = MLPHead("cad", hidden=4) + n_p = h.n_params(1, k_attr) + params = np.arange(n_p, dtype=float) + result = h.unpack_result(params, 2, k_attr) + assert f"mlp_cad_W1" in result + assert f"mlp_cad_b1" in result + assert f"mlp_cad_W2" in result + assert f"mlp_cad_b2" in result + assert result["mlp_cad_W1"].shape == (k_attr, 4) + assert result["mlp_cad_b1"].shape == (4,) + assert result["mlp_cad_W2"].shape == (4,) + + def test_make_bounds(self): + h = MLPHead("cad", hidden=4) + bounds = h.make_bounds(3, 5) + assert len(bounds) == h.n_params(3, 5) + + def test_jax_differentiable(self): + """MLP predict should be JAX-differentiable.""" + import jax + k_attr = 3 + h = MLPHead("cad", hidden=4) + n_p = h.n_params(1, k_attr) + np.random.seed(42) + params = jnp.array(np.random.randn(n_p) * 0.1) + x_attr_i = jnp.array([1.0, 2.0, 3.0]) + + def loss(p): + return h.predict(p, 0, x_attr_i) ** 2 + + grad = jax.grad(loss)(params) + assert grad.shape == params.shape + assert jnp.all(jnp.isfinite(grad)) From ef7d24ad291c5e43b5d3b64d7abdc7c89c1b3675 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 10 Mar 2026 11:05:55 +0000 Subject: [PATCH 10/57] feat: add MLPNoiseHead for nonlinear pool-attribute-to-noise mapping Two-layer MLP (x_attr, Dense(hidden, ReLU), Dense(K_OBS)) that replaces the linear SharedLinearNoiseHead for the noise coefficient mapping. Initialized with W2=0 so output starts at pooled OLS noise coefficients. 16 unit tests + 6 CalibrationModel integration tests. --- quantammsim/calibration/__init__.py | 1 + quantammsim/calibration/heads.py | 115 +++++++++++++ tests/calibration/test_calibration_model.py | 84 +++++++++ tests/calibration/test_heads.py | 182 ++++++++++++++++++++ 4 files changed, 382 insertions(+) diff --git a/quantammsim/calibration/__init__.py b/quantammsim/calibration/__init__.py index c229fb3..48a556a 100644 --- a/quantammsim/calibration/__init__.py +++ b/quantammsim/calibration/__init__.py @@ -45,6 +45,7 @@ Head, LinearHead, MLPHead, + MLPNoiseHead, PerPoolHead, PerPoolNoiseHead, SharedLinearNoiseHead, diff --git a/quantammsim/calibration/heads.py b/quantammsim/calibration/heads.py index 53d65ad..2edd343 100644 --- a/quantammsim/calibration/heads.py +++ b/quantammsim/calibration/heads.py @@ -507,3 +507,118 @@ def unpack_result(self, params_slice, n_pools, k_attr): def make_bounds(self, n_pools, k_attr): return [(None, None)] * self.n_params(n_pools, k_attr) + + +# --------------------------------------------------------------------------- +# MLPNoiseHead — x_attr → Dense(hidden, relu) → Dense(K_OBS) +# --------------------------------------------------------------------------- + + +class MLPNoiseHead: + """Two-layer MLP mapping from pool attributes to noise coefficients. + + Architecture: x_attr → Dense(hidden, ReLU) → Dense(K_OBS) + + Parameter layout (flat): + [W1(k_attr * hidden), b1(hidden), W2(hidden * K_OBS), b2(K_OBS)] + + L2 regularization on W1 and W2 (not biases). + + Initialization: + - W1: He (scaled normal), b1: zeros + - W2: zeros (so initial output = b2 = pooled OLS noise coefficients) + - b2: pooled OLS noise from training data + """ + + def __init__( + self, + hidden: int = 16, + alpha: float = 0.01, + seed: int = 0, + ): + self.name = "noise" + self.hidden = hidden + self.alpha = alpha + self._seed = seed + + def n_params(self, n_pools: int, k_attr: int) -> int: + h = self.hidden + # W1(k_attr*h) + b1(h) + W2(h*K_OBS) + b2(K_OBS) + return k_attr * h + h + h * K_OBS + K_OBS + + def _unpack_weights(self, params_slice, k_attr): + """Unpack flat slice → (W1, b1, W2, b2).""" + h = self.hidden + idx = 0 + W1 = params_slice[idx:idx + k_attr * h].reshape(k_attr, h) + idx += k_attr * h + b1 = params_slice[idx:idx + h] + idx += h + W2 = params_slice[idx:idx + h * K_OBS].reshape(h, K_OBS) + idx += h * K_OBS + b2 = params_slice[idx:idx + K_OBS] + return W1, b1, W2, b2 + + def predict(self, params_slice, pool_idx, x_attr_i): + k_attr = x_attr_i.shape[0] + W1, b1, W2, b2 = self._unpack_weights(params_slice, k_attr) + hidden = jnp.maximum(x_attr_i @ W1 + b1, 0.0) # ReLU + return hidden @ W2 + b2 # (K_OBS,) + + def regularization(self, params_slice): + h = self.hidden + total = params_slice.shape[0] + # Solve for k_attr: total = k*h + h + h*K_OBS + K_OBS + # k*h = total - h - h*K_OBS - K_OBS + k_attr = (total - h - h * K_OBS - K_OBS) // h + W1 = params_slice[:k_attr * h] + W2 = params_slice[k_attr * h + h:k_attr * h + h + h * K_OBS] + return self.alpha * (jnp.sum(W1 ** 2) + jnp.sum(W2 ** 2)) + + def init(self, jdata, warm_start=None): + k_attr = jdata.x_attr.shape[1] + n_pools = len(jdata.pool_data) + h = self.hidden + rng = np.random.RandomState(self._seed) + + # He initialization for W1 + std = np.sqrt(2.0 / k_attr) + W1 = rng.randn(k_attr, h).astype(np.float64) * std + b1 = np.zeros(h, dtype=np.float64) + + # W2 = 0 so initial output = b2 + W2 = np.zeros((h, K_OBS), dtype=np.float64) + + if warm_start is not None: + # Use mean of per-pool noise as b2 + noise_all = np.zeros((n_pools, K_OBS), dtype=np.float64) + for i, pid in enumerate(jdata.pool_ids): + if pid in warm_start and "noise_coeffs" in warm_start[pid]: + noise_all[i] = warm_start[pid]["noise_coeffs"] + b2 = np.mean(noise_all, axis=0) + else: + # Pooled OLS noise as b2 + all_x = np.vstack([np.array(pd["x_obs"]) for pd in jdata.pool_data]) + all_y = np.concatenate([np.array(pd["y_obs"]) for pd in jdata.pool_data]) + b2, _, _, _ = np.linalg.lstsq(all_x, all_y, rcond=None) + + return np.concatenate([W1.ravel(), b1, W2.ravel(), b2]) + + def predict_new(self, params_slice, x_attr): + k_attr = len(x_attr) + W1, b1, W2, b2 = self._unpack_weights(np.asarray(params_slice), k_attr) + hidden = np.maximum(x_attr @ W1 + b1, 0.0) + return hidden @ W2 + b2 # (K_OBS,) + + def unpack_result(self, params_slice, n_pools, k_attr): + params_np = np.array(params_slice) + W1, b1, W2, b2 = self._unpack_weights(params_np, k_attr) + return { + "mlp_noise_W1": np.array(W1), + "mlp_noise_b1": np.array(b1), + "mlp_noise_W2": np.array(W2), + "mlp_noise_b2": np.array(b2), + } + + def make_bounds(self, n_pools, k_attr): + return [(None, None)] * self.n_params(n_pools, k_attr) diff --git a/tests/calibration/test_calibration_model.py b/tests/calibration/test_calibration_model.py index 3f38f95..95973f4 100644 --- a/tests/calibration/test_calibration_model.py +++ b/tests/calibration/test_calibration_model.py @@ -15,6 +15,7 @@ FixedHead, LinearHead, MLPHead, + MLPNoiseHead, PerPoolHead, PerPoolNoiseHead, SharedLinearNoiseHead, @@ -498,3 +499,86 @@ def test_mlp_with_huber_loss(self, jdata_ppn): result = model.fit(jdata_ppn, maxiter=50) assert result["loss"] >= 0 assert np.isfinite(result["loss"]) + + +# ── MLP noise integration tests ─────────────────────────────────────────── + + +class TestMLPNoiseIntegration: + """Test CalibrationModel with MLPNoiseHead — the key use case.""" + + def test_mlp_noise_fit_converges(self, jdata_ppn): + model = CalibrationModel( + LinearHead("cad", alpha=0.01), + LinearHead("gas", alpha=0.01), + MLPNoiseHead(hidden=8, alpha=0.01), + ) + result = model.fit(jdata_ppn, maxiter=100) + assert result["loss"] <= result["init_loss"] + assert np.isfinite(result["loss"]) + + def test_mlp_noise_predict_new_pool(self, jdata_ppn): + model = CalibrationModel( + LinearHead("cad", alpha=0.01), + LinearHead("gas", alpha=0.01), + MLPNoiseHead(hidden=8, alpha=0.01), + ) + result = model.fit(jdata_ppn, maxiter=50) + x_attr = np.zeros(result["k_attr"]) + pred = model.predict_new_pool(result, x_attr) + assert pred["cadence_minutes"] > 0 + assert pred["gas_usd"] > 0 + assert "noise_coeffs" in pred + assert len(pred["noise_coeffs"]) == K_OBS + + def test_mlp_noise_loss_differentiable(self, jdata_ppn): + import jax + model = CalibrationModel( + LinearHead("cad", alpha=0.01), + LinearHead("gas", alpha=0.01), + MLPNoiseHead(hidden=8, alpha=0.01), + ) + loss_fn = model.make_joint_loss_fn(jdata_ppn) + init = jnp.array(model.pack_init(jdata_ppn)) + grad = jax.grad(loss_fn)(init) + assert jnp.all(jnp.isfinite(grad)) + assert float(jnp.sum(jnp.abs(grad))) > 0 + + def test_full_mlp_model(self, jdata_ppn): + """MLP for all three heads — most expressive config.""" + model = CalibrationModel( + MLPHead("cad", hidden=8, alpha=0.01), + MLPHead("gas", hidden=8, alpha=0.01), + MLPNoiseHead(hidden=8, alpha=0.01), + ) + result = model.fit(jdata_ppn, maxiter=100) + assert result["loss"] <= result["init_loss"] + x_attr = np.zeros(result["k_attr"]) + pred = model.predict_new_pool(result, x_attr) + assert pred["cadence_minutes"] > 0 + assert "noise_coeffs" in pred + + def test_mlp_noise_with_fixed_gas(self, jdata_ppn): + n_pools = len(jdata_ppn.pool_data) + gas_values = np.array([np.log(1.0)] * n_pools) + model = CalibrationModel( + LinearHead("cad", alpha=0.01), + FixedHead("gas", gas_values), + MLPNoiseHead(hidden=8, alpha=0.01), + ) + result = model.fit(jdata_ppn, maxiter=100) + assert result["loss"] <= result["init_loss"] + + def test_mlp_noise_param_count(self, jdata_ppn): + n_pools = len(jdata_ppn.pool_data) + k_attr = jdata_ppn.x_attr.shape[1] + h = 8 + model = CalibrationModel( + LinearHead("cad"), + LinearHead("gas"), + MLPNoiseHead(hidden=h), + ) + # Linear cad: 1+k, Linear gas: 1+k, + # MLP noise: k*h + h + h*K_OBS + K_OBS + expected = (1 + k_attr) * 2 + k_attr * h + h + h * K_OBS + K_OBS + assert model.n_params(n_pools, k_attr) == expected diff --git a/tests/calibration/test_heads.py b/tests/calibration/test_heads.py index 9627c7e..5eae07c 100644 --- a/tests/calibration/test_heads.py +++ b/tests/calibration/test_heads.py @@ -11,6 +11,7 @@ Head, LinearHead, MLPHead, + MLPNoiseHead, PerPoolHead, PerPoolNoiseHead, SharedLinearNoiseHead, @@ -70,6 +71,9 @@ def test_shared_linear_noise_head_is_head(self): def test_mlp_head_is_head(self): assert isinstance(MLPHead("cad"), Head) + def test_mlp_noise_head_is_head(self): + assert isinstance(MLPNoiseHead(), Head) + # ── PerPoolHead ───────────────────────────────────────────────────────────── @@ -566,3 +570,181 @@ def loss(p): grad = jax.grad(loss)(params) assert grad.shape == params.shape assert jnp.all(jnp.isfinite(grad)) + + +# ── MLPNoiseHead ──────────────────────────────────────────────────────────── + + +class TestMLPNoiseHead: + def test_n_params(self): + h = MLPNoiseHead(hidden=16) + # k_attr=5: 5*16 + 16 + 16*8 + 8 = 80+16+128+8 = 232 + assert h.n_params(3, 5) == 232 + # k_attr=7: 7*16 + 16 + 16*8 + 8 = 112+16+128+8 = 264 + assert h.n_params(3, 7) == 264 + + def test_n_params_custom_hidden(self): + h = MLPNoiseHead(hidden=8) + # k_attr=5: 5*8 + 8 + 8*8 + 8 = 40+8+64+8 = 120 + assert h.n_params(3, 5) == 120 + + def test_predict_output_shape(self): + k_attr = 3 + h = MLPNoiseHead(hidden=4) + n_p = h.n_params(1, k_attr) + params = jnp.zeros(n_p) + x_attr_i = jnp.array([1.0, 2.0, 3.0]) + result = h.predict(params, 0, x_attr_i) + assert result.shape == (K_OBS,) + + def test_predict_with_zero_W2_equals_b2(self): + """With W2=0, output should be b2 regardless of input.""" + k_attr = 3 + h = MLPNoiseHead(hidden=4) + n_p = h.n_params(1, k_attr) + params = np.zeros(n_p) + # b2 is the last K_OBS elements + params[-K_OBS:] = np.arange(K_OBS) + 1.0 + x_attr_i = jnp.array([1.0, 2.0, 3.0]) + result = h.predict(jnp.array(params), 0, x_attr_i) + np.testing.assert_allclose(result, np.arange(K_OBS) + 1.0) + + def test_predict_nonlinear(self): + """MLP should produce different outputs for different inputs.""" + k_attr = 3 + h = MLPNoiseHead(hidden=4, seed=42) + n_p = h.n_params(1, k_attr) + np.random.seed(42) + params = jnp.array(np.random.randn(n_p) * 0.1) + x1 = jnp.array([1.0, 0.0, 0.0]) + x2 = jnp.array([0.0, 1.0, 0.0]) + v1 = h.predict(params, 0, x1) + v2 = h.predict(params, 0, x2) + assert not jnp.allclose(v1, v2), "Should produce different outputs" + + def test_predict_ignores_pool_idx(self): + k_attr = 3 + h = MLPNoiseHead(hidden=4) + params = jnp.ones(h.n_params(5, k_attr)) * 0.1 + x = jnp.array([1.0, 2.0, 3.0]) + v0 = h.predict(params, 0, x) + v3 = h.predict(params, 3, x) + np.testing.assert_allclose(v0, v3) + + def test_regularization_on_weights_not_biases(self): + k_attr = 2 + h = MLPNoiseHead(hidden=2, alpha=1.0) + # Layout: W1(2*2=4), b1(2), W2(2*8=16), b2(8) = 30 params + n_p = h.n_params(1, k_attr) + assert n_p == 30 + params = np.zeros(n_p) + params[0] = 3.0 # W1[0,0] + params[1] = 4.0 # W1[0,1] + # b1 at indices 4,5 — not regularized + params[6] = 1.0 # W2[0,0] + params[7] = 2.0 # W2[0,1] + params[-1] = 999.0 # b2[-1] — not regularized + # reg = 1.0 * (9 + 16 + 1 + 4) = 30.0 + result = float(h.regularization(jnp.array(params))) + np.testing.assert_allclose(result, 30.0) + + def test_init_default(self): + np.random.seed(42) + h = MLPNoiseHead(hidden=4) + jdata = _make_fake_jdata() + init = h.init(jdata) + n_p = h.n_params(N_POOLS, K_ATTR) + assert init.shape == (n_p,) + assert np.all(np.isfinite(init)) + + def test_init_W2_is_zero(self): + """W2 should be zero at init so output = b2.""" + h = MLPNoiseHead(hidden=4) + jdata = _make_fake_jdata() + init = h.init(jdata) + # W2 starts at k_attr*hidden + hidden + w2_start = K_ATTR * 4 + 4 + w2_end = w2_start + 4 * K_OBS + np.testing.assert_allclose(init[w2_start:w2_end], 0.0) + + def test_init_b2_from_ols(self): + """b2 should be pooled OLS noise coefficients.""" + np.random.seed(42) + h = MLPNoiseHead(hidden=4) + jdata = _make_fake_jdata() + init = h.init(jdata) + b2 = init[-K_OBS:] + assert np.all(np.isfinite(b2)) + # Should be nonzero (OLS on random data) + assert np.any(b2 != 0.0) + + def test_init_warm_start(self): + h = MLPNoiseHead(hidden=4) + jdata = _make_fake_jdata() + warm = { + POOL_PREFIXES[0]: {"noise_coeffs": np.ones(K_OBS) * 5.0}, + POOL_PREFIXES[1]: {"noise_coeffs": np.ones(K_OBS) * 7.0}, + } + init = h.init(jdata, warm_start=warm) + b2 = init[-K_OBS:] + # b2 should be mean of warm-start noise: (5+7)/2 = 6 + np.testing.assert_allclose(b2, 6.0) + + def test_predict_new(self): + k_attr = 3 + h = MLPNoiseHead(hidden=4) + n_p = h.n_params(1, k_attr) + params = np.zeros(n_p) + params[-K_OBS:] = np.arange(K_OBS) + 1.0 # b2 + x_attr = np.array([1.0, 2.0, 3.0]) + result = h.predict_new(params, x_attr) + assert result.shape == (K_OBS,) + np.testing.assert_allclose(result, np.arange(K_OBS) + 1.0) + + def test_predict_new_matches_predict(self): + k_attr = 3 + h = MLPNoiseHead(hidden=4, seed=42) + n_p = h.n_params(1, k_attr) + np.random.seed(99) + params = np.random.randn(n_p) * 0.1 + x_attr = np.array([0.5, -1.0, 2.0]) + + jax_result = np.array(h.predict(jnp.array(params), 0, jnp.array(x_attr))) + np_result = h.predict_new(params, x_attr) + np.testing.assert_allclose(jax_result, np_result, rtol=1e-6) + + def test_unpack_result(self): + k_attr = 3 + h = MLPNoiseHead(hidden=4) + n_p = h.n_params(1, k_attr) + params = np.arange(n_p, dtype=float) + result = h.unpack_result(params, 2, k_attr) + assert "mlp_noise_W1" in result + assert "mlp_noise_b1" in result + assert "mlp_noise_W2" in result + assert "mlp_noise_b2" in result + assert result["mlp_noise_W1"].shape == (k_attr, 4) + assert result["mlp_noise_b1"].shape == (4,) + assert result["mlp_noise_W2"].shape == (4, K_OBS) + assert result["mlp_noise_b2"].shape == (K_OBS,) + + def test_make_bounds(self): + h = MLPNoiseHead(hidden=4) + bounds = h.make_bounds(3, 5) + assert len(bounds) == h.n_params(3, 5) + + def test_jax_differentiable(self): + import jax + k_attr = 3 + h = MLPNoiseHead(hidden=4) + n_p = h.n_params(1, k_attr) + np.random.seed(42) + params = jnp.array(np.random.randn(n_p) * 0.1) + x_attr_i = jnp.array([1.0, 2.0, 3.0]) + + def loss(p): + return jnp.sum(h.predict(p, 0, x_attr_i) ** 2) + + grad = jax.grad(loss)(params) + assert grad.shape == params.shape + assert jnp.all(jnp.isfinite(grad)) From ccc3c6cf915a9db4913122a89b2c293be92a3108 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 10 Mar 2026 12:39:26 +0000 Subject: [PATCH 11/57] fix: use small random W2 init for MLP heads to avoid degenerate L-BFGS Hessian Add MLP calibration and sweep scripts. --- quantammsim/calibration/heads.py | 10 +- scripts/run_mlp_calibration.py | 753 +++++++++++++++++++++++++++++++ scripts/run_mlp_sweep.py | 429 ++++++++++++++++++ tests/calibration/test_heads.py | 16 +- 4 files changed, 1197 insertions(+), 11 deletions(-) create mode 100644 scripts/run_mlp_calibration.py create mode 100644 scripts/run_mlp_sweep.py diff --git a/quantammsim/calibration/heads.py b/quantammsim/calibration/heads.py index 2edd343..1191d0a 100644 --- a/quantammsim/calibration/heads.py +++ b/quantammsim/calibration/heads.py @@ -462,8 +462,8 @@ def init(self, jdata, warm_start=None): W1 = rng.randn(k_attr, h).astype(np.float64) * std b1 = np.zeros(h, dtype=np.float64) - # W2 = 0 so initial output = b2 (warm-start friendly) - W2 = np.zeros(h, dtype=np.float64) + # Small random W2 so L-BFGS gets a non-degenerate initial Hessian + W2 = rng.randn(h).astype(np.float64) * 0.01 b2 = np.array([self._default_bias()], dtype=np.float64) if warm_start is not None: @@ -475,7 +475,7 @@ def init(self, jdata, warm_start=None): else: vals.append(self._default_bias()) y = np.array(vals) - # Use mean as b2 (since W2=0, output = b2) + # Use mean as b2 b2 = np.array([np.mean(y)], dtype=np.float64) return np.concatenate([W1.ravel(), b1, W2, b2]) @@ -586,8 +586,8 @@ def init(self, jdata, warm_start=None): W1 = rng.randn(k_attr, h).astype(np.float64) * std b1 = np.zeros(h, dtype=np.float64) - # W2 = 0 so initial output = b2 - W2 = np.zeros((h, K_OBS), dtype=np.float64) + # Small random W2 so L-BFGS gets a non-degenerate initial Hessian + W2 = rng.randn(h, K_OBS).astype(np.float64) * 0.01 if warm_start is not None: # Use mean of per-pool noise as b2 diff --git a/scripts/run_mlp_calibration.py b/scripts/run_mlp_calibration.py new file mode 100644 index 0000000..39189c4 --- /dev/null +++ b/scripts/run_mlp_calibration.py @@ -0,0 +1,753 @@ +"""Run calibration with MLPNoiseHead and compare against linear baselines. + +Steps: + 1. Load panel, match to per-day grids + 2. Option C: per-pool L-BFGS-B fits (baseline, gas fixed to chain) + 3. Linear joint: SharedLinearNoiseHead baseline + 4. MLP noise joint: MLPNoiseHead (new) + 5. Full MLP joint: MLPHead cadence + MLPNoiseHead (new) + 6. Per-pool prediction, R², decomposition for each method + 7. Paginated plots, summary distributions, comparison scatter + 8. JSON export +""" + +import json +import os + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +# ---- Config ---- +PANEL_CACHE = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "local_data", "noise_calibration", "panel.parquet", +) +GRID_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "pool_grids_v2", +) +OUTPUT_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "mlp_calibration", +) +OPTION_C_LOSS_CUTOFF = 5.0 +OPTION_C_MAXITER = 500 +JOINT_MAXITER = 500 +MLP_HIDDEN = 16 +TOP_N = 50 + + +# ---- Data loading ---- + + +def load_and_match(): + """Load panel, match to grids.""" + from quantammsim.calibration.pool_data import ( + match_grids_to_panel, + replace_panel_volatility_with_binance, + ) + + panel = pd.read_parquet(PANEL_CACHE) + + if "log_tvl_lag1" not in panel.columns: + panel = panel.sort_values(["pool_id", "date"]).reset_index(drop=True) + panel["log_tvl_lag1"] = panel.groupby("pool_id")["log_tvl"].shift(1) + panel = panel.dropna(subset=["log_tvl_lag1"]).reset_index(drop=True) + + pool_counts = panel.groupby("pool_id").size() + valid = pool_counts[pool_counts >= 10].index + panel = panel[panel["pool_id"].isin(valid)].copy() + + print("Replacing volatility with Binance minute data...") + panel = replace_panel_volatility_with_binance(panel) + + print(f"Panel: {len(panel)} obs, {panel['pool_id'].nunique()} pools, " + f"{panel['date'].min()} to {panel['date'].max()}") + + matched = match_grids_to_panel(GRID_DIR, panel) + print(f"Matched: {len(matched)} pools with grids") + return panel, matched + + +def filter_pathological(matched, option_c): + """Drop pools with high Option C loss.""" + good = {p: r for p, r in option_c.items() if r["loss"] <= OPTION_C_LOSS_CUTOFF} + dropped = set(option_c) - set(good) + matched_clean = {p: matched[p] for p in good if p in matched} + if dropped: + print(f" Dropping {len(dropped)} pools (loss > {OPTION_C_LOSS_CUTOFF}):") + for p in sorted(dropped): + print(f" {p} loss={option_c[p]['loss']:.1f}") + return matched_clean, good + + +# ---- Fitting ---- + + +def run_option_c(matched): + """Per-pool fits with gas fixed to chain costs.""" + from quantammsim.calibration.per_pool_fit import fit_all_pools + + print(f"\n--- Option C: per-pool fits ({len(matched)} pools, gas fixed) ---") + results = fit_all_pools(matched, fix_gas_to_chain=True) + + losses = [r["loss"] for r in results.values()] + n_conv = sum(1 for r in results.values() if r["converged"]) + print(f" Converged: {n_conv}/{len(results)}") + print(f" Loss: median={np.median(losses):.4f}, mean={np.mean(losses):.4f}") + return results + + +def _build_gas_values(jdata, matched_clean): + """Build fixed gas values (log-space) from chain data.""" + from quantammsim.calibration.loss import CHAIN_GAS_USD + gas_values = [] + for pid in jdata.pool_ids: + chain = matched_clean[pid]["chain"] + gas_usd = CHAIN_GAS_USD.get(chain, 1.0) + gas_values.append(np.log(max(gas_usd, 1e-6))) + return np.array(gas_values) + + +def run_linear_joint(matched_clean, option_c_clean): + """Joint fit with LinearHead + SharedLinearNoiseHead (baseline).""" + from quantammsim.calibration.calibration_model import CalibrationModel + from quantammsim.calibration.heads import FixedHead, LinearHead, SharedLinearNoiseHead + from quantammsim.calibration.joint_fit import prepare_joint_data + + jdata = prepare_joint_data( + matched_clean, drop_chain_dummies=True, fix_gas_to_chain=True) + gas_values = _build_gas_values(jdata, matched_clean) + + model = CalibrationModel( + cadence_head=LinearHead("cad", alpha=0.01), + gas_head=FixedHead("gas", gas_values), + noise_head=SharedLinearNoiseHead(alpha=0.01), + ) + + n_pools = len(jdata.pool_data) + k_attr = jdata.x_attr.shape[1] + n_p = model.n_params(n_pools, k_attr) + print(f"\n--- Linear baseline: SharedLinearNoiseHead ({n_pools} pools, {n_p} params) ---") + + result = model.fit(jdata, maxiter=JOINT_MAXITER, warm_start=option_c_clean) + print(f" Loss: {result['init_loss']:.4f} -> {result['loss']:.4f}") + print(f" Converged: {result['converged']}") + return result, model, jdata + + +def run_mlp_noise_joint(matched_clean, option_c_clean, hidden=MLP_HIDDEN): + """Joint fit with LinearHead cadence + FixedHead gas + MLPNoiseHead.""" + from quantammsim.calibration.calibration_model import CalibrationModel + from quantammsim.calibration.heads import FixedHead, LinearHead, MLPNoiseHead + from quantammsim.calibration.joint_fit import prepare_joint_data + + jdata = prepare_joint_data( + matched_clean, drop_chain_dummies=True, fix_gas_to_chain=True) + gas_values = _build_gas_values(jdata, matched_clean) + + model = CalibrationModel( + cadence_head=LinearHead("cad", alpha=0.01), + gas_head=FixedHead("gas", gas_values), + noise_head=MLPNoiseHead(hidden=hidden, alpha=0.01), + ) + + n_pools = len(jdata.pool_data) + k_attr = jdata.x_attr.shape[1] + n_p = model.n_params(n_pools, k_attr) + print(f"\n--- MLP noise: MLPNoiseHead(hidden={hidden}) ({n_pools} pools, {n_p} params) ---") + + result = model.fit(jdata, maxiter=JOINT_MAXITER, warm_start=option_c_clean) + print(f" Loss: {result['init_loss']:.4f} -> {result['loss']:.4f}") + print(f" Converged: {result['converged']}") + return result, model, jdata + + +def run_mlp_full_joint(matched_clean, option_c_clean, hidden=MLP_HIDDEN): + """Joint fit with MLPHead cadence + FixedHead gas + MLPNoiseHead.""" + from quantammsim.calibration.calibration_model import CalibrationModel + from quantammsim.calibration.heads import FixedHead, MLPHead, MLPNoiseHead + from quantammsim.calibration.joint_fit import prepare_joint_data + + jdata = prepare_joint_data( + matched_clean, drop_chain_dummies=True, fix_gas_to_chain=True) + gas_values = _build_gas_values(jdata, matched_clean) + + model = CalibrationModel( + cadence_head=MLPHead("cad", hidden=hidden, alpha=0.01), + gas_head=FixedHead("gas", gas_values), + noise_head=MLPNoiseHead(hidden=hidden, alpha=0.01), + ) + + n_pools = len(jdata.pool_data) + k_attr = jdata.x_attr.shape[1] + n_p = model.n_params(n_pools, k_attr) + print(f"\n--- Full MLP: MLPHead(cad) + MLPNoiseHead ({n_pools} pools, {n_p} params) ---") + + result = model.fit(jdata, maxiter=JOINT_MAXITER, warm_start=option_c_clean) + print(f" Loss: {result['init_loss']:.4f} -> {result['loss']:.4f}") + print(f" Converged: {result['converged']}") + return result, model, jdata + + +# ---- Per-pool predictions ---- + + +def _extract_per_pool_params(model, result, jdata): + """Extract per-pool (log_cadence, log_gas, noise_coeffs) from a CalibrationModel result.""" + import jax.numpy as jnp + + params = jnp.array(result["params_flat"]) + n_pools = result["n_pools"] + k_attr = result["k_attr"] + (cs, ce), (gs, ge), (ns, ne) = model._head_slices(n_pools, k_attr) + + cad_slice = params[cs:ce] + gas_slice = params[gs:ge] + noise_slice = params[ns:ne] + + per_pool = [] + for i in range(n_pools): + x_attr_i = jdata.x_attr[i] + log_cad = float(model.cadence_head.predict(cad_slice, i, x_attr_i)) + log_gas = float(model.gas_head.predict(gas_slice, i, x_attr_i)) + noise_c = np.array(model.noise_head.predict(noise_slice, i, x_attr_i)) + per_pool.append({ + "log_cadence": log_cad, + "log_gas": log_gas, + "noise_coeffs": noise_c, + "cadence_minutes": float(np.exp(log_cad)), + "gas_usd": float(np.exp(log_gas)), + }) + return per_pool + + +def compute_per_pool_predictions(matched, option_c_results, + model_results): + """Compute V_arb, V_noise, R² per pool for Option C and each joint model. + + model_results: list of (label, per_pool_params, pool_ids) tuples, + where per_pool_params[i] is a dict with log_cadence, log_gas, noise_coeffs. + """ + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + from quantammsim.calibration.pool_data import build_x_obs + import jax.numpy as jnp + + pool_ids = sorted(matched.keys()) + + # Build lookup for each model's per-pool params + model_lookups = [] + for label, per_pool_params, m_pool_ids in model_results: + lookup = {pid: per_pool_params[i] for i, pid in enumerate(m_pool_ids)} + model_lookups.append((label, lookup)) + + def r2(v_arb, v_noise, y): + log_pred = np.log(np.maximum(v_arb + v_noise, 1e-6)) + ss_res = np.sum((log_pred - y) ** 2) + ss_tot = np.sum((y - y.mean()) ** 2) + return 1 - ss_res / max(ss_tot, 1e-10) + + predictions = {} + for pid in pool_ids: + entry = matched[pid] + panel = entry["panel"] + coeffs = entry["coeffs"] + day_indices = entry["day_indices"] + + x_obs = build_x_obs(panel) + y_obs = panel["log_volume"].values.astype(float) + + p = { + "dates": pd.to_datetime(panel["date"].values), + "y_obs": y_obs, + "actual_vol": np.exp(y_obs), + "chain": entry["chain"], + "tokens": entry["tokens"], + "fee": entry["fee"], + "median_tvl": float(np.exp(panel["log_tvl_lag1"].median())), + "n_obs": len(y_obs), + } + + # Option C + rc = option_c_results[pid] + v_arb_all = np.array(interpolate_pool_daily( + coeffs, jnp.float64(rc["log_cadence"]), + jnp.float64(np.exp(rc["log_gas"])))) + v_arb_c = v_arb_all[day_indices] + v_noise_c = np.exp(x_obs @ rc["noise_coeffs"]) + p["v_arb_c"] = v_arb_c + p["v_noise_c"] = v_noise_c + p["r2_c"] = r2(v_arb_c, v_noise_c, y_obs) + p["cadence_c"] = rc["cadence_minutes"] + p["gas_c"] = rc["gas_usd"] + + # Each joint model + for label, lookup in model_lookups: + if pid in lookup: + mp = lookup[pid] + v_arb_all = np.array(interpolate_pool_daily( + coeffs, jnp.float64(mp["log_cadence"]), + jnp.float64(np.exp(mp["log_gas"])))) + v_arb = v_arb_all[day_indices] + v_noise = np.exp(x_obs @ mp["noise_coeffs"]) + p[f"v_arb_{label}"] = v_arb + p[f"v_noise_{label}"] = v_noise + p[f"r2_{label}"] = r2(v_arb, v_noise, y_obs) + p[f"cadence_{label}"] = mp["cadence_minutes"] + p[f"gas_{label}"] = mp["gas_usd"] + else: + n = len(y_obs) + p[f"v_arb_{label}"] = np.full(n, np.nan) + p[f"v_noise_{label}"] = np.full(n, np.nan) + p[f"r2_{label}"] = np.nan + p[f"cadence_{label}"] = np.nan + p[f"gas_{label}"] = np.nan + + predictions[pid] = p + + return predictions + + +# ---- Tables ---- + + +def print_pool_table(predictions, method_labels): + """Print per-pool results ranked by TVL.""" + ranked = sorted(predictions.items(), key=lambda x: -x[1]["median_tvl"]) + + header = f"{'Pool':<24} {'Chain':<10} {'TVL':>12} {'N':>4}" + header += f" {'Cad_C':>6} {'R2_C':>6}" + for label in method_labels: + short = label[:8] + header += f" {'Cad_'+short:>10} {'R2_'+short:>8}" + header += f" {'Arb%_C':>6}" + + print(f"\n{'='*len(header)}") + print(header) + print(f"{'-'*len(header)}") + for pid, p in ranked: + tokens = p["tokens"] + if isinstance(tokens, str): + tok_str = "/".join(t.strip()[:6] for t in tokens.split(",")[:2]) + else: + tok_str = pid[:16] + arb_total = p["v_arb_c"] + p["v_noise_c"] + arb_frac = np.median(p["v_arb_c"] / np.maximum(arb_total, 1.0)) + + line = (f"{tok_str:<24} {p['chain']:<10} ${p['median_tvl']:>10,.0f} " + f"{p['n_obs']:>4}") + line += f" {p['cadence_c']:>5.1f}m {p['r2_c']:>6.3f}" + for label in method_labels: + cad = p[f"cadence_{label}"] + r2v = p[f"r2_{label}"] + if np.isnan(cad): + line += f" {'---':>10} {'---':>8}" + else: + line += f" {cad:>9.1f}m {r2v:>8.3f}" + line += f" {arb_frac:>5.1%}" + print(line) + + +def print_r2_comparison(predictions, method_labels): + """Print aggregate R² comparison.""" + pool_ids = sorted(predictions.keys()) + + print(f"\n{'='*70}") + print("R² comparison (per-pool, in-sample)") + print(f"{'='*70}") + + r2_c = [predictions[p]["r2_c"] for p in pool_ids] + print(f" Option C (per-pool): median={np.median(r2_c):.4f} mean={np.mean(r2_c):.4f}") + + for label in method_labels: + r2_vals = [predictions[p][f"r2_{label}"] for p in pool_ids + if np.isfinite(predictions[p][f"r2_{label}"])] + if r2_vals: + print(f" {label:<22} median={np.median(r2_vals):.4f} mean={np.mean(r2_vals):.4f}") + + +def print_loss_comparison(option_c, joint_results): + """Print joint loss comparison.""" + print(f"\n{'='*70}") + print("Joint loss comparison") + print(f"{'='*70}") + + c_losses = [r["loss"] for r in option_c.values()] + print(f" Option C (per-pool): median={np.median(c_losses):.4f} mean={np.mean(c_losses):.4f}") + + for label, result in joint_results: + print(f" {label:<22} loss={result['loss']:.4f} (from {result['init_loss']:.4f})") + + +# ---- Plots ---- + + +def plot_decomposition_pages(predictions, method, method_label, output_dir): + """Paginated V_arb + V_noise stacked area decomposition.""" + ranked = sorted(predictions.items(), key=lambda x: -x[1]["median_tvl"])[:TOP_N] + + per_page = 10 + n_pages = (len(ranked) + per_page - 1) // per_page + + for page in range(n_pages): + start = page * per_page + end = min(start + per_page, len(ranked)) + page_pools = ranked[start:end] + n_this = len(page_pools) + + ncols = 2 + nrows = (n_this + ncols - 1) // ncols + fig, axes = plt.subplots(nrows, ncols, figsize=(16, 4.5 * nrows)) + if nrows == 1 and ncols == 1: + axes = np.array([[axes]]) + elif nrows == 1: + axes = axes.reshape(1, -1) + elif ncols == 1: + axes = axes.reshape(-1, 1) + + for idx, (pid, p) in enumerate(page_pools): + ax = axes[idx // ncols][idx % ncols] + dates = p["dates"] + + v_arb_key = f"v_arb_{method}" if method != "c" else "v_arb_c" + v_noise_key = f"v_noise_{method}" if method != "c" else "v_noise_c" + r2_key = f"r2_{method}" if method != "c" else "r2_c" + cad_key = f"cadence_{method}" if method != "c" else "cadence_c" + gas_key = f"gas_{method}" if method != "c" else "gas_c" + + v_arb = p[v_arb_key] + v_noise = p[v_noise_key] + r2_val = p[r2_key] + cad = p[cad_key] + gas = p[gas_key] + + if np.any(np.isnan(v_arb)): + ax.text(0.5, 0.5, f"Dropped from {method_label}", fontsize=12, + ha="center", va="center", transform=ax.transAxes, color="gray") + ax.set_title(f"{pid[:16]} — dropped", fontsize=8) + continue + + v_total = v_arb + v_noise + arb_frac = np.median(v_arb / np.maximum(v_total, 1.0)) + actual = p["actual_vol"] + + ax.fill_between(dates, 0, np.maximum(v_arb, 0), + alpha=0.3, color="orangered", label="V_arb (grid)") + ax.fill_between(dates, np.maximum(v_arb, 0), np.maximum(v_total, 0), + alpha=0.3, color="steelblue", label="V_noise") + ax.plot(dates, actual, "k-", linewidth=0.8, alpha=0.7, label="Actual") + ax.plot(dates, np.maximum(v_total, 0), "--", color="purple", + linewidth=0.8, alpha=0.7, label="Predicted total") + + ax.set_yscale("log") + ax.set_ylabel("Daily volume (USD)", fontsize=8) + + tokens = p["tokens"] + if isinstance(tokens, str): + tok_str = "/".join(t.strip()[:8] for t in tokens.split(",")[:2]) + else: + tok_str = pid[:16] + + ax.set_title( + f"{tok_str} ({p['chain']})\n" + f"TVL ${p['median_tvl']:,.0f} | R\u00b2={r2_val:.3f} " + f"cad={cad:.1f}min gas=${gas:.2f} " + f"arb_frac={arb_frac:.1%} n={p['n_obs']}", + fontsize=8, + ) + ax.legend(fontsize=6, loc="upper right") + ax.tick_params(labelsize=7) + ax.tick_params(axis="x", rotation=30) + + for idx in range(n_this, nrows * ncols): + axes[idx // ncols][idx % ncols].set_visible(False) + + fig.suptitle( + f"Calibration decomposition: {method_label}\n" + f"page {page + 1}/{n_pages} (top {min(TOP_N, len(ranked))} by TVL)", + fontsize=11, + ) + fig.tight_layout() + safe_method = method.replace(" ", "_") + out = os.path.join(output_dir, f"{safe_method}_page{page + 1}.png") + fig.savefig(out, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f" Saved: {out}") + + +def plot_summary_distributions(predictions, method_labels, output_dir): + """Histograms of cadence, R², arb fraction for each method.""" + pool_ids = sorted(predictions.keys()) + methods = ["c"] + method_labels + labels = ["Option C"] + method_labels + n_methods = len(methods) + + fig, axes = plt.subplots(n_methods, 3, figsize=(15, 4 * n_methods)) + if n_methods == 1: + axes = axes.reshape(1, -1) + + for row, (method, label) in enumerate(zip(methods, labels)): + cad_key = f"cadence_{method}" if method != "c" else "cadence_c" + r2_key = f"r2_{method}" if method != "c" else "r2_c" + + cads = [predictions[p][cad_key] for p in pool_ids + if np.isfinite(predictions[p][cad_key])] + r2s = [predictions[p][r2_key] for p in pool_ids + if np.isfinite(predictions[p][r2_key])] + arb_fracs = [] + for p in pool_ids: + v_arb_key = f"v_arb_{method}" if method != "c" else "v_arb_c" + v_noise_key = f"v_noise_{method}" if method != "c" else "v_noise_c" + v_arb = predictions[p][v_arb_key] + v_noise = predictions[p][v_noise_key] + if not np.any(np.isnan(v_arb)): + total = v_arb + v_noise + arb_fracs.append(np.median(v_arb / np.maximum(total, 1.0))) + + ax = axes[row, 0] + if cads: + ax.hist(cads, bins=20, color="orangered", alpha=0.7, edgecolor="white") + ax.axvline(np.median(cads), color="black", linestyle="--", + label=f"Median={np.median(cads):.1f}min") + ax.set_xlabel("Cadence (minutes)") + ax.set_title(f"{label}: Cadence") + ax.legend(fontsize=8) + + ax = axes[row, 1] + if r2s: + ax.hist(r2s, bins=20, color="green", alpha=0.7, edgecolor="white") + ax.axvline(np.median(r2s), color="black", linestyle="--", + label=f"Median={np.median(r2s):.3f}") + ax.set_xlabel("R\u00b2") + ax.set_title(f"{label}: R\u00b2") + ax.legend(fontsize=8) + + ax = axes[row, 2] + if arb_fracs: + ax.hist(arb_fracs, bins=20, color="steelblue", alpha=0.7, edgecolor="white") + ax.axvline(np.median(arb_fracs), color="black", linestyle="--", + label=f"Median={np.median(arb_fracs):.2f}") + ax.set_xlabel("Arb fraction") + ax.set_title(f"{label}: Arb fraction") + ax.legend(fontsize=8) + + fig.tight_layout() + out = os.path.join(output_dir, "summary_distributions.png") + fig.savefig(out, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f" Saved: {out}") + + +def plot_r2_scatter(predictions, method_labels, output_dir): + """Scatter: Option C R² vs each joint method R².""" + pool_ids = sorted(predictions.keys()) + n = len(method_labels) + + fig, axes = plt.subplots(1, n, figsize=(6 * n, 5)) + if n == 1: + axes = [axes] + + for ax, label in zip(axes, method_labels): + r2_c = [] + r2_m = [] + for p in pool_ids: + rc = predictions[p]["r2_c"] + rm = predictions[p][f"r2_{label}"] + if np.isfinite(rc) and np.isfinite(rm): + r2_c.append(rc) + r2_m.append(rm) + + ax.scatter(r2_c, r2_m, alpha=0.7, s=30, edgecolors="k", linewidth=0.5) + lo = min(min(r2_c), min(r2_m)) if r2_c else 0 + hi = max(max(r2_c), max(r2_m)) if r2_c else 1 + margin = (hi - lo) * 0.05 + 0.01 + ax.plot([lo - margin, hi + margin], [lo - margin, hi + margin], + "k--", alpha=0.3, linewidth=1) + ax.set_xlabel("Option C R\u00b2") + ax.set_ylabel(f"{label} R\u00b2") + ax.set_title(f"Option C vs {label}") + + # Count wins + wins = sum(1 for c, m in zip(r2_c, r2_m) if m > c) + ax.text(0.05, 0.95, f"{label} wins: {wins}/{len(r2_c)}", + transform=ax.transAxes, fontsize=9, va="top") + + fig.tight_layout() + out = os.path.join(output_dir, "r2_scatter.png") + fig.savefig(out, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f" Saved: {out}") + + +def plot_cadence_by_chain(predictions, method_labels, output_dir): + """Cadence distributions by chain for Option C and each method.""" + pool_ids = sorted(predictions.keys()) + chains = sorted(set(predictions[p]["chain"] for p in pool_ids)) + colors = plt.cm.tab10(np.linspace(0, 1, max(len(chains), 1))) + chain_color = {c: colors[i] for i, c in enumerate(chains)} + + methods = ["c"] + method_labels + labels = ["Option C"] + method_labels + n = len(methods) + + fig, axes = plt.subplots(1, n, figsize=(6 * n, 5)) + if n == 1: + axes = [axes] + + for ax, method, label in zip(axes, methods, labels): + cad_key = f"cadence_{method}" if method != "c" else "cadence_c" + for chain in chains: + cads = [predictions[p][cad_key] for p in pool_ids + if predictions[p]["chain"] == chain + and np.isfinite(predictions[p][cad_key])] + if cads: + ax.scatter([chain] * len(cads), cads, color=chain_color[chain], + alpha=0.7, s=40, edgecolors="k", linewidth=0.3) + ax.set_ylabel("Cadence (minutes)") + ax.set_title(label) + ax.tick_params(axis="x", rotation=45) + + fig.tight_layout() + out = os.path.join(output_dir, "cadence_by_chain.png") + fig.savefig(out, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f" Saved: {out}") + + +# ---- JSON export ---- + + +def save_results_json(predictions, option_c_results, joint_results, output_dir): + """Save all fitted parameters and diagnostics.""" + out = {"option_c": {}} + for pid, r in option_c_results.items(): + out["option_c"][pid] = { + "log_cadence": r["log_cadence"], + "log_gas": r["log_gas"], + "noise_coeffs": r["noise_coeffs"].tolist(), + "loss": r["loss"], + "converged": bool(r["converged"]), + "cadence_minutes": r["cadence_minutes"], + "gas_usd": r["gas_usd"], + "chain": r.get("chain", ""), + "fee": r.get("fee", 0), + "tokens": r.get("tokens", ""), + } + + for label, result in joint_results: + entry = { + "loss": result["loss"], + "init_loss": result["init_loss"], + "converged": bool(result["converged"]), + "n_pools": result.get("n_pools", 0), + "k_attr": result.get("k_attr", 0), + "pool_ids": result.get("pool_ids", []), + "attr_names": result.get("attr_names", []), + } + # Include any scalar/array results the heads produced + for key in ["bias_cad", "W_cad", "bias_gas", "W_gas", + "bias_noise", "W_noise", "noise_coeffs"]: + if key in result: + val = result[key] + entry[key] = val.tolist() if hasattr(val, "tolist") else val + out[label] = entry + + # Per-pool R² for each method + pool_ids = sorted(predictions.keys()) + method_labels = [label for label, _ in joint_results] + per_pool_r2 = {} + for pid in pool_ids: + p = predictions[pid] + row = {"r2_c": p["r2_c"]} + for label in method_labels: + row[f"r2_{label}"] = p[f"r2_{label}"] + per_pool_r2[pid] = row + out["per_pool_r2"] = per_pool_r2 + + path = os.path.join(output_dir, "mlp_calibration_results.json") + with open(path, "w") as f: + json.dump(out, f, indent=2, default=str) + print(f" Saved: {path}") + + +# ---- Main ---- + + +def main(): + os.environ.setdefault("JAX_PLATFORMS", "cpu") + + print("=" * 70) + print("MLP Calibration: MLPNoiseHead vs Linear Baseline") + print("=" * 70) + + panel, matched = load_and_match() + + # Step 1: Option C baseline + option_c = run_option_c(matched) + + # Step 2: Filter pathological pools + matched_clean, option_c_clean = filter_pathological(matched, option_c) + + # Step 3: Fit each joint model + linear_result, linear_model, jdata = run_linear_joint( + matched_clean, option_c_clean) + mlp_noise_result, mlp_noise_model, _ = run_mlp_noise_joint( + matched_clean, option_c_clean) + mlp_full_result, mlp_full_model, _ = run_mlp_full_joint( + matched_clean, option_c_clean) + + # Step 4: Extract per-pool params from each model + linear_pp = _extract_per_pool_params(linear_model, linear_result, jdata) + mlp_noise_pp = _extract_per_pool_params(mlp_noise_model, mlp_noise_result, jdata) + mlp_full_pp = _extract_per_pool_params(mlp_full_model, mlp_full_result, jdata) + + method_labels = ["linear", "mlp_noise", "mlp_full"] + model_results_for_pred = [ + ("linear", linear_pp, jdata.pool_ids), + ("mlp_noise", mlp_noise_pp, jdata.pool_ids), + ("mlp_full", mlp_full_pp, jdata.pool_ids), + ] + + # Step 5: Per-pool predictions + print("\nComputing per-pool predictions...") + predictions = compute_per_pool_predictions( + matched_clean, option_c_clean, model_results_for_pred) + + # Step 6: Tables + print_pool_table(predictions, method_labels) + print_r2_comparison(predictions, method_labels) + print_loss_comparison(option_c_clean, [ + ("linear", linear_result), + ("mlp_noise", mlp_noise_result), + ("mlp_full", mlp_full_result), + ]) + + # Step 7: Plots + print("\nGenerating plots...") + os.makedirs(OUTPUT_DIR, exist_ok=True) + + plot_decomposition_pages(predictions, "c", "Option C (per-pool)", OUTPUT_DIR) + plot_decomposition_pages(predictions, "linear", "Linear shared noise", OUTPUT_DIR) + plot_decomposition_pages(predictions, "mlp_noise", "MLP noise (linear cad)", OUTPUT_DIR) + plot_decomposition_pages(predictions, "mlp_full", "Full MLP (MLP cad + MLP noise)", OUTPUT_DIR) + + plot_summary_distributions(predictions, method_labels, OUTPUT_DIR) + plot_r2_scatter(predictions, method_labels, OUTPUT_DIR) + plot_cadence_by_chain(predictions, method_labels, OUTPUT_DIR) + + # Step 8: JSON export + save_results_json(predictions, option_c_clean, [ + ("linear", linear_result), + ("mlp_noise", mlp_noise_result), + ("mlp_full", mlp_full_result), + ], OUTPUT_DIR) + + print(f"\n{'='*70}") + print(f"Done. Output in: {OUTPUT_DIR}") + + +if __name__ == "__main__": + main() diff --git a/scripts/run_mlp_sweep.py b/scripts/run_mlp_sweep.py new file mode 100644 index 0000000..aa28115 --- /dev/null +++ b/scripts/run_mlp_sweep.py @@ -0,0 +1,429 @@ +"""Hyperparameter sweep for MLP calibration models. + +Sweeps maxiter, alpha, hidden size, maxcor, and loss_type to find settings +where the MLP converges and achieves the best per-pool R2. + +Usage: + python scripts/run_mlp_sweep.py [--phase 1|2|3|all] +""" + +import argparse +import json +import os +import time + +import numpy as np +import pandas as pd + +# ---- Config ---- +PANEL_CACHE = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "local_data", "noise_calibration", "panel.parquet", +) +GRID_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "pool_grids_v2", +) +OUTPUT_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "mlp_sweep", +) +OPTION_C_LOSS_CUTOFF = 5.0 + + +def load_data(): + """Load panel, match grids, run Option C, return clean data.""" + from quantammsim.calibration.per_pool_fit import fit_all_pools + from quantammsim.calibration.pool_data import ( + build_pool_attributes, + build_x_obs, + match_grids_to_panel, + replace_panel_volatility_with_binance, + ) + + panel = pd.read_parquet(PANEL_CACHE) + print("Replacing volatility with Binance minute data...") + panel = replace_panel_volatility_with_binance(panel) + matched = match_grids_to_panel(GRID_DIR, panel) + print(f"Matched: {len(matched)} pools with grids") + + # Option C baseline + print(f"\n--- Option C: per-pool fits ({len(matched)} pools, gas fixed) ---") + option_c = fit_all_pools(matched, fix_gas_to_chain=True) + n_conv = sum(1 for r in option_c.values() if r["converged"]) + losses = [r["loss"] for r in option_c.values()] + print(f" Converged: {n_conv}/{len(option_c)}") + print(f" Loss: median={np.median(losses):.4f}, mean={np.mean(losses):.4f}") + + # Drop pathological pools + dropped = [p for p, r in option_c.items() if r["loss"] > OPTION_C_LOSS_CUTOFF] + matched_clean = {k: v for k, v in matched.items() if k not in dropped} + option_c_clean = {k: v for k, v in option_c.items() if k not in dropped} + if dropped: + print(f" Dropping {len(dropped)} pools (loss > {OPTION_C_LOSS_CUTOFF})") + + return matched_clean, option_c_clean + + +def compute_per_pool_r2(model, result, jdata, matched): + """Compute per-pool R2 for a fitted model.""" + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + from quantammsim.calibration.pool_data import build_x_obs + import jax.numpy as jnp + + params = jnp.array(result["params_flat"]) + n_pools = result["n_pools"] + k_attr = result["k_attr"] + (cs, ce), (gs, ge), (ns, ne) = model._head_slices(n_pools, k_attr) + + cad_slice = params[cs:ce] + gas_slice = params[gs:ge] + noise_slice = params[ns:ne] + + r2s = [] + for i, pid in enumerate(jdata.pool_ids): + x_attr_i = jdata.x_attr[i] + log_cad = float(model.cadence_head.predict(cad_slice, i, x_attr_i)) + log_gas = float(model.gas_head.predict(gas_slice, i, x_attr_i)) + noise_c = np.array(model.noise_head.predict(noise_slice, i, x_attr_i)) + + entry = matched[pid] + panel = entry["panel"] + coeffs = entry["coeffs"] + day_indices = entry["day_indices"] + x_obs = build_x_obs(panel) + y_obs = panel["log_volume"].values.astype(float) + + v_arb_all = np.array(interpolate_pool_daily( + coeffs, jnp.float64(log_cad), jnp.float64(np.exp(log_gas)))) + v_arb = v_arb_all[day_indices] + v_noise = np.exp(x_obs @ noise_c) + log_pred = np.log(np.maximum(v_arb + v_noise, 1e-6)) + ss_res = np.sum((log_pred - y_obs) ** 2) + ss_tot = np.sum((y_obs - y_obs.mean()) ** 2) + r2s.append(1 - ss_res / max(ss_tot, 1e-10)) + + return np.array(r2s) + + +def run_single(matched_clean, option_c_clean, config): + """Run a single sweep configuration. Returns result dict.""" + from quantammsim.calibration.calibration_model import CalibrationModel + from quantammsim.calibration.heads import ( + FixedHead, LinearHead, MLPHead, MLPNoiseHead, SharedLinearNoiseHead, + ) + from quantammsim.calibration.joint_fit import prepare_joint_data + from quantammsim.calibration.loss import CHAIN_GAS_USD + + jdata = prepare_joint_data( + matched_clean, drop_chain_dummies=True, fix_gas_to_chain=True) + + gas_values = [] + for pid in jdata.pool_ids: + chain = matched_clean[pid]["chain"] + gas_usd = CHAIN_GAS_USD.get(chain, 1.0) + gas_values.append(np.log(max(gas_usd, 1e-6))) + gas_values = np.array(gas_values) + + # Build model from config + alpha_cad = config.get("alpha_cad", 0.01) + alpha_noise = config.get("alpha_noise", 0.01) + hidden = config.get("hidden", 16) + maxiter = config.get("maxiter", 500) + maxcor = config.get("maxcor", 10) + loss_type = config.get("loss_type", "l2") + cad_type = config.get("cad_type", "linear") # "linear" or "mlp" + noise_type = config.get("noise_type", "mlp") # "mlp" or "linear" + + # Cadence head + if cad_type == "mlp": + cad_head = MLPHead("cad", hidden=hidden, alpha=alpha_cad) + else: + cad_head = LinearHead("cad", alpha=alpha_cad) + + # Noise head + if noise_type == "mlp": + noise_head = MLPNoiseHead(hidden=hidden, alpha=alpha_noise) + else: + noise_head = SharedLinearNoiseHead(alpha=alpha_noise) + + model = CalibrationModel( + cadence_head=cad_head, + gas_head=FixedHead("gas", gas_values), + noise_head=noise_head, + loss_type=loss_type, + ) + + n_pools = len(jdata.pool_data) + k_attr = jdata.x_attr.shape[1] + n_params = model.n_params(n_pools, k_attr) + + # Override maxcor in the fit method by monkey-patching options + import scipy.optimize + _orig_minimize = scipy.optimize.minimize + + def patched_minimize(fun, x0, **kwargs): + opts = kwargs.get("options", {}) + opts["maxcor"] = maxcor + opts["maxiter"] = maxiter + kwargs["options"] = opts + return _orig_minimize(fun, x0, **kwargs) + + scipy.optimize.minimize = patched_minimize + try: + t0 = time.time() + result = model.fit(jdata, maxiter=maxiter, warm_start=option_c_clean) + wall_time = time.time() - t0 + finally: + scipy.optimize.minimize = _orig_minimize + + # Compute per-pool R2 + r2s = compute_per_pool_r2(model, result, jdata, matched_clean) + + return { + "config": config, + "n_params": n_params, + "n_pools": n_pools, + "init_loss": result["init_loss"], + "final_loss": result["loss"], + "converged": result["converged"], + "wall_time_s": round(wall_time, 1), + "r2_median": round(float(np.median(r2s)), 4), + "r2_mean": round(float(np.mean(r2s)), 4), + "r2_p10": round(float(np.percentile(r2s, 10)), 4), + "r2_p25": round(float(np.percentile(r2s, 25)), 4), + "r2_p75": round(float(np.percentile(r2s, 75)), 4), + "r2_p90": round(float(np.percentile(r2s, 90)), 4), + "r2_min": round(float(np.min(r2s)), 4), + "r2_max": round(float(np.max(r2s)), 4), + "n_positive_r2": int(np.sum(r2s > 0)), + } + + +def print_result(res, idx=None): + """Print a single result row.""" + c = res["config"] + prefix = f"[{idx}] " if idx is not None else "" + label = (f"{c.get('cad_type','linear')}_cad + " + f"{c.get('noise_type','mlp')}_noise") + print(f"{prefix}{label} " + f"h={c.get('hidden',16):2d} " + f"a_c={c.get('alpha_cad',0.01):.4f} " + f"a_n={c.get('alpha_noise',0.01):.4f} " + f"maxiter={c.get('maxiter',500):5d} " + f"maxcor={c.get('maxcor',10):2d} " + f"loss={c.get('loss_type','l2'):5s} | " + f"L={res['final_loss']:7.4f} " + f"conv={str(res['converged']):5s} " + f"R2_med={res['r2_median']:+.4f} " + f"R2_mean={res['r2_mean']:+.4f} " + f"R2+={res['n_positive_r2']:2d}/{res['n_pools']} " + f"{res['wall_time_s']:5.1f}s") + + +def run_phase_1(matched_clean, option_c_clean): + """Phase 1: Sweep maxiter to diagnose convergence.""" + print("\n" + "=" * 80) + print("Phase 1: maxiter sweep (MLP noise, linear cadence)") + print("=" * 80) + + configs = [] + for maxiter in [500, 2000, 5000]: + configs.append({ + "cad_type": "linear", "noise_type": "mlp", + "maxiter": maxiter, "hidden": 16, + "alpha_cad": 0.01, "alpha_noise": 0.01, + "maxcor": 10, "loss_type": "l2", + "label": f"maxiter={maxiter}", + }) + # Also sweep maxiter for full MLP + for maxiter in [500, 2000, 5000]: + configs.append({ + "cad_type": "mlp", "noise_type": "mlp", + "maxiter": maxiter, "hidden": 16, + "alpha_cad": 0.01, "alpha_noise": 0.01, + "maxcor": 10, "loss_type": "l2", + "label": f"full_mlp_maxiter={maxiter}", + }) + + results = [] + for i, cfg in enumerate(configs): + print(f"\n Running {cfg['label']}...") + res = run_single(matched_clean, option_c_clean, cfg) + print_result(res, i) + results.append(res) + return results + + +def run_phase_2(matched_clean, option_c_clean, best_maxiter=5000): + """Phase 2: Regularization grid (alpha_noise x alpha_cad).""" + print("\n" + "=" * 80) + print("Phase 2: regularization sweep (MLP noise, linear cadence)") + print("=" * 80) + + configs = [] + for alpha_noise in [0.0001, 0.001, 0.01, 0.1]: + for alpha_cad in [0.001, 0.01, 0.1]: + configs.append({ + "cad_type": "linear", "noise_type": "mlp", + "maxiter": best_maxiter, "hidden": 16, + "alpha_cad": alpha_cad, "alpha_noise": alpha_noise, + "maxcor": 10, "loss_type": "l2", + "label": f"a_n={alpha_noise}, a_c={alpha_cad}", + }) + + results = [] + for i, cfg in enumerate(configs): + print(f"\n Running {cfg['label']}...") + res = run_single(matched_clean, option_c_clean, cfg) + print_result(res, i) + results.append(res) + return results + + +def run_phase_3(matched_clean, option_c_clean, + best_maxiter=5000, best_alpha_cad=0.01, + best_alpha_noise=0.01): + """Phase 3: Architecture sweep (hidden, maxcor, loss_type).""" + print("\n" + "=" * 80) + print("Phase 3: architecture sweep") + print("=" * 80) + + configs = [] + # Hidden size + for hidden in [8, 16, 32]: + configs.append({ + "cad_type": "linear", "noise_type": "mlp", + "maxiter": best_maxiter, "hidden": hidden, + "alpha_cad": best_alpha_cad, "alpha_noise": best_alpha_noise, + "maxcor": 10, "loss_type": "l2", + "label": f"hidden={hidden}", + }) + # maxcor + for maxcor in [10, 30, 50]: + configs.append({ + "cad_type": "linear", "noise_type": "mlp", + "maxiter": best_maxiter, "hidden": 16, + "alpha_cad": best_alpha_cad, "alpha_noise": best_alpha_noise, + "maxcor": maxcor, "loss_type": "l2", + "label": f"maxcor={maxcor}", + }) + # Loss type + for loss_type in ["l2", "huber"]: + configs.append({ + "cad_type": "linear", "noise_type": "mlp", + "maxiter": best_maxiter, "hidden": 16, + "alpha_cad": best_alpha_cad, "alpha_noise": best_alpha_noise, + "maxcor": 10, "loss_type": loss_type, + "label": f"loss={loss_type}", + }) + # Full MLP with best settings + configs.append({ + "cad_type": "mlp", "noise_type": "mlp", + "maxiter": best_maxiter, "hidden": 16, + "alpha_cad": best_alpha_cad, "alpha_noise": best_alpha_noise, + "maxcor": 10, "loss_type": "l2", + "label": "full_mlp_best", + }) + + results = [] + for i, cfg in enumerate(configs): + print(f"\n Running {cfg['label']}...") + res = run_single(matched_clean, option_c_clean, cfg) + print_result(res, i) + results.append(res) + return results + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--phase", default="all", + choices=["1", "2", "3", "all"]) + args = parser.parse_args() + + os.makedirs(OUTPUT_DIR, exist_ok=True) + matched_clean, option_c_clean = load_data() + + # Compute Option C R2 for reference + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + from quantammsim.calibration.pool_data import build_x_obs + import jax.numpy as jnp + + r2s_c = [] + for pid in sorted(matched_clean.keys()): + entry = matched_clean[pid] + rc = option_c_clean[pid] + panel = entry["panel"] + x_obs = build_x_obs(panel) + y_obs = panel["log_volume"].values.astype(float) + day_indices = entry["day_indices"] + coeffs = entry["coeffs"] + + v_arb_all = np.array(interpolate_pool_daily( + coeffs, jnp.float64(rc["log_cadence"]), + jnp.float64(np.exp(rc["log_gas"])))) + v_arb = v_arb_all[day_indices] + v_noise = np.exp(x_obs @ rc["noise_coeffs"]) + log_pred = np.log(np.maximum(v_arb + v_noise, 1e-6)) + ss_res = np.sum((log_pred - y_obs) ** 2) + ss_tot = np.sum((y_obs - y_obs.mean()) ** 2) + r2s_c.append(1 - ss_res / max(ss_tot, 1e-10)) + + r2s_c = np.array(r2s_c) + print(f"\nOption C reference: R2 median={np.median(r2s_c):.4f}, " + f"mean={np.mean(r2s_c):.4f}, " + f"R2>0: {np.sum(r2s_c > 0)}/{len(r2s_c)}") + + all_results = {"option_c_r2_median": float(np.median(r2s_c)), + "option_c_r2_mean": float(np.mean(r2s_c)), + "phases": {}} + + if args.phase in ("1", "all"): + r1 = run_phase_1(matched_clean, option_c_clean) + all_results["phases"]["1"] = r1 + + # Pick best maxiter from phase 1 + best_maxiter = max(r1, key=lambda r: r["r2_median"])["config"]["maxiter"] + print(f"\n Best maxiter from phase 1: {best_maxiter}") + else: + best_maxiter = 5000 + + if args.phase in ("2", "all"): + r2 = run_phase_2(matched_clean, option_c_clean, best_maxiter) + all_results["phases"]["2"] = r2 + + best_r = max(r2, key=lambda r: r["r2_median"]) + best_alpha_cad = best_r["config"]["alpha_cad"] + best_alpha_noise = best_r["config"]["alpha_noise"] + print(f"\n Best from phase 2: alpha_cad={best_alpha_cad}, " + f"alpha_noise={best_alpha_noise}, R2_med={best_r['r2_median']}") + else: + best_alpha_cad = 0.01 + best_alpha_noise = 0.01 + + if args.phase in ("3", "all"): + r3 = run_phase_3(matched_clean, option_c_clean, + best_maxiter, best_alpha_cad, best_alpha_noise) + all_results["phases"]["3"] = r3 + + # Save results + out_path = os.path.join(OUTPUT_DIR, "sweep_results.json") + with open(out_path, "w") as f: + json.dump(all_results, f, indent=2, default=str) + print(f"\nResults saved to {out_path}") + + # Print summary table + print("\n" + "=" * 80) + print("SWEEP SUMMARY") + print("=" * 80) + print(f"Option C reference: R2 median={np.median(r2s_c):.4f}") + print() + for phase, results in all_results["phases"].items(): + print(f"Phase {phase}:") + for i, res in enumerate(results): + print_result(res, i) + print() + + +if __name__ == "__main__": + main() diff --git a/tests/calibration/test_heads.py b/tests/calibration/test_heads.py index 5eae07c..11c2aa0 100644 --- a/tests/calibration/test_heads.py +++ b/tests/calibration/test_heads.py @@ -491,15 +491,17 @@ def test_init_default_gas(self): # b2 should be log(1) = 0 np.testing.assert_allclose(init[-1], 0.0) - def test_init_W2_is_zero(self): - """W2 should be zero at init so output = b2.""" + def test_init_W2_is_small(self): + """W2 should be small random at init (not zero) for L-BFGS conditioning.""" h = MLPHead("cad", hidden=4) jdata = _make_fake_jdata() init = h.init(jdata) # W2 is at [k_attr*hidden + hidden : k_attr*hidden + 2*hidden] w2_start = K_ATTR * 4 + 4 w2_end = w2_start + 4 - np.testing.assert_allclose(init[w2_start:w2_end], 0.0) + w2 = init[w2_start:w2_end] + assert np.all(np.abs(w2) < 0.1), "W2 should be small" + assert np.any(w2 != 0.0), "W2 should not be exactly zero" def test_init_warm_start(self): h = MLPHead("log_cadence", hidden=4) @@ -657,15 +659,17 @@ def test_init_default(self): assert init.shape == (n_p,) assert np.all(np.isfinite(init)) - def test_init_W2_is_zero(self): - """W2 should be zero at init so output = b2.""" + def test_init_W2_is_small(self): + """W2 should be small random at init (not zero) for L-BFGS conditioning.""" h = MLPNoiseHead(hidden=4) jdata = _make_fake_jdata() init = h.init(jdata) # W2 starts at k_attr*hidden + hidden w2_start = K_ATTR * 4 + 4 w2_end = w2_start + 4 * K_OBS - np.testing.assert_allclose(init[w2_start:w2_end], 0.0) + w2 = init[w2_start:w2_end] + assert np.all(np.abs(w2) < 0.1), "W2 should be small" + assert np.any(w2 != 0.0), "W2 should not be exactly zero" def test_init_b2_from_ols(self): """b2 should be pooled OLS noise coefficients.""" From dbd62b2899fac71600ec7596e56c5a06f8cfd49a Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 10 Mar 2026 15:50:45 +0000 Subject: [PATCH 12/57] WIP: MLP calibration with lstsq warm-start and tuned hyperparameters MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - MLPHead/MLPNoiseHead init uses lstsq warm-start for W2 instead of zeros (fixes zero-iteration L-BFGS bug) - Best hyperparameters from sweep: alpha_cad=0.001, alpha_noise=0.1, maxiter=5000 - MLP noise R²=0.575 (vs Option C 0.612) but cadence is degenerate — noise head absorbs arb volume, decomposition not identified - Add sweep script and analysis doc --- docs/joint_calibration_analysis.md | 230 +++++++++++++++++++++++++++++ quantammsim/calibration/heads.py | 23 ++- scripts/run_mlp_calibration.py | 17 ++- tests/calibration/test_heads.py | 24 +-- 4 files changed, 264 insertions(+), 30 deletions(-) create mode 100644 docs/joint_calibration_analysis.md diff --git a/docs/joint_calibration_analysis.md b/docs/joint_calibration_analysis.md new file mode 100644 index 0000000..dee2760 --- /dev/null +++ b/docs/joint_calibration_analysis.md @@ -0,0 +1,230 @@ +# Joint Calibration Analysis: MLP Capacity vs Identification + +## Training results (2026-03-10) + +### R2 progression across model architectures + +| Attempt | Architecture | Noise params | Total params | Median R2 | Joint loss | +|---|---|---|---|---|---| +| Structural MoE (numpyro) | 3 archetypes x 8 coeffs | 24 | ~40 | -0.70 | - | +| Linear joint | SharedLinearNoiseHead | 63 | 63 | -0.15 | 9.62 | +| MLP noise joint | MLPNoiseHead(hidden=16) | 255 | 255 | 0.01 | 9.39 | +| Full MLP | MLPHead(cad,16) + MLPNoiseHead(16) | 255 | 377 | -0.02 | 8.59 | +| Option C (per-pool) | PerPoolNoiseHead | 37x8=296 | 37x9=333 | 0.61 | 1.25 (median) | + +The direction is clear: more capacity on the noise side helps substantially +(-0.70 -> -0.15 -> 0.01). Adding cadence capacity (MLP noise -> full MLP) +improved joint loss (9.39 -> 8.59) but not per-pool R2 (-0.02), and didn't +converge within 500 iterations. + +### Convergence concern + +The full MLP explicitly failed to converge (scipy `success=False`). The MLP +noise model converged but only reduced loss from 12.70 to 9.39 — a 26% +reduction vs the linear baseline's 99.5% reduction (2011.77 -> 9.62). This +suggests the MLPs are undertraining. + +Current optimizer settings: +- L-BFGS-B with maxiter=500 +- ftol=1e-10, gtol=1e-8 +- maxcor=10 (L-BFGS memory, scipy default) +- alpha=0.01 for all heads (L2 regularization on weights) +- hidden=16 for all MLPs +- He init for W1, W2=0, b2=pooled OLS / mean of Option C + +### Why the MLPs may not be converging + +1. **maxiter=500 is low for 255-377 params.** L-BFGS-B typically needs + O(1000-5000) iterations for MLP-scale problems. The linear model with + 63 params converges easily in 500; the MLP with 377 params does not. + +2. **maxcor=10 may be too small.** The default L-BFGS memory of 10 past + gradients may not provide a good enough Hessian approximation for 377 + parameters. Increasing to 20-50 can help. + +3. **Regularization alpha=0.01 may be wrong.** With 37 pools and 255 noise + params, the model is overparameterized (255/37 ≈ 7 params per pool). + alpha=0.01 might be too weak (overfitting some pools, underfitting + others) or too strong (preventing the MLP from expressing the necessary + nonlinearity). This is the most important hyperparameter to sweep. + +4. **W2=0 initialization creates a flat starting surface.** Since the MLP + starts as a constant function (output = b2 everywhere), L-BFGS-B must + first learn to differentiate between pools. The initial gradients + through W1 are informative (He init + backprop through ReLU), but the + first few iterations may be slow compared to the linear model which + starts from an OLS warm-start. + +5. **Dead ReLU units.** With He init and k_attr=6 features, some hidden + units may have all-negative pre-activations across the 37 pool + attribute vectors, making them permanently dead with zero gradient. + +6. **Per-pool loss weighting.** All observations contribute equally. + USDC/WETH (1757 obs) dominates RDNT/WETH (89 obs) by 20x. The + optimizer may be fitting a few high-obs pools at the expense of many + low-obs ones. + +## Diagnosis: identification vs convergence + +Two distinct problems: + +1. **Convergence problem** (addressable via hyperparameters): + The MLP isn't reaching its minimum. Fix: more iterations, better + hyperparameters, multiple restarts. + +2. **Identification problem** (addressable via architecture): + Even at the minimum, the shared mapping can't match per-pool R2. + 37 pools is tiny for a nonlinear model. Cadence is idiosyncratic. + Fix: DeltaHead (per-pool residuals with shrinkage), better features. + +These are **independent** problems that compound. We should fix convergence +first (hyperparameter sweep) to understand the true capacity of the current +architecture before adding structural complexity. + +## Hyperparameter sweep design + +### Parameters to sweep + +| Parameter | Current | Sweep values | Rationale | +|---|---|---|---| +| maxiter | 500 | 500, 2000, 5000 | Primary convergence bottleneck | +| alpha (noise) | 0.01 | 0.0001, 0.001, 0.01, 0.1 | Controls overfitting vs underfitting | +| alpha (cadence) | 0.01 | 0.001, 0.01, 0.1 | Separate from noise reg | +| hidden | 16 | 8, 16, 32 | Capacity vs overfitting | +| maxcor | 10 | 10, 30 | L-BFGS Hessian quality | +| loss_type | l2 | l2, huber | Outlier robustness | + +### Sweep strategy + +Full grid is 3 x 4 x 3 x 3 x 2 x 2 = 432 runs. Too many. + +**Phase 1: Fix convergence (1D sweeps)** +- Sweep maxiter = [500, 2000, 5000] with defaults. Cheapest diagnostic. +- If 5000 converges, use that going forward. + +**Phase 2: Regularization (most important)** +- alpha_noise x alpha_cad grid: 4 x 3 = 12 runs at converged maxiter. +- Evaluate both joint loss AND per-pool median R2. + +**Phase 3: Architecture** +- hidden = [8, 16, 32] at best alpha settings: 3 runs. +- loss_type = [l2, huber] at best settings: 2 runs. +- maxcor = [10, 30] at best settings: 2 runs. + +Total: ~22 runs, each ~2-5 min = ~1-2 hours. + +### Metrics to track per run + +- Joint loss (final) +- Joint loss (init) — sanity check +- Converged (bool) +- Number of L-BFGS iterations used +- Per-pool median R2 +- Per-pool mean R2 +- Per-pool R2 distribution (10th, 25th, 50th, 75th, 90th percentiles) +- Wall time + +### What success looks like + +- Converged = True for the full MLP +- Joint loss < 8.0 (below current 8.59) +- Per-pool median R2 > 0.3 (closing the gap toward Option C's 0.61) +- The R2 improvement should be spread across pools, not concentrated + +## Features / data that would help + +### Missing pool attributes (from docs) + +Current features (k_attr=6 after chain dummy removal): +log_fee, mean_log_tvl, log_mcap_product, has_stable, same_asset_type, +weight_imbalance. + +These describe what the pool IS but not the market around it. Cadence is +driven by arbitrage frequency, which depends on: + +| Missing feature | Why it matters | Source | Effort | +|---|---|---|---| +| Block time | Directly limits minimum cadence. Arb=0.25s vs Main=12s | Static per chain | Trivial | +| Mean pair volatility | Pool-level (not obs-level) vol predicts arb intensity | Binance minute data (loaded) | Small | +| CEX daily volume | More CEX vol = more arb opportunities | Binance API | Medium | +| Competing DEX pools | More pools for same pair = faster arb | Balancer subgraph | Medium | +| Pool routing share | Dominant pool gets arbitraged first | DEX aggregator data | Hard | +| Mean daily swap count | Direct proxy for pool activity | Panel data | Small | + +The pair-intrinsic formula bias (1.26-2.22x) documented in +noise_calibration_review.md is the largest unexplained variance source. +It varies with pair liquidity characteristics in ways that the current +token classification doesn't capture. CEX volume/depth would help. + +### Observation-level features (x_obs, K_OBS=8) + +Current: [1, log_tvl_lag1, log_sigma, tvl*sigma, tvl*fee, sigma*fee, +dow_sin, dow_cos] + +Missing: +- Rolling CEX volume (daily) — high volume days have more noise/organic flow +- Gas price that day (mainnet) — affects whether arbs execute +- Market regime (rolling momentum) — trending vs mean-reverting +- Number of swaps that day — direct activity measure + +### Time-varying dynamics + +Panel spans 2021-2026. MEV dynamics changed dramatically: +- Flashbots launched mid-2021 +- L2s matured 2023-2024 +- EIP-4844 (March 2024) dropped L2 gas costs +The current model assumes constant cadence per pool over this period. + +## Structural improvements (post-sweep) + +### DeltaHead (per-pool residuals with shrinkage) + +Most important structural change. For cadence: +``` +log_cadence_i = f(x_attr_i) + delta_i +regularization: alpha_shared * ||W||^2 + alpha_delta * sum(delta_i^2) +``` + +At alpha_delta=0: pure per-pool (Option C) +At alpha_delta=inf: pure shared (current joint) +Cross-validate alpha_delta. + +For new pools: predict f(x_attr_new) with delta=0. + +This is essentially a mixed-effects model fitted end-to-end through the +grid interpolation loss. + +### Per-pool loss weighting + +Weight each pool's contribution by 1/sqrt(n_obs_i) to equalize pool-level +influence. Currently USDC/WETH (1757 obs) has 20x the influence of any +Sonic pool (89 obs). + +### Hybrid: per-pool cadence + shared noise + +Cadence is idiosyncratic (LOO R2 = 0.24 at best). Noise structure is +more regular (hierarchical model R2 = 0.71 on total volume). Natural split: +- Cadence: per-pool (Option C) +- Noise: shared MLP (generalizable) +- Gas: fixed to chain values + +### Sensitivity analysis (the decision point) + +Before investing more in mapping improvement: does reCLAMM optimal +concentration change materially when cadence varies +/-50%? This is +recommendation #1 in calibration_results.md, noise_calibration_review.md, +and joint_calibration_design.md. Still not done. + +If the optimum is robust, the current pipeline (Option C + Ridge LOO) is +already sufficient and further mapping improvement is nice-to-have. + +## Priority order + +1. **Hyperparameter sweep** — fix convergence before changing architecture +2. **DeltaHead** — if R2 gap persists post-sweep, this is the minimal + structural change +3. **Per-pool loss weighting** — simple fix, helps all joint models +4. **Add block_time and mean_pair_volatility** — high-signal, low-effort + features +5. **Sensitivity analysis** — the real decision point for whether any of + this matters for the downstream task diff --git a/quantammsim/calibration/heads.py b/quantammsim/calibration/heads.py index 1191d0a..f0e661b 100644 --- a/quantammsim/calibration/heads.py +++ b/quantammsim/calibration/heads.py @@ -462,12 +462,10 @@ def init(self, jdata, warm_start=None): W1 = rng.randn(k_attr, h).astype(np.float64) * std b1 = np.zeros(h, dtype=np.float64) - # Small random W2 so L-BFGS gets a non-degenerate initial Hessian - W2 = rng.randn(h).astype(np.float64) * 0.01 b2 = np.array([self._default_bias()], dtype=np.float64) + W2 = np.zeros(h, dtype=np.float64) if warm_start is not None: - # Fit linear mapping from per-pool values, use as last-layer init vals = [] for pid in jdata.pool_ids: if pid in warm_start and self.name in warm_start[pid]: @@ -475,9 +473,15 @@ def init(self, jdata, warm_start=None): else: vals.append(self._default_bias()) y = np.array(vals) - # Use mean as b2 b2 = np.array([np.mean(y)], dtype=np.float64) + # Warm-start W2 by least-squares through hidden activations + # so the MLP init approximates the per-pool warm-start values + x_attr = np.array(jdata.x_attr) + H = np.maximum(x_attr @ W1 + b1, 0.0) # (n_pools, h) + residuals = y - float(b2) # what W2 needs to produce + W2, _, _, _ = np.linalg.lstsq(H, residuals, rcond=None) + return np.concatenate([W1.ravel(), b1, W2, b2]) def _default_bias(self): @@ -586,16 +590,21 @@ def init(self, jdata, warm_start=None): W1 = rng.randn(k_attr, h).astype(np.float64) * std b1 = np.zeros(h, dtype=np.float64) - # Small random W2 so L-BFGS gets a non-degenerate initial Hessian - W2 = rng.randn(h, K_OBS).astype(np.float64) * 0.01 + W2 = np.zeros((h, K_OBS), dtype=np.float64) if warm_start is not None: - # Use mean of per-pool noise as b2 noise_all = np.zeros((n_pools, K_OBS), dtype=np.float64) for i, pid in enumerate(jdata.pool_ids): if pid in warm_start and "noise_coeffs" in warm_start[pid]: noise_all[i] = warm_start[pid]["noise_coeffs"] b2 = np.mean(noise_all, axis=0) + + # Warm-start W2 by least-squares through hidden activations + # so the MLP init approximates the per-pool warm-start noise + x_attr = np.array(jdata.x_attr) + H = np.maximum(x_attr @ W1 + b1, 0.0) # (n_pools, h) + residuals = noise_all - b2 # (n_pools, K_OBS) + W2, _, _, _ = np.linalg.lstsq(H, residuals, rcond=None) else: # Pooled OLS noise as b2 all_x = np.vstack([np.array(pd["x_obs"]) for pd in jdata.pool_data]) diff --git a/scripts/run_mlp_calibration.py b/scripts/run_mlp_calibration.py index 39189c4..74d455a 100644 --- a/scripts/run_mlp_calibration.py +++ b/scripts/run_mlp_calibration.py @@ -35,9 +35,12 @@ ) OPTION_C_LOSS_CUTOFF = 5.0 OPTION_C_MAXITER = 500 -JOINT_MAXITER = 500 +JOINT_MAXITER = 5000 MLP_HIDDEN = 16 TOP_N = 50 +# Best alpha settings from sweep (phase 2) +ALPHA_CAD = 0.001 +ALPHA_NOISE = 0.1 # ---- Data loading ---- @@ -123,9 +126,9 @@ def run_linear_joint(matched_clean, option_c_clean): gas_values = _build_gas_values(jdata, matched_clean) model = CalibrationModel( - cadence_head=LinearHead("cad", alpha=0.01), + cadence_head=LinearHead("cad", alpha=ALPHA_CAD), gas_head=FixedHead("gas", gas_values), - noise_head=SharedLinearNoiseHead(alpha=0.01), + noise_head=SharedLinearNoiseHead(alpha=ALPHA_NOISE), ) n_pools = len(jdata.pool_data) @@ -150,9 +153,9 @@ def run_mlp_noise_joint(matched_clean, option_c_clean, hidden=MLP_HIDDEN): gas_values = _build_gas_values(jdata, matched_clean) model = CalibrationModel( - cadence_head=LinearHead("cad", alpha=0.01), + cadence_head=LinearHead("cad", alpha=ALPHA_CAD), gas_head=FixedHead("gas", gas_values), - noise_head=MLPNoiseHead(hidden=hidden, alpha=0.01), + noise_head=MLPNoiseHead(hidden=hidden, alpha=ALPHA_NOISE), ) n_pools = len(jdata.pool_data) @@ -177,9 +180,9 @@ def run_mlp_full_joint(matched_clean, option_c_clean, hidden=MLP_HIDDEN): gas_values = _build_gas_values(jdata, matched_clean) model = CalibrationModel( - cadence_head=MLPHead("cad", hidden=hidden, alpha=0.01), + cadence_head=MLPHead("cad", hidden=hidden, alpha=ALPHA_CAD), gas_head=FixedHead("gas", gas_values), - noise_head=MLPNoiseHead(hidden=hidden, alpha=0.01), + noise_head=MLPNoiseHead(hidden=hidden, alpha=ALPHA_NOISE), ) n_pools = len(jdata.pool_data) diff --git a/tests/calibration/test_heads.py b/tests/calibration/test_heads.py index 11c2aa0..b0f5300 100644 --- a/tests/calibration/test_heads.py +++ b/tests/calibration/test_heads.py @@ -491,17 +491,13 @@ def test_init_default_gas(self): # b2 should be log(1) = 0 np.testing.assert_allclose(init[-1], 0.0) - def test_init_W2_is_small(self): - """W2 should be small random at init (not zero) for L-BFGS conditioning.""" + def test_init_size(self): + """Init should return correct number of parameters.""" h = MLPHead("cad", hidden=4) jdata = _make_fake_jdata() init = h.init(jdata) - # W2 is at [k_attr*hidden + hidden : k_attr*hidden + 2*hidden] - w2_start = K_ATTR * 4 + 4 - w2_end = w2_start + 4 - w2 = init[w2_start:w2_end] - assert np.all(np.abs(w2) < 0.1), "W2 should be small" - assert np.any(w2 != 0.0), "W2 should not be exactly zero" + assert init.shape == (h.n_params(N_POOLS, K_ATTR),) + assert np.all(np.isfinite(init)) def test_init_warm_start(self): h = MLPHead("log_cadence", hidden=4) @@ -659,17 +655,13 @@ def test_init_default(self): assert init.shape == (n_p,) assert np.all(np.isfinite(init)) - def test_init_W2_is_small(self): - """W2 should be small random at init (not zero) for L-BFGS conditioning.""" + def test_init_size(self): + """Init should return correct number of parameters.""" h = MLPNoiseHead(hidden=4) jdata = _make_fake_jdata() init = h.init(jdata) - # W2 starts at k_attr*hidden + hidden - w2_start = K_ATTR * 4 + 4 - w2_end = w2_start + 4 * K_OBS - w2 = init[w2_start:w2_end] - assert np.all(np.abs(w2) < 0.1), "W2 should be small" - assert np.any(w2 != 0.0), "W2 should not be exactly zero" + assert init.shape == (h.n_params(N_POOLS, K_ATTR),) + assert np.all(np.isfinite(init)) def test_init_b2_from_ols(self): """b2 should be pooled OLS noise coefficients.""" From 49671f8d0d3a77468dda508e641d472d6a9a3208 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Fri, 13 Mar 2026 09:45:38 +0000 Subject: [PATCH 13/57] WIP: output clipping on heads, two-stage joint calibration - Add output_lo/output_hi to LinearHead and MLPHead for cadence bounds - Add run_two_stage_joint() and _extract_two_stage_per_pool() to MLP calibration script --- quantammsim/calibration/heads.py | 19 ++++- scripts/run_mlp_calibration.py | 140 ++++++++++++++++++++++++++++++- 2 files changed, 155 insertions(+), 4 deletions(-) diff --git a/quantammsim/calibration/heads.py b/quantammsim/calibration/heads.py index f0e661b..6f94e60 100644 --- a/quantammsim/calibration/heads.py +++ b/quantammsim/calibration/heads.py @@ -186,9 +186,12 @@ class LinearHead: L2 regularization on W (not bias) with strength ``alpha``. """ - def __init__(self, name: str, alpha: float = 0.01): + def __init__(self, name: str, alpha: float = 0.01, + output_lo: float = None, output_hi: float = None): self.name = name self.alpha = alpha + self.output_lo = output_lo + self.output_hi = output_hi def n_params(self, n_pools: int, k_attr: int) -> int: return 1 + k_attr # bias + W @@ -196,7 +199,10 @@ def n_params(self, n_pools: int, k_attr: int) -> int: def predict(self, params_slice, pool_idx, x_attr_i): bias = params_slice[0] W = params_slice[1:] - return bias + jnp.dot(x_attr_i, W) + out = bias + jnp.dot(x_attr_i, W) + if self.output_lo is not None or self.output_hi is not None: + out = jnp.clip(out, self.output_lo, self.output_hi) + return out def regularization(self, params_slice): W = params_slice[1:] @@ -404,11 +410,15 @@ def __init__( hidden: int = 16, alpha: float = 0.01, seed: int = 0, + output_lo: float = None, + output_hi: float = None, ): self.name = name self.hidden = hidden self.alpha = alpha self._seed = seed + self.output_lo = output_lo + self.output_hi = output_hi def n_params(self, n_pools: int, k_attr: int) -> int: h = self.hidden @@ -431,7 +441,10 @@ def predict(self, params_slice, pool_idx, x_attr_i): k_attr = x_attr_i.shape[0] W1, b1, W2, b2 = self._unpack_weights(params_slice, k_attr) hidden = jnp.maximum(x_attr_i @ W1 + b1, 0.0) # ReLU - return hidden @ W2 + b2 + out = hidden @ W2 + b2 + if self.output_lo is not None or self.output_hi is not None: + out = jnp.clip(out, self.output_lo, self.output_hi) + return out def regularization(self, params_slice): # Regularize W1 and W2, not biases diff --git a/scripts/run_mlp_calibration.py b/scripts/run_mlp_calibration.py index 74d455a..85db2a0 100644 --- a/scripts/run_mlp_calibration.py +++ b/scripts/run_mlp_calibration.py @@ -196,6 +196,134 @@ def run_mlp_full_joint(matched_clean, option_c_clean, hidden=MLP_HIDDEN): return result, model, jdata +def run_two_stage_joint(matched_clean, option_c_clean, hidden=MLP_HIDDEN): + """Two-stage joint fit to identify cadence separately from noise. + + Stage 1: LinearHead(cad) + FixedHead(gas) + PerPoolNoiseHead + Per-pool noise (8 coeffs/pool) can't fully absorb arb's daily + volatility pattern, so cadence is identified. + + Stage 2: FixedHead(cad, stage1_values) + FixedHead(gas) + MLPNoiseHead + Cadence frozen from stage 1, MLP learns shared noise mapping. + + For new-pool prediction: stage 1 linear coefficients give cadence, + stage 2 MLP gives noise. + """ + import jax.numpy as jnp + from quantammsim.calibration.calibration_model import CalibrationModel + from quantammsim.calibration.heads import ( + FixedHead, LinearHead, MLPNoiseHead, PerPoolNoiseHead, + ) + from quantammsim.calibration.joint_fit import prepare_joint_data + + jdata = prepare_joint_data( + matched_clean, drop_chain_dummies=True, fix_gas_to_chain=True) + gas_values = _build_gas_values(jdata, matched_clean) + n_pools = len(jdata.pool_data) + k_attr = jdata.x_attr.shape[1] + + # ---- Stage 1: fit cadence with per-pool noise ---- + stage1_model = CalibrationModel( + cadence_head=LinearHead("cad", alpha=ALPHA_CAD), + gas_head=FixedHead("gas", gas_values), + noise_head=PerPoolNoiseHead(), + ) + n_p1 = stage1_model.n_params(n_pools, k_attr) + print(f"\n--- Two-stage S1: LinearHead(cad) + PerPoolNoiseHead " + f"({n_pools} pools, {n_p1} params) ---") + + stage1_result = stage1_model.fit( + jdata, maxiter=JOINT_MAXITER, warm_start=option_c_clean) + print(f" Loss: {stage1_result['init_loss']:.4f} -> {stage1_result['loss']:.4f}") + print(f" Converged: {stage1_result['converged']}") + + # Extract per-pool cadences from stage 1 + params1 = jnp.array(stage1_result["params_flat"]) + (cs, ce), _, _ = stage1_model._head_slices(n_pools, k_attr) + cad_slice = params1[cs:ce] + stage1_cadences = np.array([ + float(stage1_model.cadence_head.predict(cad_slice, i, jdata.x_attr[i])) + for i in range(n_pools) + ]) + print(f" Cadence range: {np.exp(stage1_cadences.min()):.1f} - " + f"{np.exp(stage1_cadences.max()):.1f} min") + + # ---- Stage 2: fit MLP noise with frozen cadence ---- + stage2_model = CalibrationModel( + cadence_head=FixedHead("cad", stage1_cadences), + gas_head=FixedHead("gas", gas_values), + noise_head=MLPNoiseHead(hidden=hidden, alpha=ALPHA_NOISE), + ) + n_p2 = stage2_model.n_params(n_pools, k_attr) + print(f"\n--- Two-stage S2: FixedHead(cad) + MLPNoiseHead(hidden={hidden}) " + f"({n_pools} pools, {n_p2} params) ---") + + # Build warm-start for stage 2 noise from stage 1 per-pool noise + (_, _), (_, _), (ns, ne) = stage1_model._head_slices(n_pools, k_attr) + noise_params1 = np.array(params1[ns:ne]) + stage2_warm = {} + for i, pid in enumerate(jdata.pool_ids): + noise_c = np.array(stage1_model.noise_head.predict( + jnp.array(noise_params1), i, jdata.x_attr[i])) + stage2_warm[pid] = {"noise_coeffs": noise_c} + + stage2_result = stage2_model.fit( + jdata, maxiter=JOINT_MAXITER, warm_start=stage2_warm) + print(f" Loss: {stage2_result['init_loss']:.4f} -> {stage2_result['loss']:.4f}") + print(f" Converged: {stage2_result['converged']}") + + # Build a composite result dict for downstream use + # Cadence comes from stage 1 linear head, noise from stage 2 MLP + result = { + "stage1_result": stage1_result, + "stage2_result": stage2_result, + "loss": stage2_result["loss"], + "init_loss": stage1_result["init_loss"], + "converged": stage1_result["converged"] and stage2_result["converged"], + "n_pools": n_pools, + "k_attr": k_attr, + "pool_ids": jdata.pool_ids, + "attr_names": jdata.attr_names, + } + + return result, stage1_model, stage2_model, jdata + + +def _extract_two_stage_per_pool(stage1_model, stage2_model, result, jdata): + """Extract per-pool params from two-stage result.""" + import jax.numpy as jnp + + stage1_result = result["stage1_result"] + stage2_result = result["stage2_result"] + n_pools = result["n_pools"] + k_attr = result["k_attr"] + + params1 = jnp.array(stage1_result["params_flat"]) + params2 = jnp.array(stage2_result["params_flat"]) + + (cs1, ce1), _, (ns1, ne1) = stage1_model._head_slices(n_pools, k_attr) + _, _, (ns2, ne2) = stage2_model._head_slices(n_pools, k_attr) + + cad_slice = params1[cs1:ce1] + noise_slice = params2[ns2:ne2] + + per_pool = [] + for i in range(n_pools): + x_attr_i = jdata.x_attr[i] + log_cad = float(stage1_model.cadence_head.predict(cad_slice, i, x_attr_i)) + log_gas = float(stage2_model.gas_head.predict( + jnp.array([]), i, x_attr_i)) # FixedHead ignores params + noise_c = np.array(stage2_model.noise_head.predict(noise_slice, i, x_attr_i)) + per_pool.append({ + "log_cadence": log_cad, + "log_gas": log_gas, + "noise_coeffs": noise_c, + "cadence_minutes": float(np.exp(log_cad)), + "gas_usd": float(np.exp(log_gas)), + }) + return per_pool + + # ---- Per-pool predictions ---- @@ -702,16 +830,23 @@ def main(): mlp_full_result, mlp_full_model, _ = run_mlp_full_joint( matched_clean, option_c_clean) + # Two-stage: cadence identified with per-pool noise, then MLP noise + two_stage_result, ts_s1_model, ts_s2_model, _ = run_two_stage_joint( + matched_clean, option_c_clean) + # Step 4: Extract per-pool params from each model linear_pp = _extract_per_pool_params(linear_model, linear_result, jdata) mlp_noise_pp = _extract_per_pool_params(mlp_noise_model, mlp_noise_result, jdata) mlp_full_pp = _extract_per_pool_params(mlp_full_model, mlp_full_result, jdata) + two_stage_pp = _extract_two_stage_per_pool( + ts_s1_model, ts_s2_model, two_stage_result, jdata) - method_labels = ["linear", "mlp_noise", "mlp_full"] + method_labels = ["linear", "mlp_noise", "mlp_full", "two_stage"] model_results_for_pred = [ ("linear", linear_pp, jdata.pool_ids), ("mlp_noise", mlp_noise_pp, jdata.pool_ids), ("mlp_full", mlp_full_pp, jdata.pool_ids), + ("two_stage", two_stage_pp, jdata.pool_ids), ] # Step 5: Per-pool predictions @@ -726,6 +861,7 @@ def main(): ("linear", linear_result), ("mlp_noise", mlp_noise_result), ("mlp_full", mlp_full_result), + ("two_stage", two_stage_result), ]) # Step 7: Plots @@ -736,6 +872,7 @@ def main(): plot_decomposition_pages(predictions, "linear", "Linear shared noise", OUTPUT_DIR) plot_decomposition_pages(predictions, "mlp_noise", "MLP noise (linear cad)", OUTPUT_DIR) plot_decomposition_pages(predictions, "mlp_full", "Full MLP (MLP cad + MLP noise)", OUTPUT_DIR) + plot_decomposition_pages(predictions, "two_stage", "Two-stage (linear cad -> MLP noise)", OUTPUT_DIR) plot_summary_distributions(predictions, method_labels, OUTPUT_DIR) plot_r2_scatter(predictions, method_labels, OUTPUT_DIR) @@ -746,6 +883,7 @@ def main(): ("linear", linear_result), ("mlp_noise", mlp_noise_result), ("mlp_full", mlp_full_result), + ("two_stage", two_stage_result), ], OUTPUT_DIR) print(f"\n{'='*70}") From 71f1a3a41468900810c02cfa6a73cb8c5b3708e7 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 16 Mar 2026 01:02:31 +0000 Subject: [PATCH 14/57] feat: add reduced x_obs (k_obs=4) to calibration pipeline Remove sigma- and fee-dependent features from observation covariates so the arb channel is the only path for volatility-driven volume variation (see docs/noise_covariate_design.md). build_x_obs gains reduced=True, per_pool_fit derives k_obs from data shape, and prepare_joint_data forwards reduced_x_obs. --- quantammsim/calibration/joint_fit.py | 5 ++- quantammsim/calibration/per_pool_fit.py | 18 +++++--- quantammsim/calibration/pool_data.py | 25 ++++++++--- tests/calibration/test_joint_fit.py | 19 +++++++++ tests/calibration/test_pool_data.py | 56 +++++++++++++++++++++++++ 5 files changed, 112 insertions(+), 11 deletions(-) diff --git a/quantammsim/calibration/joint_fit.py b/quantammsim/calibration/joint_fit.py index 6dcaf85..765312d 100644 --- a/quantammsim/calibration/joint_fit.py +++ b/quantammsim/calibration/joint_fit.py @@ -39,6 +39,7 @@ def prepare_joint_data( matched: Dict[str, dict], drop_chain_dummies: bool = False, fix_gas_to_chain: bool = False, + reduced_x_obs: bool = False, ) -> JointData: """Build batched JAX arrays from matched pool data. @@ -46,6 +47,8 @@ def prepare_joint_data( matched: dict from match_grids_to_panel drop_chain_dummies: if True, remove chain_* columns from attributes fix_gas_to_chain: if True, store fixed_log_gas per pool from CHAIN_GAS_USD + reduced_x_obs: if True, use 4-column reduced x_obs + (removes sigma/fee terms to avoid identification problems) Returns: JointData with per-pool JAX arrays and shared attribute matrix. @@ -64,7 +67,7 @@ def prepare_joint_data( for pid in pool_ids: entry = matched[pid] panel = entry["panel"] - x_obs = build_x_obs(panel) + x_obs = build_x_obs(panel, reduced=reduced_x_obs) y_obs = panel["log_volume"].values.astype(float) d = { diff --git a/quantammsim/calibration/per_pool_fit.py b/quantammsim/calibration/per_pool_fit.py index 08140d9..be724a9 100644 --- a/quantammsim/calibration/per_pool_fit.py +++ b/quantammsim/calibration/per_pool_fit.py @@ -31,8 +31,9 @@ def make_initial_guess(x_obs: np.ndarray, y_obs: np.ndarray) -> np.ndarray: OLS: noise_coeffs = lstsq(x_obs, y_obs) — assumes all volume is noise. This overestimates noise but gives a reasonable starting point. """ + k_obs = x_obs.shape[1] noise_coeffs, _, _, _ = np.linalg.lstsq(x_obs, y_obs, rcond=None) - init = np.zeros(2 + K_OBS) + init = np.zeros(2 + k_obs) init[0] = np.log(12.0) # log_cadence init[1] = np.log(1.0) # log_gas (= 0.0) init[2:] = noise_coeffs @@ -41,8 +42,9 @@ def make_initial_guess(x_obs: np.ndarray, y_obs: np.ndarray) -> np.ndarray: def make_initial_guess_fixed_gas(x_obs: np.ndarray, y_obs: np.ndarray) -> np.ndarray: """Initial params for fixed-gas mode: cadence=12min, noise_coeffs from OLS.""" + k_obs = x_obs.shape[1] noise_coeffs, _, _, _ = np.linalg.lstsq(x_obs, y_obs, rcond=None) - init = np.zeros(1 + K_OBS) + init = np.zeros(1 + k_obs) init[0] = np.log(12.0) # log_cadence init[1:] = noise_coeffs return init @@ -81,7 +83,8 @@ def fit_single_pool( if init is None: init = make_initial_guess_fixed_gas(x_obs, y_obs) - scipy_bounds = [log_cad_bounds] + [(noise_bounds[0], noise_bounds[1])] * K_OBS + k_obs = x_obs.shape[1] + scipy_bounds = [log_cad_bounds] + [(noise_bounds[0], noise_bounds[1])] * k_obs @jax.jit def loss_and_grad(params_flat): @@ -122,10 +125,11 @@ def scipy_wrapper(params_np): if init is None: init = make_initial_guess(x_obs, y_obs) + k_obs = x_obs.shape[1] log_gas_bounds = bounds.get("log_gas", (np.log(0.001), np.log(50.0))) scipy_bounds = [ log_cad_bounds, log_gas_bounds, - ] + [(noise_bounds[0], noise_bounds[1])] * K_OBS + ] + [(noise_bounds[0], noise_bounds[1])] * k_obs @jax.jit def loss_and_grad(params_flat): @@ -165,11 +169,15 @@ def fit_all_pools( matched: Dict[str, dict], n_workers: int = 1, fix_gas_to_chain: bool = False, + reduced: bool = False, ) -> Dict[str, dict]: """Fit all matched pools. Returns prefix -> fit_result with metadata. If fix_gas_to_chain is True, gas is fixed to the known chain-level cost from CHAIN_GAS_USD, and only (log_cadence, noise_coeffs) are optimized. + + If reduced is True, uses the 4-covariate x_obs (intercept, log_tvl_lag1, + dow_sin, dow_cos) instead of the full 8-covariate set. """ results = {} @@ -178,7 +186,7 @@ def fit_all_pools( coeffs = entry["coeffs"] day_indices = entry["day_indices"] - x_obs = build_x_obs(panel) + x_obs = build_x_obs(panel, reduced=reduced) y_obs = panel["log_volume"].values.astype(float) fixed_gas = None diff --git a/quantammsim/calibration/pool_data.py b/quantammsim/calibration/pool_data.py index 61082cd..1b8110d 100644 --- a/quantammsim/calibration/pool_data.py +++ b/quantammsim/calibration/pool_data.py @@ -18,6 +18,7 @@ ) K_OBS = 8 # observation-level covariates +K_OBS_REDUCED = 4 # [intercept, log_tvl_lag1, dow_sin, dow_cos] # Default path for cached token market caps _MCAP_PATH = os.path.join( @@ -347,11 +348,16 @@ def match_grids_to_panel( return matched -def build_x_obs(panel_rows: pd.DataFrame) -> np.ndarray: - """Build (n_obs, 8) observation covariate matrix from panel rows. +def build_x_obs(panel_rows: pd.DataFrame, reduced: bool = False) -> np.ndarray: + """Build observation covariate matrix from panel rows. - Columns: [1, log_tvl_lag1, log_sigma, tvl*sigma, tvl*fee, - sigma*fee, dow_sin, dow_cos] + Full (reduced=False): (n_obs, 8) + [1, log_tvl_lag1, log_sigma, tvl*sigma, tvl*fee, sigma*fee, dow_sin, dow_cos] + + Reduced (reduced=True): (n_obs, 4) + [1, log_tvl_lag1, dow_sin, dow_cos] + Removes sigma- and fee-dependent terms so the arb channel is the only + path for volatility-driven volume variation. Where: log_sigma = log(max(volatility, 1e-6)) @@ -361,12 +367,21 @@ def build_x_obs(panel_rows: pd.DataFrame) -> np.ndarray: weekday: Monday=0, ..., Sunday=6 """ n = len(panel_rows) + weekdays = pd.to_datetime(panel_rows["date"]).dt.weekday.values.astype(float) + + if reduced: + x = np.zeros((n, K_OBS_REDUCED)) + x[:, 0] = 1.0 + x[:, 1] = panel_rows["log_tvl_lag1"].values.astype(float) + x[:, 2] = np.sin(2 * np.pi * weekdays / 7) + x[:, 3] = np.cos(2 * np.pi * weekdays / 7) + return x + x = np.zeros((n, K_OBS)) tvl = panel_rows["log_tvl_lag1"].values.astype(float) sigma = np.log(np.maximum(panel_rows["volatility"].values.astype(float), 1e-6)) fee = panel_rows["log_fee"].values.astype(float) - weekdays = pd.to_datetime(panel_rows["date"]).dt.weekday.values.astype(float) x[:, 0] = 1.0 # intercept x[:, 1] = tvl # log_tvl_lag1 diff --git a/tests/calibration/test_joint_fit.py b/tests/calibration/test_joint_fit.py index 50d7730..edb99f4 100644 --- a/tests/calibration/test_joint_fit.py +++ b/tests/calibration/test_joint_fit.py @@ -188,3 +188,22 @@ def test_shared_noise_predict(self, matched_data): pred = predict_new_pool_joint(result, x_attr_new) assert "noise_coeffs" in pred assert len(pred["noise_coeffs"]) == K_OBS + + +class TestPrepareJointDataReduced: + """Test prepare_joint_data with reduced_x_obs=True.""" + + def test_reduced_x_obs_shape(self, matched_data): + from quantammsim.calibration.joint_fit import prepare_joint_data + from quantammsim.calibration.pool_data import K_OBS_REDUCED + + jdata = prepare_joint_data(matched_data, reduced_x_obs=True) + for pd in jdata.pool_data: + assert pd["x_obs"].shape[1] == K_OBS_REDUCED + + def test_default_unchanged(self, matched_data): + from quantammsim.calibration.joint_fit import prepare_joint_data + + jdata = prepare_joint_data(matched_data) + for pd in jdata.pool_data: + assert pd["x_obs"].shape[1] == K_OBS diff --git a/tests/calibration/test_pool_data.py b/tests/calibration/test_pool_data.py index b3a9cf7..5544186 100644 --- a/tests/calibration/test_pool_data.py +++ b/tests/calibration/test_pool_data.py @@ -200,6 +200,62 @@ def test_x_obs_no_nans(self, synthetic_panel): assert not np.any(np.isnan(x)) +class TestBuildXObsReduced: + """Test build_x_obs with reduced=True: 4-column pruned covariates.""" + + def test_reduced_shape(self, synthetic_panel): + from quantammsim.calibration.pool_data import K_OBS_REDUCED, build_x_obs + + pool0 = synthetic_panel[synthetic_panel["pool_id"] == POOL_IDS_FULL[0]] + x = build_x_obs(pool0, reduced=True) + assert x.shape == (len(pool0), K_OBS_REDUCED) + assert K_OBS_REDUCED == 4 + + def test_reduced_columns(self, synthetic_panel): + from quantammsim.calibration.pool_data import build_x_obs + + pool0 = synthetic_panel[synthetic_panel["pool_id"] == POOL_IDS_FULL[0]] + x_full = build_x_obs(pool0) + x_red = build_x_obs(pool0, reduced=True) + + # col 0: intercept + np.testing.assert_array_equal(x_red[:, 0], 1.0) + # col 1: log_tvl_lag1 (same as full col 1) + np.testing.assert_allclose(x_red[:, 1], x_full[:, 1]) + # col 2: dow_sin (same as full col 6) + np.testing.assert_allclose(x_red[:, 2], x_full[:, 6]) + # col 3: dow_cos (same as full col 7) + np.testing.assert_allclose(x_red[:, 3], x_full[:, 7]) + + def test_reduced_no_sigma(self, synthetic_panel): + from quantammsim.calibration.pool_data import build_x_obs + + pool0 = synthetic_panel[synthetic_panel["pool_id"] == POOL_IDS_FULL[0]] + x_full = build_x_obs(pool0) + x_red = build_x_obs(pool0, reduced=True) + + # Sigma-dependent columns from full (2,3,5) should not appear + sigma_cols = x_full[:, [2, 3, 5]] + for col in range(x_red.shape[1]): + for scol in range(sigma_cols.shape[1]): + if not np.allclose(sigma_cols[:, scol], 0.0): + assert not np.allclose(x_red[:, col], sigma_cols[:, scol]) + + def test_default_unchanged(self, synthetic_panel): + from quantammsim.calibration.pool_data import build_x_obs + + pool0 = synthetic_panel[synthetic_panel["pool_id"] == POOL_IDS_FULL[0]] + x = build_x_obs(pool0) + assert x.shape == (len(pool0), K_OBS) + + def test_reduced_no_nans(self, synthetic_panel): + from quantammsim.calibration.pool_data import build_x_obs + + pool0 = synthetic_panel[synthetic_panel["pool_id"] == POOL_IDS_FULL[0]] + x = build_x_obs(pool0, reduced=True) + assert not np.any(np.isnan(x)) + + class TestBuildPoolAttributes: """Test build_pool_attributes: pool-level feature matrix.""" From ba30663ce6eae64ac108d435fd863b40778ecef4 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 16 Mar 2026 01:02:45 +0000 Subject: [PATCH 15/57] feat: parameterize k_obs in noise heads PerPoolNoiseHead, SharedLinearNoiseHead, and MLPNoiseHead accept k_obs=4 to match the reduced x_obs. Defaults to K_OBS=8 so existing usage is unchanged. --- quantammsim/calibration/heads.py | 80 +++++------ tests/calibration/test_calibration_model.py | 71 +++++++++ tests/calibration/test_heads.py | 150 ++++++++++++++++++++ 3 files changed, 261 insertions(+), 40 deletions(-) diff --git a/quantammsim/calibration/heads.py b/quantammsim/calibration/heads.py index 6f94e60..6d6a376 100644 --- a/quantammsim/calibration/heads.py +++ b/quantammsim/calibration/heads.py @@ -257,21 +257,22 @@ def make_bounds(self, n_pools, k_attr): class PerPoolNoiseHead: - """Per-pool noise coefficients: each pool has K_OBS free parameters. + """Per-pool noise coefficients: each pool has k_obs free parameters. Used for Option C noise or Option A with per-pool noise. """ - def __init__(self, alpha: float = 0.0): + def __init__(self, alpha: float = 0.0, k_obs: int = None): self.name = "noise" self.alpha = alpha + self.k_obs = k_obs if k_obs is not None else K_OBS def n_params(self, n_pools: int, k_attr: int) -> int: - return n_pools * K_OBS + return n_pools * self.k_obs def predict(self, params_slice, pool_idx, x_attr_i): - start = pool_idx * K_OBS - return params_slice[start:start + K_OBS] + start = pool_idx * self.k_obs + return params_slice[start:start + self.k_obs] def regularization(self, params_slice): if self.alpha == 0.0: @@ -282,13 +283,13 @@ def init(self, jdata, warm_start=None): n_pools = len(jdata.pool_data) if warm_start is not None: - noise_all = np.zeros((n_pools, K_OBS), dtype=np.float64) + noise_all = np.zeros((n_pools, self.k_obs), dtype=np.float64) for i, pid in enumerate(jdata.pool_ids): if pid in warm_start and "noise_coeffs" in warm_start[pid]: noise_all[i] = warm_start[pid]["noise_coeffs"] return noise_all.ravel() - noise_all = np.zeros((n_pools, K_OBS), dtype=np.float64) + noise_all = np.zeros((n_pools, self.k_obs), dtype=np.float64) for i, pd in enumerate(jdata.pool_data): x_obs_np = np.array(pd["x_obs"]) y_obs_np = np.array(pd["y_obs"]) @@ -303,11 +304,11 @@ def predict_new(self, params_slice, x_attr): def unpack_result(self, params_slice, n_pools, k_attr): return { - "noise_coeffs": np.array(params_slice).reshape(n_pools, K_OBS), + "noise_coeffs": np.array(params_slice).reshape(n_pools, self.k_obs), } def make_bounds(self, n_pools, k_attr): - return [(None, None)] * (n_pools * K_OBS) + return [(None, None)] * (n_pools * self.k_obs) # --------------------------------------------------------------------------- @@ -318,28 +319,27 @@ def make_bounds(self, n_pools, k_attr): class SharedLinearNoiseHead: """Shared linear mapping for noise: bias_noise + x_attr @ W_noise. - Output is (K_OBS,) noise coefficients, predicted from pool attributes. + Output is (k_obs,) noise coefficients, predicted from pool attributes. L2 regularization on W_noise (not bias_noise). """ - def __init__(self, alpha: float = 0.01): + def __init__(self, alpha: float = 0.01, k_obs: int = None): self.name = "noise" self.alpha = alpha + self.k_obs = k_obs if k_obs is not None else K_OBS def n_params(self, n_pools: int, k_attr: int) -> int: - return (1 + k_attr) * K_OBS + return (1 + k_attr) * self.k_obs def predict(self, params_slice, pool_idx, x_attr_i): - # params_slice is ((1+k_attr) * K_OBS,) k_attr = x_attr_i.shape[0] - W_full = params_slice.reshape(1 + k_attr, K_OBS) + W_full = params_slice.reshape(1 + k_attr, self.k_obs) bias_noise = W_full[0] W_noise = W_full[1:] return bias_noise + jnp.dot(x_attr_i, W_noise) def regularization(self, params_slice): - # Regularize W_noise only, not bias_noise - W_full = params_slice.reshape(-1, K_OBS) + W_full = params_slice.reshape(-1, self.k_obs) W_noise = W_full[1:] return self.alpha * jnp.sum(W_noise ** 2) @@ -348,8 +348,7 @@ def init(self, jdata, warm_start=None): n_pools = len(jdata.pool_data) if warm_start is not None: - # Collect per-pool noise, regress on attributes - noise_all = np.zeros((n_pools, K_OBS), dtype=np.float64) + noise_all = np.zeros((n_pools, self.k_obs), dtype=np.float64) for i, pid in enumerate(jdata.pool_ids): if pid in warm_start and "noise_coeffs" in warm_start[pid]: noise_all[i] = warm_start[pid]["noise_coeffs"] @@ -357,30 +356,29 @@ def init(self, jdata, warm_start=None): params, _, _, _ = np.linalg.lstsq(X_aug, noise_all, rcond=None) return params.ravel().astype(np.float64) - # Pool OLS noise as shared bias, W_noise = 0 all_x = np.vstack([np.array(pd["x_obs"]) for pd in jdata.pool_data]) all_y = np.concatenate([np.array(pd["y_obs"]) for pd in jdata.pool_data]) c, _, _, _ = np.linalg.lstsq(all_x, all_y, rcond=None) - params = np.zeros((1 + k_attr, K_OBS), dtype=np.float64) + params = np.zeros((1 + k_attr, self.k_obs), dtype=np.float64) params[0, :] = c return params.ravel() def predict_new(self, params_slice, x_attr): k_attr = len(x_attr) - W_full = np.array(params_slice).reshape(1 + k_attr, K_OBS) + W_full = np.array(params_slice).reshape(1 + k_attr, self.k_obs) bias_noise = W_full[0] W_noise = W_full[1:] return bias_noise + x_attr @ W_noise def unpack_result(self, params_slice, n_pools, k_attr): - W_full = np.array(params_slice).reshape(1 + k_attr, K_OBS) + W_full = np.array(params_slice).reshape(1 + k_attr, self.k_obs) return { "bias_noise": W_full[0], "W_noise": W_full[1:], } def make_bounds(self, n_pools, k_attr): - return [(None, None)] * ((1 + k_attr) * K_OBS) + return [(None, None)] * ((1 + k_attr) * self.k_obs) # --------------------------------------------------------------------------- @@ -534,10 +532,10 @@ def make_bounds(self, n_pools, k_attr): class MLPNoiseHead: """Two-layer MLP mapping from pool attributes to noise coefficients. - Architecture: x_attr → Dense(hidden, ReLU) → Dense(K_OBS) + Architecture: x_attr → Dense(hidden, ReLU) → Dense(k_obs) Parameter layout (flat): - [W1(k_attr * hidden), b1(hidden), W2(hidden * K_OBS), b2(K_OBS)] + [W1(k_attr * hidden), b1(hidden), W2(hidden * k_obs), b2(k_obs)] L2 regularization on W1 and W2 (not biases). @@ -552,50 +550,53 @@ def __init__( hidden: int = 16, alpha: float = 0.01, seed: int = 0, + k_obs: int = None, ): self.name = "noise" self.hidden = hidden self.alpha = alpha self._seed = seed + self.k_obs = k_obs if k_obs is not None else K_OBS def n_params(self, n_pools: int, k_attr: int) -> int: h = self.hidden - # W1(k_attr*h) + b1(h) + W2(h*K_OBS) + b2(K_OBS) - return k_attr * h + h + h * K_OBS + K_OBS + return k_attr * h + h + h * self.k_obs + self.k_obs def _unpack_weights(self, params_slice, k_attr): """Unpack flat slice → (W1, b1, W2, b2).""" h = self.hidden + ko = self.k_obs idx = 0 W1 = params_slice[idx:idx + k_attr * h].reshape(k_attr, h) idx += k_attr * h b1 = params_slice[idx:idx + h] idx += h - W2 = params_slice[idx:idx + h * K_OBS].reshape(h, K_OBS) - idx += h * K_OBS - b2 = params_slice[idx:idx + K_OBS] + W2 = params_slice[idx:idx + h * ko].reshape(h, ko) + idx += h * ko + b2 = params_slice[idx:idx + ko] return W1, b1, W2, b2 def predict(self, params_slice, pool_idx, x_attr_i): k_attr = x_attr_i.shape[0] W1, b1, W2, b2 = self._unpack_weights(params_slice, k_attr) hidden = jnp.maximum(x_attr_i @ W1 + b1, 0.0) # ReLU - return hidden @ W2 + b2 # (K_OBS,) + return hidden @ W2 + b2 # (k_obs,) def regularization(self, params_slice): h = self.hidden + ko = self.k_obs total = params_slice.shape[0] - # Solve for k_attr: total = k*h + h + h*K_OBS + K_OBS - # k*h = total - h - h*K_OBS - K_OBS - k_attr = (total - h - h * K_OBS - K_OBS) // h + # Solve for k_attr: total = k*h + h + h*ko + ko + k_attr = (total - h - h * ko - ko) // h W1 = params_slice[:k_attr * h] - W2 = params_slice[k_attr * h + h:k_attr * h + h + h * K_OBS] + W2 = params_slice[k_attr * h + h:k_attr * h + h + h * ko] return self.alpha * (jnp.sum(W1 ** 2) + jnp.sum(W2 ** 2)) def init(self, jdata, warm_start=None): k_attr = jdata.x_attr.shape[1] n_pools = len(jdata.pool_data) h = self.hidden + ko = self.k_obs rng = np.random.RandomState(self._seed) # He initialization for W1 @@ -603,20 +604,19 @@ def init(self, jdata, warm_start=None): W1 = rng.randn(k_attr, h).astype(np.float64) * std b1 = np.zeros(h, dtype=np.float64) - W2 = np.zeros((h, K_OBS), dtype=np.float64) + W2 = np.zeros((h, ko), dtype=np.float64) if warm_start is not None: - noise_all = np.zeros((n_pools, K_OBS), dtype=np.float64) + noise_all = np.zeros((n_pools, ko), dtype=np.float64) for i, pid in enumerate(jdata.pool_ids): if pid in warm_start and "noise_coeffs" in warm_start[pid]: noise_all[i] = warm_start[pid]["noise_coeffs"] b2 = np.mean(noise_all, axis=0) # Warm-start W2 by least-squares through hidden activations - # so the MLP init approximates the per-pool warm-start noise x_attr = np.array(jdata.x_attr) H = np.maximum(x_attr @ W1 + b1, 0.0) # (n_pools, h) - residuals = noise_all - b2 # (n_pools, K_OBS) + residuals = noise_all - b2 # (n_pools, ko) W2, _, _, _ = np.linalg.lstsq(H, residuals, rcond=None) else: # Pooled OLS noise as b2 @@ -630,7 +630,7 @@ def predict_new(self, params_slice, x_attr): k_attr = len(x_attr) W1, b1, W2, b2 = self._unpack_weights(np.asarray(params_slice), k_attr) hidden = np.maximum(x_attr @ W1 + b1, 0.0) - return hidden @ W2 + b2 # (K_OBS,) + return hidden @ W2 + b2 # (k_obs,) def unpack_result(self, params_slice, n_pools, k_attr): params_np = np.array(params_slice) diff --git a/tests/calibration/test_calibration_model.py b/tests/calibration/test_calibration_model.py index 95973f4..95812f6 100644 --- a/tests/calibration/test_calibration_model.py +++ b/tests/calibration/test_calibration_model.py @@ -582,3 +582,74 @@ def test_mlp_noise_param_count(self, jdata_ppn): # MLP noise: k*h + h + h*K_OBS + K_OBS expected = (1 + k_attr) * 2 + k_attr * h + h + h * K_OBS + K_OBS assert model.n_params(n_pools, k_attr) == expected + + +# ── Reduced k_obs=4 integration tests ──────────────────────────────────── + +K_OBS_REDUCED = 4 + + +@pytest.fixture +def jdata_reduced(matched_data): + """JointData with reduced x_obs (4 columns).""" + from quantammsim.calibration.joint_fit import prepare_joint_data + return prepare_joint_data( + matched_data, drop_chain_dummies=True, + fix_gas_to_chain=True, reduced_x_obs=True, + ) + + +class TestReducedKObsIntegration: + """CalibrationModel with k_obs=4 noise heads on reduced x_obs data.""" + + def test_reduced_n_params(self, jdata_reduced): + n_pools = len(jdata_reduced.pool_data) + k_attr = jdata_reduced.x_attr.shape[1] + h = 8 + model = CalibrationModel( + LinearHead("cad", alpha=0.01), + FixedHead("gas", np.zeros(n_pools)), + MLPNoiseHead(hidden=h, alpha=0.01, k_obs=K_OBS_REDUCED), + ) + # Linear cad: 1+k, Fixed gas: 0, + # MLP noise: k*h + h + h*4 + 4 + expected = (1 + k_attr) + 0 + k_attr * h + h + h * 4 + 4 + assert model.n_params(n_pools, k_attr) == expected + + def test_reduced_loss_runs(self, jdata_reduced): + n_pools = len(jdata_reduced.pool_data) + gas_values = np.zeros(n_pools) + model = CalibrationModel( + LinearHead("cad", alpha=0.01), + FixedHead("gas", gas_values), + MLPNoiseHead(hidden=8, alpha=0.01, k_obs=K_OBS_REDUCED), + ) + loss_fn = model.make_joint_loss_fn(jdata_reduced) + init = jnp.array(model.pack_init(jdata_reduced)) + loss = float(loss_fn(init)) + assert np.isfinite(loss) and loss >= 0 + + def test_reduced_grad_finite(self, jdata_reduced): + n_pools = len(jdata_reduced.pool_data) + gas_values = np.zeros(n_pools) + model = CalibrationModel( + LinearHead("cad", alpha=0.01), + FixedHead("gas", gas_values), + MLPNoiseHead(hidden=8, alpha=0.01, k_obs=K_OBS_REDUCED), + ) + loss_fn = model.make_joint_loss_fn(jdata_reduced) + init = jnp.array(model.pack_init(jdata_reduced)) + grad = jax.grad(loss_fn)(init) + assert jnp.all(jnp.isfinite(grad)) + + def test_reduced_fit_converges(self, jdata_reduced): + n_pools = len(jdata_reduced.pool_data) + gas_values = np.zeros(n_pools) + model = CalibrationModel( + LinearHead("cad", alpha=0.01), + FixedHead("gas", gas_values), + MLPNoiseHead(hidden=8, alpha=0.01, k_obs=K_OBS_REDUCED), + ) + result = model.fit(jdata_reduced, maxiter=100) + assert result["loss"] <= result["init_loss"] + assert np.isfinite(result["loss"]) diff --git a/tests/calibration/test_heads.py b/tests/calibration/test_heads.py index b0f5300..2c891e8 100644 --- a/tests/calibration/test_heads.py +++ b/tests/calibration/test_heads.py @@ -744,3 +744,153 @@ def loss(p): grad = jax.grad(loss)(params) assert grad.shape == params.shape assert jnp.all(jnp.isfinite(grad)) + + +# ── Reduced k_obs=4 tests ───────────────────────────────────────────────── + +K_OBS_REDUCED = 4 + + +def _make_fake_jdata_reduced(): + """JointData-like object with k_obs=4 x_obs for reduced noise testing.""" + from quantammsim.calibration.joint_fit import JointData + + pool_data = [] + for _ in range(N_POOLS): + n_obs = 14 + x_obs = np.random.randn(n_obs, K_OBS_REDUCED) + x_obs[:, 0] = 1.0 # intercept column + y_obs = np.random.randn(n_obs) * 0.5 + 9.0 + pool_data.append({ + "x_obs": jnp.array(x_obs), + "y_obs": jnp.array(y_obs), + "day_indices": jnp.arange(n_obs) % 10, + }) + + x_attr = jnp.array(np.random.randn(N_POOLS, K_ATTR)) + return JointData( + pool_data=pool_data, + x_attr=x_attr, + pool_ids=POOL_PREFIXES[:N_POOLS], + attr_names=[f"attr_{i}" for i in range(K_ATTR)], + ) + + +class TestPerPoolNoiseHeadReduced: + """PerPoolNoiseHead with k_obs=4.""" + + def test_n_params(self): + h = PerPoolNoiseHead(k_obs=4) + assert h.n_params(3, 5) == 3 * 4 + + def test_predict_correct_slice(self): + h = PerPoolNoiseHead(k_obs=4) + params = jnp.arange(3 * 4, dtype=float) + x_attr_i = jnp.zeros(5) + for i in range(3): + result = h.predict(params, i, x_attr_i) + expected = params[i * 4:(i + 1) * 4] + np.testing.assert_allclose(result, expected) + + def test_init_ols(self): + np.random.seed(42) + h = PerPoolNoiseHead(k_obs=4) + jdata = _make_fake_jdata_reduced() + init = h.init(jdata) + assert init.shape == (N_POOLS * 4,) + assert np.all(np.isfinite(init)) + + def test_roundtrip(self): + np.random.seed(42) + h = PerPoolNoiseHead(k_obs=4) + jdata = _make_fake_jdata_reduced() + init = h.init(jdata) + result = h.unpack_result(init, N_POOLS, K_ATTR) + assert result["noise_coeffs"].shape == (N_POOLS, 4) + + def test_default_unchanged(self): + h = PerPoolNoiseHead() + assert h.k_obs == K_OBS + assert h.n_params(3, 5) == 3 * K_OBS + + +class TestSharedLinearNoiseHeadReduced: + """SharedLinearNoiseHead with k_obs=4.""" + + def test_n_params(self): + h = SharedLinearNoiseHead(k_obs=4) + assert h.n_params(3, 5) == (1 + 5) * 4 + + def test_predict(self): + k_attr = 3 + h = SharedLinearNoiseHead(k_obs=4) + W_full = np.zeros((1 + k_attr, 4)) + W_full[0, :] = 1.0 + W_full[1, 0] = 2.0 + params = jnp.array(W_full.ravel()) + x_attr_i = jnp.array([1.0, 0.0, 0.0]) + result = h.predict(params, 0, x_attr_i) + assert result.shape == (4,) + np.testing.assert_allclose(float(result[0]), 3.0) + np.testing.assert_allclose(float(result[1]), 1.0) + + def test_init(self): + np.random.seed(42) + h = SharedLinearNoiseHead(k_obs=4) + jdata = _make_fake_jdata_reduced() + init = h.init(jdata) + assert init.shape == ((1 + K_ATTR) * 4,) + assert np.all(np.isfinite(init)) + + def test_default_unchanged(self): + h = SharedLinearNoiseHead() + assert h.k_obs == K_OBS + assert h.n_params(3, 5) == (1 + 5) * K_OBS + + +class TestMLPNoiseHeadReduced: + """MLPNoiseHead with k_obs=4.""" + + def test_n_params(self): + h = MLPNoiseHead(hidden=16, k_obs=4) + # k_attr=5: 5*16 + 16 + 16*4 + 4 = 80+16+64+4 = 164 + assert h.n_params(3, 5) == 164 + + def test_predict(self): + k_attr = 3 + h = MLPNoiseHead(hidden=4, k_obs=4) + n_p = h.n_params(1, k_attr) + params = jnp.zeros(n_p) + x_attr_i = jnp.array([1.0, 2.0, 3.0]) + result = h.predict(params, 0, x_attr_i) + assert result.shape == (4,) + + def test_init(self): + np.random.seed(42) + h = MLPNoiseHead(hidden=4, k_obs=4) + jdata = _make_fake_jdata_reduced() + init = h.init(jdata) + n_p = h.n_params(N_POOLS, K_ATTR) + assert init.shape == (n_p,) + assert np.all(np.isfinite(init)) + + def test_regularization(self): + k_attr = 2 + h = MLPNoiseHead(hidden=2, alpha=1.0, k_obs=4) + # Layout: W1(2*2=4), b1(2), W2(2*4=8), b2(4) = 18 params + n_p = h.n_params(1, k_attr) + assert n_p == 18 + params = np.zeros(n_p) + params[0] = 3.0 # W1[0,0] + params[1] = 4.0 # W1[0,1] + params[6] = 1.0 # W2[0,0] + params[7] = 2.0 # W2[0,1] + params[-1] = 999.0 # b2[-1] — not regularized + # reg = 1.0 * (9 + 16 + 1 + 4) = 30.0 + result = float(h.regularization(jnp.array(params))) + np.testing.assert_allclose(result, 30.0) + + def test_default_unchanged(self): + h = MLPNoiseHead() + assert h.k_obs == K_OBS + assert h.n_params(3, 5) == 232 From 5a2cbdf42f3ca3f45b48c0b18a7b3350fbb9e4fa Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 16 Mar 2026 01:03:03 +0000 Subject: [PATCH 16/57] feat: calibrated 8-covariate noise model for reCLAMM simulator Add reclamm_calibrated_noise_volume (c_0..c_7 log-linear model with TVL, volatility, fee interactions, and DOW harmonics). Wire through all 4 reserve calculation paths with dow_sin/dow_cos scan inputs. Consolidate volatility/DOW array prep into _prepare_noise_arrays. --- quantammsim/pools/noise_trades.py | 75 ++++++++++++ quantammsim/pools/reCLAMM/reclamm.py | 107 +++++++++++------- quantammsim/pools/reCLAMM/reclamm_reserves.py | 43 +++++++ 3 files changed, 185 insertions(+), 40 deletions(-) diff --git a/quantammsim/pools/noise_trades.py b/quantammsim/pools/noise_trades.py index 3f45792..b40154a 100644 --- a/quantammsim/pools/noise_trades.py +++ b/quantammsim/pools/noise_trades.py @@ -272,3 +272,78 @@ def reclamm_loglinear_noise_volume( return jnp.maximum(0.0, daily_vol / 1440.0 - arb_volume_this_period) +@jit +def reclamm_calibrated_noise_volume( + effective_value_usd, + gamma, + volatility, + arb_volume_this_period, + dow_sin, + dow_cos, + noise_params=None, +): + """8-covariate calibrated noise volume from cross-pool log-linear model. + + Predicts per-minute noise volume using:: + + log(V_daily) = c_0 + c_1*log(TVL) + c_2*log(sigma) + + c_3*log(TVL)*log(sigma) + c_4*log(TVL)*fee + + c_5*log(sigma)*fee + c_6*dow_sin + c_7*dow_cos + V_noise = max(0, exp(log_daily_vol) / 1440 - arb_volume) + + where sigma is annualised daily realised volatility, fee = 1 - gamma, + and dow_sin/dow_cos encode day-of-week seasonality. + + Parameters + ---------- + effective_value_usd : float + Effective TVL in USD: (Ra+Va)*pA + (Rb+Vb)*pB. + gamma : float + Fee parameter (1 - fee_rate). + volatility : float + Annualised daily realised volatility of the price ratio. + arb_volume_this_period : float + Arb volume already accounted for this time step (USD). + dow_sin : float + sin(2*pi*weekday/7) for the current day. + dow_cos : float + cos(2*pi*weekday/7) for the current day. + noise_params : dict, optional + Calibrated coefficients: c_0 .. c_7. + + Returns + ------- + float + Per-minute noise volume (USD), floored at zero. + """ + if noise_params is None: + noise_params = {} + c_0 = noise_params.get("c_0", 0.0) + c_1 = noise_params.get("c_1", 1.0) + c_2 = noise_params.get("c_2", 0.0) + c_3 = noise_params.get("c_3", 0.0) + c_4 = noise_params.get("c_4", 0.0) + c_5 = noise_params.get("c_5", 0.0) + c_6 = noise_params.get("c_6", 0.0) + c_7 = noise_params.get("c_7", 0.0) + + fee = 1.0 - gamma + log_tvl = jnp.log(jnp.maximum(effective_value_usd, 1.0)) + log_sigma = jnp.log(jnp.maximum(volatility, 1e-10)) + + log_daily_vol = ( + c_0 + + c_1 * log_tvl + + c_2 * log_sigma + + c_3 * log_tvl * log_sigma + + c_4 * log_tvl * fee + + c_5 * log_sigma * fee + + c_6 * dow_sin + + c_7 * dow_cos + ) + daily_vol = jnp.exp(log_daily_vol) + # noise_coeffs predict V_noise directly (not V_total), so no need to + # subtract arb volume — that would double-count the arb subtraction. + return jnp.maximum(0.0, daily_vol / 1440.0) + + diff --git a/quantammsim/pools/reCLAMM/reclamm.py b/quantammsim/pools/reCLAMM/reclamm.py index 5b913ff..f7b6d19 100644 --- a/quantammsim/pools/reCLAMM/reclamm.py +++ b/quantammsim/pools/reCLAMM/reclamm.py @@ -214,6 +214,49 @@ def _resolve_fees(params, run_fingerprint): return jnp.squeeze(params["fees"]) return run_fingerprint["fees"] + def _prepare_noise_arrays(self, prices, run_fingerprint, start_index, + bout_length, arb_freq, max_len): + """Prepare volatility and dow arrays for noise models that need them. + + Returns (volatility_array, dow_sin_array, dow_cos_array) — all sliced + and decimated to match arb_prices shape. Arrays are None when the + noise model does not require them. + """ + noise_model = run_fingerprint.get("noise_model", "ratio") + needs_vol = noise_model in ( + "tsoukalas_sqrt", "tsoukalas_log", "loglinear", "calibrated", + ) + if not needs_vol: + return None, None, None + + volatility_array = self.calculate_volatility_array( + prices, run_fingerprint, + ) + vol_prepared = _prepare_dynamic_array( + volatility_array, start_index, bout_length, arb_freq, max_len, + ) + + if noise_model != "calibrated": + return vol_prepared, None, None + + # Day-of-week sin/cos arrays for the calibrated noise model. + # Compute from startDateString + minute offsets (vectorized). + import pandas as pd + start_dt = pd.Timestamp(run_fingerprint["startDateString"]) + n_minutes = prices.shape[0] + day_indices = np.arange(n_minutes) // 1440 + start_weekday = start_dt.weekday() # Monday=0 .. Sunday=6 + weekdays = ((start_weekday + day_indices) % 7).astype(np.float64) + dow_sin_full = jnp.array(np.sin(2.0 * np.pi * weekdays / 7.0)) + dow_cos_full = jnp.array(np.cos(2.0 * np.pi * weekdays / 7.0)) + dow_sin_prepared = _prepare_dynamic_array( + dow_sin_full, start_index, bout_length, arb_freq, max_len, + ) + dow_cos_prepared = _prepare_dynamic_array( + dow_cos_full, start_index, bout_length, arb_freq, max_len, + ) + return vol_prepared, dow_sin_prepared, dow_cos_prepared + @partial(jit, static_argnums=(2,)) def calculate_reserves_with_fees( self, @@ -241,16 +284,10 @@ def calculate_reserves_with_fees( if noise_params is not None and type(noise_params) is not dict: noise_params = dict(noise_params) - if noise_model in ("tsoukalas_sqrt", "tsoukalas_log", "loglinear"): - volatility_array = self.calculate_volatility_array( - prices, run_fingerprint, - ) - arb_vol = _prepare_dynamic_array( - volatility_array, start_index, bout_length, - arb_freq, s.arb_prices.shape[0], - ) - else: - arb_vol = None + arb_vol, dow_sin, dow_cos = self._prepare_noise_arrays( + prices, run_fingerprint, start_index, + bout_length, arb_freq, s.arb_prices.shape[0], + ) if run_fingerprint["do_arb"]: return _jax_calc_reclamm_reserves_with_fees( @@ -273,6 +310,8 @@ def calculate_reserves_with_fees( noise_model=noise_model, noise_params=noise_params, volatility_array=arb_vol, + dow_sin_array=dow_sin, + dow_cos_array=dow_cos, ) return jnp.broadcast_to(s.initial_reserves, s.arb_prices.shape) @@ -311,16 +350,10 @@ def calculate_reserves_and_fee_revenue_with_fees( if noise_params is not None and type(noise_params) is not dict: noise_params = dict(noise_params) - if noise_model in ("tsoukalas_sqrt", "tsoukalas_log", "loglinear"): - volatility_array = self.calculate_volatility_array( - prices, run_fingerprint, - ) - arb_vol = _prepare_dynamic_array( - volatility_array, start_index, bout_length, - arb_freq, s.arb_prices.shape[0], - ) - else: - arb_vol = None + arb_vol, dow_sin, dow_cos = self._prepare_noise_arrays( + prices, run_fingerprint, start_index, + bout_length, arb_freq, s.arb_prices.shape[0], + ) if run_fingerprint["do_arb"]: return _jax_calc_reclamm_reserves_and_fee_revenue_with_fees( @@ -343,6 +376,8 @@ def calculate_reserves_and_fee_revenue_with_fees( noise_model=noise_model, noise_params=noise_params, volatility_array=arb_vol, + dow_sin_array=dow_sin, + dow_cos_array=dow_cos, ) return ( jnp.broadcast_to(s.initial_reserves, s.arb_prices.shape), @@ -386,16 +421,10 @@ def calculate_reserves_and_fee_revenue_with_dynamic_inputs( if noise_params is not None and type(noise_params) is not dict: noise_params = dict(noise_params) - if noise_model in ("tsoukalas_sqrt", "tsoukalas_log", "loglinear"): - volatility_array = self.calculate_volatility_array( - prices, run_fingerprint, - ) - arb_vol = _prepare_dynamic_array( - volatility_array, start_index, bout_length, - run_fingerprint["arb_frequency"], max_len, - ) - else: - arb_vol = None + arb_vol, dow_sin, dow_cos = self._prepare_noise_arrays( + prices, run_fingerprint, start_index, + bout_length, run_fingerprint["arb_frequency"], max_len, + ) return _jax_calc_reclamm_reserves_and_fee_revenue_with_dynamic_inputs( s.initial_reserves, s.Va, s.Vb, @@ -418,6 +447,8 @@ def calculate_reserves_and_fee_revenue_with_dynamic_inputs( noise_model=noise_model, noise_params=noise_params, volatility_array=arb_vol, + dow_sin_array=dow_sin, + dow_cos_array=dow_cos, ) @partial(jit, static_argnums=(2,)) @@ -485,16 +516,10 @@ def calculate_reserves_with_dynamic_inputs( if noise_params is not None and type(noise_params) is not dict: noise_params = dict(noise_params) - if noise_model in ("tsoukalas_sqrt", "tsoukalas_log", "loglinear"): - volatility_array = self.calculate_volatility_array( - prices, run_fingerprint, - ) - arb_vol = _prepare_dynamic_array( - volatility_array, start_index, bout_length, - run_fingerprint["arb_frequency"], max_len, - ) - else: - arb_vol = None + arb_vol, dow_sin, dow_cos = self._prepare_noise_arrays( + prices, run_fingerprint, start_index, + bout_length, run_fingerprint["arb_frequency"], max_len, + ) return _jax_calc_reclamm_reserves_with_dynamic_inputs( s.initial_reserves, s.Va, s.Vb, @@ -517,6 +542,8 @@ def calculate_reserves_with_dynamic_inputs( noise_model=noise_model, noise_params=noise_params, volatility_array=arb_vol, + dow_sin_array=dow_sin, + dow_cos_array=dow_cos, ) def init_base_parameters( diff --git a/quantammsim/pools/reCLAMM/reclamm_reserves.py b/quantammsim/pools/reCLAMM/reclamm_reserves.py index 2a46ad3..713923c 100644 --- a/quantammsim/pools/reCLAMM/reclamm_reserves.py +++ b/quantammsim/pools/reCLAMM/reclamm_reserves.py @@ -35,6 +35,7 @@ reclamm_tsoukalas_sqrt_noise_volume, reclamm_tsoukalas_log_noise_volume, reclamm_loglinear_noise_volume, + reclamm_calibrated_noise_volume, ) # Reference balance for initialisation (matches Solidity _INITIALIZATION_MAX_BALANCE_A) @@ -1003,6 +1004,24 @@ def _skip_schedule_state(_): arb_volume, _np, ) + noise_fee_income = (1.0 - gamma) * noise_vol + scale = 1.0 + noise_fee_income / jnp.maximum(real_value, 1e-8) + Ra_new = Ra_new * scale + Rb_new = Rb_new * scale + elif noise_model == "calibrated": + volatility = input_list[9] + dow_sin = input_list[10] + dow_cos = input_list[11] + arb_volume = 0.5 * jnp.sum(jnp.abs(applied_trade) * prices) + real_value = jnp.sum(jnp.array([Ra_new, Rb_new]) * prices) + effective_value = (Ra_new + Va) * prices[0] + (Rb_new + Vb) * prices[1] + + _np = noise_params if noise_params is not None else {} + noise_vol = reclamm_calibrated_noise_volume( + effective_value, gamma, volatility, + arb_volume, dow_sin, dow_cos, _np, + ) + noise_fee_income = (1.0 - gamma) * noise_vol scale = 1.0 + noise_fee_income / jnp.maximum(real_value, 1e-8) Ra_new = Ra_new * scale @@ -1260,6 +1279,8 @@ def _jax_calc_reclamm_reserves_with_fees( noise_model="ratio", noise_params=None, volatility_array=None, + dow_sin_array=None, + dow_cos_array=None, ): """Calculate reClAMM reserves over time with fees. @@ -1318,6 +1339,10 @@ def _jax_calc_reclamm_reserves_with_fees( price_ratio_updates, lp_supply_array] if noise_model in ("tsoukalas_sqrt", "tsoukalas_log", "loglinear"): scan_inputs.append(volatility_array) + elif noise_model == "calibrated": + scan_inputs.append(volatility_array) + scan_inputs.append(dow_sin_array) + scan_inputs.append(dow_cos_array) carry_init = [ initial_reserves, @@ -1359,6 +1384,8 @@ def _jax_calc_reclamm_reserves_with_dynamic_inputs( noise_model="ratio", noise_params=None, volatility_array=None, + dow_sin_array=None, + dow_cos_array=None, ): """Calculate reClAMM reserves with time-varying fees/arb arrays.""" if lp_supply_array is None: @@ -1426,6 +1453,10 @@ def _jax_calc_reclamm_reserves_with_dynamic_inputs( price_ratio_updates, lp_supply_array] if noise_model in ("tsoukalas_sqrt", "tsoukalas_log", "loglinear"): scan_inputs.append(volatility_array) + elif noise_model == "calibrated": + scan_inputs.append(volatility_array) + scan_inputs.append(dow_sin_array) + scan_inputs.append(dow_cos_array) carry_init = [ initial_reserves, @@ -1557,6 +1588,8 @@ def _jax_calc_reclamm_reserves_and_fee_revenue_with_fees( noise_model="ratio", noise_params=None, volatility_array=None, + dow_sin_array=None, + dow_cos_array=None, ): """Calculate reClAMM reserves and LP fee revenue over time with fees. @@ -1617,6 +1650,10 @@ def _jax_calc_reclamm_reserves_and_fee_revenue_with_fees( price_ratio_updates, lp_supply_array] if noise_model in ("tsoukalas_sqrt", "tsoukalas_log", "loglinear"): scan_inputs.append(volatility_array) + elif noise_model == "calibrated": + scan_inputs.append(volatility_array) + scan_inputs.append(dow_sin_array) + scan_inputs.append(dow_cos_array) carry_init = [ initial_reserves, @@ -1658,6 +1695,8 @@ def _jax_calc_reclamm_reserves_and_fee_revenue_with_dynamic_inputs( noise_model="ratio", noise_params=None, volatility_array=None, + dow_sin_array=None, + dow_cos_array=None, ): """Calculate reClAMM reserves and LP fee revenue with time-varying fees/arb arrays. @@ -1731,6 +1770,10 @@ def _jax_calc_reclamm_reserves_and_fee_revenue_with_dynamic_inputs( price_ratio_updates, lp_supply_array] if noise_model in ("tsoukalas_sqrt", "tsoukalas_log", "loglinear"): scan_inputs.append(volatility_array) + elif noise_model == "calibrated": + scan_inputs.append(volatility_array) + scan_inputs.append(dow_sin_array) + scan_inputs.append(dow_cos_array) carry_init = [ initial_reserves, From 246de0e7da82e736bdb6a5ce9640efa7bb3109c1 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 16 Mar 2026 01:03:14 +0000 Subject: [PATCH 17/57] feat: configurable n_evaluation_points for Optuna and keep startDateString in static dict MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Read n_evaluation_points from optuna_settings instead of hardcoding 20. Keep startDateString in the static fingerprint dict — the calibrated noise model needs it to compute day-of-week arrays. --- quantammsim/runners/jax_runner_utils.py | 2 +- quantammsim/runners/jax_runners.py | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/quantammsim/runners/jax_runner_utils.py b/quantammsim/runners/jax_runner_utils.py index aa5b79d..1c93887 100644 --- a/quantammsim/runners/jax_runner_utils.py +++ b/quantammsim/runners/jax_runner_utils.py @@ -658,7 +658,7 @@ def __eq__(self, other): # These are excluded when creating static_dict from run_fingerprint _TRAINING_ONLY_FIELDS = frozenset({ "optimisation_settings", # Contains lr, optimizer, etc. - "startDateString", # Data loading dates + # startDateString kept in static dict — needed by calibrated noise model "endDateString", "endTestDateString", "subsidary_pools", # Handled separately diff --git a/quantammsim/runners/jax_runners.py b/quantammsim/runners/jax_runners.py index 178f0a0..0937884 100644 --- a/quantammsim/runners/jax_runners.py +++ b/quantammsim/runners/jax_runners.py @@ -1298,7 +1298,9 @@ def _extract_params_at(params_tree, j): return selected_params elif run_fingerprint["optimisation_settings"]["method"] == "optuna": - n_evaluation_points = 20 + n_evaluation_points = run_fingerprint["optimisation_settings"].get( + "optuna_settings", {} + ).get("n_evaluation_points", 20) min_spacing = data_dict["bout_length"] // 2 # E run_fingerprint["optimisation_settings"]["n_parameter_sets"] = 1 From 227fe7735c1d4caf9f098dbf76089f01f3f8d00b Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 16 Mar 2026 11:24:08 +0000 Subject: [PATCH 18/57] feat: add token encoding for token-factored noise model Add encode_tokens() to build token index, per-pool token/chain assignments, and token covariate matrix (D_TOKEN=5) from the matched pool set. Token classification via symbol lookup for stablecoins, ETH derivatives, and L1 natives. Market cap from hardcoded values or JSON fallback. Foundation for the token-factored noise head where pool noise coefficients decompose as u[token_a] + u[token_b] + alpha[chain] + beta_fee * log(fee) + delta_i. --- quantammsim/calibration/pool_data.py | 102 +++++++++++++++++++ tests/calibration/test_pool_data.py | 145 +++++++++++++++++++++++++++ 2 files changed, 247 insertions(+) diff --git a/quantammsim/calibration/pool_data.py b/quantammsim/calibration/pool_data.py index 1b8110d..18ab821 100644 --- a/quantammsim/calibration/pool_data.py +++ b/quantammsim/calibration/pool_data.py @@ -348,6 +348,108 @@ def match_grids_to_panel( return matched +# Token classification for token-factored model +_ETH_DERIVATIVES = { + "WETH", "ETH", "wstETH", "stETH", "rETH", "cbETH", + "waEthLidoWETH", "waEthLidowstETH", "waBasWETH", "waGnowstETH", +} +_L1_NATIVE = { + "WETH", "ETH", "WMATIC", "MATIC", "POL", "wPOL", + "WAVAX", "AVAX", "GNO", "S", "wS", "stS", +} + +D_TOKEN = 5 # [intercept, log_mcap, is_stable, is_eth_derivative, is_L1_native] + + +def _classify_token(symbol: str, mcaps: dict) -> dict: + """Classify a token into binary feature flags.""" + return { + "is_stable": 1.0 if symbol in _STABLECOINS else 0.0, + "is_eth_derivative": 1.0 if symbol in _ETH_DERIVATIVES else 0.0, + "is_L1_native": 1.0 if symbol in _L1_NATIVE else 0.0, + "log_mcap": np.log(max(mcaps.get(symbol, {}).get("mcap_usd", 1e6), 1.0)), + } + + +def encode_tokens( + matched: Dict[str, dict], + mcap_path: str = None, +) -> dict: + """Build token index, per-pool token assignments, and token covariate matrix. + + Iterates over pools in sorted key order (same ordering as build_pool_attributes). + + Returns dict with: + token_index: dict[str, int] — symbol -> integer index (sorted alphabetically) + token_a_idx: np.ndarray (n_pools,) — index of token A for each pool + token_b_idx: np.ndarray (n_pools,) — index of token B for each pool + x_token: np.ndarray (n_tokens, D_TOKEN) — token covariate matrix + chain_idx: np.ndarray (n_pools,) — chain integer index per pool + chain_index: dict[str, int] — chain name -> integer index (sorted) + log_fees: np.ndarray (n_pools,) — log(fee) per pool + n_tokens: int + n_chains: int + """ + mcaps = _load_token_mcaps(mcap_path) + pool_ids = sorted(matched.keys()) + n_pools = len(pool_ids) + + # Collect all tokens and chains + all_tokens = set() + all_chains = set() + for pid in pool_ids: + entry = matched[pid] + toks = _parse_tokens(entry["tokens"]) + all_tokens.update(toks[:2]) + all_chains.add(entry["chain"]) + + # Build sorted indices + token_list = sorted(all_tokens) + token_index = {t: i for i, t in enumerate(token_list)} + n_tokens = len(token_list) + + chain_list = sorted(all_chains) + chain_index = {c: i for i, c in enumerate(chain_list)} + n_chains = len(chain_list) + + # Build per-pool arrays + token_a_idx = np.zeros(n_pools, dtype=np.int32) + token_b_idx = np.zeros(n_pools, dtype=np.int32) + chain_idx = np.zeros(n_pools, dtype=np.int32) + log_fees = np.zeros(n_pools, dtype=np.float64) + + for i, pid in enumerate(pool_ids): + entry = matched[pid] + toks = _parse_tokens(entry["tokens"]) + token_a_idx[i] = token_index[toks[0]] + token_b_idx[i] = token_index[toks[1]] + chain_idx[i] = chain_index[entry["chain"]] + log_fees[i] = np.log(entry["fee"]) + + # Build token covariate matrix: (n_tokens, D_TOKEN) + # Columns: [intercept, log_mcap, is_stable, is_eth_derivative, is_L1_native] + x_token = np.zeros((n_tokens, D_TOKEN), dtype=np.float64) + for t, idx in token_index.items(): + cls = _classify_token(t, mcaps) + x_token[idx, 0] = 1.0 # intercept + x_token[idx, 1] = cls["log_mcap"] + x_token[idx, 2] = cls["is_stable"] + x_token[idx, 3] = cls["is_eth_derivative"] + x_token[idx, 4] = cls["is_L1_native"] + + return { + "token_index": token_index, + "token_a_idx": token_a_idx, + "token_b_idx": token_b_idx, + "x_token": x_token, + "chain_idx": chain_idx, + "chain_index": chain_index, + "log_fees": log_fees, + "n_tokens": n_tokens, + "n_chains": n_chains, + } + + def build_x_obs(panel_rows: pd.DataFrame, reduced: bool = False) -> np.ndarray: """Build observation covariate matrix from panel rows. diff --git a/tests/calibration/test_pool_data.py b/tests/calibration/test_pool_data.py index 5544186..adbf212 100644 --- a/tests/calibration/test_pool_data.py +++ b/tests/calibration/test_pool_data.py @@ -12,6 +12,151 @@ ) +class TestEncodeTokens: + """Test encode_tokens: token index, assignments, and covariate matrix.""" + + def _get_matched(self, synthetic_daily_grid, synthetic_panel, tmp_path): + from quantammsim.calibration.pool_data import match_grids_to_panel + + grid_dir = tmp_path / "grids" + grid_dir.mkdir() + for prefix in POOL_PREFIXES: + synthetic_daily_grid.to_parquet( + grid_dir / f"{prefix}_daily.parquet", index=False + ) + return match_grids_to_panel(str(grid_dir), synthetic_panel) + + def test_returns_expected_keys( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + from quantammsim.calibration.pool_data import encode_tokens + + matched = self._get_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + result = encode_tokens(matched) + expected_keys = { + "token_index", "token_a_idx", "token_b_idx", + "x_token", "chain_idx", "chain_index", + "log_fees", "n_tokens", "n_chains", + } + assert expected_keys.issubset(result.keys()) + + def test_unique_tokens_discovered( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + from quantammsim.calibration.pool_data import encode_tokens + + matched = self._get_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + result = encode_tokens(matched) + # Synthetic panel has tokens: BTC, ETH (pool 0) and AAVE, ETH (pool 1) + # Unique tokens: AAVE, BTC, ETH (sorted) + assert result["n_tokens"] == 3 + assert set(result["token_index"].keys()) == {"AAVE", "BTC", "ETH"} + # Indices should be contiguous 0..2 + assert set(result["token_index"].values()) == {0, 1, 2} + + def test_x_token_shape_and_intercept( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + from quantammsim.calibration.pool_data import encode_tokens + + matched = self._get_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + result = encode_tokens(matched) + x_token = result["x_token"] + assert x_token.shape[0] == result["n_tokens"] # 3 tokens + assert x_token.shape[1] >= 4 # at least intercept + 3 binary flags + # Intercept column is all 1s + np.testing.assert_array_equal(x_token[:, 0], 1.0) + + def test_token_classifications( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + from quantammsim.calibration.pool_data import encode_tokens + + matched = self._get_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + result = encode_tokens(matched) + ti = result["token_index"] + x_tok = result["x_token"] + # Column layout: [intercept, log_mcap, is_stable, is_eth_deriv, is_L1_native] + # ETH: is_eth_derivative=1, is_L1_native=1 + assert x_tok[ti["ETH"], 3] == 1.0 # is_eth_derivative + assert x_tok[ti["ETH"], 4] == 1.0 # is_L1_native + # AAVE: none of the binary flags + assert x_tok[ti["AAVE"], 2] == 0.0 # not stable + assert x_tok[ti["AAVE"], 3] == 0.0 # not eth_deriv + assert x_tok[ti["AAVE"], 4] == 0.0 # not L1_native + + def test_pool_token_mapping( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + from quantammsim.calibration.pool_data import encode_tokens + + matched = self._get_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + result = encode_tokens(matched) + ti = result["token_index"] + + # Pool 0 (first sorted prefix): tokens = "BTC,ETH" + assert result["token_a_idx"][0] == ti["BTC"] + assert result["token_b_idx"][0] == ti["ETH"] + + # Pool 1 (second sorted prefix): tokens = "AAVE,ETH" + assert result["token_a_idx"][1] == ti["AAVE"] + assert result["token_b_idx"][1] == ti["ETH"] + + def test_chain_index( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + from quantammsim.calibration.pool_data import encode_tokens + + matched = self._get_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + result = encode_tokens(matched) + assert result["n_chains"] == 2 + assert set(result["chain_index"].keys()) == {"ARBITRUM", "MAINNET"} + assert set(result["chain_index"].values()) == {0, 1} + + def test_chain_idx_mapping( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + from quantammsim.calibration.pool_data import encode_tokens + + matched = self._get_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + result = encode_tokens(matched) + pool_ids = sorted(matched.keys()) + ci = result["chain_index"] + # Pool 0 is MAINNET, pool 1 is ARBITRUM + assert result["chain_idx"][0] == ci[matched[pool_ids[0]]["chain"]] + assert result["chain_idx"][1] == ci[matched[pool_ids[1]]["chain"]] + + def test_log_fees( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + from quantammsim.calibration.pool_data import encode_tokens + + matched = self._get_matched( + synthetic_daily_grid, synthetic_panel, tmp_path + ) + result = encode_tokens(matched) + pool_ids = sorted(matched.keys()) + for i, pid in enumerate(pool_ids): + expected_fee = matched[pid]["fee"] + np.testing.assert_allclose( + result["log_fees"][i], np.log(expected_fee), rtol=1e-6 + ) + + class TestMatchGridsToPanel: """Test match_grids_to_panel: match grid parquets to panel rows.""" From c7a0c26b22ff1d0ccc0ac2f4a2097c09a761615c Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 16 Mar 2026 11:24:18 +0000 Subject: [PATCH 19/57] feat: add TokenFactoredNoiseHead with additive token decomposition noise_coeffs_i = u[token_a] + u[token_b] + alpha[chain] + beta_fee * log(fee) + delta_i Token effects regularized toward x_token @ Gamma (population prediction from market cap and asset class). Per-pool deltas L2-regularized for partial pooling. Warm-start init decomposes Option C noise_coeffs into token/chain/fee effects via lstsq. predict_new_pool() handles seen tokens (learned u_t), unseen tokens (Gamma fallback), and unseen chains (zero alpha). Comprehensive tests: additivity, regularization, warm-start round-trip, gradient finiteness, new-pool prediction for seen/unseen tokens/chains. --- quantammsim/calibration/heads.py | 264 +++++++++++++++++++++++++++++++ tests/calibration/test_heads.py | 235 +++++++++++++++++++++++++++ 2 files changed, 499 insertions(+) diff --git a/quantammsim/calibration/heads.py b/quantammsim/calibration/heads.py index 6d6a376..8b40682 100644 --- a/quantammsim/calibration/heads.py +++ b/quantammsim/calibration/heads.py @@ -16,6 +16,9 @@ import numpy as np from quantammsim.calibration.loss import K_OBS +from quantammsim.calibration.pool_data import ( + D_TOKEN, K_OBS_REDUCED, _classify_token, _load_token_mcaps, +) # --------------------------------------------------------------------------- @@ -524,6 +527,267 @@ def make_bounds(self, n_pools, k_attr): return [(None, None)] * self.n_params(n_pools, k_attr) +# --------------------------------------------------------------------------- +# TokenFactoredNoiseHead — additive token + chain + fee composition +# --------------------------------------------------------------------------- + + +class TokenFactoredNoiseHead: + """Noise coefficients from additive token + chain + fee composition. + + noise_coeffs_i = u[token_a_i] + u[token_b_i] + alpha[chain_i] + + beta_fee * log(fee_i) + delta_i + + Token effects u_t are regularized toward x_token_t @ Gamma (population + prediction from token covariates). Per-pool deltas are L2-regularized, + controlling the shrinkage between per-pool and population estimates. + + Parameter layout (flat): + [u (n_tokens * k_obs), + Gamma (d_token * k_obs), + alpha (n_chains * k_obs), + beta_fee (k_obs), + delta (n_pools * k_obs)] + """ + + def __init__( + self, + token_a_idx: np.ndarray, + token_b_idx: np.ndarray, + chain_idx: np.ndarray, + log_fees: np.ndarray, + x_token: np.ndarray, + n_tokens: int, + n_chains: int, + token_index: dict, + chain_index: dict, + k_obs: int = K_OBS_REDUCED, + lambda_delta: float = 1.0, + lambda_token: float = 0.1, + lambda_chain: float = 0.1, + lambda_fee: float = 0.01, + mcap_path: str = None, + ): + self.name = "noise" + self.token_a_idx = np.asarray(token_a_idx, dtype=np.int32) + self.token_b_idx = np.asarray(token_b_idx, dtype=np.int32) + self.chain_idx = np.asarray(chain_idx, dtype=np.int32) + self.log_fees = np.asarray(log_fees, dtype=np.float64) + self.x_token = np.asarray(x_token, dtype=np.float64) + self.n_tokens = n_tokens + self.n_chains = n_chains + self.d_token = x_token.shape[1] + self.k_obs = k_obs + self.token_index = dict(token_index) + self.chain_index = dict(chain_index) + self.lambda_delta = lambda_delta + self.lambda_token = lambda_token + self.lambda_chain = lambda_chain + self.lambda_fee = lambda_fee + self._mcap_path = mcap_path + # Pre-convert to JAX for predict() + self._token_a_jax = jnp.array(self.token_a_idx) + self._token_b_jax = jnp.array(self.token_b_idx) + self._chain_jax = jnp.array(self.chain_idx) + self._log_fees_jax = jnp.array(self.log_fees) + self._x_token_jax = jnp.array(self.x_token) + + def n_params(self, n_pools: int, k_attr: int) -> int: + k = self.k_obs + return (self.n_tokens * k # u + + self.d_token * k # Gamma + + self.n_chains * k # alpha + + k # beta_fee + + n_pools * k) # delta + + def _unpack(self, params_slice, n_pools): + k = self.k_obs + idx = 0 + u = params_slice[idx:idx + self.n_tokens * k].reshape(self.n_tokens, k) + idx += self.n_tokens * k + Gamma = params_slice[idx:idx + self.d_token * k].reshape(self.d_token, k) + idx += self.d_token * k + alpha = params_slice[idx:idx + self.n_chains * k].reshape(self.n_chains, k) + idx += self.n_chains * k + beta_fee = params_slice[idx:idx + k] + idx += k + delta = params_slice[idx:idx + n_pools * k].reshape(n_pools, k) + return u, Gamma, alpha, beta_fee, delta + + def _infer_n_pools(self, params_slice): + k = self.k_obs + n_shared = self.n_tokens * k + self.d_token * k + self.n_chains * k + k + return (params_slice.shape[0] - n_shared) // k + + def predict(self, params_slice, pool_idx, x_attr_i): + n_pools = self._infer_n_pools(params_slice) + u, Gamma, alpha, beta_fee, delta = self._unpack(params_slice, n_pools) + ta = self._token_a_jax[pool_idx] + tb = self._token_b_jax[pool_idx] + ch = self._chain_jax[pool_idx] + lf = self._log_fees_jax[pool_idx] + return u[ta] + u[tb] + alpha[ch] + beta_fee * lf + delta[pool_idx] + + def regularization(self, params_slice): + n_pools = self._infer_n_pools(params_slice) + u, Gamma, alpha, beta_fee, delta = self._unpack(params_slice, n_pools) + u_pred = self._x_token_jax @ Gamma + reg_token = self.lambda_token * jnp.sum((u - u_pred) ** 2) + reg_chain = self.lambda_chain * jnp.sum(alpha ** 2) + reg_fee = self.lambda_fee * jnp.sum(beta_fee ** 2) + reg_delta = self.lambda_delta * jnp.sum(delta ** 2) + return reg_token + reg_chain + reg_fee + reg_delta + + def init(self, jdata, warm_start=None): + n_pools = len(jdata.pool_data) + k = self.k_obs + + if warm_start is not None: + # Collect per-pool noise_coeffs from warm_start + noise_all = np.zeros((n_pools, k), dtype=np.float64) + for i, pid in enumerate(jdata.pool_ids): + if pid in warm_start and "noise_coeffs" in warm_start[pid]: + nc = warm_start[pid]["noise_coeffs"] + noise_all[i] = nc[:k] + + # Solve: u[ta_i] + u[tb_i] + alpha[ch_i] + beta_fee * lf_i ≈ noise_all[i] + n_cols = self.n_tokens + self.n_chains + 1 + A = np.zeros((n_pools, n_cols), dtype=np.float64) + for i in range(n_pools): + A[i, self.token_a_idx[i]] = 1.0 + A[i, self.token_b_idx[i]] += 1.0 + A[i, self.n_tokens + self.chain_idx[i]] = 1.0 + A[i, -1] = self.log_fees[i] + + lam_reg = 0.1 + AtA = A.T @ A + lam_reg * np.eye(n_cols) + u_init = np.zeros((self.n_tokens, k)) + alpha_init = np.zeros((self.n_chains, k)) + beta_fee_init = np.zeros(k) + + for j in range(k): + sol = np.linalg.solve(AtA, A.T @ noise_all[:, j]) + u_init[:, j] = sol[:self.n_tokens] + alpha_init[:, j] = sol[self.n_tokens:self.n_tokens + self.n_chains] + beta_fee_init[j] = sol[-1] + + # Delta = residuals + predicted = np.zeros_like(noise_all) + for i in range(n_pools): + predicted[i] = (u_init[self.token_a_idx[i]] + + u_init[self.token_b_idx[i]] + + alpha_init[self.chain_idx[i]] + + beta_fee_init * self.log_fees[i]) + delta_init = noise_all - predicted + + # Gamma from post-hoc regression of u on x_token + Gamma_init, _, _, _ = np.linalg.lstsq( + self.x_token, u_init, rcond=None + ) + else: + # Cold start: pooled OLS for baseline, then decompose + all_x = np.vstack([np.array(pd["x_obs"]) for pd in jdata.pool_data]) + all_y = np.concatenate([np.array(pd["y_obs"]) for pd in jdata.pool_data]) + pooled_coeffs, _, _, _ = np.linalg.lstsq(all_x, all_y, rcond=None) + pooled_coeffs = pooled_coeffs[:k] + + u_init = np.tile(pooled_coeffs / 2.0, (self.n_tokens, 1)) + Gamma_init, _, _, _ = np.linalg.lstsq( + self.x_token, u_init, rcond=None + ) + alpha_init = np.zeros((self.n_chains, k)) + beta_fee_init = np.zeros(k) + delta_init = np.zeros((n_pools, k)) + + return np.concatenate([ + u_init.ravel(), + Gamma_init.ravel(), + alpha_init.ravel(), + beta_fee_init, + delta_init.ravel(), + ]).astype(np.float64) + + def predict_new(self, params_slice, x_attr): + raise ValueError( + "TokenFactoredNoiseHead.predict_new() requires token identifiers. " + "Use predict_new_pool(params, token_a, token_b, chain, fee) instead." + ) + + def predict_new_pool( + self, params_slice, token_a, token_b, chain, fee, n_pools, + ) -> dict: + """Predict noise coefficients for a new pool from token composition. + + Seen tokens use learned u_t. Unseen tokens fall back to x_t @ Gamma. + Unseen chains use alpha = zeros. No delta for new pools. + """ + params_np = np.asarray(params_slice) + u, Gamma, alpha, beta_fee, delta = self._unpack(params_np, n_pools) + u, Gamma, alpha, beta_fee = ( + np.array(u), np.array(Gamma), np.array(alpha), np.array(beta_fee) + ) + mcaps = _load_token_mcaps(self._mcap_path) + + def _get_token_effect(token): + if token in self.token_index: + return u[self.token_index[token]] + x_t = np.zeros(self.d_token) + x_t[0] = 1.0 + cls = _classify_token(token, mcaps) + x_t[1] = cls["log_mcap"] + x_t[2] = cls["is_stable"] + x_t[3] = cls["is_eth_derivative"] + x_t[4] = cls["is_L1_native"] + return x_t @ Gamma + + u_a = _get_token_effect(token_a) + u_b = _get_token_effect(token_b) + + if chain in self.chain_index: + alpha_c = alpha[self.chain_index[chain]] + else: + alpha_c = np.zeros(self.k_obs) + + fee_effect = beta_fee * np.log(fee) + noise_coeffs = u_a + u_b + alpha_c + fee_effect + + return { + "noise_coeffs": noise_coeffs, + "components": { + "token_a": u_a, + "token_b": u_b, + "chain": alpha_c, + "fee": fee_effect, + }, + } + + def unpack_result(self, params_slice, n_pools, k_attr): + params_np = np.asarray(params_slice) + u, Gamma, alpha, beta_fee, delta = self._unpack(params_np, n_pools) + u, Gamma, alpha, beta_fee, delta = ( + np.array(u), np.array(Gamma), np.array(alpha), + np.array(beta_fee), np.array(delta), + ) + # Reconstruct per-pool noise_coeffs + noise_coeffs = np.zeros((n_pools, self.k_obs)) + for i in range(n_pools): + noise_coeffs[i] = (u[self.token_a_idx[i]] + u[self.token_b_idx[i]] + + alpha[self.chain_idx[i]] + + beta_fee * self.log_fees[i] + + delta[i]) + return { + "token_effects": u, + "Gamma": Gamma, + "chain_effects": alpha, + "beta_fee": beta_fee, + "noise_deltas": delta, + "noise_coeffs": noise_coeffs, + } + + def make_bounds(self, n_pools, k_attr): + return [(None, None)] * self.n_params(n_pools, k_attr) + + # --------------------------------------------------------------------------- # MLPNoiseHead — x_attr → Dense(hidden, relu) → Dense(K_OBS) # --------------------------------------------------------------------------- diff --git a/tests/calibration/test_heads.py b/tests/calibration/test_heads.py index 2c891e8..d5bc644 100644 --- a/tests/calibration/test_heads.py +++ b/tests/calibration/test_heads.py @@ -16,6 +16,7 @@ PerPoolNoiseHead, SharedLinearNoiseHead, ) +from quantammsim.calibration.pool_data import K_OBS_REDUCED # ── Helpers ───────────────────────────────────────────────────────────────── @@ -894,3 +895,237 @@ def test_default_unchanged(self): h = MLPNoiseHead() assert h.k_obs == K_OBS assert h.n_params(3, 5) == 232 + + +# ── TokenFactoredNoiseHead ───────────────────────────────────────────────── + + +def _make_token_factored_head(k_obs=K_OBS_REDUCED): + """Build a TokenFactoredNoiseHead from synthetic 2-pool, 3-token data.""" + from quantammsim.calibration.heads import TokenFactoredNoiseHead + + # Pool 0: (BTC=1, ETH=2) on MAINNET=1, fee=0.003 + # Pool 1: (AAVE=0, ETH=2) on ARBITRUM=0, fee=0.01 + token_a_idx = np.array([1, 0], dtype=np.int32) # BTC, AAVE + token_b_idx = np.array([2, 2], dtype=np.int32) # ETH, ETH + chain_idx = np.array([1, 0], dtype=np.int32) # MAINNET, ARBITRUM + log_fees = np.array([np.log(0.003), np.log(0.01)]) + x_token = np.array([ + [1.0, 20.0, 0.0, 0.0, 0.0], # AAVE: volatile + [1.0, 25.0, 0.0, 0.0, 0.0], # BTC: volatile + [1.0, 26.0, 0.0, 1.0, 1.0], # ETH: eth_derivative + L1_native + ]) + token_index = {"AAVE": 0, "BTC": 1, "ETH": 2} + chain_index = {"ARBITRUM": 0, "MAINNET": 1} + + head = TokenFactoredNoiseHead( + token_a_idx=token_a_idx, + token_b_idx=token_b_idx, + chain_idx=chain_idx, + log_fees=log_fees, + x_token=x_token, + n_tokens=3, + n_chains=2, + token_index=token_index, + chain_index=chain_index, + k_obs=k_obs, + lambda_delta=1.0, + lambda_token=0.1, + lambda_chain=0.1, + lambda_fee=0.01, + ) + return head + + +class TestTokenFactoredNoiseHead: + + def test_is_head(self): + head = _make_token_factored_head() + assert isinstance(head, Head) + + def test_n_params(self): + head = _make_token_factored_head(k_obs=4) + # 3 tokens * 4 + 5 d_token * 4 + 2 chains * 4 + 4 beta_fee + 2 pools * 4 + # = 12 + 20 + 8 + 4 + 8 = 52 + assert head.n_params(2, K_ATTR) == 52 + + def test_predict_returns_k_obs_vector(self): + head = _make_token_factored_head(k_obs=4) + n_p = head.n_params(2, K_ATTR) + params = jnp.zeros(n_p) + x_attr_i = jnp.zeros(K_ATTR) + result = head.predict(params, 0, x_attr_i) + assert result.shape == (4,) + + def test_predict_additivity(self): + """predict(pool_0) = u[BTC] + u[ETH] + alpha[MAINNET] + + beta_fee * log(0.003) + delta[0]""" + head = _make_token_factored_head(k_obs=4) + n_p = head.n_params(2, K_ATTR) + + # Build params with known values + params = np.zeros(n_p) + k = 4 # k_obs + # u: (3 tokens, 4) at offset 0 + u_flat = np.array([ + 1.0, 0.0, 0.0, 0.0, # AAVE + 2.0, 0.5, 0.0, 0.0, # BTC + 3.0, 1.0, 0.0, 0.0, # ETH + ]) + params[:12] = u_flat + # Gamma: (5, 4) at offset 12 — skip (doesn't affect predict) + # alpha: (2, 4) at offset 32 + alpha_flat = np.array([ + 0.1, 0.0, 0.0, 0.0, # ARBITRUM + 0.2, 0.0, 0.0, 0.0, # MAINNET + ]) + params[32:40] = alpha_flat + # beta_fee: (4,) at offset 40 + params[40:44] = np.array([0.5, 0.0, 0.0, 0.0]) + # delta: (2, 4) at offset 44 + params[44:48] = np.array([0.05, 0.0, 0.0, 0.0]) # pool 0 delta + + result = head.predict(jnp.array(params), 0, jnp.zeros(K_ATTR)) + + # Expected for pool 0: u[BTC] + u[ETH] + alpha[MAINNET] + # + beta_fee * log(0.003) + delta[0] + expected_0 = 2.0 + 3.0 + 0.2 + 0.5 * np.log(0.003) + 0.05 + np.testing.assert_allclose(float(result[0]), expected_0, rtol=1e-5) + + def test_regularization_nonneg_and_finite(self): + head = _make_token_factored_head() + n_p = head.n_params(2, K_ATTR) + np.random.seed(42) + params = jnp.array(np.random.randn(n_p) * 0.1) + reg = float(head.regularization(params)) + assert np.isfinite(reg) + assert reg >= 0.0 + + def test_regularization_zero_when_perfect(self): + """If u = x_token @ Gamma exactly, delta=0, alpha=0, beta_fee=0, + then only the Gamma-predicted part has zero token reg.""" + head = _make_token_factored_head(k_obs=4) + n_p = head.n_params(2, K_ATTR) + params = np.zeros(n_p) + # Set Gamma to something, then set u = x_token @ Gamma + np.random.seed(7) + Gamma = np.random.randn(5, 4) * 0.1 + u = head.x_token @ Gamma # (3, 4) + params[:12] = u.ravel() + params[12:32] = Gamma.ravel() + # alpha, beta_fee, delta all zero + reg = float(head.regularization(jnp.array(params))) + # Only lambda_token * 0 + lambda_chain * 0 + lambda_fee * 0 + lambda_delta * 0 + np.testing.assert_allclose(reg, 0.0, atol=1e-10) + + def test_init_cold(self): + head = _make_token_factored_head(k_obs=4) + jdata = _make_fake_jdata_reduced() + init = head.init(jdata) + n_p = head.n_params(N_POOLS, K_ATTR) + assert init.shape == (n_p,) + assert np.all(np.isfinite(init)) + + def test_init_warm_start_roundtrip(self): + """init from warm_start → predict ≈ warm_start noise_coeffs.""" + head = _make_token_factored_head(k_obs=4) + jdata = _make_fake_jdata_reduced() + + # Warm start with known noise coefficients per pool + warm = { + POOL_PREFIXES[0]: {"noise_coeffs": np.array([9.0, 0.5, 0.1, -0.2])}, + POOL_PREFIXES[1]: {"noise_coeffs": np.array([8.5, 0.3, 0.2, -0.1])}, + } + init = head.init(jdata, warm_start=warm) + params = jnp.array(init) + x_attr_dummy = jnp.zeros(K_ATTR) + + for i, pid in enumerate(jdata.pool_ids): + predicted = np.array(head.predict(params, i, x_attr_dummy)) + target = warm[pid]["noise_coeffs"] + # Should approximately recover the warm-start values + # (not exact because the lstsq decomposition is underdetermined + # with 2 pools and 3 tokens) + np.testing.assert_allclose(predicted, target, atol=0.5) + + def test_gradient_finite(self): + """jax.grad of a simple loss at init produces finite gradients.""" + import jax + head = _make_token_factored_head(k_obs=4) + jdata = _make_fake_jdata_reduced() + init = jnp.array(head.init(jdata)) + x_attr_i = jnp.zeros(K_ATTR) + + def loss(p): + c = head.predict(p, 0, x_attr_i) + return jnp.sum(c ** 2) + head.regularization(p) + + grad = jax.grad(loss)(init) + assert grad.shape == init.shape + assert jnp.all(jnp.isfinite(grad)) + + def test_unpack_result_keys(self): + head = _make_token_factored_head(k_obs=4) + n_p = head.n_params(2, K_ATTR) + np.random.seed(42) + params = np.random.randn(n_p) + result = head.unpack_result(params, 2, K_ATTR) + for key in ["token_effects", "Gamma", "chain_effects", + "beta_fee", "noise_deltas", "noise_coeffs"]: + assert key in result, f"Missing key: {key}" + assert result["token_effects"].shape == (3, 4) + assert result["Gamma"].shape == (5, 4) + assert result["chain_effects"].shape == (2, 4) + assert result["beta_fee"].shape == (4,) + assert result["noise_deltas"].shape == (2, 4) + assert result["noise_coeffs"].shape == (2, 4) + + def test_make_bounds(self): + head = _make_token_factored_head(k_obs=4) + bounds = head.make_bounds(2, K_ATTR) + assert len(bounds) == head.n_params(2, K_ATTR) + assert all(b == (None, None) for b in bounds) + + def test_predict_new_pool_seen_tokens(self): + head = _make_token_factored_head(k_obs=4) + n_p = head.n_params(2, K_ATTR) + np.random.seed(42) + params = np.random.randn(n_p) * 0.1 + result = head.predict_new_pool( + params, "BTC", "AAVE", "MAINNET", 0.003, n_pools=2 + ) + assert "noise_coeffs" in result + assert "components" in result + nc = result["noise_coeffs"] + assert nc.shape == (4,) or len(nc) == 4 + # Should equal u[BTC] + u[AAVE] + alpha[MAINNET] + beta_fee*log(0.003) + # (no delta for new pool) + comps = result["components"] + reconstructed = (comps["token_a"] + comps["token_b"] + + comps["chain"] + comps["fee"]) + np.testing.assert_allclose(nc, reconstructed, rtol=1e-6) + + def test_predict_new_pool_unseen_token(self): + head = _make_token_factored_head(k_obs=4) + n_p = head.n_params(2, K_ATTR) + np.random.seed(42) + params = np.random.randn(n_p) * 0.1 + # "LINK" is not in token_index → should fall back to Gamma + result = head.predict_new_pool( + params, "LINK", "ETH", "MAINNET", 0.003, n_pools=2 + ) + assert "noise_coeffs" in result + assert result["noise_coeffs"].shape == (4,) or len(result["noise_coeffs"]) == 4 + + def test_predict_new_pool_unseen_chain(self): + head = _make_token_factored_head(k_obs=4) + n_p = head.n_params(2, K_ATTR) + np.random.seed(42) + params = np.random.randn(n_p) * 0.1 + # "BASE" is not in chain_index → alpha = zeros + result = head.predict_new_pool( + params, "BTC", "ETH", "BASE", 0.003, n_pools=2 + ) + np.testing.assert_allclose( + result["components"]["chain"], np.zeros(4) + ) From 7edf6ecac20ef1537a7fae0db9a69024e7bc53be Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 16 Mar 2026 11:24:29 +0000 Subject: [PATCH 20/57] feat: integrate token-factored noise into joint calibration pipeline Add prepare_token_factored_data() combining joint data preparation with token encoding. End-to-end tests verify TokenFactoredNoiseHead fits through CalibrationModel with PerPoolHead(cadence) + FixedHead(gas), both cold-start and warm-started from Option C. --- quantammsim/calibration/joint_fit.py | 23 +++++++ tests/calibration/test_joint_fit.py | 98 ++++++++++++++++++++++++++++ 2 files changed, 121 insertions(+) diff --git a/quantammsim/calibration/joint_fit.py b/quantammsim/calibration/joint_fit.py index 765312d..4a237e9 100644 --- a/quantammsim/calibration/joint_fit.py +++ b/quantammsim/calibration/joint_fit.py @@ -91,6 +91,29 @@ def prepare_joint_data( ) +def prepare_token_factored_data( + matched: Dict[str, dict], + reduced_x_obs: bool = True, + fix_gas_to_chain: bool = True, +) -> tuple: + """Prepare JointData + token encoding for TokenFactoredNoiseHead. + + Returns (jdata, token_encoding) where token_encoding is the dict from + encode_tokens() containing token/chain structure for constructing the head. + """ + from quantammsim.calibration.pool_data import encode_tokens + + jdata = prepare_joint_data( + matched, + fix_gas_to_chain=fix_gas_to_chain, + reduced_x_obs=reduced_x_obs, + ) + + token_encoding = encode_tokens(matched) + + return jdata, token_encoding + + def pack_joint_params( bias_cad: float, bias_gas: float, diff --git a/tests/calibration/test_joint_fit.py b/tests/calibration/test_joint_fit.py index edb99f4..004ef93 100644 --- a/tests/calibration/test_joint_fit.py +++ b/tests/calibration/test_joint_fit.py @@ -190,6 +190,104 @@ def test_shared_noise_predict(self, matched_data): assert len(pred["noise_coeffs"]) == K_OBS +class TestPrepareTokenFactoredData: + def test_returns_jdata_and_encoding(self, matched_data): + from quantammsim.calibration.joint_fit import prepare_token_factored_data + + jdata, token_enc = prepare_token_factored_data(matched_data) + assert hasattr(jdata, "pool_data") + assert hasattr(jdata, "x_attr") + assert "token_index" in token_enc + assert "token_a_idx" in token_enc + + def test_reduced_x_obs_by_default(self, matched_data): + from quantammsim.calibration.joint_fit import prepare_token_factored_data + from quantammsim.calibration.pool_data import K_OBS_REDUCED + + jdata, _ = prepare_token_factored_data(matched_data) + for pd in jdata.pool_data: + assert pd["x_obs"].shape[1] == K_OBS_REDUCED + + def test_encoding_matches_pools(self, matched_data): + from quantammsim.calibration.joint_fit import prepare_token_factored_data + + jdata, enc = prepare_token_factored_data(matched_data) + n_pools = len(jdata.pool_data) + assert len(enc["token_a_idx"]) == n_pools + assert len(enc["token_b_idx"]) == n_pools + assert len(enc["chain_idx"]) == n_pools + assert len(enc["log_fees"]) == n_pools + + +class TestTokenFactoredEndToEnd: + """Full pipeline: TokenFactoredNoiseHead + CalibrationModel + fit.""" + + def test_model_fits_and_converges(self, matched_data): + from quantammsim.calibration.calibration_model import CalibrationModel + from quantammsim.calibration.heads import ( + FixedHead, PerPoolHead, TokenFactoredNoiseHead, + ) + from quantammsim.calibration.joint_fit import prepare_token_factored_data + from quantammsim.calibration.loss import CHAIN_GAS_USD + from quantammsim.calibration.pool_data import K_OBS_REDUCED + + jdata, enc = prepare_token_factored_data(matched_data) + n_pools = len(jdata.pool_data) + + # Gas: fixed to chain defaults + gas_values = [] + for pid in jdata.pool_ids: + chain = matched_data[pid]["chain"] + gas_values.append(np.log(max(CHAIN_GAS_USD.get(chain, 1.0), 1e-6))) + gas_head = FixedHead("log_gas", np.array(gas_values)) + + # Cadence: per-pool + cad_head = PerPoolHead("log_cadence", default=np.log(12.0)) + + # Noise: token-factored + noise_head = TokenFactoredNoiseHead(k_obs=K_OBS_REDUCED, **enc) + + model = CalibrationModel(cad_head, gas_head, noise_head) + result = model.fit(jdata, maxiter=50) + + assert result["loss"] <= result["init_loss"] + assert "token_effects" in result + assert "noise_coeffs" in result + assert result["noise_coeffs"].shape == (n_pools, K_OBS_REDUCED) + + def test_model_with_warm_start(self, matched_data): + from quantammsim.calibration.calibration_model import CalibrationModel + from quantammsim.calibration.heads import ( + FixedHead, PerPoolHead, TokenFactoredNoiseHead, + ) + from quantammsim.calibration.joint_fit import prepare_token_factored_data + from quantammsim.calibration.loss import CHAIN_GAS_USD + from quantammsim.calibration.per_pool_fit import fit_all_pools + from quantammsim.calibration.pool_data import K_OBS_REDUCED + + # Run Option C first + option_c = fit_all_pools( + matched_data, fix_gas_to_chain=True, reduced=True + ) + + jdata, enc = prepare_token_factored_data(matched_data) + n_pools = len(jdata.pool_data) + + gas_values = [] + for pid in jdata.pool_ids: + chain = matched_data[pid]["chain"] + gas_values.append(np.log(max(CHAIN_GAS_USD.get(chain, 1.0), 1e-6))) + + noise_head = TokenFactoredNoiseHead(k_obs=K_OBS_REDUCED, **enc) + model = CalibrationModel( + PerPoolHead("log_cadence", default=np.log(12.0)), + FixedHead("log_gas", np.array(gas_values)), + noise_head, + ) + result = model.fit(jdata, maxiter=100, warm_start=option_c) + assert result["loss"] <= result["init_loss"] + + class TestPrepareJointDataReduced: """Test prepare_joint_data with reduced_x_obs=True.""" From e8e9031bbfe35825a174532c083a5d2daa9b1005 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 16 Mar 2026 11:24:38 +0000 Subject: [PATCH 21/57] feat: add reduced x_obs (k_obs=4) pipeline to calibration runner Add run_option_c_reduced() for 4-covariate per-pool fits and run_reduced_joint() for joint MLPNoiseHead with k_obs=4. Wire reduced model into the comparison pipeline with correct x_obs dispatch in compute_per_pool_predictions(). Save reduced Option C results to JSON immediately for downstream use. --- scripts/run_mlp_calibration.py | 132 +++++++++++++++++++++++++++++++-- 1 file changed, 125 insertions(+), 7 deletions(-) diff --git a/scripts/run_mlp_calibration.py b/scripts/run_mlp_calibration.py index 85db2a0..e9b377b 100644 --- a/scripts/run_mlp_calibration.py +++ b/scripts/run_mlp_calibration.py @@ -104,6 +104,21 @@ def run_option_c(matched): return results +def run_option_c_reduced(matched): + """Per-pool fits with reduced x_obs (4 covariates) and gas fixed.""" + from quantammsim.calibration.per_pool_fit import fit_all_pools + + print(f"\n--- Option C Reduced: per-pool fits ({len(matched)} pools, " + f"4-covariate x_obs, gas fixed) ---") + results = fit_all_pools(matched, fix_gas_to_chain=True, reduced=True) + + losses = [r["loss"] for r in results.values()] + n_conv = sum(1 for r in results.values() if r["converged"]) + print(f" Converged: {n_conv}/{len(results)}") + print(f" Loss: median={np.median(losses):.4f}, mean={np.mean(losses):.4f}") + return results + + def _build_gas_values(jdata, matched_clean): """Build fixed gas values (log-space) from chain data.""" from quantammsim.calibration.loss import CHAIN_GAS_USD @@ -289,6 +304,53 @@ def run_two_stage_joint(matched_clean, option_c_clean, hidden=MLP_HIDDEN): return result, stage1_model, stage2_model, jdata +def run_reduced_joint(matched_clean, option_c_clean, hidden=MLP_HIDDEN): + """Joint fit with reduced x_obs (4 cols) to avoid noise-cadence confounding. + + Removes sigma- and fee-dependent features from the noise model's x_obs + so the arb channel (grid + cadence) is the only path for volatility-driven + volume variation. See docs/noise_covariate_design.md for theory. + + Uses LinearHead(cad) + FixedHead(gas) + MLPNoiseHead(k_obs=4). + Cadence warm-started from Option C; noise cold-started (OLS on 4-col x_obs). + """ + import jax.numpy as jnp + from quantammsim.calibration.calibration_model import CalibrationModel + from quantammsim.calibration.heads import FixedHead, LinearHead, MLPNoiseHead + from quantammsim.calibration.joint_fit import prepare_joint_data + from quantammsim.calibration.pool_data import K_OBS_REDUCED + + jdata = prepare_joint_data( + matched_clean, drop_chain_dummies=True, + fix_gas_to_chain=True, reduced_x_obs=True) + gas_values = _build_gas_values(jdata, matched_clean) + n_pools = len(jdata.pool_data) + k_attr = jdata.x_attr.shape[1] + + model = CalibrationModel( + cadence_head=LinearHead("cad", alpha=ALPHA_CAD), + gas_head=FixedHead("gas", gas_values), + noise_head=MLPNoiseHead(hidden=hidden, alpha=ALPHA_NOISE, + k_obs=K_OBS_REDUCED), + ) + + n_p = model.n_params(n_pools, k_attr) + print(f"\n--- Reduced x_obs: LinearHead(cad) + MLPNoiseHead(k_obs={K_OBS_REDUCED}, " + f"hidden={hidden}) ({n_pools} pools, {n_p} params) ---") + + # Only warm-start cadence (noise dimension changed 8→4, skip noise warm-start) + warm_cad = {} + for pid in jdata.pool_ids: + if pid in option_c_clean: + warm_cad[pid] = {"cad": option_c_clean[pid]["log_cadence"]} + + result = model.fit(jdata, maxiter=JOINT_MAXITER, warm_start=warm_cad) + print(f" Loss: {result['init_loss']:.4f} -> {result['loss']:.4f}") + print(f" Converged: {result['converged']}") + + return result, model, jdata + + def _extract_two_stage_per_pool(stage1_model, stage2_model, result, jdata): """Extract per-pool params from two-stage result.""" import jax.numpy as jnp @@ -357,16 +419,22 @@ def _extract_per_pool_params(model, result, jdata): def compute_per_pool_predictions(matched, option_c_results, - model_results): + model_results, reduced_models=None): """Compute V_arb, V_noise, R² per pool for Option C and each joint model. model_results: list of (label, per_pool_params, pool_ids) tuples, where per_pool_params[i] is a dict with log_cadence, log_gas, noise_coeffs. + + reduced_models: list of label strings whose noise_coeffs correspond to + reduced x_obs (4 columns). For those, build_x_obs(reduced=True) is used. """ from quantammsim.calibration.grid_interpolation import interpolate_pool_daily from quantammsim.calibration.pool_data import build_x_obs import jax.numpy as jnp + if reduced_models is None: + reduced_models = [] + pool_ids = sorted(matched.keys()) # Build lookup for each model's per-pool params @@ -388,7 +456,8 @@ def r2(v_arb, v_noise, y): coeffs = entry["coeffs"] day_indices = entry["day_indices"] - x_obs = build_x_obs(panel) + x_obs_full = build_x_obs(panel) + x_obs_red = None # lazy-build only if needed y_obs = panel["log_volume"].values.astype(float) p = { @@ -408,7 +477,7 @@ def r2(v_arb, v_noise, y): coeffs, jnp.float64(rc["log_cadence"]), jnp.float64(np.exp(rc["log_gas"])))) v_arb_c = v_arb_all[day_indices] - v_noise_c = np.exp(x_obs @ rc["noise_coeffs"]) + v_noise_c = np.exp(x_obs_full @ rc["noise_coeffs"]) p["v_arb_c"] = v_arb_c p["v_noise_c"] = v_noise_c p["r2_c"] = r2(v_arb_c, v_noise_c, y_obs) @@ -423,7 +492,15 @@ def r2(v_arb, v_noise, y): coeffs, jnp.float64(mp["log_cadence"]), jnp.float64(np.exp(mp["log_gas"])))) v_arb = v_arb_all[day_indices] - v_noise = np.exp(x_obs @ mp["noise_coeffs"]) + + # Use reduced x_obs for models that were trained with it + if label in reduced_models: + if x_obs_red is None: + x_obs_red = build_x_obs(panel, reduced=True) + v_noise = np.exp(x_obs_red @ mp["noise_coeffs"]) + else: + v_noise = np.exp(x_obs_full @ mp["noise_coeffs"]) + p[f"v_arb_{label}"] = v_arb p[f"v_noise_{label}"] = v_noise p[f"r2_{label}"] = r2(v_arb, v_noise, y_obs) @@ -807,6 +884,28 @@ def save_results_json(predictions, option_c_results, joint_results, output_dir): # ---- Main ---- +def _save_per_pool_results(results, output_path, label="option_c_reduced"): + """Save per-pool fit results to JSON immediately.""" + out = {} + for pid, r in results.items(): + out[pid] = { + "log_cadence": r["log_cadence"], + "log_gas": r["log_gas"], + "noise_coeffs": r["noise_coeffs"].tolist(), + "loss": r["loss"], + "converged": bool(r["converged"]), + "cadence_minutes": r["cadence_minutes"], + "gas_usd": r["gas_usd"], + "chain": r.get("chain", ""), + "fee": r.get("fee", 0), + "tokens": r.get("tokens", ""), + } + os.makedirs(os.path.dirname(output_path), exist_ok=True) + with open(output_path, "w") as f: + json.dump({label: out}, f, indent=2) + print(f" Saved {len(out)} pool results to {output_path}") + + def main(): os.environ.setdefault("JAX_PLATFORMS", "cpu") @@ -816,7 +915,15 @@ def main(): panel, matched = load_and_match() - # Step 1: Option C baseline + # Step 0: 4-covariate per-pool fits (fast, save immediately) + option_c_reduced = run_option_c_reduced(matched) + _save_per_pool_results( + option_c_reduced, + os.path.join(OUTPUT_DIR, "option_c_reduced.json"), + label="option_c_reduced", + ) + + # Step 1: Option C baseline (8-covariate) option_c = run_option_c(matched) # Step 2: Filter pathological pools @@ -834,25 +941,33 @@ def main(): two_stage_result, ts_s1_model, ts_s2_model, _ = run_two_stage_joint( matched_clean, option_c_clean) + # Reduced x_obs: prune sigma/fee features from noise covariates + reduced_result, reduced_model, jdata_reduced = run_reduced_joint( + matched_clean, option_c_clean) + # Step 4: Extract per-pool params from each model linear_pp = _extract_per_pool_params(linear_model, linear_result, jdata) mlp_noise_pp = _extract_per_pool_params(mlp_noise_model, mlp_noise_result, jdata) mlp_full_pp = _extract_per_pool_params(mlp_full_model, mlp_full_result, jdata) two_stage_pp = _extract_two_stage_per_pool( ts_s1_model, ts_s2_model, two_stage_result, jdata) + reduced_pp = _extract_per_pool_params( + reduced_model, reduced_result, jdata_reduced) - method_labels = ["linear", "mlp_noise", "mlp_full", "two_stage"] + method_labels = ["linear", "mlp_noise", "mlp_full", "two_stage", "reduced"] model_results_for_pred = [ ("linear", linear_pp, jdata.pool_ids), ("mlp_noise", mlp_noise_pp, jdata.pool_ids), ("mlp_full", mlp_full_pp, jdata.pool_ids), ("two_stage", two_stage_pp, jdata.pool_ids), + ("reduced", reduced_pp, jdata_reduced.pool_ids), ] # Step 5: Per-pool predictions print("\nComputing per-pool predictions...") predictions = compute_per_pool_predictions( - matched_clean, option_c_clean, model_results_for_pred) + matched_clean, option_c_clean, model_results_for_pred, + reduced_models=["reduced"]) # Step 6: Tables print_pool_table(predictions, method_labels) @@ -862,6 +977,7 @@ def main(): ("mlp_noise", mlp_noise_result), ("mlp_full", mlp_full_result), ("two_stage", two_stage_result), + ("reduced", reduced_result), ]) # Step 7: Plots @@ -873,6 +989,7 @@ def main(): plot_decomposition_pages(predictions, "mlp_noise", "MLP noise (linear cad)", OUTPUT_DIR) plot_decomposition_pages(predictions, "mlp_full", "Full MLP (MLP cad + MLP noise)", OUTPUT_DIR) plot_decomposition_pages(predictions, "two_stage", "Two-stage (linear cad -> MLP noise)", OUTPUT_DIR) + plot_decomposition_pages(predictions, "reduced", "Reduced x_obs (k_obs=4)", OUTPUT_DIR) plot_summary_distributions(predictions, method_labels, OUTPUT_DIR) plot_r2_scatter(predictions, method_labels, OUTPUT_DIR) @@ -884,6 +1001,7 @@ def main(): ("mlp_noise", mlp_noise_result), ("mlp_full", mlp_full_result), ("two_stage", two_stage_result), + ("reduced", reduced_result), ], OUTPUT_DIR) print(f"\n{'='*70}") From 229e38d83eee16790507c78d8e2e4e42e60b0e7f Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 16 Mar 2026 11:26:39 +0000 Subject: [PATCH 22/57] feat: token-factored calibration script with Phase 0 diagnostic and LOO MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Full pipeline: Phase 0 pooled Ridge diagnostic (baseline vs pool attrs vs token dummies vs full), token-factored fit with lambda_delta sweep, token/chain/delta analysis tables, leave-one-pool-out cross-validation comparing LOO R² to Option C in-sample R², and diagnostic plots. Phase 0 results: token dummies +0.091 vs pool attrs +0.072 above baseline (R²=0.058), confirming compositional structure exists. LOO results: median R²=0.33 vs Option C 0.59, 7/36 wins — the static coefficient prediction bottleneck limits transfer to unseen pools. This motivates lagged cross-pool features. --- scripts/run_token_factored_calibration.py | 611 ++++++++++++++++++++++ 1 file changed, 611 insertions(+) create mode 100644 scripts/run_token_factored_calibration.py diff --git a/scripts/run_token_factored_calibration.py b/scripts/run_token_factored_calibration.py new file mode 100644 index 0000000..0a6a170 --- /dev/null +++ b/scripts/run_token_factored_calibration.py @@ -0,0 +1,611 @@ +"""Token-factored noise calibration: pooled diagnostic + full pipeline. + +Phase 0: Pooled Ridge diagnostic — does cross-pool signal exist? +Phase 1: Token-factored model with lambda_delta sweep +Phase 2: LOO cross-validation +Phase 3: Comparison plots and JSON export +""" + +import json +import os +import sys + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +# ---- Config ---- +PANEL_CACHE = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "local_data", "noise_calibration", "panel.parquet", +) +GRID_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "pool_grids_v2", +) +OUTPUT_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", +) +OPTION_C_LOSS_CUTOFF = 5.0 +JOINT_MAXITER = 5000 +LAMBDA_DELTAS = [0.01, 0.1, 0.5, 1.0, 5.0, 10.0] + + +# ---- Data loading (shared with run_mlp_calibration.py) ---- + + +def load_and_match(): + """Load panel, match to grids.""" + from quantammsim.calibration.pool_data import ( + match_grids_to_panel, + replace_panel_volatility_with_binance, + ) + + panel = pd.read_parquet(PANEL_CACHE) + + if "log_tvl_lag1" not in panel.columns: + panel = panel.sort_values(["pool_id", "date"]).reset_index(drop=True) + panel["log_tvl_lag1"] = panel.groupby("pool_id")["log_tvl"].shift(1) + panel = panel.dropna(subset=["log_tvl_lag1"]).reset_index(drop=True) + + pool_counts = panel.groupby("pool_id").size() + valid = pool_counts[pool_counts >= 10].index + panel = panel[panel["pool_id"].isin(valid)].copy() + + print("Replacing volatility with Binance minute data...") + panel = replace_panel_volatility_with_binance(panel) + + print(f"Panel: {len(panel)} obs, {panel['pool_id'].nunique()} pools, " + f"{panel['date'].min()} to {panel['date'].max()}") + + matched = match_grids_to_panel(GRID_DIR, panel) + print(f"Matched: {len(matched)} pools with grids") + return panel, matched + + +def filter_pathological(matched, option_c): + """Drop pools with high Option C loss.""" + good = {p: r for p, r in option_c.items() if r["loss"] <= OPTION_C_LOSS_CUTOFF} + dropped = set(option_c) - set(good) + matched_clean = {p: matched[p] for p in good if p in matched} + if dropped: + print(f" Dropping {len(dropped)} pools (loss > {OPTION_C_LOSS_CUTOFF}):") + for p in sorted(dropped): + print(f" {p} loss={option_c[p]['loss']:.1f}") + return matched_clean, good + + +# ---- Phase 0: Pooled Ridge Diagnostic ---- + + +def run_phase0_diagnostic(matched, option_c): + """Pooled Ridge + token-dummy Ridge — go/no-go gate.""" + from sklearn.linear_model import RidgeCV + + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + from quantammsim.calibration.pool_data import ( + build_pool_attributes, build_x_obs, encode_tokens, _parse_tokens, + ) + import jax.numpy as jnp + + print("\n" + "=" * 70) + print("Phase 0: Pooled Ridge Diagnostic — cross-pool signal?") + print("=" * 70) + + pool_ids = sorted(matched.keys()) + X_attr, attr_names, _ = build_pool_attributes(matched) + pool_idx_map = {pid: i for i, pid in enumerate(pool_ids)} + enc = encode_tokens(matched) + + all_x, all_y, all_pool_attrs, all_token_dummies = [], [], [], [] + + for pid in pool_ids: + entry = matched[pid] + oc = option_c[pid] + coeffs = entry["coeffs"] + + v_arb_all = np.array(interpolate_pool_daily( + coeffs, jnp.float64(oc["log_cadence"]), + jnp.float64(np.exp(oc["log_gas"])))) + v_arb = v_arb_all[entry["day_indices"]] + + x_obs = build_x_obs(entry["panel"], reduced=True) + y_obs = entry["panel"]["log_volume"].values.astype(float) + y_residual = y_obs - np.log(np.maximum(v_arb, 1e-6)) + + all_x.append(x_obs) + all_y.append(y_residual) + + # Broadcast pool attrs to each obs + x_attr_row = X_attr[pool_idx_map[pid]] + all_pool_attrs.append(np.tile(x_attr_row, (len(x_obs), 1))) + + # Token dummies: one-hot for each token in the pool + n_obs = len(x_obs) + dummies = np.zeros((n_obs, enc["n_tokens"]), dtype=np.float64) + toks = _parse_tokens(entry["tokens"]) + for t in toks[:2]: + if t in enc["token_index"]: + dummies[:, enc["token_index"][t]] = 1.0 + all_token_dummies.append(dummies) + + X_obs = np.vstack(all_x) + y_combined = np.concatenate(all_y) + X_pool_attrs = np.vstack(all_pool_attrs) + X_token_dummies = np.vstack(all_token_dummies) + + # Model 1: x_obs + pool_attrs → pooled Ridge + X_combined = np.column_stack([X_obs, X_pool_attrs]) + model1 = RidgeCV(alphas=np.logspace(-2, 4, 50)) + model1.fit(X_combined, y_combined) + r2_pooled = model1.score(X_combined, y_combined) + print(f" Pooled Ridge (x_obs + pool attrs): R² = {r2_pooled:.4f}") + + # Model 2: x_obs + token_dummies → token-dummy Ridge + X_token = np.column_stack([X_obs, X_token_dummies]) + model2 = RidgeCV(alphas=np.logspace(-2, 4, 50)) + model2.fit(X_token, y_combined) + r2_token = model2.score(X_token, y_combined) + print(f" Token-dummy Ridge (x_obs + token dummies): R² = {r2_token:.4f}") + + # Model 3: x_obs + token_dummies + chain_dummies + log_fee + chain_dummies = np.zeros((len(y_combined), enc["n_chains"]), dtype=np.float64) + log_fees = np.zeros((len(y_combined), 1), dtype=np.float64) + offset = 0 + for pid in pool_ids: + n_obs = len(matched[pid]["day_indices"]) + ci = enc["chain_idx"][pool_idx_map[pid]] + chain_dummies[offset:offset + n_obs, ci] = 1.0 + log_fees[offset:offset + n_obs, 0] = enc["log_fees"][pool_idx_map[pid]] + offset += n_obs + + X_full = np.column_stack([X_obs, X_token_dummies, chain_dummies, log_fees]) + model3 = RidgeCV(alphas=np.logspace(-2, 4, 50)) + model3.fit(X_full, y_combined) + r2_full = model3.score(X_full, y_combined) + print(f" Full Ridge (x_obs + tokens + chains + fee): R² = {r2_full:.4f}") + + # Baseline: x_obs only + model_base = RidgeCV(alphas=np.logspace(-2, 4, 50)) + model_base.fit(X_obs, y_combined) + r2_base = model_base.score(X_obs, y_combined) + print(f" Baseline (x_obs only): R² = {r2_base:.4f}") + + print(f"\n Signal above baseline:") + print(f" Pool attrs: +{r2_pooled - r2_base:.4f}") + print(f" Token dummies: +{r2_token - r2_base:.4f}") + print(f" Full (tok+ch+fee): +{r2_full - r2_base:.4f}") + + if r2_full - r2_base < 0.01: + print("\n WARNING: Very weak cross-pool signal. " + "Token factoring may not improve over per-pool fits.") + + return { + "r2_baseline": r2_base, + "r2_pool_attrs": r2_pooled, + "r2_token_dummies": r2_token, + "r2_full": r2_full, + "n_obs_total": len(y_combined), + "n_pools": len(pool_ids), + "n_tokens": enc["n_tokens"], + "n_chains": enc["n_chains"], + } + + +# ---- Phase 1: Token-Factored Model ---- + + +def _build_gas_values(jdata, matched_clean): + """Build fixed gas values (log-space) from chain data.""" + from quantammsim.calibration.loss import CHAIN_GAS_USD + gas_values = [] + for pid in jdata.pool_ids: + chain = matched_clean[pid]["chain"] + gas_usd = CHAIN_GAS_USD.get(chain, 1.0) + gas_values.append(np.log(max(gas_usd, 1e-6))) + return np.array(gas_values) + + +def run_token_factored(matched_clean, option_c_clean, lambda_delta=1.0): + """Fit TokenFactoredNoiseHead with PerPoolHead(cadence) + FixedHead(gas).""" + from quantammsim.calibration.calibration_model import CalibrationModel + from quantammsim.calibration.heads import ( + FixedHead, PerPoolHead, TokenFactoredNoiseHead, + ) + from quantammsim.calibration.joint_fit import prepare_token_factored_data + from quantammsim.calibration.pool_data import K_OBS_REDUCED + + jdata, enc = prepare_token_factored_data(matched_clean) + n_pools = len(jdata.pool_data) + + gas_values = _build_gas_values(jdata, matched_clean) + gas_head = FixedHead("log_gas", gas_values) + cad_head = PerPoolHead("log_cadence", default=np.log(12.0)) + noise_head = TokenFactoredNoiseHead( + k_obs=K_OBS_REDUCED, + lambda_delta=lambda_delta, + **enc, + ) + + model = CalibrationModel(cad_head, gas_head, noise_head) + n_p = model.n_params(n_pools, jdata.x_attr.shape[1]) + print(f"\n--- Token-factored (lambda_delta={lambda_delta}) ---") + print(f" {n_pools} pools, {enc['n_tokens']} tokens, " + f"{enc['n_chains']} chains, {n_p} params") + + result = model.fit(jdata, maxiter=JOINT_MAXITER, warm_start=option_c_clean) + print(f" Loss: {result['init_loss']:.4f} -> {result['loss']:.4f}") + print(f" Converged: {result['converged']}") + + return result, model, jdata, enc + + +# ---- Phase 2: Analysis & Visualization ---- + + +def print_token_effects(result, enc): + """Print token effect table.""" + u = result["token_effects"] + Gamma = result["Gamma"] + x_token = enc["x_token"] + token_index = enc["token_index"] + inv_index = {v: k for k, v in token_index.items()} + + u_pred = x_token @ Gamma # population prediction + + print(f"\n{'='*70}") + print("Token effects (u_t) vs population prediction (x_t @ Gamma)") + print(f"{'='*70}") + print(f"{'Token':<12} {'u[0]':>8} {'pred[0]':>8} {'delta[0]':>8} " + f"{'u[1]':>8} {'pred[1]':>8}") + print("-" * 60) + for idx in range(len(inv_index)): + name = inv_index[idx] + print(f"{name:<12} {u[idx,0]:>8.3f} {u_pred[idx,0]:>8.3f} " + f"{u[idx,0]-u_pred[idx,0]:>8.3f} " + f"{u[idx,1]:>8.3f} {u_pred[idx,1]:>8.3f}") + + +def print_chain_effects(result, enc): + """Print chain effect table.""" + alpha = result["chain_effects"] + chain_index = enc["chain_index"] + inv_index = {v: k for k, v in chain_index.items()} + + print(f"\n{'='*70}") + print("Chain effects (alpha)") + print(f"{'='*70}") + k = alpha.shape[1] + header = f"{'Chain':<12}" + "".join(f" {'a['+str(j)+']':>8}" for j in range(k)) + print(header) + print("-" * (12 + 9 * k)) + for idx in range(len(inv_index)): + name = inv_index[idx] + vals = " ".join(f"{alpha[idx, j]:>8.3f}" for j in range(k)) + print(f"{name:<12} {vals}") + + +def print_delta_analysis(result, enc, jdata, matched_clean): + """Print per-pool delta analysis.""" + delta = result["noise_deltas"] + pool_ids = jdata.pool_ids + token_index = enc["token_index"] + inv_token = {v: k for k, v in token_index.items()} + + print(f"\n{'='*70}") + print("Per-pool deltas (unexplained residual)") + print(f"{'='*70}") + print(f"{'Pool':<24} {'Tokens':<16} {'Chain':<10} " + f"{'|delta|':>8} {'delta[0]':>8}") + print("-" * 70) + + delta_norms = np.linalg.norm(delta, axis=1) + order = np.argsort(-delta_norms) + for i in order: + pid = pool_ids[i] + entry = matched_clean[pid] + print(f"{pid[:24]:<24} {entry['tokens']:<16} {entry['chain']:<10} " + f"{delta_norms[i]:>8.3f} {delta[i, 0]:>8.3f}") + + +def run_lambda_sweep(matched_clean, option_c_clean): + """Sweep lambda_delta values and report loss + delta shrinkage.""" + print(f"\n{'='*70}") + print("Lambda_delta sweep") + print(f"{'='*70}") + print(f"{'lambda':>10} {'loss':>10} {'delta_norm':>12} {'mean_|d|':>10}") + print("-" * 45) + + results = [] + for lam in LAMBDA_DELTAS: + result, model, jdata, enc = run_token_factored( + matched_clean, option_c_clean, lambda_delta=lam) + delta = result["noise_deltas"] + delta_norm = float(np.linalg.norm(delta)) + mean_abs_d = float(np.mean(np.abs(delta))) + print(f"{lam:>10.2f} {result['loss']:>10.4f} " + f"{delta_norm:>12.4f} {mean_abs_d:>10.4f}") + results.append({ + "lambda_delta": lam, + "loss": result["loss"], + "delta_norm": delta_norm, + "mean_abs_delta": mean_abs_d, + "converged": result["converged"], + }) + + return results + + +# ---- Phase 3: LOO Cross-Validation ---- + + +def run_loo_validation(matched_clean, option_c_clean, lambda_delta=1.0): + """Leave-one-pool-out cross-validation via predict_new_pool.""" + from quantammsim.calibration.calibration_model import CalibrationModel + from quantammsim.calibration.heads import ( + FixedHead, PerPoolHead, TokenFactoredNoiseHead, + ) + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + from quantammsim.calibration.joint_fit import prepare_token_factored_data + from quantammsim.calibration.pool_data import K_OBS_REDUCED, build_x_obs, _parse_tokens + import jax.numpy as jnp + + pool_ids = sorted(matched_clean.keys()) + n_pools = len(pool_ids) + + print(f"\n{'='*70}") + print(f"LOO Cross-Validation (lambda_delta={lambda_delta})") + print(f"{'='*70}") + + loo_results = [] + for hold_out_pid in pool_ids: + # Build training set without hold-out pool + train_matched = {p: matched_clean[p] for p in pool_ids if p != hold_out_pid} + train_oc = {p: option_c_clean[p] for p in pool_ids if p != hold_out_pid} + + if len(train_matched) < 3: + continue + + # Fit on training set + jdata, enc = prepare_token_factored_data(train_matched) + gas_values = _build_gas_values(jdata, train_matched) + + noise_head = TokenFactoredNoiseHead( + k_obs=K_OBS_REDUCED, + lambda_delta=lambda_delta, + **enc, + ) + model = CalibrationModel( + PerPoolHead("log_cadence", default=np.log(12.0)), + FixedHead("log_gas", gas_values), + noise_head, + ) + result = model.fit(jdata, maxiter=JOINT_MAXITER, warm_start=train_oc) + + # Extract noise params and predict for hold-out pool + n_train = len(jdata.pool_data) + k_attr = jdata.x_attr.shape[1] + (_, _), (_, _), (ns, ne) = model._head_slices(n_train, k_attr) + noise_params = result["params_flat"][ns:ne] + + ho_entry = matched_clean[hold_out_pid] + toks = _parse_tokens(ho_entry["tokens"]) + ho_pred = noise_head.predict_new_pool( + noise_params, toks[0], toks[1], + ho_entry["chain"], ho_entry["fee"], + n_pools=n_train, + ) + + # Evaluate hold-out R² + ho_panel = ho_entry["panel"] + x_obs_ho = build_x_obs(ho_panel, reduced=True) + y_obs_ho = ho_panel["log_volume"].values.astype(float) + + # Use Option C cadence for the hold-out pool (not predicting cadence) + oc_ho = option_c_clean[hold_out_pid] + v_arb_all = np.array(interpolate_pool_daily( + ho_entry["coeffs"], + jnp.float64(oc_ho["log_cadence"]), + jnp.float64(np.exp(oc_ho["log_gas"])), + )) + v_arb = v_arb_all[ho_entry["day_indices"]] + v_noise = np.exp(x_obs_ho @ ho_pred["noise_coeffs"]) + log_pred = np.log(np.maximum(v_arb + v_noise, 1e-6)) + ss_res = np.sum((log_pred - y_obs_ho) ** 2) + ss_tot = np.sum((y_obs_ho - y_obs_ho.mean()) ** 2) + r2_loo = 1 - ss_res / max(ss_tot, 1e-10) + + # Compare with Option C in-sample R² + v_noise_c = np.exp(build_x_obs(ho_panel, reduced=True) @ oc_ho["noise_coeffs"][:K_OBS_REDUCED]) + log_pred_c = np.log(np.maximum(v_arb + v_noise_c, 1e-6)) + ss_res_c = np.sum((log_pred_c - y_obs_ho) ** 2) + r2_c = 1 - ss_res_c / max(ss_tot, 1e-10) + + loo_results.append({ + "pool_id": hold_out_pid, + "r2_loo": r2_loo, + "r2_option_c": r2_c, + "tokens": ho_entry["tokens"], + "chain": ho_entry["chain"], + }) + + print(f" {hold_out_pid[:16]} ({ho_entry['tokens']:<14}) " + f"R²_LOO={r2_loo:.3f} R²_C={r2_c:.3f} " + f"{'BETTER' if r2_loo > r2_c else 'worse'}") + + if loo_results: + r2s_loo = [r["r2_loo"] for r in loo_results] + r2s_c = [r["r2_option_c"] for r in loo_results] + n_better = sum(1 for r in loo_results if r["r2_loo"] > r["r2_option_c"]) + print(f"\n LOO median R²: {np.median(r2s_loo):.4f} " + f"(Option C: {np.median(r2s_c):.4f})") + print(f" LOO wins: {n_better}/{len(loo_results)}") + + return loo_results + + +# ---- Plots ---- + + +def plot_lambda_sweep(sweep_results, output_dir): + """Plot loss and delta norm vs lambda_delta.""" + lambdas = [r["lambda_delta"] for r in sweep_results] + losses = [r["loss"] for r in sweep_results] + delta_norms = [r["delta_norm"] for r in sweep_results] + + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5)) + + ax1.semilogx(lambdas, losses, "o-", color="steelblue") + ax1.set_xlabel("lambda_delta") + ax1.set_ylabel("Loss") + ax1.set_title("Loss vs lambda_delta") + + ax2.semilogx(lambdas, delta_norms, "o-", color="orangered") + ax2.set_xlabel("lambda_delta") + ax2.set_ylabel("||delta||") + ax2.set_title("Delta norm vs lambda_delta") + + fig.tight_layout() + out = os.path.join(output_dir, "lambda_sweep.png") + fig.savefig(out, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f" Saved: {out}") + + +def plot_token_effects(result, enc, output_dir): + """Bar chart of token effects (intercept coefficient).""" + u = result["token_effects"] + token_index = enc["token_index"] + inv_index = {v: k for k, v in token_index.items()} + names = [inv_index[i] for i in range(len(inv_index))] + + fig, ax = plt.subplots(figsize=(max(8, len(names) * 0.5), 5)) + x = np.arange(len(names)) + ax.bar(x, u[:, 0], color="steelblue", alpha=0.8) + ax.set_xticks(x) + ax.set_xticklabels(names, rotation=45, ha="right", fontsize=8) + ax.set_ylabel("u_t[0] (intercept effect)") + ax.set_title("Token effects on noise intercept") + ax.axhline(0, color="black", linewidth=0.5, linestyle="--") + fig.tight_layout() + out = os.path.join(output_dir, "token_effects.png") + fig.savefig(out, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f" Saved: {out}") + + +def plot_loo_scatter(loo_results, output_dir): + """Scatter: Option C R² vs LOO R².""" + if not loo_results: + return + + r2_c = [r["r2_option_c"] for r in loo_results] + r2_loo = [r["r2_loo"] for r in loo_results] + + fig, ax = plt.subplots(figsize=(7, 6)) + ax.scatter(r2_c, r2_loo, alpha=0.7, s=40, edgecolors="k", linewidth=0.5) + lo = min(min(r2_c), min(r2_loo)) + hi = max(max(r2_c), max(r2_loo)) + margin = (hi - lo) * 0.05 + 0.01 + ax.plot([lo - margin, hi + margin], [lo - margin, hi + margin], + "k--", alpha=0.3, linewidth=1) + ax.set_xlabel("Option C R² (in-sample)") + ax.set_ylabel("Token-factored R² (LOO)") + ax.set_title("LOO Cross-Validation: Token-Factored vs Option C") + + n_better = sum(1 for c, l in zip(r2_c, r2_loo) if l > c) + ax.text(0.05, 0.95, f"LOO wins: {n_better}/{len(r2_c)}", + transform=ax.transAxes, fontsize=10, va="top") + + fig.tight_layout() + out = os.path.join(output_dir, "loo_scatter.png") + fig.savefig(out, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f" Saved: {out}") + + +# ---- Main ---- + + +def main(): + os.environ.setdefault("JAX_PLATFORMS", "cpu") + + print("=" * 70) + print("Token-Factored Noise Calibration") + print("=" * 70) + + panel, matched = load_and_match() + + # Step 1: Option C baseline (reduced x_obs) + from quantammsim.calibration.per_pool_fit import fit_all_pools + print(f"\n--- Option C Reduced: per-pool fits ({len(matched)} pools) ---") + option_c = fit_all_pools(matched, fix_gas_to_chain=True, reduced=True) + losses = [r["loss"] for r in option_c.values()] + print(f" Loss: median={np.median(losses):.4f}, mean={np.mean(losses):.4f}") + + # Step 2: Filter pathological pools + matched_clean, option_c_clean = filter_pathological(matched, option_c) + + # Phase 0: Diagnostic + diag = run_phase0_diagnostic(matched_clean, option_c_clean) + + # Phase 1: Token-factored model (default lambda) + result, model, jdata, enc = run_token_factored( + matched_clean, option_c_clean, lambda_delta=1.0) + + # Analysis + print_token_effects(result, enc) + print_chain_effects(result, enc) + print_delta_analysis(result, enc, jdata, matched_clean) + + # Lambda sweep + sweep_results = run_lambda_sweep(matched_clean, option_c_clean) + + # Phase 2: LOO cross-validation + loo_results = run_loo_validation( + matched_clean, option_c_clean, lambda_delta=1.0) + + # Phase 3: Plots & export + print("\nGenerating plots...") + os.makedirs(OUTPUT_DIR, exist_ok=True) + + plot_lambda_sweep(sweep_results, OUTPUT_DIR) + plot_token_effects(result, enc, OUTPUT_DIR) + plot_loo_scatter(loo_results, OUTPUT_DIR) + + # JSON export + export = { + "phase0_diagnostic": diag, + "token_factored": { + "loss": result["loss"], + "init_loss": result["init_loss"], + "converged": result["converged"], + "n_pools": result["n_pools"], + "n_tokens": enc["n_tokens"], + "n_chains": enc["n_chains"], + "token_index": enc["token_index"], + "chain_index": enc["chain_index"], + "token_effects": result["token_effects"].tolist(), + "Gamma": result["Gamma"].tolist(), + "chain_effects": result["chain_effects"].tolist(), + "beta_fee": result["beta_fee"].tolist(), + "noise_deltas": result["noise_deltas"].tolist(), + "noise_coeffs": result["noise_coeffs"].tolist(), + }, + "lambda_sweep": sweep_results, + "loo_results": loo_results, + } + json_path = os.path.join(OUTPUT_DIR, "token_factored_results.json") + with open(json_path, "w") as f: + json.dump(export, f, indent=2, default=str) + print(f" Saved: {json_path}") + + print(f"\n{'='*70}") + print(f"Done. Output in: {OUTPUT_DIR}") + + +if __name__ == "__main__": + main() From 5db391905d309385fd6d8352a3c1f5507a113d4a Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 16 Mar 2026 11:28:31 +0000 Subject: [PATCH 23/57] feat: support calibrated noise model in Optuna parameter tuning Load per-pool noise_coeffs from calibration JSON, derive arb_frequency from calibrated log_cadence, and pick up token pair, fee, and gas from pool metadata. Supports both 4-covariate (reduced) and 8-covariate (full) noise coefficient formats. Add --n-eval-points flag for evaluation sub-window control. --- experiments/tune_reclamm_params.py | 76 +++++++++++++++++++++++++++++- 1 file changed, 74 insertions(+), 2 deletions(-) diff --git a/experiments/tune_reclamm_params.py b/experiments/tune_reclamm_params.py index 4969923..e2448bf 100644 --- a/experiments/tune_reclamm_params.py +++ b/experiments/tune_reclamm_params.py @@ -13,9 +13,16 @@ # More trials, custom fees python experiments/tune_reclamm_params.py --n-trials 200 --fees 0.005 + + # With calibrated 8-covariate noise model and arb frequency from calibration + python experiments/tune_reclamm_params.py --noise-model calibrated \ + --noise-params-json results/mlp_calibration/option_c_reduced.json \ + --noise-pool-id 0x9d1fcf346ea1b0 """ import argparse +import json +import math from quantammsim.runners.jax_runners import train_on_historic_data PARAMETER_CONFIG = { @@ -39,6 +46,15 @@ def main(): choices=["geometric", "constant_arc_length"]) parser.add_argument("--centeredness-scaling", action="store_true") parser.add_argument("--noise-trader-ratio", type=float, default=0.0) + parser.add_argument("--noise-model", default=None, + choices=["ratio", "loglinear", "calibrated", "arb_only"], + help="Noise volume model (default: ratio via noise-trader-ratio)") + parser.add_argument("--noise-params-json", default=None, + help="JSON file with per-pool calibration results") + parser.add_argument("--noise-pool-id", default=None, + help="Pool ID to load noise params for (from --noise-params-json)") + parser.add_argument("--arb-frequency", type=int, default=None, + help="Arb frequency in minutes (default: from calibrated cadence or 1)") parser.add_argument("--start-date", default="2024-06-01 00:00:00") parser.add_argument("--end-date", default="2025-01-01 00:00:00", help="End of training / start of test") @@ -49,6 +65,8 @@ def main(): help="Validation holdout fraction (default: 0.2, use 0 to disable)") parser.add_argument("--overfitting-penalty", type=float, default=None, help="Overfitting penalty weight (default: 0.2)") + parser.add_argument("--n-eval-points", type=int, default=None, + help="Number of evaluation sub-windows (default: 20, use 1 for full-window)") args = parser.parse_args() learn_speed = args.interpolation == "constant_arc_length" @@ -56,19 +74,72 @@ def main(): if learn_speed: param_config.update(ARC_LENGTH_SPEED_CONFIG) + # --- Noise model setup --- + pool_tokens = ["AAVE", "ETH"] # default + noise_fp = {"noise_trader_ratio": args.noise_trader_ratio} + if args.noise_model: + noise_fp["noise_model"] = args.noise_model + if args.noise_params_json and args.noise_pool_id: + with open(args.noise_params_json) as f: + all_results = json.load(f) + # Support both {"option_c_reduced": {pid: ...}} and {pid: ...} formats + pool_results = all_results + for key in all_results: + if isinstance(all_results[key], dict) and args.noise_pool_id in all_results[key]: + pool_results = all_results[key] + break + pool_data = pool_results[args.noise_pool_id] + coeffs = pool_data["noise_coeffs"] + if len(coeffs) == 8: + # Full 8-covariate model: [intercept, log_tvl, log_sigma, + # tvl*sigma, tvl*fee, sigma*fee, dow_sin, dow_cos] + noise_fp["reclamm_noise_params"] = { + f"c_{i}": c for i, c in enumerate(coeffs) + } + elif len(coeffs) == 4: + # Reduced 4-covariate model: [intercept, log_tvl, dow_sin, dow_cos] + # Map to c_0, c_1, c_6, c_7 (sigma/fee terms stay at 0) + noise_fp["reclamm_noise_params"] = { + "c_0": coeffs[0], "c_1": coeffs[1], + "c_6": coeffs[2], "c_7": coeffs[3], + } + else: + raise ValueError(f"Expected 4 or 8 noise_coeffs, got {len(coeffs)}") + # Derive arb_frequency from calibrated cadence if not explicitly set + if args.arb_frequency is None: + log_cad = pool_data["log_cadence"] + args.arb_frequency = max(1, round(math.exp(log_cad))) + print(f" arb_frequency={args.arb_frequency} " + f"(from log_cadence={log_cad:.2f}, " + f"cadence={math.exp(log_cad):.1f} min)") + # Use pool's fee and gas from calibration as defaults + if "fee" in pool_data: + args.fees = pool_data["fee"] + if "gas_usd" in pool_data: + args.gas_cost = pool_data["gas_usd"] + # Pick up token pair from calibration + # Map on-chain names (WETH, WBTC) to data-file names (ETH, BTC) + _TOKEN_MAP = {"WETH": "ETH", "WBTC": "BTC"} + if "tokens" in pool_data: + pool_tokens = [ + _TOKEN_MAP.get(t, t) for t in pool_data["tokens"].split(",") + ] + print(f" tokens={pool_tokens}, fee={args.fees}, gas={args.gas_cost}") + fp = { "rule": "reclamm", - "tokens": ["AAVE", "ETH"], + "tokens": pool_tokens, "startDateString": args.start_date, "endDateString": args.end_date, "endTestDateString": args.end_test_date, "initial_pool_value": 1_000_000.0, "do_arb": True, + **({"arb_frequency": args.arb_frequency} if args.arb_frequency is not None else {}), "fees": args.fees, "gas_cost": args.gas_cost, "arb_fees": 0.0, "protocol_fee_split": 0.5, - "noise_trader_ratio": args.noise_trader_ratio, + **noise_fp, "return_val": args.objective, "reclamm_interpolation_method": args.interpolation, "reclamm_centeredness_scaling": args.centeredness_scaling, @@ -86,6 +157,7 @@ def main(): "multi_objective": False, "parameter_config": param_config, **({"overfitting_penalty": args.overfitting_penalty} if args.overfitting_penalty is not None else {}), + **({"n_evaluation_points": args.n_eval_points} if args.n_eval_points is not None else {}), }, }, } From 5398c3b5483d7600c66909767600467f039b0344 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 16 Mar 2026 12:20:34 +0000 Subject: [PATCH 24/57] feat: add token canonicalization and cross-pool lagged volume features MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Token canonicalization (_CANON_MAP) maps wrapped/derivative tokens to their base symbols (WETH→ETH, waBasWETH→ETH, WBTC→BTC, etc.), reducing the token graph from ~32 to ~22 unique tokens and thickening peer groups for cross-pool information sharing. Cross-pool lag features (build_cross_pool_x_obs, K_OBS_CROSS=7) enrich observation-level covariates with lagged peer volume averages for token A, token B, and chain — so daily noise predictions can adapt to market conditions without autoregressive cold-start issues. --- quantammsim/calibration/heads.py | 8 +- quantammsim/calibration/pool_data.py | 176 +++++++++++++++- tests/calibration/test_pool_data.py | 303 +++++++++++++++++++++++++++ 3 files changed, 481 insertions(+), 6 deletions(-) diff --git a/quantammsim/calibration/heads.py b/quantammsim/calibration/heads.py index 8b40682..ff86dfc 100644 --- a/quantammsim/calibration/heads.py +++ b/quantammsim/calibration/heads.py @@ -17,7 +17,8 @@ from quantammsim.calibration.loss import K_OBS from quantammsim.calibration.pool_data import ( - D_TOKEN, K_OBS_REDUCED, _classify_token, _load_token_mcaps, + D_TOKEN, K_OBS_REDUCED, _canonicalize_token, _classify_token, + _load_token_mcaps, ) @@ -720,6 +721,7 @@ def predict_new_pool( Seen tokens use learned u_t. Unseen tokens fall back to x_t @ Gamma. Unseen chains use alpha = zeros. No delta for new pools. + Input token names are canonicalized before lookup. """ params_np = np.asarray(params_slice) u, Gamma, alpha, beta_fee, delta = self._unpack(params_np, n_pools) @@ -728,6 +730,10 @@ def predict_new_pool( ) mcaps = _load_token_mcaps(self._mcap_path) + # Canonicalize input tokens + token_a = _canonicalize_token(token_a) + token_b = _canonicalize_token(token_b) + def _get_token_effect(token): if token in self.token_index: return u[self.token_index[token]] diff --git a/quantammsim/calibration/pool_data.py b/quantammsim/calibration/pool_data.py index 18ab821..3b05bbb 100644 --- a/quantammsim/calibration/pool_data.py +++ b/quantammsim/calibration/pool_data.py @@ -19,6 +19,9 @@ K_OBS = 8 # observation-level covariates K_OBS_REDUCED = 4 # [intercept, log_tvl_lag1, dow_sin, dow_cos] +K_OBS_CROSS = 7 # [intercept, log_tvl_lag1, dow_sin, dow_cos, + # cross_vol_token_a_{t-1}, cross_vol_token_b_{t-1}, + # cross_vol_chain_{t-1}] # Default path for cached token market caps _MCAP_PATH = os.path.join( @@ -348,6 +351,24 @@ def match_grids_to_panel( return matched +# Token canonicalization — map wrapped/LST variants to base tokens +_CANON_MAP = { + "WETH": "ETH", "waBasWETH": "ETH", "waEthLidoWETH": "ETH", + "waEthLidowstETH": "wstETH", "waGnowstETH": "wstETH", + "waBasUSDC": "USDC", "scUSD": "USDC", "USDC.e": "USDC", + "USDbC": "USDC", "waEthUSDC": "USDC", + "sDAI": "DAI", "WXDAI": "DAI", + "WBTC": "BTC", "cbBTC": "BTC", + "stS": "S", "wS": "S", + "waGnoGNO": "GNO", "osGNO": "GNO", +} + + +def _canonicalize_token(symbol: str) -> str: + """Map wrapped/derivative token to its canonical base symbol.""" + return _CANON_MAP.get(symbol, symbol) + + # Token classification for token-factored model _ETH_DERIVATIVES = { "WETH", "ETH", "wstETH", "stETH", "rETH", "cbETH", @@ -374,11 +395,16 @@ def _classify_token(symbol: str, mcaps: dict) -> dict: def encode_tokens( matched: Dict[str, dict], mcap_path: str = None, + canonicalize: bool = True, ) -> dict: """Build token index, per-pool token assignments, and token covariate matrix. Iterates over pools in sorted key order (same ordering as build_pool_attributes). + When canonicalize=True (default), wrappd/derivative tokens are mapped to + their canonical base symbol via _CANON_MAP before building the index. + Raw symbols are still used for market cap lookup. + Returns dict with: token_index: dict[str, int] — symbol -> integer index (sorted alphabetically) token_a_idx: np.ndarray (n_pools,) — index of token A for each pool @@ -394,14 +420,19 @@ def encode_tokens( pool_ids = sorted(matched.keys()) n_pools = len(pool_ids) - # Collect all tokens and chains + # Collect all tokens and chains; store per-pool canonical pairs all_tokens = set() all_chains = set() + pool_canon_toks = [] # (canon_a, canon_b) per pool in sorted order for pid in pool_ids: entry = matched[pid] toks = _parse_tokens(entry["tokens"]) - all_tokens.update(toks[:2]) + raw_a, raw_b = toks[0], toks[1] + canon_a = _canonicalize_token(raw_a) if canonicalize else raw_a + canon_b = _canonicalize_token(raw_b) if canonicalize else raw_b + all_tokens.update([canon_a, canon_b]) all_chains.add(entry["chain"]) + pool_canon_toks.append((canon_a, canon_b)) # Build sorted indices token_list = sorted(all_tokens) @@ -420,9 +451,9 @@ def encode_tokens( for i, pid in enumerate(pool_ids): entry = matched[pid] - toks = _parse_tokens(entry["tokens"]) - token_a_idx[i] = token_index[toks[0]] - token_b_idx[i] = token_index[toks[1]] + canon_a, canon_b = pool_canon_toks[i] + token_a_idx[i] = token_index[canon_a] + token_b_idx[i] = token_index[canon_b] chain_idx[i] = chain_index[entry["chain"]] log_fees[i] = np.log(entry["fee"]) @@ -497,6 +528,141 @@ def build_x_obs(panel_rows: pd.DataFrame, reduced: bool = False) -> np.ndarray: return x +def build_cross_pool_x_obs( + panel_rows: pd.DataFrame, + matched: Dict[str, dict], + pool_id: str, + exclude_pool: Optional[str] = None, + canonicalize: bool = True, +) -> np.ndarray: + """Build x_obs with cross-pool lagged volume features. + + Columns 0-3: same as build_x_obs(reduced=True) + Column 4: mean log_volume at t-1 across pools sharing token A (excl self) + Column 5: mean log_volume at t-1 across pools sharing token B (excl self) + Column 6: mean log_volume at t-1 across pools on same chain (excl self) + + The first observation (day 0) is dropped because there is no lag available. + + Args: + panel_rows: DataFrame for this pool + matched: full matched dict (all pools) + pool_id: this pool's key in matched (prefix) + exclude_pool: optional pool to exclude from peer averages (for LOO) + canonicalize: if True, canonicalize tokens before peer matching + + Returns: + (n_obs - 1, K_OBS_CROSS) array + """ + # Get this pool's tokens and chain + entry = matched[pool_id] + toks = _parse_tokens(entry["tokens"]) + tok_a_raw, tok_b_raw = toks[0], toks[1] + tok_a = _canonicalize_token(tok_a_raw) if canonicalize else tok_a_raw + tok_b = _canonicalize_token(tok_b_raw) if canonicalize else tok_b_raw + this_chain = entry["chain"] + + # Build peer sets: token→set of pool_ids, chain→set of pool_ids + token_peers = {} # canonical_token → set of (prefix, panel_df) + chain_peers = {} # chain → set of (prefix, panel_df) + all_pool_ids = sorted(matched.keys()) + + for pid in all_pool_ids: + if pid == pool_id: + continue # always exclude self + if pid == exclude_pool: + continue + peer_entry = matched[pid] + peer_toks = _parse_tokens(peer_entry["tokens"]) + peer_canonical = set() + for t in peer_toks[:2]: + ct = _canonicalize_token(t) if canonicalize else t + peer_canonical.add(ct) + + for ct in peer_canonical: + if ct not in token_peers: + token_peers[ct] = [] + token_peers[ct].append(pid) + + peer_chain = peer_entry["chain"] + if peer_chain not in chain_peers: + chain_peers[peer_chain] = [] + chain_peers[peer_chain].append(pid) + + # Build (pool_id, date_ordinal) → log_volume lookup from all pools + vol_lookup = {} # (pid, date_ordinal) → log_volume + for pid in all_pool_ids: + if pid == pool_id or pid == exclude_pool: + continue + peer_panel = matched[pid]["panel"] + peer_dates = pd.to_datetime(peer_panel["date"]) + peer_ords = np.array([d.toordinal() for d in peer_dates]) + peer_vols = peer_panel["log_volume"].values.astype(float) + for ord_val, vol_val in zip(peer_ords, peer_vols): + vol_lookup[(pid, int(ord_val))] = vol_val + + # Compute global lagged mean for fallback + all_vols = list(vol_lookup.values()) + global_mean_vol = float(np.mean(all_vols)) if all_vols else 0.0 + + # Get this pool's dates + dates = pd.to_datetime(panel_rows["date"]) + date_ords = np.array([d.toordinal() for d in dates]) + n_obs = len(panel_rows) + + def _peer_mean_at_lag(peer_pids, date_ord_prev): + """Mean log_volume of peer pools at date_ord_prev.""" + vals = [] + for pid in peer_pids: + key = (pid, date_ord_prev) + if key in vol_lookup: + vals.append(vol_lookup[key]) + if vals: + return float(np.mean(vals)) + return np.nan + + # Build cross-pool features for each obs (starting from day 1) + cross_vol_a = np.full(n_obs, np.nan) + cross_vol_b = np.full(n_obs, np.nan) + cross_vol_chain = np.full(n_obs, np.nan) + + tok_a_peers = token_peers.get(tok_a, []) + tok_b_peers = token_peers.get(tok_b, []) + chain_peer_list = chain_peers.get(this_chain, []) + + for i in range(1, n_obs): + prev_ord = int(date_ords[i - 1]) + + if tok_a_peers: + cross_vol_a[i] = _peer_mean_at_lag(tok_a_peers, prev_ord) + if tok_b_peers: + cross_vol_b[i] = _peer_mean_at_lag(tok_b_peers, prev_ord) + if chain_peer_list: + cross_vol_chain[i] = _peer_mean_at_lag(chain_peer_list, prev_ord) + + # Drop first day, fill NaN with global mean + cross_vol_a = cross_vol_a[1:] + cross_vol_b = cross_vol_b[1:] + cross_vol_chain = cross_vol_chain[1:] + + cross_vol_a = np.where(np.isnan(cross_vol_a), global_mean_vol, cross_vol_a) + cross_vol_b = np.where(np.isnan(cross_vol_b), global_mean_vol, cross_vol_b) + cross_vol_chain = np.where(np.isnan(cross_vol_chain), global_mean_vol, cross_vol_chain) + + # Build base x_obs (reduced) and drop first row + x_base = build_x_obs(panel_rows, reduced=True) + x_base = x_base[1:] # drop first day + + # Assemble + x = np.zeros((n_obs - 1, K_OBS_CROSS)) + x[:, :4] = x_base + x[:, 4] = cross_vol_a + x[:, 5] = cross_vol_b + x[:, 6] = cross_vol_chain + + return x + + def build_pool_attributes( matched: Dict[str, dict], mcap_path: str = None, diff --git a/tests/calibration/test_pool_data.py b/tests/calibration/test_pool_data.py index adbf212..4864c19 100644 --- a/tests/calibration/test_pool_data.py +++ b/tests/calibration/test_pool_data.py @@ -502,3 +502,306 @@ def test_attributes_returns_pool_order( assert isinstance(pool_ids, list) assert len(pool_ids) == len(matched) assert set(pool_ids) == set(matched.keys()) + + +class TestTokenCanonicalization: + """Test _CANON_MAP and canonicalization in encode_tokens.""" + + def test_canon_map_exists(self): + from quantammsim.calibration.pool_data import _CANON_MAP + assert isinstance(_CANON_MAP, dict) + + def test_canon_map_expected_mappings(self): + from quantammsim.calibration.pool_data import _CANON_MAP + assert _CANON_MAP["WETH"] == "ETH" + assert _CANON_MAP["waBasWETH"] == "ETH" + assert _CANON_MAP["waEthLidoWETH"] == "ETH" + assert _CANON_MAP["waEthLidowstETH"] == "wstETH" + assert _CANON_MAP["waGnowstETH"] == "wstETH" + assert _CANON_MAP["waBasUSDC"] == "USDC" + assert _CANON_MAP["scUSD"] == "USDC" + assert _CANON_MAP["sDAI"] == "DAI" + assert _CANON_MAP["WBTC"] == "BTC" + assert _CANON_MAP["waGnoGNO"] == "GNO" + assert _CANON_MAP["stS"] == "S" + + def test_canonicalize_passthrough(self): + from quantammsim.calibration.pool_data import _CANON_MAP + for tok in ["AAVE", "BTC", "ETH", "LINK", "ARB"]: + assert tok not in _CANON_MAP + + def test_canonicalize_function(self): + from quantammsim.calibration.pool_data import _canonicalize_token + assert _canonicalize_token("WETH") == "ETH" + assert _canonicalize_token("WBTC") == "BTC" + assert _canonicalize_token("ETH") == "ETH" + assert _canonicalize_token("AAVE") == "AAVE" + + def test_encode_tokens_canonicalize_false( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + """With canonicalize=False, same result as v1 (no merging).""" + from quantammsim.calibration.pool_data import encode_tokens, match_grids_to_panel + + grid_dir = tmp_path / "grids" + grid_dir.mkdir() + for prefix in POOL_PREFIXES: + synthetic_daily_grid.to_parquet( + grid_dir / f"{prefix}_daily.parquet", index=False + ) + matched = match_grids_to_panel(str(grid_dir), synthetic_panel) + result = encode_tokens(matched, canonicalize=False) + # Synthetic uses BTC, ETH, AAVE — none in canon map, same either way + assert result["n_tokens"] == 3 + assert set(result["token_index"].keys()) == {"AAVE", "BTC", "ETH"} + + def test_encode_tokens_canonicalize_default( + self, synthetic_daily_grid, synthetic_panel, tmp_path + ): + """Default canonicalize=True. Synthetic data unaffected (BTC, ETH, AAVE not in map).""" + from quantammsim.calibration.pool_data import encode_tokens, match_grids_to_panel + + grid_dir = tmp_path / "grids" + grid_dir.mkdir() + for prefix in POOL_PREFIXES: + synthetic_daily_grid.to_parquet( + grid_dir / f"{prefix}_daily.parquet", index=False + ) + matched = match_grids_to_panel(str(grid_dir), synthetic_panel) + result = encode_tokens(matched) # canonicalize=True by default + assert result["n_tokens"] == 3 + assert set(result["token_index"].keys()) == {"AAVE", "BTC", "ETH"} + + def test_encode_tokens_merges_wrapped_tokens(self): + """Synthetic matched dict with WETH+USDC and waBasWETH+WBTC → ETH, BTC, USDC.""" + from quantammsim.calibration.pool_data import encode_tokens + + # Minimal matched dict — encode_tokens only uses 'tokens' and 'fee' keys + matched = { + "pool_a": { + "tokens": "WETH,USDC", + "fee": 0.003, + "chain": "MAINNET", + }, + "pool_b": { + "tokens": "waBasWETH,WBTC", + "fee": 0.003, + "chain": "BASE", + }, + } + result = encode_tokens(matched, canonicalize=True) + # WETH→ETH, waBasWETH→ETH, WBTC→BTC → unique: {BTC, ETH, USDC} + assert result["n_tokens"] == 3 + assert set(result["token_index"].keys()) == {"BTC", "ETH", "USDC"} + + # Both pools should have ETH as token A (canonicalized) + ti = result["token_index"] + assert result["token_a_idx"][0] == ti["ETH"] # pool_a: WETH→ETH + assert result["token_a_idx"][1] == ti["ETH"] # pool_b: waBasWETH→ETH + + def test_encode_tokens_canon_false_keeps_wrapped(self): + """canonicalize=False keeps WETH and waBasWETH as separate tokens.""" + from quantammsim.calibration.pool_data import encode_tokens + + matched = { + "pool_a": { + "tokens": "WETH,USDC", + "fee": 0.003, + "chain": "MAINNET", + }, + "pool_b": { + "tokens": "waBasWETH,WBTC", + "fee": 0.003, + "chain": "BASE", + }, + } + result = encode_tokens(matched, canonicalize=False) + # No merging: WETH, USDC, waBasWETH, WBTC → 4 unique tokens + assert result["n_tokens"] == 4 + assert "WETH" in result["token_index"] + assert "waBasWETH" in result["token_index"] + + +class TestCrossPoolFeatures: + """Test build_cross_pool_x_obs: cross-pool lagged volume features.""" + + @pytest.fixture + def three_pool_panel(self): + """3 pools sharing tokens, 10 days each.""" + np.random.seed(42) + dates = pd.date_range("2025-12-01", periods=10, freq="D") + rows = [] + pool_configs = [ + ("0xpool_a_full_id_padding_to_66_chars_aaaaaaaaaaaaaaaaaaaaaaaaa", + "MAINNET", "ETH,USDC", 0.003), + ("0xpool_b_full_id_padding_to_66_chars_bbbbbbbbbbbbbbbbbbbbbbbbb", + "MAINNET", "ETH,AAVE", 0.003), + ("0xpool_c_full_id_padding_to_66_chars_ccccccccccccccccccccccccc", + "ARBITRUM", "AAVE,USDC", 0.01), + ] + for full_id, chain, tokens, fee in pool_configs: + for di, date in enumerate(dates): + tvl = 12.0 + 0.05 * np.sin(2 * np.pi * di / 7) + vol = 9.0 + 0.3 * np.random.randn() + rows.append({ + "pool_id": full_id, + "chain": chain, + "date": date, + "log_volume": vol, + "log_tvl": tvl, + "log_tvl_lag1": tvl - 0.01, + "volatility": 0.4, + "log_fee": np.log(fee), + "swap_fee": fee, + "tokens": tokens, + }) + return pd.DataFrame(rows) + + @pytest.fixture + def three_pool_matched(self, three_pool_panel): + """Minimal matched dict for 3 pools (no grid needed for x_obs tests).""" + matched = {} + for full_id in three_pool_panel["pool_id"].unique(): + prefix = full_id[:16] + rows = three_pool_panel[three_pool_panel["pool_id"] == full_id].copy() + rows = rows.reset_index(drop=True) + matched[prefix] = { + "panel": rows, + "pool_id": full_id, + "chain": rows.iloc[0]["chain"], + "fee": float(np.exp(rows.iloc[0]["log_fee"])), + "tokens": rows.iloc[0]["tokens"], + "weights": [0.5, 0.5], + } + return matched + + def test_build_cross_pool_x_obs_shape(self, three_pool_matched): + from quantammsim.calibration.pool_data import ( + K_OBS_CROSS, build_cross_pool_x_obs, + ) + + pid = sorted(three_pool_matched.keys())[0] + entry = three_pool_matched[pid] + x = build_cross_pool_x_obs(entry["panel"], three_pool_matched, pid) + # Drops first day → n_obs - 1 rows, K_OBS_CROSS=7 columns + assert x.shape[1] == K_OBS_CROSS + assert x.shape[0] == len(entry["panel"]) - 1 + + def test_first_four_cols_match_reduced(self, three_pool_matched): + from quantammsim.calibration.pool_data import ( + build_cross_pool_x_obs, build_x_obs, + ) + + pid = sorted(three_pool_matched.keys())[0] + entry = three_pool_matched[pid] + x_cross = build_cross_pool_x_obs(entry["panel"], three_pool_matched, pid) + x_reduced = build_x_obs(entry["panel"], reduced=True) + # First 4 columns should match (after dropping first row) + np.testing.assert_allclose(x_cross[:, :4], x_reduced[1:, :4]) + + def test_cross_vol_token_a_excludes_self(self, three_pool_matched): + """Peer average for token A excludes pool i itself.""" + from quantammsim.calibration.pool_data import build_cross_pool_x_obs + + pool_ids = sorted(three_pool_matched.keys()) + pid_a = pool_ids[0] # ETH,USDC + pid_b = pool_ids[1] # ETH,AAVE — shares ETH with pool_a + + x_a = build_cross_pool_x_obs( + three_pool_matched[pid_a]["panel"], + three_pool_matched, pid_a, + ) + # Column 4 = cross_vol_token_a (ETH peers excl self) + # Pool b also has ETH, so pool_a's cross_vol_token_a should use pool_b's volume + panel_b = three_pool_matched[pid_b]["panel"] + log_vol_b_lagged = panel_b["log_volume"].values[:-1] # lag by 1 + np.testing.assert_allclose(x_a[:, 4], log_vol_b_lagged, rtol=1e-6) + + def test_cross_vol_is_lagged(self, three_pool_matched): + """Features at day t use log_volume at day t-1.""" + from quantammsim.calibration.pool_data import build_cross_pool_x_obs + + pool_ids = sorted(three_pool_matched.keys()) + pid = pool_ids[0] + x = build_cross_pool_x_obs( + three_pool_matched[pid]["panel"], + three_pool_matched, pid, + ) + # x has n_obs - 1 rows (first day dropped) + # Row 0 of x corresponds to day 1 and should use day 0 volume + assert x.shape[0] > 0 + + def test_cross_vol_nan_free_after_first_day(self, three_pool_matched): + from quantammsim.calibration.pool_data import build_cross_pool_x_obs + + for pid in three_pool_matched: + x = build_cross_pool_x_obs( + three_pool_matched[pid]["panel"], + three_pool_matched, pid, + ) + assert not np.any(np.isnan(x)), f"NaNs in cross-pool x_obs for {pid}" + + def test_exclude_pool_changes_features(self, three_pool_matched): + """exclude_pool removes that pool from peer averages.""" + from quantammsim.calibration.pool_data import build_cross_pool_x_obs + + pool_ids = sorted(three_pool_matched.keys()) + pid = pool_ids[0] # ETH,USDC + + x_normal = build_cross_pool_x_obs( + three_pool_matched[pid]["panel"], + three_pool_matched, pid, + ) + x_excluded = build_cross_pool_x_obs( + three_pool_matched[pid]["panel"], + three_pool_matched, pid, + exclude_pool=pool_ids[1], # exclude the ETH peer + ) + # Chain feature (col 6) may change too; token A col (4) definitely changes + # since pool_b is the only ETH peer + # With only peer excluded, cross_vol_token_a should be NaN→fallback + assert not np.allclose(x_normal[:, 4], x_excluded[:, 4]) + + def test_single_token_pool_fallback(self): + """When a token appears in only one pool, its cross_vol uses global mean.""" + from quantammsim.calibration.pool_data import build_cross_pool_x_obs + + np.random.seed(42) + dates = pd.date_range("2025-12-01", periods=5, freq="D") + rows = [] + # Pool A: LINK,USDC — LINK is unique + for di, date in enumerate(dates): + rows.append({ + "pool_id": "0xsolo_link_pool_aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", + "chain": "MAINNET", "date": date, + "log_volume": 9.0 + 0.1 * di, + "log_tvl_lag1": 12.0, "volatility": 0.4, + "log_fee": np.log(0.003), "tokens": "LINK,USDC", + }) + # Pool B: ETH,USDC + for di, date in enumerate(dates): + rows.append({ + "pool_id": "0xpeer_eth_pool__bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb", + "chain": "MAINNET", "date": date, + "log_volume": 10.0 + 0.1 * di, + "log_tvl_lag1": 13.0, "volatility": 0.4, + "log_fee": np.log(0.003), "tokens": "ETH,USDC", + }) + panel = pd.DataFrame(rows) + + matched = {} + for full_id in panel["pool_id"].unique(): + prefix = full_id[:16] + sub = panel[panel["pool_id"] == full_id].reset_index(drop=True) + matched[prefix] = { + "panel": sub, "pool_id": full_id, + "chain": sub.iloc[0]["chain"], + "fee": float(np.exp(sub.iloc[0]["log_fee"])), + "tokens": sub.iloc[0]["tokens"], "weights": [0.5, 0.5], + } + + pid_link = [p for p in matched if matched[p]["tokens"] == "LINK,USDC"][0] + x = build_cross_pool_x_obs(panel[panel["pool_id"] == matched[pid_link]["pool_id"]].reset_index(drop=True), + matched, pid_link) + # LINK has no peers → col 4 should be a fallback (global mean), not NaN + assert not np.any(np.isnan(x[:, 4])) From e99d3e25d1b86884563016c7425a88d6da046e43 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 16 Mar 2026 12:20:48 +0000 Subject: [PATCH 25/57] feat: report data_loss and reg_loss separately in CalibrationModel.fit Re-evaluates pool loss functions at the optimum to decompose total loss into data_loss (mean per-pool MSE) and reg_loss (head regularization). Enables tracking whether lambda annealing is reducing data fit or just shrinking regularization. --- quantammsim/calibration/calibration_model.py | 10 +++ tests/calibration/test_joint_fit.py | 87 ++++++++++++++++++++ 2 files changed, 97 insertions(+) diff --git a/quantammsim/calibration/calibration_model.py b/quantammsim/calibration/calibration_model.py index 88e940c..86471ec 100644 --- a/quantammsim/calibration/calibration_model.py +++ b/quantammsim/calibration/calibration_model.py @@ -179,6 +179,7 @@ def loss_fn(params_flat): return data_loss + reg # Attach for the scipy wrapper + loss_fn._pool_loss_fns = pool_loss_fns loss_fn._pool_val_and_grad_fns = pool_val_and_grad_fns loss_fn._n_pools = n_pools loss_fn._head_slices = (cad_s, cad_e), (gas_s, gas_e), (noise_s, noise_e) @@ -282,9 +283,18 @@ def scipy_wrapper(params_np): (cad_s, cad_e), (gas_s, gas_e), (noise_s, noise_e) = \ self._head_slices(n_pools, k_attr) + # Compute data_loss and reg_loss at optimum + fitted_j = jnp.array(result.x) + data_loss_val = sum( + float(fn(fitted_j)) for fn in loss_fn._pool_loss_fns + ) / n_pools + reg_loss_val = float(result.fun) - data_loss_val + out = { "init_loss": init_loss, "loss": float(result.fun), + "data_loss": data_loss_val, + "reg_loss": reg_loss_val, "converged": result.success, "params_flat": np.array(result.x), } diff --git a/tests/calibration/test_joint_fit.py b/tests/calibration/test_joint_fit.py index 004ef93..3a3971d 100644 --- a/tests/calibration/test_joint_fit.py +++ b/tests/calibration/test_joint_fit.py @@ -288,6 +288,93 @@ def test_model_with_warm_start(self, matched_data): assert result["loss"] <= result["init_loss"] +class TestDataRegLossSeparation: + """Test that fit() reports data_loss and reg_loss separately.""" + + def test_fit_reports_data_and_reg_loss(self, matched_data): + from quantammsim.calibration.calibration_model import CalibrationModel + from quantammsim.calibration.heads import ( + FixedHead, PerPoolHead, TokenFactoredNoiseHead, + ) + from quantammsim.calibration.joint_fit import prepare_token_factored_data + from quantammsim.calibration.loss import CHAIN_GAS_USD + from quantammsim.calibration.pool_data import K_OBS_REDUCED + + jdata, enc = prepare_token_factored_data(matched_data) + n_pools = len(jdata.pool_data) + + gas_values = [] + for pid in jdata.pool_ids: + chain = matched_data[pid]["chain"] + gas_values.append(np.log(max(CHAIN_GAS_USD.get(chain, 1.0), 1e-6))) + + noise_head = TokenFactoredNoiseHead(k_obs=K_OBS_REDUCED, **enc) + model = CalibrationModel( + PerPoolHead("log_cadence", default=np.log(12.0)), + FixedHead("log_gas", np.array(gas_values)), + noise_head, + ) + result = model.fit(jdata, maxiter=50) + + assert "data_loss" in result + assert "reg_loss" in result + + def test_data_plus_reg_equals_total(self, matched_data): + from quantammsim.calibration.calibration_model import CalibrationModel + from quantammsim.calibration.heads import ( + FixedHead, PerPoolHead, TokenFactoredNoiseHead, + ) + from quantammsim.calibration.joint_fit import prepare_token_factored_data + from quantammsim.calibration.loss import CHAIN_GAS_USD + from quantammsim.calibration.pool_data import K_OBS_REDUCED + + jdata, enc = prepare_token_factored_data(matched_data) + gas_values = [] + for pid in jdata.pool_ids: + chain = matched_data[pid]["chain"] + gas_values.append(np.log(max(CHAIN_GAS_USD.get(chain, 1.0), 1e-6))) + + noise_head = TokenFactoredNoiseHead(k_obs=K_OBS_REDUCED, **enc) + model = CalibrationModel( + PerPoolHead("log_cadence", default=np.log(12.0)), + FixedHead("log_gas", np.array(gas_values)), + noise_head, + ) + result = model.fit(jdata, maxiter=50) + + np.testing.assert_allclose( + result["data_loss"] + result["reg_loss"], + result["loss"], + rtol=1e-6, + ) + + def test_data_loss_leq_total(self, matched_data): + from quantammsim.calibration.calibration_model import CalibrationModel + from quantammsim.calibration.heads import ( + FixedHead, PerPoolHead, TokenFactoredNoiseHead, + ) + from quantammsim.calibration.joint_fit import prepare_token_factored_data + from quantammsim.calibration.loss import CHAIN_GAS_USD + from quantammsim.calibration.pool_data import K_OBS_REDUCED + + jdata, enc = prepare_token_factored_data(matched_data) + gas_values = [] + for pid in jdata.pool_ids: + chain = matched_data[pid]["chain"] + gas_values.append(np.log(max(CHAIN_GAS_USD.get(chain, 1.0), 1e-6))) + + noise_head = TokenFactoredNoiseHead(k_obs=K_OBS_REDUCED, **enc) + model = CalibrationModel( + PerPoolHead("log_cadence", default=np.log(12.0)), + FixedHead("log_gas", np.array(gas_values)), + noise_head, + ) + result = model.fit(jdata, maxiter=50) + + assert result["data_loss"] <= result["loss"] + 1e-10 + assert result["reg_loss"] >= -1e-10 + + class TestPrepareJointDataReduced: """Test prepare_joint_data with reduced_x_obs=True.""" From 34751981dc460c64c070264ba37b288c0fe12e0c Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 16 Mar 2026 12:24:22 +0000 Subject: [PATCH 26/57] feat: v2 runner with lambda annealing, cross-pool ablation, and LOO MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Lambda sweep now runs descending (high→low regularization) with each fit warm-starting from the previous result. Runner runs two ablations side-by-side: baseline (K_OBS_REDUCED=4) vs cross-pool (K_OBS_CROSS=7), reporting separated data/reg loss and LOO R² for each configuration. prepare_token_factored_data() gains cross_pool parameter to swap in cross-pool lag features automatically. --- quantammsim/calibration/joint_fit.py | 35 ++- scripts/run_token_factored_calibration.py | 292 ++++++++++++++++------ 2 files changed, 246 insertions(+), 81 deletions(-) diff --git a/quantammsim/calibration/joint_fit.py b/quantammsim/calibration/joint_fit.py index 4a237e9..d71be73 100644 --- a/quantammsim/calibration/joint_fit.py +++ b/quantammsim/calibration/joint_fit.py @@ -95,21 +95,54 @@ def prepare_token_factored_data( matched: Dict[str, dict], reduced_x_obs: bool = True, fix_gas_to_chain: bool = True, + canonicalize: bool = True, + cross_pool: bool = False, ) -> tuple: """Prepare JointData + token encoding for TokenFactoredNoiseHead. + Args: + matched: dict from match_grids_to_panel + reduced_x_obs: if True, use 4-column reduced x_obs + fix_gas_to_chain: if True, fix gas to chain-level costs + canonicalize: if True, canonicalize token names before building index + cross_pool: if True, use cross-pool lag features (K_OBS_CROSS=7) + Returns (jdata, token_encoding) where token_encoding is the dict from encode_tokens() containing token/chain structure for constructing the head. """ from quantammsim.calibration.pool_data import encode_tokens + if cross_pool: + from quantammsim.calibration.pool_data import ( + K_OBS_CROSS, build_cross_pool_x_obs, + ) + jdata = prepare_joint_data( matched, fix_gas_to_chain=fix_gas_to_chain, reduced_x_obs=reduced_x_obs, ) - token_encoding = encode_tokens(matched) + if cross_pool: + # Replace x_obs with cross-pool version for each pool + pool_ids = jdata.pool_ids + new_pool_data = [] + for i, pid in enumerate(pool_ids): + entry = matched[pid] + x_obs_cross = build_cross_pool_x_obs( + entry["panel"], matched, pid, canonicalize=canonicalize, + ) + d = dict(jdata.pool_data[i]) + d["x_obs"] = jnp.array(x_obs_cross) + new_pool_data.append(d) + jdata = JointData( + pool_data=new_pool_data, + x_attr=jdata.x_attr, + pool_ids=jdata.pool_ids, + attr_names=jdata.attr_names, + ) + + token_encoding = encode_tokens(matched, canonicalize=canonicalize) return jdata, token_encoding diff --git a/scripts/run_token_factored_calibration.py b/scripts/run_token_factored_calibration.py index 0a6a170..1ccf15e 100644 --- a/scripts/run_token_factored_calibration.py +++ b/scripts/run_token_factored_calibration.py @@ -1,14 +1,13 @@ -"""Token-factored noise calibration: pooled diagnostic + full pipeline. +"""Token-factored noise calibration v2: canonicalization + cross-pool lag features. Phase 0: Pooled Ridge diagnostic — does cross-pool signal exist? -Phase 1: Token-factored model with lambda_delta sweep -Phase 2: LOO cross-validation +Phase 1: Token-factored model with lambda_delta annealing sweep +Phase 2: LOO cross-validation (baseline vs cross-pool ablation) Phase 3: Comparison plots and JSON export """ import json import os -import sys import matplotlib matplotlib.use("Agg") @@ -31,7 +30,8 @@ ) OPTION_C_LOSS_CUTOFF = 5.0 JOINT_MAXITER = 5000 -LAMBDA_DELTAS = [0.01, 0.1, 0.5, 1.0, 5.0, 10.0] +# Sorted descending for warm-start annealing (highest regularization first) +LAMBDA_DELTAS = [10.0, 5.0, 1.0, 0.5, 0.1, 0.01] # ---- Data loading (shared with run_mlp_calibration.py) ---- @@ -96,7 +96,7 @@ def run_phase0_diagnostic(matched, option_c): print("=" * 70) pool_ids = sorted(matched.keys()) - X_attr, attr_names, _ = build_pool_attributes(matched) + X_attr, _, _ = build_pool_attributes(matched) pool_idx_map = {pid: i for i, pid in enumerate(pool_ids)} enc = encode_tokens(matched) @@ -137,14 +137,14 @@ def run_phase0_diagnostic(matched, option_c): X_pool_attrs = np.vstack(all_pool_attrs) X_token_dummies = np.vstack(all_token_dummies) - # Model 1: x_obs + pool_attrs → pooled Ridge + # Model 1: x_obs + pool_attrs X_combined = np.column_stack([X_obs, X_pool_attrs]) model1 = RidgeCV(alphas=np.logspace(-2, 4, 50)) model1.fit(X_combined, y_combined) r2_pooled = model1.score(X_combined, y_combined) print(f" Pooled Ridge (x_obs + pool attrs): R² = {r2_pooled:.4f}") - # Model 2: x_obs + token_dummies → token-dummy Ridge + # Model 2: x_obs + token_dummies X_token = np.column_stack([X_obs, X_token_dummies]) model2 = RidgeCV(alphas=np.logspace(-2, 4, 50)) model2.fit(X_token, y_combined) @@ -209,35 +209,65 @@ def _build_gas_values(jdata, matched_clean): return np.array(gas_values) -def run_token_factored(matched_clean, option_c_clean, lambda_delta=1.0): +def _result_to_warm_start(result): + """Extract per-pool warm_start dict from a CalibrationModel fit result. + + Returns dict: pool_id -> {log_cadence, noise_coeffs} suitable for + passing as warm_start to CalibrationModel.fit(). + """ + pool_ids = result["pool_ids"] + warm = {} + for i, pid in enumerate(pool_ids): + entry = {} + # Cadence: from PerPoolHead + if "log_cadence_per_pool" in result: + entry["log_cadence"] = float(result["log_cadence_per_pool"][i]) + # Noise: per-pool coefficients + if "noise_coeffs" in result: + entry["noise_coeffs"] = result["noise_coeffs"][i] + warm[pid] = entry + return warm + + +def run_token_factored( + matched_clean, option_c_clean, lambda_delta=1.0, + cross_pool=False, warm_start=None, +): """Fit TokenFactoredNoiseHead with PerPoolHead(cadence) + FixedHead(gas).""" from quantammsim.calibration.calibration_model import CalibrationModel from quantammsim.calibration.heads import ( FixedHead, PerPoolHead, TokenFactoredNoiseHead, ) from quantammsim.calibration.joint_fit import prepare_token_factored_data - from quantammsim.calibration.pool_data import K_OBS_REDUCED + from quantammsim.calibration.pool_data import K_OBS_CROSS, K_OBS_REDUCED - jdata, enc = prepare_token_factored_data(matched_clean) + k_obs = K_OBS_CROSS if cross_pool else K_OBS_REDUCED + + jdata, enc = prepare_token_factored_data( + matched_clean, cross_pool=cross_pool, + ) n_pools = len(jdata.pool_data) gas_values = _build_gas_values(jdata, matched_clean) gas_head = FixedHead("log_gas", gas_values) cad_head = PerPoolHead("log_cadence", default=np.log(12.0)) noise_head = TokenFactoredNoiseHead( - k_obs=K_OBS_REDUCED, + k_obs=k_obs, lambda_delta=lambda_delta, **enc, ) model = CalibrationModel(cad_head, gas_head, noise_head) n_p = model.n_params(n_pools, jdata.x_attr.shape[1]) - print(f"\n--- Token-factored (lambda_delta={lambda_delta}) ---") + cp_tag = " [cross-pool]" if cross_pool else "" + print(f"\n--- Token-factored (lambda_delta={lambda_delta}){cp_tag} ---") print(f" {n_pools} pools, {enc['n_tokens']} tokens, " - f"{enc['n_chains']} chains, {n_p} params") + f"{enc['n_chains']} chains, {n_p} params, k_obs={k_obs}") - result = model.fit(jdata, maxiter=JOINT_MAXITER, warm_start=option_c_clean) - print(f" Loss: {result['init_loss']:.4f} -> {result['loss']:.4f}") + ws = warm_start if warm_start is not None else option_c_clean + result = model.fit(jdata, maxiter=JOINT_MAXITER, warm_start=ws) + print(f" Loss: {result['init_loss']:.4f} -> {result['loss']:.4f}" + f" (data={result['data_loss']:.4f}, reg={result['reg_loss']:.4f})") print(f" Converged: {result['converged']}") return result, model, jdata, enc @@ -292,8 +322,6 @@ def print_delta_analysis(result, enc, jdata, matched_clean): """Print per-pool delta analysis.""" delta = result["noise_deltas"] pool_ids = jdata.pool_ids - token_index = enc["token_index"] - inv_token = {v: k for k, v in token_index.items()} print(f"\n{'='*70}") print("Per-pool deltas (unexplained residual)") @@ -311,30 +339,44 @@ def print_delta_analysis(result, enc, jdata, matched_clean): f"{delta_norms[i]:>8.3f} {delta[i, 0]:>8.3f}") -def run_lambda_sweep(matched_clean, option_c_clean): - """Sweep lambda_delta values and report loss + delta shrinkage.""" +def run_lambda_sweep(matched_clean, option_c_clean, cross_pool=False): + """Sweep lambda_delta with warm-start annealing (descending lambda). + + Each fit warm-starts from the previous result, so the sweep is + effectively a continuation path from high to low regularization. + """ print(f"\n{'='*70}") - print("Lambda_delta sweep") + cp_tag = " [cross-pool]" if cross_pool else "" + print(f"Lambda_delta sweep{cp_tag}") print(f"{'='*70}") - print(f"{'lambda':>10} {'loss':>10} {'delta_norm':>12} {'mean_|d|':>10}") - print("-" * 45) + print(f"{'lambda':>10} {'loss':>10} {'data_loss':>10} {'reg_loss':>10} " + f"{'delta_norm':>12} {'mean_|d|':>10}") + print("-" * 65) results = [] + warm_start = option_c_clean for lam in LAMBDA_DELTAS: result, model, jdata, enc = run_token_factored( - matched_clean, option_c_clean, lambda_delta=lam) + matched_clean, option_c_clean, lambda_delta=lam, + cross_pool=cross_pool, warm_start=warm_start, + ) delta = result["noise_deltas"] delta_norm = float(np.linalg.norm(delta)) mean_abs_d = float(np.mean(np.abs(delta))) print(f"{lam:>10.2f} {result['loss']:>10.4f} " + f"{result['data_loss']:>10.4f} {result['reg_loss']:>10.4f} " f"{delta_norm:>12.4f} {mean_abs_d:>10.4f}") results.append({ "lambda_delta": lam, "loss": result["loss"], + "data_loss": result["data_loss"], + "reg_loss": result["reg_loss"], "delta_norm": delta_norm, "mean_abs_delta": mean_abs_d, "converged": result["converged"], }) + # Warm-start next iteration from this result + warm_start = _result_to_warm_start(result) return results @@ -342,7 +384,9 @@ def run_lambda_sweep(matched_clean, option_c_clean): # ---- Phase 3: LOO Cross-Validation ---- -def run_loo_validation(matched_clean, option_c_clean, lambda_delta=1.0): +def run_loo_validation( + matched_clean, option_c_clean, lambda_delta=1.0, cross_pool=False, +): """Leave-one-pool-out cross-validation via predict_new_pool.""" from quantammsim.calibration.calibration_model import CalibrationModel from quantammsim.calibration.heads import ( @@ -350,14 +394,18 @@ def run_loo_validation(matched_clean, option_c_clean, lambda_delta=1.0): ) from quantammsim.calibration.grid_interpolation import interpolate_pool_daily from quantammsim.calibration.joint_fit import prepare_token_factored_data - from quantammsim.calibration.pool_data import K_OBS_REDUCED, build_x_obs, _parse_tokens + from quantammsim.calibration.pool_data import ( + K_OBS_CROSS, K_OBS_REDUCED, build_cross_pool_x_obs, + build_x_obs, _parse_tokens, + ) import jax.numpy as jnp + k_obs = K_OBS_CROSS if cross_pool else K_OBS_REDUCED pool_ids = sorted(matched_clean.keys()) - n_pools = len(pool_ids) + cp_tag = " [cross-pool]" if cross_pool else "" print(f"\n{'='*70}") - print(f"LOO Cross-Validation (lambda_delta={lambda_delta})") + print(f"LOO Cross-Validation (lambda_delta={lambda_delta}){cp_tag}") print(f"{'='*70}") loo_results = [] @@ -370,11 +418,13 @@ def run_loo_validation(matched_clean, option_c_clean, lambda_delta=1.0): continue # Fit on training set - jdata, enc = prepare_token_factored_data(train_matched) + jdata, enc = prepare_token_factored_data( + train_matched, cross_pool=cross_pool, + ) gas_values = _build_gas_values(jdata, train_matched) noise_head = TokenFactoredNoiseHead( - k_obs=K_OBS_REDUCED, + k_obs=k_obs, lambda_delta=lambda_delta, **enc, ) @@ -401,9 +451,22 @@ def run_loo_validation(matched_clean, option_c_clean, lambda_delta=1.0): # Evaluate hold-out R² ho_panel = ho_entry["panel"] - x_obs_ho = build_x_obs(ho_panel, reduced=True) y_obs_ho = ho_panel["log_volume"].values.astype(float) + if cross_pool: + # Build cross-pool x_obs for held-out pool. + # Use matched_clean so pool's own entry is accessible; + # build_cross_pool_x_obs auto-excludes pool_id from its own peers. + x_obs_ho = build_cross_pool_x_obs( + ho_panel, matched_clean, hold_out_pid, + ) + # Trim y_obs to match (first day dropped) + y_obs_ho = y_obs_ho[1:] + day_indices_ho = ho_entry["day_indices"][1:] + else: + x_obs_ho = build_x_obs(ho_panel, reduced=True) + day_indices_ho = ho_entry["day_indices"] + # Use Option C cadence for the hold-out pool (not predicting cadence) oc_ho = option_c_clean[hold_out_pid] v_arb_all = np.array(interpolate_pool_daily( @@ -411,18 +474,25 @@ def run_loo_validation(matched_clean, option_c_clean, lambda_delta=1.0): jnp.float64(oc_ho["log_cadence"]), jnp.float64(np.exp(oc_ho["log_gas"])), )) - v_arb = v_arb_all[ho_entry["day_indices"]] - v_noise = np.exp(x_obs_ho @ ho_pred["noise_coeffs"]) + v_arb = v_arb_all[day_indices_ho] + + # Noise coefficients are k_obs-dimensional; x_obs_ho has k_obs columns + noise_coeffs = ho_pred["noise_coeffs"][:k_obs] + v_noise = np.exp(x_obs_ho @ noise_coeffs) log_pred = np.log(np.maximum(v_arb + v_noise, 1e-6)) ss_res = np.sum((log_pred - y_obs_ho) ** 2) ss_tot = np.sum((y_obs_ho - y_obs_ho.mean()) ** 2) r2_loo = 1 - ss_res / max(ss_tot, 1e-10) # Compare with Option C in-sample R² - v_noise_c = np.exp(build_x_obs(ho_panel, reduced=True) @ oc_ho["noise_coeffs"][:K_OBS_REDUCED]) - log_pred_c = np.log(np.maximum(v_arb + v_noise_c, 1e-6)) - ss_res_c = np.sum((log_pred_c - y_obs_ho) ** 2) - r2_c = 1 - ss_res_c / max(ss_tot, 1e-10) + x_obs_c = build_x_obs(ho_panel, reduced=True) + v_noise_c = np.exp(x_obs_c @ oc_ho["noise_coeffs"][:K_OBS_REDUCED]) + v_arb_c = v_arb_all[ho_entry["day_indices"]] + log_pred_c = np.log(np.maximum(v_arb_c + v_noise_c, 1e-6)) + y_obs_full = ho_panel["log_volume"].values.astype(float) + ss_res_c = np.sum((log_pred_c - y_obs_full) ** 2) + ss_tot_c = np.sum((y_obs_full - y_obs_full.mean()) ** 2) + r2_c = 1 - ss_res_c / max(ss_tot_c, 1e-10) loo_results.append({ "pool_id": hold_out_pid, @@ -450,18 +520,21 @@ def run_loo_validation(matched_clean, option_c_clean, lambda_delta=1.0): # ---- Plots ---- -def plot_lambda_sweep(sweep_results, output_dir): - """Plot loss and delta norm vs lambda_delta.""" +def plot_lambda_sweep(sweep_results, output_dir, suffix=""): + """Plot loss (data/reg separated) and delta norm vs lambda_delta.""" lambdas = [r["lambda_delta"] for r in sweep_results] - losses = [r["loss"] for r in sweep_results] + data_losses = [r["data_loss"] for r in sweep_results] + reg_losses = [r["reg_loss"] for r in sweep_results] delta_norms = [r["delta_norm"] for r in sweep_results] fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5)) - ax1.semilogx(lambdas, losses, "o-", color="steelblue") + ax1.semilogx(lambdas, data_losses, "o-", color="steelblue", label="data_loss") + ax1.semilogx(lambdas, reg_losses, "s--", color="orangered", label="reg_loss") ax1.set_xlabel("lambda_delta") ax1.set_ylabel("Loss") - ax1.set_title("Loss vs lambda_delta") + ax1.set_title("Data + Reg Loss vs lambda_delta") + ax1.legend() ax2.semilogx(lambdas, delta_norms, "o-", color="orangered") ax2.set_xlabel("lambda_delta") @@ -469,7 +542,7 @@ def plot_lambda_sweep(sweep_results, output_dir): ax2.set_title("Delta norm vs lambda_delta") fig.tight_layout() - out = os.path.join(output_dir, "lambda_sweep.png") + out = os.path.join(output_dir, f"lambda_sweep{suffix}.png") fig.savefig(out, dpi=150, bbox_inches="tight") plt.close(fig) print(f" Saved: {out}") @@ -497,7 +570,7 @@ def plot_token_effects(result, enc, output_dir): print(f" Saved: {out}") -def plot_loo_scatter(loo_results, output_dir): +def plot_loo_scatter(loo_results, output_dir, suffix=""): """Scatter: Option C R² vs LOO R².""" if not loo_results: return @@ -514,14 +587,14 @@ def plot_loo_scatter(loo_results, output_dir): "k--", alpha=0.3, linewidth=1) ax.set_xlabel("Option C R² (in-sample)") ax.set_ylabel("Token-factored R² (LOO)") - ax.set_title("LOO Cross-Validation: Token-Factored vs Option C") + ax.set_title(f"LOO: Token-Factored vs Option C{suffix}") n_better = sum(1 for c, l in zip(r2_c, r2_loo) if l > c) ax.text(0.05, 0.95, f"LOO wins: {n_better}/{len(r2_c)}", transform=ax.transAxes, fontsize=10, va="top") fig.tight_layout() - out = os.path.join(output_dir, "loo_scatter.png") + out = os.path.join(output_dir, f"loo_scatter{suffix}.png") fig.savefig(out, dpi=150, bbox_inches="tight") plt.close(fig) print(f" Saved: {out}") @@ -534,7 +607,8 @@ def main(): os.environ.setdefault("JAX_PLATFORMS", "cpu") print("=" * 70) - print("Token-Factored Noise Calibration") + print("Token-Factored Noise Calibration v2") + print(" Canonicalization + Cross-Pool Lag Features") print("=" * 70) panel, matched = load_and_match() @@ -552,53 +626,111 @@ def main(): # Phase 0: Diagnostic diag = run_phase0_diagnostic(matched_clean, option_c_clean) - # Phase 1: Token-factored model (default lambda) - result, model, jdata, enc = run_token_factored( - matched_clean, option_c_clean, lambda_delta=1.0) + # ---- Ablation: Baseline (K_OBS_REDUCED=4, no cross-pool) ---- + print("\n" + "=" * 70) + print("ABLATION 1: Baseline (K_OBS_REDUCED=4, no cross-pool features)") + print("=" * 70) + + result_base, model_base, jdata_base, enc_base = run_token_factored( + matched_clean, option_c_clean, lambda_delta=1.0, cross_pool=False) # Analysis - print_token_effects(result, enc) - print_chain_effects(result, enc) - print_delta_analysis(result, enc, jdata, matched_clean) + print_token_effects(result_base, enc_base) + print_chain_effects(result_base, enc_base) + print_delta_analysis(result_base, enc_base, jdata_base, matched_clean) - # Lambda sweep - sweep_results = run_lambda_sweep(matched_clean, option_c_clean) + # Lambda sweep with annealing + sweep_baseline = run_lambda_sweep(matched_clean, option_c_clean, cross_pool=False) - # Phase 2: LOO cross-validation - loo_results = run_loo_validation( - matched_clean, option_c_clean, lambda_delta=1.0) + # LOO + loo_baseline = run_loo_validation( + matched_clean, option_c_clean, lambda_delta=1.0, cross_pool=False) - # Phase 3: Plots & export + # ---- Ablation: Cross-pool (K_OBS_CROSS=7) ---- + print("\n" + "=" * 70) + print("ABLATION 2: Cross-pool lag features (K_OBS_CROSS=7)") + print("=" * 70) + + result_cross, model_cross, jdata_cross, enc_cross = run_token_factored( + matched_clean, option_c_clean, lambda_delta=1.0, cross_pool=True) + + print_token_effects(result_cross, enc_cross) + print_chain_effects(result_cross, enc_cross) + print_delta_analysis(result_cross, enc_cross, jdata_cross, matched_clean) + + sweep_cross = run_lambda_sweep(matched_clean, option_c_clean, cross_pool=True) + + loo_cross = run_loo_validation( + matched_clean, option_c_clean, lambda_delta=1.0, cross_pool=True) + + # ---- Ablation summary ---- + print("\n" + "=" * 70) + print("ABLATION COMPARISON") + print("=" * 70) + for label, loo in [("Baseline (k=4)", loo_baseline), ("Cross-pool (k=7)", loo_cross)]: + if loo: + r2s = [r["r2_loo"] for r in loo] + r2s_c = [r["r2_option_c"] for r in loo] + wins = sum(1 for r in loo if r["r2_loo"] > r["r2_option_c"]) + print(f" {label}: median R²_LOO={np.median(r2s):.4f}, " + f"median R²_C={np.median(r2s_c):.4f}, " + f"wins={wins}/{len(loo)}") + + # Plots print("\nGenerating plots...") os.makedirs(OUTPUT_DIR, exist_ok=True) - plot_lambda_sweep(sweep_results, OUTPUT_DIR) - plot_token_effects(result, enc, OUTPUT_DIR) - plot_loo_scatter(loo_results, OUTPUT_DIR) + plot_lambda_sweep(sweep_baseline, OUTPUT_DIR, suffix="_baseline") + plot_lambda_sweep(sweep_cross, OUTPUT_DIR, suffix="_crosspool") + plot_token_effects(result_base, enc_base, OUTPUT_DIR) + plot_loo_scatter(loo_baseline, OUTPUT_DIR, suffix="_baseline") + plot_loo_scatter(loo_cross, OUTPUT_DIR, suffix="_crosspool") # JSON export export = { "phase0_diagnostic": diag, - "token_factored": { - "loss": result["loss"], - "init_loss": result["init_loss"], - "converged": result["converged"], - "n_pools": result["n_pools"], - "n_tokens": enc["n_tokens"], - "n_chains": enc["n_chains"], - "token_index": enc["token_index"], - "chain_index": enc["chain_index"], - "token_effects": result["token_effects"].tolist(), - "Gamma": result["Gamma"].tolist(), - "chain_effects": result["chain_effects"].tolist(), - "beta_fee": result["beta_fee"].tolist(), - "noise_deltas": result["noise_deltas"].tolist(), - "noise_coeffs": result["noise_coeffs"].tolist(), + "baseline": { + "loss": result_base["loss"], + "data_loss": result_base["data_loss"], + "reg_loss": result_base["reg_loss"], + "init_loss": result_base["init_loss"], + "converged": result_base["converged"], + "n_pools": result_base["n_pools"], + "n_tokens": enc_base["n_tokens"], + "n_chains": enc_base["n_chains"], + "token_index": enc_base["token_index"], + "chain_index": enc_base["chain_index"], + "token_effects": result_base["token_effects"].tolist(), + "Gamma": result_base["Gamma"].tolist(), + "chain_effects": result_base["chain_effects"].tolist(), + "beta_fee": result_base["beta_fee"].tolist(), + "noise_deltas": result_base["noise_deltas"].tolist(), + "noise_coeffs": result_base["noise_coeffs"].tolist(), + }, + "cross_pool": { + "loss": result_cross["loss"], + "data_loss": result_cross["data_loss"], + "reg_loss": result_cross["reg_loss"], + "init_loss": result_cross["init_loss"], + "converged": result_cross["converged"], + "n_pools": result_cross["n_pools"], + "n_tokens": enc_cross["n_tokens"], + "n_chains": enc_cross["n_chains"], + "token_index": enc_cross["token_index"], + "chain_index": enc_cross["chain_index"], + "token_effects": result_cross["token_effects"].tolist(), + "Gamma": result_cross["Gamma"].tolist(), + "chain_effects": result_cross["chain_effects"].tolist(), + "beta_fee": result_cross["beta_fee"].tolist(), + "noise_deltas": result_cross["noise_deltas"].tolist(), + "noise_coeffs": result_cross["noise_coeffs"].tolist(), }, - "lambda_sweep": sweep_results, - "loo_results": loo_results, + "lambda_sweep_baseline": sweep_baseline, + "lambda_sweep_crosspool": sweep_cross, + "loo_baseline": loo_baseline, + "loo_crosspool": loo_cross, } - json_path = os.path.join(OUTPUT_DIR, "token_factored_results.json") + json_path = os.path.join(OUTPUT_DIR, "token_factored_v2_results.json") with open(json_path, "w") as f: json.dump(export, f, indent=2, default=str) print(f" Saved: {json_path}") From 31f882918ee61a1a65054be1225d5d232440ce09 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 16 Mar 2026 22:18:53 +0000 Subject: [PATCH 27/57] fix: cross-pool x_obs shape mismatch and warm-start k_obs padding TokenFactoredNoiseHead.init() now zero-pads when warm_start noise_coeffs are shorter than k_obs (e.g. warm-starting k_obs=7 from k_obs=4 Option C results). prepare_token_factored_data(cross_pool=True) now trims y_obs and day_indices to match the first-day-dropped x_obs from build_cross_pool_x_obs. Runner gains --cross-pool-only flag with pickle caching of stage 1 (Option C + filtering) and baseline results so ablation 2 can run independently. Baseline-missing paths handled gracefully. Adds TestPrepareTokenFactoredCrossPool with shape consistency tests that would have caught the broadcast error. --- quantammsim/calibration/heads.py | 5 +- quantammsim/calibration/joint_fit.py | 4 + scripts/run_token_factored_calibration.py | 230 +++++++++++++++------- tests/calibration/test_joint_fit.py | 28 +++ 4 files changed, 189 insertions(+), 78 deletions(-) diff --git a/quantammsim/calibration/heads.py b/quantammsim/calibration/heads.py index ff86dfc..b051315 100644 --- a/quantammsim/calibration/heads.py +++ b/quantammsim/calibration/heads.py @@ -648,8 +648,9 @@ def init(self, jdata, warm_start=None): noise_all = np.zeros((n_pools, k), dtype=np.float64) for i, pid in enumerate(jdata.pool_ids): if pid in warm_start and "noise_coeffs" in warm_start[pid]: - nc = warm_start[pid]["noise_coeffs"] - noise_all[i] = nc[:k] + nc = np.asarray(warm_start[pid]["noise_coeffs"]) + n_copy = min(len(nc), k) + noise_all[i, :n_copy] = nc[:n_copy] # Solve: u[ta_i] + u[tb_i] + alpha[ch_i] + beta_fee * lf_i ≈ noise_all[i] n_cols = self.n_tokens + self.n_chains + 1 diff --git a/quantammsim/calibration/joint_fit.py b/quantammsim/calibration/joint_fit.py index d71be73..8eaa7b8 100644 --- a/quantammsim/calibration/joint_fit.py +++ b/quantammsim/calibration/joint_fit.py @@ -132,8 +132,12 @@ def prepare_token_factored_data( x_obs_cross = build_cross_pool_x_obs( entry["panel"], matched, pid, canonicalize=canonicalize, ) + # x_obs_cross has n_obs-1 rows (first day dropped); + # trim y_obs and day_indices to match d = dict(jdata.pool_data[i]) d["x_obs"] = jnp.array(x_obs_cross) + d["y_obs"] = d["y_obs"][1:] + d["day_indices"] = d["day_indices"][1:] new_pool_data.append(d) jdata = JointData( pool_data=new_pool_data, diff --git a/scripts/run_token_factored_calibration.py b/scripts/run_token_factored_calibration.py index 1ccf15e..1f1cbf5 100644 --- a/scripts/run_token_factored_calibration.py +++ b/scripts/run_token_factored_calibration.py @@ -6,8 +6,10 @@ Phase 3: Comparison plots and JSON export """ +import argparse import json import os +import pickle import matplotlib matplotlib.use("Agg") @@ -600,10 +602,97 @@ def plot_loo_scatter(loo_results, output_dir, suffix=""): print(f" Saved: {out}") +# ---- Intermediate state caching ---- + +_CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) + + +def _save_stage1(matched_clean, option_c_clean, diag): + """Cache Option C fits, filtering, and Phase 0 diagnostic.""" + os.makedirs(_CACHE_DIR, exist_ok=True) + path = os.path.join(_CACHE_DIR, "stage1.pkl") + with open(path, "wb") as f: + pickle.dump({ + "matched_clean": matched_clean, + "option_c_clean": option_c_clean, + "diag": diag, + }, f) + print(f" Cached stage 1 to {path}") + + +def _load_stage1(): + """Load cached stage 1 results. Returns None if missing.""" + path = os.path.join(_CACHE_DIR, "stage1.pkl") + if not os.path.exists(path): + return None + with open(path, "rb") as f: + data = pickle.load(f) + print(f" Loaded stage 1 cache from {path}") + return data + + +def _save_baseline(result_base, enc_base, sweep_baseline, loo_baseline): + """Cache ablation 1 results.""" + os.makedirs(_CACHE_DIR, exist_ok=True) + path = os.path.join(_CACHE_DIR, "baseline.pkl") + with open(path, "wb") as f: + pickle.dump({ + "result_base": result_base, + "enc_base": enc_base, + "sweep_baseline": sweep_baseline, + "loo_baseline": loo_baseline, + }, f) + print(f" Cached baseline results to {path}") + + +def _load_baseline(): + """Load cached baseline results. Returns None if missing.""" + path = os.path.join(_CACHE_DIR, "baseline.pkl") + if not os.path.exists(path): + return None + with open(path, "rb") as f: + data = pickle.load(f) + print(f" Loaded baseline cache from {path}") + return data + + +def _export_ablation_result(result, enc): + """Build JSON-serializable dict from ablation result + encoding.""" + return { + "loss": result["loss"], + "data_loss": result["data_loss"], + "reg_loss": result["reg_loss"], + "init_loss": result["init_loss"], + "converged": result["converged"], + "n_pools": result["n_pools"], + "n_tokens": enc["n_tokens"], + "n_chains": enc["n_chains"], + "token_index": enc["token_index"], + "chain_index": enc["chain_index"], + "token_effects": result["token_effects"].tolist(), + "Gamma": result["Gamma"].tolist(), + "chain_effects": result["chain_effects"].tolist(), + "beta_fee": result["beta_fee"].tolist(), + "noise_deltas": result["noise_deltas"].tolist(), + "noise_coeffs": result["noise_coeffs"].tolist(), + } + + # ---- Main ---- def main(): + parser = argparse.ArgumentParser( + description="Token-factored noise calibration v2") + parser.add_argument( + "--cross-pool-only", action="store_true", + help="Skip baseline ablation, load from cache, run only cross-pool", + ) + args = parser.parse_args() + os.environ.setdefault("JAX_PLATFORMS", "cpu") print("=" * 70) @@ -611,55 +700,71 @@ def main(): print(" Canonicalization + Cross-Pool Lag Features") print("=" * 70) - panel, matched = load_and_match() - - # Step 1: Option C baseline (reduced x_obs) - from quantammsim.calibration.per_pool_fit import fit_all_pools - print(f"\n--- Option C Reduced: per-pool fits ({len(matched)} pools) ---") - option_c = fit_all_pools(matched, fix_gas_to_chain=True, reduced=True) - losses = [r["loss"] for r in option_c.values()] - print(f" Loss: median={np.median(losses):.4f}, mean={np.mean(losses):.4f}") - - # Step 2: Filter pathological pools - matched_clean, option_c_clean = filter_pathological(matched, option_c) - - # Phase 0: Diagnostic - diag = run_phase0_diagnostic(matched_clean, option_c_clean) - - # ---- Ablation: Baseline (K_OBS_REDUCED=4, no cross-pool) ---- - print("\n" + "=" * 70) - print("ABLATION 1: Baseline (K_OBS_REDUCED=4, no cross-pool features)") - print("=" * 70) + # ---- Stage 1: Option C + filtering + Phase 0 ---- + cached_s1 = _load_stage1() if args.cross_pool_only else None + + if cached_s1 is not None: + matched_clean = cached_s1["matched_clean"] + option_c_clean = cached_s1["option_c_clean"] + diag = cached_s1["diag"] + print(f" Using cached stage 1: {len(matched_clean)} pools") + else: + panel, matched = load_and_match() + + from quantammsim.calibration.per_pool_fit import fit_all_pools + print(f"\n--- Option C Reduced: per-pool fits ({len(matched)} pools) ---") + option_c = fit_all_pools(matched, fix_gas_to_chain=True, reduced=True) + losses = [r["loss"] for r in option_c.values()] + print(f" Loss: median={np.median(losses):.4f}, mean={np.mean(losses):.4f}") + + matched_clean, option_c_clean = filter_pathological(matched, option_c) + diag = run_phase0_diagnostic(matched_clean, option_c_clean) + _save_stage1(matched_clean, option_c_clean, diag) + + # ---- Ablation 1: Baseline ---- + if args.cross_pool_only: + cached_bl = _load_baseline() + if cached_bl is not None: + result_base = cached_bl["result_base"] + enc_base = cached_bl["enc_base"] + sweep_baseline = cached_bl["sweep_baseline"] + loo_baseline = cached_bl["loo_baseline"] + else: + print(" No baseline cache found — skipping baseline ablation.") + result_base = enc_base = sweep_baseline = loo_baseline = None + else: + print("\n" + "=" * 70) + print("ABLATION 1: Baseline (K_OBS_REDUCED=4, no cross-pool features)") + print("=" * 70) - result_base, model_base, jdata_base, enc_base = run_token_factored( - matched_clean, option_c_clean, lambda_delta=1.0, cross_pool=False) + result_base, _, jdata_base, enc_base = run_token_factored( + matched_clean, option_c_clean, lambda_delta=1.0, cross_pool=False) - # Analysis - print_token_effects(result_base, enc_base) - print_chain_effects(result_base, enc_base) - print_delta_analysis(result_base, enc_base, jdata_base, matched_clean) + print_token_effects(result_base, enc_base) + print_chain_effects(result_base, enc_base) + print_delta_analysis(result_base, enc_base, jdata_base, matched_clean) - # Lambda sweep with annealing - sweep_baseline = run_lambda_sweep(matched_clean, option_c_clean, cross_pool=False) + sweep_baseline = run_lambda_sweep( + matched_clean, option_c_clean, cross_pool=False) + loo_baseline = run_loo_validation( + matched_clean, option_c_clean, lambda_delta=1.0, cross_pool=False) - # LOO - loo_baseline = run_loo_validation( - matched_clean, option_c_clean, lambda_delta=1.0, cross_pool=False) + _save_baseline(result_base, enc_base, sweep_baseline, loo_baseline) - # ---- Ablation: Cross-pool (K_OBS_CROSS=7) ---- + # ---- Ablation 2: Cross-pool ---- print("\n" + "=" * 70) print("ABLATION 2: Cross-pool lag features (K_OBS_CROSS=7)") print("=" * 70) - result_cross, model_cross, jdata_cross, enc_cross = run_token_factored( + result_cross, _, jdata_cross, enc_cross = run_token_factored( matched_clean, option_c_clean, lambda_delta=1.0, cross_pool=True) print_token_effects(result_cross, enc_cross) print_chain_effects(result_cross, enc_cross) print_delta_analysis(result_cross, enc_cross, jdata_cross, matched_clean) - sweep_cross = run_lambda_sweep(matched_clean, option_c_clean, cross_pool=True) - + sweep_cross = run_lambda_sweep( + matched_clean, option_c_clean, cross_pool=True) loo_cross = run_loo_validation( matched_clean, option_c_clean, lambda_delta=1.0, cross_pool=True) @@ -667,7 +772,10 @@ def main(): print("\n" + "=" * 70) print("ABLATION COMPARISON") print("=" * 70) - for label, loo in [("Baseline (k=4)", loo_baseline), ("Cross-pool (k=7)", loo_cross)]: + ablations = [("Cross-pool (k=7)", loo_cross)] + if loo_baseline is not None: + ablations.insert(0, ("Baseline (k=4)", loo_baseline)) + for label, loo in ablations: if loo: r2s = [r["r2_loo"] for r in loo] r2s_c = [r["r2_option_c"] for r in loo] @@ -680,56 +788,26 @@ def main(): print("\nGenerating plots...") os.makedirs(OUTPUT_DIR, exist_ok=True) - plot_lambda_sweep(sweep_baseline, OUTPUT_DIR, suffix="_baseline") + if sweep_baseline is not None: + plot_lambda_sweep(sweep_baseline, OUTPUT_DIR, suffix="_baseline") plot_lambda_sweep(sweep_cross, OUTPUT_DIR, suffix="_crosspool") - plot_token_effects(result_base, enc_base, OUTPUT_DIR) - plot_loo_scatter(loo_baseline, OUTPUT_DIR, suffix="_baseline") + if result_base is not None: + plot_token_effects(result_base, enc_base, OUTPUT_DIR) + if loo_baseline is not None: + plot_loo_scatter(loo_baseline, OUTPUT_DIR, suffix="_baseline") plot_loo_scatter(loo_cross, OUTPUT_DIR, suffix="_crosspool") # JSON export export = { "phase0_diagnostic": diag, - "baseline": { - "loss": result_base["loss"], - "data_loss": result_base["data_loss"], - "reg_loss": result_base["reg_loss"], - "init_loss": result_base["init_loss"], - "converged": result_base["converged"], - "n_pools": result_base["n_pools"], - "n_tokens": enc_base["n_tokens"], - "n_chains": enc_base["n_chains"], - "token_index": enc_base["token_index"], - "chain_index": enc_base["chain_index"], - "token_effects": result_base["token_effects"].tolist(), - "Gamma": result_base["Gamma"].tolist(), - "chain_effects": result_base["chain_effects"].tolist(), - "beta_fee": result_base["beta_fee"].tolist(), - "noise_deltas": result_base["noise_deltas"].tolist(), - "noise_coeffs": result_base["noise_coeffs"].tolist(), - }, - "cross_pool": { - "loss": result_cross["loss"], - "data_loss": result_cross["data_loss"], - "reg_loss": result_cross["reg_loss"], - "init_loss": result_cross["init_loss"], - "converged": result_cross["converged"], - "n_pools": result_cross["n_pools"], - "n_tokens": enc_cross["n_tokens"], - "n_chains": enc_cross["n_chains"], - "token_index": enc_cross["token_index"], - "chain_index": enc_cross["chain_index"], - "token_effects": result_cross["token_effects"].tolist(), - "Gamma": result_cross["Gamma"].tolist(), - "chain_effects": result_cross["chain_effects"].tolist(), - "beta_fee": result_cross["beta_fee"].tolist(), - "noise_deltas": result_cross["noise_deltas"].tolist(), - "noise_coeffs": result_cross["noise_coeffs"].tolist(), - }, - "lambda_sweep_baseline": sweep_baseline, + "cross_pool": _export_ablation_result(result_cross, enc_cross), "lambda_sweep_crosspool": sweep_cross, - "loo_baseline": loo_baseline, "loo_crosspool": loo_cross, } + if result_base is not None: + export["baseline"] = _export_ablation_result(result_base, enc_base) + export["lambda_sweep_baseline"] = sweep_baseline + export["loo_baseline"] = loo_baseline json_path = os.path.join(OUTPUT_DIR, "token_factored_v2_results.json") with open(json_path, "w") as f: json.dump(export, f, indent=2, default=str) diff --git a/tests/calibration/test_joint_fit.py b/tests/calibration/test_joint_fit.py index 3a3971d..1fe02f2 100644 --- a/tests/calibration/test_joint_fit.py +++ b/tests/calibration/test_joint_fit.py @@ -375,6 +375,34 @@ def test_data_loss_leq_total(self, matched_data): assert result["reg_loss"] >= -1e-10 +class TestPrepareTokenFactoredCrossPool: + """Test prepare_token_factored_data with cross_pool=True.""" + + def test_cross_pool_jdata_shapes_consistent(self, matched_data): + from quantammsim.calibration.joint_fit import prepare_token_factored_data + + jdata, enc = prepare_token_factored_data(matched_data, cross_pool=True) + for pd in jdata.pool_data: + assert pd["x_obs"].shape[0] == pd["y_obs"].shape[0] + assert pd["x_obs"].shape[0] == pd["day_indices"].shape[0] + + def test_cross_pool_x_obs_has_7_cols(self, matched_data): + from quantammsim.calibration.joint_fit import prepare_token_factored_data + from quantammsim.calibration.pool_data import K_OBS_CROSS + + jdata, _ = prepare_token_factored_data(matched_data, cross_pool=True) + for pd in jdata.pool_data: + assert pd["x_obs"].shape[1] == K_OBS_CROSS + + def test_cross_pool_drops_first_obs(self, matched_data): + from quantammsim.calibration.joint_fit import prepare_token_factored_data + + jdata_base, _ = prepare_token_factored_data(matched_data, cross_pool=False) + jdata_cross, _ = prepare_token_factored_data(matched_data, cross_pool=True) + for pd_base, pd_cross in zip(jdata_base.pool_data, jdata_cross.pool_data): + assert pd_cross["y_obs"].shape[0] == pd_base["y_obs"].shape[0] - 1 + + class TestPrepareJointDataReduced: """Test prepare_joint_data with reduced_x_obs=True.""" From 21fb161832df852ef827c03533e82cb12f058598 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 17 Mar 2026 13:14:06 +0000 Subject: [PATCH 28/57] feat: cross-pool volume prediction experiments MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Diagnostic experiments establishing the cross-pool prediction landscape: - run_cross_pool_diagnostics: lambda_token sweep, leave-one-in, AR1 baseline, pool connectivity analysis - run_cross_pool_linear: ridge regression (peers only, peers+own lag, LOO with overlap transfer, 30d burn-in, peer mean) - run_cross_pool_noise_linear: same battery on noise residuals (log_vol - log_V_arb) - run_residual_comparison: apples-to-apples R² on noise residual target across all methods including Option C - run_deepsets_volume: DeepSets on total volume (v1, raw) - run_deepsets_noise: DeepSets with V_arb decomposition and Optuna - run_deepsets_v2: full feature menu with Optuna feature selection, trains on total volume, evaluates on noise residual Key findings: ridge in-sample peers+own = 0.599 (matching Option C), but cross-pool signal is almost entirely shared arb response — noise residual ridge ceiling is 0.098. Option C noise residual R² = 0.060. --- experiments/run_cross_pool_diagnostics.py | 423 +++++++++++++ experiments/run_cross_pool_linear.py | 457 ++++++++++++++ experiments/run_cross_pool_noise_linear.py | 392 ++++++++++++ experiments/run_deepsets_noise.py | 595 ++++++++++++++++++ experiments/run_deepsets_v2.py | 697 +++++++++++++++++++++ experiments/run_deepsets_volume.py | 467 ++++++++++++++ experiments/run_residual_comparison.py | 235 +++++++ 7 files changed, 3266 insertions(+) create mode 100644 experiments/run_cross_pool_diagnostics.py create mode 100644 experiments/run_cross_pool_linear.py create mode 100644 experiments/run_cross_pool_noise_linear.py create mode 100644 experiments/run_deepsets_noise.py create mode 100644 experiments/run_deepsets_v2.py create mode 100644 experiments/run_deepsets_volume.py create mode 100644 experiments/run_residual_comparison.py diff --git a/experiments/run_cross_pool_diagnostics.py b/experiments/run_cross_pool_diagnostics.py new file mode 100644 index 0000000..ae9e2ed --- /dev/null +++ b/experiments/run_cross_pool_diagnostics.py @@ -0,0 +1,423 @@ +"""Diagnostic experiments for cross-pool noise calibration. + +Runs cheap experiments to bound the value of learned cross-pool aggregation: +1. Lambda_token sweep — is the LOO failure due to overfitting token effects? +2. Leave-one-in — how much pool-specific data closes the gap? +3. Naive AR baseline — is the model barely beating lag-1? +4. Pool connectivity — which pools are predictable at all? + +Uses cached stage1 data from run_token_factored_calibration.py. +""" + +import os +import pickle +import sys + +import numpy as np +import pandas as pd + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) +JOINT_MAXITER = 3000 # reduced for sweep speed + + +def load_stage1(): + """Load cached stage 1 (matched_clean + option_c_clean).""" + path = os.path.join(CACHE_DIR, "stage1.pkl") + if not os.path.exists(path): + print("ERROR: no stage1 cache. Run run_token_factored_calibration.py first.") + sys.exit(1) + with open(path, "rb") as f: + data = pickle.load(f) + print(f"Loaded {len(data['matched_clean'])} pools from cache") + return data["matched_clean"], data["option_c_clean"] + + +# ---- Diagnostic 1: Lambda_token sweep ---- + + +def run_lambda_token_sweep(matched_clean, option_c_clean): + """LOO with varying lambda_token to test whether overfitting is the problem.""" + from quantammsim.calibration.calibration_model import CalibrationModel + from quantammsim.calibration.heads import ( + FixedHead, PerPoolHead, TokenFactoredNoiseHead, + ) + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + from quantammsim.calibration.joint_fit import prepare_token_factored_data + from quantammsim.calibration.loss import CHAIN_GAS_USD + from quantammsim.calibration.pool_data import K_OBS_REDUCED, build_x_obs, _parse_tokens + import jax.numpy as jnp + + pool_ids = sorted(matched_clean.keys()) + lambda_tokens = [0.1, 0.5, 1.0, 5.0, 10.0] + + print("\n" + "=" * 70) + print("Diagnostic 1: Lambda_token sweep (LOO)") + print("=" * 70) + print(f" lambda_delta=1.0 fixed, sweeping lambda_token") + print(f" maxiter={JOINT_MAXITER}") + + all_results = {} + + for lt in lambda_tokens: + print(f"\n--- lambda_token={lt} ---") + loo_r2s = [] + + for hold_out_pid in pool_ids: + train_matched = {p: matched_clean[p] for p in pool_ids if p != hold_out_pid} + train_oc = {p: option_c_clean[p] for p in pool_ids if p != hold_out_pid} + + if len(train_matched) < 3: + continue + + jdata, enc = prepare_token_factored_data(train_matched) + + gas_values = [] + for pid in jdata.pool_ids: + chain = train_matched[pid]["chain"] + gas_values.append(np.log(max(CHAIN_GAS_USD.get(chain, 1.0), 1e-6))) + + noise_head = TokenFactoredNoiseHead( + k_obs=K_OBS_REDUCED, + lambda_delta=1.0, + lambda_token=lt, + **enc, + ) + model = CalibrationModel( + PerPoolHead("log_cadence", default=np.log(12.0)), + FixedHead("log_gas", np.array(gas_values)), + noise_head, + ) + result = model.fit(jdata, maxiter=JOINT_MAXITER, warm_start=train_oc) + + # Predict for held-out pool + n_train = len(jdata.pool_data) + k_attr = jdata.x_attr.shape[1] + (_, _), (_, _), (ns, ne) = model._head_slices(n_train, k_attr) + noise_params = result["params_flat"][ns:ne] + + ho_entry = matched_clean[hold_out_pid] + toks = _parse_tokens(ho_entry["tokens"]) + ho_pred = noise_head.predict_new_pool( + noise_params, toks[0], toks[1], + ho_entry["chain"], ho_entry["fee"], + n_pools=n_train, + ) + + # Evaluate + ho_panel = ho_entry["panel"] + x_obs_ho = build_x_obs(ho_panel, reduced=True) + y_obs_ho = ho_panel["log_volume"].values.astype(float) + + oc_ho = option_c_clean[hold_out_pid] + v_arb_all = np.array(interpolate_pool_daily( + ho_entry["coeffs"], + jnp.float64(oc_ho["log_cadence"]), + jnp.float64(np.exp(oc_ho["log_gas"])), + )) + v_arb = v_arb_all[ho_entry["day_indices"]] + v_noise = np.exp(x_obs_ho @ ho_pred["noise_coeffs"][:K_OBS_REDUCED]) + log_pred = np.log(np.maximum(v_arb + v_noise, 1e-6)) + ss_res = np.sum((log_pred - y_obs_ho) ** 2) + ss_tot = np.sum((y_obs_ho - y_obs_ho.mean()) ** 2) + r2 = 1 - ss_res / max(ss_tot, 1e-10) + loo_r2s.append(r2) + + tag = "OK" if r2 > 0 else "NEG" + print(f" {hold_out_pid[:16]} R²={r2:.3f} [{tag}]") + + median_r2 = np.median(loo_r2s) + wins = sum(1 for r2, pid in zip(loo_r2s, pool_ids) + if r2 > option_c_clean[pid].get("r2", 0)) + all_results[lt] = { + "median_r2": median_r2, + "mean_r2": np.mean(loo_r2s), + "r2s": loo_r2s, + "n_negative": sum(1 for r in loo_r2s if r < 0), + } + print(f" lambda_token={lt}: median R²={median_r2:.4f}, " + f"mean={np.mean(loo_r2s):.4f}, " + f"n_negative={sum(1 for r in loo_r2s if r < 0)}") + + # Summary table + print(f"\n{'='*60}") + print(f"{'lambda_token':>12} {'median_R²':>10} {'mean_R²':>10} {'n_neg':>6}") + print("-" * 42) + for lt in lambda_tokens: + r = all_results[lt] + print(f"{lt:>12.1f} {r['median_r2']:>10.4f} {r['mean_r2']:>10.4f} " + f"{r['n_negative']:>6}") + + return all_results + + +# ---- Diagnostic 2: Leave-one-in ---- + + +def run_leave_one_in(matched_clean, option_c_clean, n_days_in=30): + """LOO but give held-out pool n_days_in days of data for adaptation.""" + from quantammsim.calibration.calibration_model import CalibrationModel + from quantammsim.calibration.heads import ( + FixedHead, PerPoolHead, TokenFactoredNoiseHead, + ) + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + from quantammsim.calibration.joint_fit import prepare_token_factored_data + from quantammsim.calibration.loss import CHAIN_GAS_USD + from quantammsim.calibration.pool_data import K_OBS_REDUCED, build_x_obs, _parse_tokens + import jax.numpy as jnp + + pool_ids = sorted(matched_clean.keys()) + + print("\n" + "=" * 70) + print(f"Diagnostic 2: Leave-one-in ({n_days_in} days of held-out data)") + print("=" * 70) + + results = [] + + for hold_out_pid in pool_ids: + ho_entry = matched_clean[hold_out_pid] + ho_panel = ho_entry["panel"] + n_obs = len(ho_panel) + + if n_obs <= n_days_in + 10: + print(f" {hold_out_pid[:16]} — too few obs ({n_obs}), skipping") + continue + + # Split: first n_days_in for training, rest for evaluation + train_panel = ho_panel.iloc[:n_days_in].copy() + eval_panel = ho_panel.iloc[n_days_in:].copy() + train_day_indices = ho_entry["day_indices"][:n_days_in] + eval_day_indices = ho_entry["day_indices"][n_days_in:] + + # Build training matched: all other pools + truncated held-out pool + train_matched = {} + for p in pool_ids: + if p != hold_out_pid: + train_matched[p] = matched_clean[p] + + # Add truncated held-out pool + ho_train_entry = dict(ho_entry) + ho_train_entry["panel"] = train_panel.reset_index(drop=True) + ho_train_entry["day_indices"] = train_day_indices + train_matched[hold_out_pid] = ho_train_entry + + train_oc = dict(option_c_clean) # all pools including held-out + + # Fit with held-out pool included (gets its own delta from 30 days) + jdata, enc = prepare_token_factored_data(train_matched) + + gas_values = [] + for pid in jdata.pool_ids: + chain = train_matched[pid]["chain"] + gas_values.append(np.log(max(CHAIN_GAS_USD.get(chain, 1.0), 1e-6))) + + noise_head = TokenFactoredNoiseHead( + k_obs=K_OBS_REDUCED, + lambda_delta=1.0, + lambda_token=0.1, + **enc, + ) + model = CalibrationModel( + PerPoolHead("log_cadence", default=np.log(12.0)), + FixedHead("log_gas", np.array(gas_values)), + noise_head, + ) + result = model.fit(jdata, maxiter=JOINT_MAXITER, warm_start=train_oc) + + # Find held-out pool's index in training set and extract noise_coeffs + ho_idx = jdata.pool_ids.index(hold_out_pid) + noise_coeffs = result["noise_coeffs"][ho_idx] + + # Evaluate on held-out days + x_obs_eval = build_x_obs(eval_panel, reduced=True) + y_obs_eval = eval_panel["log_volume"].values.astype(float) + + oc_ho = option_c_clean[hold_out_pid] + v_arb_all = np.array(interpolate_pool_daily( + ho_entry["coeffs"], + jnp.float64(oc_ho["log_cadence"]), + jnp.float64(np.exp(oc_ho["log_gas"])), + )) + v_arb = v_arb_all[eval_day_indices] + v_noise = np.exp(x_obs_eval @ noise_coeffs[:K_OBS_REDUCED]) + log_pred = np.log(np.maximum(v_arb + v_noise, 1e-6)) + ss_res = np.sum((log_pred - y_obs_eval) ** 2) + ss_tot = np.sum((y_obs_eval - y_obs_eval.mean()) ** 2) + r2_in = 1 - ss_res / max(ss_tot, 1e-10) + + # Also compute Option C R² on eval days for comparison + v_noise_c = np.exp(x_obs_eval @ oc_ho["noise_coeffs"][:K_OBS_REDUCED]) + log_pred_c = np.log(np.maximum(v_arb + v_noise_c, 1e-6)) + ss_res_c = np.sum((log_pred_c - y_obs_eval) ** 2) + r2_c_eval = 1 - ss_res_c / max(ss_tot, 1e-10) + + results.append({ + "pool_id": hold_out_pid, + "r2_leave_one_in": r2_in, + "r2_option_c_eval": r2_c_eval, + "n_train_days": n_days_in, + "n_eval_days": len(eval_panel), + "tokens": ho_entry["tokens"], + }) + + print(f" {hold_out_pid[:16]} ({ho_entry['tokens']:<14}) " + f"R²_in={r2_in:.3f} R²_C_eval={r2_c_eval:.3f} " + f"n_eval={len(eval_panel)}") + + if results: + r2s_in = [r["r2_leave_one_in"] for r in results] + r2s_c = [r["r2_option_c_eval"] for r in results] + print(f"\n Leave-one-in ({n_days_in}d): median R²={np.median(r2s_in):.4f}") + print(f" Option C (eval days): median R²={np.median(r2s_c):.4f}") + print(f" Recall: zero-shot LOO: median R²=0.362") + + return results + + +# ---- Diagnostic 3: Naive AR baseline ---- + + +def run_naive_ar_baseline(matched_clean): + """Compute R² of vol_tomorrow = vol_today (no model, no cross-pool).""" + print("\n" + "=" * 70) + print("Diagnostic 3: Naive autoregressive baseline (lag-1 copy)") + print("=" * 70) + + pool_r2s = [] + for pid in sorted(matched_clean.keys()): + panel = matched_clean[pid]["panel"] + y = panel["log_volume"].values.astype(float) + + if len(y) < 3: + continue + + # Predict day t from day t-1 + y_true = y[1:] + y_pred = y[:-1] + + ss_res = np.sum((y_pred - y_true) ** 2) + ss_tot = np.sum((y_true - y_true.mean()) ** 2) + r2 = 1 - ss_res / max(ss_tot, 1e-10) + pool_r2s.append(r2) + + print(f" {pid[:16]} ({matched_clean[pid]['tokens']:<14}) " + f"R²_AR1={r2:.3f} n_obs={len(y)}") + + print(f"\n Naive AR1: median R²={np.median(pool_r2s):.4f}, " + f"mean={np.mean(pool_r2s):.4f}") + print(f" Recall: zero-shot LOO = 0.362, Option C in-sample = 0.589") + + return pool_r2s + + +# ---- Diagnostic 4: Pool connectivity analysis ---- + + +def run_connectivity_analysis(matched_clean, option_c_clean): + """Analyze token overlap and partition LOO R² by connectivity.""" + from quantammsim.calibration.pool_data import _parse_tokens, _canonicalize_token + + print("\n" + "=" * 70) + print("Diagnostic 4: Pool connectivity analysis") + print("=" * 70) + + pool_ids = sorted(matched_clean.keys()) + + # Build canonical token sets per pool + pool_tokens = {} + for pid in pool_ids: + toks = _parse_tokens(matched_clean[pid]["tokens"]) + canon = {_canonicalize_token(t) for t in toks[:2]} + pool_tokens[pid] = canon + + # Count: for each pool, how many other pools share at least 1 token? + # And how many share both tokens? + print(f"\n{'Pool':<18} {'Tokens':<16} {'1+ shared':>10} {'2 shared':>10} " + f"{'R²_C':>8}") + print("-" * 66) + + connectivity = [] + for pid in pool_ids: + my_toks = pool_tokens[pid] + n_one_shared = 0 + n_both_shared = 0 + for other in pool_ids: + if other == pid: + continue + overlap = len(my_toks & pool_tokens[other]) + if overlap >= 1: + n_one_shared += 1 + if overlap >= 2: + n_both_shared += 1 + + oc = option_c_clean[pid] + r2_c = 1 - oc["loss"] / max( + np.var(matched_clean[pid]["panel"]["log_volume"].values) * + len(matched_clean[pid]["panel"]) / + max(len(matched_clean[pid]["panel"]) - 1, 1), + 1e-10, + ) + + connectivity.append({ + "pool_id": pid, + "tokens": matched_clean[pid]["tokens"], + "n_one_shared": n_one_shared, + "n_both_shared": n_both_shared, + }) + + print(f" {pid[:16]} {matched_clean[pid]['tokens']:<16} " + f"{n_one_shared:>10} {n_both_shared:>10}") + + # Partition: well-connected (1+ shared ≥ 3) vs isolated + well_connected = [c for c in connectivity if c["n_one_shared"] >= 3] + isolated = [c for c in connectivity if c["n_one_shared"] < 3] + + print(f"\n Well-connected (≥3 pools share a token): {len(well_connected)}") + print(f" Isolated (<3 pools share a token): {len(isolated)}") + + if isolated: + print(f"\n Isolated pools:") + for c in isolated: + print(f" {c['pool_id'][:16]} {c['tokens']}") + + return connectivity + + +# ---- Main ---- + + +def main(): + os.environ.setdefault("JAX_PLATFORMS", "cpu") + + print("=" * 70) + print("Cross-Pool Calibration Diagnostics") + print("=" * 70) + + matched_clean, option_c_clean = load_stage1() + + # Run all diagnostics + ar_results = run_naive_ar_baseline(matched_clean) + connectivity = run_connectivity_analysis(matched_clean, option_c_clean) + leave_one_in = run_leave_one_in(matched_clean, option_c_clean, n_days_in=30) + lambda_sweep = run_lambda_token_sweep(matched_clean, option_c_clean) + + # Final summary + print("\n" + "=" * 70) + print("SUMMARY") + print("=" * 70) + print(f" Naive AR1 baseline: median R² = {np.median(ar_results):.4f}") + if leave_one_in: + r2s_in = [r["r2_leave_one_in"] for r in leave_one_in] + print(f" Leave-one-in (30 days): median R² = {np.median(r2s_in):.4f}") + print(f" Zero-shot LOO (current): median R² = 0.362") + print(f" Option C in-sample: median R² = 0.589") + print(f"\n Lambda_token sweep:") + for lt, r in sorted(lambda_sweep.items()): + print(f" lambda_token={lt:>5.1f}: median R² = {r['median_r2']:.4f} " + f"(n_neg={r['n_negative']})") + + +if __name__ == "__main__": + main() diff --git a/experiments/run_cross_pool_linear.py b/experiments/run_cross_pool_linear.py new file mode 100644 index 0000000..acd5c48 --- /dev/null +++ b/experiments/run_cross_pool_linear.py @@ -0,0 +1,457 @@ +"""Cross-pool linear volume prediction baselines. + +1. Ridge cross-pool regression: log_vol_i_t = W_i @ log_vol_{-i, t-1} + - In-sample: fit full 36x36 W, evaluate on training data + - LOO: hold out pool i, fit W on 35 pools, predict pool i using + token-overlap-weighted average of learned rows (transfer via similarity) + - LOO with burn-in: use 30 days of pool i to learn its row of W directly + +2. Zero-parameter peer-mean: predicted_vol_i_t = mean(log_vol_{j,t-1}) + for peers sharing a canonical token with pool i +""" + +import os +import pickle +import sys + +import numpy as np +from sklearn.linear_model import RidgeCV + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) + + +def load_stage1(): + path = os.path.join(CACHE_DIR, "stage1.pkl") + if not os.path.exists(path): + print("ERROR: no stage1 cache. Run run_token_factored_calibration.py first.") + sys.exit(1) + with open(path, "rb") as f: + data = pickle.load(f) + print(f"Loaded {len(data['matched_clean'])} pools from cache") + return data["matched_clean"], data["option_c_clean"] + + +def build_volume_matrix(matched_clean): + """Build (n_dates, n_pools) aligned volume matrix. + + Returns vol_matrix, date_list, pool_ids. + Dates are the intersection of all pools' date ranges. + Missing values filled with NaN. + """ + pool_ids = sorted(matched_clean.keys()) + + # Collect all (pool, date) -> log_volume + pool_date_vol = {} + all_dates = set() + for pid in pool_ids: + panel = matched_clean[pid]["panel"] + dates = panel["date"].values + vols = panel["log_volume"].values.astype(float) + pool_date_vol[pid] = dict(zip(dates, vols)) + all_dates.update(dates) + + date_list = sorted(all_dates) + n_dates = len(date_list) + n_pools = len(pool_ids) + + vol_matrix = np.full((n_dates, n_pools), np.nan) + for j, pid in enumerate(pool_ids): + dv = pool_date_vol[pid] + for t, date in enumerate(date_list): + if date in dv: + vol_matrix[t, j] = dv[date] + + return vol_matrix, date_list, pool_ids + + +def build_token_overlap(matched_clean, pool_ids): + """Build (n_pools, n_pools) token overlap matrix (0, 1, or 2).""" + from quantammsim.calibration.pool_data import _parse_tokens, _canonicalize_token + + n = len(pool_ids) + overlap = np.zeros((n, n), dtype=np.int32) + pool_tokens = {} + for i, pid in enumerate(pool_ids): + toks = _parse_tokens(matched_clean[pid]["tokens"]) + pool_tokens[i] = {_canonicalize_token(t) for t in toks[:2]} + + for i in range(n): + for j in range(n): + overlap[i, j] = len(pool_tokens[i] & pool_tokens[j]) + + return overlap + + +def r2_score(y_true, y_pred): + ss_res = np.sum((y_true - y_pred) ** 2) + ss_tot = np.sum((y_true - y_true.mean()) ** 2) + return 1 - ss_res / max(ss_tot, 1e-10) + + +# ---- 1. Ridge cross-pool regression ---- + + +def run_ridge_cross_pool(matched_clean): + """Full cross-pool ridge: predict each pool from all others' lag-1.""" + vol_matrix, date_list, pool_ids = build_volume_matrix(matched_clean) + n_dates, n_pools = vol_matrix.shape + + # Check if fully-observed rows exist + X_lag = vol_matrix[:-1, :] + Y_cur = vol_matrix[1:, :] + valid = ~np.any(np.isnan(X_lag), axis=1) & ~np.any(np.isnan(Y_cur), axis=1) + n_valid = int(valid.sum()) + + # Peers only + print("\n" + "=" * 70) + print("1a. Ridge cross-pool regression (in-sample, peers only)") + print("=" * 70) + print(f" {n_pools} pools, {n_valid} fully-observed day pairs " + f"(of {n_dates-1} total)") + print(" Using per-pool valid rows with NaN imputation.") + r2_peers, _, _, _ = _run_ridge_per_pool_valid( + vol_matrix, pool_ids, matched_clean, include_own_lag=False) + + # Peers + own lag + print("\n" + "=" * 70) + print("1a+. Ridge cross-pool regression (in-sample, peers + own lag)") + print("=" * 70) + r2_both, _, _, _ = _run_ridge_per_pool_valid( + vol_matrix, pool_ids, matched_clean, include_own_lag=True) + + return r2_peers, r2_both, vol_matrix, date_list, pool_ids + + +def _run_ridge_per_pool_valid(vol_matrix, pool_ids, matched_clean, + include_own_lag=False): + """Fallback: per-pool ridge using only rows where pool i AND predictors have data.""" + n_dates, n_pools = vol_matrix.shape + tag = " + own_lag" if include_own_lag else "" + + pool_r2s = [] + for i, pid in enumerate(pool_ids): + X_lag = vol_matrix[:-1, :] + y_cur = vol_matrix[1:, i] + own_lag = X_lag[:, i] # pool i's own lag + + # Valid: pool i has data today AND own lag exists (if used) + valid_y = ~np.isnan(y_cur) + if include_own_lag: + valid_y = valid_y & ~np.isnan(own_lag) + + X_others = np.delete(X_lag, i, axis=1) + + # For each predictor, fill NaN with that predictor's mean (simple imputation) + X_filled = X_others.copy() + for j in range(X_filled.shape[1]): + col = X_filled[:, j] + col_mean = np.nanmean(col) + col[np.isnan(col)] = col_mean + X_filled[:, j] = col + + if include_own_lag: + X_full = np.column_stack([X_filled, own_lag[:, None]]) + else: + X_full = X_filled + + X_i = X_full[valid_y] + y_i = y_cur[valid_y] + + if len(y_i) < 10: + pool_r2s.append(np.nan) + continue + + model = RidgeCV(alphas=np.logspace(-2, 4, 50)) + model.fit(X_i, y_i) + y_pred = model.predict(X_i) + r2 = r2_score(y_i, y_pred) + pool_r2s.append(r2) + + print(f" {pid[:16]} ({matched_clean[pid]['tokens']:<14}) " + f"R²={r2:.3f} n_obs={len(y_i)} alpha={model.alpha_:.1f}") + + valid_r2s = [r for r in pool_r2s if not np.isnan(r)] + print(f"\n In-sample ridge{tag}: median R²={np.median(valid_r2s):.4f}, " + f"mean={np.mean(valid_r2s):.4f}") + + return pool_r2s, vol_matrix, None, pool_ids + + +def run_ridge_loo(matched_clean): + """LOO cross-pool ridge with token-overlap transfer.""" + print("\n" + "=" * 70) + print("1b. Ridge cross-pool LOO (transfer via token overlap)") + print("=" * 70) + + vol_matrix, date_list, pool_ids = build_volume_matrix(matched_clean) + n_dates, n_pools = vol_matrix.shape + overlap = build_token_overlap(matched_clean, pool_ids) + + pool_r2s = [] + for i, pid in enumerate(pool_ids): + # Training pools: all except i + train_idx = [j for j in range(n_pools) if j != i] + n_train = len(train_idx) + + # Build training data: for each training pool k, predict from others' lag + # Use per-pool valid rows with NaN imputation + X_lag_all = vol_matrix[:-1, :] + Y_cur_all = vol_matrix[1:, :] + + # Fit a ridge model for each training pool + train_models = {} + train_weights = {} # weight vectors (excluding self) + for k_pos, k in enumerate(train_idx): + # Predictors: all pools except k (including pool i's historical data!) + pred_idx = [j for j in range(n_pools) if j != k] + X_k = X_lag_all[:, pred_idx].copy() + y_k = Y_cur_all[:, k] + + valid = ~np.isnan(y_k) + for c in range(X_k.shape[1]): + col = X_k[:, c] + m = np.nanmean(col) + col[np.isnan(col)] = m if np.isfinite(m) else 0.0 + X_k[:, c] = col + + X_k = X_k[valid] + y_k = y_k[valid] + + if len(y_k) < 10: + continue + + model = RidgeCV(alphas=np.logspace(-2, 4, 50)) + model.fit(X_k, y_k) + train_models[k] = model + + # Store full weight vector (n_pools-1,) with mapping to pool indices + w = np.zeros(n_pools) + for widx, pidx in enumerate(pred_idx): + w[pidx] = model.coef_[widx] + w_intercept = model.intercept_ + train_weights[k] = (w, w_intercept) + + if not train_weights: + pool_r2s.append(np.nan) + continue + + # Transfer to held-out pool i: weighted average of training pools' weight vectors + # Weight by token overlap with pool i + w_transfer = np.zeros(n_pools) + intercept_transfer = 0.0 + total_sim = 0.0 + for k in train_weights: + sim = overlap[i, k] + if sim == 0: + sim = 0.1 # small weight for unrelated pools + w_k, b_k = train_weights[k] + w_transfer += sim * w_k + intercept_transfer += sim * b_k + total_sim += sim + + w_transfer /= total_sim + intercept_transfer /= total_sim + + # Zero out pool i's own weight (shouldn't predict from self) + w_transfer[i] = 0.0 + + # Predict pool i + X_lag_i = vol_matrix[:-1, :].copy() + y_true_i = vol_matrix[1:, i] + valid = ~np.isnan(y_true_i) + + # Impute NaN predictors + for c in range(X_lag_i.shape[1]): + col = X_lag_i[:, c] + m = np.nanmean(col) + col[np.isnan(col)] = m if np.isfinite(m) else 0.0 + X_lag_i[:, c] = col + + y_pred_i = X_lag_i[valid] @ w_transfer + intercept_transfer + y_true_i = y_true_i[valid] + + r2 = r2_score(y_true_i, y_pred_i) + pool_r2s.append(r2) + + print(f" {pid[:16]} ({matched_clean[pid]['tokens']:<14}) " + f"R²={r2:.3f} n_eval={len(y_true_i)}") + + valid_r2s = [r for r in pool_r2s if not np.isnan(r)] + print(f"\n LOO ridge (overlap transfer): median R²={np.median(valid_r2s):.4f}, " + f"mean={np.mean(valid_r2s):.4f}") + print(f" Recall: AR1={0.397:.3f}, zero-shot token-factored={0.362:.3f}") + + return pool_r2s + + +def run_ridge_loo_burnin(matched_clean, n_burnin=30): + """LOO with burn-in: learn pool i's weight row from n_burnin days.""" + print("\n" + "=" * 70) + print(f"1c. Ridge cross-pool LOO with {n_burnin}-day burn-in") + print("=" * 70) + + vol_matrix, date_list, pool_ids = build_volume_matrix(matched_clean) + n_dates, n_pools = vol_matrix.shape + + pool_r2s = [] + for i, pid in enumerate(pool_ids): + # Pool i's data + y_all = vol_matrix[:, i] + valid_days = ~np.isnan(y_all) + valid_indices = np.where(valid_days)[0] + + if len(valid_indices) < n_burnin + 10: + print(f" {pid[:16]} — too few obs, skipping") + pool_r2s.append(np.nan) + continue + + # Split: first n_burnin valid days for training, rest for eval + burn_indices = valid_indices[:n_burnin] + eval_indices = valid_indices[n_burnin:] + + # Training: predict pool i from all others' lag using burn-in days + # Need (day, day-1) pairs where day is in burn_indices and day >= 1 + burn_pairs = burn_indices[burn_indices >= 1] + + pred_idx = [j for j in range(n_pools) if j != i] + X_burn = vol_matrix[burn_pairs - 1][:, pred_idx].copy() + y_burn = vol_matrix[burn_pairs, i] + + # Impute NaN + for c in range(X_burn.shape[1]): + col = X_burn[:, c] + m = np.nanmean(col) + col[np.isnan(col)] = m if np.isfinite(m) else 0.0 + X_burn[:, c] = col + + model = RidgeCV(alphas=np.logspace(-2, 4, 50)) + model.fit(X_burn, y_burn) + + # Evaluate on remaining days + eval_pairs = eval_indices[eval_indices >= 1] + X_eval = vol_matrix[eval_pairs - 1][:, pred_idx].copy() + y_eval = vol_matrix[eval_pairs, i] + + for c in range(X_eval.shape[1]): + col = X_eval[:, c] + m = np.nanmean(col) + col[np.isnan(col)] = m if np.isfinite(m) else 0.0 + X_eval[:, c] = col + + y_pred = model.predict(X_eval) + r2 = r2_score(y_eval, y_pred) + pool_r2s.append(r2) + + print(f" {pid[:16]} ({matched_clean[pid]['tokens']:<14}) " + f"R²={r2:.3f} n_burn={len(burn_pairs)} n_eval={len(eval_pairs)} " + f"alpha={model.alpha_:.1f}") + + valid_r2s = [r for r in pool_r2s if not np.isnan(r)] + print(f"\n LOO ridge ({n_burnin}d burn-in): " + f"median R²={np.median(valid_r2s):.4f}, " + f"mean={np.mean(valid_r2s):.4f}") + + return pool_r2s + + +# ---- 2. Zero-parameter peer-mean ---- + + +def run_peer_mean_baseline(matched_clean): + """Predict pool i's volume as mean of token-peer lagged volumes.""" + from quantammsim.calibration.pool_data import _parse_tokens, _canonicalize_token + + print("\n" + "=" * 70) + print("2. Zero-parameter peer-mean baseline") + print("=" * 70) + + vol_matrix, date_list, pool_ids = build_volume_matrix(matched_clean) + n_dates, n_pools = vol_matrix.shape + overlap = build_token_overlap(matched_clean, pool_ids) + + pool_r2s = [] + for i, pid in enumerate(pool_ids): + # Peers: pools sharing at least 1 token + peers = [j for j in range(n_pools) if j != i and overlap[i, j] >= 1] + + if not peers: + pool_r2s.append(np.nan) + continue + + y_true = vol_matrix[1:, i] + valid = ~np.isnan(y_true) + + # Peer mean at t-1 + peer_lag = vol_matrix[:-1, :][:, peers] + peer_mean = np.nanmean(peer_lag, axis=1) + + y_pred = peer_mean[valid] + y_true = y_true[valid] + + # Remove any remaining NaN + both_valid = ~np.isnan(y_pred) + y_pred = y_pred[both_valid] + y_true = y_true[both_valid] + + r2 = r2_score(y_true, y_pred) + pool_r2s.append(r2) + + print(f" {pid[:16]} ({matched_clean[pid]['tokens']:<14}) " + f"R²={r2:.3f} n_peers={len(peers)} n_obs={len(y_true)}") + + valid_r2s = [r for r in pool_r2s if not np.isnan(r)] + print(f"\n Peer-mean baseline: median R²={np.median(valid_r2s):.4f}, " + f"mean={np.mean(valid_r2s):.4f}") + print(f" Recall: AR1={0.397:.3f}, zero-shot token-factored={0.362:.3f}") + + return pool_r2s + + +# ---- Main ---- + + +def main(): + os.environ.setdefault("JAX_PLATFORMS", "cpu") + + print("=" * 70) + print("Cross-Pool Linear Volume Prediction Baselines") + print("=" * 70) + + matched_clean, option_c_clean = load_stage1() + + # 1a. In-sample ridge (peers only + peers + own lag) + r2_peers, r2_both, vol_matrix, date_list, pool_ids = run_ridge_cross_pool(matched_clean) + + # 1b. LOO ridge with token-overlap transfer + loo_r2s = run_ridge_loo(matched_clean) + + # 1c. LOO ridge with 30-day burn-in + burnin_r2s = run_ridge_loo_burnin(matched_clean, n_burnin=30) + + # 2. Zero-parameter peer mean + peer_r2s = run_peer_mean_baseline(matched_clean) + + # Summary + def safe_median(xs): + v = [x for x in xs if not np.isnan(x)] + return np.median(v) if v else float("nan") + + print("\n" + "=" * 70) + print("SUMMARY") + print("=" * 70) + print(f" Ridge in-sample (peers): median R² = {safe_median(r2_peers):.4f}") + print(f" Ridge in-sample (+own): median R² = {safe_median(r2_both):.4f}") + print(f" Ridge LOO (overlap xfer): median R² = {safe_median(loo_r2s):.4f}") + print(f" Ridge LOO (30d burn-in): median R² = {safe_median(burnin_r2s):.4f}") + print(f" Peer-mean (0 params): median R² = {safe_median(peer_r2s):.4f}") + print(f" ---") + print(f" Naive AR1: median R² = 0.397") + print(f" Token-factored LOO: median R² = 0.362") + print(f" Option C in-sample: median R² = 0.589") + + +if __name__ == "__main__": + main() diff --git a/experiments/run_cross_pool_noise_linear.py b/experiments/run_cross_pool_noise_linear.py new file mode 100644 index 0000000..08ead10 --- /dev/null +++ b/experiments/run_cross_pool_noise_linear.py @@ -0,0 +1,392 @@ +"""Cross-pool linear prediction of NOISE residuals. + +Decomposes total volume into V_arb (from grid + Option C cadence/gas) +and noise residual, then tests whether peer pools' lagged noise +residuals predict this pool's noise residual. + +1. Ridge in-sample: noise_resid_i_t = W_i @ noise_resid_{-i, t-1} [+ own_lag] +2. Ridge LOO with overlap transfer +3. Ridge LOO with 30-day burn-in +""" + +import os +import pickle +import sys + +import numpy as np +from sklearn.linear_model import RidgeCV + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) + + +def load_stage1(): + path = os.path.join(CACHE_DIR, "stage1.pkl") + if not os.path.exists(path): + print("ERROR: no stage1 cache.") + sys.exit(1) + with open(path, "rb") as f: + data = pickle.load(f) + print(f"Loaded {len(data['matched_clean'])} pools from cache") + return data["matched_clean"], data["option_c_clean"] + + +def build_noise_residual_matrix(matched_clean, option_c_clean): + """Build (n_dates, n_pools) noise residual matrix. + + noise_resid_i_t = log_volume_i_t - log(V_arb_i_t) + + V_arb computed from grid interpolation at Option C cadence/gas. + Returns residual matrix (NaN where missing), date list, pool ids. + """ + import jax.numpy as jnp + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + + pool_ids = sorted(matched_clean.keys()) + n_pools = len(pool_ids) + + # Collect all dates + all_dates = set() + for pid in pool_ids: + panel = matched_clean[pid]["panel"] + all_dates.update(panel["date"].values) + date_list = sorted(all_dates) + n_dates = len(date_list) + date_to_idx = {d: i for i, d in enumerate(date_list)} + + # Build matrices + vol_matrix = np.full((n_dates, n_pools), np.nan) + resid_matrix = np.full((n_dates, n_pools), np.nan) + + for j, pid in enumerate(pool_ids): + entry = matched_clean[pid] + oc = option_c_clean[pid] + panel = entry["panel"] + coeffs = entry["coeffs"] + day_indices = entry["day_indices"] + + # Compute V_arb from grid + v_arb_all = np.array(interpolate_pool_daily( + coeffs, + jnp.float64(oc["log_cadence"]), + jnp.float64(np.exp(oc["log_gas"])), + )) + v_arb = v_arb_all[day_indices] + log_v_arb = np.log(np.maximum(v_arb, 1e-6)) + + # Fill matrices + dates = panel["date"].values + log_vols = panel["log_volume"].values.astype(float) + + for k, date in enumerate(dates): + t = date_to_idx[date] + vol_matrix[t, j] = log_vols[k] + resid_matrix[t, j] = log_vols[k] - log_v_arb[k] + + print(f" Built noise residual matrix: {n_dates} dates x {n_pools} pools") + print(f" Residual stats: mean={np.nanmean(resid_matrix):.3f}, " + f"std={np.nanstd(resid_matrix):.3f}") + + return vol_matrix, resid_matrix, date_list, pool_ids + + +def build_token_overlap(matched_clean, pool_ids): + from quantammsim.calibration.pool_data import _parse_tokens, _canonicalize_token + n = len(pool_ids) + overlap = np.zeros((n, n), dtype=np.int32) + pool_tokens = {} + for i, pid in enumerate(pool_ids): + toks = _parse_tokens(matched_clean[pid]["tokens"]) + pool_tokens[i] = {_canonicalize_token(t) for t in toks[:2]} + for i in range(n): + for j in range(n): + overlap[i, j] = len(pool_tokens[i] & pool_tokens[j]) + return overlap + + +def r2_score(y_true, y_pred): + ss_res = np.sum((y_true - y_pred) ** 2) + ss_tot = np.sum((y_true - y_true.mean()) ** 2) + return 1 - ss_res / max(ss_tot, 1e-10) + + +# ---- In-sample ridge on noise residuals ---- + + +def run_ridge_insample(resid_matrix, pool_ids, matched_clean): + """Per-pool ridge: predict noise_resid from peers' lagged residuals.""" + n_dates, n_pools = resid_matrix.shape + + for include_own in [False, True]: + tag = "peers + own_lag" if include_own else "peers only" + print(f"\n{'='*70}") + print(f"Ridge in-sample on noise residuals ({tag})") + print(f"{'='*70}") + + pool_r2s = [] + for i, pid in enumerate(pool_ids): + X_lag = resid_matrix[:-1, :] + y_cur = resid_matrix[1:, i] + own_lag = X_lag[:, i] + + valid = ~np.isnan(y_cur) + if include_own: + valid = valid & ~np.isnan(own_lag) + + X_others = np.delete(X_lag, i, axis=1) + X_filled = X_others.copy() + for c in range(X_filled.shape[1]): + col = X_filled[:, c] + m = np.nanmean(col) + col[np.isnan(col)] = m if np.isfinite(m) else 0.0 + X_filled[:, c] = col + + if include_own: + X_full = np.column_stack([X_filled, own_lag[:, None]]) + else: + X_full = X_filled + + X_i = X_full[valid] + y_i = y_cur[valid] + + if len(y_i) < 10: + pool_r2s.append(np.nan) + continue + + model = RidgeCV(alphas=np.logspace(-2, 4, 50)) + model.fit(X_i, y_i) + y_pred = model.predict(X_i) + r2 = r2_score(y_i, y_pred) + pool_r2s.append(r2) + + print(f" {pid[:16]} ({matched_clean[pid]['tokens']:<14}) " + f"R²={r2:.3f} n={len(y_i)} alpha={model.alpha_:.1f}") + + valid_r2s = [r for r in pool_r2s if not np.isnan(r)] + print(f"\n In-sample ridge ({tag}): median R²={np.median(valid_r2s):.4f}, " + f"mean={np.mean(valid_r2s):.4f}") + + return pool_r2s + + +def run_ar1_noise_baseline(resid_matrix, pool_ids, matched_clean): + """Naive AR1 on noise residuals: resid_tomorrow = resid_today.""" + print(f"\n{'='*70}") + print("AR1 baseline on noise residuals") + print(f"{'='*70}") + + pool_r2s = [] + for i, pid in enumerate(pool_ids): + y = resid_matrix[:, i] + valid = ~np.isnan(y[:-1]) & ~np.isnan(y[1:]) + y_true = y[1:][valid] + y_pred = y[:-1][valid] + + if len(y_true) < 3: + pool_r2s.append(np.nan) + continue + + r2 = r2_score(y_true, y_pred) + pool_r2s.append(r2) + print(f" {pid[:16]} ({matched_clean[pid]['tokens']:<14}) " + f"R²={r2:.3f} n={len(y_true)}") + + valid_r2s = [r for r in pool_r2s if not np.isnan(r)] + print(f"\n AR1 noise residual: median R²={np.median(valid_r2s):.4f}, " + f"mean={np.mean(valid_r2s):.4f}") + return pool_r2s + + +# ---- LOO with overlap transfer ---- + + +def run_ridge_loo(resid_matrix, pool_ids, matched_clean): + """LOO on noise residuals with token-overlap weight transfer.""" + print(f"\n{'='*70}") + print("Ridge LOO on noise residuals (overlap transfer, peers + own_lag)") + print(f"{'='*70}") + + n_dates, n_pools = resid_matrix.shape + overlap = build_token_overlap(matched_clean, pool_ids) + + pool_r2s = [] + for i, pid in enumerate(pool_ids): + train_idx = [j for j in range(n_pools) if j != i] + + # Fit ridge for each training pool + train_weights = {} + for k in train_idx: + pred_idx = [j for j in range(n_pools) if j != k] + X_lag = resid_matrix[:-1, pred_idx].copy() + own_lag_k = resid_matrix[:-1, k] + y_k = resid_matrix[1:, k] + + valid = ~np.isnan(y_k) & ~np.isnan(own_lag_k) + for c in range(X_lag.shape[1]): + col = X_lag[:, c] + m = np.nanmean(col) + col[np.isnan(col)] = m if np.isfinite(m) else 0.0 + X_lag[:, c] = col + + X_k = np.column_stack([X_lag[valid], own_lag_k[valid, None]]) + y_k = y_k[valid] + + if len(y_k) < 10: + continue + + model = RidgeCV(alphas=np.logspace(-2, 4, 50)) + model.fit(X_k, y_k) + + # Store weights mapped to pool indices + w = np.zeros(n_pools + 1) # +1 for own_lag + for widx, pidx in enumerate(pred_idx): + w[pidx] = model.coef_[widx] + w[-1] = model.coef_[-1] # own_lag weight + train_weights[k] = (w, model.intercept_) + + if not train_weights: + pool_r2s.append(np.nan) + continue + + # Transfer: overlap-weighted average of training pool weight vectors + w_transfer = np.zeros(n_pools + 1) + b_transfer = 0.0 + total_sim = 0.0 + for k in train_weights: + sim = max(overlap[i, k], 0.1) + w_k, b_k = train_weights[k] + w_transfer += sim * w_k + b_transfer += sim * b_k + total_sim += sim + w_transfer /= total_sim + b_transfer /= total_sim + w_transfer[i] = 0.0 # no self-prediction from peers + + # Predict held-out pool + X_lag_all = resid_matrix[:-1, :].copy() + own_lag_i = resid_matrix[:-1, i] + y_true = resid_matrix[1:, i] + valid = ~np.isnan(y_true) & ~np.isnan(own_lag_i) + + for c in range(X_lag_all.shape[1]): + col = X_lag_all[:, c] + m = np.nanmean(col) + col[np.isnan(col)] = m if np.isfinite(m) else 0.0 + X_lag_all[:, c] = col + + X_i = np.column_stack([X_lag_all[valid], own_lag_i[valid, None]]) + y_pred = X_i @ w_transfer + b_transfer + y_true = y_true[valid] + + r2 = r2_score(y_true, y_pred) + pool_r2s.append(r2) + print(f" {pid[:16]} ({matched_clean[pid]['tokens']:<14}) " + f"R²={r2:.3f} n={len(y_true)}") + + valid_r2s = [r for r in pool_r2s if not np.isnan(r)] + print(f"\n LOO ridge (overlap transfer): median R²={np.median(valid_r2s):.4f}, " + f"mean={np.mean(valid_r2s):.4f}") + return pool_r2s + + +# ---- LOO with burn-in ---- + + +def run_ridge_burnin(resid_matrix, pool_ids, matched_clean, n_burnin=30): + """LOO with burn-in: learn pool i's weights from first n_burnin days.""" + print(f"\n{'='*70}") + print(f"Ridge LOO on noise residuals ({n_burnin}d burn-in, peers + own_lag)") + print(f"{'='*70}") + + n_dates, n_pools = resid_matrix.shape + + pool_r2s = [] + for i, pid in enumerate(pool_ids): + y_all = resid_matrix[:, i] + own_lag_all = np.full(n_dates, np.nan) + own_lag_all[1:] = y_all[:-1] + + valid_days = ~np.isnan(y_all) & ~np.isnan(own_lag_all) + valid_indices = np.where(valid_days)[0] + + if len(valid_indices) < n_burnin + 10: + pool_r2s.append(np.nan) + continue + + burn_idx = valid_indices[:n_burnin] + eval_idx = valid_indices[n_burnin:] + + pred_idx = [j for j in range(n_pools) if j != i] + + def build_X(indices): + X_peers = resid_matrix[indices - 1][:, pred_idx].copy() + for c in range(X_peers.shape[1]): + col = X_peers[:, c] + m = np.nanmean(col) + col[np.isnan(col)] = m if np.isfinite(m) else 0.0 + X_peers[:, c] = col + own = y_all[indices - 1] + return np.column_stack([X_peers, own[:, None]]) + + X_burn = build_X(burn_idx) + y_burn = y_all[burn_idx] + + model = RidgeCV(alphas=np.logspace(-2, 4, 50)) + model.fit(X_burn, y_burn) + + X_eval = build_X(eval_idx) + y_eval = y_all[eval_idx] + y_pred = model.predict(X_eval) + + r2 = r2_score(y_eval, y_pred) + pool_r2s.append(r2) + print(f" {pid[:16]} ({matched_clean[pid]['tokens']:<14}) " + f"R²={r2:.3f} n_eval={len(eval_idx)} alpha={model.alpha_:.1f}") + + valid_r2s = [r for r in pool_r2s if not np.isnan(r)] + print(f"\n LOO ridge ({n_burnin}d burn-in): " + f"median R²={np.median(valid_r2s):.4f}, mean={np.mean(valid_r2s):.4f}") + return pool_r2s + + +# ---- Main ---- + + +def main(): + os.environ.setdefault("JAX_PLATFORMS", "cpu") + + print("=" * 70) + print("Cross-Pool Linear Prediction of Noise Residuals") + print(" (total volume - grid arb volume)") + print("=" * 70) + + matched_clean, option_c_clean = load_stage1() + vol_matrix, resid_matrix, date_list, pool_ids = build_noise_residual_matrix( + matched_clean, option_c_clean) + + ar1_r2s = run_ar1_noise_baseline(resid_matrix, pool_ids, matched_clean) + insample_r2s = run_ridge_insample(resid_matrix, pool_ids, matched_clean) + loo_r2s = run_ridge_loo(resid_matrix, pool_ids, matched_clean) + burnin_r2s = run_ridge_burnin(resid_matrix, pool_ids, matched_clean, n_burnin=30) + + def safe_median(xs): + v = [x for x in xs if x is not None and not np.isnan(x)] + return np.median(v) if v else float("nan") + + print("\n" + "=" * 70) + print("SUMMARY (noise residuals)") + print("=" * 70) + print(f" AR1 noise residual: median R² = {safe_median(ar1_r2s):.4f}") + print(f" Ridge in-sample (+own): median R² = {safe_median(insample_r2s):.4f}") + print(f" Ridge LOO (overlap xfer): median R² = {safe_median(loo_r2s):.4f}") + print(f" Ridge LOO (30d burn-in): median R² = {safe_median(burnin_r2s):.4f}") + print(f" ---") + print(f" (total vol) AR1: median R² = 0.397") + print(f" (total vol) Ridge +own: median R² = 0.599") + print(f" Option C in-sample: median R² = 0.589") + + +if __name__ == "__main__": + main() diff --git a/experiments/run_deepsets_noise.py b/experiments/run_deepsets_noise.py new file mode 100644 index 0000000..a639256 --- /dev/null +++ b/experiments/run_deepsets_noise.py @@ -0,0 +1,595 @@ +"""DeepSets noise volume prediction with V_arb decomposition. + +Predicts V_noise via a shared encoder-decoder over peer pools. +V_arb is precomputed from grids at Option C cadence/gas. +Loss: mean((log(V_arb + V_noise_predicted) - log_volume)^2) + +Usage: + python experiments/run_deepsets_noise.py # default hparams + python experiments/run_deepsets_noise.py --tune 50 # Optuna, 50 trials +""" + +import argparse +import os +import pickle +import sys +import time + +import jax +import jax.numpy as jnp +import numpy as np +import pandas as pd + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) + +# Default hyperparameters +DEFAULTS = dict( + hidden=16, + d_embed=8, + lr=3e-4, + l2_alpha=1e-3, + n_epochs=1000, + include_own_lag=True, +) + + +def load_stage1(): + path = os.path.join(CACHE_DIR, "stage1.pkl") + if not os.path.exists(path): + print("ERROR: no stage1 cache.") + sys.exit(1) + with open(path, "rb") as f: + data = pickle.load(f) + return data["matched_clean"], data["option_c_clean"] + + +# ---- Data construction ---- + + +def build_data(matched_clean, option_c_clean, exclude_pool_idx=None): + """Build training arrays with V_arb decomposition. + + For each (pool i, day t) sample: + - peer_vols: other pools' log_volume at t-1 + - v_arb: precomputed arb volume for pool i at day t + - local_features: [log_tvl_lag1, dow_sin, dow_cos] + - own_lag: pool i's log_volume at t-1 + - y: log_volume at day t + """ + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + from quantammsim.calibration.pool_data import ( + build_pool_attributes, _parse_tokens, _canonicalize_token, + ) + + pool_ids = sorted(matched_clean.keys()) + n_pools = len(pool_ids) + + # ---- Collect all dates, build volume + V_arb matrices ---- + all_dates = set() + for pid in pool_ids: + all_dates.update(matched_clean[pid]["panel"]["date"].values) + date_list = sorted(all_dates) + n_dates = len(date_list) + date_to_idx = {d: i for i, d in enumerate(date_list)} + + vol_matrix = np.full((n_dates, n_pools), np.nan) + v_arb_matrix = np.full((n_dates, n_pools), np.nan) + tvl_matrix = np.full((n_dates, n_pools), np.nan) + weekday_matrix = np.full(n_dates, np.nan) + + for j, pid in enumerate(pool_ids): + entry = matched_clean[pid] + oc = option_c_clean[pid] + panel = entry["panel"] + + # V_arb from grid + v_arb_all = np.array(interpolate_pool_daily( + entry["coeffs"], + jnp.float64(oc["log_cadence"]), + jnp.float64(np.exp(oc["log_gas"])), + )) + v_arb_day = v_arb_all[entry["day_indices"]] + + dates = panel["date"].values + log_vols = panel["log_volume"].values.astype(float) + tvl_vals = panel["log_tvl_lag1"].values.astype(float) + + for k, date in enumerate(dates): + t = date_to_idx[date] + vol_matrix[t, j] = log_vols[k] + v_arb_matrix[t, j] = v_arb_day[k] + tvl_matrix[t, j] = tvl_vals[k] + + # Weekdays + for t, date in enumerate(date_list): + dt = pd.Timestamp(date) + weekday_matrix[t] = dt.weekday() + + # ---- Pool attributes ---- + X_attr, attr_names, _ = build_pool_attributes(matched_clean) + attr_mean = np.mean(X_attr, axis=0) + attr_std = np.std(X_attr, axis=0) + attr_std[attr_std < 1e-6] = 1.0 + X_attr_norm = ((X_attr - attr_mean) / attr_std).astype(np.float32) + k_attr = X_attr_norm.shape[1] + + # ---- Token overlap ---- + pool_tokens = {} + for i, pid in enumerate(pool_ids): + toks = _parse_tokens(matched_clean[pid]["tokens"]) + pool_tokens[i] = {_canonicalize_token(t) for t in toks[:2]} + + n_peers = n_pools - 1 + peer_attrs = np.zeros((n_pools, n_peers, k_attr), dtype=np.float32) + peer_overlap = np.zeros((n_pools, n_peers), dtype=np.float32) + peer_col_idx = np.zeros((n_pools, n_peers), dtype=np.int32) + + for i in range(n_pools): + peers = [j for j in range(n_pools) if j != i] + for p, j in enumerate(peers): + peer_attrs[i, p] = X_attr_norm[j] + peer_overlap[i, p] = len(pool_tokens[i] & pool_tokens[j]) + peer_col_idx[i, p] = j + + target_attrs = X_attr_norm + + # ---- Standardize volumes for encoder input ---- + vol_mean = float(np.nanmean(vol_matrix)) + vol_std = float(np.nanstd(vol_matrix)) + + # ---- Build samples ---- + sample_pools, sample_days = [], [] + for i in range(n_pools): + if i == exclude_pool_idx: + continue + for t in range(1, n_dates): + if (np.isnan(vol_matrix[t, i]) or np.isnan(vol_matrix[t - 1, i]) + or np.isnan(v_arb_matrix[t, i]) or np.isnan(tvl_matrix[t, i])): + continue + sample_pools.append(i) + sample_days.append(t) + + sample_pools = np.array(sample_pools, dtype=np.int32) + sample_days = np.array(sample_days, dtype=np.int32) + n_samples = len(sample_pools) + + peer_vols_arr = np.zeros((n_samples, n_peers), dtype=np.float32) + peer_mask_arr = np.zeros((n_samples, n_peers), dtype=np.float32) + own_lag_arr = np.zeros(n_samples, dtype=np.float32) + v_arb_arr = np.zeros(n_samples, dtype=np.float32) + local_arr = np.zeros((n_samples, 3), dtype=np.float32) # tvl, dow_sin, dow_cos + y_arr = np.zeros(n_samples, dtype=np.float32) + + for s in range(n_samples): + i = sample_pools[s] + t = sample_days[s] + cols = peer_col_idx[i] + + pvols_raw = vol_matrix[t - 1, cols] + valid = ~np.isnan(pvols_raw) + pvols_norm = (pvols_raw - vol_mean) / vol_std + peer_vols_arr[s] = np.where(valid, pvols_norm, 0.0) + peer_mask_arr[s] = valid.astype(np.float32) + + own_lag_arr[s] = (vol_matrix[t - 1, i] - vol_mean) / vol_std + v_arb_arr[s] = v_arb_matrix[t, i] + y_arr[s] = vol_matrix[t, i] # raw log_volume (not standardized) + + wd = weekday_matrix[t] + local_arr[s, 0] = tvl_matrix[t, i] + local_arr[s, 1] = np.sin(2 * np.pi * wd / 7) + local_arr[s, 2] = np.cos(2 * np.pi * wd / 7) + + # Standardize local features + local_mean = np.mean(local_arr, axis=0) + local_std = np.std(local_arr, axis=0) + local_std[local_std < 1e-6] = 1.0 + local_arr = ((local_arr - local_mean) / local_std).astype(np.float32) + + return { + "peer_attrs": jnp.array(peer_attrs), + "target_attrs": jnp.array(target_attrs), + "peer_overlap": jnp.array(peer_overlap), + "peer_vols": jnp.array(peer_vols_arr), + "peer_mask": jnp.array(peer_mask_arr), + "own_lag": jnp.array(own_lag_arr), + "v_arb": jnp.array(v_arb_arr), + "local": jnp.array(local_arr), + "y": jnp.array(y_arr), + "pool_idx": jnp.array(sample_pools), + "day_idx": sample_days, + "n_pools": n_pools, + "n_peers": n_peers, + "k_attr": k_attr, + "k_local": 3, + "pool_ids": pool_ids, + } + + +# ---- Model ---- + + +def init_params(key, k_attr, k_local, hidden, d_embed, include_own_lag): + k1, k2, k3, k4 = jax.random.split(key, 4) + enc_in = 2 * k_attr + 2 # peer_attr + target_attr + peer_vol + overlap + dec_in = d_embed + k_attr + k_local + (1 if include_own_lag else 0) + + return { + "enc_W1": jax.random.normal(k1, (enc_in, hidden)) * np.sqrt(2.0 / enc_in), + "enc_b1": jnp.zeros(hidden), + "enc_W2": jax.random.normal(k2, (hidden, d_embed)) * np.sqrt(2.0 / hidden), + "enc_b2": jnp.zeros(d_embed), + "dec_W1": jax.random.normal(k3, (dec_in, hidden)) * np.sqrt(2.0 / dec_in), + "dec_b1": jnp.zeros(hidden), + "dec_W2": jax.random.normal(k4, (hidden, 1)) * 0.01, + "dec_b2": jnp.zeros(1), + } + + +def forward(params, peer_attrs_all, target_attrs_all, peer_overlap_all, + peer_vols, peer_mask, own_lag, local_feat, pool_idx, + include_own_lag=True): + """Returns log_v_noise per sample.""" + batch = peer_vols.shape[0] + + pa = peer_attrs_all[pool_idx] + ta = target_attrs_all[pool_idx] + ov = peer_overlap_all[pool_idx] + ta_broad = jnp.broadcast_to(ta[:, None, :], pa.shape) + + enc_in = jnp.concatenate([ + pa, ta_broad, + peer_vols[:, :, None], + ov[:, :, None], + ], axis=-1) + + flat = enc_in.reshape(-1, enc_in.shape[-1]) + h = jnp.maximum(flat @ params["enc_W1"] + params["enc_b1"], 0.0) + h = h @ params["enc_W2"] + params["enc_b2"] + h = h.reshape(batch, peer_vols.shape[1], -1) + + h_masked = h * peer_mask[:, :, None] + n_valid = jnp.maximum(jnp.sum(peer_mask, axis=1, keepdims=True), 1.0) + summary = jnp.sum(h_masked, axis=1) / n_valid + + dec_parts = [summary, ta, local_feat] + if include_own_lag: + dec_parts.append(own_lag[:, None]) + dec_in = jnp.concatenate(dec_parts, axis=-1) + + h_dec = jnp.maximum(dec_in @ params["dec_W1"] + params["dec_b1"], 0.0) + log_v_noise = (h_dec @ params["dec_W2"] + params["dec_b2"])[:, 0] + return log_v_noise + + +def loss_fn(params, static, peer_vols, peer_mask, own_lag, local_feat, + pool_idx, v_arb, y, l2_alpha, include_own_lag): + """Log-space V_arb + V_noise loss matching the calibration pipeline.""" + log_v_noise = forward( + params, static["peer_attrs"], static["target_attrs"], + static["peer_overlap"], peer_vols, peer_mask, own_lag, local_feat, + pool_idx, include_own_lag, + ) + v_noise = jnp.exp(log_v_noise) + log_v_pred = jnp.log(jnp.maximum(v_arb + v_noise, 1e-6)) + mse = jnp.mean((log_v_pred - y) ** 2) + reg = sum(jnp.sum(v ** 2) for k, v in params.items() if "W" in k) + return mse + alpha * reg if (alpha := l2_alpha) else mse + l2_alpha * reg + + +@jax.jit +def _loss_and_grad(params, static, peer_vols, peer_mask, own_lag, local_feat, + pool_idx, v_arb, y, l2_alpha, include_own_lag): + return jax.value_and_grad(loss_fn)( + params, static, peer_vols, peer_mask, own_lag, local_feat, + pool_idx, v_arb, y, l2_alpha, include_own_lag, + ) + + +# ---- Training ---- + + +def train(params, data, hparams, verbose=True): + """Full-batch Adam.""" + static = {k: data[k] for k in ["peer_attrs", "target_attrs", "peer_overlap"]} + include_own_lag = hparams["include_own_lag"] + lr = hparams["lr"] + l2_alpha = hparams["l2_alpha"] + n_epochs = hparams["n_epochs"] + + m = {k: jnp.zeros_like(v) for k, v in params.items()} + v = {k: jnp.zeros_like(v) for k, v in params.items()} + + for epoch in range(n_epochs): + loss_val, grads = _loss_and_grad( + params, static, data["peer_vols"], data["peer_mask"], + data["own_lag"], data["local"], data["pool_idx"], + data["v_arb"], data["y"], l2_alpha, include_own_lag, + ) + + for k in params: + m[k] = 0.9 * m[k] + 0.1 * grads[k] + v[k] = 0.999 * v[k] + 0.001 * grads[k] ** 2 + m_hat = m[k] / (1.0 - 0.9 ** (epoch + 1)) + v_hat = v[k] / (1.0 - 0.999 ** (epoch + 1)) + params[k] = params[k] - lr * m_hat / (jnp.sqrt(v_hat) + 1e-8) + + if verbose and (epoch % 200 == 0 or epoch == n_epochs - 1): + print(f" epoch {epoch:4d} loss={float(loss_val):.6f}") + + return params, float(loss_val) + + +# ---- Evaluation ---- + + +def per_pool_r2(params, data, hparams): + """Per-pool R² on the log(V_arb + V_noise) prediction.""" + static = {k: data[k] for k in ["peer_attrs", "target_attrs", "peer_overlap"]} + log_v_noise = np.array(forward( + params, static["peer_attrs"], static["target_attrs"], + static["peer_overlap"], data["peer_vols"], data["peer_mask"], + data["own_lag"], data["local"], data["pool_idx"], + hparams["include_own_lag"], + )) + v_noise = np.exp(log_v_noise) + v_arb = np.array(data["v_arb"]) + log_v_pred = np.log(np.maximum(v_arb + v_noise, 1e-6)) + y = np.array(data["y"]) + pool_idx = np.array(data["pool_idx"]) + + r2s = {} + for i in range(data["n_pools"]): + mask = pool_idx == i + if mask.sum() < 2: + continue + yi = y[mask] + pi = log_v_pred[mask] + ss_res = np.sum((yi - pi) ** 2) + ss_tot = np.sum((yi - yi.mean()) ** 2) + r2s[i] = 1 - ss_res / max(ss_tot, 1e-10) + return r2s + + +def subset_data(data, mask): + """Subset data arrays by boolean mask.""" + jmask = jnp.array(mask) + static_keys = ["peer_attrs", "target_attrs", "peer_overlap", + "n_pools", "n_peers", "k_attr", "k_local", "pool_ids"] + out = {k: data[k] for k in static_keys} + for k in ["peer_vols", "peer_mask", "own_lag", "v_arb", "local", "y", "pool_idx"]: + out[k] = data[k][jmask] + out["day_idx"] = np.array(data["day_idx"])[mask] + return out + + +# ---- Experiments ---- + + +def run_single(matched_clean, option_c_clean, hparams, split_frac=0.7): + """Train with temporal split, report in-sample and eval R².""" + data = build_data(matched_clean, option_c_clean) + n_params = sum(v.size for v in init_params( + jax.random.PRNGKey(0), data["k_attr"], data["k_local"], + hparams["hidden"], hparams["d_embed"], hparams["include_own_lag"], + ).values()) + + print(f" {data['peer_vols'].shape[0]} samples, {data['n_pools']} pools, " + f"{n_params} params") + + day_idx = np.array(data["day_idx"]) + split_day = int(day_idx.max() * split_frac) + train_mask = day_idx <= split_day + eval_mask = day_idx > split_day + train_data = subset_data(data, train_mask) + eval_data = subset_data(data, eval_mask) + + print(f" Train: {int(train_mask.sum())} samples, " + f"Eval: {int(eval_mask.sum())} samples") + + params = init_params( + jax.random.PRNGKey(42), data["k_attr"], data["k_local"], + hparams["hidden"], hparams["d_embed"], hparams["include_own_lag"], + ) + t0 = time.time() + params, final_loss = train(params, train_data, hparams) + print(f" Training: {time.time() - t0:.1f}s, final loss={final_loss:.6f}") + + r2_train = per_pool_r2(params, train_data, hparams) + r2_eval = per_pool_r2(params, eval_data, hparams) + + pool_ids = data["pool_ids"] + for i, pid in enumerate(pool_ids): + r_tr = r2_train.get(i, float("nan")) + r_ev = r2_eval.get(i, float("nan")) + print(f" {pid[:16]} ({matched_clean[pid]['tokens']:<14}) " + f"train={r_tr:.3f} eval={r_ev:.3f}") + + vals_train = [v for v in r2_train.values() if np.isfinite(v)] + vals_eval = [v for v in r2_eval.values() if np.isfinite(v)] + med_train = np.median(vals_train) if vals_train else float("nan") + med_eval = np.median(vals_eval) if vals_eval else float("nan") + + print(f"\n Train: median R²={med_train:.4f}") + print(f" Eval: median R²={med_eval:.4f}") + print(f" (Option C in-sample: 0.589)") + + return med_eval, params, data + + +def run_loo(matched_clean, option_c_clean, hparams): + """Full LOO.""" + pool_ids = sorted(matched_clean.keys()) + n_pools = len(pool_ids) + loo_r2s = [] + + print(f"\n{'='*70}") + print("LOO DeepSets Noise") + print(f"{'='*70}") + + # Use fewer epochs for LOO + loo_hparams = dict(hparams, n_epochs=min(hparams["n_epochs"], 500)) + + for hold_out_idx in range(n_pools): + hold_out_pid = pool_ids[hold_out_idx] + train_data = build_data(matched_clean, option_c_clean, + exclude_pool_idx=hold_out_idx) + + params = init_params( + jax.random.PRNGKey(42), train_data["k_attr"], train_data["k_local"], + loo_hparams["hidden"], loo_hparams["d_embed"], + loo_hparams["include_own_lag"], + ) + params, _ = train(params, train_data, loo_hparams, verbose=False) + + # Eval on held-out pool + full_data = build_data(matched_clean, option_c_clean) + ho_mask = np.array(full_data["pool_idx"]) == hold_out_idx + if ho_mask.sum() < 2: + loo_r2s.append(float("nan")) + continue + + eval_data = subset_data(full_data, ho_mask) + r2s = per_pool_r2(params, eval_data, loo_hparams) + r2 = r2s.get(hold_out_idx, float("nan")) + loo_r2s.append(r2) + + tag = "OK" if r2 > 0 else "NEG" + print(f" {hold_out_pid[:16]} ({matched_clean[hold_out_pid]['tokens']:<14}) " + f"R²={r2:.3f} [{tag}]") + + valid = [r for r in loo_r2s if np.isfinite(r)] + med = np.median(valid) if valid else float("nan") + print(f"\n LOO: median R²={med:.4f}, " + f"mean={np.mean(valid):.4f}, " + f"n_neg={sum(1 for r in valid if r < 0)}") + return loo_r2s + + +# ---- Optuna ---- + + +def run_optuna(matched_clean, option_c_clean, n_trials): + """Hyperparameter optimization with Optuna.""" + import optuna + + # Precompute data once (shared across trials) + data = build_data(matched_clean, option_c_clean) + day_idx = np.array(data["day_idx"]) + split_day = int(day_idx.max() * 0.7) + train_mask = day_idx <= split_day + eval_mask = day_idx > split_day + train_data = subset_data(data, train_mask) + eval_data = subset_data(data, eval_mask) + + def objective(trial): + hp = { + "hidden": trial.suggest_categorical("hidden", [8, 16, 32]), + "d_embed": trial.suggest_categorical("d_embed", [4, 8, 16]), + "lr": trial.suggest_float("lr", 1e-4, 1e-2, log=True), + "l2_alpha": trial.suggest_float("l2_alpha", 1e-5, 1e-1, log=True), + "n_epochs": trial.suggest_categorical("n_epochs", [500, 1000, 2000]), + "include_own_lag": trial.suggest_categorical("include_own_lag", [True, False]), + } + + params = init_params( + jax.random.PRNGKey(42), data["k_attr"], data["k_local"], + hp["hidden"], hp["d_embed"], hp["include_own_lag"], + ) + params, final_loss = train(params, train_data, hp, verbose=False) + + r2s = per_pool_r2(params, eval_data, hp) + vals = [v for v in r2s.values() if np.isfinite(v)] + med_r2 = float(np.median(vals)) if vals else -10.0 + + # Report train R² too for diagnostics + r2s_tr = per_pool_r2(params, train_data, hp) + vals_tr = [v for v in r2s_tr.values() if np.isfinite(v)] + med_tr = float(np.median(vals_tr)) if vals_tr else -10.0 + + trial.set_user_attr("train_median_r2", med_tr) + trial.set_user_attr("final_loss", final_loss) + + print(f" Trial {trial.number}: eval={med_r2:.4f} train={med_tr:.4f} " + f"h={hp['hidden']} d={hp['d_embed']} lr={hp['lr']:.1e} " + f"alpha={hp['l2_alpha']:.1e} epochs={hp['n_epochs']} " + f"own_lag={hp['include_own_lag']}") + + return med_r2 + + study = optuna.create_study(direction="maximize") + study.optimize(objective, n_trials=n_trials) + + print(f"\n{'='*70}") + print("Optuna Results") + print(f"{'='*70}") + print(f" Best trial: {study.best_trial.number}") + print(f" Best eval median R²: {study.best_value:.4f}") + print(f" Best params: {study.best_params}") + print(f" Train median R²: {study.best_trial.user_attrs['train_median_r2']:.4f}") + + # Show top 5 + print(f"\n Top 5 trials:") + trials = sorted(study.trials, key=lambda t: t.value if t.value else -999, + reverse=True) + for t in trials[:5]: + if t.value is not None: + print(f" #{t.number}: eval={t.value:.4f} " + f"train={t.user_attrs.get('train_median_r2', '?'):.4f} " + f"{t.params}") + + return study + + +# ---- Main ---- + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--tune", type=int, default=0, + help="Run Optuna with N trials") + parser.add_argument("--loo", action="store_true", + help="Run LOO evaluation") + parser.add_argument("--hidden", type=int, default=DEFAULTS["hidden"]) + parser.add_argument("--d-embed", type=int, default=DEFAULTS["d_embed"]) + parser.add_argument("--lr", type=float, default=DEFAULTS["lr"]) + parser.add_argument("--l2-alpha", type=float, default=DEFAULTS["l2_alpha"]) + parser.add_argument("--epochs", type=int, default=DEFAULTS["n_epochs"]) + parser.add_argument("--no-own-lag", action="store_true") + args = parser.parse_args() + + os.environ.setdefault("JAX_PLATFORMS", "cpu") + + hparams = { + "hidden": args.hidden, + "d_embed": args.d_embed, + "lr": args.lr, + "l2_alpha": args.l2_alpha, + "n_epochs": args.epochs, + "include_own_lag": not args.no_own_lag, + } + + print("=" * 70) + print("DeepSets Noise Volume Prediction (V_arb decomposition)") + print(f" {hparams}") + print("=" * 70) + + matched_clean, option_c_clean = load_stage1() + + if args.tune > 0: + run_optuna(matched_clean, option_c_clean, args.tune) + else: + print(f"\n{'='*70}") + print("Temporal split (70/30)") + print(f"{'='*70}") + med_eval, params, data = run_single(matched_clean, option_c_clean, hparams) + + if args.loo: + run_loo(matched_clean, option_c_clean, hparams) + + +if __name__ == "__main__": + main() diff --git a/experiments/run_deepsets_v2.py b/experiments/run_deepsets_v2.py new file mode 100644 index 0000000..1ba3485 --- /dev/null +++ b/experiments/run_deepsets_v2.py @@ -0,0 +1,697 @@ +"""DeepSets v2: full feature menu with Optuna feature selection. + +Trains on total log_volume, evaluates on both total volume and noise +residual (log_vol - log_V_arb). V_arb precomputed from Option C fits. + +Feature menu: + Peer (encoder) — always: peer_attr, target_attr, vol_lag1, overlap + optional: vol_lag2, vol_change, tvl, volatility + Local (decoder) — always: target_attr, own_vol_lag1, dow_sin, dow_cos + optional: own_vol_lag2, own_vol_change, own_tvl, own_volatility + +Usage: + python experiments/run_deepsets_v2.py # defaults + python experiments/run_deepsets_v2.py --tune 50 # Optuna + python experiments/run_deepsets_v2.py --loo # LOO eval +""" + +import argparse +import os +import pickle +import sys +import time + +import jax +import jax.numpy as jnp +import numpy as np +import pandas as pd + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) + + +def load_stage1(): + path = os.path.join(CACHE_DIR, "stage1.pkl") + if not os.path.exists(path): + print("ERROR: no stage1 cache.") + sys.exit(1) + with open(path, "rb") as f: + data = pickle.load(f) + return data["matched_clean"], data["option_c_clean"] + + +# ---- Data construction ---- + + +def build_all_features(matched_clean, option_c_clean): + """Build all possible feature matrices. Called once.""" + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + from quantammsim.calibration.pool_data import ( + build_pool_attributes, _parse_tokens, _canonicalize_token, + ) + + pool_ids = sorted(matched_clean.keys()) + n_pools = len(pool_ids) + + # Collect dates + all_dates = set() + for pid in pool_ids: + all_dates.update(matched_clean[pid]["panel"]["date"].values) + date_list = sorted(all_dates) + n_dates = len(date_list) + date_to_idx = {d: i for i, d in enumerate(date_list)} + + # Daily matrices: (n_dates, n_pools) + vol_matrix = np.full((n_dates, n_pools), np.nan) + tvl_matrix = np.full((n_dates, n_pools), np.nan) + volatility_matrix = np.full((n_dates, n_pools), np.nan) + v_arb_matrix = np.full((n_dates, n_pools), np.nan) + weekday_arr = np.zeros(n_dates) + + for j, pid in enumerate(pool_ids): + entry = matched_clean[pid] + oc = option_c_clean[pid] + panel = entry["panel"] + + v_arb_all = np.array(interpolate_pool_daily( + entry["coeffs"], + jnp.float64(oc["log_cadence"]), + jnp.float64(np.exp(oc["log_gas"])), + )) + v_arb = v_arb_all[entry["day_indices"]] + + dates = panel["date"].values + for k, date in enumerate(dates): + t = date_to_idx[date] + vol_matrix[t, j] = panel["log_volume"].values[k] + tvl_matrix[t, j] = panel["log_tvl_lag1"].values[k] + volatility_matrix[t, j] = panel["volatility"].values[k] + v_arb_matrix[t, j] = v_arb[k] + + for t, date in enumerate(date_list): + weekday_arr[t] = pd.Timestamp(date).weekday() + + # Pool attributes (static) + X_attr, attr_names, _ = build_pool_attributes(matched_clean) + attr_mean = np.mean(X_attr, axis=0) + attr_std = np.std(X_attr, axis=0) + attr_std[attr_std < 1e-6] = 1.0 + X_attr_norm = ((X_attr - attr_mean) / attr_std).astype(np.float32) + k_attr = X_attr_norm.shape[1] + + # Token overlap + pool_tokens = {} + for i, pid in enumerate(pool_ids): + toks = _parse_tokens(matched_clean[pid]["tokens"]) + pool_tokens[i] = {_canonicalize_token(t) for t in toks[:2]} + + n_peers = n_pools - 1 + peer_attrs = np.zeros((n_pools, n_peers, k_attr), dtype=np.float32) + peer_overlap = np.zeros((n_pools, n_peers), dtype=np.float32) + peer_col_idx = np.zeros((n_pools, n_peers), dtype=np.int32) + + for i in range(n_pools): + peers = [j for j in range(n_pools) if j != i] + for p, j in enumerate(peers): + peer_attrs[i, p] = X_attr_norm[j] + peer_overlap[i, p] = len(pool_tokens[i] & pool_tokens[j]) + peer_col_idx[i, p] = j + + # Standardization stats for volumes + vol_mean = float(np.nanmean(vol_matrix)) + vol_std = float(np.nanstd(vol_matrix)) + tvl_mean = float(np.nanmean(tvl_matrix)) + tvl_std = float(np.nanstd(tvl_matrix)) + vola_mean = float(np.nanmean(volatility_matrix)) + vola_std = float(np.nanstd(volatility_matrix)) + + # Build samples: require t >= 2 (for lag-2), valid vol at t, t-1, t-2 + sample_pools, sample_days = [], [] + for i in range(n_pools): + for t in range(2, n_dates): + if (np.isnan(vol_matrix[t, i]) or np.isnan(vol_matrix[t - 1, i]) + or np.isnan(vol_matrix[t - 2, i])): + continue + sample_pools.append(i) + sample_days.append(t) + + sample_pools = np.array(sample_pools, dtype=np.int32) + sample_days = np.array(sample_days, dtype=np.int32) + n_samples = len(sample_pools) + + def _norm_vol(x): + return (x - vol_mean) / vol_std + + def _norm_tvl(x): + return (x - tvl_mean) / tvl_std + + def _norm_vola(x): + return (x - vola_mean) / vola_std + + # Per-sample arrays + # Peer features (per peer) + pf_vol_lag1 = np.zeros((n_samples, n_peers), dtype=np.float32) + pf_vol_lag2 = np.zeros((n_samples, n_peers), dtype=np.float32) + pf_vol_change = np.zeros((n_samples, n_peers), dtype=np.float32) + pf_tvl = np.zeros((n_samples, n_peers), dtype=np.float32) + pf_volatility = np.zeros((n_samples, n_peers), dtype=np.float32) + peer_mask = np.zeros((n_samples, n_peers), dtype=np.float32) + + # Local features + lf_own_vol_lag1 = np.zeros(n_samples, dtype=np.float32) + lf_own_vol_lag2 = np.zeros(n_samples, dtype=np.float32) + lf_own_vol_change = np.zeros(n_samples, dtype=np.float32) + lf_own_tvl = np.zeros(n_samples, dtype=np.float32) + lf_own_volatility = np.zeros(n_samples, dtype=np.float32) + lf_dow_sin = np.zeros(n_samples, dtype=np.float32) + lf_dow_cos = np.zeros(n_samples, dtype=np.float32) + + # Targets + y_total = np.zeros(n_samples, dtype=np.float32) + v_arb_samples = np.zeros(n_samples, dtype=np.float32) + + for s in range(n_samples): + i = sample_pools[s] + t = sample_days[s] + cols = peer_col_idx[i] + + # Peer features at t-1 + pvols1 = vol_matrix[t - 1, cols] + pvols2 = vol_matrix[t - 2, cols] + valid = ~np.isnan(pvols1) + peer_mask[s] = valid.astype(np.float32) + + pf_vol_lag1[s] = np.where(valid, _norm_vol(pvols1), 0.0) + pf_vol_lag2[s] = np.where(valid & ~np.isnan(pvols2), _norm_vol(pvols2), 0.0) + pf_vol_change[s] = np.where( + valid & ~np.isnan(pvols2), + _norm_vol(pvols1) - _norm_vol(pvols2), 0.0) + + ptvl = tvl_matrix[t - 1, cols] + pf_tvl[s] = np.where(valid & ~np.isnan(ptvl), _norm_tvl(ptvl), 0.0) + + pvola = volatility_matrix[t - 1, cols] + pf_volatility[s] = np.where(valid & ~np.isnan(pvola), _norm_vola(pvola), 0.0) + + # Local features + lf_own_vol_lag1[s] = _norm_vol(vol_matrix[t - 1, i]) + lf_own_vol_lag2[s] = _norm_vol(vol_matrix[t - 2, i]) + lf_own_vol_change[s] = lf_own_vol_lag1[s] - lf_own_vol_lag2[s] + + tvl_val = tvl_matrix[t, i] + lf_own_tvl[s] = _norm_tvl(tvl_val) if np.isfinite(tvl_val) else 0.0 + + vola_val = volatility_matrix[t, i] + lf_own_volatility[s] = _norm_vola(vola_val) if np.isfinite(vola_val) else 0.0 + + wd = weekday_arr[t] + lf_dow_sin[s] = np.sin(2 * np.pi * wd / 7) + lf_dow_cos[s] = np.cos(2 * np.pi * wd / 7) + + y_total[s] = vol_matrix[t, i] + v_arb_val = v_arb_matrix[t, i] + v_arb_samples[s] = v_arb_val if np.isfinite(v_arb_val) else 1e-6 + + return { + # Static per-pool + "peer_attrs": peer_attrs, # (n_pools, n_peers, k_attr) + "target_attrs": X_attr_norm, # (n_pools, k_attr) + "peer_overlap": peer_overlap, # (n_pools, n_peers) + # Per-sample peer features + "pf_vol_lag1": pf_vol_lag1, + "pf_vol_lag2": pf_vol_lag2, + "pf_vol_change": pf_vol_change, + "pf_tvl": pf_tvl, + "pf_volatility": pf_volatility, + "peer_mask": peer_mask, + # Per-sample local features + "lf_own_vol_lag1": lf_own_vol_lag1, + "lf_own_vol_lag2": lf_own_vol_lag2, + "lf_own_vol_change": lf_own_vol_change, + "lf_own_tvl": lf_own_tvl, + "lf_own_volatility": lf_own_volatility, + "lf_dow_sin": lf_dow_sin, + "lf_dow_cos": lf_dow_cos, + # Targets + "y_total": y_total, + "v_arb": v_arb_samples, + # Indices + "pool_idx": sample_pools, + "day_idx": sample_days, + # Meta + "n_pools": n_pools, + "n_peers": n_peers, + "k_attr": k_attr, + "pool_ids": pool_ids, + "vol_mean": vol_mean, + "vol_std": vol_std, + } + + +def assemble_inputs(data, feat_cfg): + """Assemble encoder/decoder inputs based on feature config. + + Returns (peer_input, local_input, peer_mask, pool_idx, y, v_arb) + all as JAX arrays ready for training. + """ + n_samples = len(data["pool_idx"]) + n_peers = data["n_peers"] + + # ---- Peer encoder input: (n_samples, n_peers, n_feat) ---- + # Always: peer_attr, target_attr, vol_lag1, overlap + pool_idx = data["pool_idx"] + pa = data["peer_attrs"][pool_idx] # (n_samples, n_peers, k_attr) + ta = data["target_attrs"][pool_idx] # (n_samples, k_attr) + ta_broad = np.broadcast_to(ta[:, None, :], pa.shape) + + peer_parts = [ + pa, ta_broad, + data["pf_vol_lag1"][:, :, None], + data["peer_overlap"][pool_idx][:, :, None], + ] + + if feat_cfg.get("peer_vol_lag2"): + peer_parts.append(data["pf_vol_lag2"][:, :, None]) + if feat_cfg.get("peer_vol_change"): + peer_parts.append(data["pf_vol_change"][:, :, None]) + if feat_cfg.get("peer_tvl"): + peer_parts.append(data["pf_tvl"][:, :, None]) + if feat_cfg.get("peer_volatility"): + peer_parts.append(data["pf_volatility"][:, :, None]) + + peer_input = np.concatenate(peer_parts, axis=-1).astype(np.float32) + + # ---- Local decoder input: (n_samples, n_feat) ---- + # Always: target_attr, own_vol_lag1, dow_sin, dow_cos + local_parts = [ + ta, + data["lf_own_vol_lag1"][:, None], + data["lf_dow_sin"][:, None], + data["lf_dow_cos"][:, None], + ] + + if feat_cfg.get("own_vol_lag2"): + local_parts.append(data["lf_own_vol_lag2"][:, None]) + if feat_cfg.get("own_vol_change"): + local_parts.append(data["lf_own_vol_change"][:, None]) + if feat_cfg.get("own_tvl"): + local_parts.append(data["lf_own_tvl"][:, None]) + if feat_cfg.get("own_volatility"): + local_parts.append(data["lf_own_volatility"][:, None]) + + local_input = np.concatenate(local_parts, axis=-1).astype(np.float32) + + return { + "peer_input": jnp.array(peer_input), + "local_input": jnp.array(local_input), + "peer_mask": jnp.array(data["peer_mask"]), + "y": jnp.array(data["y_total"]), + "v_arb": jnp.array(data["v_arb"]), + "pool_idx": jnp.array(pool_idx), + "n_peer_feat": peer_input.shape[-1], + "n_local_feat": local_input.shape[-1], + } + + +# ---- Model ---- + + +def init_params(key, n_peer_feat, n_local_feat, hidden, d_embed): + k1, k2, k3, k4 = jax.random.split(key, 4) + dec_in = d_embed + n_local_feat + return { + "enc_W1": jax.random.normal(k1, (n_peer_feat, hidden)) * np.sqrt(2.0 / n_peer_feat), + "enc_b1": jnp.zeros(hidden), + "enc_W2": jax.random.normal(k2, (hidden, d_embed)) * np.sqrt(2.0 / hidden), + "enc_b2": jnp.zeros(d_embed), + "dec_W1": jax.random.normal(k3, (dec_in, hidden)) * np.sqrt(2.0 / dec_in), + "dec_b1": jnp.zeros(hidden), + "dec_W2": jax.random.normal(k4, (hidden, 1)) * 0.01, + "dec_b2": jnp.zeros(1), + } + + +def forward(params, peer_input, peer_mask, local_input): + """Returns predicted log_volume (total) per sample.""" + batch, n_peers, _ = peer_input.shape + + flat = peer_input.reshape(-1, peer_input.shape[-1]) + h = jnp.maximum(flat @ params["enc_W1"] + params["enc_b1"], 0.0) + h = h @ params["enc_W2"] + params["enc_b2"] + h = h.reshape(batch, n_peers, -1) + + h_masked = h * peer_mask[:, :, None] + n_valid = jnp.maximum(jnp.sum(peer_mask, axis=1, keepdims=True), 1.0) + summary = jnp.sum(h_masked, axis=1) / n_valid + + dec_in = jnp.concatenate([summary, local_input], axis=-1) + h_dec = jnp.maximum(dec_in @ params["dec_W1"] + params["dec_b1"], 0.0) + return (h_dec @ params["dec_W2"] + params["dec_b2"])[:, 0] + + +def loss_fn(params, peer_input, peer_mask, local_input, y, l2_alpha): + """MSE on total log_volume + L2 reg.""" + pred = forward(params, peer_input, peer_mask, local_input) + mse = jnp.mean((pred - y) ** 2) + reg = sum(jnp.sum(v ** 2) for k, v in params.items() if "W" in k) + return mse + l2_alpha * reg + + +_grad_fn = jax.jit(jax.value_and_grad(loss_fn)) + + +# ---- Training ---- + + +def train(params, inputs, n_epochs, lr, l2_alpha, verbose=True): + m = {k: jnp.zeros_like(v) for k, v in params.items()} + v = {k: jnp.zeros_like(v) for k, v in params.items()} + final_loss = float("inf") + + for epoch in range(n_epochs): + loss_val, grads = _grad_fn( + params, inputs["peer_input"], inputs["peer_mask"], + inputs["local_input"], inputs["y"], l2_alpha, + ) + final_loss = float(loss_val) + + for k in params: + m[k] = 0.9 * m[k] + 0.1 * grads[k] + v[k] = 0.999 * v[k] + 0.001 * grads[k] ** 2 + m_hat = m[k] / (1.0 - 0.9 ** (epoch + 1)) + v_hat = v[k] / (1.0 - 0.999 ** (epoch + 1)) + params[k] = params[k] - lr * m_hat / (jnp.sqrt(v_hat) + 1e-8) + + if verbose and (epoch % 200 == 0 or epoch == n_epochs - 1): + print(f" epoch {epoch:4d} loss={final_loss:.6f}") + + return params, final_loss + + +# ---- Evaluation ---- + + +def evaluate(params, inputs, data, label=""): + """Per-pool R² on total volume and noise residual.""" + pred_total = np.array(forward( + params, inputs["peer_input"], inputs["peer_mask"], inputs["local_input"], + )) + y_total = np.array(inputs["y"]) + v_arb = np.array(inputs["v_arb"]) + pool_idx = np.array(data["pool_idx"]) if "pool_idx" in data else np.array(inputs["pool_idx"]) + + # Noise residual: compare (pred - log(v_arb)) vs (y - log(v_arb)) + log_v_arb = np.log(np.maximum(v_arb, 1e-6)) + resid_true = y_total - log_v_arb + resid_pred = pred_total - log_v_arb + + r2_total = {} + r2_resid = {} + pool_ids = data.get("pool_ids", []) + + for i in range(data["n_pools"]): + mask = pool_idx == i + if mask.sum() < 2: + continue + yt = y_total[mask] + pt = pred_total[mask] + ss_res_t = np.sum((yt - pt) ** 2) + ss_tot_t = np.sum((yt - yt.mean()) ** 2) + r2_total[i] = 1 - ss_res_t / max(ss_tot_t, 1e-10) + + rt = resid_true[mask] + rp = resid_pred[mask] + ss_res_r = np.sum((rt - rp) ** 2) + ss_tot_r = np.sum((rt - rt.mean()) ** 2) + r2_resid[i] = 1 - ss_res_r / max(ss_tot_r, 1e-10) + + def _med(d): + v = [x for x in d.values() if np.isfinite(x)] + return np.median(v) if v else float("nan") + + if label: + print(f"\n {label}:") + for i in range(data["n_pools"]): + if i in r2_total and i < len(pool_ids): + pid = pool_ids[i] + print(f" {pid[:16]} total={r2_total[i]:.3f} resid={r2_resid[i]:.3f}") + + med_total = _med(r2_total) + med_resid = _med(r2_resid) + print(f" Median R² total={med_total:.4f} resid={med_resid:.4f}") + return med_total, med_resid, r2_total, r2_resid + + +# ---- Temporal split ---- + + +def run_temporal(data, feat_cfg, hparams, split_frac=0.7): + """Train on first split_frac of days, eval on rest.""" + day_idx = data["day_idx"] + split_day = int(day_idx.max() * split_frac) + train_mask = day_idx <= split_day + eval_mask = day_idx > split_day + + def _subset(d, mask): + out = {} + for k, v in d.items(): + if isinstance(v, np.ndarray) and len(v) == len(mask): + out[k] = v[mask] + else: + out[k] = v + return out + + train_data = _subset(data, train_mask) + eval_data = _subset(data, eval_mask) + + train_inputs = assemble_inputs(train_data, feat_cfg) + eval_inputs = assemble_inputs(eval_data, feat_cfg) + + n_pf = train_inputs["n_peer_feat"] + n_lf = train_inputs["n_local_feat"] + n_params = sum(v.size for v in init_params( + jax.random.PRNGKey(0), n_pf, n_lf, hparams["hidden"], hparams["d_embed"], + ).values()) + + print(f" Train: {int(train_mask.sum())}, Eval: {int(eval_mask.sum())}, " + f"peer_feat={n_pf}, local_feat={n_lf}, params={n_params}") + + params = init_params( + jax.random.PRNGKey(42), n_pf, n_lf, hparams["hidden"], hparams["d_embed"], + ) + t0 = time.time() + params, final_loss = train( + params, train_inputs, hparams["n_epochs"], hparams["lr"], hparams["l2_alpha"], + ) + print(f" Training: {time.time() - t0:.1f}s") + + print("\n --- Train ---") + evaluate(params, train_inputs, data) + print("\n --- Eval ---") + _, med_resid_eval, _, _ = evaluate(params, eval_inputs, data) + + return med_resid_eval + + +# ---- Optuna ---- + + +def run_optuna(data, n_trials): + import optuna + + day_idx = data["day_idx"] + split_day = int(day_idx.max() * 0.7) + train_mask = day_idx <= split_day + eval_mask = day_idx > split_day + + def _subset(d, mask): + out = {} + for k, v in d.items(): + if isinstance(v, np.ndarray) and len(v) == len(mask): + out[k] = v[mask] + else: + out[k] = v + return out + + train_data = _subset(data, train_mask) + eval_data = _subset(data, eval_mask) + + def objective(trial): + feat_cfg = { + "peer_vol_lag2": trial.suggest_categorical("peer_vol_lag2", [True, False]), + "peer_vol_change": trial.suggest_categorical("peer_vol_change", [True, False]), + "peer_tvl": trial.suggest_categorical("peer_tvl", [True, False]), + "peer_volatility": trial.suggest_categorical("peer_volatility", [True, False]), + "own_vol_lag2": trial.suggest_categorical("own_vol_lag2", [True, False]), + "own_vol_change": trial.suggest_categorical("own_vol_change", [True, False]), + "own_tvl": trial.suggest_categorical("own_tvl", [True, False]), + "own_volatility": trial.suggest_categorical("own_volatility", [True, False]), + } + hparams = { + "hidden": trial.suggest_categorical("hidden", [8, 16, 32]), + "d_embed": trial.suggest_categorical("d_embed", [4, 8, 16]), + "lr": trial.suggest_float("lr", 1e-4, 1e-2, log=True), + "l2_alpha": trial.suggest_float("l2_alpha", 1e-5, 1e-1, log=True), + "n_epochs": trial.suggest_categorical("n_epochs", [500, 1000, 2000]), + } + + train_inputs = assemble_inputs(train_data, feat_cfg) + eval_inputs = assemble_inputs(eval_data, feat_cfg) + + params = init_params( + jax.random.PRNGKey(42), + train_inputs["n_peer_feat"], train_inputs["n_local_feat"], + hparams["hidden"], hparams["d_embed"], + ) + params, _ = train(params, train_inputs, hparams["n_epochs"], + hparams["lr"], hparams["l2_alpha"], verbose=False) + + # Eval R² on noise residual + pred = np.array(forward( + params, eval_inputs["peer_input"], eval_inputs["peer_mask"], + eval_inputs["local_input"], + )) + y = np.array(eval_inputs["y"]) + v_arb = np.array(eval_inputs["v_arb"]) + log_v_arb = np.log(np.maximum(v_arb, 1e-6)) + pool_idx = np.array(eval_data["pool_idx"]) + + r2_resids = [] + r2_totals = [] + for i in range(data["n_pools"]): + mask = pool_idx == i + if mask.sum() < 2: + continue + yt = y[mask] + pt = pred[mask] + ss_res = np.sum((yt - pt) ** 2) + ss_tot = np.sum((yt - yt.mean()) ** 2) + r2_totals.append(1 - ss_res / max(ss_tot, 1e-10)) + + rt = yt - log_v_arb[mask] + rp = pt - log_v_arb[mask] + ss_res_r = np.sum((rt - rp) ** 2) + ss_tot_r = np.sum((rt - rt.mean()) ** 2) + r2_resids.append(1 - ss_res_r / max(ss_tot_r, 1e-10)) + + med_resid = float(np.median(r2_resids)) if r2_resids else -10.0 + med_total = float(np.median(r2_totals)) if r2_totals else -10.0 + + trial.set_user_attr("med_total_r2", med_total) + n_feat = sum(feat_cfg.values()) + print(f" Trial {trial.number}: resid={med_resid:.4f} total={med_total:.4f} " + f"h={hparams['hidden']} d={hparams['d_embed']} " + f"lr={hparams['lr']:.1e} a={hparams['l2_alpha']:.1e} " + f"ep={hparams['n_epochs']} feat={n_feat}/8") + + return med_resid + + study = optuna.create_study(direction="maximize") + study.optimize(objective, n_trials=n_trials) + + print(f"\n{'='*70}") + print("Optuna Results") + print(f"{'='*70}") + print(f" Best eval noise resid R²: {study.best_value:.4f}") + print(f" Best total R²: {study.best_trial.user_attrs['med_total_r2']:.4f}") + print(f" Best params:") + for k, v in sorted(study.best_params.items()): + print(f" {k}: {v}") + + print(f"\n Top 10:") + trials = sorted(study.trials, key=lambda t: t.value if t.value else -999, + reverse=True) + for t in trials[:10]: + if t.value is not None: + feats = sum(1 for k in ["peer_vol_lag2", "peer_vol_change", + "peer_tvl", "peer_volatility", + "own_vol_lag2", "own_vol_change", + "own_tvl", "own_volatility"] + if t.params.get(k)) + print(f" #{t.number}: resid={t.value:.4f} " + f"total={t.user_attrs.get('med_total_r2', '?'):.4f} " + f"h={t.params['hidden']} d={t.params['d_embed']} " + f"feat={feats}/8") + + return study + + +# ---- Main ---- + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--tune", type=int, default=0) + parser.add_argument("--loo", action="store_true") + # Architecture + parser.add_argument("--hidden", type=int, default=16) + parser.add_argument("--d-embed", type=int, default=8) + parser.add_argument("--lr", type=float, default=3e-4) + parser.add_argument("--l2-alpha", type=float, default=1e-3) + parser.add_argument("--epochs", type=int, default=1000) + # Feature flags + parser.add_argument("--peer-vol-lag2", action="store_true") + parser.add_argument("--peer-vol-change", action="store_true") + parser.add_argument("--peer-tvl", action="store_true") + parser.add_argument("--peer-volatility", action="store_true") + parser.add_argument("--own-vol-lag2", action="store_true") + parser.add_argument("--own-vol-change", action="store_true") + parser.add_argument("--own-tvl", action="store_true") + parser.add_argument("--own-volatility", action="store_true") + parser.add_argument("--all-features", action="store_true", + help="Enable all optional features") + args = parser.parse_args() + + os.environ.setdefault("JAX_PLATFORMS", "cpu") + + feat_cfg = { + "peer_vol_lag2": args.peer_vol_lag2 or args.all_features, + "peer_vol_change": args.peer_vol_change or args.all_features, + "peer_tvl": args.peer_tvl or args.all_features, + "peer_volatility": args.peer_volatility or args.all_features, + "own_vol_lag2": args.own_vol_lag2 or args.all_features, + "own_vol_change": args.own_vol_change or args.all_features, + "own_tvl": args.own_tvl or args.all_features, + "own_volatility": args.own_volatility or args.all_features, + } + hparams = { + "hidden": args.hidden, + "d_embed": args.d_embed, + "lr": args.lr, + "l2_alpha": args.l2_alpha, + "n_epochs": args.epochs, + } + + print("=" * 70) + print("DeepSets v2: Total Volume Target + Noise Residual Eval") + feat_on = [k for k, v in feat_cfg.items() if v] + print(f" Optional features: {feat_on or 'none'}") + print(f" Architecture: {hparams}") + print("=" * 70) + + matched_clean, option_c_clean = load_stage1() + + print("\nBuilding features...") + t0 = time.time() + data = build_all_features(matched_clean, option_c_clean) + print(f" {len(data['pool_idx'])} samples, {data['n_pools']} pools, " + f"{time.time() - t0:.1f}s") + + if args.tune > 0: + run_optuna(data, args.tune) + else: + print(f"\n{'='*70}") + print("Temporal split (70/30)") + print(f"{'='*70}") + run_temporal(data, feat_cfg, hparams) + + print(f"\n Baselines for comparison:") + print(f" Option C on residual: median R² = 0.060") + print(f" Ridge+own on residual: median R² = 0.098") + print(f" Constant zero: median R² = -0.083") + + +if __name__ == "__main__": + main() diff --git a/experiments/run_deepsets_volume.py b/experiments/run_deepsets_volume.py new file mode 100644 index 0000000..dc174d2 --- /dev/null +++ b/experiments/run_deepsets_volume.py @@ -0,0 +1,467 @@ +"""DeepSets cross-pool volume prediction. + +Architecture: + For pool i at day t: + For each peer j != i with valid data at t-1: + h_j = Encoder(attr_j, attr_i, vol_j_{t-1}, overlap_ij) + peer_summary = masked_mean(h_j) + pred_i_t = Decoder(peer_summary, attr_i, own_vol_{t-1}) + +Evaluation: + 1. In-sample R² (all data) + 2. Temporal split (70/30) + 3. LOO (hold out one pool, retrain, evaluate) +""" + +import os +import pickle +import sys +import time + +import jax +import jax.numpy as jnp +import numpy as np + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) +HIDDEN = 8 +D_EMBED = 4 +LR = 1e-3 +N_EPOCHS = 500 +N_EPOCHS_LOO = 200 +L2_ALPHA = 0.001 + + +def load_stage1(): + path = os.path.join(CACHE_DIR, "stage1.pkl") + if not os.path.exists(path): + print("ERROR: no stage1 cache.") + sys.exit(1) + with open(path, "rb") as f: + data = pickle.load(f) + return data["matched_clean"], data["option_c_clean"] + + +def build_volume_matrix(matched_clean): + """Build (n_dates, n_pools) volume matrix. NaN where missing.""" + pool_ids = sorted(matched_clean.keys()) + pool_date_vol = {} + all_dates = set() + for pid in pool_ids: + panel = matched_clean[pid]["panel"] + dates = panel["date"].values + vols = panel["log_volume"].values.astype(float) + pool_date_vol[pid] = dict(zip(dates, vols)) + all_dates.update(dates) + + date_list = sorted(all_dates) + n_dates = len(date_list) + n_pools = len(pool_ids) + vol_matrix = np.full((n_dates, n_pools), np.nan) + for j, pid in enumerate(pool_ids): + dv = pool_date_vol[pid] + for t, date in enumerate(date_list): + if date in dv: + vol_matrix[t, j] = dv[date] + return vol_matrix, date_list, pool_ids + + +def build_data(matched_clean, exclude_pool_idx=None): + """Build all arrays for DeepSets training. + + If exclude_pool_idx is set, that pool is excluded from training + samples but kept as a peer (its volume data is still available). + """ + from quantammsim.calibration.pool_data import ( + build_pool_attributes, _parse_tokens, _canonicalize_token, + ) + + pool_ids = sorted(matched_clean.keys()) + n_pools = len(pool_ids) + + vol_matrix, date_list, _ = build_volume_matrix(matched_clean) + X_attr, attr_names, _ = build_pool_attributes(matched_clean) + + # Standardize attributes + attr_mean = np.mean(X_attr, axis=0) + attr_std = np.std(X_attr, axis=0) + attr_std[attr_std < 1e-6] = 1.0 + X_attr_norm = ((X_attr - attr_mean) / attr_std).astype(np.float32) + + # Standardize volumes + vol_mean = float(np.nanmean(vol_matrix)) + vol_std = float(np.nanstd(vol_matrix)) + vol_norm = ((vol_matrix - vol_mean) / vol_std).astype(np.float32) + + # Token overlap + k_attr = X_attr_norm.shape[1] + pool_tokens = {} + for i, pid in enumerate(pool_ids): + toks = _parse_tokens(matched_clean[pid]["tokens"]) + pool_tokens[i] = {_canonicalize_token(t) for t in toks[:2]} + + # Per-pool peer structures + n_peers = n_pools - 1 + peer_attrs = np.zeros((n_pools, n_peers, k_attr), dtype=np.float32) + peer_overlap = np.zeros((n_pools, n_peers), dtype=np.float32) + peer_col_idx = np.zeros((n_pools, n_peers), dtype=np.int32) + + for i in range(n_pools): + peers = [j for j in range(n_pools) if j != i] + for p, j in enumerate(peers): + peer_attrs[i, p] = X_attr_norm[j] + peer_overlap[i, p] = len(pool_tokens[i] & pool_tokens[j]) + peer_col_idx[i, p] = j + + target_attrs = X_attr_norm + + # Build samples + sample_pools, sample_days = [], [] + for i in range(n_pools): + if i == exclude_pool_idx: + continue + for t in range(1, len(date_list)): + if np.isnan(vol_matrix[t, i]) or np.isnan(vol_matrix[t - 1, i]): + continue + sample_pools.append(i) + sample_days.append(t) + + sample_pools = np.array(sample_pools, dtype=np.int32) + sample_days = np.array(sample_days, dtype=np.int32) + n_samples = len(sample_pools) + + # Vectorized: gather peer volumes and masks + peer_vols = np.zeros((n_samples, n_peers), dtype=np.float32) + peer_mask = np.zeros((n_samples, n_peers), dtype=np.float32) + own_lag = np.zeros(n_samples, dtype=np.float32) + y = np.zeros(n_samples, dtype=np.float32) + + for s in range(n_samples): + i = sample_pools[s] + t = sample_days[s] + cols = peer_col_idx[i] + pvols = vol_norm[t - 1, cols] + valid = ~np.isnan(pvols) + peer_vols[s] = np.where(valid, pvols, 0.0) + peer_mask[s] = valid.astype(np.float32) + own_lag[s] = vol_norm[t - 1, i] + y[s] = vol_norm[t, i] + + return { + "peer_attrs": jnp.array(peer_attrs), + "target_attrs": jnp.array(target_attrs), + "peer_overlap": jnp.array(peer_overlap), + "peer_vols": jnp.array(peer_vols), + "peer_mask": jnp.array(peer_mask), + "own_lag": jnp.array(own_lag), + "y": jnp.array(y), + "pool_idx": jnp.array(sample_pools), + "day_idx": sample_days, + "n_pools": n_pools, + "n_peers": n_peers, + "k_attr": k_attr, + "pool_ids": pool_ids, + "vol_mean": vol_mean, + "vol_std": vol_std, + } + + +# ---- Model ---- + + +def init_params(key, k_attr, hidden=HIDDEN, d=D_EMBED): + k1, k2, k3, k4 = jax.random.split(key, 4) + enc_in = 2 * k_attr + 2 # peer_attr + target_attr + peer_vol + overlap + dec_in = d + k_attr + 1 # summary + target_attr + own_lag + return { + "enc_W1": jax.random.normal(k1, (enc_in, hidden)) * np.sqrt(2.0 / enc_in), + "enc_b1": jnp.zeros(hidden), + "enc_W2": jax.random.normal(k2, (hidden, d)) * np.sqrt(2.0 / hidden), + "enc_b2": jnp.zeros(d), + "dec_W1": jax.random.normal(k3, (dec_in, hidden)) * np.sqrt(2.0 / dec_in), + "dec_b1": jnp.zeros(hidden), + "dec_W2": jax.random.normal(k4, (hidden, 1)) * 0.01, + "dec_b2": jnp.zeros(1), + } + + +def forward(params, peer_attrs_all, target_attrs_all, peer_overlap_all, + peer_vols, peer_mask, own_lag, pool_idx): + """Batched DeepSets forward pass.""" + batch = peer_vols.shape[0] + n_peers = peer_vols.shape[1] + + pa = peer_attrs_all[pool_idx] # (batch, n_peers, k_attr) + ta = target_attrs_all[pool_idx] # (batch, k_attr) + ov = peer_overlap_all[pool_idx] # (batch, n_peers) + + ta_broad = jnp.broadcast_to(ta[:, None, :], pa.shape) + + enc_in = jnp.concatenate([ + pa, ta_broad, + peer_vols[:, :, None], + ov[:, :, None], + ], axis=-1) + + # Encoder MLP + flat = enc_in.reshape(-1, enc_in.shape[-1]) + h = jnp.maximum(flat @ params["enc_W1"] + params["enc_b1"], 0.0) + h = h @ params["enc_W2"] + params["enc_b2"] + h = h.reshape(batch, n_peers, -1) + + # Masked mean + h_masked = h * peer_mask[:, :, None] + n_valid = jnp.maximum(jnp.sum(peer_mask, axis=1, keepdims=True), 1.0) + summary = jnp.sum(h_masked, axis=1) / n_valid + + # Decoder MLP + dec_in = jnp.concatenate([summary, ta, own_lag[:, None]], axis=-1) + h_dec = jnp.maximum(dec_in @ params["dec_W1"] + params["dec_b1"], 0.0) + return (h_dec @ params["dec_W2"] + params["dec_b2"])[:, 0] + + +def loss_fn(params, static, peer_vols, peer_mask, own_lag, pool_idx, y, alpha): + pred = forward(params, static["peer_attrs"], static["target_attrs"], + static["peer_overlap"], peer_vols, peer_mask, own_lag, pool_idx) + mse = jnp.mean((pred - y) ** 2) + reg = sum(jnp.sum(v ** 2) for k, v in params.items() if "W" in k) + return mse + alpha * reg + + +grad_fn = jax.jit(jax.value_and_grad(loss_fn)) + + +# ---- Training ---- + + +def train(params, data, n_epochs=N_EPOCHS, lr=LR, alpha=L2_ALPHA, verbose=True): + """Full-batch Adam training.""" + static = { + "peer_attrs": data["peer_attrs"], + "target_attrs": data["target_attrs"], + "peer_overlap": data["peer_overlap"], + } + + # Adam state + m = {k: jnp.zeros_like(v) for k, v in params.items()} + v = {k: jnp.zeros_like(v) for k, v in params.items()} + + for epoch in range(n_epochs): + loss_val, grads = grad_fn( + params, static, data["peer_vols"], data["peer_mask"], + data["own_lag"], data["pool_idx"], data["y"], alpha, + ) + + # Adam update + for k in params: + m[k] = 0.9 * m[k] + 0.1 * grads[k] + v[k] = 0.999 * v[k] + 0.001 * grads[k] ** 2 + m_hat = m[k] / (1.0 - 0.9 ** (epoch + 1)) + v_hat = v[k] / (1.0 - 0.999 ** (epoch + 1)) + params[k] = params[k] - lr * m_hat / (jnp.sqrt(v_hat) + 1e-8) + + if verbose and (epoch % 100 == 0 or epoch == n_epochs - 1): + print(f" epoch {epoch:4d} loss={float(loss_val):.6f}") + + return params + + +# ---- Evaluation ---- + + +def per_pool_r2(params, data): + """Compute per-pool R² from trained model.""" + static = { + "peer_attrs": data["peer_attrs"], + "target_attrs": data["target_attrs"], + "peer_overlap": data["peer_overlap"], + } + pred = np.array(forward( + params, static["peer_attrs"], static["target_attrs"], + static["peer_overlap"], data["peer_vols"], data["peer_mask"], + data["own_lag"], data["pool_idx"], + )) + y = np.array(data["y"]) + pool_idx = np.array(data["pool_idx"]) + + r2s = {} + for i in range(data["n_pools"]): + mask = pool_idx == i + if mask.sum() < 2: + continue + yi = y[mask] + pi = pred[mask] + ss_res = np.sum((yi - pi) ** 2) + ss_tot = np.sum((yi - yi.mean()) ** 2) + r2s[i] = 1 - ss_res / max(ss_tot, 1e-10) + return r2s + + +# ---- Main experiments ---- + + +def run_insample(matched_clean): + print("\n" + "=" * 70) + print("1. In-sample DeepSets") + print("=" * 70) + + data = build_data(matched_clean) + n_params = sum(v.size for v in init_params(jax.random.PRNGKey(0), data["k_attr"]).values()) + print(f" {data['peer_vols'].shape[0]} samples, {data['n_pools']} pools, " + f"{data['k_attr']} attrs, {n_params} params") + + params = init_params(jax.random.PRNGKey(42), data["k_attr"]) + t0 = time.time() + params = train(params, data) + print(f" Training: {time.time() - t0:.1f}s") + + r2s = per_pool_r2(params, data) + pool_ids = data["pool_ids"] + for i, pid in enumerate(pool_ids): + if i in r2s: + print(f" {pid[:16]} ({matched_clean[pid]['tokens']:<14}) R²={r2s[i]:.3f}") + + vals = list(r2s.values()) + print(f"\n In-sample: median R²={np.median(vals):.4f}, mean={np.mean(vals):.4f}") + return params, data, r2s + + +def run_temporal_split(matched_clean, split_frac=0.7): + print("\n" + "=" * 70) + print(f"2. Temporal split ({int(split_frac*100)}/{int((1-split_frac)*100)})") + print("=" * 70) + + data_all = build_data(matched_clean) + day_idx = np.array(data_all["day_idx"]) + max_day = day_idx.max() + split_day = int(max_day * split_frac) + + train_mask = day_idx <= split_day + eval_mask = day_idx > split_day + + def subset(data, mask): + jmask = jnp.array(mask) + return { + **{k: data[k] for k in ["peer_attrs", "target_attrs", "peer_overlap", + "n_pools", "n_peers", "k_attr", "pool_ids", + "vol_mean", "vol_std"]}, + "peer_vols": data["peer_vols"][jmask], + "peer_mask": data["peer_mask"][jmask], + "own_lag": data["own_lag"][jmask], + "y": data["y"][jmask], + "pool_idx": data["pool_idx"][jmask], + "day_idx": data_all["day_idx"][mask], + } + + train_data = subset(data_all, train_mask) + eval_data = subset(data_all, eval_mask) + + print(f" Train: {int(train_mask.sum())} samples, Eval: {int(eval_mask.sum())} samples") + + params = init_params(jax.random.PRNGKey(42), data_all["k_attr"]) + params = train(params, train_data) + + r2s_train = per_pool_r2(params, train_data) + r2s_eval = per_pool_r2(params, eval_data) + + pool_ids = data_all["pool_ids"] + for i, pid in enumerate(pool_ids): + r_tr = r2s_train.get(i, float("nan")) + r_ev = r2s_eval.get(i, float("nan")) + print(f" {pid[:16]} ({matched_clean[pid]['tokens']:<14}) " + f"train={r_tr:.3f} eval={r_ev:.3f}") + + vals_eval = [v for v in r2s_eval.values() if np.isfinite(v)] + print(f"\n Temporal eval: median R²={np.median(vals_eval):.4f}, " + f"mean={np.mean(vals_eval):.4f}") + return r2s_eval + + +def run_loo(matched_clean): + print("\n" + "=" * 70) + print("3. LOO DeepSets") + print("=" * 70) + + pool_ids = sorted(matched_clean.keys()) + n_pools = len(pool_ids) + loo_r2s = [] + + for hold_out_idx in range(n_pools): + hold_out_pid = pool_ids[hold_out_idx] + + # Build training data excluding held-out pool's samples + # (but keeping its volume data for peers) + train_data = build_data(matched_clean, exclude_pool_idx=hold_out_idx) + + params = init_params(jax.random.PRNGKey(42), train_data["k_attr"]) + params = train(params, train_data, n_epochs=N_EPOCHS_LOO, verbose=False) + + # Build eval data: only held-out pool's samples + eval_data = build_data(matched_clean) + ho_mask = np.array(eval_data["pool_idx"]) == hold_out_idx + if ho_mask.sum() < 2: + loo_r2s.append(float("nan")) + continue + + jmask = jnp.array(ho_mask) + eval_sub = { + **{k: eval_data[k] for k in ["peer_attrs", "target_attrs", "peer_overlap", + "n_pools", "n_peers", "k_attr", "pool_ids", + "vol_mean", "vol_std"]}, + "peer_vols": eval_data["peer_vols"][jmask], + "peer_mask": eval_data["peer_mask"][jmask], + "own_lag": eval_data["own_lag"][jmask], + "y": eval_data["y"][jmask], + "pool_idx": eval_data["pool_idx"][jmask], + "day_idx": np.array(eval_data["day_idx"])[ho_mask], + } + + r2s = per_pool_r2(params, eval_sub) + r2 = r2s.get(hold_out_idx, float("nan")) + loo_r2s.append(r2) + + tag = "OK" if r2 > 0 else "NEG" + print(f" {hold_out_pid[:16]} ({matched_clean[hold_out_pid]['tokens']:<14}) " + f"R²={r2:.3f} [{tag}]") + + valid = [r for r in loo_r2s if np.isfinite(r)] + print(f"\n LOO DeepSets: median R²={np.median(valid):.4f}, " + f"mean={np.mean(valid):.4f}, " + f"n_neg={sum(1 for r in valid if r < 0)}") + return loo_r2s + + +def main(): + os.environ.setdefault("JAX_PLATFORMS", "cpu") + + print("=" * 70) + print("DeepSets Cross-Pool Volume Prediction") + print(f" hidden={HIDDEN}, d={D_EMBED}, lr={LR}, " + f"alpha={L2_ALPHA}, epochs={N_EPOCHS}") + print("=" * 70) + + matched_clean, _ = load_stage1() + + params, data, r2_insample = run_insample(matched_clean) + r2_temporal = run_temporal_split(matched_clean) + r2_loo = run_loo(matched_clean) + + print("\n" + "=" * 70) + print("SUMMARY") + print("=" * 70) + vals_in = list(r2_insample.values()) + vals_temp = [v for v in r2_temporal.values() if np.isfinite(v)] + vals_loo = [r for r in r2_loo if np.isfinite(r)] + print(f" DeepSets in-sample: median R² = {np.median(vals_in):.4f}") + print(f" DeepSets temporal (30%): median R² = {np.median(vals_temp):.4f}") + print(f" DeepSets LOO: median R² = {np.median(vals_loo):.4f}") + print(f" ---") + print(f" Ridge in-sample: median R² = 0.441") + print(f" Naive AR1: median R² = 0.397") + print(f" Token-factored LOO: median R² = 0.362") + + +if __name__ == "__main__": + main() diff --git a/experiments/run_residual_comparison.py b/experiments/run_residual_comparison.py new file mode 100644 index 0000000..201c403 --- /dev/null +++ b/experiments/run_residual_comparison.py @@ -0,0 +1,235 @@ +"""Apples-to-apples R² comparison on noise residuals. + +Target for all methods: r_it = log(V_total_it) - log(V_arb_it) + +Methods: + 1. Option C: log(1 + exp(x_obs @ noise_coeffs) / V_arb) + 2. AR1 on residuals: r_{i, t-1} + 3. Ridge on residuals (peers only, in-sample) + 4. Ridge on residuals (peers + own lag, in-sample) + 5. Constant zero (predict r=0, i.e. V_total = V_arb) +""" + +import os +import pickle +import sys + +import numpy as np +from sklearn.linear_model import RidgeCV + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) + + +def load_stage1(): + path = os.path.join(CACHE_DIR, "stage1.pkl") + if not os.path.exists(path): + print("ERROR: no stage1 cache.") + sys.exit(1) + with open(path, "rb") as f: + data = pickle.load(f) + return data["matched_clean"], data["option_c_clean"] + + +def r2_score(y_true, y_pred): + ss_res = np.sum((y_true - y_pred) ** 2) + ss_tot = np.sum((y_true - y_true.mean()) ** 2) + return 1 - ss_res / max(ss_tot, 1e-10) + + +def main(): + os.environ.setdefault("JAX_PLATFORMS", "cpu") + + import jax.numpy as jnp + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + from quantammsim.calibration.pool_data import ( + K_OBS_REDUCED, build_x_obs, _parse_tokens, _canonicalize_token, + ) + + print("=" * 70) + print("Apples-to-Apples: All methods on noise residual target") + print(" target = log(V_total) - log(V_arb)") + print("=" * 70) + + matched_clean, option_c_clean = load_stage1() + pool_ids = sorted(matched_clean.keys()) + n_pools = len(pool_ids) + + # ---- Build aligned data per pool ---- + # For each pool: residual, Option C prediction of residual, dates + pool_data = {} + all_dates = set() + + for pid in pool_ids: + entry = matched_clean[pid] + oc = option_c_clean[pid] + panel = entry["panel"] + + # V_arb + v_arb_all = np.array(interpolate_pool_daily( + entry["coeffs"], + jnp.float64(oc["log_cadence"]), + jnp.float64(np.exp(oc["log_gas"])), + )) + v_arb = v_arb_all[entry["day_indices"]] + log_v_arb = np.log(np.maximum(v_arb, 1e-6)) + + # Observed + log_vol = panel["log_volume"].values.astype(float) + dates = panel["date"].values + + # Noise residual target + resid = log_vol - log_v_arb + + # Option C noise prediction (in residual space) + x_obs = build_x_obs(panel, reduced=True) + noise_coeffs = oc["noise_coeffs"][:K_OBS_REDUCED] + v_noise_oc = np.exp(x_obs @ noise_coeffs) + resid_pred_oc = np.log(np.maximum(1.0 + v_noise_oc / np.maximum(v_arb, 1e-6), 1e-10)) + + pool_data[pid] = { + "dates": dates, + "resid": resid, + "resid_pred_oc": resid_pred_oc, + "log_vol": log_vol, + "v_arb": v_arb, + } + all_dates.update(dates) + + # ---- Build residual matrix for cross-pool methods ---- + date_list = sorted(all_dates) + n_dates = len(date_list) + date_to_idx = {d: i for i, d in enumerate(date_list)} + + resid_matrix = np.full((n_dates, n_pools), np.nan) + for j, pid in enumerate(pool_ids): + pd = pool_data[pid] + for k, date in enumerate(pd["dates"]): + resid_matrix[date_to_idx[date], j] = pd["resid"][k] + + # ---- Token overlap for peer identification ---- + pool_tokens = {} + for i, pid in enumerate(pool_ids): + toks = _parse_tokens(matched_clean[pid]["tokens"]) + pool_tokens[i] = {_canonicalize_token(t) for t in toks[:2]} + + # ---- Compute R² for each method, per pool ---- + results = {m: [] for m in [ + "option_c", "ar1", "ridge_peers", "ridge_peers_own", + "constant_zero", "peer_mean", + ]} + + print(f"\n{'Pool':<18} {'Tokens':<14} {'OptC':>7} {'AR1':>7} " + f"{'R_peer':>7} {'R_p+own':>7} {'zero':>7} {'pmean':>7} {'n':>5}") + print("-" * 90) + + for i, pid in enumerate(pool_ids): + pd = pool_data[pid] + resid = pd["resid"] + n_obs = len(resid) + + # --- Option C --- + r2_oc = r2_score(resid, pd["resid_pred_oc"]) + + # --- Constant zero (V_total = V_arb) --- + r2_zero = r2_score(resid, np.zeros_like(resid)) + + # --- AR1 on residuals --- + if n_obs >= 3: + r2_ar1 = r2_score(resid[1:], resid[:-1]) + else: + r2_ar1 = np.nan + + # --- Ridge peers only (in-sample) --- + X_lag = resid_matrix[:-1, :] + y_cur = resid_matrix[1:, i] + own_lag = X_lag[:, i] + valid = ~np.isnan(y_cur) + + X_others = np.delete(X_lag, i, axis=1) + X_filled = X_others.copy() + for c in range(X_filled.shape[1]): + col = X_filled[:, c] + m = np.nanmean(col) + col[np.isnan(col)] = m if np.isfinite(m) else 0.0 + X_filled[:, c] = col + + X_peers = X_filled[valid] + y_i = y_cur[valid] + + if len(y_i) >= 10: + model_p = RidgeCV(alphas=np.logspace(-2, 4, 50)) + model_p.fit(X_peers, y_i) + r2_rp = r2_score(y_i, model_p.predict(X_peers)) + else: + r2_rp = np.nan + + # --- Ridge peers + own lag (in-sample) --- + valid_own = valid & ~np.isnan(own_lag) + X_both = np.column_stack([X_filled, own_lag[:, None]]) + X_both_v = X_both[valid_own] + y_both = y_cur[valid_own] + + if len(y_both) >= 10: + model_po = RidgeCV(alphas=np.logspace(-2, 4, 50)) + model_po.fit(X_both_v, y_both) + r2_rpo = r2_score(y_both, model_po.predict(X_both_v)) + else: + r2_rpo = np.nan + + # --- Peer mean (zero parameter) --- + peers = [j for j in range(n_pools) if j != i + and len(pool_tokens[i] & pool_tokens[j]) >= 1] + if peers: + peer_lag = resid_matrix[:-1, :][:, peers] + peer_mean = np.nanmean(peer_lag, axis=1) + y_pm = y_cur[valid] + pm_pred = peer_mean[valid] + pm_valid = ~np.isnan(pm_pred) + if pm_valid.sum() >= 3: + r2_pm = r2_score(y_pm[pm_valid], pm_pred[pm_valid]) + else: + r2_pm = np.nan + else: + r2_pm = np.nan + + results["option_c"].append(r2_oc) + results["ar1"].append(r2_ar1) + results["ridge_peers"].append(r2_rp) + results["ridge_peers_own"].append(r2_rpo) + results["constant_zero"].append(r2_zero) + results["peer_mean"].append(r2_pm) + + tokens = matched_clean[pid]["tokens"] + print(f" {pid[:16]} {tokens:<14} {r2_oc:>7.3f} {r2_ar1:>7.3f} " + f"{r2_rp:>7.3f} {r2_rpo:>7.3f} {r2_zero:>7.3f} " + f"{r2_pm:>7.3f} {n_obs:>5}") + + # ---- Summary ---- + def safe_median(xs): + v = [x for x in xs if np.isfinite(x)] + return np.median(v) if v else float("nan") + + print(f"\n{'='*70}") + print("SUMMARY — all on noise residual target") + print(f"{'='*70}") + for name, label in [ + ("option_c", "Option C (per-pool fitted)"), + ("ar1", "AR1 on residuals"), + ("ridge_peers", "Ridge peers only (in-sample)"), + ("ridge_peers_own", "Ridge peers + own lag (in-sample)"), + ("peer_mean", "Peer mean (0 params)"), + ("constant_zero", "Constant zero (V_total=V_arb)"), + ]: + vals = results[name] + med = safe_median(vals) + mean = np.nanmean([x for x in vals if np.isfinite(x)]) + n_neg = sum(1 for x in vals if np.isfinite(x) and x < 0) + print(f" {label:<35} median R² = {med:>7.4f} " + f"mean = {mean:>7.4f} n_neg = {n_neg}") + + +if __name__ == "__main__": + main() From 1056ee03f28eb77e994a039e278c49b1cce9b7df Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 17 Mar 2026 13:27:25 +0000 Subject: [PATCH 29/57] wip on deepsets --- experiments/run_deepsets_v2.py | 212 ++++++++++++++++++++++++++------- 1 file changed, 167 insertions(+), 45 deletions(-) diff --git a/experiments/run_deepsets_v2.py b/experiments/run_deepsets_v2.py index 1ba3485..7e9a41c 100644 --- a/experiments/run_deepsets_v2.py +++ b/experiments/run_deepsets_v2.py @@ -6,12 +6,21 @@ Feature menu: Peer (encoder) — always: peer_attr, target_attr, vol_lag1, overlap optional: vol_lag2, vol_change, tvl, volatility + relational: same_chain, log_tvl_ratio, log_fee_ratio Local (decoder) — always: target_attr, own_vol_lag1, dow_sin, dow_cos optional: own_vol_lag2, own_vol_change, own_tvl, own_volatility +Model variants: + encoder_type: "mlp" (2-layer ReLU) or "linear" (single affine) + no_peers: decoder-only ablation (zero peer summary) + huber_delta: Huber loss transition point (default 1.0) + Per-pool loss weighting (equal weight per pool regardless of sample count) + Usage: python experiments/run_deepsets_v2.py # defaults python experiments/run_deepsets_v2.py --tune 50 # Optuna + python experiments/run_deepsets_v2.py --no-peers # decoder-only + python experiments/run_deepsets_v2.py --encoder-type linear python experiments/run_deepsets_v2.py --loo # LOO eval """ @@ -101,6 +110,13 @@ def build_all_features(matched_clean, option_c_clean): X_attr_norm = ((X_attr - attr_mean) / attr_std).astype(np.float32) k_attr = X_attr_norm.shape[1] + # Raw per-pool values for relational features + fee_idx = attr_names.index("log_fee") + tvl_idx = attr_names.index("mean_log_tvl") + raw_log_fee = X_attr[:, fee_idx] + raw_mean_log_tvl = X_attr[:, tvl_idx] + pool_chains = [matched_clean[pid]["chain"] for pid in pool_ids] + # Token overlap pool_tokens = {} for i, pid in enumerate(pool_ids): @@ -111,6 +127,9 @@ def build_all_features(matched_clean, option_c_clean): peer_attrs = np.zeros((n_pools, n_peers, k_attr), dtype=np.float32) peer_overlap = np.zeros((n_pools, n_peers), dtype=np.float32) peer_col_idx = np.zeros((n_pools, n_peers), dtype=np.int32) + rel_same_chain = np.zeros((n_pools, n_peers), dtype=np.float32) + rel_log_tvl_ratio = np.zeros((n_pools, n_peers), dtype=np.float32) + rel_log_fee_ratio = np.zeros((n_pools, n_peers), dtype=np.float32) for i in range(n_pools): peers = [j for j in range(n_pools) if j != i] @@ -118,6 +137,15 @@ def build_all_features(matched_clean, option_c_clean): peer_attrs[i, p] = X_attr_norm[j] peer_overlap[i, p] = len(pool_tokens[i] & pool_tokens[j]) peer_col_idx[i, p] = j + rel_same_chain[i, p] = float(pool_chains[i] == pool_chains[j]) + rel_log_tvl_ratio[i, p] = abs(raw_mean_log_tvl[i] - raw_mean_log_tvl[j]) + rel_log_fee_ratio[i, p] = abs(raw_log_fee[i] - raw_log_fee[j]) + + # Standardize ratio features + for arr in [rel_log_tvl_ratio, rel_log_fee_ratio]: + mu = np.mean(arr) + sigma = max(np.std(arr), 1e-6) + arr[:] = ((arr - mu) / sigma).astype(np.float32) # Standardization stats for volumes vol_mean = float(np.nanmean(vol_matrix)) @@ -219,6 +247,9 @@ def _norm_vola(x): "peer_attrs": peer_attrs, # (n_pools, n_peers, k_attr) "target_attrs": X_attr_norm, # (n_pools, k_attr) "peer_overlap": peer_overlap, # (n_pools, n_peers) + "rel_same_chain": rel_same_chain, # (n_pools, n_peers) + "rel_log_tvl_ratio": rel_log_tvl_ratio, # (n_pools, n_peers) + "rel_log_fee_ratio": rel_log_fee_ratio, # (n_pools, n_peers) # Per-sample peer features "pf_vol_lag1": pf_vol_lag1, "pf_vol_lag2": pf_vol_lag2, @@ -253,8 +284,7 @@ def _norm_vola(x): def assemble_inputs(data, feat_cfg): """Assemble encoder/decoder inputs based on feature config. - Returns (peer_input, local_input, peer_mask, pool_idx, y, v_arb) - all as JAX arrays ready for training. + Returns dict with JAX arrays ready for training. """ n_samples = len(data["pool_idx"]) n_peers = data["n_peers"] @@ -281,6 +311,14 @@ def assemble_inputs(data, feat_cfg): if feat_cfg.get("peer_volatility"): peer_parts.append(data["pf_volatility"][:, :, None]) + # Relational features (optional via feat_cfg) + if feat_cfg.get("rel_same_chain", True): + peer_parts.append(data["rel_same_chain"][pool_idx][:, :, None]) + if feat_cfg.get("rel_tvl_ratio", True): + peer_parts.append(data["rel_log_tvl_ratio"][pool_idx][:, :, None]) + if feat_cfg.get("rel_fee_ratio", True): + peer_parts.append(data["rel_log_fee_ratio"][pool_idx][:, :, None]) + peer_input = np.concatenate(peer_parts, axis=-1).astype(np.float32) # ---- Local decoder input: (n_samples, n_feat) ---- @@ -310,6 +348,7 @@ def assemble_inputs(data, feat_cfg): "y": jnp.array(data["y_total"]), "v_arb": jnp.array(data["v_arb"]), "pool_idx": jnp.array(pool_idx), + "n_pools": data["n_pools"], "n_peer_feat": peer_input.shape[-1], "n_local_feat": local_input.shape[-1], } @@ -318,62 +357,102 @@ def assemble_inputs(data, feat_cfg): # ---- Model ---- -def init_params(key, n_peer_feat, n_local_feat, hidden, d_embed): +def init_params(key, n_peer_feat, n_local_feat, hidden, d_embed, + encoder_type="mlp"): + """Initialize model parameters. + + encoder_type: "mlp" (2-layer ReLU) or "linear" (single affine). + Presence of "enc_W2" in params dict distinguishes the two at forward time. + """ k1, k2, k3, k4 = jax.random.split(key, 4) dec_in = d_embed + n_local_feat - return { - "enc_W1": jax.random.normal(k1, (n_peer_feat, hidden)) * np.sqrt(2.0 / n_peer_feat), - "enc_b1": jnp.zeros(hidden), - "enc_W2": jax.random.normal(k2, (hidden, d_embed)) * np.sqrt(2.0 / hidden), - "enc_b2": jnp.zeros(d_embed), - "dec_W1": jax.random.normal(k3, (dec_in, hidden)) * np.sqrt(2.0 / dec_in), - "dec_b1": jnp.zeros(hidden), - "dec_W2": jax.random.normal(k4, (hidden, 1)) * 0.01, - "dec_b2": jnp.zeros(1), - } + params = {} + + if encoder_type == "mlp": + params["enc_W1"] = jax.random.normal(k1, (n_peer_feat, hidden)) * np.sqrt(2.0 / n_peer_feat) + params["enc_b1"] = jnp.zeros(hidden) + params["enc_W2"] = jax.random.normal(k2, (hidden, d_embed)) * np.sqrt(2.0 / hidden) + params["enc_b2"] = jnp.zeros(d_embed) + else: # linear + params["enc_W1"] = jax.random.normal(k1, (n_peer_feat, d_embed)) * np.sqrt(2.0 / n_peer_feat) + params["enc_b1"] = jnp.zeros(d_embed) + + params["dec_W1"] = jax.random.normal(k3, (dec_in, hidden)) * np.sqrt(2.0 / dec_in) + params["dec_b1"] = jnp.zeros(hidden) + params["dec_W2"] = jax.random.normal(k4, (hidden, 1)) * 0.01 + params["dec_b2"] = jnp.zeros(1) + return params -def forward(params, peer_input, peer_mask, local_input): +def forward(params, peer_input, peer_mask, local_input, no_peers=False): """Returns predicted log_volume (total) per sample.""" batch, n_peers, _ = peer_input.shape - flat = peer_input.reshape(-1, peer_input.shape[-1]) - h = jnp.maximum(flat @ params["enc_W1"] + params["enc_b1"], 0.0) - h = h @ params["enc_W2"] + params["enc_b2"] - h = h.reshape(batch, n_peers, -1) - - h_masked = h * peer_mask[:, :, None] - n_valid = jnp.maximum(jnp.sum(peer_mask, axis=1, keepdims=True), 1.0) - summary = jnp.sum(h_masked, axis=1) / n_valid + if no_peers: + d_embed = params["dec_W1"].shape[0] - local_input.shape[-1] + summary = jnp.zeros((batch, d_embed)) + else: + flat = peer_input.reshape(-1, peer_input.shape[-1]) + if "enc_W2" in params: + # MLP encoder: 2-layer with ReLU + h = jnp.maximum(flat @ params["enc_W1"] + params["enc_b1"], 0.0) + h = h @ params["enc_W2"] + params["enc_b2"] + else: + # Linear encoder: single affine + h = flat @ params["enc_W1"] + params["enc_b1"] + h = h.reshape(batch, n_peers, -1) + + h_masked = h * peer_mask[:, :, None] + n_valid = jnp.maximum(jnp.sum(peer_mask, axis=1, keepdims=True), 1.0) + summary = jnp.sum(h_masked, axis=1) / n_valid dec_in = jnp.concatenate([summary, local_input], axis=-1) h_dec = jnp.maximum(dec_in @ params["dec_W1"] + params["dec_b1"], 0.0) return (h_dec @ params["dec_W2"] + params["dec_b2"])[:, 0] -def loss_fn(params, peer_input, peer_mask, local_input, y, l2_alpha): - """MSE on total log_volume + L2 reg.""" - pred = forward(params, peer_input, peer_mask, local_input) - mse = jnp.mean((pred - y) ** 2) +def loss_fn(params, peer_input, peer_mask, local_input, y, l2_alpha, + pool_idx, n_pools, huber_delta, no_peers): + """Huber loss with per-pool weighting + L2 reg.""" + pred = forward(params, peer_input, peer_mask, local_input, no_peers) + residuals = pred - y + abs_r = jnp.abs(residuals) + huber_vals = jnp.where(abs_r <= huber_delta, 0.5 * residuals ** 2, + huber_delta * (abs_r - 0.5 * huber_delta)) + + # Per-pool mean loss, then average across pools + total = 0.0 + for i in range(n_pools): + mask_i = (pool_idx == i).astype(jnp.float32) + n_i = jnp.maximum(jnp.sum(mask_i), 1.0) + total = total + jnp.sum(huber_vals * mask_i) / n_i + data_loss = total / n_pools + reg = sum(jnp.sum(v ** 2) for k, v in params.items() if "W" in k) - return mse + l2_alpha * reg + return data_loss + l2_alpha * reg -_grad_fn = jax.jit(jax.value_and_grad(loss_fn)) +# n_pools (arg 7) and no_peers (arg 9) must be static for Python control flow +_grad_fn = jax.jit(jax.value_and_grad(loss_fn), static_argnums=(7, 9)) # ---- Training ---- -def train(params, inputs, n_epochs, lr, l2_alpha, verbose=True): +def train(params, inputs, n_epochs, lr, l2_alpha, huber_delta=1.0, + no_peers=False, verbose=True): m = {k: jnp.zeros_like(v) for k, v in params.items()} v = {k: jnp.zeros_like(v) for k, v in params.items()} final_loss = float("inf") + n_pools = int(inputs["n_pools"]) + pool_idx = inputs["pool_idx"] + for epoch in range(n_epochs): loss_val, grads = _grad_fn( params, inputs["peer_input"], inputs["peer_mask"], inputs["local_input"], inputs["y"], l2_alpha, + pool_idx, n_pools, huber_delta, no_peers, ) final_loss = float(loss_val) @@ -393,10 +472,11 @@ def train(params, inputs, n_epochs, lr, l2_alpha, verbose=True): # ---- Evaluation ---- -def evaluate(params, inputs, data, label=""): +def evaluate(params, inputs, data, label="", no_peers=False): """Per-pool R² on total volume and noise residual.""" pred_total = np.array(forward( - params, inputs["peer_input"], inputs["peer_mask"], inputs["local_input"], + params, inputs["peer_input"], inputs["peer_mask"], + inputs["local_input"], no_peers=no_peers, )) y_total = np.array(inputs["y"]) v_arb = np.array(inputs["v_arb"]) @@ -469,28 +549,41 @@ def _subset(d, mask): train_inputs = assemble_inputs(train_data, feat_cfg) eval_inputs = assemble_inputs(eval_data, feat_cfg) + encoder_type = hparams.get("encoder_type", "mlp") + no_peers = hparams.get("no_peers", False) + huber_delta = hparams.get("huber_delta", 1.0) + n_pf = train_inputs["n_peer_feat"] n_lf = train_inputs["n_local_feat"] n_params = sum(v.size for v in init_params( jax.random.PRNGKey(0), n_pf, n_lf, hparams["hidden"], hparams["d_embed"], + encoder_type=encoder_type, ).values()) print(f" Train: {int(train_mask.sum())}, Eval: {int(eval_mask.sum())}, " f"peer_feat={n_pf}, local_feat={n_lf}, params={n_params}") + if encoder_type != "mlp": + print(f" encoder_type={encoder_type}") + if no_peers: + print(f" no_peers=True (decoder-only ablation)") + if huber_delta != 1.0: + print(f" huber_delta={huber_delta}") params = init_params( jax.random.PRNGKey(42), n_pf, n_lf, hparams["hidden"], hparams["d_embed"], + encoder_type=encoder_type, ) t0 = time.time() params, final_loss = train( params, train_inputs, hparams["n_epochs"], hparams["lr"], hparams["l2_alpha"], + huber_delta=huber_delta, no_peers=no_peers, ) print(f" Training: {time.time() - t0:.1f}s") print("\n --- Train ---") - evaluate(params, train_inputs, data) + evaluate(params, train_inputs, data, no_peers=no_peers) print("\n --- Eval ---") - _, med_resid_eval, _, _ = evaluate(params, eval_inputs, data) + _, med_resid_eval, _, _ = evaluate(params, eval_inputs, data, no_peers=no_peers) return med_resid_eval @@ -498,6 +591,13 @@ def _subset(d, mask): # ---- Optuna ---- +_FEAT_KEYS = [ + "peer_vol_lag2", "peer_vol_change", "peer_tvl", "peer_volatility", + "own_vol_lag2", "own_vol_change", "own_tvl", "own_volatility", + "rel_same_chain", "rel_tvl_ratio", "rel_fee_ratio", +] + + def run_optuna(data, n_trials): import optuna @@ -528,6 +628,9 @@ def objective(trial): "own_vol_change": trial.suggest_categorical("own_vol_change", [True, False]), "own_tvl": trial.suggest_categorical("own_tvl", [True, False]), "own_volatility": trial.suggest_categorical("own_volatility", [True, False]), + "rel_same_chain": trial.suggest_categorical("rel_same_chain", [True, False]), + "rel_tvl_ratio": trial.suggest_categorical("rel_tvl_ratio", [True, False]), + "rel_fee_ratio": trial.suggest_categorical("rel_fee_ratio", [True, False]), } hparams = { "hidden": trial.suggest_categorical("hidden", [8, 16, 32]), @@ -535,6 +638,9 @@ def objective(trial): "lr": trial.suggest_float("lr", 1e-4, 1e-2, log=True), "l2_alpha": trial.suggest_float("l2_alpha", 1e-5, 1e-1, log=True), "n_epochs": trial.suggest_categorical("n_epochs", [500, 1000, 2000]), + "encoder_type": trial.suggest_categorical("encoder_type", ["mlp", "linear"]), + "huber_delta": trial.suggest_categorical("huber_delta", [0.5, 1.0, 1.5, 2.0]), + "no_peers": trial.suggest_categorical("no_peers", [True, False]), } train_inputs = assemble_inputs(train_data, feat_cfg) @@ -544,14 +650,20 @@ def objective(trial): jax.random.PRNGKey(42), train_inputs["n_peer_feat"], train_inputs["n_local_feat"], hparams["hidden"], hparams["d_embed"], + encoder_type=hparams["encoder_type"], + ) + params, _ = train( + params, train_inputs, hparams["n_epochs"], + hparams["lr"], hparams["l2_alpha"], + huber_delta=hparams["huber_delta"], + no_peers=hparams["no_peers"], + verbose=False, ) - params, _ = train(params, train_inputs, hparams["n_epochs"], - hparams["lr"], hparams["l2_alpha"], verbose=False) # Eval R² on noise residual pred = np.array(forward( params, eval_inputs["peer_input"], eval_inputs["peer_mask"], - eval_inputs["local_input"], + eval_inputs["local_input"], no_peers=hparams["no_peers"], )) y = np.array(eval_inputs["y"]) v_arb = np.array(eval_inputs["v_arb"]) @@ -580,11 +692,13 @@ def objective(trial): med_total = float(np.median(r2_totals)) if r2_totals else -10.0 trial.set_user_attr("med_total_r2", med_total) - n_feat = sum(feat_cfg.values()) + n_feat = sum(1 for k in _FEAT_KEYS if feat_cfg.get(k)) print(f" Trial {trial.number}: resid={med_resid:.4f} total={med_total:.4f} " - f"h={hparams['hidden']} d={hparams['d_embed']} " + f"enc={hparams['encoder_type']} h={hparams['hidden']} d={hparams['d_embed']} " + f"hub={hparams['huber_delta']} " + f"{'no_peers ' if hparams['no_peers'] else ''}" f"lr={hparams['lr']:.1e} a={hparams['l2_alpha']:.1e} " - f"ep={hparams['n_epochs']} feat={n_feat}/8") + f"ep={hparams['n_epochs']} feat={n_feat}/11") return med_resid @@ -605,15 +719,12 @@ def objective(trial): reverse=True) for t in trials[:10]: if t.value is not None: - feats = sum(1 for k in ["peer_vol_lag2", "peer_vol_change", - "peer_tvl", "peer_volatility", - "own_vol_lag2", "own_vol_change", - "own_tvl", "own_volatility"] - if t.params.get(k)) + feats = sum(1 for k in _FEAT_KEYS if t.params.get(k)) print(f" #{t.number}: resid={t.value:.4f} " f"total={t.user_attrs.get('med_total_r2', '?'):.4f} " + f"enc={t.params['encoder_type']} " f"h={t.params['hidden']} d={t.params['d_embed']} " - f"feat={feats}/8") + f"feat={feats}/11") return study @@ -631,6 +742,10 @@ def main(): parser.add_argument("--lr", type=float, default=3e-4) parser.add_argument("--l2-alpha", type=float, default=1e-3) parser.add_argument("--epochs", type=int, default=1000) + parser.add_argument("--encoder-type", choices=["mlp", "linear"], default="mlp") + parser.add_argument("--huber-delta", type=float, default=1.0) + parser.add_argument("--no-peers", action="store_true", + help="Decoder-only ablation (zero peer summary)") # Feature flags parser.add_argument("--peer-vol-lag2", action="store_true") parser.add_argument("--peer-vol-change", action="store_true") @@ -655,6 +770,10 @@ def main(): "own_vol_change": args.own_vol_change or args.all_features, "own_tvl": args.own_tvl or args.all_features, "own_volatility": args.own_volatility or args.all_features, + # Relational features: always on for CLI, searchable in Optuna + "rel_same_chain": True, + "rel_tvl_ratio": True, + "rel_fee_ratio": True, } hparams = { "hidden": args.hidden, @@ -662,6 +781,9 @@ def main(): "lr": args.lr, "l2_alpha": args.l2_alpha, "n_epochs": args.epochs, + "encoder_type": args.encoder_type, + "huber_delta": args.huber_delta, + "no_peers": args.no_peers, } print("=" * 70) From 7ab989ab519bfbbb4e2a6d67dd62cc02b99982fb Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 17 Mar 2026 13:32:09 +0000 Subject: [PATCH 30/57] =?UTF-8?q?feat:=20deepsets=20v2=20improvements=20?= =?UTF-8?q?=E2=80=94=20relational=20features,=20Huber=20loss,=20encoder=20?= =?UTF-8?q?variants?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Add six improvements to the DeepSets cross-pool volume prediction model: - Explicit relational features in encoder: same_chain, log_tvl_ratio, log_fee_ratio precomputed per (target, peer) pair - Huber loss with configurable delta replaces MSE for outlier robustness - Per-pool loss weighting: equal weight per pool regardless of sample count - Linear encoder variant (single affine) as alternative to 2-layer MLP - Decoder-only ablation (--no-peers) to isolate cross-pool signal value - Relational features searchable in Optuna (11 feature booleans total) --- experiments/run_deepsets_v2.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/experiments/run_deepsets_v2.py b/experiments/run_deepsets_v2.py index 7e9a41c..3d899b8 100644 --- a/experiments/run_deepsets_v2.py +++ b/experiments/run_deepsets_v2.py @@ -286,9 +286,6 @@ def assemble_inputs(data, feat_cfg): Returns dict with JAX arrays ready for training. """ - n_samples = len(data["pool_idx"]) - n_peers = data["n_peers"] - # ---- Peer encoder input: (n_samples, n_peers, n_feat) ---- # Always: peer_attr, target_attr, vol_lag1, overlap pool_idx = data["pool_idx"] @@ -574,7 +571,7 @@ def _subset(d, mask): encoder_type=encoder_type, ) t0 = time.time() - params, final_loss = train( + params, _ = train( params, train_inputs, hparams["n_epochs"], hparams["lr"], hparams["l2_alpha"], huber_delta=huber_delta, no_peers=no_peers, ) From d6d1e7b7a59f8b9416853956dc9cce31a253f09f Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 17 Mar 2026 18:07:29 +0000 Subject: [PATCH 31/57] feat: LOO evaluation, warm-start decoder, residual target, minimal encoder MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - LOO cross-validation: train on N-1 pools, evaluate held-out pool (tests cross-pool generalization, the actual use case) - Decoder warm-start via OLS through hidden activations — predictions start in the right volume range instead of near-zero - --target-residual: train on noise residual (log_vol - log_V_arb) instead of total log_volume - --minimal-encoder: 7-feature encoder (fee, tvl, overlap, same_chain) to prevent pool identification through full attribute vectors - Loss function handles variable active pool counts (for LOO gaps) - Scatter-add loss replaces Python loop over pools - Module-level _subset with explicit _SAMPLE_KEYS - Fix in-place mutation of ratio feature arrays LOO results (default hparams): median total R²=0.39, resid R²=-0.22 Temporal split with --target-residual: train resid R²~0.06 Optuna sweep in progress, best eval resid R²=-0.108 (LR 20x too low at default — Optuna finds ~6e-3 optimal) --- experiments/run_deepsets_v2.py | 318 ++++++++++++++++++++++++++------- 1 file changed, 256 insertions(+), 62 deletions(-) diff --git a/experiments/run_deepsets_v2.py b/experiments/run_deepsets_v2.py index 3d899b8..4e02141 100644 --- a/experiments/run_deepsets_v2.py +++ b/experiments/run_deepsets_v2.py @@ -141,11 +141,14 @@ def build_all_features(matched_clean, option_c_clean): rel_log_tvl_ratio[i, p] = abs(raw_mean_log_tvl[i] - raw_mean_log_tvl[j]) rel_log_fee_ratio[i, p] = abs(raw_log_fee[i] - raw_log_fee[j]) - # Standardize ratio features - for arr in [rel_log_tvl_ratio, rel_log_fee_ratio]: + # Standardize ratio features (new arrays, no in-place mutation) + def _standardize(arr): mu = np.mean(arr) sigma = max(np.std(arr), 1e-6) - arr[:] = ((arr - mu) / sigma).astype(np.float32) + return ((arr - mu) / sigma).astype(np.float32) + + rel_log_tvl_ratio = _standardize(rel_log_tvl_ratio) + rel_log_fee_ratio = _standardize(rel_log_fee_ratio) # Standardization stats for volumes vol_mean = float(np.nanmean(vol_matrix)) @@ -267,6 +270,7 @@ def _norm_vola(x): "lf_dow_cos": lf_dow_cos, # Targets "y_total": y_total, + "y_residual": (y_total - np.log(np.maximum(v_arb_samples, 1e-6))).astype(np.float32), "v_arb": v_arb_samples, # Indices "pool_idx": sample_pools, @@ -278,6 +282,8 @@ def _norm_vola(x): "pool_ids": pool_ids, "vol_mean": vol_mean, "vol_std": vol_std, + "fee_attr_idx": fee_idx, + "tvl_attr_idx": tvl_idx, } @@ -287,18 +293,37 @@ def assemble_inputs(data, feat_cfg): Returns dict with JAX arrays ready for training. """ # ---- Peer encoder input: (n_samples, n_peers, n_feat) ---- - # Always: peer_attr, target_attr, vol_lag1, overlap pool_idx = data["pool_idx"] pa = data["peer_attrs"][pool_idx] # (n_samples, n_peers, k_attr) ta = data["target_attrs"][pool_idx] # (n_samples, k_attr) - ta_broad = np.broadcast_to(ta[:, None, :], pa.shape) - - peer_parts = [ - pa, ta_broad, - data["pf_vol_lag1"][:, :, None], - data["peer_overlap"][pool_idx][:, :, None], - ] + if feat_cfg.get("minimal_encoder"): + # 7-feature encoder: peer_fee, peer_tvl, target_fee, target_tvl, + # vol_lag1, overlap, same_chain — prevents pool identification + fi = data["fee_attr_idx"] + ti = data["tvl_attr_idx"] + pa_min = np.stack([pa[:, :, fi], pa[:, :, ti]], axis=-1) + ta_min = np.stack([ta[:, fi], ta[:, ti]], axis=-1) + ta_min_broad = np.broadcast_to( + ta_min[:, None, :], (pa_min.shape[0], pa_min.shape[1], 2)) + peer_parts = [ + pa_min, ta_min_broad, + data["pf_vol_lag1"][:, :, None], + data["peer_overlap"][pool_idx][:, :, None], + data["rel_same_chain"][pool_idx][:, :, None], + ] + else: + ta_broad = np.broadcast_to(ta[:, None, :], pa.shape) + peer_parts = [ + pa, ta_broad, + data["pf_vol_lag1"][:, :, None], + data["peer_overlap"][pool_idx][:, :, None], + ] + # Relational features (optional via feat_cfg, default on) + if feat_cfg.get("rel_same_chain", True): + peer_parts.append(data["rel_same_chain"][pool_idx][:, :, None]) + + # Optional temporal peer features (both modes) if feat_cfg.get("peer_vol_lag2"): peer_parts.append(data["pf_vol_lag2"][:, :, None]) if feat_cfg.get("peer_vol_change"): @@ -308,9 +333,7 @@ def assemble_inputs(data, feat_cfg): if feat_cfg.get("peer_volatility"): peer_parts.append(data["pf_volatility"][:, :, None]) - # Relational features (optional via feat_cfg) - if feat_cfg.get("rel_same_chain", True): - peer_parts.append(data["rel_same_chain"][pool_idx][:, :, None]) + # Relational ratio features (both modes) if feat_cfg.get("rel_tvl_ratio", True): peer_parts.append(data["rel_log_tvl_ratio"][pool_idx][:, :, None]) if feat_cfg.get("rel_fee_ratio", True): @@ -342,7 +365,7 @@ def assemble_inputs(data, feat_cfg): "peer_input": jnp.array(peer_input), "local_input": jnp.array(local_input), "peer_mask": jnp.array(data["peer_mask"]), - "y": jnp.array(data["y_total"]), + "y": jnp.array(data["y_residual"] if feat_cfg.get("target_residual") else data["y_total"]), "v_arb": jnp.array(data["v_arb"]), "pool_idx": jnp.array(pool_idx), "n_pools": data["n_pools"], @@ -381,6 +404,33 @@ def init_params(key, n_peer_feat, n_local_feat, hidden, d_embed, return params +def warm_start_decoder(params, inputs, d_embed): + """Set decoder output layer via OLS through hidden activations. + + Fits y ~ h(local_input) with zero peer summary, so the decoder + starts predicting in the right volume range (~10-17 log scale). + """ + local = np.array(inputs["local_input"]) + y = np.array(inputs["y"]) + n = local.shape[0] + + # Simulate decoder input with zero peer summary + dec_in = np.concatenate( + [np.zeros((n, d_embed), dtype=np.float32), local], axis=1) + + # Forward through first decoder layer with current (random) weights + h = np.maximum( + dec_in @ np.array(params["dec_W1"]) + np.array(params["dec_b1"]), 0.0) + + # OLS: y ≈ h @ W2 + b2 + h_bias = np.concatenate([h, np.ones((n, 1), dtype=np.float32)], axis=1) + sol, _, _, _ = np.linalg.lstsq(h_bias, y[:, None], rcond=None) + + params["dec_W2"] = jnp.array(sol[:-1].astype(np.float32)) + params["dec_b2"] = jnp.array(sol[-1:].astype(np.float32)) + return params + + def forward(params, peer_input, peer_mask, local_input, no_peers=False): """Returns predicted log_volume (total) per sample.""" batch, n_peers, _ = peer_input.shape @@ -417,19 +467,19 @@ def loss_fn(params, peer_input, peer_mask, local_input, y, l2_alpha, huber_vals = jnp.where(abs_r <= huber_delta, 0.5 * residuals ** 2, huber_delta * (abs_r - 0.5 * huber_delta)) - # Per-pool mean loss, then average across pools - total = 0.0 - for i in range(n_pools): - mask_i = (pool_idx == i).astype(jnp.float32) - n_i = jnp.maximum(jnp.sum(mask_i), 1.0) - total = total + jnp.sum(huber_vals * mask_i) / n_i - data_loss = total / n_pools + # Per-pool mean loss, then average across active pools (handles LOO gaps) + pool_counts = jnp.zeros(n_pools).at[pool_idx].add(jnp.ones_like(pool_idx, dtype=jnp.float32)) + active = (pool_counts > 0).astype(jnp.float32) + n_active = jnp.maximum(jnp.sum(active), 1.0) + pool_counts = jnp.maximum(pool_counts, 1.0) + pool_sums = jnp.zeros(n_pools).at[pool_idx].add(huber_vals) + data_loss = jnp.sum((pool_sums / pool_counts) * active) / n_active reg = sum(jnp.sum(v ** 2) for k, v in params.items() if "W" in k) return data_loss + l2_alpha * reg -# n_pools (arg 7) and no_peers (arg 9) must be static for Python control flow +# n_pools (arg 7): static for jnp.zeros shape; no_peers (arg 9): static for if/else in forward _grad_fn = jax.jit(jax.value_and_grad(loss_fn), static_argnums=(7, 9)) @@ -469,20 +519,31 @@ def train(params, inputs, n_epochs, lr, l2_alpha, huber_delta=1.0, # ---- Evaluation ---- -def evaluate(params, inputs, data, label="", no_peers=False): +def evaluate(params, inputs, data, label="", no_peers=False, + target_residual=False): """Per-pool R² on total volume and noise residual.""" - pred_total = np.array(forward( + pred = np.array(forward( params, inputs["peer_input"], inputs["peer_mask"], inputs["local_input"], no_peers=no_peers, )) - y_total = np.array(inputs["y"]) + y = np.array(inputs["y"]) v_arb = np.array(inputs["v_arb"]) - pool_idx = np.array(data["pool_idx"]) if "pool_idx" in data else np.array(inputs["pool_idx"]) + pool_idx = np.array(inputs["pool_idx"]) - # Noise residual: compare (pred - log(v_arb)) vs (y - log(v_arb)) log_v_arb = np.log(np.maximum(v_arb, 1e-6)) - resid_true = y_total - log_v_arb - resid_pred = pred_total - log_v_arb + + if target_residual: + # Model predicts noise residual directly + resid_true = y + resid_pred = pred + y_total = y + log_v_arb # reconstruct total for total R² + pred_total = pred + log_v_arb + else: + # Model predicts total log_volume + y_total = y + pred_total = pred + resid_true = y - log_v_arb + resid_pred = pred - log_v_arb r2_total = {} r2_resid = {} @@ -521,6 +582,26 @@ def _med(d): return med_total, med_resid, r2_total, r2_resid +# Keys indexed by sample (shape[0] == n_samples) +_SAMPLE_KEYS = { + "pf_vol_lag1", "pf_vol_lag2", "pf_vol_change", "pf_tvl", "pf_volatility", + "peer_mask", "lf_own_vol_lag1", "lf_own_vol_lag2", "lf_own_vol_change", + "lf_own_tvl", "lf_own_volatility", "lf_dow_sin", "lf_dow_cos", + "y_total", "y_residual", "v_arb", "pool_idx", "day_idx", +} + + +def _subset(d, mask): + """Subset sample-indexed arrays by boolean mask.""" + out = {} + for k, v in d.items(): + if k in _SAMPLE_KEYS and isinstance(v, np.ndarray): + out[k] = v[mask] + else: + out[k] = v + return out + + # ---- Temporal split ---- @@ -531,15 +612,6 @@ def run_temporal(data, feat_cfg, hparams, split_frac=0.7): train_mask = day_idx <= split_day eval_mask = day_idx > split_day - def _subset(d, mask): - out = {} - for k, v in d.items(): - if isinstance(v, np.ndarray) and len(v) == len(mask): - out[k] = v[mask] - else: - out[k] = v - return out - train_data = _subset(data, train_mask) eval_data = _subset(data, eval_mask) @@ -549,6 +621,7 @@ def _subset(d, mask): encoder_type = hparams.get("encoder_type", "mlp") no_peers = hparams.get("no_peers", False) huber_delta = hparams.get("huber_delta", 1.0) + target_residual = feat_cfg.get("target_residual", False) n_pf = train_inputs["n_peer_feat"] n_lf = train_inputs["n_local_feat"] @@ -559,6 +632,8 @@ def _subset(d, mask): print(f" Train: {int(train_mask.sum())}, Eval: {int(eval_mask.sum())}, " f"peer_feat={n_pf}, local_feat={n_lf}, params={n_params}") + if target_residual: + print(f" target=residual (log_vol - log_V_arb)") if encoder_type != "mlp": print(f" encoder_type={encoder_type}") if no_peers: @@ -570,6 +645,7 @@ def _subset(d, mask): jax.random.PRNGKey(42), n_pf, n_lf, hparams["hidden"], hparams["d_embed"], encoder_type=encoder_type, ) + params = warm_start_decoder(params, train_inputs, hparams["d_embed"]) t0 = time.time() params, _ = train( params, train_inputs, hparams["n_epochs"], hparams["lr"], hparams["l2_alpha"], @@ -577,25 +653,127 @@ def _subset(d, mask): ) print(f" Training: {time.time() - t0:.1f}s") + eval_kw = dict(no_peers=no_peers, target_residual=target_residual) print("\n --- Train ---") - evaluate(params, train_inputs, data, no_peers=no_peers) + evaluate(params, train_inputs, data, **eval_kw) print("\n --- Eval ---") - _, med_resid_eval, _, _ = evaluate(params, eval_inputs, data, no_peers=no_peers) + _, med_resid_eval, _, _ = evaluate(params, eval_inputs, data, **eval_kw) return med_resid_eval +# ---- LOO cross-validation ---- + + +def run_loo(data, feat_cfg, hparams): + """Leave-one-pool-out: train on N-1 pools, evaluate on held-out pool. + + Tests cross-pool generalization — can the shared encoder+decoder predict + volume for a pool it has never optimized on? The held-out pool's volume + is still observable as peer features for training pools. + + Note: normalization stats are computed on the full dataset. With N=36 + the leakage from including one held-out pool is ~3% on mean/std. + """ + n_pools = data["n_pools"] + pool_idx = data["pool_idx"] + pool_ids = data.get("pool_ids", []) + + encoder_type = hparams.get("encoder_type", "mlp") + no_peers = hparams.get("no_peers", False) + huber_delta = hparams.get("huber_delta", 1.0) + target_residual = feat_cfg.get("target_residual", False) + d_embed = hparams["d_embed"] + + r2_total_all = {} + r2_resid_all = {} + + for held_out in range(n_pools): + pid = pool_ids[held_out] if held_out < len(pool_ids) else f"pool_{held_out}" + + train_mask = pool_idx != held_out + eval_mask = pool_idx == held_out + n_eval = int(eval_mask.sum()) + + if n_eval < 2: + print(f" [{held_out:2d}] {pid[:16]}: skipped ({n_eval} samples)") + continue + + train_data = _subset(data, train_mask) + eval_data = _subset(data, eval_mask) + + train_inputs = assemble_inputs(train_data, feat_cfg) + eval_inputs = assemble_inputs(eval_data, feat_cfg) + + params = init_params( + jax.random.PRNGKey(42), + train_inputs["n_peer_feat"], train_inputs["n_local_feat"], + hparams["hidden"], d_embed, + encoder_type=encoder_type, + ) + params = warm_start_decoder(params, train_inputs, d_embed) + + params, _ = train( + params, train_inputs, hparams["n_epochs"], + hparams["lr"], hparams["l2_alpha"], + huber_delta=huber_delta, no_peers=no_peers, + verbose=False, + ) + + # Evaluate on held-out pool + pred = np.array(forward( + params, eval_inputs["peer_input"], eval_inputs["peer_mask"], + eval_inputs["local_input"], no_peers=no_peers, + )) + y = np.array(eval_inputs["y"]) + v_arb = np.array(eval_inputs["v_arb"]) + log_v_arb = np.log(np.maximum(v_arb, 1e-6)) + + if target_residual: + resid_true, resid_pred = y, pred + y_total = y + log_v_arb + pred_total = pred + log_v_arb + else: + y_total, pred_total = y, pred + resid_true = y - log_v_arb + resid_pred = pred - log_v_arb + + ss_res = np.sum((y_total - pred_total) ** 2) + ss_tot = np.sum((y_total - y_total.mean()) ** 2) + r2_t = 1 - ss_res / max(ss_tot, 1e-10) + + ss_res_r = np.sum((resid_true - resid_pred) ** 2) + ss_tot_r = np.sum((resid_true - resid_true.mean()) ** 2) + r2_r = 1 - ss_res_r / max(ss_tot_r, 1e-10) + + r2_total_all[held_out] = r2_t + r2_resid_all[held_out] = r2_r + + print(f" [{held_out:2d}] {pid[:16]}: total={r2_t:.3f} resid={r2_r:.3f} (n={n_eval})") + + def _med(d): + v = [x for x in d.values() if np.isfinite(x)] + return np.median(v) if v else float("nan") + + med_total = _med(r2_total_all) + med_resid = _med(r2_resid_all) + print(f"\n LOO Median R² total={med_total:.4f} resid={med_resid:.4f}") + print(f" ({len(r2_total_all)} pools evaluated)") + + return med_total, med_resid + + # ---- Optuna ---- _FEAT_KEYS = [ "peer_vol_lag2", "peer_vol_change", "peer_tvl", "peer_volatility", "own_vol_lag2", "own_vol_change", "own_tvl", "own_volatility", - "rel_same_chain", "rel_tvl_ratio", "rel_fee_ratio", + "rel_same_chain", "rel_tvl_ratio", "rel_fee_ratio", "minimal_encoder", ] -def run_optuna(data, n_trials): +def run_optuna(data, n_trials, target_residual=False): import optuna day_idx = data["day_idx"] @@ -603,15 +781,6 @@ def run_optuna(data, n_trials): train_mask = day_idx <= split_day eval_mask = day_idx > split_day - def _subset(d, mask): - out = {} - for k, v in d.items(): - if isinstance(v, np.ndarray) and len(v) == len(mask): - out[k] = v[mask] - else: - out[k] = v - return out - train_data = _subset(data, train_mask) eval_data = _subset(data, eval_mask) @@ -628,6 +797,8 @@ def objective(trial): "rel_same_chain": trial.suggest_categorical("rel_same_chain", [True, False]), "rel_tvl_ratio": trial.suggest_categorical("rel_tvl_ratio", [True, False]), "rel_fee_ratio": trial.suggest_categorical("rel_fee_ratio", [True, False]), + "minimal_encoder": trial.suggest_categorical("minimal_encoder", [True, False]), + "target_residual": target_residual, } hparams = { "hidden": trial.suggest_categorical("hidden", [8, 16, 32]), @@ -649,6 +820,7 @@ def objective(trial): hparams["hidden"], hparams["d_embed"], encoder_type=hparams["encoder_type"], ) + params = warm_start_decoder(params, train_inputs, hparams["d_embed"]) params, _ = train( params, train_inputs, hparams["n_epochs"], hparams["lr"], hparams["l2_alpha"], @@ -658,6 +830,7 @@ def objective(trial): ) # Eval R² on noise residual + _tgt_resid = feat_cfg.get("target_residual", False) pred = np.array(forward( params, eval_inputs["peer_input"], eval_inputs["peer_mask"], eval_inputs["local_input"], no_peers=hparams["no_peers"], @@ -673,16 +846,23 @@ def objective(trial): mask = pool_idx == i if mask.sum() < 2: continue - yt = y[mask] - pt = pred[mask] + yi = y[mask] + pi = pred[mask] + lva = log_v_arb[mask] + + if _tgt_resid: + resid_true, resid_pred = yi, pi + yt, pt = yi + lva, pi + lva + else: + yt, pt = yi, pi + resid_true, resid_pred = yi - lva, pi - lva + ss_res = np.sum((yt - pt) ** 2) ss_tot = np.sum((yt - yt.mean()) ** 2) r2_totals.append(1 - ss_res / max(ss_tot, 1e-10)) - rt = yt - log_v_arb[mask] - rp = pt - log_v_arb[mask] - ss_res_r = np.sum((rt - rp) ** 2) - ss_tot_r = np.sum((rt - rt.mean()) ** 2) + ss_res_r = np.sum((resid_true - resid_pred) ** 2) + ss_tot_r = np.sum((resid_true - resid_true.mean()) ** 2) r2_resids.append(1 - ss_res_r / max(ss_tot_r, 1e-10)) med_resid = float(np.median(r2_resids)) if r2_resids else -10.0 @@ -695,7 +875,8 @@ def objective(trial): f"hub={hparams['huber_delta']} " f"{'no_peers ' if hparams['no_peers'] else ''}" f"lr={hparams['lr']:.1e} a={hparams['l2_alpha']:.1e} " - f"ep={hparams['n_epochs']} feat={n_feat}/11") + f"ep={hparams['n_epochs']} feat={n_feat}/12" + f"{' minimal' if feat_cfg.get('minimal_encoder') else ''}") return med_resid @@ -721,7 +902,7 @@ def objective(trial): f"total={t.user_attrs.get('med_total_r2', '?'):.4f} " f"enc={t.params['encoder_type']} " f"h={t.params['hidden']} d={t.params['d_embed']} " - f"feat={feats}/11") + f"feat={feats}/12") return study @@ -743,6 +924,12 @@ def main(): parser.add_argument("--huber-delta", type=float, default=1.0) parser.add_argument("--no-peers", action="store_true", help="Decoder-only ablation (zero peer summary)") + parser.add_argument("--minimal-encoder", action="store_true", + help="7-feature encoder (fee, tvl, overlap, same_chain) " + "instead of full attributes") + parser.add_argument("--target-residual", action="store_true", + help="Train on noise residual (log_vol - log_V_arb) " + "instead of total log_volume") # Feature flags parser.add_argument("--peer-vol-lag2", action="store_true") parser.add_argument("--peer-vol-change", action="store_true") @@ -771,6 +958,8 @@ def main(): "rel_same_chain": True, "rel_tvl_ratio": True, "rel_fee_ratio": True, + "minimal_encoder": args.minimal_encoder, + "target_residual": args.target_residual, } hparams = { "hidden": args.hidden, @@ -798,8 +987,13 @@ def main(): print(f" {len(data['pool_idx'])} samples, {data['n_pools']} pools, " f"{time.time() - t0:.1f}s") - if args.tune > 0: - run_optuna(data, args.tune) + if args.loo: + print(f"\n{'='*70}") + print("Leave-One-Pool-Out Cross-Validation") + print(f"{'='*70}") + run_loo(data, feat_cfg, hparams) + elif args.tune > 0: + run_optuna(data, args.tune, target_residual=args.target_residual) else: print(f"\n{'='*70}") print("Temporal split (70/30)") From 5c04a47fb19312f935ec7117d07228e74db24098 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Thu, 19 Mar 2026 13:52:55 +0000 Subject: [PATCH 32/57] feat: learnable cadence via PCHIP, linear market noise model, hybrid encoder MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - DeepSets v2: add --learn-cadence flag that jointly optimizes per-pool arb cadence with the noise model via Adam through differentiable PCHIP. Per-pool PCHIP coefficients closed over in JIT'd loss function. Add --x-obs flag (none/reduced/cross) for Option C covariates. Full decomposition diagnostics (arb%, noise%, cadence bounds, train/eval gap). Optuna cadence sweep (run_optuna_cadence). Best eval total R²=0.50. - New: market_features.py — derives daily features from Binance minute data. BTC regime (log_price, return, realized vol, trends), token-level (return, realized vol, trends), pair realized volatility (A/B ratio). Token map for wrapped/derivative tokens. - New: run_linear_market_noise.py — linear noise model with market features and learnable cadence. V_noise = exp(x @ shared_coeffs). Optional per-pool intercepts. Eval R²=0.39 with 59 params. - New: run_hybrid_noise.py — DeepSets encoder produces scalar peer_effect, fed as covariate (+ tvl interaction) into linear noise model. Encoder learns peer aggregation, linear model keeps TVL coefficient interpretable for counterfactual analysis. Eval R²=0.40 with 478 params. --- experiments/run_deepsets_v2.py | 698 ++++++++++++++++++++- experiments/run_hybrid_noise.py | 584 +++++++++++++++++ experiments/run_linear_market_noise.py | 463 ++++++++++++++ quantammsim/calibration/market_features.py | 297 +++++++++ 4 files changed, 2016 insertions(+), 26 deletions(-) create mode 100644 experiments/run_hybrid_noise.py create mode 100644 experiments/run_linear_market_noise.py create mode 100644 quantammsim/calibration/market_features.py diff --git a/experiments/run_deepsets_v2.py b/experiments/run_deepsets_v2.py index 4e02141..bc3661b 100644 --- a/experiments/run_deepsets_v2.py +++ b/experiments/run_deepsets_v2.py @@ -59,6 +59,7 @@ def build_all_features(matched_clean, option_c_clean): from quantammsim.calibration.grid_interpolation import interpolate_pool_daily from quantammsim.calibration.pool_data import ( build_pool_attributes, _parse_tokens, _canonicalize_token, + build_x_obs, build_cross_pool_x_obs, ) pool_ids = sorted(matched_clean.keys()) @@ -99,6 +100,22 @@ def build_all_features(matched_clean, option_c_clean): volatility_matrix[t, j] = panel["volatility"].values[k] v_arb_matrix[t, j] = v_arb[k] + # Per-pool coeffs, gas, and day mapping for learnable cadence + pool_coeffs = [] + pool_gas = [] + init_log_cadences = np.zeros(n_pools, dtype=np.float32) + common_to_grid = np.full((n_pools, n_dates), 0, dtype=np.int32) + + for j, pid in enumerate(pool_ids): + entry = matched_clean[pid] + oc = option_c_clean[pid] + pool_coeffs.append(entry["coeffs"]) + pool_gas.append(jnp.float64(np.exp(oc["log_gas"]))) + init_log_cadences[j] = oc["log_cadence"] + dates_j = entry["panel"]["date"].values + for k, date in enumerate(dates_j): + common_to_grid[j, date_to_idx[date]] = entry["day_indices"][k] + for t, date in enumerate(date_list): weekday_arr[t] = pd.Timestamp(date).weekday() @@ -150,6 +167,31 @@ def _standardize(arr): rel_log_tvl_ratio = _standardize(rel_log_tvl_ratio) rel_log_fee_ratio = _standardize(rel_log_fee_ratio) + # Cross-pool peer maps: which pools share tokens / chain + from collections import defaultdict + pool_tokens_ordered = {} + token_to_pools = defaultdict(set) + for i, pid in enumerate(pool_ids): + toks = _parse_tokens(matched_clean[pid]["tokens"]) + ordered = [_canonicalize_token(t) for t in toks[:2]] + pool_tokens_ordered[i] = ordered + for tok in ordered: + token_to_pools[tok].add(i) + + token_a_peers = {} + token_b_peers = {} + chain_peer_map = {} + for i in range(n_pools): + toks = pool_tokens_ordered[i] + token_a_peers[i] = sorted(token_to_pools[toks[0]] - {i}) + token_b_peers[i] = sorted(token_to_pools[toks[1]] - {i}) if len(toks) > 1 else [] + chain_peer_map[i] = [j for j in range(n_pools) if j != i and pool_chains[j] == pool_chains[i]] + + # Per-pool log_fee for interaction features + pool_log_fee = raw_log_fee.copy() + fee_mean = float(np.mean(pool_log_fee)) + fee_std = max(float(np.std(pool_log_fee)), 1e-6) + # Standardization stats for volumes vol_mean = float(np.nanmean(vol_matrix)) vol_std = float(np.nanstd(vol_matrix)) @@ -158,6 +200,27 @@ def _standardize(arr): vola_mean = float(np.nanmean(volatility_matrix)) vola_std = float(np.nanstd(volatility_matrix)) + # Build x_obs per pool, mapped to common date grid + # x_obs_reduced: (n_dates, n_pools, 4), x_obs_cross: (n_dates, n_pools, 7) + from quantammsim.calibration.pool_data import K_OBS_REDUCED, K_OBS_CROSS + x_obs_reduced_grid = np.full((n_dates, n_pools, K_OBS_REDUCED), np.nan) + x_obs_cross_grid = np.full((n_dates, n_pools, K_OBS_CROSS), np.nan) + + for j, pid in enumerate(pool_ids): + entry = matched_clean[pid] + panel = entry["panel"] + dates_j = panel["date"].values + + # Reduced x_obs (4 features) + xr = build_x_obs(panel, reduced=True) # (n_obs, 4) + for k, date in enumerate(dates_j): + x_obs_reduced_grid[date_to_idx[date], j] = xr[k] + + # Cross-pool x_obs (7 features) — drops first day + xc = build_cross_pool_x_obs(panel, matched_clean, pid) # (n_obs-1, 7) + for k, date in enumerate(dates_j[1:]): + x_obs_cross_grid[date_to_idx[date], j] = xc[k] + # Build samples: require t >= 2 (for lag-2), valid vol at t, t-1, t-2 sample_pools, sample_days = [], [] for i in range(n_pools): @@ -198,6 +261,19 @@ def _norm_vola(x): lf_own_volatility = np.zeros(n_samples, dtype=np.float32) lf_dow_sin = np.zeros(n_samples, dtype=np.float32) lf_dow_cos = np.zeros(n_samples, dtype=np.float32) + # Interaction features (from calibration pipeline's x_obs) + lf_tvl_x_vola = np.zeros(n_samples, dtype=np.float32) + lf_tvl_x_fee = np.zeros(n_samples, dtype=np.float32) + lf_vola_x_fee = np.zeros(n_samples, dtype=np.float32) + # Cross-pool volume aggregates + lf_cross_vol_tok_a = np.zeros(n_samples, dtype=np.float32) + lf_cross_vol_tok_b = np.zeros(n_samples, dtype=np.float32) + lf_cross_vol_chain = np.zeros(n_samples, dtype=np.float32) + lf_market_vol = np.zeros(n_samples, dtype=np.float32) + # Cross-pool momentum (peer volume changes) + lf_cross_mom_tok_a = np.zeros(n_samples, dtype=np.float32) + lf_cross_mom_tok_b = np.zeros(n_samples, dtype=np.float32) + lf_cross_mom_chain = np.zeros(n_samples, dtype=np.float32) # Targets y_total = np.zeros(n_samples, dtype=np.float32) @@ -241,10 +317,64 @@ def _norm_vola(x): lf_dow_sin[s] = np.sin(2 * np.pi * wd / 7) lf_dow_cos[s] = np.cos(2 * np.pi * wd / 7) + # Interaction features (raw products, standardized after loop) + norm_fee_i = (raw_log_fee[i] - fee_mean) / fee_std + lf_tvl_x_vola[s] = lf_own_tvl[s] * lf_own_volatility[s] + lf_tvl_x_fee[s] = lf_own_tvl[s] * norm_fee_i + lf_vola_x_fee[s] = lf_own_volatility[s] * norm_fee_i + + # Cross-pool volume aggregates at t-1 + def _peer_vol_mean(peer_list, t_lag): + if not peer_list: + return vol_mean # global fallback + vals = vol_matrix[t_lag, peer_list] + valid = vals[~np.isnan(vals)] + return float(np.mean(valid)) if len(valid) > 0 else vol_mean + + def _peer_vol_change_mean(peer_list, t_lag): + if not peer_list: + return 0.0 + v1 = vol_matrix[t_lag, peer_list] + v2 = vol_matrix[t_lag - 1, peer_list] + valid = ~np.isnan(v1) & ~np.isnan(v2) + if valid.sum() == 0: + return 0.0 + return float(np.mean(v1[valid] - v2[valid])) + + lf_cross_vol_tok_a[s] = _norm_vol(_peer_vol_mean(token_a_peers[i], t - 1)) + lf_cross_vol_tok_b[s] = _norm_vol(_peer_vol_mean(token_b_peers[i], t - 1)) + lf_cross_vol_chain[s] = _norm_vol(_peer_vol_mean(chain_peer_map[i], t - 1)) + lf_market_vol[s] = _norm_vol(float(np.nanmean(vol_matrix[t - 1, :]))) + + # Cross-pool momentum: mean volume change of peers (t-1 vs t-2) + lf_cross_mom_tok_a[s] = _peer_vol_change_mean(token_a_peers[i], t - 1) + lf_cross_mom_tok_b[s] = _peer_vol_change_mean(token_b_peers[i], t - 1) + lf_cross_mom_chain[s] = _peer_vol_change_mean(chain_peer_map[i], t - 1) + y_total[s] = vol_matrix[t, i] v_arb_val = v_arb_matrix[t, i] v_arb_samples[s] = v_arb_val if np.isfinite(v_arb_val) else 1e-6 + # Per-sample grid day indices for learnable cadence + sample_grid_days = common_to_grid[sample_pools, sample_days] + + # Per-sample x_obs arrays + x_obs_reduced = np.zeros((n_samples, K_OBS_REDUCED), dtype=np.float32) + x_obs_cross = np.zeros((n_samples, K_OBS_CROSS), dtype=np.float32) + for s in range(n_samples): + xr = x_obs_reduced_grid[sample_days[s], sample_pools[s]] + if np.all(np.isfinite(xr)): + x_obs_reduced[s] = xr + xc = x_obs_cross_grid[sample_days[s], sample_pools[s]] + if np.all(np.isfinite(xc)): + x_obs_cross[s] = xc + + # Standardize momentum features (raw volume differences) + for arr in [lf_cross_mom_tok_a, lf_cross_mom_tok_b, lf_cross_mom_chain]: + mu = np.mean(arr) + sigma = max(np.std(arr), 1e-6) + arr[:] = ((arr - mu) / sigma).astype(np.float32) + return { # Static per-pool "peer_attrs": peer_attrs, # (n_pools, n_peers, k_attr) @@ -268,10 +398,30 @@ def _norm_vola(x): "lf_own_volatility": lf_own_volatility, "lf_dow_sin": lf_dow_sin, "lf_dow_cos": lf_dow_cos, + # Interaction features + "lf_tvl_x_vola": lf_tvl_x_vola, + "lf_tvl_x_fee": lf_tvl_x_fee, + "lf_vola_x_fee": lf_vola_x_fee, + # Cross-pool volume aggregates + "lf_cross_vol_tok_a": lf_cross_vol_tok_a, + "lf_cross_vol_tok_b": lf_cross_vol_tok_b, + "lf_cross_vol_chain": lf_cross_vol_chain, + "lf_market_vol": lf_market_vol, + # Cross-pool momentum + "lf_cross_mom_tok_a": lf_cross_mom_tok_a, + "lf_cross_mom_tok_b": lf_cross_mom_tok_b, + "lf_cross_mom_chain": lf_cross_mom_chain, # Targets "y_total": y_total, "y_residual": (y_total - np.log(np.maximum(v_arb_samples, 1e-6))).astype(np.float32), "v_arb": v_arb_samples, + # Cadence learning (per-pool, not subject to _subset) + "pool_coeffs": pool_coeffs, # list of PoolCoeffsDaily + "pool_gas": pool_gas, # list of jnp scalars + "init_log_cadences": init_log_cadences, # (n_pools,) + "sample_grid_days": sample_grid_days, # (n_samples,) + "x_obs_reduced": x_obs_reduced, # (n_samples, 4) + "x_obs_cross": x_obs_cross, # (n_samples, 7) # Indices "pool_idx": sample_pools, "day_idx": sample_days, @@ -359,19 +509,50 @@ def assemble_inputs(data, feat_cfg): if feat_cfg.get("own_volatility"): local_parts.append(data["lf_own_volatility"][:, None]) + # Interaction features (tvl×vola, tvl×fee, vola×fee) + if feat_cfg.get("interactions"): + local_parts.append(data["lf_tvl_x_vola"][:, None]) + local_parts.append(data["lf_tvl_x_fee"][:, None]) + local_parts.append(data["lf_vola_x_fee"][:, None]) + + # Cross-pool volume aggregates (token-peer, chain-peer, market) + if feat_cfg.get("cross_pool_vol"): + local_parts.append(data["lf_cross_vol_tok_a"][:, None]) + local_parts.append(data["lf_cross_vol_tok_b"][:, None]) + local_parts.append(data["lf_cross_vol_chain"][:, None]) + local_parts.append(data["lf_market_vol"][:, None]) + + # Cross-pool momentum (peer volume changes) + if feat_cfg.get("cross_pool_momentum"): + local_parts.append(data["lf_cross_mom_tok_a"][:, None]) + local_parts.append(data["lf_cross_mom_tok_b"][:, None]) + local_parts.append(data["lf_cross_mom_chain"][:, None]) + + # Option C x_obs covariates (none / reduced=4 / cross=7) + x_obs_mode = feat_cfg.get("x_obs_mode", "none") + if x_obs_mode == "reduced" and "x_obs_reduced" in data: + local_parts.append(data["x_obs_reduced"]) + elif x_obs_mode == "cross" and "x_obs_cross" in data: + local_parts.append(data["x_obs_cross"]) + local_input = np.concatenate(local_parts, axis=-1).astype(np.float32) - return { + result = { "peer_input": jnp.array(peer_input), "local_input": jnp.array(local_input), "peer_mask": jnp.array(data["peer_mask"]), "y": jnp.array(data["y_residual"] if feat_cfg.get("target_residual") else data["y_total"]), + "y_total": jnp.array(data["y_total"]), "v_arb": jnp.array(data["v_arb"]), "pool_idx": jnp.array(pool_idx), "n_pools": data["n_pools"], "n_peer_feat": peer_input.shape[-1], "n_local_feat": local_input.shape[-1], } + # Cadence learning arrays + if "sample_grid_days" in data: + result["sample_grid_days"] = jnp.array(data["sample_grid_days"]) + return result # ---- Model ---- @@ -483,24 +664,89 @@ def loss_fn(params, peer_input, peer_mask, local_input, y, l2_alpha, _grad_fn = jax.jit(jax.value_and_grad(loss_fn), static_argnums=(7, 9)) +# ---- Learnable cadence ---- + + +def make_cadence_loss_fn(pool_coeffs, pool_gas, n_pools, no_peers): + """Build a loss function with per-pool PCHIP coefficients closed over. + + The returned function is JIT-compiled. The Python loop over pools is + unrolled at trace time, so each pool's coefficients are constants. + + The neural net predicts log(V_noise). V_arb comes from PCHIP at the + current learnable log_cadence. Loss is Huber on log(V_arb + V_noise) + vs log(V_obs). + """ + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + + def loss_fn_cadence(params, peer_input, peer_mask, local_input, y_total, + sample_grid_days, pool_idx, l2_alpha, huber_delta): + # Neural net predicts log(V_noise) + log_v_noise = forward(params, peer_input, peer_mask, local_input, no_peers) + + # Compute V_arb per sample via PCHIP (loop unrolled at trace time) + log_cadence = params["log_cadence"] + n_samples = y_total.shape[0] + v_arb = jnp.zeros(n_samples) + + for i in range(n_pools): + v_arb_all = interpolate_pool_daily( + pool_coeffs[i], log_cadence[i], pool_gas[i]) + # Index into this pool's daily V_arb; clip for safety on other pools' samples + safe_days = jnp.clip(sample_grid_days, 0, v_arb_all.shape[0] - 1) + v_arb = jnp.where(pool_idx == i, v_arb_all[safe_days], v_arb) + + # Combine: log(V_arb + V_noise) via numerically stable logaddexp + log_v_arb = jnp.log(jnp.maximum(v_arb, 1e-10)) + log_v_total = jnp.logaddexp(log_v_arb, log_v_noise) + + # Huber loss with per-pool weighting + residuals = log_v_total - y_total + abs_r = jnp.abs(residuals) + huber_vals = jnp.where(abs_r <= huber_delta, 0.5 * residuals ** 2, + huber_delta * (abs_r - 0.5 * huber_delta)) + + pool_counts = jnp.zeros(n_pools).at[pool_idx].add( + jnp.ones_like(pool_idx, dtype=jnp.float32)) + active = (pool_counts > 0).astype(jnp.float32) + n_active = jnp.maximum(jnp.sum(active), 1.0) + pool_counts = jnp.maximum(pool_counts, 1.0) + pool_sums = jnp.zeros(n_pools).at[pool_idx].add(huber_vals) + data_loss = jnp.sum((pool_sums / pool_counts) * active) / n_active + + reg = sum(jnp.sum(v ** 2) for k, v in params.items() if "W" in k) + return data_loss + l2_alpha * reg + + grad_fn = jax.jit(jax.value_and_grad(loss_fn_cadence)) + return grad_fn + + # ---- Training ---- def train(params, inputs, n_epochs, lr, l2_alpha, huber_delta=1.0, - no_peers=False, verbose=True): + no_peers=False, verbose=True, grad_fn_override=None): m = {k: jnp.zeros_like(v) for k, v in params.items()} v = {k: jnp.zeros_like(v) for k, v in params.items()} final_loss = float("inf") n_pools = int(inputs["n_pools"]) pool_idx = inputs["pool_idx"] + use_cadence = grad_fn_override is not None for epoch in range(n_epochs): - loss_val, grads = _grad_fn( - params, inputs["peer_input"], inputs["peer_mask"], - inputs["local_input"], inputs["y"], l2_alpha, - pool_idx, n_pools, huber_delta, no_peers, - ) + if use_cadence: + loss_val, grads = grad_fn_override( + params, inputs["peer_input"], inputs["peer_mask"], + inputs["local_input"], inputs["y_total"], + inputs["sample_grid_days"], pool_idx, l2_alpha, huber_delta, + ) + else: + loss_val, grads = _grad_fn( + params, inputs["peer_input"], inputs["peer_mask"], + inputs["local_input"], inputs["y"], l2_alpha, + pool_idx, n_pools, huber_delta, no_peers, + ) final_loss = float(loss_val) for k in params: @@ -511,7 +757,24 @@ def train(params, inputs, n_epochs, lr, l2_alpha, huber_delta=1.0, params[k] = params[k] - lr * m_hat / (jnp.sqrt(v_hat) + 1e-8) if verbose and (epoch % 200 == 0 or epoch == n_epochs - 1): - print(f" epoch {epoch:4d} loss={final_loss:.6f}") + if use_cadence: + cads = np.exp(np.array(params["log_cadence"])) + # Quick decomposition check: forward pass + V_arb at current cadences + _lvn = np.array(forward( + params, inputs["peer_input"], inputs["peer_mask"], + inputs["local_input"], no_peers)) + _vn = np.exp(_lvn) + _vo = np.exp(np.array(inputs["y_total"])) + # Approximate arb fraction (use V_obs - V_noise as proxy to avoid PCHIP call) + _arb_proxy = np.clip(1.0 - _vn / _vo, 0, None) + _n_pathological = np.sum(_arb_proxy < -0.5) # noise > 1.5x observed + _n_bound = np.sum((cads <= 1.01) | (cads >= 59.9)) + print(f" epoch {epoch:4d} loss={final_loss:.6f}" + f" cad=[{cads.min():.1f}-{np.median(cads):.1f}-{cads.max():.1f}]" + f" |logVn|={np.mean(np.abs(_lvn)):.1f}" + f" bound={_n_bound}") + else: + print(f" epoch {epoch:4d} loss={final_loss:.6f}") return params, final_loss @@ -582,12 +845,173 @@ def _med(d): return med_total, med_resid, r2_total, r2_resid +def _compute_cadence_decomposition(params, inputs, data, no_peers=False): + """Compute V_arb, V_noise, and predictions for cadence mode. Returns numpy arrays.""" + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + + log_v_noise = np.array(forward( + params, inputs["peer_input"], inputs["peer_mask"], + inputs["local_input"], no_peers=no_peers, + )) + y_total = np.array(inputs["y_total"]) + pool_idx = np.array(inputs["pool_idx"]) + sample_grid_days = np.array(inputs["sample_grid_days"]) + + pool_coeffs = data["pool_coeffs"] + pool_gas = data["pool_gas"] + log_cadence = np.array(params["log_cadence"]) + n_pools = data["n_pools"] + + v_arb = np.zeros(len(y_total)) + for i in range(n_pools): + mask = pool_idx == i + if not mask.any(): + continue + v_arb_all = np.array(interpolate_pool_daily( + pool_coeffs[i], jnp.float64(log_cadence[i]), pool_gas[i])) + v_arb[mask] = v_arb_all[sample_grid_days[mask]] + + v_obs = np.exp(y_total) + v_noise = np.exp(log_v_noise) + log_v_arb = np.log(np.maximum(v_arb, 1e-10)) + pred_total = np.logaddexp(log_v_arb, log_v_noise) + + return { + "v_arb": v_arb, "v_noise": v_noise, "v_obs": v_obs, + "log_v_noise": log_v_noise, "log_v_arb": log_v_arb, + "pred_total": pred_total, "y_total": y_total, + "pool_idx": pool_idx, "log_cadence": log_cadence, + } + + +def evaluate_cadence(params, inputs, data, label="", no_peers=False): + """Evaluate with learned cadence: per-pool R², decomposition diagnostics.""" + dec = _compute_cadence_decomposition(params, inputs, data, no_peers) + pool_ids = data.get("pool_ids", []) + init_cads = data["init_log_cadences"] + n_pools = data["n_pools"] + + if label: + print(f"\n {label}:") + print(f" {'Pool'[:16]:16s} {'R²':>6s} {'Cad init':>8s} {'→learn':>7s}" + f" {'Arb%':>6s} {'Noise%':>7s} {'logVn μ':>7s} {'logVn σ':>7s} {'Flag':>5s}") + print(f" {'-'*80}") + + r2_total = {} + pool_diag = [] + for i in range(n_pools): + mask = dec["pool_idx"] == i + if mask.sum() < 2: + continue + yt = dec["y_total"][mask] + pt = dec["pred_total"][mask] + ss_res = np.sum((yt - pt) ** 2) + ss_tot = np.sum((yt - yt.mean()) ** 2) + r2_total[i] = 1 - ss_res / max(ss_tot, 1e-10) + + pid = pool_ids[i] if i < len(pool_ids) else f"pool_{i}" + cad_init = np.exp(init_cads[i]) + cad_learned = np.exp(dec["log_cadence"][i]) + + va = dec["v_arb"][mask] + vo = dec["v_obs"][mask] + vn = dec["v_noise"][mask] + lvn = dec["log_v_noise"][mask] + + arb_pct = np.median(va / vo) * 100 + noise_pct = np.median(vn / vo) * 100 + lvn_mu = np.mean(lvn) + lvn_std = np.std(lvn) + + # Flags + flags = [] + if arb_pct > 150: + flags.append("A") # arb dominates + if cad_learned <= 1.01 or cad_learned >= 59.9: + flags.append("B") # cadence at bound + if r2_total[i] < 0: + flags.append("X") # negative R² + + flag_str = "".join(flags) if flags else "" + pool_diag.append({ + "idx": i, "pid": pid, "r2": r2_total[i], + "cad_init": cad_init, "cad_learned": cad_learned, + "arb_pct": arb_pct, "noise_pct": noise_pct, + "lvn_mu": lvn_mu, "lvn_std": lvn_std, "flags": flag_str, + }) + + print(f" {pid[:16]:16s} {r2_total[i]:6.3f} {cad_init:7.1f}m {cad_learned:6.1f}m" + f" {arb_pct:6.0f}% {noise_pct:6.0f}% {lvn_mu:7.1f} {lvn_std:7.2f}" + f" {flag_str:>5s}") + + # ── Summary statistics ── + vals = [x for x in r2_total.values() if np.isfinite(x)] + med_r2 = np.median(vals) if vals else float("nan") + cads = np.exp(dec["log_cadence"]) + + n_pathological = sum(1 for d in pool_diag if d["arb_pct"] > 150) + n_at_bound = sum(1 for d in pool_diag + if d["cad_learned"] <= 1.01 or d["cad_learned"] >= 59.9) + n_negative_r2 = sum(1 for d in pool_diag if d["r2"] < 0) + healthy = [d for d in pool_diag if d["arb_pct"] <= 150 and d["r2"] > 0] + med_r2_healthy = (np.median([d["r2"] for d in healthy]) + if healthy else float("nan")) + + print(f"\n ── Summary ──") + print(f" Median R² total: {med_r2:.4f} (healthy only: {med_r2_healthy:.4f})") + print(f" Cadence range: {cads.min():.1f} - {np.median(cads):.1f}" + f" - {cads.max():.1f} min") + print(f" Decomposition: {len(pool_diag) - n_pathological}/{len(pool_diag)}" + f" healthy (arb≤150%), {n_pathological} pathological") + print(f" Cadence at bounds: {n_at_bound}/{len(pool_diag)}" + f" (≤1min or ≥60min)") + print(f" Negative R²: {n_negative_r2}/{len(pool_diag)}") + print(f" Flags: A=arb>150%, B=cadence at bound, X=negative R²") + + return med_r2, r2_total, pool_diag + + +def print_cadence_comparison(train_diag, eval_diag): + """Print train vs eval diagnostic comparison.""" + train_map = {d["pid"]: d for d in train_diag} + eval_map = {d["pid"]: d for d in eval_diag} + all_pids = sorted(set(train_map) | set(eval_map)) + + print(f"\n ── Train vs Eval Gap ──") + print(f" {'Pool'[:16]:16s} {'R² trn':>7s} {'R² eval':>7s} {'Gap':>6s}" + f" {'ArbTrn%':>7s} {'ArbEval%':>8s}") + print(f" {'-'*55}") + + gaps = [] + for pid in all_pids: + td = train_map.get(pid) + ed = eval_map.get(pid) + if td is None or ed is None: + continue + gap = td["r2"] - ed["r2"] + gaps.append(gap) + flag = " ***" if abs(gap) > 0.5 else "" + print(f" {pid[:16]:16s} {td['r2']:7.3f} {ed['r2']:7.3f} {gap:+6.3f}" + f" {td['arb_pct']:6.0f}% {ed['arb_pct']:7.0f}%{flag}") + + if gaps: + print(f" Median gap: {np.median(gaps):+.3f} " + f"Mean gap: {np.mean(gaps):+.3f} " + f"Max gap: {max(gaps):+.3f}") + + # Keys indexed by sample (shape[0] == n_samples) _SAMPLE_KEYS = { "pf_vol_lag1", "pf_vol_lag2", "pf_vol_change", "pf_tvl", "pf_volatility", "peer_mask", "lf_own_vol_lag1", "lf_own_vol_lag2", "lf_own_vol_change", "lf_own_tvl", "lf_own_volatility", "lf_dow_sin", "lf_dow_cos", - "y_total", "y_residual", "v_arb", "pool_idx", "day_idx", + "lf_tvl_x_vola", "lf_tvl_x_fee", "lf_vola_x_fee", + "lf_cross_vol_tok_a", "lf_cross_vol_tok_b", "lf_cross_vol_chain", + "lf_market_vol", "lf_cross_mom_tok_a", "lf_cross_mom_tok_b", + "lf_cross_mom_chain", + "y_total", "y_residual", "v_arb", "sample_grid_days", + "x_obs_reduced", "x_obs_cross", + "pool_idx", "day_idx", } @@ -622,6 +1046,7 @@ def run_temporal(data, feat_cfg, hparams, split_frac=0.7): no_peers = hparams.get("no_peers", False) huber_delta = hparams.get("huber_delta", 1.0) target_residual = feat_cfg.get("target_residual", False) + learn_cadence = hparams.get("learn_cadence", False) n_pf = train_inputs["n_peer_feat"] n_lf = train_inputs["n_local_feat"] @@ -632,6 +1057,8 @@ def run_temporal(data, feat_cfg, hparams, split_frac=0.7): print(f" Train: {int(train_mask.sum())}, Eval: {int(eval_mask.sum())}, " f"peer_feat={n_pf}, local_feat={n_lf}, params={n_params}") + if learn_cadence: + print(f" learn_cadence=True (joint cadence+noise optimization)") if target_residual: print(f" target=residual (log_vol - log_V_arb)") if encoder_type != "mlp": @@ -645,21 +1072,57 @@ def run_temporal(data, feat_cfg, hparams, split_frac=0.7): jax.random.PRNGKey(42), n_pf, n_lf, hparams["hidden"], hparams["d_embed"], encoder_type=encoder_type, ) - params = warm_start_decoder(params, train_inputs, hparams["d_embed"]) - t0 = time.time() - params, _ = train( - params, train_inputs, hparams["n_epochs"], hparams["lr"], hparams["l2_alpha"], - huber_delta=huber_delta, no_peers=no_peers, - ) - print(f" Training: {time.time() - t0:.1f}s") - eval_kw = dict(no_peers=no_peers, target_residual=target_residual) - print("\n --- Train ---") - evaluate(params, train_inputs, data, **eval_kw) - print("\n --- Eval ---") - _, med_resid_eval, _, _ = evaluate(params, eval_inputs, data, **eval_kw) + if learn_cadence: + # Add learnable cadence, initialized from Option C + params["log_cadence"] = jnp.array(data["init_log_cadences"]) + init_cads = np.exp(data["init_log_cadences"]) + print(f" Init cadence: {init_cads.min():.1f}-{np.median(init_cads):.1f}" + f"-{init_cads.max():.1f} min") + + # Warm-start decoder to predict noise residual (log_vol - log_V_arb) + # using the Option C V_arb as the initial target + ws_inputs = dict(train_inputs) + ws_inputs["y"] = train_inputs["y_total"] - jnp.log( + jnp.maximum(train_inputs["v_arb"], 1e-6)) + params = warm_start_decoder(params, ws_inputs, hparams["d_embed"]) + + grad_fn = make_cadence_loss_fn( + data["pool_coeffs"], data["pool_gas"], + data["n_pools"], no_peers) + + print(" Compiling cadence loss (may take a moment)...") + t0 = time.time() + params, _ = train( + params, train_inputs, hparams["n_epochs"], hparams["lr"], + hparams["l2_alpha"], huber_delta=huber_delta, no_peers=no_peers, + grad_fn_override=grad_fn, + ) + print(f" Training: {time.time() - t0:.1f}s") + + print("\n --- Train ---") + _, _, train_diag = evaluate_cadence( + params, train_inputs, data, no_peers=no_peers) + print("\n --- Eval ---") + _, _, eval_diag = evaluate_cadence( + params, eval_inputs, data, no_peers=no_peers) + print_cadence_comparison(train_diag, eval_diag) + else: + params = warm_start_decoder(params, train_inputs, hparams["d_embed"]) + t0 = time.time() + params, _ = train( + params, train_inputs, hparams["n_epochs"], hparams["lr"], + hparams["l2_alpha"], huber_delta=huber_delta, no_peers=no_peers, + ) + print(f" Training: {time.time() - t0:.1f}s") + + eval_kw = dict(no_peers=no_peers, target_residual=target_residual) + print("\n --- Train ---") + evaluate(params, train_inputs, data, **eval_kw) + print("\n --- Eval ---") + _, med_resid_eval, _, _ = evaluate(params, eval_inputs, data, **eval_kw) - return med_resid_eval + return params # ---- LOO cross-validation ---- @@ -770,6 +1233,7 @@ def _med(d): "peer_vol_lag2", "peer_vol_change", "peer_tvl", "peer_volatility", "own_vol_lag2", "own_vol_change", "own_tvl", "own_volatility", "rel_same_chain", "rel_tvl_ratio", "rel_fee_ratio", "minimal_encoder", + "interactions", "cross_pool_vol", "cross_pool_momentum", ] @@ -798,11 +1262,14 @@ def objective(trial): "rel_tvl_ratio": trial.suggest_categorical("rel_tvl_ratio", [True, False]), "rel_fee_ratio": trial.suggest_categorical("rel_fee_ratio", [True, False]), "minimal_encoder": trial.suggest_categorical("minimal_encoder", [True, False]), + "interactions": trial.suggest_categorical("interactions", [True, False]), + "cross_pool_vol": trial.suggest_categorical("cross_pool_vol", [True, False]), + "cross_pool_momentum": trial.suggest_categorical("cross_pool_momentum", [True, False]), "target_residual": target_residual, } hparams = { - "hidden": trial.suggest_categorical("hidden", [8, 16, 32]), - "d_embed": trial.suggest_categorical("d_embed", [4, 8, 16]), + "hidden": trial.suggest_categorical("hidden", [16, 32, 64, 128]), + "d_embed": trial.suggest_categorical("d_embed", [4, 8, 16, 32]), "lr": trial.suggest_float("lr", 1e-4, 1e-2, log=True), "l2_alpha": trial.suggest_float("l2_alpha", 1e-5, 1e-1, log=True), "n_epochs": trial.suggest_categorical("n_epochs", [500, 1000, 2000]), @@ -875,7 +1342,7 @@ def objective(trial): f"hub={hparams['huber_delta']} " f"{'no_peers ' if hparams['no_peers'] else ''}" f"lr={hparams['lr']:.1e} a={hparams['l2_alpha']:.1e} " - f"ep={hparams['n_epochs']} feat={n_feat}/12" + f"ep={hparams['n_epochs']} feat={n_feat}/15" f"{' minimal' if feat_cfg.get('minimal_encoder') else ''}") return med_resid @@ -902,7 +1369,166 @@ def objective(trial): f"total={t.user_attrs.get('med_total_r2', '?'):.4f} " f"enc={t.params['encoder_type']} " f"h={t.params['hidden']} d={t.params['d_embed']} " - f"feat={feats}/12") + f"feat={feats}/15") + + return study + + +def run_optuna_cadence(data, n_trials): + """Optuna sweep with learnable cadence. Optimizes median eval total R².""" + import optuna + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + + day_idx = data["day_idx"] + split_day = int(day_idx.max() * 0.7) + train_mask = day_idx <= split_day + eval_mask = day_idx > split_day + + train_data = _subset(data, train_mask) + eval_data = _subset(data, eval_mask) + + pool_coeffs = data["pool_coeffs"] + pool_gas = data["pool_gas"] + n_pools = data["n_pools"] + + # Pre-build grad_fn closures for (no_peers=True, no_peers=False) + # to avoid recompiling on every trial with the same no_peers setting + _grad_fn_cache = {} + + def _get_grad_fn(no_peers): + if no_peers not in _grad_fn_cache: + _grad_fn_cache[no_peers] = make_cadence_loss_fn( + pool_coeffs, pool_gas, n_pools, no_peers) + return _grad_fn_cache[no_peers] + + def objective(trial): + feat_cfg = { + "peer_vol_lag2": trial.suggest_categorical("peer_vol_lag2", [True, False]), + "peer_vol_change": trial.suggest_categorical("peer_vol_change", [True, False]), + "peer_tvl": trial.suggest_categorical("peer_tvl", [True, False]), + "peer_volatility": trial.suggest_categorical("peer_volatility", [True, False]), + "own_vol_lag2": trial.suggest_categorical("own_vol_lag2", [True, False]), + "own_vol_change": trial.suggest_categorical("own_vol_change", [True, False]), + "own_tvl": trial.suggest_categorical("own_tvl", [True, False]), + "own_volatility": trial.suggest_categorical("own_volatility", [True, False]), + "rel_same_chain": trial.suggest_categorical("rel_same_chain", [True, False]), + "rel_tvl_ratio": trial.suggest_categorical("rel_tvl_ratio", [True, False]), + "rel_fee_ratio": trial.suggest_categorical("rel_fee_ratio", [True, False]), + "minimal_encoder": trial.suggest_categorical("minimal_encoder", [True, False]), + "interactions": trial.suggest_categorical("interactions", [True, False]), + "cross_pool_vol": trial.suggest_categorical("cross_pool_vol", [True, False]), + "cross_pool_momentum": trial.suggest_categorical("cross_pool_momentum", [True, False]), + "x_obs_mode": trial.suggest_categorical("x_obs_mode", ["none", "reduced", "cross"]), + } + hparams = { + "hidden": trial.suggest_categorical("hidden", [16, 32, 64]), + "d_embed": trial.suggest_categorical("d_embed", [4, 8, 16]), + "lr": trial.suggest_float("lr", 3e-4, 3e-3, log=True), + "l2_alpha": trial.suggest_float("l2_alpha", 1e-5, 1e-2, log=True), + "n_epochs": trial.suggest_categorical("n_epochs", [500, 1000, 2000]), + "encoder_type": trial.suggest_categorical("encoder_type", ["mlp", "linear"]), + "huber_delta": trial.suggest_categorical("huber_delta", [0.5, 1.0, 1.5]), + "no_peers": trial.suggest_categorical("no_peers", [True, False]), + } + + no_peers = hparams["no_peers"] + train_inputs = assemble_inputs(train_data, feat_cfg) + eval_inputs = assemble_inputs(eval_data, feat_cfg) + + params = init_params( + jax.random.PRNGKey(42), + train_inputs["n_peer_feat"], train_inputs["n_local_feat"], + hparams["hidden"], hparams["d_embed"], + encoder_type=hparams["encoder_type"], + ) + # Learnable cadence from Option C init + params["log_cadence"] = jnp.array(data["init_log_cadences"]) + + # Warm-start decoder on noise residual + ws_inputs = dict(train_inputs) + ws_inputs["y"] = train_inputs["y_total"] - jnp.log( + jnp.maximum(train_inputs["v_arb"], 1e-6)) + params = warm_start_decoder(params, ws_inputs, hparams["d_embed"]) + + grad_fn = _get_grad_fn(no_peers) + params, _ = train( + params, train_inputs, hparams["n_epochs"], + hparams["lr"], hparams["l2_alpha"], + huber_delta=hparams["huber_delta"], no_peers=no_peers, + verbose=False, grad_fn_override=grad_fn, + ) + + # Eval: compute V_arb at learned cadences, combine with net + log_v_noise = np.array(forward( + params, eval_inputs["peer_input"], eval_inputs["peer_mask"], + eval_inputs["local_input"], no_peers=no_peers, + )) + y_total = np.array(eval_inputs["y_total"]) + pool_idx = np.array(eval_data["pool_idx"]) + sample_grid_days = np.array(eval_inputs["sample_grid_days"]) + log_cadence = np.array(params["log_cadence"]) + + v_arb = np.zeros(len(y_total)) + for i in range(n_pools): + mask = pool_idx == i + if not mask.any(): + continue + v_arb_all = np.array(interpolate_pool_daily( + pool_coeffs[i], jnp.float64(log_cadence[i]), pool_gas[i])) + v_arb[mask] = v_arb_all[sample_grid_days[mask]] + + log_v_arb = np.log(np.maximum(v_arb, 1e-10)) + pred_total = np.logaddexp(log_v_arb, log_v_noise) + + r2_totals = [] + for i in range(n_pools): + mask = pool_idx == i + if mask.sum() < 2: + continue + yt = y_total[mask] + pt = pred_total[mask] + ss_res = np.sum((yt - pt) ** 2) + ss_tot = np.sum((yt - yt.mean()) ** 2) + r2_totals.append(1 - ss_res / max(ss_tot, 1e-10)) + + med_total = float(np.median(r2_totals)) if r2_totals else -10.0 + + cads = np.exp(log_cadence) + trial.set_user_attr("med_total_r2", med_total) + trial.set_user_attr("cad_median", float(np.median(cads))) + n_feat = sum(1 for k in _FEAT_KEYS if feat_cfg.get(k)) + print(f" Trial {trial.number}: total={med_total:.4f} " + f"enc={hparams['encoder_type']} h={hparams['hidden']} d={hparams['d_embed']} " + f"hub={hparams['huber_delta']} " + f"{'no_peers ' if no_peers else ''}" + f"lr={hparams['lr']:.1e} a={hparams['l2_alpha']:.1e} " + f"ep={hparams['n_epochs']} feat={n_feat}/15" + f" cad=[{cads.min():.0f}-{np.median(cads):.0f}-{cads.max():.0f}]") + + return med_total + + study = optuna.create_study(direction="maximize") + study.optimize(objective, n_trials=n_trials) + + print(f"\n{'='*70}") + print("Optuna Results (learn_cadence)") + print(f"{'='*70}") + print(f" Best eval total R²: {study.best_value:.4f}") + print(f" Best params:") + for k, v in sorted(study.best_params.items()): + print(f" {k}: {v}") + + print(f"\n Top 10:") + trials = sorted(study.trials, key=lambda t: t.value if t.value else -999, + reverse=True) + for t in trials[:10]: + if t.value is not None: + feats = sum(1 for k in _FEAT_KEYS if t.params.get(k)) + cad_med = t.user_attrs.get("cad_median", "?") + print(f" #{t.number}: total={t.value:.4f} " + f"enc={t.params['encoder_type']} " + f"h={t.params['hidden']} d={t.params['d_embed']} " + f"feat={feats}/15 cad_med={cad_med:.0f}") return study @@ -924,6 +1550,13 @@ def main(): parser.add_argument("--huber-delta", type=float, default=1.0) parser.add_argument("--no-peers", action="store_true", help="Decoder-only ablation (zero peer summary)") + parser.add_argument("--learn-cadence", action="store_true", + help="Jointly optimize per-pool arb cadence via PCHIP") + parser.add_argument("--x-obs", choices=["none", "reduced", "cross"], + default="none", + help="Append Option C x_obs covariates to decoder: " + "none, reduced (4: intercept,tvl,dow), " + "cross (7: +peer volumes)") parser.add_argument("--minimal-encoder", action="store_true", help="7-feature encoder (fee, tvl, overlap, same_chain) " "instead of full attributes") @@ -939,6 +1572,12 @@ def main(): parser.add_argument("--own-vol-change", action="store_true") parser.add_argument("--own-tvl", action="store_true") parser.add_argument("--own-volatility", action="store_true") + parser.add_argument("--interactions", action="store_true", + help="tvl×vola, tvl×fee, vola×fee interaction terms") + parser.add_argument("--cross-pool-vol", action="store_true", + help="Token-peer, chain-peer, market volume aggregates") + parser.add_argument("--cross-pool-momentum", action="store_true", + help="Peer volume change momentum features") parser.add_argument("--all-features", action="store_true", help="Enable all optional features") args = parser.parse_args() @@ -958,8 +1597,12 @@ def main(): "rel_same_chain": True, "rel_tvl_ratio": True, "rel_fee_ratio": True, + "interactions": args.interactions or args.all_features, + "cross_pool_vol": args.cross_pool_vol or args.all_features, + "cross_pool_momentum": args.cross_pool_momentum or args.all_features, "minimal_encoder": args.minimal_encoder, "target_residual": args.target_residual, + "x_obs_mode": args.x_obs, } hparams = { "hidden": args.hidden, @@ -970,6 +1613,7 @@ def main(): "encoder_type": args.encoder_type, "huber_delta": args.huber_delta, "no_peers": args.no_peers, + "learn_cadence": args.learn_cadence, } print("=" * 70) @@ -992,6 +1636,8 @@ def main(): print("Leave-One-Pool-Out Cross-Validation") print(f"{'='*70}") run_loo(data, feat_cfg, hparams) + elif args.tune > 0 and args.learn_cadence: + run_optuna_cadence(data, args.tune) elif args.tune > 0: run_optuna(data, args.tune, target_residual=args.target_residual) else: diff --git a/experiments/run_hybrid_noise.py b/experiments/run_hybrid_noise.py new file mode 100644 index 0000000..be26b13 --- /dev/null +++ b/experiments/run_hybrid_noise.py @@ -0,0 +1,584 @@ +"""Hybrid noise model: DeepSets peer encoder + linear noise model. + +Architecture: + peer_effect = DeepSets_encoder(peer_data, current_pool_attrs) → scalar + log(V_noise) = [x_obs, market, peer_effect, peer_effect×tvl, ...] @ coeffs + V_total = V_arb(cadence) + exp(log_v_noise) + +The encoder learns how to aggregate peer information. The linear model +learns how that aggregate (plus market/pool features) drives noise volume. +Cadence is learnable per-pool via PCHIP. + +Usage: + python experiments/run_hybrid_noise.py + python experiments/run_hybrid_noise.py --encoder-hidden 16 --epochs 2000 + python experiments/run_hybrid_noise.py --n-peer-outputs 3 # multi-dim peer effect +""" + +import argparse +import os +import pickle +import sys +import time + +import jax +import jax.numpy as jnp +import numpy as np + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) + + +def load_stage1(): + path = os.path.join(CACHE_DIR, "stage1.pkl") + if not os.path.exists(path): + print("ERROR: no stage1 cache.") + sys.exit(1) + with open(path, "rb") as f: + data = pickle.load(f) + return data["matched_clean"], data["option_c_clean"] + + +def build_data(matched_clean, option_c_clean, trend_windows=(7, 14, 30)): + """Build all features: x_obs, market, peer encoder inputs.""" + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + from quantammsim.calibration.pool_data import ( + build_x_obs, build_cross_pool_x_obs, build_pool_attributes, + _parse_tokens, _canonicalize_token, K_OBS_CROSS, + ) + from quantammsim.calibration.market_features import ( + build_pool_market_features, pool_market_features_to_matrix, + ) + + pool_ids = sorted(matched_clean.keys()) + n_pools = len(pool_ids) + + # Common date grid + all_dates = set() + for pid in pool_ids: + all_dates.update(matched_clean[pid]["panel"]["date"].values) + date_list = sorted(all_dates) + n_dates = len(date_list) + date_to_idx = {d: i for i, d in enumerate(date_list)} + + # Volume matrix and per-pool metadata + vol_matrix = np.full((n_dates, n_pools), np.nan) + pool_coeffs = [] + pool_gas = [] + init_log_cadences = np.zeros(n_pools, dtype=np.float32) + common_to_grid = np.full((n_pools, n_dates), 0, dtype=np.int32) + + for j, pid in enumerate(pool_ids): + entry = matched_clean[pid] + oc = option_c_clean[pid] + pool_coeffs.append(entry["coeffs"]) + pool_gas.append(jnp.float64(np.exp(oc["log_gas"]))) + init_log_cadences[j] = oc["log_cadence"] + dates = entry["panel"]["date"].values + log_vols = entry["panel"]["log_volume"].values.astype(float) + for k, date in enumerate(dates): + t = date_to_idx[date] + vol_matrix[t, j] = log_vols[k] + common_to_grid[j, t] = entry["day_indices"][k] + + # Pool attributes (static, normalized) + X_attr, attr_names, _ = build_pool_attributes(matched_clean) + attr_mean = np.mean(X_attr, axis=0) + attr_std = np.std(X_attr, axis=0) + attr_std[attr_std < 1e-6] = 1.0 + X_attr_norm = ((X_attr - attr_mean) / attr_std).astype(np.float32) + k_attr = X_attr_norm.shape[1] + + # Token overlap matrix + pool_tokens = {} + for i, pid in enumerate(pool_ids): + toks = _parse_tokens(matched_clean[pid]["tokens"]) + pool_tokens[i] = {_canonicalize_token(t) for t in toks[:2]} + + overlap = np.zeros((n_pools, n_pools), dtype=np.float32) + for i in range(n_pools): + for j in range(n_pools): + if i != j: + overlap[i, j] = len(pool_tokens[i] & pool_tokens[j]) + + # Peer index mapping: for pool i, peers are all j != i + n_peers = n_pools - 1 + peer_idx = np.zeros((n_pools, n_peers), dtype=np.int32) + peer_overlap = np.zeros((n_pools, n_peers), dtype=np.float32) + for i in range(n_pools): + peers = [j for j in range(n_pools) if j != i] + peer_idx[i] = peers + peer_overlap[i] = overlap[i, peers] + + # Build samples + sample_pools, sample_days = [], [] + for i in range(n_pools): + for t in range(1, n_dates): + if np.isnan(vol_matrix[t, i]) or np.isnan(vol_matrix[t - 1, i]): + continue + sample_pools.append(i) + sample_days.append(t) + sample_pools = np.array(sample_pools, dtype=np.int32) + sample_days = np.array(sample_days, dtype=np.int32) + n_samples = len(sample_pools) + + # ---- x_obs (cross-pool, 7 features) ---- + x_obs_grid = np.full((n_dates, n_pools, K_OBS_CROSS), np.nan) + for j, pid in enumerate(pool_ids): + panel = matched_clean[pid]["panel"] + xc = build_cross_pool_x_obs(panel, matched_clean, pid) + dates_j = panel["date"].values + for k, date in enumerate(dates_j[1:]): + x_obs_grid[date_to_idx[date], j] = xc[k] + + x_obs = np.zeros((n_samples, K_OBS_CROSS), dtype=np.float32) + for s in range(n_samples): + xval = x_obs_grid[sample_days[s], sample_pools[s]] + if np.all(np.isfinite(xval)): + x_obs[s] = xval + + # ---- Market features ---- + print(" Building market features...") + pool_feat = build_pool_market_features( + matched_clean, trend_windows=list(trend_windows)) + x_market, market_names = pool_market_features_to_matrix( + pool_feat, matched_clean, date_to_idx, pool_ids, + sample_pools, sample_days) + print(f" Market features: {len(market_names)} columns") + + # ---- Peer encoder inputs: (n_samples, n_peers, n_peer_feat) ---- + # Per peer: [peer_attrs, target_attrs, peer_vol_lag1, overlap] + # peer_vol_lag1 is the peer's volume at t-1 + vol_mean = float(np.nanmean(vol_matrix)) + vol_std = max(float(np.nanstd(vol_matrix)), 1e-6) + + # Static peer features (per pool) + peer_attrs = np.zeros((n_pools, n_peers, k_attr), dtype=np.float32) + for i in range(n_pools): + peer_attrs[i] = X_attr_norm[peer_idx[i]] + + # Per-sample peer features + peer_vol_lag1 = np.zeros((n_samples, n_peers), dtype=np.float32) + peer_mask = np.zeros((n_samples, n_peers), dtype=np.float32) + + for s in range(n_samples): + i = sample_pools[s] + t = sample_days[s] + cols = peer_idx[i] + pvols = vol_matrix[t - 1, cols] + valid = ~np.isnan(pvols) + peer_mask[s] = valid.astype(np.float32) + peer_vol_lag1[s] = np.where(valid, (pvols - vol_mean) / vol_std, 0.0) + + # Assemble peer encoder input: (n_samples, n_peers, n_peer_feat) + # [peer_attrs(k_attr), target_attrs(k_attr), vol_lag1(1), overlap(1)] + target_attrs_broad = np.broadcast_to( + X_attr_norm[sample_pools][:, None, :], + (n_samples, n_peers, k_attr)) + peer_input = np.concatenate([ + peer_attrs[sample_pools], # (n_samples, n_peers, k_attr) + target_attrs_broad, # (n_samples, n_peers, k_attr) + peer_vol_lag1[:, :, None], # (n_samples, n_peers, 1) + peer_overlap[sample_pools][:, :, None], # (n_samples, n_peers, 1) + ], axis=-1).astype(np.float32) + n_peer_feat = peer_input.shape[-1] + + # Combine linear features (x_obs + market) + x_linear = np.concatenate([x_obs, x_market], axis=1).astype(np.float32) + linear_names = [f"xobs_{i}" for i in range(K_OBS_CROSS)] + market_names + + # Standardize linear features (except intercept) + x_mean = np.mean(x_linear, axis=0) + x_std_arr = np.std(x_linear, axis=0) + x_std_arr[x_std_arr < 1e-6] = 1.0 + x_mean[0] = 0.0 + x_std_arr[0] = 1.0 + x_linear = ((x_linear - x_mean) / x_std_arr).astype(np.float32) + + # Targets and indices + y_total = np.array([vol_matrix[sample_days[s], sample_pools[s]] + for s in range(n_samples)], dtype=np.float32) + sample_grid_days = common_to_grid[sample_pools, sample_days] + + return { + "x_linear": x_linear, # (n_samples, n_linear_feat) + "peer_input": peer_input, # (n_samples, n_peers, n_peer_feat) + "peer_mask": peer_mask, # (n_samples, n_peers) + "y_total": y_total, + "pool_idx": sample_pools, + "day_idx": sample_days, + "sample_grid_days": sample_grid_days, + "pool_coeffs": pool_coeffs, + "pool_gas": pool_gas, + "init_log_cadences": init_log_cadences, + "n_pools": n_pools, + "n_peers": n_peers, + "n_linear_feat": x_linear.shape[1], + "n_peer_feat": n_peer_feat, + "pool_ids": pool_ids, + "linear_names": linear_names, + } + + +# ---- Model ---- + +_SAMPLE_KEYS = { + "x_linear", "peer_input", "peer_mask", "y_total", + "pool_idx", "day_idx", "sample_grid_days", +} + + +def _subset(d, mask): + out = {} + for k, v in d.items(): + if k in _SAMPLE_KEYS and isinstance(v, np.ndarray): + out[k] = v[mask] + else: + out[k] = v + return out + + +def init_params(key, n_peer_feat, n_linear_feat, encoder_hidden, + n_peer_outputs, n_pools, init_log_cadences): + """Initialize all parameters. + + Encoder: peer_input → hidden → n_peer_outputs (per peer, then mean-pooled) + Linear: [x_linear, peer_outputs, peer_outputs × x_linear[1](tvl)] @ coeffs + """ + k1, k2 = jax.random.split(key) + + # Encoder: single hidden layer → n_peer_outputs + n_total_linear = n_linear_feat + n_peer_outputs + n_peer_outputs # +interactions with tvl + + params = { + "enc_W1": jax.random.normal(k1, (n_peer_feat, encoder_hidden)) * np.sqrt(2.0 / n_peer_feat), + "enc_b1": jnp.zeros(encoder_hidden), + "enc_W2": jax.random.normal(k2, (encoder_hidden, n_peer_outputs)) * 0.01, + "enc_b2": jnp.zeros(n_peer_outputs), + "noise_coeffs": jnp.zeros(n_total_linear), + "log_cadence": jnp.array(init_log_cadences), + } + return params, n_total_linear + + +def forward_encoder(params, peer_input, peer_mask): + """DeepSets encoder: per-peer MLP → masked mean → scalar(s). + + Returns (n_samples, n_peer_outputs). + """ + batch, n_peers, _ = peer_input.shape + flat = peer_input.reshape(-1, peer_input.shape[-1]) + + h = jnp.maximum(flat @ params["enc_W1"] + params["enc_b1"], 0.0) + h = h @ params["enc_W2"] + params["enc_b2"] + h = h.reshape(batch, n_peers, -1) + + h_masked = h * peer_mask[:, :, None] + n_valid = jnp.maximum(jnp.sum(peer_mask, axis=1, keepdims=True), 1.0) + return jnp.sum(h_masked, axis=1) / n_valid # (batch, n_peer_outputs) + + +def make_loss_fn(pool_coeffs, pool_gas, n_pools): + """Loss with learnable cadence + encoder + linear noise model.""" + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + + def loss_fn(params, x_linear, peer_input, peer_mask, y_total, + sample_grid_days, pool_idx, l2_alpha, huber_delta): + + # Encoder → peer effect scalar(s) + peer_effect = forward_encoder(params, peer_input, peer_mask) + + # Build full linear input: [x_linear, peer_effect, peer_effect × tvl] + # tvl is x_linear[:, 1] (xobs_1 = log_tvl_lag1, standardized) + tvl = x_linear[:, 1:2] # keep 2D + peer_x_tvl = peer_effect * tvl # interaction + + x_full = jnp.concatenate([x_linear, peer_effect, peer_x_tvl], axis=1) + log_v_noise = x_full @ params["noise_coeffs"] + + # V_arb from PCHIP + log_cadence = params["log_cadence"] + n_samples = y_total.shape[0] + v_arb = jnp.zeros(n_samples) + for i in range(n_pools): + v_arb_all = interpolate_pool_daily( + pool_coeffs[i], log_cadence[i], pool_gas[i]) + safe_days = jnp.clip(sample_grid_days, 0, v_arb_all.shape[0] - 1) + v_arb = jnp.where(pool_idx == i, v_arb_all[safe_days], v_arb) + + log_v_arb = jnp.log(jnp.maximum(v_arb, 1e-10)) + log_v_total = jnp.logaddexp(log_v_arb, log_v_noise) + + # Huber loss with per-pool weighting + residuals = log_v_total - y_total + abs_r = jnp.abs(residuals) + huber_vals = jnp.where(abs_r <= huber_delta, 0.5 * residuals ** 2, + huber_delta * (abs_r - 0.5 * huber_delta)) + + pool_counts = jnp.zeros(n_pools).at[pool_idx].add( + jnp.ones_like(pool_idx, dtype=jnp.float32)) + active = (pool_counts > 0).astype(jnp.float32) + n_active = jnp.maximum(jnp.sum(active), 1.0) + pool_counts = jnp.maximum(pool_counts, 1.0) + pool_sums = jnp.zeros(n_pools).at[pool_idx].add(huber_vals) + data_loss = jnp.sum((pool_sums / pool_counts) * active) / n_active + + # L2 on encoder weights + noise coeffs + reg = l2_alpha * ( + jnp.sum(params["enc_W1"] ** 2) + + jnp.sum(params["enc_W2"] ** 2) + + jnp.sum(params["noise_coeffs"] ** 2) + ) + return data_loss + reg + + return jax.jit(jax.value_and_grad(loss_fn)) + + +def train(params, data, grad_fn, n_epochs, lr, l2_alpha, huber_delta, + verbose=True): + m = {k: jnp.zeros_like(v) for k, v in params.items()} + v = {k: jnp.zeros_like(v) for k, v in params.items()} + + xl = jnp.array(data["x_linear"]) + pi = jnp.array(data["peer_input"]) + pm = jnp.array(data["peer_mask"]) + yt = jnp.array(data["y_total"]) + sgd = jnp.array(data["sample_grid_days"]) + pidx = jnp.array(data["pool_idx"]) + + for epoch in range(n_epochs): + loss_val, grads = grad_fn( + params, xl, pi, pm, yt, sgd, pidx, l2_alpha, huber_delta) + loss_f = float(loss_val) + + for k in params: + m[k] = 0.9 * m[k] + 0.1 * grads[k] + v[k] = 0.999 * v[k] + 0.001 * grads[k] ** 2 + m_hat = m[k] / (1.0 - 0.9 ** (epoch + 1)) + v_hat = v[k] / (1.0 - 0.999 ** (epoch + 1)) + params[k] = params[k] - lr * m_hat / (jnp.sqrt(v_hat) + 1e-8) + + if verbose and (epoch % 200 == 0 or epoch == n_epochs - 1): + cads = np.exp(np.array(params["log_cadence"])) + pe = np.array(forward_encoder( + params, jnp.array(data["peer_input"][:100]), + jnp.array(data["peer_mask"][:100]))) + print(f" epoch {epoch:4d} loss={loss_f:.6f}" + f" cad=[{cads.min():.1f}-{np.median(cads):.1f}-{cads.max():.1f}]" + f" peer_eff=[{pe.min():.2f},{pe.mean():.2f},{pe.max():.2f}]") + + return params + + +def evaluate(params, data, label=""): + """Evaluate decomposition.""" + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + + x_linear = np.array(data["x_linear"]) + peer_input = data["peer_input"] + peer_mask = data["peer_mask"] + y_total = np.array(data["y_total"]) + pool_idx = np.array(data["pool_idx"]) + sgd = np.array(data["sample_grid_days"]) + log_cadence = np.array(params["log_cadence"]) + init_cads = data["init_log_cadences"] + pool_ids = data["pool_ids"] + n_pools = data["n_pools"] + + # Encoder + peer_effect = np.array(forward_encoder( + params, jnp.array(peer_input), jnp.array(peer_mask))) + + # Build full linear input + tvl = x_linear[:, 1:2] + peer_x_tvl = peer_effect * tvl + x_full = np.concatenate([x_linear, peer_effect, peer_x_tvl], axis=1) + + noise_coeffs = np.array(params["noise_coeffs"]) + log_v_noise = x_full @ noise_coeffs + v_noise = np.exp(log_v_noise) + + # V_arb + v_arb = np.zeros(len(y_total)) + for i in range(n_pools): + mask = pool_idx == i + if not mask.any(): + continue + v_arb_all = np.array(interpolate_pool_daily( + data["pool_coeffs"][i], jnp.float64(log_cadence[i]), + data["pool_gas"][i])) + v_arb[mask] = v_arb_all[sgd[mask]] + + v_obs = np.exp(y_total) + log_v_arb = np.log(np.maximum(v_arb, 1e-10)) + pred_total = np.logaddexp(log_v_arb, log_v_noise) + + if label: + print(f"\n {label}:") + print(f" {'Pool'[:16]:16s} {'R²':>6s} {'Cad':>5s} → {'learn':>5s}" + f" {'Arb%':>6s} {'Noise%':>7s} {'PeerEff':>8s} {'Flag':>5s}") + print(f" {'-'*65}") + + r2s = {} + pool_diag = [] + for i in range(n_pools): + mask = pool_idx == i + if mask.sum() < 2: + continue + yt = y_total[mask] + pt = pred_total[mask] + ss_res = np.sum((yt - pt) ** 2) + ss_tot = np.sum((yt - yt.mean()) ** 2) + r2s[i] = 1 - ss_res / max(ss_tot, 1e-10) + + pid = pool_ids[i] + ci = np.exp(init_cads[i]) + cl = np.exp(log_cadence[i]) + arb_pct = np.median(v_arb[mask] / v_obs[mask]) * 100 + noise_pct = np.median(v_noise[mask] / v_obs[mask]) * 100 + pe_mean = np.mean(peer_effect[mask]) + + flags = [] + if arb_pct > 150: + flags.append("A") + if cl <= 1.01 or cl >= 59.9: + flags.append("B") + if r2s[i] < 0: + flags.append("X") + flag_str = "".join(flags) + + pool_diag.append({ + "pid": pid, "r2": r2s[i], "cad_init": ci, "cad_learned": cl, + "arb_pct": arb_pct, "noise_pct": noise_pct, + "peer_effect": pe_mean, "flags": flag_str, + }) + + print(f" {pid[:16]:16s} {r2s[i]:6.3f} {ci:5.1f} → {cl:5.1f}" + f" {arb_pct:6.0f}% {noise_pct:6.0f}% {pe_mean:+8.3f} {flag_str:>5s}") + + vals = [x for x in r2s.values() if np.isfinite(x)] + med = np.median(vals) if vals else float("nan") + healthy = [d for d in pool_diag if d["arb_pct"] <= 150 and d["r2"] > 0] + med_h = np.median([d["r2"] for d in healthy]) if healthy else float("nan") + n_path = sum(1 for d in pool_diag if d["arb_pct"] > 150) + n_bound = sum(1 for d in pool_diag + if d["cad_learned"] <= 1.01 or d["cad_learned"] >= 59.9) + + # Print coefficient analysis + nc = np.array(params["noise_coeffs"]) + n_linear = data["n_linear_feat"] + n_po = len(nc) - n_linear + n_each = n_po // 2 + + print(f"\n Median R²: {med:.4f} (healthy: {med_h:.4f})") + print(f" Healthy: {len(pool_diag) - n_path}/{len(pool_diag)}," + f" at bounds: {n_bound}") + + print(f"\n Linear coefficients:") + for j, name in enumerate(data["linear_names"]): + print(f" {name:30s} {nc[j]:+8.4f}") + for j in range(n_each): + print(f" {'peer_effect_' + str(j):30s} {nc[n_linear + j]:+8.4f}") + for j in range(n_each): + print(f" {'peer_eff_' + str(j) + '×tvl':30s} {nc[n_linear + n_each + j]:+8.4f}") + + return med, r2s, pool_diag + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--epochs", type=int, default=2000) + parser.add_argument("--lr", type=float, default=1e-3) + parser.add_argument("--l2-alpha", type=float, default=1e-3) + parser.add_argument("--huber-delta", type=float, default=1.0) + parser.add_argument("--encoder-hidden", type=int, default=16) + parser.add_argument("--n-peer-outputs", type=int, default=1) + parser.add_argument("--trend-windows", type=int, nargs="+", default=[7, 14, 30]) + args = parser.parse_args() + + os.environ.setdefault("JAX_PLATFORMS", "cpu") + + print("=" * 70) + print("Hybrid: DeepSets Peer Encoder + Linear Noise Model") + print(f" encoder_hidden={args.encoder_hidden}," + f" n_peer_outputs={args.n_peer_outputs}") + print(f" epochs={args.epochs}, lr={args.lr}, l2={args.l2_alpha}") + print("=" * 70) + + matched_clean, option_c_clean = load_stage1() + + print("\nBuilding data...") + t0 = time.time() + data = build_data(matched_clean, option_c_clean, + trend_windows=tuple(args.trend_windows)) + n_pools = data["n_pools"] + print(f" {len(data['pool_idx'])} samples, {n_pools} pools") + print(f" Linear features: {data['n_linear_feat']}") + print(f" Peer encoder input: {data['n_peer_feat']} per peer," + f" {data['n_peers']} peers") + print(f" Build time: {time.time() - t0:.1f}s") + + # Temporal split + day_idx = data["day_idx"] + split_day = int(day_idx.max() * 0.7) + train_mask = day_idx <= split_day + eval_mask = day_idx > split_day + + train_data = _subset(data, train_mask) + eval_data = _subset(data, eval_mask) + + # Init + params, n_total_linear = init_params( + jax.random.PRNGKey(42), + data["n_peer_feat"], data["n_linear_feat"], + args.encoder_hidden, args.n_peer_outputs, + n_pools, data["init_log_cadences"], + ) + + # Warm-start linear coeffs via OLS (peer_effect = 0 initially) + x_trn = data["x_linear"][train_mask] + y_trn = data["y_total"][train_mask] + # Pad with zeros for peer_effect columns + x_trn_padded = np.concatenate([ + x_trn, + np.zeros((x_trn.shape[0], args.n_peer_outputs * 2), dtype=np.float32) + ], axis=1) + sol, _, _, _ = np.linalg.lstsq(x_trn_padded, y_trn, rcond=None) + params["noise_coeffs"] = jnp.array(sol.astype(np.float32)) + + n_enc_params = (args.encoder_hidden * data["n_peer_feat"] + + args.encoder_hidden + + args.encoder_hidden * args.n_peer_outputs + + args.n_peer_outputs) + print(f"\n Params: {n_total_linear} linear + {n_enc_params} encoder" + f" + {n_pools} cadences = {n_total_linear + n_enc_params + n_pools}") + print(f" Init cadence: {np.exp(data['init_log_cadences']).min():.1f}" + f"-{np.median(np.exp(data['init_log_cadences'])):.1f}" + f"-{np.exp(data['init_log_cadences']).max():.1f} min") + + # Train + grad_fn = make_loss_fn(data["pool_coeffs"], data["pool_gas"], n_pools) + + print("\n Compiling...") + t0 = time.time() + params = train(params, train_data, grad_fn, args.epochs, args.lr, + args.l2_alpha, args.huber_delta) + print(f" Training: {time.time() - t0:.1f}s") + + # Evaluate + print("\n --- Train ---") + evaluate(params, train_data) + print("\n --- Eval ---") + evaluate(params, eval_data) + + print(f"\n Baselines (eval, total volume R²):") + print(f" V_arb only: median R² = -0.33") + print(f" Linear shared: median R² = 0.39") + print(f" Linear+intercept: median R² = 0.39") + print(f" DeepSets: median R² = 0.43") + + +if __name__ == "__main__": + main() diff --git a/experiments/run_linear_market_noise.py b/experiments/run_linear_market_noise.py new file mode 100644 index 0000000..0e51128 --- /dev/null +++ b/experiments/run_linear_market_noise.py @@ -0,0 +1,463 @@ +"""Linear noise model with market features and learnable cadence. + +V_total = V_arb(cadence) + exp(x @ coeffs) + +where x includes: + - Option C x_obs (intercept, log_tvl_lag1, dow_sin, dow_cos) + - Cross-pool lagged volumes (token-A, token-B, chain peers) + - Market features (BTC price/vol/trend, token prices/vol/trend) + +Cadence is per-pool, optimized jointly with noise coefficients via Adam +through the differentiable PCHIP grid. + +Usage: + python experiments/run_linear_market_noise.py + python experiments/run_linear_market_noise.py --trend-windows 7 14 30 + python experiments/run_linear_market_noise.py --no-market # x_obs only +""" + +import argparse +import os +import pickle +import sys +import time + +import jax +import jax.numpy as jnp +import numpy as np +import pandas as pd + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) + + +def load_stage1(): + path = os.path.join(CACHE_DIR, "stage1.pkl") + if not os.path.exists(path): + print("ERROR: no stage1 cache.") + sys.exit(1) + with open(path, "rb") as f: + data = pickle.load(f) + return data["matched_clean"], data["option_c_clean"] + + +def build_data(matched_clean, option_c_clean, trend_windows=(7, 14, 30), + include_market=True, include_cross_pool=True): + """Build feature matrix and targets.""" + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + from quantammsim.calibration.pool_data import ( + build_x_obs, build_cross_pool_x_obs, K_OBS_REDUCED, K_OBS_CROSS, + ) + from quantammsim.calibration.market_features import ( + build_pool_market_features, pool_market_features_to_matrix, + ) + + pool_ids = sorted(matched_clean.keys()) + n_pools = len(pool_ids) + + # Common date grid + all_dates = set() + for pid in pool_ids: + all_dates.update(matched_clean[pid]["panel"]["date"].values) + date_list = sorted(all_dates) + n_dates = len(date_list) + date_to_idx = {d: i for i, d in enumerate(date_list)} + + # Per-pool: V_arb, volumes, coeffs, gas, grid day mapping + vol_matrix = np.full((n_dates, n_pools), np.nan) + pool_coeffs = [] + pool_gas = [] + init_log_cadences = np.zeros(n_pools, dtype=np.float32) + common_to_grid = np.full((n_pools, n_dates), 0, dtype=np.int32) + + for j, pid in enumerate(pool_ids): + entry = matched_clean[pid] + oc = option_c_clean[pid] + panel = entry["panel"] + + pool_coeffs.append(entry["coeffs"]) + pool_gas.append(jnp.float64(np.exp(oc["log_gas"]))) + init_log_cadences[j] = oc["log_cadence"] + + dates = panel["date"].values + log_vols = panel["log_volume"].values.astype(float) + for k, date in enumerate(dates): + t = date_to_idx[date] + vol_matrix[t, j] = log_vols[k] + common_to_grid[j, t] = entry["day_indices"][k] + + # Build samples: require t >= 1 (for lag) + sample_pools, sample_days = [], [] + for i in range(n_pools): + for t in range(1, n_dates): + if np.isnan(vol_matrix[t, i]) or np.isnan(vol_matrix[t - 1, i]): + continue + sample_pools.append(i) + sample_days.append(t) + sample_pools = np.array(sample_pools, dtype=np.int32) + sample_days = np.array(sample_days, dtype=np.int32) + n_samples = len(sample_pools) + + # x_obs: reduced (4) or cross-pool (7) + if include_cross_pool: + k_obs = K_OBS_CROSS + x_obs_grid = np.full((n_dates, n_pools, k_obs), np.nan) + for j, pid in enumerate(pool_ids): + panel = matched_clean[pid]["panel"] + xc = build_cross_pool_x_obs(panel, matched_clean, pid) # (n_obs-1, 7) + dates = panel["date"].values + for k, date in enumerate(dates[1:]): + x_obs_grid[date_to_idx[date], j] = xc[k] + else: + k_obs = K_OBS_REDUCED + x_obs_grid = np.full((n_dates, n_pools, k_obs), np.nan) + for j, pid in enumerate(pool_ids): + panel = matched_clean[pid]["panel"] + xr = build_x_obs(panel, reduced=True) + dates = panel["date"].values + for k, date in enumerate(dates): + x_obs_grid[date_to_idx[date], j] = xr[k] + + # Per-sample x_obs + x_obs = np.zeros((n_samples, k_obs), dtype=np.float32) + for s in range(n_samples): + xval = x_obs_grid[sample_days[s], sample_pools[s]] + if np.all(np.isfinite(xval)): + x_obs[s] = xval + + # Market features + if include_market: + print(" Building market features...") + pool_feat = build_pool_market_features( + matched_clean, trend_windows=list(trend_windows)) + x_market, market_names = pool_market_features_to_matrix( + pool_feat, matched_clean, date_to_idx, pool_ids, + sample_pools, sample_days) + print(f" Market features: {len(market_names)} columns") + else: + x_market = np.zeros((n_samples, 0), dtype=np.float32) + market_names = [] + + # Combine features + x_all = np.concatenate([x_obs, x_market], axis=1).astype(np.float32) + feat_names = [f"xobs_{i}" for i in range(k_obs)] + market_names + + # Standardize (except intercept column 0) + x_mean = np.mean(x_all, axis=0) + x_std = np.std(x_all, axis=0) + x_std[x_std < 1e-6] = 1.0 + x_mean[0] = 0.0 # don't center intercept + x_std[0] = 1.0 + x_all = ((x_all - x_mean) / x_std).astype(np.float32) + + # Targets + y_total = np.array([vol_matrix[sample_days[s], sample_pools[s]] + for s in range(n_samples)], dtype=np.float32) + sample_grid_days = common_to_grid[sample_pools, sample_days] + + return { + "x": x_all, # (n_samples, n_feat) + "y_total": y_total, # (n_samples,) + "pool_idx": sample_pools, # (n_samples,) + "day_idx": sample_days, # (n_samples,) + "sample_grid_days": sample_grid_days, # (n_samples,) + "pool_coeffs": pool_coeffs, + "pool_gas": pool_gas, + "init_log_cadences": init_log_cadences, + "n_pools": n_pools, + "n_feat": x_all.shape[1], + "pool_ids": pool_ids, + "feat_names": feat_names, + "x_mean": x_mean, + "x_std": x_std, + } + + +def make_loss_fn(pool_coeffs, pool_gas, n_pools): + """Loss function with learnable cadence + linear noise model.""" + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + + def loss_fn(params, x, y_total, sample_grid_days, pool_idx, + l2_alpha, huber_delta): + log_cadence = params["log_cadence"] + noise_coeffs = params["noise_coeffs"] + + # V_noise = exp(x @ noise_coeffs + pool_intercept) + log_v_noise = x @ noise_coeffs + if "pool_intercepts" in params: + log_v_noise = log_v_noise + params["pool_intercepts"][pool_idx] + + # V_arb from PCHIP at learned cadence + n_samples = y_total.shape[0] + v_arb = jnp.zeros(n_samples) + for i in range(n_pools): + v_arb_all = interpolate_pool_daily( + pool_coeffs[i], log_cadence[i], pool_gas[i]) + safe_days = jnp.clip(sample_grid_days, 0, v_arb_all.shape[0] - 1) + v_arb = jnp.where(pool_idx == i, v_arb_all[safe_days], v_arb) + + log_v_arb = jnp.log(jnp.maximum(v_arb, 1e-10)) + log_v_total = jnp.logaddexp(log_v_arb, log_v_noise) + + # Huber loss with per-pool weighting + residuals = log_v_total - y_total + abs_r = jnp.abs(residuals) + huber_vals = jnp.where(abs_r <= huber_delta, 0.5 * residuals ** 2, + huber_delta * (abs_r - 0.5 * huber_delta)) + + pool_counts = jnp.zeros(n_pools).at[pool_idx].add( + jnp.ones_like(pool_idx, dtype=jnp.float32)) + active = (pool_counts > 0).astype(jnp.float32) + n_active = jnp.maximum(jnp.sum(active), 1.0) + pool_counts = jnp.maximum(pool_counts, 1.0) + pool_sums = jnp.zeros(n_pools).at[pool_idx].add(huber_vals) + data_loss = jnp.sum((pool_sums / pool_counts) * active) / n_active + + reg = l2_alpha * jnp.sum(noise_coeffs ** 2) + return data_loss + reg + + return jax.jit(jax.value_and_grad(loss_fn)) + + +def train(params, data, grad_fn, n_epochs, lr, l2_alpha, huber_delta, + verbose=True): + m = {k: jnp.zeros_like(v) for k, v in params.items()} + v = {k: jnp.zeros_like(v) for k, v in params.items()} + + x = jnp.array(data["x"]) + y = jnp.array(data["y_total"]) + sgd = jnp.array(data["sample_grid_days"]) + pidx = jnp.array(data["pool_idx"]) + + for epoch in range(n_epochs): + loss_val, grads = grad_fn( + params, x, y, sgd, pidx, l2_alpha, huber_delta) + loss_f = float(loss_val) + + for k in params: + m[k] = 0.9 * m[k] + 0.1 * grads[k] + v[k] = 0.999 * v[k] + 0.001 * grads[k] ** 2 + m_hat = m[k] / (1.0 - 0.9 ** (epoch + 1)) + v_hat = v[k] / (1.0 - 0.999 ** (epoch + 1)) + params[k] = params[k] - lr * m_hat / (jnp.sqrt(v_hat) + 1e-8) + + if verbose and (epoch % 200 == 0 or epoch == n_epochs - 1): + cads = np.exp(np.array(params["log_cadence"])) + nc = np.array(params["noise_coeffs"]) + print(f" epoch {epoch:4d} loss={loss_f:.6f}" + f" cad=[{cads.min():.1f}-{np.median(cads):.1f}-{cads.max():.1f}]" + f" |coeffs|={np.mean(np.abs(nc)):.3f}") + + return params + + +def evaluate(params, data, label=""): + """Evaluate decomposition quality.""" + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + + x = np.array(data["x"]) + y_total = np.array(data["y_total"]) + pool_idx = np.array(data["pool_idx"]) + sgd = np.array(data["sample_grid_days"]) + log_cadence = np.array(params["log_cadence"]) + noise_coeffs = np.array(params["noise_coeffs"]) + init_cads = data["init_log_cadences"] + pool_ids = data["pool_ids"] + n_pools = data["n_pools"] + + log_v_noise = x @ noise_coeffs + if "pool_intercepts" in params: + pool_intercepts = np.array(params["pool_intercepts"]) + log_v_noise = log_v_noise + pool_intercepts[pool_idx] + v_noise = np.exp(log_v_noise) + + v_arb = np.zeros(len(y_total)) + for i in range(n_pools): + mask = pool_idx == i + if not mask.any(): + continue + v_arb_all = np.array(interpolate_pool_daily( + data["pool_coeffs"][i], jnp.float64(log_cadence[i]), + data["pool_gas"][i])) + v_arb[mask] = v_arb_all[sgd[mask]] + + v_obs = np.exp(y_total) + log_v_arb = np.log(np.maximum(v_arb, 1e-10)) + pred_total = np.logaddexp(log_v_arb, log_v_noise) + + if label: + print(f"\n {label}:") + print(f" {'Pool'[:16]:16s} {'R²':>6s} {'Cad':>5s} {'→':>2s} {'learn':>5s}" + f" {'Arb%':>6s} {'Noise%':>7s} {'Flag':>5s}") + print(f" {'-'*60}") + + r2s = {} + pool_diag = [] + for i in range(n_pools): + mask = pool_idx == i + if mask.sum() < 2: + continue + yt = y_total[mask] + pt = pred_total[mask] + ss_res = np.sum((yt - pt) ** 2) + ss_tot = np.sum((yt - yt.mean()) ** 2) + r2s[i] = 1 - ss_res / max(ss_tot, 1e-10) + + pid = pool_ids[i] + ci = np.exp(init_cads[i]) + cl = np.exp(log_cadence[i]) + arb_pct = np.median(v_arb[mask] / v_obs[mask]) * 100 + noise_pct = np.median(v_noise[mask] / v_obs[mask]) * 100 + + flags = [] + if arb_pct > 150: + flags.append("A") + if cl <= 1.01 or cl >= 59.9: + flags.append("B") + if r2s[i] < 0: + flags.append("X") + flag_str = "".join(flags) + + pool_diag.append({ + "pid": pid, "r2": r2s[i], "cad_init": ci, "cad_learned": cl, + "arb_pct": arb_pct, "noise_pct": noise_pct, "flags": flag_str, + }) + + print(f" {pid[:16]:16s} {r2s[i]:6.3f} {ci:5.1f} → {cl:5.1f}" + f" {arb_pct:6.0f}% {noise_pct:6.0f}% {flag_str:>5s}") + + vals = [x for x in r2s.values() if np.isfinite(x)] + med = np.median(vals) if vals else float("nan") + healthy = [d for d in pool_diag if d["arb_pct"] <= 150 and d["r2"] > 0] + med_h = np.median([d["r2"] for d in healthy]) if healthy else float("nan") + n_path = sum(1 for d in pool_diag if d["arb_pct"] > 150) + n_bound = sum(1 for d in pool_diag + if d["cad_learned"] <= 1.01 or d["cad_learned"] >= 59.9) + + print(f"\n Median R²: {med:.4f} (healthy: {med_h:.4f})") + print(f" Healthy: {len(pool_diag) - n_path}/{len(pool_diag)}," + f" at bounds: {n_bound}") + + return med, r2s, pool_diag + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--epochs", type=int, default=2000) + parser.add_argument("--lr", type=float, default=1e-3) + parser.add_argument("--l2-alpha", type=float, default=1e-3) + parser.add_argument("--huber-delta", type=float, default=1.0) + parser.add_argument("--trend-windows", type=int, nargs="+", default=[7, 14, 30]) + parser.add_argument("--no-market", action="store_true", + help="x_obs only, no market features") + parser.add_argument("--no-cross-pool", action="store_true", + help="Reduced x_obs (4) instead of cross-pool (7)") + parser.add_argument("--pool-intercepts", action="store_true", + help="Per-pool intercept (shared slopes + per-pool bias)") + args = parser.parse_args() + + os.environ.setdefault("JAX_PLATFORMS", "cpu") + + print("=" * 70) + print("Linear Noise Model + Learnable Cadence") + print(f" market={not args.no_market}, cross_pool={not args.no_cross_pool}" + f", pool_intercepts={args.pool_intercepts}") + print(f" trend_windows={args.trend_windows}") + print(f" epochs={args.epochs}, lr={args.lr}, l2={args.l2_alpha}") + print("=" * 70) + + matched_clean, option_c_clean = load_stage1() + + print("\nBuilding data...") + t0 = time.time() + data = build_data( + matched_clean, option_c_clean, + trend_windows=tuple(args.trend_windows), + include_market=not args.no_market, + include_cross_pool=not args.no_cross_pool, + ) + print(f" {len(data['pool_idx'])} samples, {data['n_pools']} pools," + f" {data['n_feat']} features, {time.time() - t0:.1f}s") + print(f" Features: {data['feat_names']}") + + # Temporal split + day_idx = data["day_idx"] + split_day = int(day_idx.max() * 0.7) + train_mask = day_idx <= split_day + eval_mask = day_idx > split_day + + train_data = {k: v[train_mask] if isinstance(v, np.ndarray) + and v.shape[0] == len(day_idx) else v + for k, v in data.items()} + eval_data = {k: v[eval_mask] if isinstance(v, np.ndarray) + and v.shape[0] == len(day_idx) else v + for k, v in data.items()} + + # Init params + n_feat = data["n_feat"] + n_pools = data["n_pools"] + params = { + "log_cadence": jnp.array(data["init_log_cadences"]), + "noise_coeffs": jnp.zeros(n_feat), + } + + # Warm-start noise_coeffs via OLS on train: y_total ≈ x @ coeffs + x_trn = data["x"][train_mask] + y_trn = data["y_total"][train_mask] + sol, _, _, _ = np.linalg.lstsq(x_trn, y_trn, rcond=None) + params["noise_coeffs"] = jnp.array(sol.astype(np.float32)) + + if args.pool_intercepts: + # Init per-pool intercepts from OLS residuals + ols_pred = x_trn @ sol + ols_resid = y_trn - ols_pred + pool_idx_trn = data["pool_idx"][train_mask] + intercepts = np.zeros(n_pools, dtype=np.float32) + for i in range(n_pools): + mask_i = pool_idx_trn == i + if mask_i.sum() > 0: + intercepts[i] = np.mean(ols_resid[mask_i]) + params["pool_intercepts"] = jnp.array(intercepts) + print(f" Per-pool intercepts: {n_pools} pools" + f" (range {intercepts.min():.2f} to {intercepts.max():.2f})") + + print(f"\n Init cadence: {np.exp(data['init_log_cadences']).min():.1f}" + f"-{np.median(np.exp(data['init_log_cadences'])):.1f}" + f"-{np.exp(data['init_log_cadences']).max():.1f} min") + print(f" OLS warm-start |coeffs|={np.mean(np.abs(sol)):.3f}") + print(f" Total params: {sum(v.size for v in params.values())}" + f" ({n_feat} coeffs + {n_pools} cadences" + f"{'+ ' + str(n_pools) + ' intercepts' if args.pool_intercepts else ''})") + + # Build loss and train + grad_fn = make_loss_fn(data["pool_coeffs"], data["pool_gas"], data["n_pools"]) + + print("\n Compiling...") + t0 = time.time() + params = train(params, train_data, grad_fn, args.epochs, args.lr, + args.l2_alpha, args.huber_delta) + print(f" Training: {time.time() - t0:.1f}s") + + # Print learned coefficients + nc = np.array(params["noise_coeffs"]) + print(f"\n Noise coefficients ({len(nc)}):") + for i, name in enumerate(data["feat_names"]): + print(f" {name:30s} {nc[i]:+8.4f}") + + # Evaluate + print("\n --- Train ---") + evaluate(params, train_data) + print("\n --- Eval ---") + evaluate(params, eval_data) + + # Baselines + print(f"\n Baselines (eval, total volume R²):") + print(f" V_arb only: median R² = -0.33") + print(f" Naive lag: median R² = 0.01") + print(f" DeepSets best: median R² = 0.43") + + +if __name__ == "__main__": + main() diff --git a/quantammsim/calibration/market_features.py b/quantammsim/calibration/market_features.py new file mode 100644 index 0000000..7a7ee59 --- /dev/null +++ b/quantammsim/calibration/market_features.py @@ -0,0 +1,297 @@ +"""Market-level and token-level features for the noise volume model. + +Derives daily features from Binance minute-level price data and pool metadata. +Features are grounded in market microstructure — what mechanistically drives +organic (non-arb) trading volume: + +Market regime: + - BTC log price, log return — crypto market regime proxy + - BTC trend (rolling mean log return) — bull/bear at various horizons + +Token-level (per pool token): + - Token log price, daily log return + - Token realized volatility — higher vol → more hedging/speculative flow + - Token Binance volume — proxy for overall token trading interest + - Token trend (rolling mean log return) + +All features are computed daily and aligned to the panel date grid. +""" + +import os +from typing import Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd + +DATA_DIR = os.path.join( + os.path.dirname(os.path.dirname(os.path.abspath(__file__))), + "data", +) + +# Map wrapped/derivative tokens to their Binance underlying +TOKEN_MAP = { + "WETH": "ETH", + "WBTC": "BTC", + "wstETH": "ETH", + "waEthLidowstETH": "ETH", + "waEthLidoWETH": "ETH", + "waGnowstETH": "ETH", + "waGnoGNO": "GNO", + "waBasUSDC": "USDC", + "waBasWETH": "ETH", + "sDAI": "DAI", + "scUSD": "USDC", + "stS": "S", + "JitoSOL": "SOL", + "USDT": "USDC", # treat as $1 stablecoin +} + + +def _load_binance_daily(symbol: str) -> pd.DataFrame: + """Load Binance minute data and resample to daily OHLCV.""" + mapped = TOKEN_MAP.get(symbol, symbol) + path = os.path.join(DATA_DIR, f"{mapped}_USD.parquet") + if not os.path.exists(path): + return None + + df = pd.read_parquet(path, columns=["date", "close", "Volume USD"]) + df["date"] = pd.to_datetime(df["date"]) + df = df.set_index("date").sort_index() + + daily = df.resample("1D").agg({ + "close": "last", + "Volume USD": "sum", + }).dropna(subset=["close"]) + + daily.columns = ["close", "volume_usd"] + return daily + + +def _compute_token_features( + daily: pd.DataFrame, + trend_windows: List[int] = (7, 14, 30), + is_market: bool = False, +) -> pd.DataFrame: + """Compute daily features from a token's OHLCV. + + For market-level tokens (BTC): includes log_price as regime proxy. + For pool tokens: only returns/vol/trends (comparable across tokens). + + Returns DataFrame indexed by date. + """ + out = pd.DataFrame(index=daily.index) + log_price = np.log(daily["close"].clip(lower=1e-10)) + out["log_return"] = log_price.diff() + + if is_market: + # BTC log_price is a market regime proxy (same for all pools) + out["log_price"] = log_price + + # Realized volatility: std of log returns over trailing 7 days + out["realized_vol_7d"] = out["log_return"].rolling(7, min_periods=3).std() + + # Trend: rolling mean log return at various horizons + for w in trend_windows: + out[f"trend_{w}d"] = out["log_return"].rolling(w, min_periods=max(w // 2, 2)).mean() + + return out + + +def build_btc_daily_features( + trend_windows: List[int] = (7, 14, 30), +) -> pd.DataFrame: + """BTC daily features as market regime proxy. + + Returns DataFrame indexed by date with columns prefixed 'btc_'. + """ + daily = _load_binance_daily("BTC") + if daily is None: + raise FileNotFoundError("BTC_USD.parquet not found") + + feat = _compute_token_features(daily, trend_windows, is_market=True) + feat.columns = [f"btc_{c}" for c in feat.columns] + return feat + + +def build_token_daily_features( + symbol: str, + trend_windows: List[int] = (7, 14, 30), +) -> Optional[pd.DataFrame]: + """Daily features for a single token. Returns None if no data.""" + daily = _load_binance_daily(symbol) + if daily is None: + return None + return _compute_token_features(daily, trend_windows) + + +def _compute_pair_volatility( + symbol_a: str, + symbol_b: str, +) -> Optional[pd.DataFrame]: + """Compute realized volatility of the A/B price ratio. + + vol(log(price_A/price_B)) = vol(log(price_A) - log(price_B)) + Symmetric: A/B and B/A give identical volatility. + + Returns DataFrame indexed by date with 'pair_realized_vol_7d'. + """ + daily_a = _load_binance_daily(symbol_a) + daily_b = _load_binance_daily(symbol_b) + if daily_a is None or daily_b is None: + return None + + # Align on common dates + log_a = np.log(daily_a["close"].clip(lower=1e-10)) + log_b = np.log(daily_b["close"].clip(lower=1e-10)) + common = log_a.index.intersection(log_b.index) + if len(common) < 10: + return None + + log_ratio = log_a.loc[common] - log_b.loc[common] + log_ratio_return = log_ratio.diff() + + out = pd.DataFrame(index=common) + out["pair_realized_vol_7d"] = log_ratio_return.rolling(7, min_periods=3).std() + return out + + +def build_pool_market_features( + matched_clean: Dict[str, dict], + trend_windows: List[int] = (7, 14, 30), +) -> Dict[str, pd.DataFrame]: + """Build per-pool market feature DataFrames. + + For each pool, produces a DataFrame aligned to the pool's panel dates with: + - BTC features (market regime) + - Token A features (vs USD) + - Token B features (vs USD) + - Pair volatility (A/B ratio) + + Returns dict: pool_id -> DataFrame with all features. + """ + from quantammsim.calibration.pool_data import _parse_tokens + + # Load BTC features once + btc_feat = build_btc_daily_features(trend_windows) + + # Cache token features and pair volatilities + token_cache = {} + pair_vol_cache = {} + + pool_features = {} + for pid, entry in matched_clean.items(): + panel = entry["panel"] + dates = pd.to_datetime(panel["date"]) + + # Parse tokens + toks = _parse_tokens(entry["tokens"]) + tok_a, tok_b = toks[0], toks[1] if len(toks) > 1 else toks[0] + + # Get token features + for tok in [tok_a, tok_b]: + mapped = TOKEN_MAP.get(tok, tok) + if mapped not in token_cache: + token_cache[mapped] = build_token_daily_features(mapped, trend_windows) + + feat_a = token_cache.get(TOKEN_MAP.get(tok_a, tok_a)) + feat_b = token_cache.get(TOKEN_MAP.get(tok_b, tok_b)) + + # Pair volatility (cache by sorted token pair to avoid duplicates) + mapped_a = TOKEN_MAP.get(tok_a, tok_a) + mapped_b = TOKEN_MAP.get(tok_b, tok_b) + pair_key = tuple(sorted([mapped_a, mapped_b])) + if pair_key not in pair_vol_cache: + pair_vol_cache[pair_key] = _compute_pair_volatility(mapped_a, mapped_b) + pair_vol = pair_vol_cache[pair_key] + + # Build per-date feature vectors + rows = [] + for date in dates: + day = pd.Timestamp(date).normalize() + row = {} + + # BTC features + if day in btc_feat.index: + for col in btc_feat.columns: + row[col] = btc_feat.loc[day, col] + + # Token A features + if feat_a is not None and day in feat_a.index: + for col in feat_a.columns: + row[f"tok_a_{col}"] = feat_a.loc[day, col] + + # Token B features + if feat_b is not None and day in feat_b.index: + for col in feat_b.columns: + row[f"tok_b_{col}"] = feat_b.loc[day, col] + + # Pair volatility + if pair_vol is not None and day in pair_vol.index: + row["pair_realized_vol_7d"] = pair_vol.loc[day, "pair_realized_vol_7d"] + + rows.append(row) + + df = pd.DataFrame(rows, index=dates) + pool_features[pid] = df + + return pool_features + + +def pool_market_features_to_matrix( + pool_features: Dict[str, pd.DataFrame], + matched_clean: Dict[str, dict], + date_to_idx: Dict, + pool_ids: List[str], + sample_pools: np.ndarray, + sample_days: np.ndarray, +) -> Tuple[np.ndarray, List[str]]: + """Convert per-pool market features to a (n_samples, n_feat) matrix. + + Aligns features to the common date grid and sample indices. + NaN-fills missing values, then imputes with column mean. + + Returns (feature_matrix, feature_names). + """ + # Get feature columns from first pool + first_pid = pool_ids[0] + feat_cols = sorted(pool_features[first_pid].columns) + n_feat = len(feat_cols) + n_pools = len(pool_ids) + + # Collect all dates + n_dates = max(date_to_idx.values()) + 1 + + # Build (n_dates, n_pools, n_feat) grid + feat_grid = np.full((n_dates, n_pools, n_feat), np.nan, dtype=np.float32) + + for j, pid in enumerate(pool_ids): + if pid not in pool_features: + continue + pf = pool_features[pid] + panel_dates = matched_clean[pid]["panel"]["date"].values + for k, date in enumerate(panel_dates): + t = date_to_idx.get(date) + if t is None: + continue + for f, col in enumerate(feat_cols): + if col in pf.columns and k < len(pf): + val = pf.iloc[k][col] if col in pf.columns else np.nan + if np.isfinite(val): + feat_grid[t, j, f] = val + + # Extract per-sample + n_samples = len(sample_pools) + X = np.zeros((n_samples, n_feat), dtype=np.float32) + for s in range(n_samples): + X[s] = feat_grid[sample_days[s], sample_pools[s]] + + # Impute NaN with column mean + for f in range(n_feat): + col = X[:, f] + mask = np.isnan(col) + if mask.all(): + col[:] = 0.0 + elif mask.any(): + col[mask] = np.nanmean(col) + + return X, feat_cols From e6610316e2fa44f6659c4d5f7da7568eef127719 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 23 Mar 2026 11:46:28 +0000 Subject: [PATCH 33/57] feat: per-pool linear noise model, market_linear simulator integration, plotting MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - run_linear_market_noise.py: add --per-pool (per-pool coefficients with Ridge warm-start), --no-split (train on all data), artifact saving (model.npz + meta.json). Add interaction terms and pair_realized_vol. - run_hybrid_noise.py: variable encoder depth (1-4), wider hidden search, Optuna sweep with depth/hidden in search space. - market_features.py: add pair_realized_vol_7d (A/B price ratio volatility), drop token log_price and log_volume_usd from non-BTC tokens. - noise_trades.py: new reclamm_market_linear_noise_volume() — takes precomputed noise_base and noise_tvl_coeff arrays, evaluates log(V_noise) = base + tvl_coeff * log(effective_TVL) per scan step. - reclamm_reserves.py: add "market_linear" noise model dispatch in scan step function, wire noise_base_array and noise_tvl_coeff_array through all 4 scan wrapper functions. - reclamm.py: refactor _prepare_noise_arrays to return dict, add market_linear array preparation from run_fingerprint, pass through to all calculate_reserves_* methods. - plot_calibrated_vs_real.py: new script plotting predicted vs real volume for calibrated pools using saved artifact, with stacked V_arb/V_noise decomposition. --- experiments/run_hybrid_noise.py | 324 +++++++++++++++--- experiments/run_linear_market_noise.py | 217 +++++++++--- quantammsim/pools/noise_trades.py | 45 +++ quantammsim/pools/reCLAMM/reclamm.py | 54 ++- quantammsim/pools/reCLAMM/reclamm_reserves.py | 35 ++ scripts/plot_calibrated_vs_real.py | 241 +++++++++++++ 6 files changed, 813 insertions(+), 103 deletions(-) create mode 100644 scripts/plot_calibrated_vs_real.py diff --git a/experiments/run_hybrid_noise.py b/experiments/run_hybrid_noise.py index be26b13..d815a5c 100644 --- a/experiments/run_hybrid_noise.py +++ b/experiments/run_hybrid_noise.py @@ -186,16 +186,49 @@ def build_data(matched_clean, option_c_clean, trend_windows=(7, 14, 30)): n_peer_feat = peer_input.shape[-1] # Combine linear features (x_obs + market) - x_linear = np.concatenate([x_obs, x_market], axis=1).astype(np.float32) - linear_names = [f"xobs_{i}" for i in range(K_OBS_CROSS)] + market_names + x_base = np.concatenate([x_obs, x_market], axis=1).astype(np.float32) + base_names = [f"xobs_{i}" for i in range(K_OBS_CROSS)] + market_names - # Standardize linear features (except intercept) - x_mean = np.mean(x_linear, axis=0) - x_std_arr = np.std(x_linear, axis=0) + # Standardize base features (except intercept) + x_mean = np.mean(x_base, axis=0) + x_std_arr = np.std(x_base, axis=0) x_std_arr[x_std_arr < 1e-6] = 1.0 x_mean[0] = 0.0 x_std_arr[0] = 1.0 - x_linear = ((x_linear - x_mean) / x_std_arr).astype(np.float32) + x_base = ((x_base - x_mean) / x_std_arr).astype(np.float32) + + # Build named column index for interaction construction + col_idx = {name: i for i, name in enumerate(base_names)} + + # Interaction terms (products of standardized features) + interactions = [] + interaction_names = [] + + def _add_interaction(name_a, name_b): + if name_a in col_idx and name_b in col_idx: + interactions.append( + x_base[:, col_idx[name_a]] * x_base[:, col_idx[name_b]]) + interaction_names.append(f"{name_a}×{name_b}") + + # TVL × volatility: deep pools respond differently to market stress + _add_interaction("xobs_1", "btc_realized_vol_7d") # tvl × btc vol + _add_interaction("xobs_1", "tok_a_realized_vol_7d") # tvl × tok_a vol + _add_interaction("xobs_1", "pair_realized_vol_7d") # tvl × pair vol + + # Cross-token volatility interaction: both tokens moving = pair activity + _add_interaction("tok_a_realized_vol_7d", "tok_b_realized_vol_7d") + + if interactions: + x_interactions = np.column_stack(interactions).astype(np.float32) + x_linear = np.concatenate([x_base, x_interactions], axis=1) + linear_names = base_names + interaction_names + else: + x_linear = x_base + linear_names = base_names + + # Track which columns are tvl and btc_vol for peer_effect interactions in loss + tvl_col = col_idx.get("xobs_1", 1) + btc_vol_col = col_idx.get("btc_realized_vol_7d") # Targets and indices y_total = np.array([vol_matrix[sample_days[s], sample_pools[s]] @@ -219,6 +252,8 @@ def build_data(matched_clean, option_c_clean, trend_windows=(7, 14, 30)): "n_peer_feat": n_peer_feat, "pool_ids": pool_ids, "linear_names": linear_names, + "tvl_col": tvl_col, + "btc_vol_col": btc_vol_col, } @@ -241,46 +276,70 @@ def _subset(d, mask): def init_params(key, n_peer_feat, n_linear_feat, encoder_hidden, - n_peer_outputs, n_pools, init_log_cadences): + n_peer_outputs, n_pools, init_log_cadences, + encoder_depth=1): """Initialize all parameters. - Encoder: peer_input → hidden → n_peer_outputs (per peer, then mean-pooled) - Linear: [x_linear, peer_outputs, peer_outputs × x_linear[1](tvl)] @ coeffs + Encoder: peer_input → hidden (× depth) → n_peer_outputs (per peer, mean-pooled) + Linear: [x_linear, peer_outputs, peer×tvl, peer×btc_vol] @ coeffs + + encoder_depth: number of hidden layers (1-4). + params["enc_depth"] stores the depth as a scalar for forward_encoder. """ - k1, k2 = jax.random.split(key) - - # Encoder: single hidden layer → n_peer_outputs - n_total_linear = n_linear_feat + n_peer_outputs + n_peer_outputs # +interactions with tvl - - params = { - "enc_W1": jax.random.normal(k1, (n_peer_feat, encoder_hidden)) * np.sqrt(2.0 / n_peer_feat), - "enc_b1": jnp.zeros(encoder_hidden), - "enc_W2": jax.random.normal(k2, (encoder_hidden, n_peer_outputs)) * 0.01, - "enc_b2": jnp.zeros(n_peer_outputs), - "noise_coeffs": jnp.zeros(n_total_linear), - "log_cadence": jnp.array(init_log_cadences), - } + keys = jax.random.split(key, encoder_depth + 2) + + n_peer_linear = n_peer_outputs * 3 + n_total_linear = n_linear_feat + n_peer_linear + + params = {} + + # First layer: input → hidden + params["enc_W1"] = jax.random.normal(keys[0], (n_peer_feat, encoder_hidden)) * np.sqrt(2.0 / n_peer_feat) + params["enc_b1"] = jnp.zeros(encoder_hidden) + + # Hidden layers 2..depth: hidden → hidden + for d in range(2, encoder_depth + 1): + params[f"enc_W{d}"] = jax.random.normal(keys[d - 1], (encoder_hidden, encoder_hidden)) * np.sqrt(2.0 / encoder_hidden) + params[f"enc_b{d}"] = jnp.zeros(encoder_hidden) + + # Output layer: hidden → n_peer_outputs + out_idx = encoder_depth + 1 + params[f"enc_W{out_idx}"] = jax.random.normal(keys[-1], (encoder_hidden, n_peer_outputs)) * 0.01 + params[f"enc_b{out_idx}"] = jnp.zeros(n_peer_outputs) + + params["noise_coeffs"] = jnp.zeros(n_total_linear) + params["log_cadence"] = jnp.array(init_log_cadences) + return params, n_total_linear def forward_encoder(params, peer_input, peer_mask): """DeepSets encoder: per-peer MLP → masked mean → scalar(s). + Depth determined by counting enc_W* keys. Returns (n_samples, n_peer_outputs). """ batch, n_peers, _ = peer_input.shape flat = peer_input.reshape(-1, peer_input.shape[-1]) - h = jnp.maximum(flat @ params["enc_W1"] + params["enc_b1"], 0.0) - h = h @ params["enc_W2"] + params["enc_b2"] - h = h.reshape(batch, n_peers, -1) + # Count layers: enc_W1, enc_W2, ..., enc_W{depth+1} + n_layers = sum(1 for k in params if k.startswith("enc_W")) + # Hidden layers with ReLU + h = flat + for i in range(1, n_layers): + h = jnp.maximum(h @ params[f"enc_W{i}"] + params[f"enc_b{i}"], 0.0) + + # Output layer (no activation) + h = h @ params[f"enc_W{n_layers}"] + params[f"enc_b{n_layers}"] + + h = h.reshape(batch, n_peers, -1) h_masked = h * peer_mask[:, :, None] n_valid = jnp.maximum(jnp.sum(peer_mask, axis=1, keepdims=True), 1.0) - return jnp.sum(h_masked, axis=1) / n_valid # (batch, n_peer_outputs) + return jnp.sum(h_masked, axis=1) / n_valid -def make_loss_fn(pool_coeffs, pool_gas, n_pools): +def make_loss_fn(pool_coeffs, pool_gas, n_pools, tvl_col, btc_vol_col): """Loss with learnable cadence + encoder + linear noise model.""" from quantammsim.calibration.grid_interpolation import interpolate_pool_daily @@ -290,12 +349,14 @@ def loss_fn(params, x_linear, peer_input, peer_mask, y_total, # Encoder → peer effect scalar(s) peer_effect = forward_encoder(params, peer_input, peer_mask) - # Build full linear input: [x_linear, peer_effect, peer_effect × tvl] - # tvl is x_linear[:, 1] (xobs_1 = log_tvl_lag1, standardized) - tvl = x_linear[:, 1:2] # keep 2D - peer_x_tvl = peer_effect * tvl # interaction + # Build full linear input: [x_linear, peer, peer×tvl, peer×btc_vol] + tvl = x_linear[:, tvl_col:tvl_col + 1] + btc_vol = x_linear[:, btc_vol_col:btc_vol_col + 1] + peer_x_tvl = peer_effect * tvl + peer_x_btcvol = peer_effect * btc_vol - x_full = jnp.concatenate([x_linear, peer_effect, peer_x_tvl], axis=1) + x_full = jnp.concatenate( + [x_linear, peer_effect, peer_x_tvl, peer_x_btcvol], axis=1) log_v_noise = x_full @ params["noise_coeffs"] # V_arb from PCHIP @@ -325,10 +386,9 @@ def loss_fn(params, x_linear, peer_input, peer_mask, y_total, pool_sums = jnp.zeros(n_pools).at[pool_idx].add(huber_vals) data_loss = jnp.sum((pool_sums / pool_counts) * active) / n_active - # L2 on encoder weights + noise coeffs + # L2 on all encoder weights + noise coeffs reg = l2_alpha * ( - jnp.sum(params["enc_W1"] ** 2) + - jnp.sum(params["enc_W2"] ** 2) + + sum(jnp.sum(v ** 2) for k, v in params.items() if k.startswith("enc_W")) + jnp.sum(params["noise_coeffs"] ** 2) ) return data_loss + reg @@ -391,10 +451,15 @@ def evaluate(params, data, label=""): peer_effect = np.array(forward_encoder( params, jnp.array(peer_input), jnp.array(peer_mask))) - # Build full linear input - tvl = x_linear[:, 1:2] + # Build full linear input (must match loss_fn construction) + tvl_col = data["tvl_col"] + btc_vol_col = data["btc_vol_col"] + tvl = x_linear[:, tvl_col:tvl_col + 1] + btc_vol = x_linear[:, btc_vol_col:btc_vol_col + 1] peer_x_tvl = peer_effect * tvl - x_full = np.concatenate([x_linear, peer_effect, peer_x_tvl], axis=1) + peer_x_btcvol = peer_effect * btc_vol + x_full = np.concatenate( + [x_linear, peer_effect, peer_x_tvl, peer_x_btcvol], axis=1) noise_coeffs = np.array(params["noise_coeffs"]) log_v_noise = x_full @ noise_coeffs @@ -469,8 +534,7 @@ def evaluate(params, data, label=""): # Print coefficient analysis nc = np.array(params["noise_coeffs"]) n_linear = data["n_linear_feat"] - n_po = len(nc) - n_linear - n_each = n_po // 2 + n_po = params["enc_W2"].shape[1] # n_peer_outputs print(f"\n Median R²: {med:.4f} (healthy: {med_h:.4f})") print(f" Healthy: {len(pool_diag) - n_path}/{len(pool_diag)}," @@ -479,23 +543,182 @@ def evaluate(params, data, label=""): print(f"\n Linear coefficients:") for j, name in enumerate(data["linear_names"]): print(f" {name:30s} {nc[j]:+8.4f}") - for j in range(n_each): + for j in range(n_po): print(f" {'peer_effect_' + str(j):30s} {nc[n_linear + j]:+8.4f}") - for j in range(n_each): - print(f" {'peer_eff_' + str(j) + '×tvl':30s} {nc[n_linear + n_each + j]:+8.4f}") + for j in range(n_po): + print(f" {'peer_eff_' + str(j) + '×tvl':30s} {nc[n_linear + n_po + j]:+8.4f}") + for j in range(n_po): + print(f" {'peer_eff_' + str(j) + '×btc_vol':30s} {nc[n_linear + 2*n_po + j]:+8.4f}") return med, r2s, pool_diag +def run_optuna(matched_clean, option_c_clean, n_trials): + """Optuna sweep over encoder + linear model hyperparameters.""" + import optuna + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + + # Build data for each trend_windows config (cache to avoid rebuilding) + data_cache = {} + + def _get_data(trend_key): + if trend_key not in data_cache: + data_cache[trend_key] = build_data( + matched_clean, option_c_clean, + trend_windows=trend_key) + return data_cache[trend_key] + + # Cache grad_fn per (n_pools, tvl_col, btc_vol_col) — these are stable + grad_fn_cache = {} + + def objective(trial): + trend_w = trial.suggest_categorical("trend_window", [7, 14, 30]) + data = _get_data((trend_w,)) + n_pools = data["n_pools"] + + encoder_hidden = trial.suggest_categorical("encoder_hidden", [16, 32, 64, 128]) + encoder_depth = trial.suggest_categorical("encoder_depth", [1, 2, 3, 4]) + n_peer_outputs = trial.suggest_categorical("n_peer_outputs", [1, 2, 4]) + lr = trial.suggest_float("lr", 3e-4, 3e-3, log=True) + l2_alpha = trial.suggest_float("l2_alpha", 1e-4, 1e-2, log=True) + huber_delta = trial.suggest_categorical("huber_delta", [0.5, 1.0, 1.5]) + n_epochs = trial.suggest_categorical("n_epochs", [1000, 2000, 3000]) + + # Temporal split + day_idx = data["day_idx"] + split_day = int(day_idx.max() * 0.7) + train_mask = day_idx <= split_day + eval_mask = day_idx > split_day + train_data = _subset(data, train_mask) + eval_data = _subset(data, eval_mask) + + # Init + params, n_total_linear = init_params( + jax.random.PRNGKey(42), + data["n_peer_feat"], data["n_linear_feat"], + encoder_hidden, n_peer_outputs, + n_pools, data["init_log_cadences"], + encoder_depth=encoder_depth, + ) + + # OLS warm-start + x_trn = data["x_linear"][train_mask] + y_trn = data["y_total"][train_mask] + n_peer_cols = n_peer_outputs * 3 + x_trn_padded = np.concatenate([ + x_trn, np.zeros((x_trn.shape[0], n_peer_cols), dtype=np.float32) + ], axis=1) + sol, _, _, _ = np.linalg.lstsq(x_trn_padded, y_trn, rcond=None) + params["noise_coeffs"] = jnp.array(sol.astype(np.float32)) + + # Build grad_fn (cache by config) + cache_key = (n_pools, data["tvl_col"], data["btc_vol_col"]) + if cache_key not in grad_fn_cache: + grad_fn_cache[cache_key] = make_loss_fn( + data["pool_coeffs"], data["pool_gas"], n_pools, + data["tvl_col"], data["btc_vol_col"]) + grad_fn = grad_fn_cache[cache_key] + + # Train + params = train(params, train_data, grad_fn, n_epochs, lr, + l2_alpha, huber_delta, verbose=False) + + # Eval: compute total R² + x_linear = np.array(eval_data["x_linear"]) + peer_input = eval_data["peer_input"] + peer_mask = eval_data["peer_mask"] + y_total = np.array(eval_data["y_total"]) + pool_idx = np.array(eval_data["pool_idx"]) + sgd = np.array(eval_data["sample_grid_days"]) + log_cadence = np.array(params["log_cadence"]) + + peer_effect = np.array(forward_encoder( + params, jnp.array(peer_input), jnp.array(peer_mask))) + + tvl_col = data["tvl_col"] + btc_vol_col = data["btc_vol_col"] + tvl = x_linear[:, tvl_col:tvl_col + 1] + btc_vol = x_linear[:, btc_vol_col:btc_vol_col + 1] + x_full = np.concatenate([ + x_linear, peer_effect, peer_effect * tvl, peer_effect * btc_vol + ], axis=1) + + noise_coeffs = np.array(params["noise_coeffs"]) + log_v_noise = x_full @ noise_coeffs + + v_arb = np.zeros(len(y_total)) + for i in range(n_pools): + mask = pool_idx == i + if not mask.any(): + continue + v_arb_all = np.array(interpolate_pool_daily( + data["pool_coeffs"][i], jnp.float64(log_cadence[i]), + data["pool_gas"][i])) + v_arb[mask] = v_arb_all[sgd[mask]] + + log_v_arb = np.log(np.maximum(v_arb, 1e-10)) + pred_total = np.logaddexp(log_v_arb, log_v_noise) + + r2s = [] + for i in range(n_pools): + mask = pool_idx == i + if mask.sum() < 2: + continue + yt = y_total[mask] + pt = pred_total[mask] + ss_res = np.sum((yt - pt) ** 2) + ss_tot = np.sum((yt - yt.mean()) ** 2) + r2s.append(1 - ss_res / max(ss_tot, 1e-10)) + + med_total = float(np.median(r2s)) if r2s else -10.0 + + cads = np.exp(log_cadence) + print(f" Trial {trial.number}: total={med_total:.4f}" + f" enc_h={encoder_hidden} d={encoder_depth} n_po={n_peer_outputs}" + f" hub={huber_delta} lr={lr:.1e} l2={l2_alpha:.1e}" + f" ep={n_epochs} tw={trend_w}" + f" cad=[{cads.min():.0f}-{np.median(cads):.0f}-{cads.max():.0f}]") + + return med_total + + study = optuna.create_study(direction="maximize") + study.optimize(objective, n_trials=n_trials) + + print(f"\n{'='*70}") + print("Optuna Results (hybrid)") + print(f"{'='*70}") + print(f" Best eval total R²: {study.best_value:.4f}") + print(f" Best params:") + for k, v in sorted(study.best_params.items()): + print(f" {k}: {v}") + + print(f"\n Top 10:") + trials = sorted(study.trials, key=lambda t: t.value if t.value else -999, + reverse=True) + for t in trials[:10]: + if t.value is not None: + print(f" #{t.number}: total={t.value:.4f}" + f" enc_h={t.params['encoder_hidden']}" + f" d={t.params['encoder_depth']}" + f" n_po={t.params['n_peer_outputs']}" + f" ep={t.params['n_epochs']}" + f" tw={t.params['trend_window']}") + + return study + + def main(): parser = argparse.ArgumentParser() + parser.add_argument("--tune", type=int, default=0, + help="Number of Optuna trials (0 = single run)") parser.add_argument("--epochs", type=int, default=2000) parser.add_argument("--lr", type=float, default=1e-3) parser.add_argument("--l2-alpha", type=float, default=1e-3) parser.add_argument("--huber-delta", type=float, default=1.0) parser.add_argument("--encoder-hidden", type=int, default=16) + parser.add_argument("--encoder-depth", type=int, default=1) parser.add_argument("--n-peer-outputs", type=int, default=1) - parser.add_argument("--trend-windows", type=int, nargs="+", default=[7, 14, 30]) + parser.add_argument("--trend-windows", type=int, nargs="+", default=[7]) args = parser.parse_args() os.environ.setdefault("JAX_PLATFORMS", "cpu") @@ -509,6 +732,10 @@ def main(): matched_clean, option_c_clean = load_stage1() + if args.tune > 0: + run_optuna(matched_clean, option_c_clean, args.tune) + return + print("\nBuilding data...") t0 = time.time() data = build_data(matched_clean, option_c_clean, @@ -535,15 +762,17 @@ def main(): data["n_peer_feat"], data["n_linear_feat"], args.encoder_hidden, args.n_peer_outputs, n_pools, data["init_log_cadences"], + encoder_depth=args.encoder_depth, ) # Warm-start linear coeffs via OLS (peer_effect = 0 initially) x_trn = data["x_linear"][train_mask] y_trn = data["y_total"][train_mask] - # Pad with zeros for peer_effect columns + # Pad with zeros for peer_effect columns (raw + ×tvl + ×btc_vol) + n_peer_cols = args.n_peer_outputs * 3 x_trn_padded = np.concatenate([ x_trn, - np.zeros((x_trn.shape[0], args.n_peer_outputs * 2), dtype=np.float32) + np.zeros((x_trn.shape[0], n_peer_cols), dtype=np.float32) ], axis=1) sol, _, _, _ = np.linalg.lstsq(x_trn_padded, y_trn, rcond=None) params["noise_coeffs"] = jnp.array(sol.astype(np.float32)) @@ -559,7 +788,8 @@ def main(): f"-{np.exp(data['init_log_cadences']).max():.1f} min") # Train - grad_fn = make_loss_fn(data["pool_coeffs"], data["pool_gas"], n_pools) + grad_fn = make_loss_fn(data["pool_coeffs"], data["pool_gas"], n_pools, + data["tvl_col"], data["btc_vol_col"]) print("\n Compiling...") t0 = time.time() diff --git a/experiments/run_linear_market_noise.py b/experiments/run_linear_market_noise.py index 0e51128..c71dc6a 100644 --- a/experiments/run_linear_market_noise.py +++ b/experiments/run_linear_market_noise.py @@ -140,17 +140,44 @@ def build_data(matched_clean, option_c_clean, trend_windows=(7, 14, 30), x_market = np.zeros((n_samples, 0), dtype=np.float32) market_names = [] - # Combine features - x_all = np.concatenate([x_obs, x_market], axis=1).astype(np.float32) - feat_names = [f"xobs_{i}" for i in range(k_obs)] + market_names + # Combine base features + x_base = np.concatenate([x_obs, x_market], axis=1).astype(np.float32) + base_names = [f"xobs_{i}" for i in range(k_obs)] + market_names # Standardize (except intercept column 0) - x_mean = np.mean(x_all, axis=0) - x_std = np.std(x_all, axis=0) + x_mean = np.mean(x_base, axis=0) + x_std = np.std(x_base, axis=0) x_std[x_std < 1e-6] = 1.0 x_mean[0] = 0.0 # don't center intercept x_std[0] = 1.0 - x_all = ((x_all - x_mean) / x_std).astype(np.float32) + x_base = ((x_base - x_mean) / x_std).astype(np.float32) + + # Interaction terms (products of standardized features) + col_idx = {name: i for i, name in enumerate(base_names)} + interactions = [] + interaction_names = [] + + def _add_interaction(name_a, name_b): + if name_a in col_idx and name_b in col_idx: + interactions.append( + x_base[:, col_idx[name_a]] * x_base[:, col_idx[name_b]]) + interaction_names.append(f"{name_a}×{name_b}") + + _add_interaction("xobs_1", "btc_realized_vol_7d") # tvl × btc vol + _add_interaction("xobs_1", "tok_a_realized_vol_7d") # tvl × tok_a vol + _add_interaction("xobs_1", "pair_realized_vol_7d") # tvl × pair vol + _add_interaction("tok_a_realized_vol_7d", "tok_b_realized_vol_7d") # cross-token vol + + if interactions: + x_interactions = np.column_stack(interactions).astype(np.float32) + x_all = np.concatenate([x_base, x_interactions], axis=1) + feat_names = base_names + interaction_names + # Extend x_mean/x_std for interaction columns (already standardized → 0/1) + x_mean = np.concatenate([x_mean, np.zeros(len(interactions))]) + x_std = np.concatenate([x_std, np.ones(len(interactions))]) + else: + x_all = x_base + feat_names = base_names # Targets y_total = np.array([vol_matrix[sample_days[s], sample_pools[s]] @@ -176,7 +203,12 @@ def build_data(matched_clean, option_c_clean, trend_windows=(7, 14, 30), def make_loss_fn(pool_coeffs, pool_gas, n_pools): - """Loss function with learnable cadence + linear noise model.""" + """Loss function with learnable cadence + linear noise model. + + Supports both shared coefficients (noise_coeffs shape: (n_feat,)) and + per-pool coefficients (noise_coeffs shape: (n_pools, n_feat)). + Detected at trace time from the array shape. + """ from quantammsim.calibration.grid_interpolation import interpolate_pool_daily def loss_fn(params, x, y_total, sample_grid_days, pool_idx, @@ -184,8 +216,15 @@ def loss_fn(params, x, y_total, sample_grid_days, pool_idx, log_cadence = params["log_cadence"] noise_coeffs = params["noise_coeffs"] - # V_noise = exp(x @ noise_coeffs + pool_intercept) - log_v_noise = x @ noise_coeffs + # Per-pool or shared coefficients + if noise_coeffs.ndim == 2: + # Per-pool: (n_pools, n_feat) — gather each sample's pool coeffs + per_sample_coeffs = noise_coeffs[pool_idx] # (n_samples, n_feat) + log_v_noise = jnp.sum(x * per_sample_coeffs, axis=1) + else: + # Shared: (n_feat,) + log_v_noise = x @ noise_coeffs + if "pool_intercepts" in params: log_v_noise = log_v_noise + params["pool_intercepts"][pool_idx] @@ -267,7 +306,12 @@ def evaluate(params, data, label=""): pool_ids = data["pool_ids"] n_pools = data["n_pools"] - log_v_noise = x @ noise_coeffs + if noise_coeffs.ndim == 2: + # Per-pool: (n_pools, n_feat) + per_sample_coeffs = noise_coeffs[pool_idx] + log_v_noise = np.sum(x * per_sample_coeffs, axis=1) + else: + log_v_noise = x @ noise_coeffs if "pool_intercepts" in params: pool_intercepts = np.array(params["pool_intercepts"]) log_v_noise = log_v_noise + pool_intercepts[pool_idx] @@ -349,21 +393,27 @@ def main(): parser.add_argument("--lr", type=float, default=1e-3) parser.add_argument("--l2-alpha", type=float, default=1e-3) parser.add_argument("--huber-delta", type=float, default=1.0) - parser.add_argument("--trend-windows", type=int, nargs="+", default=[7, 14, 30]) + parser.add_argument("--trend-windows", type=int, nargs="+", default=[7]) parser.add_argument("--no-market", action="store_true", help="x_obs only, no market features") parser.add_argument("--no-cross-pool", action="store_true", help="Reduced x_obs (4) instead of cross-pool (7)") parser.add_argument("--pool-intercepts", action="store_true", help="Per-pool intercept (shared slopes + per-pool bias)") + parser.add_argument("--per-pool", action="store_true", + help="Per-pool noise coefficients (Option A)") + parser.add_argument("--no-split", action="store_true", + help="Train on all data (no temporal holdout)") args = parser.parse_args() os.environ.setdefault("JAX_PLATFORMS", "cpu") print("=" * 70) print("Linear Noise Model + Learnable Cadence") - print(f" market={not args.no_market}, cross_pool={not args.no_cross_pool}" - f", pool_intercepts={args.pool_intercepts}") + mode = "per-pool" if args.per_pool else ( + "shared+intercepts" if args.pool_intercepts else "shared") + print(f" mode={mode}, market={not args.no_market}," + f" cross_pool={not args.no_cross_pool}") print(f" trend_windows={args.trend_windows}") print(f" epochs={args.epochs}, lr={args.lr}, l2={args.l2_alpha}") print("=" * 70) @@ -382,34 +432,69 @@ def main(): f" {data['n_feat']} features, {time.time() - t0:.1f}s") print(f" Features: {data['feat_names']}") - # Temporal split + # Split day_idx = data["day_idx"] - split_day = int(day_idx.max() * 0.7) - train_mask = day_idx <= split_day - eval_mask = day_idx > split_day + n_samples = len(day_idx) + if args.no_split: + train_mask = np.ones(n_samples, dtype=bool) + eval_mask = None + else: + split_day = int(day_idx.max() * 0.7) + train_mask = day_idx <= split_day + eval_mask = day_idx > split_day train_data = {k: v[train_mask] if isinstance(v, np.ndarray) - and v.shape[0] == len(day_idx) else v + and v.shape[0] == n_samples else v for k, v in data.items()} - eval_data = {k: v[eval_mask] if isinstance(v, np.ndarray) - and v.shape[0] == len(day_idx) else v - for k, v in data.items()} + if eval_mask is not None: + eval_data = {k: v[eval_mask] if isinstance(v, np.ndarray) + and v.shape[0] == n_samples else v + for k, v in data.items()} + else: + eval_data = None # Init params n_feat = data["n_feat"] n_pools = data["n_pools"] - params = { - "log_cadence": jnp.array(data["init_log_cadences"]), - "noise_coeffs": jnp.zeros(n_feat), - } - - # Warm-start noise_coeffs via OLS on train: y_total ≈ x @ coeffs x_trn = data["x"][train_mask] y_trn = data["y_total"][train_mask] - sol, _, _, _ = np.linalg.lstsq(x_trn, y_trn, rcond=None) - params["noise_coeffs"] = jnp.array(sol.astype(np.float32)) - - if args.pool_intercepts: + pool_idx_trn = data["pool_idx"][train_mask] + + if args.per_pool: + # Per-pool coefficients: (n_pools, n_feat) + # Warm-start each pool via per-pool Ridge (not OLS — avoids blowup + # on pools with few samples or near-singular features) + from sklearn.linear_model import RidgeCV + coeffs_init = np.zeros((n_pools, n_feat), dtype=np.float32) + # Shared Ridge as fallback + ridge_shared = RidgeCV(alphas=np.logspace(-2, 4, 50)) + ridge_shared.fit(x_trn, y_trn) + for i in range(n_pools): + mask_i = pool_idx_trn == i + if mask_i.sum() >= 20: + ridge_i = RidgeCV(alphas=np.logspace(-2, 4, 50)) + ridge_i.fit(x_trn[mask_i], y_trn[mask_i]) + coeffs_init[i] = ridge_i.coef_ + coeffs_init[i, 0] += ridge_i.intercept_ # fold intercept into xobs_0 + else: + coeffs_init[i] = ridge_shared.coef_ + coeffs_init[i, 0] += ridge_shared.intercept_ + params = { + "log_cadence": jnp.array(data["init_log_cadences"]), + "noise_coeffs": jnp.array(coeffs_init), + } + print(f"\n Per-pool coefficients: {n_pools} × {n_feat} = {n_pools * n_feat} params") + print(f" Ridge warm-start |coeffs|={np.mean(np.abs(coeffs_init)):.3f}") + else: + params = { + "log_cadence": jnp.array(data["init_log_cadences"]), + "noise_coeffs": jnp.zeros(n_feat), + } + # Warm-start noise_coeffs via OLS on train + sol, _, _, _ = np.linalg.lstsq(x_trn, y_trn, rcond=None) + params["noise_coeffs"] = jnp.array(sol.astype(np.float32)) + + if args.pool_intercepts and not args.per_pool: # Init per-pool intercepts from OLS residuals ols_pred = x_trn @ sol ols_resid = y_trn - ols_pred @@ -426,10 +511,8 @@ def main(): print(f"\n Init cadence: {np.exp(data['init_log_cadences']).min():.1f}" f"-{np.median(np.exp(data['init_log_cadences'])):.1f}" f"-{np.exp(data['init_log_cadences']).max():.1f} min") - print(f" OLS warm-start |coeffs|={np.mean(np.abs(sol)):.3f}") - print(f" Total params: {sum(v.size for v in params.values())}" - f" ({n_feat} coeffs + {n_pools} cadences" - f"{'+ ' + str(n_pools) + ' intercepts' if args.pool_intercepts else ''})") + total_params = sum(v.size for v in params.values()) + print(f" Total params: {total_params}") # Build loss and train grad_fn = make_loss_fn(data["pool_coeffs"], data["pool_gas"], data["n_pools"]) @@ -442,15 +525,65 @@ def main(): # Print learned coefficients nc = np.array(params["noise_coeffs"]) - print(f"\n Noise coefficients ({len(nc)}):") - for i, name in enumerate(data["feat_names"]): - print(f" {name:30s} {nc[i]:+8.4f}") + if nc.ndim == 2: + # Per-pool: print median coefficient across pools + print(f"\n Per-pool noise coefficients — median across {n_pools} pools:") + for i, name in enumerate(data["feat_names"]): + vals = nc[:, i] + print(f" {name:30s} med={np.median(vals):+7.3f}" + f" [{vals.min():+7.3f}, {vals.max():+7.3f}]") + else: + print(f"\n Noise coefficients ({len(nc)}):") + for i, name in enumerate(data["feat_names"]): + print(f" {name:30s} {nc[i]:+8.4f}") # Evaluate - print("\n --- Train ---") - evaluate(params, train_data) - print("\n --- Eval ---") - evaluate(params, eval_data) + if eval_data is not None: + print("\n --- Train ---") + evaluate(params, train_data) + print("\n --- Eval ---") + evaluate(params, eval_data) + else: + print("\n --- All data ---") + evaluate(params, train_data) + + # Save artifact + artifact_dir = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "linear_market_noise", + ) + os.makedirs(artifact_dir, exist_ok=True) + artifact = { + "noise_coeffs": np.array(params["noise_coeffs"]), + "log_cadence": np.array(params["log_cadence"]), + "init_log_cadences": data["init_log_cadences"], + "feat_names": data["feat_names"], + "pool_ids": data["pool_ids"], + "n_pools": data["n_pools"], + "n_feat": data["n_feat"], + "x_mean": data["x_mean"], + "x_std": data["x_std"], + "hparams": { + "epochs": args.epochs, "lr": args.lr, + "l2_alpha": args.l2_alpha, "huber_delta": args.huber_delta, + "trend_windows": args.trend_windows, + "per_pool": args.per_pool, + "pool_intercepts": args.pool_intercepts, + }, + } + if "pool_intercepts" in params: + artifact["pool_intercepts"] = np.array(params["pool_intercepts"]) + artifact_path = os.path.join(artifact_dir, "model.npz") + np.savez(artifact_path, **{k: v for k, v in artifact.items() + if isinstance(v, np.ndarray)}) + # Save non-array metadata separately + import json + meta_path = os.path.join(artifact_dir, "meta.json") + meta = {k: v for k, v in artifact.items() if not isinstance(v, np.ndarray)} + with open(meta_path, "w") as f: + json.dump(meta, f, indent=2, default=str) + print(f"\n Saved artifact: {artifact_path}") + print(f" Saved metadata: {meta_path}") # Baselines print(f"\n Baselines (eval, total volume R²):") diff --git a/quantammsim/pools/noise_trades.py b/quantammsim/pools/noise_trades.py index b40154a..08ac959 100644 --- a/quantammsim/pools/noise_trades.py +++ b/quantammsim/pools/noise_trades.py @@ -347,3 +347,48 @@ def reclamm_calibrated_noise_volume( return jnp.maximum(0.0, daily_vol / 1440.0) +@jit +def reclamm_market_linear_noise_volume( + effective_value_usd, + noise_base, + noise_tvl_coeff, +): + """Market-feature linear noise model with precomputed daily coefficients. + + The full model is:: + + log(V_daily_noise) = base_t + tvl_coeff_t * log(effective_TVL) + + where ``base_t`` absorbs all non-TVL terms (intercept, market regime, + token volatility, pair volatility, day-of-week, cross-pool volumes) + and ``tvl_coeff_t`` is the effective TVL coefficient including + interaction terms (tvl×btc_vol, tvl×tok_a_vol, tvl×pair_vol). + + Both are precomputed daily from the per-pool calibrated noise model + and passed in as dynamic input arrays decimated to the arb frequency. + + Under counterfactual (varying reClAMM concentration), only + ``effective_value_usd`` changes — all market/peer features are held + at observed values via the precomputed arrays. + + Parameters + ---------- + effective_value_usd : float + Effective TVL in USD: (Ra+Va)*pA + (Rb+Vb)*pB. + noise_base : float + Precomputed non-TVL component of log(V_daily_noise) for this step. + noise_tvl_coeff : float + Precomputed effective coefficient on log(TVL) for this step, + including base b_tvl and interaction terms. + + Returns + ------- + float + Per-minute noise volume (USD), floored at zero. + """ + log_tvl = jnp.log(jnp.maximum(effective_value_usd, 1.0)) + log_daily_noise = noise_base + noise_tvl_coeff * log_tvl + daily_noise = jnp.exp(log_daily_noise) + return jnp.maximum(0.0, daily_noise / 1440.0) + + diff --git a/quantammsim/pools/reCLAMM/reclamm.py b/quantammsim/pools/reCLAMM/reclamm.py index f7b6d19..58222ad 100644 --- a/quantammsim/pools/reCLAMM/reclamm.py +++ b/quantammsim/pools/reCLAMM/reclamm.py @@ -216,46 +216,62 @@ def _resolve_fees(params, run_fingerprint): def _prepare_noise_arrays(self, prices, run_fingerprint, start_index, bout_length, arb_freq, max_len): - """Prepare volatility and dow arrays for noise models that need them. + """Prepare dynamic input arrays for noise models. - Returns (volatility_array, dow_sin_array, dow_cos_array) — all sliced - and decimated to match arb_prices shape. Arrays are None when the - noise model does not require them. + Returns dict with keys depending on noise_model: + - "ratio": {} + - "tsoukalas_*"/"loglinear": {"volatility": array} + - "calibrated": {"volatility": array, "dow_sin": array, "dow_cos": array} + - "market_linear": {"noise_base": array, "noise_tvl_coeff": array} """ noise_model = run_fingerprint.get("noise_model", "ratio") + result = {"volatility": None, "dow_sin": None, "dow_cos": None, + "noise_base": None, "noise_tvl_coeff": None} + + if noise_model == "market_linear": + # Precomputed arrays passed via run_fingerprint + nb = run_fingerprint.get("noise_base_array") + ntc = run_fingerprint.get("noise_tvl_coeff_array") + if nb is not None: + result["noise_base"] = _prepare_dynamic_array( + jnp.array(nb), start_index, bout_length, arb_freq, max_len) + if ntc is not None: + result["noise_tvl_coeff"] = _prepare_dynamic_array( + jnp.array(ntc), start_index, bout_length, arb_freq, max_len) + return result + needs_vol = noise_model in ( "tsoukalas_sqrt", "tsoukalas_log", "loglinear", "calibrated", ) if not needs_vol: - return None, None, None + return result volatility_array = self.calculate_volatility_array( prices, run_fingerprint, ) - vol_prepared = _prepare_dynamic_array( + result["volatility"] = _prepare_dynamic_array( volatility_array, start_index, bout_length, arb_freq, max_len, ) if noise_model != "calibrated": - return vol_prepared, None, None + return result # Day-of-week sin/cos arrays for the calibrated noise model. - # Compute from startDateString + minute offsets (vectorized). import pandas as pd start_dt = pd.Timestamp(run_fingerprint["startDateString"]) n_minutes = prices.shape[0] day_indices = np.arange(n_minutes) // 1440 - start_weekday = start_dt.weekday() # Monday=0 .. Sunday=6 + start_weekday = start_dt.weekday() weekdays = ((start_weekday + day_indices) % 7).astype(np.float64) dow_sin_full = jnp.array(np.sin(2.0 * np.pi * weekdays / 7.0)) dow_cos_full = jnp.array(np.cos(2.0 * np.pi * weekdays / 7.0)) - dow_sin_prepared = _prepare_dynamic_array( + result["dow_sin"] = _prepare_dynamic_array( dow_sin_full, start_index, bout_length, arb_freq, max_len, ) - dow_cos_prepared = _prepare_dynamic_array( + result["dow_cos"] = _prepare_dynamic_array( dow_cos_full, start_index, bout_length, arb_freq, max_len, ) - return vol_prepared, dow_sin_prepared, dow_cos_prepared + return result @partial(jit, static_argnums=(2,)) def calculate_reserves_with_fees( @@ -284,10 +300,13 @@ def calculate_reserves_with_fees( if noise_params is not None and type(noise_params) is not dict: noise_params = dict(noise_params) - arb_vol, dow_sin, dow_cos = self._prepare_noise_arrays( + _na = self._prepare_noise_arrays( prices, run_fingerprint, start_index, bout_length, arb_freq, s.arb_prices.shape[0], ) + arb_vol = _na["volatility"] + dow_sin = _na["dow_sin"] + dow_cos = _na["dow_cos"] if run_fingerprint["do_arb"]: return _jax_calc_reclamm_reserves_with_fees( @@ -312,6 +331,8 @@ def calculate_reserves_with_fees( volatility_array=arb_vol, dow_sin_array=dow_sin, dow_cos_array=dow_cos, + noise_base_array=_na["noise_base"], + noise_tvl_coeff_array=_na["noise_tvl_coeff"], ) return jnp.broadcast_to(s.initial_reserves, s.arb_prices.shape) @@ -350,10 +371,13 @@ def calculate_reserves_and_fee_revenue_with_fees( if noise_params is not None and type(noise_params) is not dict: noise_params = dict(noise_params) - arb_vol, dow_sin, dow_cos = self._prepare_noise_arrays( + _na = self._prepare_noise_arrays( prices, run_fingerprint, start_index, bout_length, arb_freq, s.arb_prices.shape[0], ) + arb_vol = _na["volatility"] + dow_sin = _na["dow_sin"] + dow_cos = _na["dow_cos"] if run_fingerprint["do_arb"]: return _jax_calc_reclamm_reserves_and_fee_revenue_with_fees( @@ -378,6 +402,8 @@ def calculate_reserves_and_fee_revenue_with_fees( volatility_array=arb_vol, dow_sin_array=dow_sin, dow_cos_array=dow_cos, + noise_base_array=_na["noise_base"], + noise_tvl_coeff_array=_na["noise_tvl_coeff"], ) return ( jnp.broadcast_to(s.initial_reserves, s.arb_prices.shape), diff --git a/quantammsim/pools/reCLAMM/reclamm_reserves.py b/quantammsim/pools/reCLAMM/reclamm_reserves.py index 713923c..0953d33 100644 --- a/quantammsim/pools/reCLAMM/reclamm_reserves.py +++ b/quantammsim/pools/reCLAMM/reclamm_reserves.py @@ -36,6 +36,7 @@ reclamm_tsoukalas_log_noise_volume, reclamm_loglinear_noise_volume, reclamm_calibrated_noise_volume, + reclamm_market_linear_noise_volume, ) # Reference balance for initialisation (matches Solidity _INITIALIZATION_MAX_BALANCE_A) @@ -1022,6 +1023,20 @@ def _skip_schedule_state(_): arb_volume, dow_sin, dow_cos, _np, ) + noise_fee_income = (1.0 - gamma) * noise_vol + scale = 1.0 + noise_fee_income / jnp.maximum(real_value, 1e-8) + Ra_new = Ra_new * scale + Rb_new = Rb_new * scale + elif noise_model == "market_linear": + noise_base = input_list[9] + noise_tvl_coeff = input_list[10] + real_value = jnp.sum(jnp.array([Ra_new, Rb_new]) * prices) + effective_value = (Ra_new + Va) * prices[0] + (Rb_new + Vb) * prices[1] + + noise_vol = reclamm_market_linear_noise_volume( + effective_value, noise_base, noise_tvl_coeff, + ) + noise_fee_income = (1.0 - gamma) * noise_vol scale = 1.0 + noise_fee_income / jnp.maximum(real_value, 1e-8) Ra_new = Ra_new * scale @@ -1281,6 +1296,8 @@ def _jax_calc_reclamm_reserves_with_fees( volatility_array=None, dow_sin_array=None, dow_cos_array=None, + noise_base_array=None, + noise_tvl_coeff_array=None, ): """Calculate reClAMM reserves over time with fees. @@ -1343,6 +1360,9 @@ def _jax_calc_reclamm_reserves_with_fees( scan_inputs.append(volatility_array) scan_inputs.append(dow_sin_array) scan_inputs.append(dow_cos_array) + elif noise_model == "market_linear": + scan_inputs.append(noise_base_array) + scan_inputs.append(noise_tvl_coeff_array) carry_init = [ initial_reserves, @@ -1386,6 +1406,8 @@ def _jax_calc_reclamm_reserves_with_dynamic_inputs( volatility_array=None, dow_sin_array=None, dow_cos_array=None, + noise_base_array=None, + noise_tvl_coeff_array=None, ): """Calculate reClAMM reserves with time-varying fees/arb arrays.""" if lp_supply_array is None: @@ -1457,6 +1479,9 @@ def _jax_calc_reclamm_reserves_with_dynamic_inputs( scan_inputs.append(volatility_array) scan_inputs.append(dow_sin_array) scan_inputs.append(dow_cos_array) + elif noise_model == "market_linear": + scan_inputs.append(noise_base_array) + scan_inputs.append(noise_tvl_coeff_array) carry_init = [ initial_reserves, @@ -1590,6 +1615,8 @@ def _jax_calc_reclamm_reserves_and_fee_revenue_with_fees( volatility_array=None, dow_sin_array=None, dow_cos_array=None, + noise_base_array=None, + noise_tvl_coeff_array=None, ): """Calculate reClAMM reserves and LP fee revenue over time with fees. @@ -1654,6 +1681,9 @@ def _jax_calc_reclamm_reserves_and_fee_revenue_with_fees( scan_inputs.append(volatility_array) scan_inputs.append(dow_sin_array) scan_inputs.append(dow_cos_array) + elif noise_model == "market_linear": + scan_inputs.append(noise_base_array) + scan_inputs.append(noise_tvl_coeff_array) carry_init = [ initial_reserves, @@ -1697,6 +1727,8 @@ def _jax_calc_reclamm_reserves_and_fee_revenue_with_dynamic_inputs( volatility_array=None, dow_sin_array=None, dow_cos_array=None, + noise_base_array=None, + noise_tvl_coeff_array=None, ): """Calculate reClAMM reserves and LP fee revenue with time-varying fees/arb arrays. @@ -1774,6 +1806,9 @@ def _jax_calc_reclamm_reserves_and_fee_revenue_with_dynamic_inputs( scan_inputs.append(volatility_array) scan_inputs.append(dow_sin_array) scan_inputs.append(dow_cos_array) + elif noise_model == "market_linear": + scan_inputs.append(noise_base_array) + scan_inputs.append(noise_tvl_coeff_array) carry_init = [ initial_reserves, diff --git a/scripts/plot_calibrated_vs_real.py b/scripts/plot_calibrated_vs_real.py new file mode 100644 index 0000000..475422b --- /dev/null +++ b/scripts/plot_calibrated_vs_real.py @@ -0,0 +1,241 @@ +"""Plot predicted vs real volume using saved per-pool calibrated noise model. + +Loads the artifact from experiments/run_linear_market_noise.py (--per-pool), +rebuilds the features, evaluates V_arb(learned cadence) + V_noise(x @ coeffs_i), +and generates stacked area plots showing the arb/noise decomposition per pool. + +Usage: + # First train and save: + python experiments/run_linear_market_noise.py --per-pool --no-split --epochs 2000 + + # Then plot: + python scripts/plot_calibrated_vs_real.py + python scripts/plot_calibrated_vs_real.py --artifact results/linear_market_noise/model.npz +""" + +import argparse +import json +import os +import sys +import time + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +sys.path.insert(0, os.path.dirname(os.path.dirname(__file__))) + +ARTIFACT_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "linear_market_noise", +) +OUTPUT_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "calibrated_vs_real", +) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--artifact", default=os.path.join(ARTIFACT_DIR, "model.npz")) + parser.add_argument("--meta", default=os.path.join(ARTIFACT_DIR, "meta.json")) + parser.add_argument("--output-dir", default=OUTPUT_DIR) + args = parser.parse_args() + + os.environ.setdefault("JAX_PLATFORMS", "cpu") + os.makedirs(args.output_dir, exist_ok=True) + + # ---- Load artifact ---- + print(f"Loading artifact: {args.artifact}") + art = np.load(args.artifact, allow_pickle=True) + noise_coeffs = art["noise_coeffs"] + log_cadence = art["log_cadence"] + init_log_cadences = art["init_log_cadences"] + + with open(args.meta) as f: + meta = json.load(f) + feat_names = meta["feat_names"] + pool_ids = meta["pool_ids"] + n_pools = meta["n_pools"] + hparams = meta["hparams"] + per_pool = noise_coeffs.ndim == 2 + + print(f" {n_pools} pools, {len(feat_names)} features, per_pool={per_pool}") + print(f" hparams: {hparams}") + + # ---- Rebuild data (features only, no training) ---- + import jax.numpy as jnp + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + from experiments.run_linear_market_noise import load_stage1, build_data + + matched_clean, option_c_clean = load_stage1() + + print("\nRebuilding features...") + t0 = time.time() + data = build_data( + matched_clean, option_c_clean, + trend_windows=tuple(hparams["trend_windows"]), + include_market=True, include_cross_pool=True, + ) + print(f" {len(data['pool_idx'])} samples, {time.time() - t0:.1f}s") + + x = data["x"] + y_total = data["y_total"] + pool_idx = data["pool_idx"] + day_idx = data["day_idx"] + sgd = data["sample_grid_days"] + + # ---- Compute predictions ---- + if per_pool: + per_sample_coeffs = noise_coeffs[pool_idx] + log_v_noise = np.sum(x * per_sample_coeffs, axis=1) + else: + log_v_noise = x @ noise_coeffs + + if "pool_intercepts" in art: + log_v_noise = log_v_noise + art["pool_intercepts"][pool_idx] + + v_noise = np.exp(log_v_noise) + + v_arb = np.zeros(len(y_total)) + for i in range(n_pools): + mask = pool_idx == i + if not mask.any(): + continue + v_arb_all = np.array(interpolate_pool_daily( + data["pool_coeffs"][i], jnp.float64(log_cadence[i]), + data["pool_gas"][i])) + v_arb[mask] = v_arb_all[sgd[mask]] + + v_obs = np.exp(y_total) + log_v_arb = np.log(np.maximum(v_arb, 1e-10)) + pred_total_log = np.logaddexp(log_v_arb, log_v_noise) + + # ---- Reconstruct dates ---- + all_dates = set() + for pid in pool_ids: + all_dates.update(matched_clean[pid]["panel"]["date"].values) + date_list = sorted(all_dates) + + # ---- Plot: stacked area per pool ---- + per_page = 9 + n_pages = (n_pools + per_page - 1) // per_page + + for page in range(n_pages): + start = page * per_page + end = min(start + per_page, n_pools) + n_this = end - start + + ncols = 3 + nrows = (n_this + ncols - 1) // ncols + fig, axes = plt.subplots(nrows, ncols, figsize=(16, 4 * nrows)) + if nrows == 1: + axes = axes.reshape(1, -1) + + for idx, i in enumerate(range(start, end)): + ax = axes[idx // ncols][idx % ncols] + mask = pool_idx == i + if mask.sum() < 5: + ax.set_visible(False) + continue + + days = day_idx[mask] + dates = [pd.Timestamp(date_list[d]) for d in days] + vo = v_obs[mask] + va = v_arb[mask] + vn = v_noise[mask] + + ax.fill_between(dates, 0, va, alpha=0.3, color="steelblue", + label="V_arb") + ax.fill_between(dates, va, va + vn, alpha=0.3, color="coral", + label="V_noise") + ax.plot(dates, vo, "k-", linewidth=0.8, alpha=0.7, label="V_obs") + ax.plot(dates, va + vn, "--", color="darkred", linewidth=0.8, + alpha=0.7, label="V_pred") + + ax.set_yscale("log") + ax.set_ylabel("USD/day", fontsize=7) + ax.tick_params(labelsize=6) + ax.tick_params(axis="x", rotation=30) + + yt = y_total[mask] + pt = pred_total_log[mask] + ss_res = np.sum((yt - pt) ** 2) + ss_tot = np.sum((yt - yt.mean()) ** 2) + r2 = 1 - ss_res / max(ss_tot, 1e-10) + + pid = pool_ids[i] + tokens = matched_clean[pid]["tokens"] + chain = matched_clean[pid]["chain"] + ci = np.exp(init_log_cadences[i]) + cl = np.exp(log_cadence[i]) + arb_share = np.median(va / vo) * 100 + noise_share = np.median(vn / vo) * 100 + b_tvl = noise_coeffs[i, 1] if per_pool else noise_coeffs[1] + + ax.set_title( + f"{tokens} ({chain})\n" + f"R\u00b2={r2:.3f} cad={ci:.0f}\u2192{cl:.0f}min " + f"arb={arb_share:.0f}% noise={noise_share:.0f}% " + f"b_tvl={b_tvl:.2f}", + fontsize=7) + ax.legend(fontsize=6, loc="upper right") + + for idx in range(n_this, nrows * ncols): + axes[idx // ncols][idx % ncols].set_visible(False) + + fig.suptitle( + f"Per-pool calibrated noise model \u2014 V_arb + V_noise " + f"(page {page+1}/{n_pages})", fontsize=10) + fig.tight_layout() + out = os.path.join(args.output_dir, f"calibrated_page{page+1}.png") + fig.savefig(out, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f" Saved: {out}") + + # ---- Summary ---- + summary = [] + for i in range(n_pools): + mask = pool_idx == i + if mask.sum() < 2: + continue + yt = y_total[mask] + pt = pred_total_log[mask] + ss_res = np.sum((yt - pt) ** 2) + ss_tot = np.sum((yt - yt.mean()) ** 2) + r2 = 1 - ss_res / max(ss_tot, 1e-10) + + pid = pool_ids[i] + va = v_arb[mask] + vn = v_noise[mask] + vo = v_obs[mask] + + summary.append({ + "pool_id": pid, + "tokens": matched_clean[pid]["tokens"], + "chain": matched_clean[pid]["chain"], + "n_obs": int(mask.sum()), + "R2": r2, + "cadence_init": float(np.exp(init_log_cadences[i])), + "cadence_learned": float(np.exp(log_cadence[i])), + "median_arb_pct": float(np.median(va / vo) * 100), + "median_noise_pct": float(np.median(vn / vo) * 100), + "b_tvl": float(noise_coeffs[i, 1] if per_pool else noise_coeffs[1]), + }) + + summary_df = pd.DataFrame(summary) + csv_path = os.path.join(args.output_dir, "summary.csv") + summary_df.to_csv(csv_path, index=False) + print(f"\n Summary: {csv_path}") + print(f" Median R\u00b2: {summary_df['R2'].median():.4f}") + print(f" Median arb: {summary_df['median_arb_pct'].median():.0f}%") + print(f" b_tvl: [{summary_df['b_tvl'].min():.2f}," + f" {summary_df['b_tvl'].max():.2f}]," + f" median={summary_df['b_tvl'].median():.2f}") + + +if __name__ == "__main__": + main() From 825c8a7f2d5973bfc379def6c576cf3d81e6902e Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Mon, 23 Mar 2026 13:08:01 +0000 Subject: [PATCH 34/57] feat: simulator integration for market_linear noise model, TVL standardization fix MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - noise_model_arrays.py: new module to precompute noise_base and noise_tvl_coeff arrays from trained artifact. Decomposes per-pool coefficients into TVL-dependent and TVL-independent components. Returns tvl_mean/tvl_std for runtime standardization. - noise_trades.py: add tvl_mean/tvl_std params to reclamm_market_linear_noise_volume() — standardizes log(TVL) at runtime to match training scale. Fixes NaN blowup from raw TVL. - reclamm_reserves.py: pass noise_params (tvl_mean, tvl_std) through to market_linear dispatch. - reclamm.py: cache loaded noise arrays on pool instance to avoid repeated disk reads. Support noise_arrays_path in fingerprint. - tune_reclamm_calibrated_noise.py: add --noise-model flag (calibrated vs market_linear), --artifact-dir, --initial-pool-value. Save precomputed arrays to disk, pass path + tvl stats via fingerprint. Default dates adjusted to panel coverage period. 100 trials × 3 objectives all complete successfully. - plot_reclamm_optuna_result.py: forward noise_arrays_path in run_full_period() for market_linear re-runs. --- experiments/tune_reclamm_calibrated_noise.py | 259 ++++++++++++++++ quantammsim/calibration/noise_model_arrays.py | 285 ++++++++++++++++++ quantammsim/pools/noise_trades.py | 21 +- quantammsim/pools/reCLAMM/reclamm.py | 9 +- quantammsim/pools/reCLAMM/reclamm_reserves.py | 3 + scripts/plot_reclamm_optuna_result.py | 241 +++++++++------ 6 files changed, 716 insertions(+), 102 deletions(-) create mode 100644 experiments/tune_reclamm_calibrated_noise.py create mode 100644 quantammsim/calibration/noise_model_arrays.py diff --git a/experiments/tune_reclamm_calibrated_noise.py b/experiments/tune_reclamm_calibrated_noise.py new file mode 100644 index 0000000..4eda1a6 --- /dev/null +++ b/experiments/tune_reclamm_calibrated_noise.py @@ -0,0 +1,259 @@ +"""Optuna tuning of reClAMM pool parameters with calibrated noise models. + +Supports two noise model modes: + --noise-model calibrated (legacy 8-covariate model) + --noise-model market_linear (new per-pool model with market features) + +The market_linear model uses precomputed daily arrays from the per-pool +calibrated noise model artifact (results/linear_market_noise/). It evaluates: + + log(V_noise) = base_t + tvl_coeff_t * log(effective_TVL) + +where base_t absorbs all non-TVL terms (market regime, token volatility, +pair volatility, day-of-week, cross-pool volumes) and tvl_coeff_t is the +effective TVL coefficient including interaction terms. + +Pool: 0x9d1fcf346ea1b0 = AAVE/WETH Mainnet + +Usage: + cd + source ~/miniconda3/etc/profile.d/conda.sh && conda activate qsim_reclamm_public + + # New market_linear model (default) + python experiments/tune_reclamm_calibrated_noise.py + + # Legacy 8-covariate model + python experiments/tune_reclamm_calibrated_noise.py --noise-model calibrated + + # All three objectives + python experiments/tune_reclamm_calibrated_noise.py --all-objectives + + # More trials + python experiments/tune_reclamm_calibrated_noise.py --n-trials 200 +""" + +import argparse +import json +import math +import numpy as np +from pathlib import Path +from quantammsim.runners.jax_runners import train_on_historic_data + +POOL_ID = "0x9d1fcf346ea1b0" # AAVE/WETH Mainnet + +# --- Legacy 8-covariate noise coefficients --- +NOISE_COEFFS_LEGACY = [ + -0.453, # c_0: intercept + 0.025, # c_1: log(TVL) + -0.060, # c_2: log(sigma) + 0.310, # c_3: log(TVL) * log(sigma) + -0.149, # c_4: log(TVL) * fee + 0.359, # c_5: log(sigma) * fee + 0.061, # c_6: dow_sin + 0.060, # c_7: dow_cos +] +LEGACY_LOG_CADENCE = 2.68 +LEGACY_ARB_FREQUENCY = max(1, round(math.exp(LEGACY_LOG_CADENCE))) # ~15 min + +PARAMETER_CONFIG = { + "price_ratio": {"low": 1.01, "high": 200.0, "log_scale": True, "scalar": True}, + "centeredness_margin": {"low": 0.01, "high": 0.99, "scalar": True}, + "shift_exponent": {"low": 1e-5, "high": 125.0, "log_scale": True, "scalar": True}, +} + +OBJECTIVES = ["daily_log_sharpe", "returns_over_hodl", "fee_revenue_over_value"] + + +def _build_market_linear_arrays(args): + """Precompute noise arrays from the per-pool market noise model artifact.""" + from quantammsim.calibration.noise_model_arrays import build_simulator_arrays + + # Parse dates — strip time component for the array builder + start = args.start_date.split(" ")[0] + end = args.end_test_date.split(" ")[0] + + print(f" Building market_linear noise arrays for {POOL_ID}...") + print(f" Date range: {start} → {end}") + arrays = build_simulator_arrays( + pool_id=POOL_ID, + start_date=start, + end_date=end, + artifact_dir=args.artifact_dir, + ) + print(f" {arrays['n_days']} days, {arrays['n_minutes']} minutes") + print(f" noise_base range: [{arrays['noise_base'].min():.2f}," + f" {arrays['noise_base'].max():.2f}]") + print(f" noise_tvl_coeff range: [{arrays['noise_tvl_coeff'].min():.4f}," + f" {arrays['noise_tvl_coeff'].max():.4f}]") + + # Save arrays to disk (fingerprint can't hold numpy arrays — it gets JSON-serialized) + import os + cache_dir = os.path.join(args.artifact_dir, "_sim_arrays") + os.makedirs(cache_dir, exist_ok=True) + arrays_path = os.path.join(cache_dir, f"{POOL_ID}_{start}_{end}.npz") + np.savez(arrays_path, + noise_base=arrays["noise_base"], + noise_tvl_coeff=arrays["noise_tvl_coeff"], + tvl_mean=arrays["tvl_mean"], + tvl_std=arrays["tvl_std"]) + print(f" Saved arrays: {arrays_path}") + + # Get learned cadence from artifact + from quantammsim.calibration.noise_model_arrays import load_artifact, _find_pool_index + art, meta = load_artifact(args.artifact_dir) + pool_idx = _find_pool_index(POOL_ID, meta["pool_ids"]) + if pool_idx >= 0: + learned_cadence = float(np.exp(art["log_cadence"][pool_idx])) + print(f" Learned cadence: {learned_cadence:.1f} min") + else: + learned_cadence = 5.0 + print(f" Pool not in calibration set, using default cadence: {learned_cadence}") + + return arrays_path, max(1, round(learned_cadence)) + + +def build_fingerprint(objective, args, noise_arrays_path=None, arb_freq=None): + """Build run fingerprint with calibrated noise model.""" + if args.noise_model == "market_linear" and noise_arrays_path is not None: + # Load tvl standardization stats from the saved arrays + _arr = np.load(noise_arrays_path) + noise_block = { + "noise_trader_ratio": 0.0, + "noise_model": "market_linear", + "noise_arrays_path": noise_arrays_path, + "reclamm_noise_params": { + "tvl_mean": float(_arr["tvl_mean"]), + "tvl_std": float(_arr["tvl_std"]), + }, + } + freq = arb_freq or 5 + else: + noise_block = { + "noise_trader_ratio": 0.0, + "noise_model": "calibrated", + "reclamm_noise_params": { + f"c_{i}": NOISE_COEFFS_LEGACY[i] for i in range(8) + }, + } + freq = LEGACY_ARB_FREQUENCY + + return { + "rule": "reclamm", + "tokens": ["AAVE", "ETH"], + "startDateString": args.start_date, + "endDateString": args.end_date, + "endTestDateString": args.end_test_date, + "initial_pool_value": args.initial_pool_value, + "do_arb": True, + "arb_frequency": freq, + "fees": args.fees, + "gas_cost": args.gas_cost, + "arb_fees": 0.0, + "protocol_fee_split": 0.5, + **noise_block, + "return_val": objective, + "reclamm_interpolation_method": args.interpolation, + "reclamm_centeredness_scaling": args.centeredness_scaling, + "reclamm_learn_arc_length_speed": False, + "reclamm_use_shift_exponent": True, + **({"bout_offset": args.bout_offset} if args.bout_offset is not None else {}), + "optimisation_settings": { + "method": "optuna", + "n_parameter_sets": 1, + **({"val_fraction": args.val_fraction} if args.val_fraction is not None else {}), + "optuna_settings": { + "make_scalar": True, + "expand_around": False, + "n_trials": args.n_trials, + "multi_objective": False, + "parameter_config": PARAMETER_CONFIG, + **({"overfitting_penalty": args.overfitting_penalty} if args.overfitting_penalty is not None else {}), + }, + }, + } + + +def run_single(objective, args, noise_arrays_path=None, arb_freq=None): + """Run Optuna tuning for a single objective.""" + print(f"\n{'='*60}") + print(f" Objective: {objective}") + print(f" Noise model: {args.noise_model}") + print(f" Pool: AAVE/WETH Mainnet ({POOL_ID})") + print(f" Train: {args.start_date} → {args.end_date}") + print(f" Test: {args.end_date} → {args.end_test_date}") + if arb_freq: + print(f" Arb frequency: {arb_freq} min (learned)") + print(f"{'='*60}\n") + + fp = build_fingerprint(objective, args, noise_arrays_path, arb_freq) + result = train_on_historic_data(fp, verbose=True) + + if result is not None: + print(f"\n=== Result ({objective}) ===") + for k, v in result.items(): + print(f" {k}: {v}") + + return result + + +def main(): + parser = argparse.ArgumentParser( + description="Tune reClAMM params with calibrated 8-covariate noise model" + ) + parser.add_argument("--n-trials", type=int, default=50) + parser.add_argument("--noise-model", default="market_linear", + choices=["calibrated", "market_linear"], + help="Noise model variant") + parser.add_argument("--artifact-dir", + default="results/linear_market_noise", + help="Artifact dir for market_linear model") + parser.add_argument("--initial-pool-value", type=float, default=20_000_000.0, + help="Initial pool TVL in USD (default: 20M)") + parser.add_argument("--fees", type=float, default=0.0025, + help="Pool fee rate (default: 0.0025 matching calibration)") + parser.add_argument("--gas-cost", type=float, default=1.0) + parser.add_argument("--objective", default="fee_revenue_over_value", + choices=OBJECTIVES) + parser.add_argument("--all-objectives", action="store_true", + help="Run all three objectives sequentially") + parser.add_argument("--interpolation", default="geometric", + choices=["geometric", "constant_arc_length"]) + parser.add_argument("--centeredness-scaling", action="store_true") + parser.add_argument("--start-date", default="2025-08-03 00:00:00") + parser.add_argument("--end-date", default="2025-12-01 00:00:00", + help="End of training / start of test") + parser.add_argument("--end-test-date", default="2026-02-18 00:00:00", + help="End of test (latest available data)") + parser.add_argument("--bout-offset", type=int, default=None) + parser.add_argument("--val-fraction", type=float, default=None) + parser.add_argument("--overfitting-penalty", type=float, default=None) + parser.add_argument("--output", type=str, default=None, + help="Save results to JSON file") + args = parser.parse_args() + + if args.all_objectives: + objectives = OBJECTIVES + else: + objectives = [args.objective] + + # Precompute noise arrays once (if using market_linear) + noise_arrays_path = None + arb_freq = None + if args.noise_model == "market_linear": + noise_arrays_path, arb_freq = _build_market_linear_arrays(args) + + all_results = {} + for obj in objectives: + result = run_single(obj, args, noise_arrays_path, arb_freq) + all_results[obj] = result + + if args.output: + out_path = Path(args.output) + out_path.parent.mkdir(parents=True, exist_ok=True) + with open(out_path, "w") as f: + json.dump(all_results, f, indent=2, default=str) + print(f"\nResults saved to {out_path}") + + +if __name__ == "__main__": + main() diff --git a/quantammsim/calibration/noise_model_arrays.py b/quantammsim/calibration/noise_model_arrays.py new file mode 100644 index 0000000..b8798c3 --- /dev/null +++ b/quantammsim/calibration/noise_model_arrays.py @@ -0,0 +1,285 @@ +"""Precompute noise_base and noise_tvl_coeff arrays for the simulator. + +Takes a trained per-pool noise model artifact and produces the two daily +arrays needed by reclamm_market_linear_noise_volume(): + + log(V_daily_noise) = noise_base_t + noise_tvl_coeff_t * log(effective_TVL) + +The arrays are at daily resolution and need to be expanded to minute-level +(by repeating each day's value 1440 times) before passing to the simulator. + +Usage: + from quantammsim.calibration.noise_model_arrays import build_simulator_arrays + + arrays = build_simulator_arrays( + pool_id="0x0b09dea16768f0", + start_date="2025-06-01", + end_date="2026-03-01", + artifact_dir="results/linear_market_noise", + ) + # arrays["noise_base"] — (n_minutes,) float64 + # arrays["noise_tvl_coeff"] — (n_minutes,) float64 +""" + +import json +import os +from datetime import date, timedelta +from typing import Dict, Optional, Tuple + +import numpy as np +import pandas as pd + + +def load_artifact(artifact_dir: str) -> Tuple[dict, dict]: + """Load model.npz + meta.json from artifact directory.""" + art = np.load(os.path.join(artifact_dir, "model.npz"), allow_pickle=True) + with open(os.path.join(artifact_dir, "meta.json")) as f: + meta = json.load(f) + return dict(art), meta + + +def _find_pool_index(pool_id: str, pool_ids: list) -> int: + """Match pool_id (full or prefix) to calibration pool list.""" + for i, cid in enumerate(pool_ids): + if pool_id.startswith(cid) or cid.startswith(pool_id): + return i + return -1 + + +def _identify_tvl_columns(feat_names: list) -> Tuple[int, list]: + """Identify which feature columns involve TVL. + + Returns: + tvl_col: index of the pure log_tvl feature (xobs_1) + tvl_interaction_cols: list of (col_idx, paired_col_idx) for + interaction terms that multiply TVL with another feature + """ + tvl_col = None + tvl_interaction_cols = [] + + for i, name in enumerate(feat_names): + if name == "xobs_1": + tvl_col = i + elif "xobs_1×" in name: + # e.g. "xobs_1×btc_realized_vol_7d" — find the paired feature + paired_name = name.split("×")[1] + for j, n2 in enumerate(feat_names): + if n2 == paired_name: + tvl_interaction_cols.append((i, j)) + break + + if tvl_col is None: + raise ValueError("xobs_1 (log_tvl) not found in feature names") + + return tvl_col, tvl_interaction_cols + + +def build_daily_features( + pool_id: str, + matched_clean: dict, + start_date: str, + end_date: str, + feat_names: list, + x_mean: np.ndarray, + x_std: np.ndarray, + trend_windows: tuple = (7,), +) -> Tuple[np.ndarray, list]: + """Build the full standardized feature matrix for a pool over a date range. + + Returns (x_daily, dates) where x_daily is (n_days, n_feat) and dates + is the list of dates. TVL column (xobs_1) is filled with the pool's + observed log_tvl_lag1 where available, 0 otherwise. + """ + from quantammsim.calibration.pool_data import ( + build_x_obs, build_cross_pool_x_obs, K_OBS_CROSS, + ) + from quantammsim.calibration.market_features import ( + build_pool_market_features, + ) + + # Find the pool + pid_match = None + for pid in matched_clean: + if pool_id.startswith(pid) or pid.startswith(pool_id): + pid_match = pid + break + if pid_match is None: + raise ValueError(f"Pool {pool_id} not found in matched_clean") + + entry = matched_clean[pid_match] + panel = entry["panel"] + + # Filter panel to date range + start = pd.Timestamp(start_date) + end = pd.Timestamp(end_date) + panel_dates = pd.to_datetime(panel["date"]) + mask = (panel_dates >= start) & (panel_dates <= end) + panel_sub = panel[mask.values].copy() + n_days = len(panel_sub) + + if n_days < 2: + raise ValueError(f"Only {n_days} days in range for pool {pool_id}") + + dates = panel_sub["date"].values + + # x_obs (cross-pool, 7 features) — need at least 1 lag + xc = build_cross_pool_x_obs(panel_sub, matched_clean, pid_match) + # xc drops first row; align + if len(xc) < n_days: + # Pad first row with zeros + xc = np.vstack([np.zeros((1, xc.shape[1])), xc]) + + # Market features + pool_feat = build_pool_market_features( + matched_clean, trend_windows=list(trend_windows)) + pf = pool_feat.get(pid_match) + if pf is None: + raise ValueError(f"No market features for {pool_id}") + + # Align market features to panel dates + n_base = K_OBS_CROSS + market_cols = [c for c in sorted(pf.columns)] + n_market = len(market_cols) + + x_base = np.zeros((n_days, n_base + n_market), dtype=np.float32) + x_base[:, :n_base] = xc[:n_days] + + for k, d in enumerate(dates): + day = pd.Timestamp(d).normalize() + if day in pf.index: + for m, col in enumerate(market_cols): + val = pf.loc[day, col] + if np.isfinite(val): + x_base[k, n_base + m] = val + + # Standardize using saved stats (base features only) + n_base_total = n_base + n_market + x_base = ((x_base - x_mean[:n_base_total]) / x_std[:n_base_total]).astype(np.float32) + + # Interaction terms + base_names = [f"xobs_{i}" for i in range(n_base)] + market_cols + col_idx = {name: i for i, name in enumerate(base_names)} + + interactions = [] + for fname in feat_names[n_base_total:]: + if "×" in fname: + parts = fname.split("×") + if parts[0] in col_idx and parts[1] in col_idx: + interactions.append( + x_base[:, col_idx[parts[0]]] * x_base[:, col_idx[parts[1]]]) + else: + interactions.append(np.zeros(n_days, dtype=np.float32)) + else: + interactions.append(np.zeros(n_days, dtype=np.float32)) + + if interactions: + x_all = np.concatenate( + [x_base, np.column_stack(interactions)], axis=1).astype(np.float32) + else: + x_all = x_base + + return x_all, dates.tolist() + + +def build_simulator_arrays( + pool_id: str, + start_date: str, + end_date: str, + artifact_dir: str = "results/linear_market_noise", + matched_clean: Optional[dict] = None, + arb_frequency: int = 1, +) -> Dict[str, np.ndarray]: + """Build noise_base and noise_tvl_coeff arrays for the simulator. + + Parameters + ---------- + pool_id : str + Pool ID (full or prefix). + start_date, end_date : str + Date range (inclusive). + artifact_dir : str + Directory containing model.npz and meta.json. + matched_clean : dict, optional + Pre-loaded matched_clean dict. If None, loads from stage1.pkl. + arb_frequency : int + Arb frequency in minutes. Arrays are at minute resolution, + repeated from daily values. + + Returns + ------- + dict with: + noise_base : (n_minutes,) array + noise_tvl_coeff : (n_minutes,) array + dates : list of dates + pool_index : int (index in calibration set, or -1) + """ + art, meta = load_artifact(artifact_dir) + noise_coeffs = art["noise_coeffs"] + feat_names = meta["feat_names"] + pool_ids = meta["pool_ids"] + x_mean = art["x_mean"] + x_std = art["x_std"] + per_pool = noise_coeffs.ndim == 2 + trend_windows = tuple(meta["hparams"]["trend_windows"]) + + # Load matched_clean if needed + if matched_clean is None: + import pickle + cache_dir = os.path.join( + os.path.dirname(os.path.dirname(os.path.dirname( + os.path.abspath(__file__)))), + "results", "token_factored_calibration", "_cache", + ) + with open(os.path.join(cache_dir, "stage1.pkl"), "rb") as f: + data = pickle.load(f) + matched_clean = data["matched_clean"] + + # Find pool coefficients + pool_idx = _find_pool_index(pool_id, pool_ids) + if pool_idx >= 0 and per_pool: + coeffs = noise_coeffs[pool_idx] + elif per_pool: + print(f" Warning: pool {pool_id} not in calibration set, using median coeffs") + coeffs = np.median(noise_coeffs, axis=0) + else: + coeffs = noise_coeffs + + # Build daily features + x_daily, dates = build_daily_features( + pool_id, matched_clean, start_date, end_date, + feat_names, x_mean, x_std, trend_windows, + ) + n_days = len(dates) + + # Decompose into base (non-TVL) and tvl_coeff + tvl_col, tvl_interactions = _identify_tvl_columns(feat_names) + + # tvl_coeff_t = coeffs[tvl_col] + sum(coeffs[inter_col] * x[paired_col]) + tvl_coeff_daily = np.full(n_days, coeffs[tvl_col], dtype=np.float64) + for inter_col, paired_col in tvl_interactions: + tvl_coeff_daily += coeffs[inter_col] * x_daily[:, paired_col] + + # base_t = sum(coeffs[j] * x[j]) for j not in {tvl_col, interaction_cols} + tvl_related = {tvl_col} | {ic for ic, _ in tvl_interactions} + base_daily = np.zeros(n_days, dtype=np.float64) + for j in range(len(feat_names)): + if j not in tvl_related: + base_daily += coeffs[j] * x_daily[:, j] + + # Expand to minute resolution: each day's value repeats 1440 times + n_minutes = n_days * 1440 + noise_base = np.repeat(base_daily, 1440) + noise_tvl_coeff = np.repeat(tvl_coeff_daily, 1440) + + return { + "noise_base": noise_base, + "noise_tvl_coeff": noise_tvl_coeff, + "tvl_mean": float(x_mean[tvl_col]), + "tvl_std": float(x_std[tvl_col]), + "dates": dates, + "pool_index": pool_idx, + "n_days": n_days, + "n_minutes": n_minutes, + "coeffs": coeffs, + "tvl_col": tvl_col, + } diff --git a/quantammsim/pools/noise_trades.py b/quantammsim/pools/noise_trades.py index 08ac959..bfef86c 100644 --- a/quantammsim/pools/noise_trades.py +++ b/quantammsim/pools/noise_trades.py @@ -352,20 +352,25 @@ def reclamm_market_linear_noise_volume( effective_value_usd, noise_base, noise_tvl_coeff, + tvl_mean=0.0, + tvl_std=1.0, ): """Market-feature linear noise model with precomputed daily coefficients. The full model is:: - log(V_daily_noise) = base_t + tvl_coeff_t * log(effective_TVL) + log(V_daily_noise) = base_t + tvl_coeff_t * standardized_log_tvl where ``base_t`` absorbs all non-TVL terms (intercept, market regime, token volatility, pair volatility, day-of-week, cross-pool volumes) and ``tvl_coeff_t`` is the effective TVL coefficient including interaction terms (tvl×btc_vol, tvl×tok_a_vol, tvl×pair_vol). - Both are precomputed daily from the per-pool calibrated noise model - and passed in as dynamic input arrays decimated to the arb frequency. + The log(TVL) is standardized using the same mean/std from training + to ensure the coefficient scale matches. + + Both base_t and tvl_coeff_t are precomputed daily from the per-pool + calibrated noise model and passed in as dynamic input arrays. Under counterfactual (varying reClAMM concentration), only ``effective_value_usd`` changes — all market/peer features are held @@ -378,8 +383,11 @@ def reclamm_market_linear_noise_volume( noise_base : float Precomputed non-TVL component of log(V_daily_noise) for this step. noise_tvl_coeff : float - Precomputed effective coefficient on log(TVL) for this step, - including base b_tvl and interaction terms. + Precomputed effective coefficient on log(TVL) for this step. + tvl_mean : float + Mean of log(TVL) from training data standardization. + tvl_std : float + Std of log(TVL) from training data standardization. Returns ------- @@ -387,7 +395,8 @@ def reclamm_market_linear_noise_volume( Per-minute noise volume (USD), floored at zero. """ log_tvl = jnp.log(jnp.maximum(effective_value_usd, 1.0)) - log_daily_noise = noise_base + noise_tvl_coeff * log_tvl + standardized_log_tvl = (log_tvl - tvl_mean) / tvl_std + log_daily_noise = noise_base + noise_tvl_coeff * standardized_log_tvl daily_noise = jnp.exp(log_daily_noise) return jnp.maximum(0.0, daily_noise / 1440.0) diff --git a/quantammsim/pools/reCLAMM/reclamm.py b/quantammsim/pools/reCLAMM/reclamm.py index 58222ad..32e0c3e 100644 --- a/quantammsim/pools/reCLAMM/reclamm.py +++ b/quantammsim/pools/reCLAMM/reclamm.py @@ -229,9 +229,16 @@ def _prepare_noise_arrays(self, prices, run_fingerprint, start_index, "noise_base": None, "noise_tvl_coeff": None} if noise_model == "market_linear": - # Precomputed arrays passed via run_fingerprint + # Load precomputed arrays from path (cached on instance) or direct nb = run_fingerprint.get("noise_base_array") ntc = run_fingerprint.get("noise_tvl_coeff_array") + if nb is None and "noise_arrays_path" in run_fingerprint: + path = run_fingerprint["noise_arrays_path"] + if not hasattr(self, "_market_linear_cache") or self._market_linear_cache[0] != path: + arrays = np.load(path) + self._market_linear_cache = (path, arrays["noise_base"], arrays["noise_tvl_coeff"]) + nb = self._market_linear_cache[1] + ntc = self._market_linear_cache[2] if nb is not None: result["noise_base"] = _prepare_dynamic_array( jnp.array(nb), start_index, bout_length, arb_freq, max_len) diff --git a/quantammsim/pools/reCLAMM/reclamm_reserves.py b/quantammsim/pools/reCLAMM/reclamm_reserves.py index 0953d33..26dcd7a 100644 --- a/quantammsim/pools/reCLAMM/reclamm_reserves.py +++ b/quantammsim/pools/reCLAMM/reclamm_reserves.py @@ -1033,8 +1033,11 @@ def _skip_schedule_state(_): real_value = jnp.sum(jnp.array([Ra_new, Rb_new]) * prices) effective_value = (Ra_new + Va) * prices[0] + (Rb_new + Vb) * prices[1] + _np = noise_params if noise_params is not None else {} noise_vol = reclamm_market_linear_noise_volume( effective_value, noise_base, noise_tvl_coeff, + tvl_mean=_np.get("tvl_mean", 0.0), + tvl_std=_np.get("tvl_std", 1.0), ) noise_fee_income = (1.0 - gamma) * noise_vol diff --git a/scripts/plot_reclamm_optuna_result.py b/scripts/plot_reclamm_optuna_result.py index 35242da..b90ba40 100644 --- a/scripts/plot_reclamm_optuna_result.py +++ b/scripts/plot_reclamm_optuna_result.py @@ -1,15 +1,20 @@ #!/usr/bin/env python3 """Plot reClAMM pool performance from Optuna tuning results. -Reads the SGD-compatible JSON output of tune_reclamm_params.py (or any Optuna -run), extracts the best trial's pool params, re-runs a forward pass over the -full train+test window, and produces a value-over-time plot with on-chain -baselines and cumulative fee revenue. +Reads SGD-compatible JSON output(s) of tune_reclamm_params.py, extracts the +best trial's pool params, re-runs a forward pass over the full train+test +window, and produces a value-over-time plot with on-chain baselines and +cumulative fee revenue. Usage: + # Single result python scripts/plot_reclamm_optuna_result.py results/run_.json - python scripts/plot_reclamm_optuna_result.py results/run_.json --output my_plot.png - python scripts/plot_reclamm_optuna_result.py results/run_.json --top-k 3 + + # Multiple results (comparison across objectives / noise models) + python scripts/plot_reclamm_optuna_result.py results/run_*.json + + # Top-3 trials from each result + python scripts/plot_reclamm_optuna_result.py results/run_*.json --top-k 3 """ import argparse @@ -36,12 +41,20 @@ BG = "#162536" TEXT_COLOR = "#E6CE97" +# Extended palette for multi-file comparison COLORS = [ - "#3498db", "#2ecc71", "#e74c3c", # top-k - "#f39c12", # on-chain launch - "#9b59b6", # on-chain current + "#3498db", "#2ecc71", "#e74c3c", "#f39c12", "#9b59b6", + "#1abc9c", "#e67e22", "#2980b9", "#c0392b", "#8e44ad", + "#27ae60", "#d35400", "#16a085", "#f1c40f", "#7f8c8d", ] +# Short labels for objectives +_OBJ_SHORT = { + "daily_log_sharpe": "sharpe", + "returns_over_hodl": "ret/hodl", + "fee_revenue_over_value": "fee_rev", +} + def _plot_order(configs): """Yield (name, meta, color_idx) with baselines first, optimized trials last.""" @@ -60,9 +73,10 @@ def parse_args(): description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter, ) - p.add_argument("results_json", help="Path to run_.json from Optuna") + p.add_argument("results_json", nargs="+", + help="Path(s) to run_.json from Optuna") p.add_argument("--top-k", type=int, default=1, - help="Plot top K trials by objective (default 1)") + help="Plot top K trials per result file (default 1)") p.add_argument("--output", default=None, help="Output PNG path (default: auto-generated)") p.add_argument("--no-onchain", action="store_true", @@ -100,6 +114,18 @@ def extract_pool_params(trial, config): return params +def _noise_model_label(config): + """Short label describing the noise model in the config.""" + nm = config.get("noise_model", "ratio") + if nm != "calibrated": + ntr = config.get("noise_trader_ratio", 0.0) + return f"{nm}(ntr={ntr})" + nc = config.get("reclamm_noise_params", {}) + n_coeffs = len(nc) + arb_freq = config.get("arb_frequency", 1) + return f"cal-{n_coeffs}cov(af={arb_freq})" + + def run_full_period(params, config, fees_override=None): """Run forward pass over the full train+test window.""" fees = fees_override if fees_override is not None else config["fees"] @@ -120,19 +146,28 @@ def run_full_period(params, config, fees_override=None): "reclamm_centeredness_scaling": config.get("reclamm_centeredness_scaling", False), "reclamm_learn_arc_length_speed": config.get("reclamm_learn_arc_length_speed", False), } + # Forward noise model settings + if "noise_model" in config: + fp["noise_model"] = config["noise_model"] + if "reclamm_noise_params" in config: + fp["reclamm_noise_params"] = config["reclamm_noise_params"] + if "noise_arrays_path" in config: + fp["noise_arrays_path"] = config["noise_arrays_path"] + if "arb_frequency" in config: + fp["arb_frequency"] = config["arb_frequency"] jax_params = {k: jnp.array(v) for k, v in params.items()} return do_run_on_historic_data(run_fingerprint=fp, params=jax_params) -def plot_results(configs, time_series, hodl_values, config, args): +def plot_results(configs, time_series, hodl_values, ref_config, args): """Two-panel plot: value-over-time + cumulative fee revenue.""" - train_end_str = config["endDateString"] + train_end_str = ref_config["endDateString"] train_end_dt = datetime.strptime(train_end_str, "%Y-%m-%d %H:%M:%S") first_out = next(iter(time_series.values())) n_minutes = len(first_out["value"]) dates = pd.date_range( - start=datetime.strptime(config["startDateString"], "%Y-%m-%d %H:%M:%S"), + start=datetime.strptime(ref_config["startDateString"], "%Y-%m-%d %H:%M:%S"), periods=n_minutes, freq="1min", ) step = 1440 @@ -157,7 +192,7 @@ def plot_results(configs, time_series, hodl_values, config, args): vals = np.array(out["value"][::step]) / 1e6 label = f"{name}" if "test_objective" in meta: - obj_name = config.get("return_val", "objective") + obj_name = meta.get("obj_name", "objective") label += f" (OOS {obj_name}={meta['test_objective']:.4f})" is_optimized = "On-Chain" not in name ax_val.plot(dates_daily[:len(vals)], vals, @@ -171,21 +206,19 @@ def plot_results(configs, time_series, hodl_values, config, args): ax_val.axvline(x=train_end_dt, color="white", linestyle=":", alpha=0.5, linewidth=1.5) ylims = ax_val.get_ylim() - ax_val.text(train_end_dt - pd.Timedelta(days=10), ylims[1] * 0.97, "Train", + ax_val.text(train_end_dt - pd.Timedelta(days=5), ylims[1] * 0.97, "Train", color="white", alpha=0.6, fontsize=11, ha="right", va="top") - ax_val.text(train_end_dt + pd.Timedelta(days=10), ylims[1] * 0.97, "Test", + ax_val.text(train_end_dt + pd.Timedelta(days=5), ylims[1] * 0.97, "Test", color="white", alpha=0.6, fontsize=11, ha="left", va="top") _style_axis(ax_val) ax_val.set_ylabel("Pool Value ($M USD)", color=TEXT_COLOR, fontsize=12) - tokens_str = "/".join(config["tokens"]) - obj_name = config.get("return_val", "objective") - ntr = config.get("noise_trader_ratio", 0.0) + tokens_str = "/".join(ref_config["tokens"]) ax_val.set_title( - f"reClAMM Optuna-Optimized ({obj_name}, noise={ntr}) — {tokens_str}", + f"reClAMM Optuna Comparison — {tokens_str}", color=TEXT_COLOR, fontsize=13, pad=15, ) - ax_val.legend(loc="upper left", fontsize=9, facecolor=BG, + ax_val.legend(loc="upper left", fontsize=8, facecolor=BG, edgecolor=TEXT_COLOR, labelcolor=TEXT_COLOR) # ── Panel 2: Cumulative fee revenue ─────────────────────────────── @@ -208,7 +241,7 @@ def plot_results(configs, time_series, hodl_values, config, args): _style_axis(ax_fee) ax_fee.set_ylabel("Cumulative Fee Revenue ($K)", color=TEXT_COLOR, fontsize=12) ax_fee.set_xlabel("Date", color=TEXT_COLOR, fontsize=12) - ax_fee.legend(loc="upper left", fontsize=9, facecolor=BG, + ax_fee.legend(loc="upper left", fontsize=8, facecolor=BG, edgecolor=TEXT_COLOR, labelcolor=TEXT_COLOR) else: ax_val.set_xlabel("Date", color=TEXT_COLOR, fontsize=12) @@ -222,11 +255,11 @@ def plot_results(configs, time_series, hodl_values, config, args): plt.close() -def plot_test_only(configs, time_series, hodl_values, config, args): +def plot_test_only(configs, time_series, hodl_values, ref_config, args): """Test-period plot with all curves normalised to start at 1.0.""" - train_end_str = config["endDateString"] + train_end_str = ref_config["endDateString"] train_end_dt = datetime.strptime(train_end_str, "%Y-%m-%d %H:%M:%S") - start_dt = datetime.strptime(config["startDateString"], "%Y-%m-%d %H:%M:%S") + start_dt = datetime.strptime(ref_config["startDateString"], "%Y-%m-%d %H:%M:%S") first_out = next(iter(time_series.values())) n_minutes = len(first_out["value"]) @@ -262,14 +295,12 @@ def plot_test_only(configs, time_series, hodl_values, config, args): ax.axhline(1.0, color="white", linestyle=":", alpha=0.3, linewidth=1) _style_axis(ax) - tokens_str = "/".join(config["tokens"]) - obj_name = config.get("return_val", "objective") - ntr = config.get("noise_trader_ratio", 0.0) - ax.set_title(f"Test Period Only (normalised) — {obj_name}, noise={ntr} — {tokens_str}", + tokens_str = "/".join(ref_config["tokens"]) + ax.set_title(f"Test Period Only (normalised) — {tokens_str}", color=TEXT_COLOR, fontsize=13, pad=15) ax.set_ylabel("Normalised Value", color=TEXT_COLOR, fontsize=12) ax.set_xlabel("Date", color=TEXT_COLOR, fontsize=12) - ax.legend(loc="best", fontsize=9, facecolor=BG, + ax.legend(loc="best", fontsize=8, facecolor=BG, edgecolor=TEXT_COLOR, labelcolor=TEXT_COLOR) fig.patch.set_facecolor(BG) @@ -281,10 +312,10 @@ def plot_test_only(configs, time_series, hodl_values, config, args): plt.close() -def plot_weights(configs, time_series, config, args): +def plot_weights(configs, time_series, ref_config, args): """Effective weight (value fraction) of token 0 over time.""" - start_dt = datetime.strptime(config["startDateString"], "%Y-%m-%d %H:%M:%S") - train_end_dt = datetime.strptime(config["endDateString"], "%Y-%m-%d %H:%M:%S") + start_dt = datetime.strptime(ref_config["startDateString"], "%Y-%m-%d %H:%M:%S") + train_end_dt = datetime.strptime(ref_config["endDateString"], "%Y-%m-%d %H:%M:%S") first_out = next(iter(time_series.values())) n_minutes = len(first_out["value"]) @@ -292,7 +323,7 @@ def plot_weights(configs, time_series, config, args): step = 1440 dates_daily = dates[::step] - token_name = config["tokens"][0] + token_name = ref_config["tokens"][0] fig, ax = plt.subplots(1, 1, figsize=(14, 5)) @@ -310,18 +341,18 @@ def plot_weights(configs, time_series, config, args): ax.axhline(0.5, color="white", linestyle="--", alpha=0.3, linewidth=1) ax.axvline(x=train_end_dt, color="white", linestyle=":", alpha=0.5, linewidth=1.5) ylims = ax.get_ylim() - ax.text(train_end_dt - pd.Timedelta(days=10), ylims[1] * 0.97, "Train", + ax.text(train_end_dt - pd.Timedelta(days=5), ylims[1] * 0.97, "Train", color="white", alpha=0.6, fontsize=11, ha="right", va="top") - ax.text(train_end_dt + pd.Timedelta(days=10), ylims[1] * 0.97, "Test", + ax.text(train_end_dt + pd.Timedelta(days=5), ylims[1] * 0.97, "Test", color="white", alpha=0.6, fontsize=11, ha="left", va="top") _style_axis(ax) - tokens_str = "/".join(config["tokens"]) + tokens_str = "/".join(ref_config["tokens"]) ax.set_title(f"Effective {token_name} Weight — {tokens_str}", color=TEXT_COLOR, fontsize=13, pad=15) ax.set_ylabel(f"{token_name} weight (value fraction)", color=TEXT_COLOR, fontsize=12) ax.set_xlabel("Date", color=TEXT_COLOR, fontsize=12) - ax.legend(loc="best", fontsize=9, facecolor=BG, + ax.legend(loc="best", fontsize=8, facecolor=BG, edgecolor=TEXT_COLOR, labelcolor=TEXT_COLOR) fig.patch.set_facecolor(BG) @@ -346,60 +377,79 @@ def _style_axis(ax): def main(): args = parse_args() - config, trials = load_results(args.results_json) - if args.end_test_date: - config["endTestDateString"] = args.end_test_date - if args.noise_trader_ratio is not None: - config["noise_trader_ratio"] = args.noise_trader_ratio - tokens = config["tokens"] - obj_name = config.get("return_val", "objective") - - # Sort trials by penalised objective - trials_sorted = sorted(trials, key=lambda t: t.get("objective", 0), reverse=True) - top_trials = trials_sorted[:args.top_k] - - print("=" * 80) - print(f"reClAMM Optuna Result Plotter — objective: {obj_name}") - print("=" * 80) - print(f" Results: {args.results_json}") + + # ── Load all result files ───────────────────────────────────────── + all_loaded = [] + for path in args.results_json: + config, trials = load_results(path) + if args.end_test_date: + config["endTestDateString"] = args.end_test_date + if args.noise_trader_ratio is not None: + config["noise_trader_ratio"] = args.noise_trader_ratio + all_loaded.append((path, config, trials)) + + # Use first file's config as reference for dates/tokens + ref_config = all_loaded[0][1] + tokens = ref_config["tokens"] + + print("=" * 100) + print(f"reClAMM Optuna Result Plotter — {len(all_loaded)} result file(s)") + print("=" * 100) print(f" Tokens: {'/'.join(tokens)}") - print(f" Train: {config['startDateString']} → {config['endDateString']}") - print(f" Test: {config['endDateString']} → {config['endTestDateString']}") - print(f" Fees: {config['fees']}, Gas: {config.get('gas_cost', 1.0)}") - print(f" Trials: {len(trials)} total, plotting top {len(top_trials)}") + print(f" Train: {ref_config['startDateString']} → {ref_config['endDateString']}") + print(f" Test: {ref_config['endDateString']} → {ref_config['endTestDateString']}") + # ── Build configs dict from all files ───────────────────────────── configs = {} - for i, trial in enumerate(top_trials): - params = extract_pool_params(trial, config) - name = f"#{trial.get('optuna_trial_number', i)} (rank {i+1})" - configs[name] = { - "params": params, - "objective": trial.get("objective", 0), - "train_objective": trial.get("train_objective", 0), - "test_objective": trial.get("test_objective", 0), - "train_sharpe": trial.get("train_sharpe", 0), - "validation_sharpe": trial.get("validation_sharpe", 0), - } - print(f"\n {name}:") - print(f" {obj_name}: train={trial.get('train_objective', 0):.4f} " - f"test={trial.get('test_objective', 0):.4f} " - f"penalised={trial.get('objective', 0):.4f}") - print(f" sharpe: train={trial.get('train_sharpe', 0):+.4f} " - f"val={trial.get('validation_sharpe', 0):+.4f}") - for k, v in params.items(): - print(f" {k}: {v:.6g}") + for path, config, trials in all_loaded: + obj_name = config.get("return_val", "objective") + obj_short = _OBJ_SHORT.get(obj_name, obj_name) + noise_label = _noise_model_label(config) + + trials_sorted = sorted(trials, key=lambda t: t.get("objective", 0), reverse=True) + top_trials = trials_sorted[:args.top_k] + + for i, trial in enumerate(top_trials): + params = extract_pool_params(trial, config) + rank_suffix = f" r{i+1}" if args.top_k > 1 else "" + name = f"{obj_short} {noise_label}{rank_suffix}" + configs[name] = { + "params": params, + "config": config, # per-file config for noise model + "objective": trial.get("objective", 0), + "train_objective": trial.get("train_objective", 0), + "test_objective": trial.get("test_objective", 0), + "train_sharpe": trial.get("train_sharpe", 0), + "validation_sharpe": trial.get("validation_sharpe", 0), + "obj_name": obj_name, + } + print(f"\n {name}:") + print(f" {obj_name}: train={trial.get('train_objective', 0):.4f} " + f"test={trial.get('test_objective', 0):.4f} " + f"penalised={trial.get('objective', 0):.4f}") + print(f" sharpe: train={trial.get('train_sharpe', 0):+.4f} " + f"val={trial.get('validation_sharpe', 0):+.4f}") + for k, v in params.items(): + print(f" {k}: {v:.6g}") if not args.no_onchain: - configs["On-Chain (launch)"] = {"params": dict(ONCHAIN_LAUNCH_PARAMS)} - configs["On-Chain (current)"] = {"params": dict(ONCHAIN_CURRENT_PARAMS)} + configs["On-Chain (launch)"] = { + "params": dict(ONCHAIN_LAUNCH_PARAMS), + "config": ref_config, + } + configs["On-Chain (current)"] = { + "params": dict(ONCHAIN_CURRENT_PARAMS), + "config": ref_config, + } # ── Full-period runs ────────────────────────────────────────────── - print(f"\n--- Running full-period simulations ({config['startDateString']} → " - f"{config['endTestDateString']}) ---") + print(f"\n--- Running full-period simulations ({ref_config['startDateString']} → " + f"{ref_config['endTestDateString']}) ---") time_series = {} for name, cfg in configs.items(): print(f" {name}...", end=" ", flush=True) - out = run_full_period(cfg["params"], config) + run_config = cfg.get("config", ref_config) + out = run_full_period(cfg["params"], run_config) time_series[name] = out fv = float(out["final_value"]) fr = out.get("fee_revenue") @@ -415,28 +465,29 @@ def main(): ) # ── Plots ───────────────────────────────────────────────────────── - plot_results(configs, time_series, hodl_values, config, args) - plot_test_only(configs, time_series, hodl_values, config, args) - plot_weights(configs, time_series, config, args) + plot_results(configs, time_series, hodl_values, ref_config, args) + plot_test_only(configs, time_series, hodl_values, ref_config, args) + plot_weights(configs, time_series, ref_config, args) # ── Summary table ───────────────────────────────────────────────── - print(f"\n{'=' * 120}") - print(f"SUMMARY — {'/'.join(tokens)} — {obj_name}") - print(f"{'=' * 120}") - hdr = (f"{'Config':<28s} {'Train '+obj_name:>20s} {'Test '+obj_name:>20s} " + print(f"\n{'=' * 130}") + print(f"SUMMARY — {'/'.join(tokens)}") + print(f"{'=' * 130}") + hdr = (f"{'Config':<35s} {'Objective':>12s} {'Train':>10s} {'Test':>10s} " f"{'Train SR':>10s} {'Val SR':>10s} " f"{'PR':>7s} {'Margin':>7s} {'ShiftExp':>10s} {'Full RoH':>10s}") print(hdr) - print("-" * 120) + print("-" * 130) for name, cfg in configs.items(): cp = cfg["params"] fv = float(time_series[name]["final_value"]) full_roh = fv / float(hodl_values[-1]) - 1 print( - f"{name:<28s} " - f"{cfg.get('train_objective', float('nan')):>20.4f} " - f"{cfg.get('test_objective', float('nan')):>20.4f} " + f"{name:<35s} " + f"{cfg.get('obj_name', ''):>12s} " + f"{cfg.get('train_objective', float('nan')):>10.4f} " + f"{cfg.get('test_objective', float('nan')):>10.4f} " f"{cfg.get('train_sharpe', float('nan')):>+10.4f} " f"{cfg.get('validation_sharpe', float('nan')):>+10.4f} " f"{cp.get('price_ratio', float('nan')):>7.3f} " @@ -444,7 +495,7 @@ def main(): f"{cp.get('shift_exponent', float('nan')):>10.4g} " f"{full_roh * 100:>+9.2f}%" ) - print("=" * 120) + print("=" * 130) if __name__ == "__main__": From afb8511e1b786dcf05c8305c8e5b32486e9fed38 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Thu, 26 Mar 2026 11:21:43 +0000 Subject: [PATCH 35/57] =?UTF-8?q?feat:=20causal=20TVL=20elasticity=20analy?= =?UTF-8?q?sis=20=E2=80=94=20deconfounder=20+=20LP=20event=20study?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - run_deconfounder_noise.py: Four-strategy causal analysis of b_tvl: 1. Variance decomposition (62% between-pool, 38% within-pool) 2. Within-pool Δ regressions (median b_tvl=+0.12, daily too fast) 2b. Lagged-average TVL across windows (stable ~0.95 at all horizons) 3. TVL decomposition: price-driven vs flow-driven (IV-style) 4. Deconfounder sensitivity (Wang & Blei 2019, n_factors sweep) D'Amour critique acknowledged in docstring. Ridge warm-start, standardized Z_hat. Convergent finding: per-pool b_tvl ~1.0 is the right working estimate for counterfactuals. - scan_lp_events.py: Scan all pools for large LP deposit/withdrawal events (semi-exogenous TVL shocks). Filters pool creation events via min-age and min-tvl. Computes per-event elasticity from ±window day volume comparison. 836 events across 118 pools, median elasticity +0.84 (clean: +0.98, OLS: +0.89). Saves CSV + generates plots: elasticity histograms, deposits vs withdrawals, elasticity vs pool size, log-log scatter with OLS, boxplot by chain. No asymmetry between deposits/withdrawals, flat across pool sizes and chains. --- experiments/run_deconfounder_noise.py | 555 ++++++++++++++++++++++++++ experiments/scan_lp_events.py | 522 ++++++++++++++++++++++++ 2 files changed, 1077 insertions(+) create mode 100644 experiments/run_deconfounder_noise.py create mode 100644 experiments/scan_lp_events.py diff --git a/experiments/run_deconfounder_noise.py b/experiments/run_deconfounder_noise.py new file mode 100644 index 0000000..cd761b9 --- /dev/null +++ b/experiments/run_deconfounder_noise.py @@ -0,0 +1,555 @@ +"""Causal noise volume estimation: TVL decomposition + deconfounder sensitivity. + +Two identification strategies for the causal effect of TVL on noise volume: + +**Primary: TVL decomposition (IV-style)** + Decomposes Δlog(TVL) into: + - Price-driven: Δlog(TVL) - Δlog(shares) — market price moves, more + exogenous to pool-specific trading activity (conditional on BTC/token + market features already in the model) + - Flow-driven: Δlog(shares) — LP deposits/withdrawals, endogenous + (LPs deposit when they expect fees → correlated with noise) + If b_tvl estimated from price-driven variation ≈ observational b_tvl, + the coefficient is likely causal. + +**Secondary: Deconfounder sensitivity analysis (Wang & Blei 2019)** + Fit a factor model (PPCA) on covariates only (no outcome), extract + latent factors Z_hat, include them in the outcome model. + This is a sensitivity analysis: if b_tvl shifts substantially when + conditioning on Z_hat, there's evidence of unobserved confounding. + If it's stable, confounding through the covariate structure is small. + + NB: The deconfounder has known theoretical limitations (D'Amour 2019). + Wang & Blei (2020, arXiv:2003.04948) respond that D'Amour's + counterexamples violate the required assumptions (pinpointability). + The theory holds under its assumptions, but the key assumption (no + unobserved single-cause confounders) is domain-specific and + uncheckable. Results should be interpreted as sensitivity bounds. + +**Diagnostics:** + - Variance decomposition of log_tvl: between-pool vs within-pool + - Within-pool simple regression: Δlog(V_obs) on Δlog(TVL) per pool + - These test whether the observational b_tvl reflects cross-sectional + or temporal variation + +Usage: + python experiments/run_deconfounder_noise.py + python experiments/run_deconfounder_noise.py --n-factors 1 2 3 5 +""" + +import argparse +import os +import time + +import jax.numpy as jnp +import numpy as np + + +# ---- Factor model ---- + + +def fit_ppca(X, n_components): + """Probabilistic PCA. Returns Z_hat and the model.""" + from sklearn.decomposition import PCA + pca = PCA(n_components=n_components) + Z_hat = pca.fit_transform(X) + print(f" PPCA({n_components}): explained var = " + f"{pca.explained_variance_ratio_.sum():.3f} " + f"per-component: {np.round(pca.explained_variance_ratio_, 3)}") + return Z_hat, pca + + +def build_augmented_data(data, Z_hat): + """Augment covariate matrix with standardized substitute confounders.""" + x_orig = data["x"] + n_z = Z_hat.shape[1] + + z_mean = Z_hat.mean(axis=0) + z_std = Z_hat.std(axis=0) + z_std[z_std < 1e-6] = 1.0 + Z_std = ((Z_hat - z_mean) / z_std).astype(np.float32) + + x_aug = np.concatenate([x_orig, Z_std], axis=1) + data_aug = dict(data) + data_aug["x"] = x_aug + data_aug["n_feat"] = x_aug.shape[1] + data_aug["feat_names"] = data["feat_names"] + [f"Z_{k}" for k in range(n_z)] + data_aug["x_mean"] = np.concatenate([ + data["x_mean"], z_mean.astype(np.float32)]) + data_aug["x_std"] = np.concatenate([ + data["x_std"], z_std.astype(np.float32)]) + return data_aug + + +def _tvl_col_index(feat_names): + """Find TVL column index from feature names (robust to reordering).""" + return feat_names.index("xobs_1") + + +def _intercept_col_index(feat_names): + """Find intercept column index.""" + return feat_names.index("xobs_0") + + +# ---- TVL decomposition ---- + + +def decompose_tvl(matched_clean, pool_ids, sample_pools, sample_days, + date_to_idx, n_dates, n_pools): + """Decompose Δlog(TVL) into price-driven and flow-driven components. + + Uses log_tvl (not log_tvl_lag1) for the decomposition to avoid + mixing lags. total_shares is assumed to be contemporaneous with TVL. + + flow = Δlog(shares) — LP deposits/withdrawals + price = Δlog(tvl) - Δlog(shares) — price changes + + Returns per-sample arrays and a validity mask. + """ + log_shares = np.full((n_dates, n_pools), np.nan) + log_tvl = np.full((n_dates, n_pools), np.nan) + + for j, pid in enumerate(pool_ids): + panel = matched_clean[pid]["panel"] + dates = panel["date"].values + + has_shares = ("total_shares" in panel.columns and + panel["total_shares"].notna().any()) + if not has_shares: + continue + + shares = panel["total_shares"].values.astype(float) + shares = np.maximum(shares, 1e-10) + + # Use log_tvl (not lag) for contemporaneous decomposition + if "log_tvl" in panel.columns: + tvl_vals = panel["log_tvl"].values.astype(float) + else: + tvl_vals = panel["log_tvl_lag1"].values.astype(float) + + for k, date in enumerate(dates): + t = date_to_idx.get(date) + if t is not None: + log_shares[t, j] = np.log(shares[k]) + log_tvl[t, j] = tvl_vals[k] + + n_samples = len(sample_pools) + tvl_flow = np.full(n_samples, np.nan, dtype=np.float32) + tvl_price = np.full(n_samples, np.nan, dtype=np.float32) + + for s in range(n_samples): + i = sample_pools[s] + t = sample_days[s] + if t >= 1: + d_log_shares = log_shares[t, i] - log_shares[t - 1, i] + d_log_tvl = log_tvl[t, i] - log_tvl[t - 1, i] + if np.isfinite(d_log_shares) and np.isfinite(d_log_tvl): + tvl_flow[s] = d_log_shares + tvl_price[s] = d_log_tvl - d_log_shares + + valid = np.isfinite(tvl_flow) & np.isfinite(tvl_price) + return tvl_flow, tvl_price, valid + + +def run_tvl_decomposition_analysis(data, matched_clean, tvl_flow, tvl_price, valid): + """Primary identification: compare b_tvl from price-driven vs all TVL.""" + from sklearn.linear_model import RidgeCV + + y = data["y_total"] + x = data["x"] + tvl_idx = _tvl_col_index(data["feat_names"]) + + print(f"\n Valid samples (have LP shares data): {valid.sum()}/{len(valid)}") + if valid.sum() < 100: + print(" Insufficient LP shares data for TVL decomposition.") + return None + + x_valid = x[valid] + y_valid = y[valid] + tvl_price_valid = tvl_price[valid] + tvl_flow_valid = tvl_flow[valid] + + print(f" Price component: mean={tvl_price_valid.mean():.4f}," + f" std={tvl_price_valid.std():.4f}") + print(f" Flow component: mean={tvl_flow_valid.mean():.4f}," + f" std={tvl_flow_valid.std():.4f}") + + # Observational b_tvl (Ridge on all features) + ridge_obs = RidgeCV(alphas=np.logspace(-2, 4, 50)) + ridge_obs.fit(x_valid, y_valid) + b_tvl_obs = ridge_obs.coef_[tvl_idx] + + # Replace TVL column with price-driven component only + x_price = x_valid.copy() + ps = tvl_price_valid.std() + x_price[:, tvl_idx] = (tvl_price_valid - tvl_price_valid.mean()) / max(ps, 1e-6) + ridge_price = RidgeCV(alphas=np.logspace(-2, 4, 50)) + ridge_price.fit(x_price, y_valid) + b_tvl_price = ridge_price.coef_[tvl_idx] + + # Replace TVL column with flow-driven component only + x_flow = x_valid.copy() + fs = tvl_flow_valid.std() + x_flow[:, tvl_idx] = (tvl_flow_valid - tvl_flow_valid.mean()) / max(fs, 1e-6) + ridge_flow = RidgeCV(alphas=np.logspace(-2, 4, 50)) + ridge_flow.fit(x_flow, y_valid) + b_tvl_flow = ridge_flow.coef_[tvl_idx] + + print(f"\n b_tvl estimates (Ridge, all 22 features):") + print(f" All TVL variation: {b_tvl_obs:+.4f}") + print(f" Price-driven only: {b_tvl_price:+.4f}" + f" (more exogenous)") + print(f" Flow-driven only: {b_tvl_flow:+.4f}" + f" (endogenous)") + + if abs(b_tvl_price - b_tvl_obs) < 0.3 * abs(b_tvl_obs): + print(f"\n → Price-driven ≈ observational: confounding small.") + else: + print(f"\n → Price-driven ≠ observational: potential confounding.") + + return {"obs": b_tvl_obs, "price": b_tvl_price, "flow": b_tvl_flow} + + +# ---- Variance decomposition ---- + + +def run_variance_decomposition(matched_clean, pool_ids): + """Decompose log_tvl variance into between-pool and within-pool.""" + all_tvls = [] + pool_labels = [] + + for j, pid in enumerate(pool_ids): + panel = matched_clean[pid]["panel"] + tvls = panel["log_tvl_lag1"].values.astype(float) + valid = np.isfinite(tvls) + all_tvls.extend(tvls[valid]) + pool_labels.extend([j] * valid.sum()) + + all_tvls = np.array(all_tvls) + pool_labels = np.array(pool_labels) + + total_var = np.var(all_tvls) + pool_means = np.array([all_tvls[pool_labels == j].mean() + for j in range(len(pool_ids))]) + between_var = np.var(pool_means) + within_vars = [np.var(all_tvls[pool_labels == j]) + for j in range(len(pool_ids))] + within_var = np.mean(within_vars) + + print(f" Total variance: {total_var:.4f}") + print(f" Between-pool: {between_var:.4f} ({between_var/total_var*100:.1f}%)") + print(f" Within-pool (avg): {within_var:.4f} ({within_var/total_var*100:.1f}%)") + print(f" Pool mean range: {pool_means.min():.1f} to {pool_means.max():.1f}") + + return {"total": total_var, "between": between_var, "within": within_var} + + +# ---- Within-pool simple regression ---- + + +def run_within_pool_regressions(matched_clean, pool_ids): + """Per-pool: Δlog(V_obs) on Δlog(TVL), no other covariates.""" + print(f"\n {'Pool':16s} {'Tokens':16s} {'b_tvl':>8s} {'R²':>6s}" + f" {'n':>5s} {'ΔTVL_std':>8s}") + print(f" {'-'*65}") + + b_tvls = [] + for pid in pool_ids: + panel = matched_clean[pid]["panel"] + log_vol = panel["log_volume"].values.astype(float) + log_tvl = panel["log_tvl_lag1"].values.astype(float) + + d_vol = np.diff(log_vol) + d_tvl = np.diff(log_tvl) + + valid = np.isfinite(d_vol) & np.isfinite(d_tvl) + if valid.sum() < 10: + continue + + dv = d_vol[valid] + dt = d_tvl[valid] + + # OLS: Δlog_vol = a + b * Δlog_tvl + X = np.column_stack([np.ones(len(dt)), dt]) + sol, _, _, _ = np.linalg.lstsq(X, dv, rcond=None) + b = sol[1] + pred = X @ sol + ss_res = np.sum((dv - pred) ** 2) + ss_tot = np.sum((dv - dv.mean()) ** 2) + r2 = 1 - ss_res / max(ss_tot, 1e-10) + + b_tvls.append(b) + tokens = matched_clean[pid]["tokens"] + print(f" {pid[:16]:16s} {tokens[:16]:16s} {b:+8.3f} {r2:6.3f}" + f" {valid.sum():5d} {dt.std():8.4f}") + + if b_tvls: + print(f"\n Median within-pool b_tvl: {np.median(b_tvls):+.4f}") + print(f" Mean: {np.mean(b_tvls):+.4f}") + print(f" Std across pools: {np.std(b_tvls):.4f}") + return b_tvls + + +# ---- Lagged-average TVL analysis ---- + + +def run_lagged_average_analysis(matched_clean, pool_ids): + """Test TVL→noise at different timescales. + + If the daily Δ elasticity is ~0 but the level elasticity is ~2.5, + the effect may operate on longer timescales. Test by regressing + noise on rolling-average TVL at windows of 7, 14, 30, 60, 90 days. + If b_tvl grows with window size, the relationship is real but slow. + """ + windows = [1, 7, 14, 30, 60, 90] + + print(f"\n Window Median b_tvl Mean b_tvl Pools w/ data") + print(f" {'-'*55}") + + for w in windows: + b_tvls = [] + n_pools_used = 0 + for pid in pool_ids: + panel = matched_clean[pid]["panel"] + log_vol = panel["log_volume"].values.astype(float) + log_tvl = panel["log_tvl_lag1"].values.astype(float) + + if len(log_vol) < w + 10: + continue + + # Rolling mean TVL over window w + if w == 1: + tvl_avg = log_tvl + else: + # Simple trailing average + tvl_avg = np.full_like(log_tvl, np.nan) + for t in range(w, len(log_tvl)): + vals = log_tvl[t - w:t] + if np.all(np.isfinite(vals)): + tvl_avg[t] = np.mean(vals) + + # Within-pool: demean both series + valid = np.isfinite(log_vol) & np.isfinite(tvl_avg) + if valid.sum() < 15: + continue + + vol = log_vol[valid] + tvl = tvl_avg[valid] + vol_dm = vol - vol.mean() + tvl_dm = tvl - tvl.mean() + + # OLS: demeaned_vol = b * demeaned_tvl + if np.var(tvl_dm) < 1e-10: + continue + b = np.sum(vol_dm * tvl_dm) / np.sum(tvl_dm ** 2) + b_tvls.append(b) + n_pools_used += 1 + + if b_tvls: + print(f" {w:5d}d {np.median(b_tvls):+11.4f} {np.mean(b_tvls):+10.4f}" + f" {n_pools_used:>13d}") + + return windows + + +# ---- Deconfounder sensitivity ---- + + +def run_deconfounder(data, n_factors_list, args): + """Secondary: deconfounder sensitivity analysis across n_factors.""" + from experiments.run_linear_market_noise import make_loss_fn, train + from sklearn.linear_model import RidgeCV + + X = data["x"] + tvl_idx = _tvl_col_index(data["feat_names"]) + intercept_idx = _intercept_col_index(data["feat_names"]) + results = {} + + for n_f in n_factors_list: + print(f"\n --- n_factors={n_f} ---") + Z_hat, _ = fit_ppca(X, n_f) + data_aug = build_augmented_data(data, Z_hat) + + n_feat = data_aug["n_feat"] + n_pools = data_aug["n_pools"] + + # Ridge warm-start + ridge = RidgeCV(alphas=np.logspace(-2, 4, 50)) + ridge.fit(data_aug["x"], data_aug["y_total"]) + sol = ridge.coef_.copy() + sol[intercept_idx] += ridge.intercept_ + + params = { + "log_cadence": jnp.array(data_aug["init_log_cadences"]), + "noise_coeffs": jnp.array(sol.astype(np.float32)), + } + + grad_fn = make_loss_fn(data_aug["pool_coeffs"], data_aug["pool_gas"], n_pools) + params = train(params, data_aug, grad_fn, args.epochs, args.lr, + args.l2_alpha, args.huber_delta, verbose=False) + + nc = np.array(params["noise_coeffs"]) + b_tvl = nc[tvl_idx] + n_orig = data["n_feat"] + z_coeffs = nc[n_orig:n_orig + n_f] + + print(f" b_tvl = {b_tvl:+.4f} " + f" Z coeffs: {np.round(z_coeffs, 3)}") + + results[n_f] = { + "b_tvl": float(b_tvl), + "z_coeffs": z_coeffs.tolist(), + "explained_var": float(Z_hat.var(axis=0).sum() / X.var(axis=0).sum()), + } + + return results + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--n-factors", type=int, nargs="+", default=[1, 2, 3, 5], + help="Number of latent factors to sweep") + parser.add_argument("--epochs", type=int, default=2000) + parser.add_argument("--lr", type=float, default=3e-4) + parser.add_argument("--l2-alpha", type=float, default=1e-3) + parser.add_argument("--huber-delta", type=float, default=1.0) + parser.add_argument("--trend-windows", type=int, nargs="+", default=[7]) + args = parser.parse_args() + + os.environ.setdefault("JAX_PLATFORMS", "cpu") + + print("=" * 70) + print("Causal Noise Volume Estimation") + print(" 1. Variance decomposition (between vs within pool)") + print(" 2. Within-pool simple regressions") + print(" 3. TVL decomposition (price vs flow)") + print(" 4. Deconfounder sensitivity (PPCA factors)") + print(f" n_factors sweep: {args.n_factors}") + print("=" * 70) + + from experiments.run_linear_market_noise import load_stage1, build_data + + matched_clean, option_c_clean = load_stage1() + + print("\nBuilding data...") + t0 = time.time() + data = build_data( + matched_clean, option_c_clean, + trend_windows=tuple(args.trend_windows), + include_market=True, include_cross_pool=True, + ) + pool_ids = data["pool_ids"] + n_pools = data["n_pools"] + print(f" {len(data['pool_idx'])} samples, {n_pools} pools," + f" {data['n_feat']} features, {time.time() - t0:.1f}s") + + # ---- 1. Variance decomposition ---- + print(f"\n{'='*70}") + print("1. Variance Decomposition of log_tvl_lag1") + print(f"{'='*70}") + var_results = run_variance_decomposition(matched_clean, pool_ids) + + # ---- 2. Within-pool simple regressions ---- + print(f"\n{'='*70}") + print("2. Within-pool: Δlog(V_obs) ~ Δlog(TVL) (no other covariates)") + print(f"{'='*70}") + within_b_tvls = run_within_pool_regressions(matched_clean, pool_ids) + + # ---- 2b. Lagged-average TVL (timescale test) ---- + print(f"\n{'='*70}") + print("2b. Lagged-Average TVL: Does b_tvl grow with averaging window?") + print(f" (Tests whether the TVL→noise effect is slow-moving)") + print(f"{'='*70}") + run_lagged_average_analysis(matched_clean, pool_ids) + + # ---- 3. TVL decomposition ---- + print(f"\n{'='*70}") + print("3. TVL Decomposition: Price-driven vs Flow-driven") + print(f"{'='*70}") + + all_dates = set() + for pid in pool_ids: + all_dates.update(matched_clean[pid]["panel"]["date"].values) + date_list = sorted(all_dates) + date_to_idx = {d: i for i, d in enumerate(date_list)} + + tvl_flow, tvl_price, valid = decompose_tvl( + matched_clean, pool_ids, data["pool_idx"], data["day_idx"], + date_to_idx, len(date_list), n_pools, + ) + tvl_results = run_tvl_decomposition_analysis( + data, matched_clean, tvl_flow, tvl_price, valid) + + # ---- 4. Deconfounder sensitivity ---- + print(f"\n{'='*70}") + print("4. Deconfounder Sensitivity Analysis") + print(f" (Wang & Blei 2019; D'Amour 2019; Wang & Blei 2020)") + print(f"{'='*70}") + deconf_results = run_deconfounder(data, args.n_factors, args) + + # ---- Summary ---- + print(f"\n{'='*70}") + print("SUMMARY: b_tvl across identification strategies") + print(f"{'='*70}") + + # Observational baseline from artifact + obs_path = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "linear_market_noise", "model.npz", + ) + if os.path.exists(obs_path): + obs_nc = np.load(obs_path)["noise_coeffs"] + tvl_idx = _tvl_col_index(data["feat_names"]) + if obs_nc.ndim == 2: + b_obs = float(np.median(obs_nc[:, tvl_idx])) + print(f" Observational (per-pool median): {b_obs:+.4f}") + else: + b_obs = float(obs_nc[tvl_idx]) + print(f" Observational (shared): {b_obs:+.4f}") + + between_pct = var_results["between"] / var_results["total"] * 100 + print(f"\n Variance decomposition: {between_pct:.0f}% between-pool," + f" {100-between_pct:.0f}% within-pool") + + if within_b_tvls: + print(f" Within-pool Δ regressions: " + f"median={np.median(within_b_tvls):+.4f}" + f" (mean={np.mean(within_b_tvls):+.4f})") + + if tvl_results: + print(f"\n TVL decomposition (Ridge, 22 features):") + print(f" All variation: {tvl_results['obs']:+.4f}") + print(f" Price-driven (exogenous): {tvl_results['price']:+.4f}") + print(f" Flow-driven (endogenous): {tvl_results['flow']:+.4f}") + + print(f"\n Deconfounder sensitivity (shared, learnable cadence):") + print(f" {'n_factors':>10s} {'b_tvl':>8s}") + for n_f, r in sorted(deconf_results.items()): + print(f" {n_f:>10d} {r['b_tvl']:+8.4f}") + + # Stability + b_tvls_d = [r["b_tvl"] for r in deconf_results.values()] + rng = max(b_tvls_d) - min(b_tvls_d) + mn = np.mean(b_tvls_d) + stable = rng < 0.3 * abs(mn) + print(f"\n Deconfounder: {'STABLE' if stable else 'VARIES'}" + f" (range {rng:.3f}, mean {mn:+.3f})") + + print(f"\n Interpretation:") + if tvl_results and abs(tvl_results['price']) < 0.5: + print(f" Daily b_tvl (Δ regression, price-driven) is near zero.") + print(f" This does NOT mean the long-run effect is zero:") + print(f" - Noise may respond slowly to TVL (routing updates,") + print(f" aggregator discovery, ecosystem integration)") + print(f" - The lagged-average analysis above tests this") + print(f" - The per-pool b_tvl of ~1.0 captures medium-frequency") + print(f" within-pool variation and is the best working estimate") + print(f" - Changing reClAMM concentration is a structural change") + print(f" (like being a different pool), not a daily TVL shock") + print(f" → Use per-pool b_tvl (~1.0) for counterfactuals, with") + print(f" sensitivity analysis across [0.5, 1.0, 2.0]") + + +if __name__ == "__main__": + main() diff --git a/experiments/scan_lp_events.py b/experiments/scan_lp_events.py new file mode 100644 index 0000000..7194b84 --- /dev/null +++ b/experiments/scan_lp_events.py @@ -0,0 +1,522 @@ +"""Scan all pools for large LP deposit/withdrawal events and estimate TVL→noise elasticity. + +Identifies "semi-exogenous" LP flow events — large share changes that represent +genuine deposit/withdrawal decisions, not pool creation or dust. + +Filters: + - |Δlog(shares)| > threshold (default 20%) + - Pool must have been active for at least --min-age days before the event + - Pre-event TVL must be above --min-tvl (filters out pool creation events + where initial TVL is dust) + - Enough pre/post data to estimate volume change + +For each event, computes the volume response and implied elasticity. + +Usage: + python experiments/scan_lp_events.py + python experiments/scan_lp_events.py --threshold 0.1 --window 7 + python experiments/scan_lp_events.py --use-api # fetch fresh snapshots +""" + +import argparse +import os +import time + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + + +def load_panel_data(use_api=False): + """Load pool panel data from calibration cache or API.""" + import pickle + + pools = {} + + # Stage1 calibration pools + cache_path = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", "stage1.pkl", + ) + if os.path.exists(cache_path): + with open(cache_path, "rb") as f: + data = pickle.load(f) + for pid, entry in data["matched_clean"].items(): + panel = entry["panel"].copy() + panel["pool_id"] = pid + panel["chain"] = entry["chain"] + panel["tokens"] = entry["tokens"] + pools[pid] = panel + + # Noise calibration panel (broader set) + noise_panel_path = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "local_data", "noise_calibration", "panel.parquet", + ) + if os.path.exists(noise_panel_path): + panel_all = pd.read_parquet(noise_panel_path) + for pid in panel_all["pool_id"].unique(): + if pid[:16] not in pools: # don't duplicate + pp = panel_all[panel_all["pool_id"] == pid].copy() + if len(pp) >= 30: + pools[pid[:16]] = pp + + # Top50 snapshots (even broader) + snap_dir = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "local_data", "noise_top50", "snapshots", + ) + if os.path.exists(snap_dir): + import glob + for f in glob.glob(os.path.join(snap_dir, "*.parquet")): + pid = os.path.basename(f).replace(".parquet", "") + if pid[:16] not in pools: + try: + df = pd.read_parquet(f) + if len(df) >= 30 and "total_shares" in df.columns: + df["pool_id"] = pid + pools[pid[:16]] = df + except Exception: + pass + + if use_api: + print(" Fetching fresh snapshots from Balancer API...") + from quantammsim.noise_calibration import ( + fetch_pool_snapshots, BALANCER_API_CHAINS, + ) + for pid_short, panel in list(pools.items()): + if "chain" in panel.columns: + chain = panel["chain"].iloc[0] + else: + chain = "MAINNET" + full_pid = panel["pool_id"].iloc[0] if "pool_id" in panel.columns else pid_short + try: + fresh = fetch_pool_snapshots(full_pid, chain) + if len(fresh) > len(panel): + fresh["pool_id"] = full_pid + fresh["chain"] = chain + if "tokens" in panel.columns: + fresh["tokens"] = panel["tokens"].iloc[0] + pools[pid_short] = fresh + time.sleep(0.3) + except Exception: + pass + + print(f" Loaded {len(pools)} pools") + return pools + + +def find_lp_events(panel, threshold=0.2, min_age_days=30, min_tvl=10_000): + """Find large LP deposit/withdrawal events in a single pool's panel. + + Returns list of event dicts. + """ + dates = pd.to_datetime(panel["date"]) + + # Need shares and TVL + if "total_shares" not in panel.columns: + return [] + shares = panel["total_shares"].values.astype(float) + if np.all(shares <= 0) or np.all(np.isnan(shares)): + return [] + + # TVL + if "total_liquidity_usd" in panel.columns: + tvl = panel["total_liquidity_usd"].values.astype(float) + elif "log_tvl" in panel.columns: + tvl = np.exp(panel["log_tvl"].values.astype(float)) + elif "log_tvl_lag1" in panel.columns: + tvl = np.exp(panel["log_tvl_lag1"].values.astype(float)) + else: + return [] + + # Volume + if "volume_usd" in panel.columns: + vol = panel["volume_usd"].values.astype(float) + elif "log_volume" in panel.columns: + vol = np.exp(panel["log_volume"].values.astype(float)) + else: + return [] + + log_shares = np.log(np.maximum(shares, 1e-10)) + d_log_shares = np.diff(log_shares) + + events = [] + for i in range(len(d_log_shares)): + if abs(d_log_shares[i]) < np.log(1 + threshold): + continue + + # Check min age: pool must have been active for min_age_days + days_active = (dates.iloc[i + 1] - dates.iloc[0]).days + if days_active < min_age_days: + continue + + # Check min TVL before event + if tvl[i] < min_tvl: + continue + + # Check shares aren't near-zero before (not pool creation) + if shares[i] < 1: + continue + + pct_change = (np.exp(d_log_shares[i]) - 1) * 100 + event_type = "deposit" if d_log_shares[i] > 0 else "withdrawal" + + events.append({ + "date": dates.iloc[i + 1], + "idx": i + 1, + "type": event_type, + "d_log_shares": float(d_log_shares[i]), + "pct_change": float(pct_change), + "shares_before": float(shares[i]), + "shares_after": float(shares[i + 1]), + "tvl_before": float(tvl[i]), + "tvl_after": float(tvl[i + 1]), + "vol_on_day": float(vol[i + 1]), + }) + + return events + + +def compute_event_elasticity(panel, event, window=7): + """Compute volume response around an LP event. + + Compares median volume in [event-window, event) vs [event+1, event+window+1). + """ + dates = pd.to_datetime(panel["date"]) + idx = event["idx"] + + if "volume_usd" in panel.columns: + vol = panel["volume_usd"].values.astype(float) + elif "log_volume" in panel.columns: + vol = np.exp(panel["log_volume"].values.astype(float)) + else: + return None + + if "total_liquidity_usd" in panel.columns: + tvl = panel["total_liquidity_usd"].values.astype(float) + elif "log_tvl" in panel.columns: + tvl = np.exp(panel["log_tvl"].values.astype(float)) + elif "log_tvl_lag1" in panel.columns: + tvl = np.exp(panel["log_tvl_lag1"].values.astype(float)) + else: + return None + + pre_start = max(0, idx - window) + post_end = min(len(vol), idx + 1 + window) + + if idx - pre_start < 3 or post_end - (idx + 1) < 3: + return None + + vol_pre = np.median(vol[pre_start:idx]) + vol_post = np.median(vol[idx + 1:post_end]) + tvl_pre = np.median(tvl[pre_start:idx]) + tvl_post = np.median(tvl[idx + 1:post_end]) + + if vol_pre <= 0 or tvl_pre <= 0 or tvl_post <= 0: + return None + + vol_ratio = vol_post / vol_pre + tvl_ratio = tvl_post / tvl_pre + + if abs(np.log(tvl_ratio)) < 0.05: # TVL didn't actually change much + return None + + elasticity = np.log(vol_ratio) / np.log(tvl_ratio) + + return { + "vol_pre": vol_pre, + "vol_post": vol_post, + "tvl_pre": tvl_pre, + "tvl_post": tvl_post, + "vol_ratio": vol_ratio, + "tvl_ratio": tvl_ratio, + "elasticity": elasticity, + } + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--threshold", type=float, default=0.2, + help="Min |share change| to count as event (0.2 = 20%%)") + parser.add_argument("--window", type=int, default=7, + help="Days before/after event for volume comparison") + parser.add_argument("--min-age", type=int, default=30, + help="Min days pool must be active before event") + parser.add_argument("--min-tvl", type=float, default=10_000, + help="Min TVL before event (filters pool creation)") + parser.add_argument("--use-api", action="store_true", + help="Fetch fresh snapshots from Balancer API") + parser.add_argument("--output-dir", default="results/lp_events", + help="Output directory for CSV and plots") + args = parser.parse_args() + + print("=" * 70) + print("LP Event Scanner: Semi-Exogenous TVL Shocks") + print(f" threshold={args.threshold:.0%}, window={args.window}d," + f" min_age={args.min_age}d, min_tvl=${args.min_tvl:,.0f}") + print("=" * 70) + + pools = load_panel_data(use_api=args.use_api) + + all_events = [] + print(f"\nScanning {len(pools)} pools for LP events...") + + for pid_short, panel in pools.items(): + tokens = (panel["tokens"].iloc[0] if "tokens" in panel.columns + else "?") + chain = (panel["chain"].iloc[0] if "chain" in panel.columns + else "?") + + events = find_lp_events( + panel, threshold=args.threshold, + min_age_days=args.min_age, min_tvl=args.min_tvl) + + for ev in events: + result = compute_event_elasticity(panel, ev, window=args.window) + ev["pool_id"] = pid_short + ev["tokens"] = tokens + ev["chain"] = chain + ev["result"] = result + all_events.append(ev) + + # Sort by absolute share change + all_events.sort(key=lambda e: abs(e["d_log_shares"]), reverse=True) + + print(f"\nFound {len(all_events)} LP events across {len(pools)} pools") + events_with_elasticity = [e for e in all_events if e["result"] is not None] + print(f" {len(events_with_elasticity)} with computable elasticity") + + # Print event table + print(f"\n{'Date':12s} {'Pool':16s} {'Tokens':18s} {'Type':10s}" + f" {'Δshares':>8s} {'TVL before':>12s} {'TVL after':>12s}" + f" {'VolPre':>10s} {'VolPost':>10s} {'Elast':>7s}") + print("-" * 120) + + for ev in all_events: + r = ev["result"] + if r: + elast_str = f"{r['elasticity']:+7.2f}" + vol_pre_str = f"${r['vol_pre']:>9,.0f}" + vol_post_str = f"${r['vol_post']:>9,.0f}" + else: + elast_str = " n/a" + vol_pre_str = " n/a" + vol_post_str = " n/a" + + print(f"{str(ev['date'].date()):12s} {ev['pool_id'][:16]:16s}" + f" {str(ev['tokens'])[:18]:18s} {ev['type']:10s}" + f" {ev['pct_change']:+7.0f}%" + f" ${ev['tvl_before']:>11,.0f} ${ev['tvl_after']:>11,.0f}" + f" {vol_pre_str} {vol_post_str} {elast_str}") + + # Summary statistics + if not events_with_elasticity: + print("No events with computable elasticity.") + return + + deposits = [e for e in events_with_elasticity if e["type"] == "deposit"] + withdrawals = [e for e in events_with_elasticity if e["type"] == "withdrawal"] + + all_elast = [e["result"]["elasticity"] for e in events_with_elasticity] + dep_elast = [e["result"]["elasticity"] for e in deposits] + wth_elast = [e["result"]["elasticity"] for e in withdrawals] + clean = [e for e in events_with_elasticity + if -1 < e["result"]["elasticity"] < 5] + clean_elast = [e["result"]["elasticity"] for e in clean] + + print(f"\n{'='*70}") + print("Summary: Implied TVL→Volume Elasticity") + print(f"{'='*70}") + print(f" All events ({len(all_elast)}):" + f" median={np.median(all_elast):+.2f}" + f" mean={np.mean(all_elast):+.2f}" + f" std={np.std(all_elast):.2f}") + if dep_elast: + print(f" Deposits ({len(dep_elast)}):" + f" median={np.median(dep_elast):+.2f}" + f" mean={np.mean(dep_elast):+.2f}") + if wth_elast: + print(f" Withdrawals ({len(wth_elast)}):" + f" median={np.median(wth_elast):+.2f}" + f" mean={np.mean(wth_elast):+.2f}") + if clean_elast: + print(f"\n Clean events (elasticity in [-1, 5], n={len(clean_elast)}):") + print(f" median={np.median(clean_elast):+.2f}" + f" mean={np.mean(clean_elast):+.2f}" + f" [Q25={np.percentile(clean_elast, 25):+.2f}," + f" Q75={np.percentile(clean_elast, 75):+.2f}]") + + print(f"\n For comparison:") + print(f" Per-pool observational b_tvl: ~1.0") + print(f" Shared observational b_tvl: ~2.5") + print(f" Daily Δ within-pool: ~0.1") + + # ---- Save CSV ---- + out_dir = args.output_dir + os.makedirs(out_dir, exist_ok=True) + + rows = [] + for ev in all_events: + r = ev.get("result") or {} + rows.append({ + "date": ev["date"], + "pool_id": ev["pool_id"], + "tokens": str(ev["tokens"]), + "chain": str(ev["chain"]), + "type": ev["type"], + "pct_change": ev["pct_change"], + "tvl_before": ev["tvl_before"], + "tvl_after": ev["tvl_after"], + "shares_before": ev["shares_before"], + "shares_after": ev["shares_after"], + "vol_pre": r.get("vol_pre"), + "vol_post": r.get("vol_post"), + "tvl_ratio": r.get("tvl_ratio"), + "vol_ratio": r.get("vol_ratio"), + "elasticity": r.get("elasticity"), + }) + df = pd.DataFrame(rows) + csv_path = os.path.join(out_dir, "lp_events.csv") + df.to_csv(csv_path, index=False) + print(f"\n Saved: {csv_path} ({len(df)} events)") + + # ---- Plots ---- + # 1. Elasticity histogram (clean events, deposits vs withdrawals) + fig, axes = plt.subplots(1, 3, figsize=(16, 5)) + + ax = axes[0] + ax.hist(clean_elast, bins=40, color="steelblue", alpha=0.7, edgecolor="white") + ax.axvline(np.median(clean_elast), color="red", linestyle="--", linewidth=2, + label=f"median={np.median(clean_elast):+.2f}") + ax.axvline(1.0, color="gray", linestyle=":", alpha=0.5, label="elasticity=1") + ax.set_xlabel("Elasticity (Δlog vol / Δlog TVL)") + ax.set_ylabel("Count") + ax.set_title(f"All clean events (n={len(clean_elast)})") + ax.legend(fontsize=8) + + ax = axes[1] + dep_clean = [e["result"]["elasticity"] for e in clean if e["type"] == "deposit"] + wth_clean = [e["result"]["elasticity"] for e in clean if e["type"] == "withdrawal"] + ax.hist(dep_clean, bins=30, color="green", alpha=0.6, label=f"deposits (n={len(dep_clean)})", edgecolor="white") + ax.hist(wth_clean, bins=30, color="coral", alpha=0.6, label=f"withdrawals (n={len(wth_clean)})", edgecolor="white") + ax.axvline(1.0, color="gray", linestyle=":", alpha=0.5) + ax.set_xlabel("Elasticity") + ax.set_title("Deposits vs Withdrawals") + ax.legend(fontsize=8) + + # 2. Elasticity vs event size (|Δlog shares|) + ax = axes[2] + sizes = [abs(e["d_log_shares"]) for e in clean] + elasts = [e["result"]["elasticity"] for e in clean] + colors = ["green" if e["type"] == "deposit" else "coral" for e in clean] + ax.scatter(sizes, elasts, c=colors, alpha=0.4, s=15, edgecolors="none") + ax.axhline(1.0, color="gray", linestyle=":", alpha=0.5) + ax.axhline(np.median(elasts), color="red", linestyle="--", alpha=0.7, + label=f"median={np.median(elasts):+.2f}") + ax.set_xlabel("|Δlog(shares)| (event size)") + ax.set_ylabel("Elasticity") + ax.set_title("Elasticity vs Event Size") + ax.legend(fontsize=8) + + fig.suptitle(f"LP Event Elasticity Analysis — {len(clean)} clean events" + f" from {len(pools)} pools", fontsize=11) + fig.tight_layout() + p1 = os.path.join(out_dir, "elasticity_histograms.png") + fig.savefig(p1, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f" Saved: {p1}") + + # 3. Elasticity vs pre-event TVL (does pool size affect elasticity?) + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + + ax = axes[0] + tvl_pre = [e["result"]["tvl_pre"] for e in clean] + ax.scatter(tvl_pre, elasts, c=colors, alpha=0.4, s=15, edgecolors="none") + ax.set_xscale("log") + ax.axhline(1.0, color="gray", linestyle=":", alpha=0.5) + ax.set_xlabel("Pre-event TVL (USD)") + ax.set_ylabel("Elasticity") + ax.set_title("Elasticity vs Pool Size") + + # Bin by TVL decile and show median elasticity + tvl_arr = np.array(tvl_pre) + el_arr = np.array(elasts) + for q_lo, q_hi in [(0, 25), (25, 50), (50, 75), (75, 100)]: + lo = np.percentile(tvl_arr, q_lo) + hi = np.percentile(tvl_arr, q_hi) + mask = (tvl_arr >= lo) & (tvl_arr < hi + 1) + if mask.sum() > 5: + med_tvl = np.median(tvl_arr[mask]) + med_el = np.median(el_arr[mask]) + ax.plot(med_tvl, med_el, "rs", markersize=10, zorder=5) + ax.annotate(f"{med_el:.2f}", (med_tvl, med_el), + textcoords="offset points", xytext=(8, 5), fontsize=7) + + # 4. log(vol_post/vol_pre) vs log(tvl_post/tvl_pre) scatter + ax = axes[1] + log_tvl_ratio = [np.log(e["result"]["tvl_ratio"]) for e in clean] + log_vol_ratio = [np.log(e["result"]["vol_ratio"]) for e in clean] + ax.scatter(log_tvl_ratio, log_vol_ratio, c=colors, alpha=0.4, s=15, + edgecolors="none") + + # OLS fit line + x_fit = np.array(log_tvl_ratio) + y_fit = np.array(log_vol_ratio) + slope, intercept = np.polyfit(x_fit, y_fit, 1) + x_line = np.linspace(x_fit.min(), x_fit.max(), 100) + ax.plot(x_line, slope * x_line + intercept, "r-", linewidth=2, + label=f"OLS slope={slope:.2f}") + ax.plot(x_line, x_line, "k--", alpha=0.3, label="1:1 line") + ax.set_xlabel("Δlog(TVL)") + ax.set_ylabel("Δlog(Volume)") + ax.set_title("Volume Response to TVL Shocks") + ax.legend(fontsize=8) + + fig.suptitle("TVL→Volume Elasticity: Event Study", fontsize=11) + fig.tight_layout() + p2 = os.path.join(out_dir, "elasticity_scatter.png") + fig.savefig(p2, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f" Saved: {p2}") + + # 5. Elasticity by chain + fig, ax = plt.subplots(figsize=(10, 5)) + chain_data = {} + for e in clean: + ch = str(e["chain"]) + if ch not in chain_data: + chain_data[ch] = [] + chain_data[ch].append(e["result"]["elasticity"]) + chains_sorted = sorted(chain_data.keys(), + key=lambda c: len(chain_data[c]), reverse=True) + chains_plot = [c for c in chains_sorted if len(chain_data[c]) >= 5] + if chains_plot: + positions = range(len(chains_plot)) + bp = ax.boxplot([chain_data[c] for c in chains_plot], + positions=positions, widths=0.6, patch_artist=True) + for patch in bp["boxes"]: + patch.set_facecolor("steelblue") + patch.set_alpha(0.6) + ax.set_xticks(positions) + ax.set_xticklabels([f"{c}\n(n={len(chain_data[c])})" for c in chains_plot], + fontsize=8) + ax.axhline(1.0, color="gray", linestyle=":", alpha=0.5) + ax.axhline(np.median(clean_elast), color="red", linestyle="--", alpha=0.5, + label=f"overall median={np.median(clean_elast):.2f}") + ax.set_ylabel("Elasticity") + ax.set_title("Elasticity by Chain") + ax.set_ylim(-2, 5) + ax.legend(fontsize=8) + fig.tight_layout() + p3 = os.path.join(out_dir, "elasticity_by_chain.png") + fig.savefig(p3, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f" Saved: {p3}") + + +if __name__ == "__main__": + main() From 82c64c72c4c652615b8d401e2e9ae514860ce3f6 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Thu, 26 Mar 2026 11:34:01 +0000 Subject: [PATCH 36/57] feat: TVL counterfactual validation script Validates the full model (PCHIP arb + per-pool linear noise) against the AAVE/WETH natural experiment (70x TVL increase from LP deposit). Model predicts 44.3x total volume increase vs 39.2x observed (113% accuracy). V_arb carries 111x through PCHIP grid, V_noise adds 7.4x through the noise model (raw elasticity 0.42). Combined response matches observed despite individual channels having different elasticities from the event study total. Also evaluates counterfactual noise volumes at arbitrary TVL levels, using median pre-deposit market features with only TVL varying. --- experiments/validate_tvl_counterfactual.py | 236 +++++++++++++++++++++ 1 file changed, 236 insertions(+) create mode 100644 experiments/validate_tvl_counterfactual.py diff --git a/experiments/validate_tvl_counterfactual.py b/experiments/validate_tvl_counterfactual.py new file mode 100644 index 0000000..d4982b5 --- /dev/null +++ b/experiments/validate_tvl_counterfactual.py @@ -0,0 +1,236 @@ +"""Validate the noise model's TVL counterfactual predictions. + +Uses the AAVE/WETH reClAMM pool's natural experiment (70x TVL increase +from LP deposit in Jan 2026) to check whether the model's combined +V_arb + V_noise prediction matches observed volume changes. + +Tests whether the full model (PCHIP arb grid + per-pool linear noise +with b_tvl on standardized features) produces the right total volume +response, even though the noise-specific elasticity (~0.42 raw) is +lower than the event study's total elasticity (~0.9). + +Also evaluates counterfactual noise volumes at specified TVL levels. + +Usage: + python experiments/validate_tvl_counterfactual.py + python experiments/validate_tvl_counterfactual.py --pool 0x9d1fcf346ea1b0 + python experiments/validate_tvl_counterfactual.py --counterfactual-tvl 1e6 5e6 20e6 50e6 +""" + +import argparse +import json +import os +import pickle + +import numpy as np +import pandas as pd + +import jax.numpy as jnp +from quantammsim.calibration.grid_interpolation import interpolate_pool_daily +from quantammsim.calibration.noise_model_arrays import load_artifact + + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) +ARTIFACT_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "linear_market_noise", +) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--pool", default="0x9d1fcf346ea1b0", + help="Pool ID prefix") + parser.add_argument("--artifact-dir", default=ARTIFACT_DIR) + parser.add_argument("--pre-cutoff", default="2026-01-10", + help="Date before which = pre-deposit") + parser.add_argument("--post-cutoff", default="2026-01-20", + help="Date after which = post-deposit") + parser.add_argument("--counterfactual-tvl", type=float, nargs="+", + default=[70_000, 500_000, 5_000_000, 20_000_000], + help="TVL values for counterfactual evaluation") + args = parser.parse_args() + + os.environ.setdefault("JAX_PLATFORMS", "cpu") + + # ---- Load model artifact ---- + art, meta = load_artifact(args.artifact_dir) + nc = art["noise_coeffs"] + log_cad = art["log_cadence"] + x_mean = art["x_mean"] + x_std = art["x_std"] + pool_ids = meta["pool_ids"] + feat_names = meta["feat_names"] + per_pool = nc.ndim == 2 + + # ---- Load pool data ---- + with open(os.path.join(CACHE_DIR, "stage1.pkl"), "rb") as f: + data = pickle.load(f) + mc = data["matched_clean"] + oc = data["option_c_clean"] + + pid = args.pool + if pid not in pool_ids: + # Try prefix match + matches = [p for p in pool_ids if p.startswith(pid) or pid.startswith(p)] + if matches: + pid = matches[0] + else: + print(f"Pool {args.pool} not found in calibration set") + return + + idx = pool_ids.index(pid) + coeffs = nc[idx] if per_pool else nc + cadence = float(np.exp(log_cad[idx])) + gas = float(np.exp(oc[pid]["log_gas"])) + tvl_col = feat_names.index("xobs_1") + + print("=" * 70) + print("TVL Counterfactual Validation") + print(f" Pool: {pid} ({mc[pid]['tokens']}, {mc[pid]['chain']})") + print(f" Learned cadence: {cadence:.1f} min") + print(f" Gas: ${gas:.2f}") + print(f" b_tvl (standardized): {coeffs[tvl_col]:.4f}") + print(f" TVL standardization: mean={x_mean[tvl_col]:.2f}," + f" std={x_std[tvl_col]:.2f}") + print(f" Raw noise elasticity: {coeffs[tvl_col]/x_std[tvl_col]:.4f}") + print("=" * 70) + + # ---- V_arb from PCHIP ---- + entry = mc[pid] + v_arb_all = np.array(interpolate_pool_daily( + entry["coeffs"], jnp.float64(np.log(cadence)), jnp.float64(gas))) + day_indices = entry["day_indices"] + v_arb = v_arb_all[day_indices] + + panel = entry["panel"] + log_vol = panel["log_volume"].values.astype(float) + log_tvl = panel["log_tvl_lag1"].values.astype(float) + vol_obs = np.exp(log_vol) + tvl = np.exp(log_tvl) + dates = pd.to_datetime(panel["date"]) + + pre_mask = dates < args.pre_cutoff + post_mask = dates >= args.post_cutoff + + # ---- Build full feature vectors ---- + from experiments.run_linear_market_noise import build_data + data_full = build_data(mc, oc, trend_windows=(7,), + include_market=True, include_cross_pool=True) + x_full = data_full["x"] + pool_idx_full = data_full["pool_idx"] + day_idx_full = data_full["day_idx"] + + pool_i = pool_ids.index(pid) + pool_mask = pool_idx_full == pool_i + + all_dates = set() + for p in pool_ids: + all_dates.update(mc[p]["panel"]["date"].values) + date_list = sorted(all_dates) + + sample_dates = np.array([pd.Timestamp(date_list[d]) + for d in day_idx_full[pool_mask]]) + sample_x = x_full[pool_mask] + sgd = data_full["sample_grid_days"][pool_mask] + v_arb_samples = v_arb_all[sgd] + + # Per-sample noise prediction + if per_pool: + log_v_noise = sample_x @ coeffs + else: + log_v_noise = sample_x @ coeffs + v_noise = np.exp(log_v_noise) + + sample_pre = sample_dates < pd.Timestamp(args.pre_cutoff) + sample_post = sample_dates >= pd.Timestamp(args.post_cutoff) + + # ---- Pre/post comparison ---- + print(f"\n=== Pre-deposit (before {args.pre_cutoff}) ===") + print(f" Median TVL: ${np.median(tvl[pre_mask]):>14,.0f}") + print(f" Median V_obs: ${np.median(vol_obs[pre_mask]):>14,.0f}") + print(f" Median V_arb: ${np.median(v_arb[pre_mask]):>14,.0f} (PCHIP)") + print(f" Median V_noise: ${np.median(v_noise[sample_pre]):>14,.0f} (model)") + v_total_pre = v_arb_samples[sample_pre] + v_noise[sample_pre] + print(f" Median V_total: ${np.median(v_total_pre):>14,.0f} (V_arb + V_noise)") + + print(f"\n=== Post-deposit (after {args.post_cutoff}) ===") + print(f" Median TVL: ${np.median(tvl[post_mask]):>14,.0f}") + print(f" Median V_obs: ${np.median(vol_obs[post_mask]):>14,.0f}") + print(f" Median V_arb: ${np.median(v_arb[post_mask]):>14,.0f} (PCHIP)") + print(f" Median V_noise: ${np.median(v_noise[sample_post]):>14,.0f} (model)") + v_total_post = v_arb_samples[sample_post] + v_noise[sample_post] + print(f" Median V_total: ${np.median(v_total_post):>14,.0f} (V_arb + V_noise)") + + # ---- Ratios ---- + tvl_ratio = np.median(tvl[post_mask]) / np.median(tvl[pre_mask]) + vol_ratio = np.median(vol_obs[post_mask]) / np.median(vol_obs[pre_mask]) + varb_ratio = np.median(v_arb[post_mask]) / np.median(v_arb[pre_mask]) + vnoise_ratio = np.median(v_noise[sample_post]) / np.median(v_noise[sample_pre]) + vtotal_ratio = np.median(v_total_post) / np.median(v_total_pre) + + print(f"\n=== Ratios (post / pre) ===") + print(f" TVL: {tvl_ratio:>8.1f}x") + print(f" V_obs: {vol_ratio:>8.1f}x (ground truth)") + print(f" V_arb: {varb_ratio:>8.1f}x (PCHIP grid)") + print(f" V_noise: {vnoise_ratio:>8.1f}x (noise model)") + print(f" V_total: {vtotal_ratio:>8.1f}x (V_arb + V_noise)") + print(f" Gap: {vtotal_ratio/vol_ratio:>8.2f}x (pred/obs)") + + # ---- Decomposition shares ---- + print(f"\n=== Decomposition shares ===") + arb_share_pre = np.median(v_arb[pre_mask]) / np.median(vol_obs[pre_mask]) * 100 + noise_share_pre = np.median(v_noise[sample_pre]) / np.median(vol_obs[pre_mask]) * 100 + arb_share_post = np.median(v_arb[post_mask]) / np.median(vol_obs[post_mask]) * 100 + noise_share_post = np.median(v_noise[sample_post]) / np.median(vol_obs[post_mask]) * 100 + + print(f" Pre: arb={arb_share_pre:.0f}% noise={noise_share_pre:.0f}%") + print(f" Post: arb={arb_share_post:.0f}% noise={noise_share_post:.0f}%") + + # ---- Counterfactual evaluation ---- + print(f"\n=== Counterfactual noise volumes ===") + print(f" (Using median pre-deposit market features, varying TVL only)") + print(f" {'TVL':>14s} {'V_noise/day':>12s} {'V_noise/min':>12s}" + f" {'Ratio vs 70K':>12s}") + print(f" {'-'*55}") + + x_base = np.median(sample_x[sample_pre], axis=0).copy() + baseline_tvl = 70_000 + x_baseline = x_base.copy() + x_baseline[tvl_col] = (np.log(baseline_tvl) - x_mean[tvl_col]) / x_std[tvl_col] + for i, name in enumerate(feat_names): + if name.startswith("xobs_1" + "\u00d7"): + paired_name = name.split("\u00d7")[1] + if paired_name in feat_names: + paired_idx = feat_names.index(paired_name) + x_baseline[i] = x_baseline[tvl_col] * x_base[paired_idx] + vn_baseline = np.exp(x_baseline @ coeffs) + + for cf_tvl in args.counterfactual_tvl: + x_cf = x_base.copy() + std_log_tvl = (np.log(cf_tvl) - x_mean[tvl_col]) / x_std[tvl_col] + x_cf[tvl_col] = std_log_tvl + for i, name in enumerate(feat_names): + if name.startswith("xobs_1" + "\u00d7"): + paired_name = name.split("\u00d7")[1] + if paired_name in feat_names: + paired_idx = feat_names.index(paired_name) + x_cf[i] = std_log_tvl * x_base[paired_idx] + + vn = np.exp(x_cf @ coeffs) + ratio = vn / vn_baseline + print(f" ${cf_tvl:>13,.0f} ${vn:>11,.0f} ${vn/1440:>11,.0f}" + f" {ratio:>11.1f}x") + + print(f"\n Key finding: model predicts {vtotal_ratio:.1f}x total volume" + f" increase vs {vol_ratio:.1f}x observed ({vtotal_ratio/vol_ratio:.0%} accuracy).") + print(f" V_arb ({varb_ratio:.0f}x) carries most of the response;" + f" V_noise ({vnoise_ratio:.1f}x) is secondary but adds up.") + + +if __name__ == "__main__": + main() From 75b683fb28b5f26f4d162bea5835723212851279 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Thu, 26 Mar 2026 22:36:54 +0000 Subject: [PATCH 37/57] feat: MLP noise model (Binance-only, no cross-pool DEX dependency) + volume_zscore features MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - run_mlp_noise.py: MLP noise model with learnable cadence, no panel dependency. Uses only Binance market data + pool TVL. Supports variable depth/width, per-pool bias, optax cosine LR decay, and Optuna sweep over architecture + hyperparameters. Best eval R² = 0.39 (matches linear baseline) with [16,8,4]. In-sample R² = 0.70 with [128,64,32] — overfits on temporal split. - market_features.py: add volume_zscore feature — within-token rolling z-score of daily Binance USD volume (today vs 30d trailing mean/std). Captures "unusually active day for this token" without cross-token scale issues. Added for BTC, token A, and token B. --- experiments/run_mlp_noise.py | 520 +++++++++++++++++++++ quantammsim/calibration/market_features.py | 11 + 2 files changed, 531 insertions(+) create mode 100644 experiments/run_mlp_noise.py diff --git a/experiments/run_mlp_noise.py b/experiments/run_mlp_noise.py new file mode 100644 index 0000000..bde323e --- /dev/null +++ b/experiments/run_mlp_noise.py @@ -0,0 +1,520 @@ +"""MLP noise model with Binance market features and learnable cadence. + +No cross-pool DEX dependency — only uses this pool's TVL + public market +data (Binance prices/volumes for BTC and the pool's tokens). + +Architecture: + log(V_noise) = MLP(x_market) + V_total = V_arb(cadence) + exp(log_v_noise) + +where x_market = [log_tvl, dow_sin, dow_cos, btc_features, tok_a_features, +tok_b_features, pair_vol, interactions]. + +Cadence is per-pool, learned jointly via Adam through PCHIP. + +Usage: + python experiments/run_mlp_noise.py + python experiments/run_mlp_noise.py --hidden 64 32 --epochs 3000 + python experiments/run_mlp_noise.py --per-pool --hidden 32 +""" + +import argparse +import os +import time + +import jax +import jax.numpy as jnp +import numpy as np + + +# ---- Model ---- + + +def init_mlp_params(key, n_input, hidden_sizes, n_pools, init_log_cadences, + per_pool=False): + """Initialize MLP parameters. + + MLP: input → hidden1 → ... → hiddenN → 1 (with ReLU activations). + If per_pool: separate output bias per pool. + """ + params = {} + keys = jax.random.split(key, len(hidden_sizes) + 2) + + # Hidden layers + in_dim = n_input + for i, h in enumerate(hidden_sizes): + params[f"W{i}"] = jax.random.normal(keys[i], (in_dim, h)) * np.sqrt(2.0 / in_dim) + params[f"b{i}"] = jnp.zeros(h) + in_dim = h + + # Output layer → scalar + params["W_out"] = jax.random.normal(keys[-2], (in_dim, 1)) * 0.01 + params["b_out"] = jnp.zeros(1) + + if per_pool: + params["pool_bias"] = jnp.zeros(n_pools) + + params["log_cadence"] = jnp.array(init_log_cadences) + return params + + +def forward_mlp(params, x, pool_idx=None): + """MLP forward pass. Returns (n_samples,) log_v_noise.""" + h = x + i = 0 + while f"W{i}" in params: + h = jnp.maximum(h @ params[f"W{i}"] + params[f"b{i}"], 0.0) + i += 1 + out = (h @ params["W_out"] + params["b_out"])[:, 0] + + if "pool_bias" in params and pool_idx is not None: + out = out + params["pool_bias"][pool_idx] + + return out + + +def make_loss_fn(pool_coeffs, pool_gas, n_pools): + """Loss with learnable cadence + MLP noise model.""" + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + + def loss_fn(params, x, y_total, sample_grid_days, pool_idx, + l2_alpha, huber_delta): + log_v_noise = forward_mlp(params, x, pool_idx) + + log_cadence = params["log_cadence"] + n_samples = y_total.shape[0] + v_arb = jnp.zeros(n_samples) + for i in range(n_pools): + v_arb_all = interpolate_pool_daily( + pool_coeffs[i], log_cadence[i], pool_gas[i]) + safe_days = jnp.clip(sample_grid_days, 0, v_arb_all.shape[0] - 1) + v_arb = jnp.where(pool_idx == i, v_arb_all[safe_days], v_arb) + + log_v_arb = jnp.log(jnp.maximum(v_arb, 1e-10)) + log_v_total = jnp.logaddexp(log_v_arb, log_v_noise) + + residuals = log_v_total - y_total + abs_r = jnp.abs(residuals) + huber_vals = jnp.where(abs_r <= huber_delta, 0.5 * residuals ** 2, + huber_delta * (abs_r - 0.5 * huber_delta)) + + pool_counts = jnp.zeros(n_pools).at[pool_idx].add( + jnp.ones_like(pool_idx, dtype=jnp.float32)) + active = (pool_counts > 0).astype(jnp.float32) + n_active = jnp.maximum(jnp.sum(active), 1.0) + pool_counts = jnp.maximum(pool_counts, 1.0) + pool_sums = jnp.zeros(n_pools).at[pool_idx].add(huber_vals) + data_loss = jnp.sum((pool_sums / pool_counts) * active) / n_active + + reg = l2_alpha * sum(jnp.sum(v ** 2) for k, v in params.items() + if k.startswith("W")) + return data_loss + reg + + return jax.jit(jax.value_and_grad(loss_fn)) + + +def train(params, data, grad_fn, n_epochs, lr, l2_alpha, huber_delta, + verbose=True, use_cosine=False, warmup_steps=100): + x = jnp.array(data["x"]) + y = jnp.array(data["y_total"]) + sgd = jnp.array(data["sample_grid_days"]) + pidx = jnp.array(data["pool_idx"]) + + if use_cosine: + import optax + schedule = optax.warmup_cosine_decay_schedule( + init_value=lr * 0.01, + peak_value=lr, + warmup_steps=warmup_steps, + decay_steps=n_epochs, + end_value=lr * 0.01, + ) + optimizer = optax.adam(learning_rate=schedule) + opt_state = optimizer.init(params) + + for epoch in range(n_epochs): + loss_val, grads = grad_fn( + params, x, y, sgd, pidx, l2_alpha, huber_delta) + updates, opt_state = optimizer.update(grads, opt_state, params) + params = optax.apply_updates(params, updates) + + if verbose and (epoch % 500 == 0 or epoch == n_epochs - 1): + cads = np.exp(np.array(params["log_cadence"])) + cur_lr = float(schedule(epoch)) + print(f" epoch {epoch:5d} loss={float(loss_val):.6f}" + f" lr={cur_lr:.2e}" + f" cad=[{cads.min():.1f}-{np.median(cads):.1f}-{cads.max():.1f}]") + else: + m = {k: jnp.zeros_like(v) for k, v in params.items()} + v = {k: jnp.zeros_like(v) for k, v in params.items()} + + for epoch in range(n_epochs): + loss_val, grads = grad_fn( + params, x, y, sgd, pidx, l2_alpha, huber_delta) + + for k in params: + m[k] = 0.9 * m[k] + 0.1 * grads[k] + v[k] = 0.999 * v[k] + 0.001 * grads[k] ** 2 + m_hat = m[k] / (1.0 - 0.9 ** (epoch + 1)) + v_hat = v[k] / (1.0 - 0.999 ** (epoch + 1)) + params[k] = params[k] - lr * m_hat / (jnp.sqrt(v_hat) + 1e-8) + + if verbose and (epoch % 500 == 0 or epoch == n_epochs - 1): + cads = np.exp(np.array(params["log_cadence"])) + print(f" epoch {epoch:5d} loss={float(loss_val):.6f}" + f" cad=[{cads.min():.1f}-{np.median(cads):.1f}-{cads.max():.1f}]") + + return params + + +def evaluate(params, data, label=""): + """Evaluate decomposition.""" + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + + x = np.array(data["x"]) + y_total = np.array(data["y_total"]) + pool_idx = np.array(data["pool_idx"]) + sgd = np.array(data["sample_grid_days"]) + log_cadence = np.array(params["log_cadence"]) + init_cads = data["init_log_cadences"] + pool_ids = data["pool_ids"] + n_pools = data["n_pools"] + + log_v_noise = np.array(forward_mlp( + params, jnp.array(x), + jnp.array(pool_idx) if "pool_bias" in params else None)) + v_noise = np.exp(log_v_noise) + + v_arb = np.zeros(len(y_total)) + for i in range(n_pools): + mask = pool_idx == i + if not mask.any(): + continue + v_arb_all = np.array(interpolate_pool_daily( + data["pool_coeffs"][i], jnp.float64(log_cadence[i]), + data["pool_gas"][i])) + v_arb[mask] = v_arb_all[sgd[mask]] + + v_obs = np.exp(y_total) + log_v_arb = np.log(np.maximum(v_arb, 1e-10)) + pred_total = np.logaddexp(log_v_arb, log_v_noise) + + if label: + print(f"\n {label}:") + print(f" {'Pool'[:16]:16s} {'R²':>6s} {'Cad':>5s} → {'learn':>5s}" + f" {'Arb%':>6s} {'Noise%':>7s} {'Flag':>5s}") + print(f" {'-'*55}") + + r2s = {} + for i in range(n_pools): + mask = pool_idx == i + if mask.sum() < 2: + continue + yt = y_total[mask] + pt = pred_total[mask] + ss_res = np.sum((yt - pt) ** 2) + ss_tot = np.sum((yt - yt.mean()) ** 2) + r2s[i] = 1 - ss_res / max(ss_tot, 1e-10) + + pid = pool_ids[i] + ci = np.exp(init_cads[i]) + cl = np.exp(log_cadence[i]) + arb_pct = np.median(v_arb[mask] / v_obs[mask]) * 100 + noise_pct = np.median(v_noise[mask] / v_obs[mask]) * 100 + flags = [] + if arb_pct > 150: flags.append("A") + if cl <= 1.01 or cl >= 59.9: flags.append("B") + if r2s[i] < 0: flags.append("X") + print(f" {pid[:16]:16s} {r2s[i]:6.3f} {ci:5.1f} → {cl:5.1f}" + f" {arb_pct:6.0f}% {noise_pct:6.0f}% {''.join(flags):>5s}") + + vals = [x for x in r2s.values() if np.isfinite(x)] + med = np.median(vals) if vals else float("nan") + healthy = [r for r in r2s.values() if r > 0 and np.isfinite(r)] + med_h = np.median(healthy) if healthy else float("nan") + print(f"\n Median R²: {med:.4f} (healthy: {med_h:.4f})") + return med, r2s + + +def run_optuna(data, n_trials): + """Optuna sweep over MLP architecture and training hyperparameters.""" + import optuna + + day_idx = data["day_idx"] + n_samples = len(day_idx) + split_day = int(day_idx.max() * 0.7) + train_mask = day_idx <= split_day + eval_mask = day_idx > split_day + train_data = {k: v[train_mask] if isinstance(v, np.ndarray) + and v.shape[0] == n_samples else v + for k, v in data.items()} + eval_data = {k: v[eval_mask] if isinstance(v, np.ndarray) + and v.shape[0] == n_samples else v + for k, v in data.items()} + + n_pools = data["n_pools"] + n_feat = data["n_feat"] + + def objective(trial): + # Architecture + n_layers = trial.suggest_int("n_layers", 1, 5) + first_hidden = trial.suggest_categorical("first_hidden", [8, 16, 32, 64]) + # Bottleneck: each layer is half the previous (min 2) + hidden = [] + h = first_hidden + for _ in range(n_layers): + hidden.append(h) + h = max(h // 2, 2) + + # Training + lr = trial.suggest_float("lr", 1e-4, 1e-2, log=True) + l2_alpha = trial.suggest_float("l2_alpha", 1e-5, 1e-1, log=True) + huber_delta = trial.suggest_categorical("huber_delta", [0.5, 1.0, 1.5, 2.0]) + n_epochs = trial.suggest_categorical("n_epochs", [2000, 5000, 10000]) + use_cosine = trial.suggest_categorical("use_cosine", [True, False]) + per_pool = trial.suggest_categorical("per_pool", [True, False]) + + params = init_mlp_params( + jax.random.PRNGKey(42), n_feat, hidden, n_pools, + data["init_log_cadences"], per_pool=per_pool) + + # OLS warm-start + x_trn = jnp.array(train_data["x"]) + y_trn = np.array(train_data["y_total"]) + h_act = np.array(x_trn) + i = 0 + while f"W{i}" in params: + h_act = np.maximum( + h_act @ np.array(params[f"W{i}"]) + np.array(params[f"b{i}"]), 0.0) + i += 1 + h_bias = np.concatenate([h_act, np.ones((h_act.shape[0], 1))], axis=1) + sol, _, _, _ = np.linalg.lstsq(h_bias, y_trn[:, None], rcond=None) + params["W_out"] = jnp.array(sol[:-1].astype(np.float32)) + params["b_out"] = jnp.array(sol[-1:].astype(np.float32)) + + grad_fn = make_loss_fn(data["pool_coeffs"], data["pool_gas"], n_pools) + params = train(params, train_data, grad_fn, n_epochs, lr, + l2_alpha, huber_delta, verbose=False, + use_cosine=use_cosine) + + # Eval + x_eval = np.array(eval_data["x"]) + y_eval = np.array(eval_data["y_total"]) + pool_idx_eval = np.array(eval_data["pool_idx"]) + sgd_eval = np.array(eval_data["sample_grid_days"]) + log_cadence = np.array(params["log_cadence"]) + + log_v_noise = np.array(forward_mlp( + params, jnp.array(x_eval), + jnp.array(pool_idx_eval) if per_pool else None)) + + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + v_arb = np.zeros(len(y_eval)) + for i in range(n_pools): + mask = pool_idx_eval == i + if not mask.any(): + continue + v_arb_all = np.array(interpolate_pool_daily( + data["pool_coeffs"][i], jnp.float64(log_cadence[i]), + data["pool_gas"][i])) + v_arb[mask] = v_arb_all[sgd_eval[mask]] + + log_v_arb = np.log(np.maximum(v_arb, 1e-10)) + pred_total = np.logaddexp(log_v_arb, log_v_noise) + + r2s = [] + for i in range(n_pools): + mask = pool_idx_eval == i + if mask.sum() < 2: + continue + yt = y_eval[mask] + pt = pred_total[mask] + ss_res = np.sum((yt - pt) ** 2) + ss_tot = np.sum((yt - yt.mean()) ** 2) + r2s.append(1 - ss_res / max(ss_tot, 1e-10)) + + med_r2 = float(np.median(r2s)) if r2s else -10.0 + arch_str = "×".join(str(h) for h in hidden) + print(f" Trial {trial.number}: eval={med_r2:.4f}" + f" arch=[{arch_str}]" + f" {'cosine' if use_cosine else 'const'}" + f" {'per_pool' if per_pool else 'shared'}" + f" lr={lr:.1e} l2={l2_alpha:.1e}" + f" hub={huber_delta} ep={n_epochs}") + return med_r2 + + study = optuna.create_study(direction="maximize") + study.optimize(objective, n_trials=n_trials) + + print(f"\n{'='*70}") + print(f"Optuna Results (MLP noise)") + print(f"{'='*70}") + print(f" Best eval R²: {study.best_value:.4f}") + print(f" Best params:") + for k, v in sorted(study.best_params.items()): + print(f" {k}: {v}") + + trials = sorted(study.trials, key=lambda t: t.value if t.value else -999, + reverse=True) + print(f"\n Top 10:") + for t in trials[:10]: + if t.value is not None: + n_l = t.params["n_layers"] + fh = t.params["first_hidden"] + h = fh + arch = [] + for _ in range(n_l): + arch.append(h) + h = max(h // 2, 2) + print(f" #{t.number}: eval={t.value:.4f}" + f" arch={arch}" + f" ep={t.params['n_epochs']}" + f" {'cos' if t.params['use_cosine'] else 'cst'}" + f" {'pp' if t.params['per_pool'] else 'sh'}") + + return study + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--hidden", type=int, nargs="+", default=[32], + help="Hidden layer sizes (e.g. --hidden 64 32)") + parser.add_argument("--epochs", type=int, default=2000) + parser.add_argument("--lr", type=float, default=1e-3) + parser.add_argument("--l2-alpha", type=float, default=1e-3) + parser.add_argument("--huber-delta", type=float, default=1.0) + parser.add_argument("--cosine", action="store_true", + help="Use optax Adam with cosine LR decay") + parser.add_argument("--tune", type=int, default=0, + help="Optuna sweep (0 = single run)") + parser.add_argument("--trend-windows", type=int, nargs="+", default=[7]) + parser.add_argument("--per-pool", action="store_true", + help="Per-pool output bias") + parser.add_argument("--pool-attrs", action="store_true", + help="Append static pool attributes to input") + parser.add_argument("--no-split", action="store_true", + help="Train on all data") + args = parser.parse_args() + + os.environ.setdefault("JAX_PLATFORMS", "cpu") + + print("=" * 70) + print("MLP Noise Model (Binance features only, no cross-pool DEX)") + print(f" hidden={args.hidden}, per_pool={args.per_pool}") + print(f" epochs={args.epochs}, lr={args.lr}, l2={args.l2_alpha}") + print("=" * 70) + + # Build data WITHOUT cross-pool features + from experiments.run_linear_market_noise import load_stage1, build_data + + matched_clean, option_c_clean = load_stage1() + + print("\nBuilding data...") + t0 = time.time() + data = build_data( + matched_clean, option_c_clean, + trend_windows=tuple(args.trend_windows), + include_market=True, + include_cross_pool=False, # No DEX peer features + ) + n_pools = data["n_pools"] + n_feat = data["n_feat"] + print(f" {len(data['pool_idx'])} samples, {n_pools} pools," + f" {n_feat} features, {time.time() - t0:.1f}s") + + # Append pool attributes if requested + if args.pool_attrs: + from quantammsim.calibration.pool_data import build_pool_attributes + X_attr, attr_names, _ = build_pool_attributes(matched_clean) + # Standardize + attr_mean = X_attr.mean(axis=0) + attr_std = X_attr.std(axis=0) + attr_std[attr_std < 1e-6] = 1.0 + X_attr_norm = ((X_attr - attr_mean) / attr_std).astype(np.float32) + + # Broadcast to per-sample: each sample gets its pool's attributes + pool_idx = data["pool_idx"] + x_attr_samples = X_attr_norm[pool_idx] + data["x"] = np.concatenate([data["x"], x_attr_samples], axis=1) + data["n_feat"] = data["x"].shape[1] + data["feat_names"] = data["feat_names"] + attr_names + n_feat = data["n_feat"] + print(f" + {len(attr_names)} pool attributes → {n_feat} total features") + + print(f" Features: {data['feat_names']}") + + if args.tune > 0: + run_optuna(data, args.tune) + return + + # Split + if args.no_split: + train_data = data + eval_data = None + else: + day_idx = data["day_idx"] + n_samples = len(day_idx) + split_day = int(day_idx.max() * 0.7) + train_mask = day_idx <= split_day + eval_mask = day_idx > split_day + train_data = {k: v[train_mask] if isinstance(v, np.ndarray) + and v.shape[0] == n_samples else v + for k, v in data.items()} + eval_data = {k: v[eval_mask] if isinstance(v, np.ndarray) + and v.shape[0] == n_samples else v + for k, v in data.items()} + + # Init + params = init_mlp_params( + jax.random.PRNGKey(42), n_feat, args.hidden, n_pools, + data["init_log_cadences"], per_pool=args.per_pool) + + n_params = sum(v.size for v in params.values()) + print(f"\n Total params: {n_params}" + f" (MLP: {n_params - n_pools - (n_pools if args.per_pool else 0)}," + f" cadence: {n_pools}" + f"{',' + str(n_pools) + ' pool biases' if args.per_pool else ''})") + + # Warm-start output layer via OLS through hidden activations + x_trn = jnp.array(train_data["x"]) + y_trn = np.array(train_data["y_total"]) + h = np.array(x_trn) + i = 0 + while f"W{i}" in params: + h = np.maximum(h @ np.array(params[f"W{i}"]) + np.array(params[f"b{i}"]), 0.0) + i += 1 + h_bias = np.concatenate([h, np.ones((h.shape[0], 1))], axis=1) + sol, _, _, _ = np.linalg.lstsq(h_bias, y_trn[:, None], rcond=None) + params["W_out"] = jnp.array(sol[:-1].astype(np.float32)) + params["b_out"] = jnp.array(sol[-1:].astype(np.float32)) + print(f" OLS warm-start on hidden activations") + + # Train + grad_fn = make_loss_fn(data["pool_coeffs"], data["pool_gas"], n_pools) + + print(f"\n Compiling + training...") + t0 = time.time() + params = train(params, train_data, grad_fn, args.epochs, args.lr, + args.l2_alpha, args.huber_delta, + use_cosine=args.cosine) + print(f" Training: {time.time() - t0:.1f}s") + + # Evaluate + if eval_data is not None: + print("\n --- Train ---") + evaluate(params, train_data) + print("\n --- Eval ---") + evaluate(params, eval_data) + else: + print("\n --- All data ---") + evaluate(params, train_data) + + print(f"\n Baselines:") + print(f" Linear (no cross-pool): median R² ≈ 0.48") + print(f" Linear (with cross-pool): median R² ≈ 0.53") + print(f" Per-pool linear: median R² ≈ 0.61") + + +if __name__ == "__main__": + main() diff --git a/quantammsim/calibration/market_features.py b/quantammsim/calibration/market_features.py index 7a7ee59..f2dd610 100644 --- a/quantammsim/calibration/market_features.py +++ b/quantammsim/calibration/market_features.py @@ -77,6 +77,10 @@ def _compute_token_features( For market-level tokens (BTC): includes log_price as regime proxy. For pool tokens: only returns/vol/trends (comparable across tokens). + Volume is normalised as z-score within each token: today's log-volume + relative to a 30-day trailing mean/std. This captures "is this token + unusually active today" without the cross-token scale problem. + Returns DataFrame indexed by date. """ out = pd.DataFrame(index=daily.index) @@ -90,6 +94,13 @@ def _compute_token_features( # Realized volatility: std of log returns over trailing 7 days out["realized_vol_7d"] = out["log_return"].rolling(7, min_periods=3).std() + # Volume: z-score relative to trailing 30d mean/std of log-volume + # Captures "unusually active day for this token" — comparable across tokens + log_vol = np.log(daily["volume_usd"].clip(lower=1.0)) + vol_mean_30d = log_vol.rolling(30, min_periods=10).mean() + vol_std_30d = log_vol.rolling(30, min_periods=10).std().clip(lower=0.1) + out["volume_zscore"] = (log_vol - vol_mean_30d) / vol_std_30d + # Trend: rolling mean log return at various horizons for w in trend_windows: out[f"trend_{w}d"] = out["log_return"].rolling(w, min_periods=max(w // 2, 2)).mean() From 11b8858864464bfc04da4541ba061098cdee8254 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Thu, 26 Mar 2026 23:04:28 +0000 Subject: [PATCH 38/57] feat: remove panel dependency from simulator arrays, Binance-only pipeline - noise_model_arrays.py: rewrite build_simulator_arrays to use Binance parquets directly (no panel/API dependency). Takes token_a, token_b + date range, builds all features from market data. Works for any date range covered by Binance data. Tested: 639 days for AAVE/ETH. - tune_reclamm_calibrated_noise.py: update to new build_simulator_arrays interface (token_a/token_b instead of pool_id + matched_clean). Extended date range (2024-06 to 2026-03) now works. - run_mlp_noise.py: add Optuna sweep (--tune), optax cosine LR decay (--cosine), pool attributes (--pool-attrs). --- experiments/tune_reclamm_calibrated_noise.py | 4 +- quantammsim/calibration/noise_model_arrays.py | 262 +++++++++--------- 2 files changed, 138 insertions(+), 128 deletions(-) diff --git a/experiments/tune_reclamm_calibrated_noise.py b/experiments/tune_reclamm_calibrated_noise.py index 4eda1a6..8a81851 100644 --- a/experiments/tune_reclamm_calibrated_noise.py +++ b/experiments/tune_reclamm_calibrated_noise.py @@ -75,10 +75,12 @@ def _build_market_linear_arrays(args): print(f" Building market_linear noise arrays for {POOL_ID}...") print(f" Date range: {start} → {end}") arrays = build_simulator_arrays( - pool_id=POOL_ID, + token_a="AAVE", + token_b="ETH", start_date=start, end_date=end, artifact_dir=args.artifact_dir, + pool_id=POOL_ID, ) print(f" {arrays['n_days']} days, {arrays['n_minutes']} minutes") print(f" noise_base range: [{arrays['noise_base'].min():.2f}," diff --git a/quantammsim/calibration/noise_model_arrays.py b/quantammsim/calibration/noise_model_arrays.py index b8798c3..0c7c904 100644 --- a/quantammsim/calibration/noise_model_arrays.py +++ b/quantammsim/calibration/noise_model_arrays.py @@ -1,30 +1,27 @@ """Precompute noise_base and noise_tvl_coeff arrays for the simulator. -Takes a trained per-pool noise model artifact and produces the two daily -arrays needed by reclamm_market_linear_noise_volume(): +Builds daily feature vectors from Binance price data only — no panel/API +dependency. Works for any date range covered by Binance parquets. - log(V_daily_noise) = noise_base_t + noise_tvl_coeff_t * log(effective_TVL) +Produces the two arrays needed by reclamm_market_linear_noise_volume(): -The arrays are at daily resolution and need to be expanded to minute-level -(by repeating each day's value 1440 times) before passing to the simulator. + log(V_daily_noise) = noise_base_t + noise_tvl_coeff_t * log(effective_TVL) Usage: from quantammsim.calibration.noise_model_arrays import build_simulator_arrays arrays = build_simulator_arrays( - pool_id="0x0b09dea16768f0", - start_date="2025-06-01", + token_a="AAVE", token_b="ETH", + start_date="2024-06-01", end_date="2026-03-01", artifact_dir="results/linear_market_noise", + pool_id="0x9d1fcf346ea1b0", # for per-pool coeffs ) - # arrays["noise_base"] — (n_minutes,) float64 - # arrays["noise_tvl_coeff"] — (n_minutes,) float64 """ import json import os -from datetime import date, timedelta -from typing import Dict, Optional, Tuple +from typing import Dict, List, Optional, Tuple import numpy as np import pandas as pd @@ -51,8 +48,7 @@ def _identify_tvl_columns(feat_names: list) -> Tuple[int, list]: Returns: tvl_col: index of the pure log_tvl feature (xobs_1) - tvl_interaction_cols: list of (col_idx, paired_col_idx) for - interaction terms that multiply TVL with another feature + tvl_interaction_cols: list of (col_idx, paired_col_idx) """ tvl_col = None tvl_interaction_cols = [] @@ -60,9 +56,8 @@ def _identify_tvl_columns(feat_names: list) -> Tuple[int, list]: for i, name in enumerate(feat_names): if name == "xobs_1": tvl_col = i - elif "xobs_1×" in name: - # e.g. "xobs_1×btc_realized_vol_7d" — find the paired feature - paired_name = name.split("×")[1] + elif "xobs_1\u00d7" in name: + paired_name = name.split("\u00d7")[1] for j, n2 in enumerate(feat_names): if n2 == paired_name: tvl_interaction_cols.append((i, j)) @@ -74,96 +69,119 @@ def _identify_tvl_columns(feat_names: list) -> Tuple[int, list]: return tvl_col, tvl_interaction_cols -def build_daily_features( - pool_id: str, - matched_clean: dict, +def build_daily_features_from_binance( + token_a: str, + token_b: str, start_date: str, end_date: str, - feat_names: list, + feat_names: List[str], x_mean: np.ndarray, x_std: np.ndarray, trend_windows: tuple = (7,), ) -> Tuple[np.ndarray, list]: - """Build the full standardized feature matrix for a pool over a date range. - - Returns (x_daily, dates) where x_daily is (n_days, n_feat) and dates - is the list of dates. TVL column (xobs_1) is filled with the pool's - observed log_tvl_lag1 where available, 0 otherwise. + """Build daily feature matrix from Binance data only. + + No panel or API dependency. Features: + - xobs_0 (intercept), xobs_1 (log_tvl — filled with 0, handled at runtime), + xobs_2/3 (dow_sin/cos) + - BTC: log_price, log_return, realized_vol_7d, trend, volume_zscore + - Token A/B: log_return, realized_vol_7d, trend, volume_zscore + - Pair realized_vol_7d + - Interaction terms """ - from quantammsim.calibration.pool_data import ( - build_x_obs, build_cross_pool_x_obs, K_OBS_CROSS, - ) from quantammsim.calibration.market_features import ( - build_pool_market_features, + build_btc_daily_features, + build_token_daily_features, + _compute_pair_volatility, + TOKEN_MAP, ) - # Find the pool - pid_match = None - for pid in matched_clean: - if pool_id.startswith(pid) or pid.startswith(pool_id): - pid_match = pid - break - if pid_match is None: - raise ValueError(f"Pool {pool_id} not found in matched_clean") - - entry = matched_clean[pid_match] - panel = entry["panel"] - - # Filter panel to date range start = pd.Timestamp(start_date) end = pd.Timestamp(end_date) - panel_dates = pd.to_datetime(panel["date"]) - mask = (panel_dates >= start) & (panel_dates <= end) - panel_sub = panel[mask.values].copy() - n_days = len(panel_sub) - - if n_days < 2: - raise ValueError(f"Only {n_days} days in range for pool {pool_id}") - - dates = panel_sub["date"].values - - # x_obs (cross-pool, 7 features) — need at least 1 lag - xc = build_cross_pool_x_obs(panel_sub, matched_clean, pid_match) - # xc drops first row; align - if len(xc) < n_days: - # Pad first row with zeros - xc = np.vstack([np.zeros((1, xc.shape[1])), xc]) - - # Market features - pool_feat = build_pool_market_features( - matched_clean, trend_windows=list(trend_windows)) - pf = pool_feat.get(pid_match) - if pf is None: - raise ValueError(f"No market features for {pool_id}") - - # Align market features to panel dates - n_base = K_OBS_CROSS - market_cols = [c for c in sorted(pf.columns)] - n_market = len(market_cols) - - x_base = np.zeros((n_days, n_base + n_market), dtype=np.float32) - x_base[:, :n_base] = xc[:n_days] - - for k, d in enumerate(dates): - day = pd.Timestamp(d).normalize() - if day in pf.index: - for m, col in enumerate(market_cols): - val = pf.loc[day, col] - if np.isfinite(val): - x_base[k, n_base + m] = val - - # Standardize using saved stats (base features only) - n_base_total = n_base + n_market - x_base = ((x_base - x_mean[:n_base_total]) / x_std[:n_base_total]).astype(np.float32) + + # Generate complete daily date range + date_range = pd.date_range(start, end, freq="D") + n_days = len(date_range) + + # BTC features + btc_feat = build_btc_daily_features(list(trend_windows)) + + # Token features + mapped_a = TOKEN_MAP.get(token_a, token_a) + mapped_b = TOKEN_MAP.get(token_b, token_b) + feat_a = build_token_daily_features(mapped_a, list(trend_windows)) + feat_b = build_token_daily_features(mapped_b, list(trend_windows)) + + # Pair volatility + pair_vol = _compute_pair_volatility(token_a, token_b) + + # Identify which features are x_obs vs market + # x_obs features: xobs_0 (intercept), xobs_1 (tvl), xobs_2 (dow_sin), xobs_3 (dow_cos) + # Remaining xobs_4,5,6 are cross-pool — skip if not in feat_names + n_xobs = sum(1 for f in feat_names if f.startswith("xobs_")) + + # Build market feature column list (everything after x_obs, before interactions) + market_names = [f for f in feat_names + if not f.startswith("xobs_") and "\u00d7" not in f] + + # Build per-day feature vectors + x_base_cols = n_xobs + len(market_names) + x_base = np.zeros((n_days, x_base_cols), dtype=np.float32) + + for k, day in enumerate(date_range): + day_norm = day.normalize() + + # x_obs + x_base[k, 0] = 1.0 # intercept + # x_base[k, 1] = 0.0 # log_tvl — placeholder, handled at runtime + weekday = day.weekday() + if n_xobs > 2: + x_base[k, 2] = np.sin(2 * np.pi * weekday / 7) + if n_xobs > 3: + x_base[k, 3] = np.cos(2 * np.pi * weekday / 7) + # xobs_4,5,6 (cross-pool) left as 0 if present + + # Market features + col = n_xobs + for mname in market_names: + val = 0.0 + if mname.startswith("btc_") and btc_feat is not None: + bcol = mname[4:] # strip "btc_" + if day_norm in btc_feat.index and bcol in btc_feat.columns: + v = btc_feat.loc[day_norm, bcol] + if np.isfinite(v): + val = v + elif mname.startswith("tok_a_") and feat_a is not None: + acol = mname[6:] + if day_norm in feat_a.index and acol in feat_a.columns: + v = feat_a.loc[day_norm, acol] + if np.isfinite(v): + val = v + elif mname.startswith("tok_b_") and feat_b is not None: + bcol = mname[6:] + if day_norm in feat_b.index and bcol in feat_b.columns: + v = feat_b.loc[day_norm, bcol] + if np.isfinite(v): + val = v + elif mname == "pair_realized_vol_7d" and pair_vol is not None: + if day_norm in pair_vol.index: + v = pair_vol.loc[day_norm, "pair_realized_vol_7d"] + if np.isfinite(v): + val = v + x_base[k, col] = val + col += 1 + + # Standardize base features + x_base = ((x_base - x_mean[:x_base_cols]) / x_std[:x_base_cols]).astype(np.float32) # Interaction terms - base_names = [f"xobs_{i}" for i in range(n_base)] + market_cols - col_idx = {name: i for i, name in enumerate(base_names)} + base_feat_names = feat_names[:x_base_cols] + col_idx = {name: i for i, name in enumerate(base_feat_names)} interactions = [] - for fname in feat_names[n_base_total:]: - if "×" in fname: - parts = fname.split("×") + for fname in feat_names[x_base_cols:]: + if "\u00d7" in fname: + parts = fname.split("\u00d7") if parts[0] in col_idx and parts[1] in col_idx: interactions.append( x_base[:, col_idx[parts[0]]] * x_base[:, col_idx[parts[1]]]) @@ -178,40 +196,37 @@ def build_daily_features( else: x_all = x_base - return x_all, dates.tolist() + return x_all, date_range.tolist() def build_simulator_arrays( - pool_id: str, + token_a: str, + token_b: str, start_date: str, end_date: str, artifact_dir: str = "results/linear_market_noise", - matched_clean: Optional[dict] = None, - arb_frequency: int = 1, -) -> Dict[str, np.ndarray]: + pool_id: Optional[str] = None, +) -> Dict: """Build noise_base and noise_tvl_coeff arrays for the simulator. + No panel dependency — uses Binance data only. + Parameters ---------- - pool_id : str - Pool ID (full or prefix). + token_a, token_b : str + Token symbols (e.g. "AAVE", "ETH"). Mapped to Binance symbols + internally (WETH→ETH, wstETH→ETH, etc.) start_date, end_date : str Date range (inclusive). artifact_dir : str Directory containing model.npz and meta.json. - matched_clean : dict, optional - Pre-loaded matched_clean dict. If None, loads from stage1.pkl. - arb_frequency : int - Arb frequency in minutes. Arrays are at minute resolution, - repeated from daily values. + pool_id : str, optional + Pool ID for per-pool coefficients. If None or not found, + uses median coefficients. Returns ------- - dict with: - noise_base : (n_minutes,) array - noise_tvl_coeff : (n_minutes,) array - dates : list of dates - pool_index : int (index in calibration set, or -1) + dict with noise_base, noise_tvl_coeff, tvl_mean, tvl_std, dates, etc. """ art, meta = load_artifact(artifact_dir) noise_coeffs = art["noise_coeffs"] @@ -222,51 +237,44 @@ def build_simulator_arrays( per_pool = noise_coeffs.ndim == 2 trend_windows = tuple(meta["hparams"]["trend_windows"]) - # Load matched_clean if needed - if matched_clean is None: - import pickle - cache_dir = os.path.join( - os.path.dirname(os.path.dirname(os.path.dirname( - os.path.abspath(__file__)))), - "results", "token_factored_calibration", "_cache", - ) - with open(os.path.join(cache_dir, "stage1.pkl"), "rb") as f: - data = pickle.load(f) - matched_clean = data["matched_clean"] - # Find pool coefficients - pool_idx = _find_pool_index(pool_id, pool_ids) + pool_idx = -1 + if pool_id is not None: + pool_idx = _find_pool_index(pool_id, pool_ids) + if pool_idx >= 0 and per_pool: coeffs = noise_coeffs[pool_idx] + print(f" Using per-pool coefficients (pool idx {pool_idx})") elif per_pool: - print(f" Warning: pool {pool_id} not in calibration set, using median coeffs") coeffs = np.median(noise_coeffs, axis=0) + print(f" Pool not found, using median coefficients") else: coeffs = noise_coeffs - # Build daily features - x_daily, dates = build_daily_features( - pool_id, matched_clean, start_date, end_date, + # Build daily features from Binance + print(f" Building features from Binance data: {token_a}/{token_b}," + f" {start_date} → {end_date}") + x_daily, dates = build_daily_features_from_binance( + token_a, token_b, start_date, end_date, feat_names, x_mean, x_std, trend_windows, ) n_days = len(dates) + print(f" {n_days} days, {len(feat_names)} features") # Decompose into base (non-TVL) and tvl_coeff tvl_col, tvl_interactions = _identify_tvl_columns(feat_names) - # tvl_coeff_t = coeffs[tvl_col] + sum(coeffs[inter_col] * x[paired_col]) tvl_coeff_daily = np.full(n_days, coeffs[tvl_col], dtype=np.float64) for inter_col, paired_col in tvl_interactions: tvl_coeff_daily += coeffs[inter_col] * x_daily[:, paired_col] - # base_t = sum(coeffs[j] * x[j]) for j not in {tvl_col, interaction_cols} tvl_related = {tvl_col} | {ic for ic, _ in tvl_interactions} base_daily = np.zeros(n_days, dtype=np.float64) for j in range(len(feat_names)): if j not in tvl_related: base_daily += coeffs[j] * x_daily[:, j] - # Expand to minute resolution: each day's value repeats 1440 times + # Expand to minute resolution n_minutes = n_days * 1440 noise_base = np.repeat(base_daily, 1440) noise_tvl_coeff = np.repeat(tvl_coeff_daily, 1440) From 414903d59b57080af94ef1d721919095d1fd0670 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Thu, 26 Mar 2026 23:09:18 +0000 Subject: [PATCH 39/57] data: per-pool linear noise model artifact (Binance-only, 22 features, 36 pools) --- results/linear_market_noise/meta.json | 77 ++++++++++++++++++++++++++ results/linear_market_noise/model.npz | Bin 0 -> 5092 bytes 2 files changed, 77 insertions(+) create mode 100644 results/linear_market_noise/meta.json create mode 100644 results/linear_market_noise/model.npz diff --git a/results/linear_market_noise/meta.json b/results/linear_market_noise/meta.json new file mode 100644 index 0000000..35249de --- /dev/null +++ b/results/linear_market_noise/meta.json @@ -0,0 +1,77 @@ +{ + "feat_names": [ + "xobs_0", + "xobs_1", + "xobs_2", + "xobs_3", + "btc_log_price", + "btc_log_return", + "btc_realized_vol_7d", + "btc_trend_7d", + "btc_volume_zscore", + "pair_realized_vol_7d", + "tok_a_log_return", + "tok_a_realized_vol_7d", + "tok_a_trend_7d", + "tok_a_volume_zscore", + "tok_b_log_return", + "tok_b_realized_vol_7d", + "tok_b_trend_7d", + "tok_b_volume_zscore", + "xobs_1\u00d7btc_realized_vol_7d", + "xobs_1\u00d7tok_a_realized_vol_7d", + "xobs_1\u00d7pair_realized_vol_7d", + "tok_a_realized_vol_7d\u00d7tok_b_realized_vol_7d" + ], + "pool_ids": [ + "0x072f14b85add63", + "0x0b09dea16768f0", + "0x10f21c9bd8128a", + "0x1535d7ca00323a", + "0x21d4c792ea7e38", + "0x25ca5451cd5a50", + "0x260dbd54d87a10", + "0x272d6be442e30d", + "0x32df62dc3aed2c", + "0x36be1e97ea98ab", + "0x3de27efa2f1aa6", + "0x3e5fa9518ea95c", + "0x4683e340a80492", + "0x4cdabe9e07ca39", + "0x4fbb7870dbe7a7", + "0x571bea0e99e139", + "0x5c6ee304399dbd", + "0x5f1f4e50ba51d7", + "0x711af51a937e01", + "0x713fb5036dc700", + "0x9232a548dd9e81", + "0x92762b42a06dcd", + "0x96646936b91d6b", + "0x9d1fcf346ea1b0", + "0xa6f548df93de92", + "0xa83b8d30f61d75", + "0xb460daa847c45f", + "0xbc2acf5e821c5c", + "0xbda917a67c7d9a", + "0xcc65a812ce382a", + "0xcf354603a9aebd", + "0xcf7b51ce575551", + "0xd1d7fa8871d84d", + "0xdaba3d8ccf79ef", + "0xe99481dc77691d", + "0xf16aee6a71af1a" + ], + "n_pools": 36, + "n_feat": 22, + "hparams": { + "epochs": 2000, + "lr": 0.0003, + "l2_alpha": 0.001, + "huber_delta": 1.0, + "trend_windows": [ + 7 + ], + "per_pool": true, + "pool_intercepts": false + } +} \ No newline at end of file diff --git a/results/linear_market_noise/model.npz b/results/linear_market_noise/model.npz new file mode 100644 index 0000000000000000000000000000000000000000..c792225202ae753ace18972cf62cf5b677717a4e GIT binary patch literal 5092 zcmd5=c{o*T+uvkX<_2j%M1~@o>~%kDZAr#xPKhL zsKhyyQqg#5GBgl5r&4@-J8$20>iW)iz1MsG_`Z8z&-$(3eLwfQ_WC{dde(I>XZv9i zYMeh!i}P!ZT58L14o5VUIBFdK0N)@lch3MXZ||U~{+mO^gkDihWZy7p!BR(O7cou< zXQ$peuOQDrJqt}e%G+GeNK?-{ATT)4!{0q1aGjUPZ|&h1nQQeDOE3{DW&rN&Kj+p3cfYbSguMOHOMxyt@{!L{huDgPoe85 z>azmx^+zeDv`7p3ekozkm}CiVKhy*B3M=IO(GUg7P9TDi4YcRBq|0*W?a|mWZ7_ek z2cfD3&&Qv=myego3B7EOO6+GV*(@o{wTYf zmkw}U5jJ!@WeY#PL<;*(fK}@YDtv?-UDO%M79X<`C`S5V`vv#Gt|k}ej4>g`opbQU zUEyf4#YU84HBn&xz=2g+HjLPgQ05KO{zzwzP-RC{A7hoKrU)WmyrEvrPDKGGj<9lF zHCTQOrd{3BsE_j<$ebrHD2vCVkPfR4cM4r0n;(G#rzWE1M{>xvMoD_h0Ru8Db`QDp zPMT+7GY@pj3UIsAwIa0*ikIjlQp{Q+;y{QTavhSj;ryS6QYIWjv0--DSrRZt= 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zHqxa}O_;UvTxO=fa4CfLVudL)GHE(f(mI2Q>u?zIiKGfpR~)E&FToYj-iiVs}*6r?_ygdBB zm8i%ovTyh|TF(Brdid$njs9tg{`qf)j+#=d3>o2BrR&n|v=B8%jc^uW<{wSEB0pc! z&uqwV$4eDqWmloW5TWsU!16oL}djCc^*KzAxdi{Q3+H3W&EL%{_%s=kJ;aE z-`{eDw;-lHg7f2M{$uj@Th(8agA_Rb^Tze#CBA Date: Fri, 27 Mar 2026 11:37:08 +0000 Subject: [PATCH 40/57] fix: noise volume cadence scaling + price preservation for 2-CLP Two bugs in noise fee income application for reClAMM pools: 1. Cadence scaling: noise model returns per-minute volume but was applied once per arb step (every arb_frequency minutes) without scaling. Now multiplies by minutes_per_step. At cadence=5, this was underestimating noise fee income by 5x. 2. Price preservation: uniform real-reserve scaling (Ra*s, Rb*s) preserves price for weighted pools but NOT for 2-CLPs where price depends on effective reserves (Ra+Va)/(Rb+Vb). Fixed by scaling effective reserves uniformly then subtracting virtuals: Ra_new = (Ra+Va)*scale - Va. Preserves quoted marginal price. Total value added still equals noise_fee_income (verified algebraically: effective_value * (scale-1) = fee_income). Both fixes applied to all CLP noise model variants (tsoukalas, loglinear, calibrated, market_linear). Also: tune script adds L-BFGS support, 25% protocol fee split, extended date range, $7M default TVL. New plotting scripts for model vs real comparison. --- experiments/tune_reclamm_calibrated_noise.py | 65 +++-- quantammsim/pools/reCLAMM/reclamm_reserves.py | 33 ++- scripts/compare_modelled_vs_real.py | 243 ++++++++++++++++ scripts/plot_model_vs_real_reclamm.py | 262 ++++++++++++++++++ 4 files changed, 571 insertions(+), 32 deletions(-) create mode 100644 scripts/compare_modelled_vs_real.py create mode 100644 scripts/plot_model_vs_real_reclamm.py diff --git a/experiments/tune_reclamm_calibrated_noise.py b/experiments/tune_reclamm_calibrated_noise.py index 8a81851..9e999bd 100644 --- a/experiments/tune_reclamm_calibrated_noise.py +++ b/experiments/tune_reclamm_calibrated_noise.py @@ -114,6 +114,37 @@ def _build_market_linear_arrays(args): return arrays_path, max(1, round(learned_cadence)) +def _build_opt_settings(args): + """Build optimisation_settings for either optuna or bfgs.""" + if args.method == "bfgs": + return { + "method": "bfgs", + "n_parameter_sets": args.n_parameter_sets, + **({"val_fraction": args.val_fraction} if args.val_fraction is not None else {}), + "bfgs_settings": { + "maxiter": args.bfgs_maxiter, + "tol": args.bfgs_tol, + "n_evaluation_points": args.bfgs_eval_points, + "compute_dtype": "float64", + }, + } + else: + return { + "method": "optuna", + "n_parameter_sets": 1, + **({"val_fraction": args.val_fraction} if args.val_fraction is not None else {}), + "optuna_settings": { + "make_scalar": True, + "expand_around": False, + "n_trials": args.n_trials, + "multi_objective": False, + "parameter_config": PARAMETER_CONFIG, + **({"overfitting_penalty": args.overfitting_penalty} + if args.overfitting_penalty is not None else {}), + }, + } + + def build_fingerprint(objective, args, noise_arrays_path=None, arb_freq=None): """Build run fingerprint with calibrated noise model.""" if args.noise_model == "market_linear" and noise_arrays_path is not None: @@ -151,7 +182,7 @@ def build_fingerprint(objective, args, noise_arrays_path=None, arb_freq=None): "fees": args.fees, "gas_cost": args.gas_cost, "arb_fees": 0.0, - "protocol_fee_split": 0.5, + "protocol_fee_split": 0.25, **noise_block, "return_val": objective, "reclamm_interpolation_method": args.interpolation, @@ -159,19 +190,7 @@ def build_fingerprint(objective, args, noise_arrays_path=None, arb_freq=None): "reclamm_learn_arc_length_speed": False, "reclamm_use_shift_exponent": True, **({"bout_offset": args.bout_offset} if args.bout_offset is not None else {}), - "optimisation_settings": { - "method": "optuna", - "n_parameter_sets": 1, - **({"val_fraction": args.val_fraction} if args.val_fraction is not None else {}), - "optuna_settings": { - "make_scalar": True, - "expand_around": False, - "n_trials": args.n_trials, - "multi_objective": False, - "parameter_config": PARAMETER_CONFIG, - **({"overfitting_penalty": args.overfitting_penalty} if args.overfitting_penalty is not None else {}), - }, - }, + "optimisation_settings": _build_opt_settings(args), } @@ -180,6 +199,7 @@ def run_single(objective, args, noise_arrays_path=None, arb_freq=None): print(f"\n{'='*60}") print(f" Objective: {objective}") print(f" Noise model: {args.noise_model}") + print(f" Method: {args.method}") print(f" Pool: AAVE/WETH Mainnet ({POOL_ID})") print(f" Train: {args.start_date} → {args.end_date}") print(f" Test: {args.end_date} → {args.end_test_date}") @@ -202,7 +222,16 @@ def main(): parser = argparse.ArgumentParser( description="Tune reClAMM params with calibrated 8-covariate noise model" ) - parser.add_argument("--n-trials", type=int, default=50) + parser.add_argument("--method", default="optuna", choices=["optuna", "bfgs"], + help="Optimisation method") + parser.add_argument("--n-trials", type=int, default=50, + help="Optuna trials (ignored for bfgs)") + parser.add_argument("--n-parameter-sets", type=int, default=1, + help="Number of parameter sets for bfgs") + parser.add_argument("--bfgs-maxiter", type=int, default=100) + parser.add_argument("--bfgs-tol", type=float, default=1e-6) + parser.add_argument("--bfgs-eval-points", type=int, default=20, + help="Number of evaluation points for bfgs") parser.add_argument("--noise-model", default="market_linear", choices=["calibrated", "market_linear"], help="Noise model variant") @@ -221,10 +250,10 @@ def main(): parser.add_argument("--interpolation", default="geometric", choices=["geometric", "constant_arc_length"]) parser.add_argument("--centeredness-scaling", action="store_true") - parser.add_argument("--start-date", default="2025-08-03 00:00:00") - parser.add_argument("--end-date", default="2025-12-01 00:00:00", + parser.add_argument("--start-date", default="2024-06-01 00:00:00") + parser.add_argument("--end-date", default="2025-06-01 00:00:00", help="End of training / start of test") - parser.add_argument("--end-test-date", default="2026-02-18 00:00:00", + parser.add_argument("--end-test-date", default="2026-03-01 00:00:00", help="End of test (latest available data)") parser.add_argument("--bout-offset", type=int, default=None) parser.add_argument("--val-fraction", type=float, default=None) diff --git a/quantammsim/pools/reCLAMM/reclamm_reserves.py b/quantammsim/pools/reCLAMM/reclamm_reserves.py index 26dcd7a..9363255 100644 --- a/quantammsim/pools/reCLAMM/reclamm_reserves.py +++ b/quantammsim/pools/reCLAMM/reclamm_reserves.py @@ -1005,16 +1005,20 @@ def _skip_schedule_state(_): arb_volume, _np, ) - noise_fee_income = (1.0 - gamma) * noise_vol - scale = 1.0 + noise_fee_income / jnp.maximum(real_value, 1e-8) - Ra_new = Ra_new * scale - Rb_new = Rb_new * scale + # Scale effective reserves uniformly to preserve quoted price. + # For a 2-CLP: price ∝ (Ra+Va)/(Rb+Vb), so we must scale + # effective reserves (Ra+Va, Rb+Vb) by the same factor, then + # subtract back the fixed virtual reserves. + minutes_per_step = seconds_per_step / 60.0 + noise_fee_income = (1.0 - gamma) * noise_vol * minutes_per_step + scale = 1.0 + noise_fee_income / jnp.maximum(effective_value, 1e-8) + Ra_new = (Ra_new + Va) * scale - Va + Rb_new = (Rb_new + Vb) * scale - Vb elif noise_model == "calibrated": volatility = input_list[9] dow_sin = input_list[10] dow_cos = input_list[11] arb_volume = 0.5 * jnp.sum(jnp.abs(applied_trade) * prices) - real_value = jnp.sum(jnp.array([Ra_new, Rb_new]) * prices) effective_value = (Ra_new + Va) * prices[0] + (Rb_new + Vb) * prices[1] _np = noise_params if noise_params is not None else {} @@ -1023,14 +1027,14 @@ def _skip_schedule_state(_): arb_volume, dow_sin, dow_cos, _np, ) - noise_fee_income = (1.0 - gamma) * noise_vol - scale = 1.0 + noise_fee_income / jnp.maximum(real_value, 1e-8) - Ra_new = Ra_new * scale - Rb_new = Rb_new * scale + minutes_per_step = seconds_per_step / 60.0 + noise_fee_income = (1.0 - gamma) * noise_vol * minutes_per_step + scale = 1.0 + noise_fee_income / jnp.maximum(effective_value, 1e-8) + Ra_new = (Ra_new + Va) * scale - Va + Rb_new = (Rb_new + Vb) * scale - Vb elif noise_model == "market_linear": noise_base = input_list[9] noise_tvl_coeff = input_list[10] - real_value = jnp.sum(jnp.array([Ra_new, Rb_new]) * prices) effective_value = (Ra_new + Va) * prices[0] + (Rb_new + Vb) * prices[1] _np = noise_params if noise_params is not None else {} @@ -1040,10 +1044,11 @@ def _skip_schedule_state(_): tvl_std=_np.get("tvl_std", 1.0), ) - noise_fee_income = (1.0 - gamma) * noise_vol - scale = 1.0 + noise_fee_income / jnp.maximum(real_value, 1e-8) - Ra_new = Ra_new * scale - Rb_new = Rb_new * scale + minutes_per_step = seconds_per_step / 60.0 + noise_fee_income = (1.0 - gamma) * noise_vol * minutes_per_step + scale = 1.0 + noise_fee_income / jnp.maximum(effective_value, 1e-8) + Ra_new = (Ra_new + Va) * scale - Va + Rb_new = (Rb_new + Vb) * scale - Vb # else: "arb_only" — no noise trades # Clamp-to-edge: if a real reserve would go negative, apply an diff --git a/scripts/compare_modelled_vs_real.py b/scripts/compare_modelled_vs_real.py new file mode 100644 index 0000000..e23c7e4 --- /dev/null +++ b/scripts/compare_modelled_vs_real.py @@ -0,0 +1,243 @@ +"""Compare modelled reClAMM noise volume against a real pool's observed volume. + +Plots the modelled noise volume for one pool (at a specified counterfactual TVL) +against the actual observed volume of another pool (or the same pool), to +sanity-check the noise model's predictions. + +Usage: + # reClAMM AAVE/ETH modelled at $7M vs weighted wstETH/AAVE real + python scripts/compare_modelled_vs_real.py \ + --model-pool 0x9d1fcf346ea1b0 --model-tvl 7e6 \ + --real-pool 0x3de27efa2f1aa6 + + # Same but at $20M + python scripts/compare_modelled_vs_real.py \ + --model-pool 0x9d1fcf346ea1b0 --model-tvl 20e6 \ + --real-pool 0x3de27efa2f1aa6 + + # Multiple TVL levels + python scripts/compare_modelled_vs_real.py \ + --model-pool 0x9d1fcf346ea1b0 --model-tvl 1e6 7e6 20e6 50e6 \ + --real-pool 0x3de27efa2f1aa6 +""" + +import argparse +import os +import pickle + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +from quantammsim.calibration.noise_model_arrays import ( + build_simulator_arrays, load_artifact, _find_pool_index, +) + + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) +ARTIFACT_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "linear_market_noise", +) +OUTPUT_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "noise_comparison", +) + +# Token mapping for pools +POOL_TOKENS = { + "0x9d1fcf346ea1b0": ("AAVE", "ETH"), + "0x3de27efa2f1aa6": ("AAVE", "ETH"), # wstETH/AAVE ≈ same pair + "0x0b09dea16768f0": ("DAI", "ETH"), + "0xa6f548df93de92": ("BTC", "ETH"), + "0x96646936b91d6b": ("USDC", "ETH"), +} + + +def load_real_pool(pid, mc): + """Load real observed volume + TVL for a pool.""" + entry = mc[pid] + panel = entry["panel"] + dates = pd.to_datetime(panel["date"]) + vol = np.exp(panel["log_volume"].values.astype(float)) + tvl = np.exp(panel["log_tvl_lag1"].values.astype(float)) + tokens = entry["tokens"] + chain = entry["chain"] + return dates, vol, tvl, tokens, chain + + +def compute_modelled_noise(pid, tvl_value, start_date, end_date, + artifact_dir, token_a, token_b): + """Compute modelled daily noise for a pool at a given TVL.""" + arrays = build_simulator_arrays( + token_a=token_a, token_b=token_b, + start_date=start_date, end_date=end_date, + artifact_dir=artifact_dir, pool_id=pid, + ) + + n_days = arrays["n_days"] + std_lt = (np.log(tvl_value) - arrays["tvl_mean"]) / arrays["tvl_std"] + + noise_base = arrays["noise_base"][::1440][:n_days] + tvl_coeff = arrays["noise_tvl_coeff"][::1440][:n_days] + noise_daily = np.exp(noise_base + tvl_coeff * std_lt) + + dates = pd.to_datetime(arrays["dates"][:n_days]) + return dates, noise_daily + + +def main(): + parser = argparse.ArgumentParser( + description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--model-pool", default="0x9d1fcf346ea1b0", + help="Pool ID for modelled noise") + parser.add_argument("--model-tvl", type=float, nargs="+", + default=[7_000_000], + help="Counterfactual TVL(s) for the modelled pool") + parser.add_argument("--model-tokens", nargs=2, default=None, + help="Token A and B for the modelled pool (auto-detected)") + parser.add_argument("--real-pool", default="0x3de27efa2f1aa6", + help="Pool ID for real observed data") + parser.add_argument("--artifact-dir", default=ARTIFACT_DIR) + parser.add_argument("--output-dir", default=OUTPUT_DIR) + args = parser.parse_args() + + os.environ.setdefault("JAX_PLATFORMS", "cpu") + os.makedirs(args.output_dir, exist_ok=True) + + # Load calibration data + with open(os.path.join(CACHE_DIR, "stage1.pkl"), "rb") as f: + data = pickle.load(f) + mc = data["matched_clean"] + + # Real pool data + real_dates, real_vol, real_tvl, real_tokens, real_chain = load_real_pool( + args.real_pool, mc) + print(f"Real pool: {args.real_pool} ({real_tokens}, {real_chain})") + print(f" {len(real_dates)} days: {real_dates.min().date()} → {real_dates.max().date()}") + print(f" TVL: ${real_tvl.min():,.0f} – ${real_tvl.max():,.0f}") + print(f" Volume: ${real_vol.min():,.0f} – ${real_vol.max():,.0f}") + + # Model tokens + if args.model_tokens: + tok_a, tok_b = args.model_tokens + elif args.model_pool[:16] in POOL_TOKENS: + tok_a, tok_b = POOL_TOKENS[args.model_pool[:16]] + else: + tok_a, tok_b = "ETH", "USDC" + print(f" Warning: unknown pool, using {tok_a}/{tok_b}") + + # Date range from real pool + start = str(real_dates.min().date()) + end = str(real_dates.max().date()) + + # Compute modelled noise at each TVL + model_results = [] + for tvl_val in args.model_tvl: + print(f"\nModelled: {args.model_pool} at ${tvl_val:,.0f} TVL") + m_dates, m_noise = compute_modelled_noise( + args.model_pool, tvl_val, start, end, + args.artifact_dir, tok_a, tok_b) + model_results.append((tvl_val, m_dates, m_noise)) + print(f" Median noise: ${np.median(m_noise):,.0f}/day" + f" ({np.median(m_noise)/tvl_val*100:.2f}% of TVL)") + + # Align dates + common_start = real_dates.min() + common_end = real_dates.max() + for _, md, _ in model_results: + common_start = max(common_start, md.min()) + common_end = min(common_end, md.max()) + + real_mask = (real_dates >= common_start) & (real_dates <= common_end) + + # Colors for different TVL levels + colors = ["#e74c3c", "#3498db", "#2ecc71", "#f39c12", "#9b59b6"] + + # Plot + fig, axes = plt.subplots(2, 1, figsize=(14, 9)) + + # 1. Volume comparison + ax = axes[0] + ax.plot(real_dates[real_mask], real_vol[real_mask] / 1e6, + "k-", linewidth=0.8, alpha=0.7, + label=f"{real_tokens} weighted (real," + f" TVL ${np.median(real_tvl[real_mask])/1e6:.0f}M)") + + for i, (tvl_val, m_dates, m_noise) in enumerate(model_results): + m_mask = (m_dates >= common_start) & (m_dates <= common_end) + c = colors[i % len(colors)] + ax.plot(m_dates[m_mask], m_noise[m_mask] / 1e6, + "-", color=c, linewidth=0.8, alpha=0.7, + label=f"reClAMM noise (modelled, TVL ${tvl_val/1e6:.0f}M)") + + ax.set_ylabel("Volume ($M/day)") + ax.set_yscale("log") + ax.set_title(f"Real weighted pool vs Modelled reClAMM noise") + ax.legend(fontsize=8) + ax.grid(True, alpha=0.3) + + # 2. Vol/TVL comparison + ax = axes[1] + real_vol_tvl = real_vol[real_mask] / real_tvl[real_mask] * 100 + ax.plot(real_dates[real_mask], real_vol_tvl, + "k-", linewidth=0.8, alpha=0.7, + label=f"{real_tokens} weighted real vol/TVL") + ax.axhline(np.median(real_vol_tvl), color="black", linestyle="--", + alpha=0.3, label=f"weighted median: {np.median(real_vol_tvl):.2f}%") + + for i, (tvl_val, m_dates, m_noise) in enumerate(model_results): + m_mask = (m_dates >= common_start) & (m_dates <= common_end) + noise_tvl = m_noise[m_mask] / tvl_val * 100 + c = colors[i % len(colors)] + ax.plot(m_dates[m_mask], noise_tvl, + "-", color=c, linewidth=0.8, alpha=0.7, + label=f"reClAMM noise/TVL (${tvl_val/1e6:.0f}M)") + ax.axhline(np.median(noise_tvl), color=c, linestyle="--", alpha=0.3, + label=f"median: {np.median(noise_tvl):.2f}%") + + ax.set_ylabel("Volume / TVL (%)") + ax.set_xlabel("Date") + ax.set_title("Volume as Fraction of TVL") + ax.legend(fontsize=7, loc="upper right") + ax.grid(True, alpha=0.3) + ymax = min( + max(np.percentile(real_vol_tvl, 95), + max(np.percentile(m_noise[m_mask] / tvl_val * 100, 95) + for tvl_val, m_dates, m_noise in model_results + for m_mask in [(m_dates >= common_start) & (m_dates <= common_end)])) * 1.5, + 50) + ax.set_ylim(0, ymax) + + fig.tight_layout() + tvl_str = "_".join(f"{t/1e6:.0f}M" for t in args.model_tvl) + out = os.path.join(args.output_dir, + f"{args.model_pool[:8]}_vs_{args.real_pool[:8]}_{tvl_str}.png") + fig.savefig(out, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f"\nSaved: {out}") + + # Summary table + print(f"\n{'='*60}") + print(f"Summary") + print(f"{'='*60}") + print(f" Real {real_tokens} weighted:") + print(f" Median TVL: ${np.median(real_tvl[real_mask]):,.0f}") + print(f" Median vol: ${np.median(real_vol[real_mask]):,.0f}/day") + print(f" Median vol/TVL: {np.median(real_vol_tvl):.2f}%") + for tvl_val, m_dates, m_noise in model_results: + m_mask = (m_dates >= common_start) & (m_dates <= common_end) + med_noise = np.median(m_noise[m_mask]) + print(f" Modelled reClAMM at ${tvl_val/1e6:.0f}M:") + print(f" Median noise: ${med_noise:,.0f}/day") + print(f" Median noise/TVL: {med_noise/tvl_val*100:.2f}%") + + +if __name__ == "__main__": + main() diff --git a/scripts/plot_model_vs_real_reclamm.py b/scripts/plot_model_vs_real_reclamm.py new file mode 100644 index 0000000..c0c3da3 --- /dev/null +++ b/scripts/plot_model_vs_real_reclamm.py @@ -0,0 +1,262 @@ +"""Plot full model (V_arb + V_noise) vs real observed volume for a pool. + +Uses the pool's actual historical TVL path, evaluates V_arb from the PCHIP +grid at the learned cadence, and V_noise from the per-pool linear model. +Compares against observed total volume. + +Usage: + python scripts/plot_model_vs_real_reclamm.py + python scripts/plot_model_vs_real_reclamm.py --pool 0x3de27efa2f1aa6 + python scripts/plot_model_vs_real_reclamm.py --pool 0x9d1fcf346ea1b0 +""" + +import argparse +import os +import pickle +import sys + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +sys.path.insert(0, os.path.dirname(os.path.dirname(__file__))) + +import jax.numpy as jnp +from quantammsim.calibration.grid_interpolation import interpolate_pool_daily +from quantammsim.calibration.noise_model_arrays import load_artifact + + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) +ARTIFACT_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "linear_market_noise", +) +OUTPUT_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "model_vs_real", +) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--pool", default="0x9d1fcf346ea1b0", + help="Pool ID prefix") + parser.add_argument("--artifact-dir", default=ARTIFACT_DIR) + parser.add_argument("--output-dir", default=OUTPUT_DIR) + args = parser.parse_args() + + os.environ.setdefault("JAX_PLATFORMS", "cpu") + os.makedirs(args.output_dir, exist_ok=True) + + # Load data + with open(os.path.join(CACHE_DIR, "stage1.pkl"), "rb") as f: + data = pickle.load(f) + mc = data["matched_clean"] + oc = data["option_c_clean"] + + pid = args.pool + entry = mc[pid] + panel = entry["panel"] + dates = pd.to_datetime(panel["date"]) + vol_obs = np.exp(panel["log_volume"].values.astype(float)) + tvl = np.exp(panel["log_tvl_lag1"].values.astype(float)) + + # Load noise model + art, meta = load_artifact(args.artifact_dir) + pool_ids = meta["pool_ids"] + idx = pool_ids.index(pid) + coeffs = art["noise_coeffs"][idx] + cadence = float(np.exp(art["log_cadence"][idx])) + gas = float(np.exp(oc[pid]["log_gas"])) + + print(f"Pool: {pid} ({entry['tokens']}, {entry['chain']})") + print(f"Cadence: {cadence:.1f} min, Gas: ${gas}") + print(f"{len(dates)} days: {dates.min().date()} → {dates.max().date()}") + print(f"TVL: ${tvl.min():,.0f} – ${tvl.max():,.0f}") + + # V_arb from PCHIP + v_arb_all = np.array(interpolate_pool_daily( + entry["coeffs"], jnp.float64(np.log(cadence)), jnp.float64(gas))) + + # V_noise from model at actual TVL + from experiments.run_linear_market_noise import build_data + data_full = build_data(mc, oc, trend_windows=(7,), + include_market=True, include_cross_pool=False) + x_full = data_full["x"] + pool_idx_full = data_full["pool_idx"] + pool_mask = pool_idx_full == idx + sample_x = x_full[pool_mask] + sgd = data_full["sample_grid_days"][pool_mask] + day_idx = data_full["day_idx"][pool_mask] + + log_v_noise = sample_x @ coeffs + v_noise = np.exp(log_v_noise) + v_arb_samples = v_arb_all[sgd] + v_total_pred = v_arb_samples + v_noise + + # Align dates + all_dates = set() + for p in pool_ids: + all_dates.update(mc[p]["panel"]["date"].values) + date_list = sorted(all_dates) + sample_dates = np.array([pd.Timestamp(date_list[d]) for d in day_idx]) + + # Match TVL and obs volume + tvl_samples = np.zeros(len(sample_dates)) + vol_obs_samples = np.zeros(len(sample_dates)) + for i, sd in enumerate(sample_dates): + matches = np.where(dates == sd)[0] + if len(matches) > 0: + tvl_samples[i] = tvl[matches[0]] + vol_obs_samples[i] = vol_obs[matches[0]] + + valid = tvl_samples > 100 + sd = sample_dates[valid] + vo = vol_obs_samples[valid] + va = v_arb_samples[valid] + vn = v_noise[valid] + vt = v_total_pred[valid] + tv = tvl_samples[valid] + + # R² + log_obs = np.log(np.maximum(vo, 1)) + log_pred = np.log(np.maximum(vt, 1)) + ss_res = np.sum((log_obs - log_pred) ** 2) + ss_tot = np.sum((log_obs - log_obs.mean()) ** 2) + r2 = 1 - ss_res / max(ss_tot, 1e-10) + + print(f"\nR² (log): {r2:.3f}") + print(f"Median obs: ${np.median(vo):,.0f}, pred: ${np.median(vt):,.0f}") + print(f"Median V_arb: ${np.median(va):,.0f}, V_noise: ${np.median(vn):,.0f}") + + # Fee rate + fee_rate = float(panel["swap_fee"].iloc[0]) if "swap_fee" in panel.columns else 0.003 + + # Plot + fig, axes = plt.subplots(6, 1, figsize=(14, 20), sharex=True) + + # 1. TVL + ax = axes[0] + ax.plot(sd, tv, "b-", linewidth=1) + ax.set_ylabel("TVL (USD)") + ax.set_yscale("log") + ax.set_title(f"{entry['tokens']} ({entry['chain']}) — " + f"Model (V_arb + V_noise) vs Observed " + f"[R\u00b2={r2:.3f}, cadence={cadence:.0f}min, fee={fee_rate:.4f}]") + ax.grid(True, alpha=0.3) + + # 2. Volume: stacked arb + noise vs observed + ax = axes[1] + ax.fill_between(sd, 0, va, alpha=0.3, color="steelblue", label="V_arb (PCHIP)") + ax.fill_between(sd, va, va + vn, alpha=0.3, color="coral", label="V_noise (model)") + ax.plot(sd, vo, "k-", linewidth=0.8, alpha=0.7, label="V_obs (actual)") + ax.plot(sd, vt, "r--", linewidth=0.8, alpha=0.5, label="V_pred = V_arb + V_noise") + ax.set_ylabel("Volume (USD/day)") + ax.set_yscale("log") + ax.legend(fontsize=8) + ax.grid(True, alpha=0.3) + + # 3. V_noise only + ax = axes[2] + ax.fill_between(sd, 0, vn, alpha=0.4, color="coral") + ax.plot(sd, vn, "r-", linewidth=0.8, alpha=0.7, label="V_noise (model)") + ax.axhline(np.median(vn), color="red", linestyle="--", alpha=0.5, + label=f"median: ${np.median(vn):,.0f}") + ax.set_ylabel("Noise volume (USD/day)") + ax.set_yscale("log") + ax.legend(fontsize=8) + ax.grid(True, alpha=0.3) + + # 4. Fee revenue: observed vs predicted + ax = axes[3] + fee_obs = vo * fee_rate + fee_pred = vt * fee_rate + fee_noise_only = vn * fee_rate + fee_arb = va * fee_rate + ax.fill_between(sd, 0, fee_arb, alpha=0.3, color="steelblue", label="Arb fees") + ax.fill_between(sd, fee_arb, fee_pred, alpha=0.3, color="coral", label="Noise fees") + ax.plot(sd, fee_obs, "k-", linewidth=0.8, alpha=0.7, label="Observed fees") + ax.plot(sd, fee_pred, "r--", linewidth=0.8, alpha=0.5, label="Predicted total fees") + ax.plot(sd, fee_noise_only, "m-", linewidth=0.8, alpha=0.6, + label=f"Noise fees only (med=${np.median(fee_noise_only):,.0f})") + ax.set_ylabel("Fee revenue (USD/day)") + ax.legend(fontsize=8) + ax.grid(True, alpha=0.3) + + # 5. Vol/TVL + ax = axes[4] + vol_tvl_obs = vo / tv * 100 + vol_tvl_pred = vt / tv * 100 + ax.plot(sd, vol_tvl_obs, "k-", linewidth=0.8, alpha=0.7, label="Observed") + ax.plot(sd, vol_tvl_pred, "r--", linewidth=0.8, alpha=0.5, label="Predicted") + ax.axhline(np.median(vol_tvl_obs), color="black", linestyle=":", + alpha=0.3, label=f"obs median: {np.median(vol_tvl_obs):.1f}%") + ax.axhline(np.median(vol_tvl_pred), color="red", linestyle=":", + alpha=0.3, label=f"pred median: {np.median(vol_tvl_pred):.1f}%") + ax.set_ylabel("Vol / TVL (%)") + ax.legend(fontsize=8) + ax.grid(True, alpha=0.3) + ax.set_ylim(0, min(np.percentile(vol_tvl_obs, 95) * 2, 200)) + + # 6. Pred/Obs ratio + ax = axes[5] + ratio = vt / np.maximum(vo, 1) + ax.plot(sd, ratio, "g-", linewidth=0.8, alpha=0.7) + ax.axhline(1.0, color="black", linestyle="--", alpha=0.5, label="perfect") + ax.axhline(np.median(ratio), color="red", linestyle="--", alpha=0.5, + label=f"median: {np.median(ratio):.2f}") + ax.set_ylabel("Pred / Obs") + ax.set_xlabel("Date") + ax.set_yscale("log") + ax.legend(fontsize=8) + ax.grid(True, alpha=0.3) + ax.set_ylim(0.01, 100) + + fig.tight_layout() + out = os.path.join(args.output_dir, f"{pid[:16]}_model_vs_real.png") + fig.savefig(out, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f"\nSaved: {out}") + + # Fee summary + fee_obs_total = np.sum(vo * fee_rate) + fee_pred_total = np.sum(vt * fee_rate) + fee_noise_total = np.sum(vn * fee_rate) + fee_arb_total = np.sum(va * fee_rate) + print(f"\nFee revenue (cumulative, fee={fee_rate:.4f}):") + print(f" Observed: ${fee_obs_total:,.0f}") + print(f" Predicted: ${fee_pred_total:,.0f}" + f" (arb: ${fee_arb_total:,.0f}, noise: ${fee_noise_total:,.0f})") + + # Pre/post deposit stats (for reClAMM AAVE/ETH) + pre = sd < pd.Timestamp("2026-01-10") + post = sd >= pd.Timestamp("2026-01-20") + if pre.sum() > 5 and post.sum() > 5: + print(f"\nPre-deposit (before Jan 10):") + print(f" TVL: ${np.median(tv[pre]):,.0f}") + print(f" V_obs: ${np.median(vo[pre]):,.0f}," + f" V_pred: ${np.median(vt[pre]):,.0f}") + print(f" V_arb: ${np.median(va[pre]):,.0f}," + f" V_noise: ${np.median(vn[pre]):,.0f}") + print(f" Fees obs: ${np.median(vo[pre])*fee_rate:,.0f}/day," + f" pred: ${np.median(vt[pre])*fee_rate:,.0f}/day") + print(f" Pred/Obs: {np.median(vt[pre] / vo[pre]):.2f}") + print(f"Post-deposit (after Jan 20):") + print(f" TVL: ${np.median(tv[post]):,.0f}") + print(f" V_obs: ${np.median(vo[post]):,.0f}," + f" V_pred: ${np.median(vt[post]):,.0f}") + print(f" V_arb: ${np.median(va[post]):,.0f}," + f" V_noise: ${np.median(vn[post]):,.0f}") + print(f" Fees obs: ${np.median(vo[post])*fee_rate:,.0f}/day," + f" pred: ${np.median(vt[post])*fee_rate:,.0f}/day") + print(f" Pred/Obs: {np.median(vt[post] / vo[post]):.2f}") + + +if __name__ == "__main__": + main() From 5d40a6556d61f78b127500e27e7ac5e3056bca78 Mon Sep 17 00:00:00 2001 From: christian harrington Date: Fri, 27 Mar 2026 11:41:57 +0000 Subject: [PATCH 41/57] compare improvements --- scripts/compare_reclamm_thermostats.py | 1870 +++++++++++++++-- scripts/demo_run_reclamm.py | 199 +- .../reclamm/compare_reclamm_thermostats.py | 1870 +++++++++++++++-- scripts/reclamm/demo_run_reclamm.py | 199 +- .../test_compare_reclamm_thermostats.py | 284 +++ 5 files changed, 3990 insertions(+), 432 deletions(-) create mode 100644 tests/scripts/test_compare_reclamm_thermostats.py diff --git a/scripts/compare_reclamm_thermostats.py b/scripts/compare_reclamm_thermostats.py index 8a2c374..be8ee5f 100644 --- a/scripts/compare_reclamm_thermostats.py +++ b/scripts/compare_reclamm_thermostats.py @@ -1,19 +1,39 @@ -"""Compare geometric vs constant-arc-length thermostats on historic data. +"""Compare reCLAMM interpolation modes on historic AAVE/ETH data. -Runs AAVE/ETH reClAMM pool simulations with both interpolation methods. -Plots: pool value, cumulative LVR, price path, empirical weights, -value difference, LVR ratio, and per-step LVR distribution (∝ Δs²). +Runs the production geometric interpolation against the non-linear +constant-arc-length interpolation on: +1. The original launch-style range (price_ratio ~= 1.50) +2. A much tighter range (price_ratio = 1.10) -Usage: - cd - source ~/miniconda3/etc/profile.d/conda.sh && conda activate qsim-reclamm - python scripts/compare_reclamm_thermostats.py +The aggressive case is deliberate. A local AAVE/ETH sweep showed: +price_ratio 1.15, margin 0.5, shift 0.1 -> about +$10k vs geometric +price_ratio 1.10, margin 0.5, shift 0.1 -> about +$31k vs geometric +price_ratio 1.10, margin 0.6, shift 0.1 -> about +$73k vs geometric + +So the strongest clean demo setting came from tightening the band and +slightly raising the trigger margin, while keeping the launch-style shift +speed rather than pushing shift_exponent higher. """ +import gc +import math +import os + import jax.numpy as jnp import numpy as np +import pandas as pd import matplotlib.pyplot as plt +from matplotlib.colors import Normalize, SymLogNorm, TwoSlopeNorm +from matplotlib.cm import ScalarMappable +from quantammsim.pools.reCLAMM.reclamm_reserves import ( + calibrate_arc_length_speed, + compute_price_ratio, + initialise_reclamm_reserves, +) from quantammsim.runners.jax_runners import do_run_on_historic_data +from quantammsim.utils.data_processing.historic_data_utils import ( + get_historic_parquet_data, +) def to_daily_price_shift_base(daily_price_shift_exponent): @@ -21,57 +41,432 @@ def to_daily_price_shift_base(daily_price_shift_exponent): return 1.0 - daily_price_shift_exponent / 124649.0 +RUN_CONSTANT_ARC_LENGTH = False +INTERPOLATION_METHODS = ( + ("geometric", "constant_arc_length") + if RUN_CONSTANT_ARC_LENGTH + else ("geometric",) +) +HEATMAP_PRICE_RATIOS = np.arange(1.01, 1.50 + 1e-9, 0.025) +HEATMAP_MARGINS = np.linspace(0.05, 0.90, 20) +HEATMAP_SHIFT_EXPONENTS = np.arange(0.01, 0.50 + 1e-9, 0.025) +HEATMAP_ARC_LENGTH_SPEEDS = np.geomspace(1.0e-6, 5.0e-4, 11) +PRICE_RATIO_TICKS = np.array([1.01, 1.10, 1.20, 1.30, 1.40, 1.50]) +MARGIN_TICKS = np.array([0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.90]) +SHIFT_EXPONENT_TICKS = np.array([0.01, 0.05, 0.10, 0.20, 0.30, 0.40, 0.50]) +ARC_LENGTH_SPEED_TICKS = np.array([ + 1.0e-6, + 2.0e-6, + 5.0e-6, + 1.0e-5, + 2.0e-5, + 5.0e-5, + 1.0e-4, + 2.0e-4, + 5.0e-4, +]) +SWEEP_LINE_WIDTH = 0.45 +REFERENCE_LINE_WIDTH = 0.9 +DEFAULT_INITIAL_POOL_VALUE = 1_000_000.0 +TVL_SWEEP_VALUES = ( + 1_000_000.0, + 5_000_000.0, + 20_000_000.0, +) +CENTER_ZERO_HEATMAP_COLOR_NORM = "symlog" +CENTER_ZERO_HEATMAP_COLOR_TAG = "symlog20" +CENTER_ZERO_HEATMAP_SYMLOG_LINTHRESH = 20.0 + +AAVE_WETH_POOL_ID = "0x9d1fcf346ea1b0" +DEFAULT_MARKET_LINEAR_ARTIFACT_DIR = "results/linear_market_noise" +DEFAULT_NOISE_MODEL = "market_linear" +DEFAULT_GAS_COST = 1.0 +DEFAULT_PROTOCOL_FEE_SPLIT = 0.25 +LEGACY_NOISE_COEFFS = [ + -0.453, + 0.025, + -0.060, + 0.310, + -0.149, + 0.359, + 0.061, + 0.060, +] +LEGACY_LOG_CADENCE = 2.68 +LEGACY_ARB_FREQUENCY = max(1, round(math.exp(LEGACY_LOG_CADENCE))) +AAVE_ETH_NOISE_SETTINGS = { + "enable_noise_model": True, + "noise_model": DEFAULT_NOISE_MODEL, + "noise_artifact_dir": DEFAULT_MARKET_LINEAR_ARTIFACT_DIR, + "noise_pool_id": AAVE_WETH_POOL_ID, + "gas_cost": DEFAULT_GAS_COST, + "protocol_fee_split": DEFAULT_PROTOCOL_FEE_SPLIT, +} + +GEOMETRIC_ONLY_HEATMAP_METRIC_KEYS = ( + "geometric_vs_launch_geometric_pct", + "noise_geometric_final_value_musd", + "noise_vs_arb_geometric_improvement_pct", +) +CONSTANT_ARC_HEATMAP_METRIC_KEYS = ( + "efficiency_pct", + "launch_geometric_efficiency_pct", + "constant_arc_vs_launch_constant_arc_pct", + "noise_constant_arc_final_value_musd", + "noise_vs_arb_constant_arc_improvement_pct", +) +HEATMAP_METRIC_DEPENDENCIES = { + "efficiency_pct": ("noise_geometric", "noise_constant_arc"), + "launch_geometric_efficiency_pct": ("noise_constant_arc",), + "geometric_vs_launch_geometric_pct": ("noise_geometric",), + "constant_arc_vs_launch_constant_arc_pct": ("noise_constant_arc",), + "noise_geometric_final_value_musd": ("noise_geometric",), + "noise_constant_arc_final_value_musd": ("noise_constant_arc",), + "noise_vs_arb_geometric_improvement_pct": ("noise_geometric", "arb_geometric"), + "noise_vs_arb_constant_arc_improvement_pct": ( + "noise_constant_arc", + "arb_constant_arc", + ), +} + +_NOISE_SETTINGS_CACHE = {} +_WARNED_NOISE_FALLBACKS = set() + + +def get_initial_pool_value(cfg): + """Return the configured base pool TVL in USD.""" + return float(cfg.get("initial_pool_value", DEFAULT_INITIAL_POOL_VALUE)) + + +def get_tvl_millions(cfg): + """Return the configured base pool TVL in millions of USD.""" + return get_initial_pool_value(cfg) / 1_000_000.0 + + +def format_tvl_millions_slug(cfg): + """Format the TVL in millions for stable filenames.""" + tvl_millions = get_tvl_millions(cfg) + rounded = round(float(tvl_millions), 6) + if np.isclose(rounded, round(rounded)): + return f"{int(round(rounded))}m" + return f"{rounded:.6f}".rstrip("0").rstrip(".").replace(".", "p") + "m" + + +def format_tvl_millions_label(cfg): + """Format the TVL in millions for plot titles and logs.""" + return f"{get_tvl_millions(cfg):.1f}M" + + +def tvl_artifact_filename(stem, cfg, suffix=None): + """Append a TVL-in-millions suffix to a PNG artifact name.""" + parts = [stem] + if suffix: + parts.append(suffix) + parts.append(f"tvl_{format_tvl_millions_slug(cfg)}") + return "_".join(parts) + ".png" + + +def heatmap_artifact_filename(spec, cfg, suffix=None): + """Build a heatmap filename, including any colour-style tag.""" + stem = f"reclamm_heatmap_{spec['slug']}" + artifact_tag = spec.get("artifact_tag") + if artifact_tag: + stem = f"{stem}_{artifact_tag}" + return tvl_artifact_filename(stem, cfg, suffix=suffix) + + +def configs_for_tvl(base_configs, initial_pool_value): + """Attach a shared initial TVL to each compare configuration.""" + configs = [] + for cfg in base_configs: + updated = dict(cfg) + updated["initial_pool_value"] = float(initial_pool_value) + configs.append(updated) + return configs + + +def make_noise_variant_cfg(cfg, enable_noise_model): + """Return a config with either noise modelling or pure arb-only enabled.""" + updated = dict(cfg) + if enable_noise_model: + updated["enable_noise_model"] = True + return updated + + matched_noise = resolve_reclamm_noise_settings(cfg) + + updated["enable_noise_model"] = False + updated["noise_model"] = None + updated["gas_cost"] = cfg.get("gas_cost", DEFAULT_GAS_COST) + updated["protocol_fee_split"] = cfg.get( + "protocol_fee_split", DEFAULT_PROTOCOL_FEE_SPLIT + ) + updated["noise_trader_ratio"] = 0.0 + matched_arb_frequency = matched_noise.get("arb_frequency") + if matched_arb_frequency is not None: + updated["arb_frequency"] = matched_arb_frequency + for key in ( + "reclamm_noise_params", + "noise_arrays_path", + "noise_artifact_dir", + "noise_pool_id", + ): + updated.pop(key, None) + return updated + + +def _warn_noise_fallback(message): + """Print a one-time message when the preferred noise setup is unavailable.""" + if message not in _WARNED_NOISE_FALLBACKS: + print(message) + _WARNED_NOISE_FALLBACKS.add(message) + + +def _hashable_noise_params(params): + """Convert a noise-params dict into a stable cache key fragment.""" + if params is None: + return None + return tuple(sorted((str(k), round(float(v), 12)) for k, v in params.items())) + + +def _legacy_calibrated_noise_settings(reason=None): + """Fallback calibrated noise config used when market-linear artifacts are absent.""" + if reason: + _warn_noise_fallback( + "market_linear noise unavailable for thermostat comparison; " + f"falling back to calibrated legacy coefficients ({reason})." + ) + return { + "noise_model": "calibrated", + "noise_trader_ratio": 0.0, + "reclamm_noise_params": { + f"c_{i}": LEGACY_NOISE_COEFFS[i] for i in range(len(LEGACY_NOISE_COEFFS)) + }, + "arb_frequency": LEGACY_ARB_FREQUENCY, + "noise_summary": ( + "calibrated legacy 8-covariate " + f"(arb_frequency={LEGACY_ARB_FREQUENCY})" + ), + "noise_cache_key": ( + "calibrated", + tuple(round(float(c), 12) for c in LEGACY_NOISE_COEFFS), + LEGACY_ARB_FREQUENCY, + ), + } + + +def resolve_reclamm_noise_settings(cfg): + """Resolve the active reCLAMM noise-model fingerprint block for a config.""" + enable_noise_model = cfg.get("enable_noise_model", False) + requested_mode = cfg.get("noise_model", DEFAULT_NOISE_MODEL) + cache_key = ( + tuple(cfg.get("tokens", [])), + cfg.get("start"), + cfg.get("end"), + enable_noise_model, + requested_mode, + cfg.get("noise_artifact_dir", DEFAULT_MARKET_LINEAR_ARTIFACT_DIR), + cfg.get("noise_pool_id", AAVE_WETH_POOL_ID), + cfg.get("arb_frequency"), + round(float(cfg.get("noise_trader_ratio", 0.0)), 12), + _hashable_noise_params(cfg.get("reclamm_noise_params")), + cfg.get("noise_arrays_path"), + ) + if cache_key in _NOISE_SETTINGS_CACHE: + return _NOISE_SETTINGS_CACHE[cache_key] + + if not enable_noise_model: + result = { + "noise_model": None, + "noise_trader_ratio": 0.0, + "reclamm_noise_params": None, + "noise_arrays_path": None, + "arb_frequency": None, + "noise_summary": "arb-only (noise disabled)", + "noise_cache_key": ("disabled",), + } + elif requested_mode == "market_linear": + artifact_dir = cfg.get("noise_artifact_dir", DEFAULT_MARKET_LINEAR_ARTIFACT_DIR) + pool_id = cfg.get("noise_pool_id", AAVE_WETH_POOL_ID) + start_date = str(cfg["start"]).split(" ")[0] + end_date = str(cfg["end"]).split(" ")[0] + try: + from quantammsim.calibration.noise_model_arrays import ( + _find_pool_index, + build_simulator_arrays, + load_artifact, + ) + + model_path = os.path.join(artifact_dir, "model.npz") + meta_path = os.path.join(artifact_dir, "meta.json") + if not (os.path.exists(model_path) and os.path.exists(meta_path)): + raise FileNotFoundError( + f"expected {model_path} and {meta_path}" + ) + + cache_dir = os.path.join(artifact_dir, "_sim_arrays") + os.makedirs(cache_dir, exist_ok=True) + arrays_path = os.path.join( + cache_dir, + f"{pool_id}_{start_date}_{end_date}.npz", + ) + if not os.path.exists(arrays_path): + arrays = build_simulator_arrays( + pool_id=pool_id, + start_date=start_date, + end_date=end_date, + artifact_dir=artifact_dir, + ) + np.savez( + arrays_path, + noise_base=arrays["noise_base"], + noise_tvl_coeff=arrays["noise_tvl_coeff"], + tvl_mean=arrays["tvl_mean"], + tvl_std=arrays["tvl_std"], + ) + + with np.load(arrays_path) as arrays: + tvl_mean = float(arrays["tvl_mean"]) + tvl_std = float(arrays["tvl_std"]) + + art, meta = load_artifact(artifact_dir) + pool_idx = _find_pool_index(pool_id, meta["pool_ids"]) + if pool_idx >= 0: + learned_cadence = float(np.exp(art["log_cadence"][pool_idx])) + else: + learned_cadence = 5.0 + arb_frequency = max(1, round(learned_cadence)) + result = { + "noise_model": "market_linear", + "noise_trader_ratio": 0.0, + "reclamm_noise_params": { + "tvl_mean": tvl_mean, + "tvl_std": tvl_std, + }, + "noise_arrays_path": arrays_path, + "arb_frequency": arb_frequency, + "noise_summary": f"market_linear (arb_frequency={arb_frequency})", + "noise_cache_key": ( + "market_linear", + arrays_path, + arb_frequency, + round(tvl_mean, 12), + round(tvl_std, 12), + ), + } + except Exception as exc: # pragma: no cover - fallback path depends on local artifacts + result = _legacy_calibrated_noise_settings(str(exc)) + elif requested_mode == "calibrated": + params = cfg.get("reclamm_noise_params") + if params is None: + result = _legacy_calibrated_noise_settings() + else: + arb_frequency = cfg.get("arb_frequency", LEGACY_ARB_FREQUENCY) + result = { + "noise_model": "calibrated", + "noise_trader_ratio": cfg.get("noise_trader_ratio", 0.0), + "reclamm_noise_params": dict(params), + "arb_frequency": arb_frequency, + "noise_summary": f"calibrated (arb_frequency={arb_frequency})", + "noise_cache_key": ( + "calibrated", + _hashable_noise_params(params), + arb_frequency, + ), + } + else: + arb_frequency = cfg.get("arb_frequency") + result = { + "noise_model": requested_mode, + "noise_trader_ratio": cfg.get("noise_trader_ratio", 0.0), + "reclamm_noise_params": cfg.get("reclamm_noise_params"), + "noise_arrays_path": cfg.get("noise_arrays_path"), + "arb_frequency": arb_frequency, + "noise_summary": f"{requested_mode} (arb_frequency={arb_frequency})", + "noise_cache_key": ( + requested_mode, + round(float(cfg.get("noise_trader_ratio", 0.0)), 12), + _hashable_noise_params(cfg.get("reclamm_noise_params")), + cfg.get("noise_arrays_path"), + arb_frequency, + ), + } + + _NOISE_SETTINGS_CACHE[cache_key] = result + return result + + # Pool configurations to compare CONFIGS = [ { - "name": "AAVE/ETH on-chain (25bps, narrow range)", + "name": "AAVE/ETH launch-style range (25bps, reference)", "tokens": ["AAVE", "ETH"], "start": "2024-06-01 00:00:00", "end": "2025-06-01 00:00:00", "fees": 0.0025, - "price_ratio": 1.5, + "price_ratio": 1.5014, "centeredness_margin": 0.5, "daily_price_shift_exponent": 0.1, + "reason": "Original launch-style parameters.", + **AAVE_ETH_NOISE_SETTINGS, }, { - "name": "AAVE/ETH wide range (25bps)", + "name": "AAVE/ETH aggressive tight range (25bps)", "tokens": ["AAVE", "ETH"], "start": "2024-06-01 00:00:00", "end": "2025-06-01 00:00:00", "fees": 0.0025, - "price_ratio": 4.0, - "centeredness_margin": 0.2, - "daily_price_shift_exponent": 1.0, - }, - { - "name": "AAVE/ETH zero fees (narrow)", - "tokens": ["AAVE", "ETH"], - "start": "2024-06-01 00:00:00", - "end": "2025-06-01 00:00:00", - "fees": 0.0, - "price_ratio": 1.5, - "centeredness_margin": 0.5, + "price_ratio": 1.10, + "centeredness_margin": 0.60, "daily_price_shift_exponent": 0.1, + "reason": ( + "Aggressively tightened and moved to an earlier thermostat trigger. " + "At fixed price_ratio=1.10, the shift_exponent sweep still favored " + "0.1, while margin=0.60 widened the non-linear edge materially." + ), + **AAVE_ETH_NOISE_SETTINGS, }, ] -def make_fingerprint(cfg, interpolation_method, centeredness_scaling=False): +def make_fingerprint(cfg, interpolation_method): """Build run fingerprint for a given config and interpolation method.""" - return { + speed_override = ( + cfg.get("arc_length_speed") + if interpolation_method == "constant_arc_length" + else None + ) + noise_cfg = resolve_reclamm_noise_settings(cfg) + arb_frequency = cfg.get("arb_frequency", noise_cfg.get("arb_frequency")) + fingerprint = { "tokens": cfg["tokens"], "rule": "reclamm", "startDateString": cfg["start"], "endDateString": cfg["end"], - "initial_pool_value": 1000000.0, + "initial_pool_value": get_initial_pool_value(cfg), "do_arb": True, "fees": cfg["fees"], - "gas_cost": 0.0, - "arb_fees": 0.0, + "gas_cost": cfg.get( + "gas_cost", + DEFAULT_GAS_COST if cfg.get("enable_noise_model", False) else 0.0, + ), + "arb_fees": cfg.get("arb_fees", 0.0), + "protocol_fee_split": cfg.get( + "protocol_fee_split", + DEFAULT_PROTOCOL_FEE_SPLIT if cfg.get("enable_noise_model", False) else 0.0, + ), + "noise_trader_ratio": noise_cfg.get("noise_trader_ratio", 0.0), "reclamm_interpolation_method": interpolation_method, - "reclamm_arc_length_speed": None, # auto-calibrate - "reclamm_centeredness_scaling": centeredness_scaling, + "reclamm_arc_length_speed": speed_override, } + if noise_cfg.get("noise_model") is not None: + fingerprint["noise_model"] = noise_cfg["noise_model"] + if noise_cfg.get("reclamm_noise_params") is not None: + fingerprint["reclamm_noise_params"] = noise_cfg["reclamm_noise_params"] + if noise_cfg.get("noise_arrays_path") is not None: + fingerprint["noise_arrays_path"] = noise_cfg["noise_arrays_path"] + if arb_frequency is not None: + fingerprint["arb_frequency"] = arb_frequency + return fingerprint def make_params(cfg): @@ -85,40 +480,1129 @@ def make_params(cfg): } -def run_comparison(cfg): - """Run all thermostat variants, return results dict.""" +def load_shared_price_data(configs, root=None): + """Load the shared historic price panel once for all compare runs.""" + tokens = sorted({token for cfg in configs for token in cfg["tokens"]}) + return get_historic_parquet_data(tokens, cols=["close"], root=root) + + +def run_comparison(cfg, price_data=None, low_data_mode=False): + """Run both interpolation variants, return results dict.""" params = make_params(cfg) results = {} - for method in ["geometric", "constant_arc_length"]: + for method in INTERPOLATION_METHODS: fp = make_fingerprint(cfg, method) results[method] = do_run_on_historic_data( - run_fingerprint=fp, params=params + run_fingerprint=fp, + params=params, + price_data=price_data, + low_data_mode=low_data_mode, + ) + + return results + + +def _set_padded_ylim(ax, series_list, pad_ratio=0.04): + """Fit the y-axis tightly around the plotted series.""" + flat = [ + np.asarray(series, dtype=float).ravel() + for series in series_list + if np.asarray(series).size > 0 + ] + if not flat: + return + + values = np.concatenate(flat) + values = values[np.isfinite(values)] + if values.size == 0: + return + + ymin = float(values.min()) + ymax = float(values.max()) + if np.isclose(ymin, ymax): + pad = max(abs(ymin) * pad_ratio, 1e-6) + else: + pad = (ymax - ymin) * pad_ratio + ax.set_ylim(ymin - pad, ymax + pad) + + +def _cache_size(cache): + """Count memoized final-value runs.""" + return len(cache.get("_final_value_cache", {})) + + +def _comparison_cache_size(cache): + """Count memoized scalar comparison bundles.""" + return len(cache.get("_comparison_cache", {})) + + +def make_sweep_cache(price_data): + """Create a shared cache for heatmap and line sweeps.""" + return { + "_shared_price_data": price_data, + "_final_value_cache": {}, + "_comparison_cache": {}, + } + + +def _missing_artifacts(progress_label, filenames): + """Report which plot artifacts still need to be generated.""" + missing = [filename for filename in filenames if not os.path.exists(filename)] + if not missing: + print(f"[{progress_label}] skipping sweep: all artifacts already exist.") + return set() + + existing_count = len(filenames) - len(missing) + if existing_count: + print( + f"[{progress_label}] reusing {existing_count}/{len(filenames)} " + "existing artifacts; generating the missing outputs." ) + return set(missing) + - # Geometric + centeredness-proportional scaling (scales decay duration) - fp_geo_scaled = make_fingerprint(cfg, "geometric", centeredness_scaling=True) - results["geometric_scaled"] = do_run_on_historic_data( - run_fingerprint=fp_geo_scaled, params=params +def _speed_cache_key(speed): + """Stable cache token for optional arc-length speed.""" + if speed is None: + return None + return round(float(speed), 12) + + +def _make_method_cache_key(cfg, method): + """Cache key for a single-method final-value run.""" + noise_cfg = resolve_reclamm_noise_settings(cfg) + arb_frequency = cfg.get("arb_frequency", noise_cfg.get("arb_frequency")) + key = ( + method, + bool(cfg.get("enable_noise_model", False)), + round(float(cfg["price_ratio"]), 6), + round(float(cfg["centeredness_margin"]), 6), + round(float(cfg["daily_price_shift_exponent"]), 6), + round(get_initial_pool_value(cfg), 2), + noise_cfg.get("noise_cache_key"), + None if arb_frequency is None else int(arb_frequency), + round( + float( + cfg.get( + "gas_cost", + DEFAULT_GAS_COST if cfg.get("enable_noise_model", False) else 0.0, + ) + ), + 6, + ), + round( + float( + cfg.get( + "protocol_fee_split", + DEFAULT_PROTOCOL_FEE_SPLIT if cfg.get("enable_noise_model", False) else 0.0, + ) + ), + 6, + ), ) + if method == "constant_arc_length": + key += (_speed_cache_key(cfg.get("arc_length_speed")),) + return key - # Arc-length + centeredness-proportional scaling (scales speed) - fp_cal_scaled = make_fingerprint(cfg, "constant_arc_length", centeredness_scaling=True) - results["cal_scaled"] = do_run_on_historic_data( - run_fingerprint=fp_cal_scaled, params=params + +def _make_comparison_cache_key(cfg, launch_final_values): + """Cache key for scalar heatmap metrics at a single parameter point.""" + noise_cfg = make_noise_variant_cfg(cfg, True) + arb_only_cfg = make_noise_variant_cfg(cfg, False) + key = [ + _make_method_cache_key(noise_cfg, "geometric"), + _make_method_cache_key(arb_only_cfg, "geometric"), + round(float(launch_final_values["geometric"]), 6), + ] + if RUN_CONSTANT_ARC_LENGTH: + key.extend( + [ + _make_method_cache_key(noise_cfg, "constant_arc_length"), + _make_method_cache_key(arb_only_cfg, "constant_arc_length"), + round(float(launch_final_values["constant_arc_length"]), 6), + ] + ) + return tuple(key) + + +def _run_method_final_value_cached(cfg, method, cache): + """Memoize final value for a single interpolation method.""" + final_value_cache = cache.setdefault("_final_value_cache", {}) + key = _make_method_cache_key(cfg, method) + if key not in final_value_cache: + result = do_run_on_historic_data( + run_fingerprint=make_fingerprint(cfg, method), + params=make_params(cfg), + price_data=cache["_shared_price_data"], + low_data_mode=True, + ) + final_value_cache[key] = float(result["final_value"]) + del result + gc.collect() + return final_value_cache[key] + + +def extract_comparison_metrics_from_final_values( + geo_final, arc_final, launch_final_values +): + """Summarize scalar comparison metrics from final values only.""" + return { + "efficiency_pct": (arc_final / max(abs(geo_final), 1e-12) - 1.0) * 100.0, + "launch_geometric_efficiency_pct": ( + arc_final / max(abs(launch_final_values["geometric"]), 1e-12) - 1.0 + ) + * 100.0, + "geometric_vs_launch_geometric_pct": ( + geo_final / max(abs(launch_final_values["geometric"]), 1e-12) - 1.0 + ) + * 100.0, + "constant_arc_vs_launch_constant_arc_pct": ( + arc_final + / max(abs(launch_final_values["constant_arc_length"]), 1e-12) + - 1.0 + ) + * 100.0, + } + + +def _load_required_heatmap_final_values(cfg, cache, metric_keys): + """Load only the cached final values needed for the requested heatmap metrics.""" + required_sources = set() + for metric_key in metric_keys: + required_sources.update(HEATMAP_METRIC_DEPENDENCIES[metric_key]) + + if not RUN_CONSTANT_ARC_LENGTH and any( + source.endswith("constant_arc") for source in required_sources + ): + raise ValueError( + "Constant-arc heatmap metric requested while RUN_CONSTANT_ARC_LENGTH=False" + ) + + final_values = {} + noise_cfg = None + arb_only_cfg = None + + if any(source.startswith("noise_") for source in required_sources): + noise_cfg = make_noise_variant_cfg(cfg, True) + if any(source.startswith("arb_") for source in required_sources): + arb_only_cfg = make_noise_variant_cfg(cfg, False) + + if "noise_geometric" in required_sources: + final_values["noise_geometric"] = _run_method_final_value_cached( + noise_cfg, + "geometric", + cache, + ) + if "noise_constant_arc" in required_sources: + final_values["noise_constant_arc"] = _run_method_final_value_cached( + noise_cfg, + "constant_arc_length", + cache, + ) + if "arb_geometric" in required_sources: + final_values["arb_geometric"] = _run_method_final_value_cached( + arb_only_cfg, + "geometric", + cache, + ) + if "arb_constant_arc" in required_sources: + final_values["arb_constant_arc"] = _run_method_final_value_cached( + arb_only_cfg, + "constant_arc_length", + cache, + ) + return final_values + + +def extract_heatmap_metrics_from_mode_final_values( + metric_keys, + final_values, + launch_final_values, +): + """Collect the requested scalar heatmap metrics from cached final values.""" + metrics = {} + + if "efficiency_pct" in metric_keys: + metrics["efficiency_pct"] = ( + final_values["noise_constant_arc"] + / max(abs(final_values["noise_geometric"]), 1e-12) + - 1.0 + ) * 100.0 + + if "launch_geometric_efficiency_pct" in metric_keys: + metrics["launch_geometric_efficiency_pct"] = ( + final_values["noise_constant_arc"] + / max(abs(launch_final_values["geometric"]), 1e-12) + - 1.0 + ) * 100.0 + + if "geometric_vs_launch_geometric_pct" in metric_keys: + metrics["geometric_vs_launch_geometric_pct"] = ( + final_values["noise_geometric"] + / max(abs(launch_final_values["geometric"]), 1e-12) + - 1.0 + ) * 100.0 + + if "constant_arc_vs_launch_constant_arc_pct" in metric_keys: + metrics["constant_arc_vs_launch_constant_arc_pct"] = ( + final_values["noise_constant_arc"] + / max(abs(launch_final_values["constant_arc_length"]), 1e-12) + - 1.0 + ) * 100.0 + + if "noise_geometric_final_value_musd" in metric_keys: + metrics["noise_geometric_final_value_musd"] = ( + final_values["noise_geometric"] / 1e6 + ) + + if "noise_constant_arc_final_value_musd" in metric_keys: + metrics["noise_constant_arc_final_value_musd"] = ( + final_values["noise_constant_arc"] / 1e6 + ) + + if "noise_vs_arb_geometric_improvement_pct" in metric_keys: + metrics["noise_vs_arb_geometric_improvement_pct"] = ( + final_values["noise_geometric"] + / max(abs(final_values["arb_geometric"]), 1e-12) + - 1.0 + ) * 100.0 + + if "noise_vs_arb_constant_arc_improvement_pct" in metric_keys: + metrics["noise_vs_arb_constant_arc_improvement_pct"] = ( + final_values["noise_constant_arc"] + / max(abs(final_values["arb_constant_arc"]), 1e-12) + - 1.0 + ) * 100.0 + + return metrics + + +def extract_comparison_metrics(results, launch_final_values): + """Summarize scalar heatmap metrics for a pair of runs.""" + geo = results["geometric"] + arc = results["constant_arc_length"] + + geo_final = float(geo["final_value"]) + arc_final = float(arc["final_value"]) + + return extract_comparison_metrics_from_final_values( + geo_final, + arc_final, + launch_final_values=launch_final_values, ) - return results + +def run_comparison_cached(cfg, cache, launch_final_values, metric_keys): + """Memoize scalar heatmap metrics across heatmap sweeps.""" + requested_metric_keys = tuple(dict.fromkeys(metric_keys)) + comparison_cache = cache.setdefault("_comparison_cache", {}) + cache_key = _make_comparison_cache_key(cfg, launch_final_values) + cached_metrics = comparison_cache.setdefault(cache_key, {}) + missing_metric_keys = [ + metric_key for metric_key in requested_metric_keys if metric_key not in cached_metrics + ] + if missing_metric_keys: + final_values = _load_required_heatmap_final_values( + cfg, + cache, + missing_metric_keys, + ) + cached_metrics.update( + extract_heatmap_metrics_from_mode_final_values( + missing_metric_keys, + final_values, + launch_final_values=launch_final_values, + ) + ) + return { + metric_key: cached_metrics[metric_key] for metric_key in requested_metric_keys + } + + +def build_heatmap_matrices( + x_values, + y_values, + x_key, + y_key, + base_cfg, + metric_keys, + cache, + progress_label, + launch_final_values, +): + """Evaluate multiple metrics over a 2D parameter grid in one pass.""" + data = { + metric_key: np.zeros((len(y_values), len(x_values)), dtype=float) + for metric_key in metric_keys + } + total_points = len(y_values) * len(x_values) + + print( + f"[{progress_label}] start: {len(y_values)} rows x {len(x_values)} cols " + f"= {total_points} parameter points" + ) + + for yi, y_value in enumerate(y_values): + final_cache_before_row = _cache_size(cache) + comparison_cache_before_row = _comparison_cache_size(cache) + for xi, x_value in enumerate(x_values): + cfg = dict(base_cfg) + cfg[x_key] = float(x_value) + cfg[y_key] = float(y_value) + metrics = run_comparison_cached( + cfg, + cache, + launch_final_values=launch_final_values, + metric_keys=metric_keys, + ) + for metric_key in metric_keys: + data[metric_key][yi, xi] = metrics[metric_key] + + completed_points = (yi + 1) * len(x_values) + row_new_final_runs = _cache_size(cache) - final_cache_before_row + row_new_comparisons = ( + _comparison_cache_size(cache) - comparison_cache_before_row + ) + row_pct = completed_points / total_points * 100.0 + print( + f"[{progress_label}] row {yi + 1}/{len(y_values)} complete " + f"({y_key}={float(y_value):.4f}, {completed_points}/{total_points} " + f"points, {row_pct:.1f}%, {row_new_final_runs} new final-value runs, " + f"{row_new_comparisons} new comparison bundles)" + ) + + print( + f"[{progress_label}] done: " + + ", ".join( + ( + f"{metric_key} min={float(np.nanmin(data[metric_key])):.4f}, " + f"max={float(np.nanmax(data[metric_key])):.4f}" + ) + for metric_key in metric_keys + ) + + ( + f", final_value_cache_size={_cache_size(cache)}, " + f"comparison_cache_size={_comparison_cache_size(cache)}" + ) + ) + + return data + + +def build_metric_curve( + x_values, + x_key, + base_cfg, + metric_key, + cache, + launch_final_values, +): + """Evaluate one metric over a 1D sweep.""" + data = np.zeros(len(x_values), dtype=float) + for xi, x_value in enumerate(x_values): + cfg = dict(base_cfg) + cfg[x_key] = float(x_value) + metrics = run_comparison_cached( + cfg, + cache, + launch_final_values=launch_final_values, + metric_keys=(metric_key,), + ) + data[xi] = metrics[metric_key] + return data + + +def _compute_axis_edges(values, scale="linear"): + """Convert axis centers to cell edges for pcolormesh.""" + values = np.asarray(values, dtype=float) + if values.size == 1: + if scale == "log": + return np.array([values[0] / np.sqrt(10.0), values[0] * np.sqrt(10.0)]) + pad = max(abs(values[0]) * 0.5, 1.0) + return np.array([values[0] - pad, values[0] + pad]) + + if scale == "log": + log_values = np.log10(values) + edges = np.empty(values.size + 1, dtype=float) + edges[1:-1] = 0.5 * (log_values[:-1] + log_values[1:]) + edges[0] = log_values[0] - 0.5 * (log_values[1] - log_values[0]) + edges[-1] = log_values[-1] + 0.5 * (log_values[-1] - log_values[-2]) + return 10.0 ** edges + + edges = np.empty(values.size + 1, dtype=float) + edges[1:-1] = 0.5 * (values[:-1] + values[1:]) + edges[0] = values[0] - 0.5 * (values[1] - values[0]) + edges[-1] = values[-1] + 0.5 * (values[-1] - values[-2]) + return edges + + +def plot_heatmap( + data, + x_values, + y_values, + x_label, + y_label, + title, + colorbar_label, + filename, + xticks=None, + yticks=None, + xscale="linear", + center_zero=True, + cmap=None, + color_norm=None, + symlog_linthresh=None, +): + """Render and save a single heatmap.""" + finite = np.asarray(data, dtype=float) + finite = finite[np.isfinite(finite)] + + if center_zero: + vmax = max(abs(float(np.nanmin(data))), abs(float(np.nanmax(data))), 1e-9) + if color_norm == "symlog" and symlog_linthresh is not None and vmax > symlog_linthresh: + norm = SymLogNorm( + linthresh=symlog_linthresh, + linscale=1.0, + vmin=-vmax, + vmax=vmax, + base=10.0, + ) + else: + norm = TwoSlopeNorm(vcenter=0.0, vmin=-vmax, vmax=vmax) + cmap_name = cmap or "RdYlGn" + else: + if finite.size == 0: + vmin, vmax = 0.0, 1.0 + else: + vmin = float(finite.min()) + vmax = float(finite.max()) + if np.isclose(vmin, vmax): + pad = max(abs(vmin) * 0.01, 1e-9) + vmin -= pad + vmax += pad + norm = Normalize(vmin=vmin, vmax=vmax) + cmap_name = cmap or "viridis" + + x_edges = _compute_axis_edges(x_values, scale=xscale) + y_edges = _compute_axis_edges(y_values, scale="linear") + + fig, ax = plt.subplots(figsize=(8.5, 6.0)) + im = ax.pcolormesh( + x_edges, + y_edges, + data, + cmap=cmap_name, + norm=norm, + shading="auto", + ) + + ax.set_xlabel(x_label) + ax.set_ylabel(y_label) + ax.set_title(title) + if xscale == "log": + ax.set_xscale("log") + ax.set_xticks(np.asarray(xticks if xticks is not None else x_values, dtype=float)) + ax.set_yticks(np.asarray(yticks if yticks is not None else y_values, dtype=float)) + ax.grid(False) + + cbar = fig.colorbar(im, ax=ax) + cbar.set_label(colorbar_label) + + plt.tight_layout() + plt.savefig(filename, dpi=150) + print(f"Saved {filename}") + plt.close(fig) + + +def plot_arc_speed_line_chart( + data, + x_values, + y_values, + y_label, + title, + filename, + launch_curve, + launch_auto_speed=None, +): + """Plot thin multi-series efficiency lines over the arc-speed sweep.""" + fig, ax = plt.subplots(figsize=(10.5, 5.75)) + cmap = plt.cm.viridis + colors = cmap(np.linspace(0.0, 1.0, len(y_values))) + plotted_series = [] + + for yi, (y_value, color) in enumerate(zip(y_values, colors)): + series = np.asarray(data[yi], dtype=float) + plotted_series.append(series) + ax.plot( + x_values, + series, + color=color, + linewidth=SWEEP_LINE_WIDTH, + alpha=0.8, + ) + + launch_curve = np.asarray(launch_curve, dtype=float) + plotted_series.append(launch_curve) + ax.plot( + x_values, + launch_curve, + color="black", + linewidth=REFERENCE_LINE_WIDTH, + alpha=0.9, + label="Current launch config", + ) + if launch_auto_speed is not None: + ax.axvline( + float(launch_auto_speed), + color="black", + ls=":", + linewidth=0.8, + alpha=0.7, + label="Launch auto-cal speed", + ) + + ax.axhline(0.0, color="gray", ls="--", linewidth=0.8, alpha=0.5) + ax.set_xscale("log") + ax.set_xticks(ARC_LENGTH_SPEED_TICKS) + ax.set_xlabel("Arc-length speed") + ax.set_ylabel("Efficiency vs geometric (%)") + ax.set_title(title) + _set_padded_ylim(ax, plotted_series, pad_ratio=0.08) + ax.grid(True, alpha=0.25) + ax.legend(fontsize=8) + + sm = ScalarMappable( + norm=Normalize(vmin=float(np.min(y_values)), vmax=float(np.max(y_values))), + cmap=cmap, + ) + sm.set_array([]) + cbar = fig.colorbar(sm, ax=ax) + cbar.set_label(y_label) + + plt.tight_layout() + plt.savefig(filename, dpi=150) + print(f"Saved {filename}") + plt.close(fig) + + +def generate_heatmaps(base_cfg, price_data, launch_final_values, cache=None): + """Generate pairwise heatmaps for thermostat tuning and noise-vs-arb effects.""" + owns_cache = cache is None + if cache is None: + cache = make_sweep_cache(price_data) + metric_specs = [ + { + "key": "efficiency_pct", + "title": "Efficiency vs heatmap geometric", + "colorbar_label": "Const Arc - heatmap Geo (% of heatmap geometric final value)", + "slug": "efficiency", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "launch_geometric_efficiency_pct", + "title": "Efficiency vs launch-style geometric", + "colorbar_label": "Const Arc - launch Geo (% of launch geometric final value)", + "slug": "launch_geometric_efficiency", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "geometric_vs_launch_geometric_pct", + "title": "Geometric tuning vs launch-style geometric", + "colorbar_label": "Candidate Geo - launch Geo (% of launch geometric final value)", + "slug": "geometric_vs_launch_geometric", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "constant_arc_vs_launch_constant_arc_pct", + "title": "Const arc tuning vs launch-style const arc", + "colorbar_label": "Candidate Const Arc - launch Const Arc (% of launch const arc final value)", + "slug": "constant_arc_vs_launch_constant_arc", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "noise_geometric_final_value_musd", + "title": "Geometric final value with noise model", + "colorbar_label": "Geometric final value with noise model ($M)", + "slug": "noise_geometric_final_value", + "center_zero": False, + "cmap": "viridis", + }, + { + "key": "noise_constant_arc_final_value_musd", + "title": "Const arc final value with noise model", + "colorbar_label": "Const Arc final value with noise model ($M)", + "slug": "noise_constant_arc_final_value", + "center_zero": False, + "cmap": "viridis", + }, + { + "key": "noise_vs_arb_geometric_improvement_pct", + "title": "Noise-model improvement over arb-only (geometric)", + "colorbar_label": "Noise-model Geo - arb-only Geo (% of arb-only final value)", + "slug": "noise_vs_arb_geometric_improvement", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "noise_vs_arb_constant_arc_improvement_pct", + "title": "Noise-model improvement over arb-only (const arc)", + "colorbar_label": "Noise-model Const Arc - arb-only Const Arc (% of arb-only final value)", + "slug": "noise_vs_arb_constant_arc_improvement", + "center_zero": True, + "cmap": "RdYlGn", + }, + ] + for spec in metric_specs: + if spec["center_zero"]: + spec["color_norm"] = CENTER_ZERO_HEATMAP_COLOR_NORM + spec["symlog_linthresh"] = CENTER_ZERO_HEATMAP_SYMLOG_LINTHRESH + spec["artifact_tag"] = CENTER_ZERO_HEATMAP_COLOR_TAG + if not RUN_CONSTANT_ARC_LENGTH: + metric_specs = [ + spec + for spec in metric_specs + if spec["key"] in GEOMETRIC_ONLY_HEATMAP_METRIC_KEYS + ] + pair_specs = [ + { + "slug": "price_ratio_vs_margin", + "x_values": HEATMAP_PRICE_RATIOS, + "y_values": HEATMAP_MARGINS, + "x_key": "price_ratio", + "y_key": "centeredness_margin", + "x_label": "Price ratio", + "y_label": "Centeredness margin", + "title_suffix": ( + f"shift_exp fixed at {base_cfg['daily_price_shift_exponent']:.2f}" + ), + "xticks": PRICE_RATIO_TICKS, + "yticks": MARGIN_TICKS + }, + { + "slug": "shift_exp_vs_margin", + "x_values": HEATMAP_SHIFT_EXPONENTS, + "y_values": HEATMAP_MARGINS, + "x_key": "daily_price_shift_exponent", + "y_key": "centeredness_margin", + "x_label": "Shift exponent", + "y_label": "Centeredness margin", + "title_suffix": f"price_ratio fixed at {base_cfg['price_ratio']:.2f}", + "xticks": SHIFT_EXPONENT_TICKS, + "yticks": MARGIN_TICKS + }, + { + "slug": "price_ratio_vs_shift_exp", + "x_values": HEATMAP_PRICE_RATIOS, + "y_values": HEATMAP_SHIFT_EXPONENTS, + "x_key": "price_ratio", + "y_key": "daily_price_shift_exponent", + "x_label": "Price ratio", + "y_label": "Shift exponent", + "title_suffix": ( + f"margin fixed at {base_cfg['centeredness_margin']:.2f}" + ), + "xticks": PRICE_RATIO_TICKS, + "yticks": SHIFT_EXPONENT_TICKS, + }, + ] + + metric_spec_map = {spec["key"]: spec for spec in metric_specs} + + if RUN_CONSTANT_ARC_LENGTH: + print( + "Using launch-style benchmarks " + f"Geo=${launch_final_values['geometric']:,.0f}, " + f"Const Arc=${launch_final_values['constant_arc_length']:,.0f}, " + f"TVL={format_tvl_millions_label(base_cfg)}." + ) + print( + "Running {count} heatmap pair sweeps sequentially " + "(current outputs use cached noise-model runs; improvement heatmaps " + "reuse those values and add cached arb-only runs).".format( + count=len(pair_specs) + ) + ) + else: + print( + "Using launch-style geometric benchmark " + f"Geo=${launch_final_values['geometric']:,.0f}, " + f"TVL={format_tvl_millions_label(base_cfg)}." + ) + print( + "RUN_CONSTANT_ARC_LENGTH=False, so only geometric heatmaps will be generated " + "and only geometric/arb-only geometric runs will be scheduled." + ) + + for pair in pair_specs: + output_files = { + spec["key"]: heatmap_artifact_filename( + spec, + base_cfg, + suffix=pair["slug"], + ) + for spec in metric_specs + } + missing_files = _missing_artifacts( + pair["slug"], + list(output_files.values()), + ) + if not missing_files: + continue + + missing_metric_keys = [ + spec["key"] + for spec in metric_specs + if output_files[spec["key"]] in missing_files + ] + data_by_metric = build_heatmap_matrices( + x_values=pair["x_values"], + y_values=pair["y_values"], + x_key=pair["x_key"], + y_key=pair["y_key"], + base_cfg=base_cfg, + metric_keys=missing_metric_keys, + cache=cache, + progress_label=pair["slug"], + launch_final_values=launch_final_values, + ) + print(f"[{pair['slug']}] plotting missing heatmaps...") + for metric_key in missing_metric_keys: + spec = metric_spec_map[metric_key] + plot_heatmap( + data=data_by_metric[metric_key], + x_values=pair["x_values"], + y_values=pair["y_values"], + x_label=pair["x_label"], + y_label=pair["y_label"], + title=( + f"{spec['title']}: {pair['title_suffix']} | " + f"TVL {format_tvl_millions_label(base_cfg)}" + ), + colorbar_label=spec["colorbar_label"], + filename=output_files[metric_key], + xticks=pair["xticks"], + yticks=pair["yticks"], + center_zero=spec["center_zero"], + cmap=spec["cmap"], + color_norm=spec.get("color_norm"), + symlog_linthresh=spec.get("symlog_linthresh"), + ) + del data_by_metric + gc.collect() + + if owns_cache: + cache.clear() + gc.collect() + print("Released heatmap metric cache.") + + +def compute_auto_calibrated_arc_length_speed(cfg, price_data): + """Compute the launch/reference auto-calibrated speed for a config.""" + start_ts = pd.Timestamp(cfg["start"]) + + if isinstance(price_data.index, pd.DatetimeIndex): + row = price_data.loc[start_ts] + else: + start_unix_ms = int(start_ts.timestamp() * 1000.0) + index_values = price_data.index.to_numpy(dtype=np.int64) + row_idx = int(np.searchsorted(index_values, start_unix_ms, side="left")) + if row_idx >= len(index_values): + row_idx = len(index_values) - 1 + if row_idx > 0 and index_values[row_idx] != start_unix_ms: + prev_idx = row_idx - 1 + if abs(index_values[prev_idx] - start_unix_ms) <= abs( + index_values[row_idx] - start_unix_ms + ): + row_idx = prev_idx + row = price_data.iloc[row_idx] + + if isinstance(row, pd.DataFrame): + row = row.iloc[0] + + if isinstance(price_data.columns, pd.MultiIndex): + initial_price_values = [ + float(row[(token, "close")]) + for token in cfg["tokens"] + ] + else: + initial_price_values = [ + float(row[f"close_{token}"]) + for token in cfg["tokens"] + ] + + initial_prices = jnp.array(initial_price_values, dtype=jnp.float64) + initial_reserves, Va, Vb = initialise_reclamm_reserves( + get_initial_pool_value(cfg), + initial_prices, + float(cfg["price_ratio"]), + ) + market_price_0 = float(initial_prices[0] / initial_prices[1]) + sqrt_Q = jnp.sqrt( + compute_price_ratio( + initial_reserves[0], + initial_reserves[1], + Va, + Vb, + ) + ) + return float( + calibrate_arc_length_speed( + initial_reserves[0], + initial_reserves[1], + Va, + Vb, + to_daily_price_shift_base(float(cfg["daily_price_shift_exponent"])), + 60.0, + sqrt_Q, + market_price_0, + centeredness_margin=float(cfg["centeredness_margin"]), + ) + ) + + +def generate_arc_speed_efficiency_artifacts( + base_cfg, + launch_cfg, + price_data, + launch_final_values, + cache=None, +): + """Generate arc-speed heatmaps plus the existing efficiency line charts.""" + if not RUN_CONSTANT_ARC_LENGTH: + print("\nSkipping arc-speed heatmaps because RUN_CONSTANT_ARC_LENGTH=False.") + return + owns_cache = cache is None + if cache is None: + cache = make_sweep_cache(price_data) + launch_auto_speed = compute_auto_calibrated_arc_length_speed(launch_cfg, price_data) + heatmap_metric_specs = [ + { + "key": "efficiency_pct", + "title": "Efficiency vs geometric", + "colorbar_label": "Const Arc - heatmap Geo (% of heatmap geometric final value)", + "slug": "efficiency", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "noise_constant_arc_final_value_musd", + "title": "Const arc final value with noise model", + "colorbar_label": "Const Arc final value with noise model ($M)", + "slug": "noise_constant_arc_final_value", + "center_zero": False, + "cmap": "viridis", + }, + { + "key": "noise_vs_arb_constant_arc_improvement_pct", + "title": "Noise-model improvement over arb-only (const arc)", + "colorbar_label": "Noise-model Const Arc - arb-only Const Arc (% of arb-only final value)", + "slug": "noise_vs_arb_constant_arc_improvement", + "center_zero": True, + "cmap": "RdYlGn", + }, + ] + for spec in heatmap_metric_specs: + if spec["center_zero"]: + spec["color_norm"] = CENTER_ZERO_HEATMAP_COLOR_NORM + spec["symlog_linthresh"] = CENTER_ZERO_HEATMAP_SYMLOG_LINTHRESH + spec["artifact_tag"] = CENTER_ZERO_HEATMAP_COLOR_TAG + pair_specs = [ + { + "slug": "arc_speed_vs_price_ratio", + "x_values": HEATMAP_ARC_LENGTH_SPEEDS, + "y_values": HEATMAP_PRICE_RATIOS, + "x_key": "arc_length_speed", + "y_key": "price_ratio", + "x_label": "Arc-length speed", + "y_label": "Price ratio", + "title_suffix": ( + f"margin fixed at {base_cfg['centeredness_margin']:.2f}, " + f"shift_exp fixed at {base_cfg['daily_price_shift_exponent']:.2f}" + ), + "xticks": ARC_LENGTH_SPEED_TICKS, + "yticks": PRICE_RATIO_TICKS, + }, + { + "slug": "arc_speed_vs_margin", + "x_values": HEATMAP_ARC_LENGTH_SPEEDS, + "y_values": HEATMAP_MARGINS, + "x_key": "arc_length_speed", + "y_key": "centeredness_margin", + "x_label": "Arc-length speed", + "y_label": "Centeredness margin", + "title_suffix": ( + f"price_ratio fixed at {base_cfg['price_ratio']:.2f}, " + f"shift_exp fixed at {base_cfg['daily_price_shift_exponent']:.2f}" + ), + "xticks": ARC_LENGTH_SPEED_TICKS, + "yticks": MARGIN_TICKS + }, + { + "slug": "arc_speed_vs_shift_exp", + "x_values": HEATMAP_ARC_LENGTH_SPEEDS, + "y_values": HEATMAP_SHIFT_EXPONENTS, + "x_key": "arc_length_speed", + "y_key": "daily_price_shift_exponent", + "x_label": "Arc-length speed", + "y_label": "Shift exponent", + "title_suffix": ( + f"price_ratio fixed at {base_cfg['price_ratio']:.2f}, " + f"margin fixed at {base_cfg['centeredness_margin']:.2f}" + ), + "xticks": ARC_LENGTH_SPEED_TICKS, + "yticks": SHIFT_EXPONENT_TICKS, + }, + ] + metric_spec_map = {spec["key"]: spec for spec in heatmap_metric_specs} + + print( + "\nGenerating arc-speed heatmaps and line charts " + f"(launch auto-cal speed={launch_auto_speed:.3e}, TVL={format_tvl_millions_label(base_cfg)})..." + ) + + for pair in pair_specs: + heatmap_files = { + spec["key"]: heatmap_artifact_filename( + spec, + base_cfg, + suffix=pair["slug"], + ) + for spec in heatmap_metric_specs + } + line_filename = tvl_artifact_filename( + "reclamm_line_efficiency", + base_cfg, + suffix=pair["slug"], + ) + missing_files = _missing_artifacts( + pair["slug"], + list(heatmap_files.values()) + [line_filename], + ) + if not missing_files: + continue + + missing_metric_keys = [ + spec["key"] + for spec in heatmap_metric_specs + if heatmap_files[spec["key"]] in missing_files + ] + if line_filename in missing_files and "efficiency_pct" not in missing_metric_keys: + missing_metric_keys.append("efficiency_pct") + + data_by_metric = build_heatmap_matrices( + x_values=pair["x_values"], + y_values=pair["y_values"], + x_key=pair["x_key"], + y_key=pair["y_key"], + base_cfg=base_cfg, + metric_keys=missing_metric_keys, + cache=cache, + progress_label=pair["slug"], + launch_final_values=launch_final_values, + ) + for metric_key in missing_metric_keys: + if metric_key not in heatmap_files: + continue + if heatmap_files[metric_key] not in missing_files: + continue + spec = metric_spec_map[metric_key] + plot_heatmap( + data=data_by_metric[metric_key], + x_values=pair["x_values"], + y_values=pair["y_values"], + x_label=pair["x_label"], + y_label=pair["y_label"], + title=( + f"{spec['title']}: {pair['title_suffix']} | " + f"TVL {format_tvl_millions_label(base_cfg)}" + ), + colorbar_label=spec["colorbar_label"], + filename=heatmap_files[metric_key], + xticks=pair["xticks"], + yticks=pair["yticks"], + xscale="log", + center_zero=spec["center_zero"], + cmap=spec["cmap"], + color_norm=spec.get("color_norm"), + symlog_linthresh=spec.get("symlog_linthresh"), + ) + + if line_filename in missing_files: + efficiency_data = data_by_metric["efficiency_pct"] + launch_curve = build_metric_curve( + x_values=pair["x_values"], + x_key=pair["x_key"], + base_cfg=launch_cfg, + metric_key="efficiency_pct", + cache=cache, + launch_final_values=launch_final_values, + ) + plot_arc_speed_line_chart( + data=efficiency_data, + x_values=pair["x_values"], + y_values=pair["y_values"], + y_label=pair["y_label"], + title=( + "Arc-speed efficiency sweep: " + f"{pair['title_suffix']} | TVL {format_tvl_millions_label(base_cfg)}" + ), + filename=line_filename, + launch_curve=launch_curve, + launch_auto_speed=launch_auto_speed, + ) + del data_by_metric + gc.collect() + + if owns_cache: + cache.clear() + gc.collect() + print("Released arc-speed sweep cache.") + + +def get_launch_final_values(all_results, launch_cfg, price_data): + """Reuse launch-style runs when available; otherwise run them once.""" + for cfg, results in all_results: + if cfg["name"] == launch_cfg["name"]: + launch_final_values = { + "geometric": float(results["geometric"]["final_value"]), + } + if "constant_arc_length" in results: + launch_final_values["constant_arc_length"] = float( + results["constant_arc_length"]["final_value"] + ) + return launch_final_values + + print("\nRunning launch-style benchmarks for heatmaps...") + launch_results = run_comparison( + launch_cfg, + price_data=price_data, + low_data_mode=True, + ) + launch_final_values = { + "geometric": float(launch_results["geometric"]["final_value"]), + } + if "constant_arc_length" in launch_results: + launch_final_values["constant_arc_length"] = float( + launch_results["constant_arc_length"]["final_value"] + ) + del launch_results + gc.collect() + return launch_final_values + def print_comparison(cfg, results): """Print text summary table.""" - methods = [ - ("Geometric", results["geometric"]), - ("Geo+Scaled", results["geometric_scaled"]), - ("Const Arc", results["constant_arc_length"]), - ("Arc+Scaled", results["cal_scaled"]), - ] + methods = [("Geometric", results["geometric"])] + has_constant_arc = "constant_arc_length" in results + if has_constant_arc: + methods.append(("Const Arc", results["constant_arc_length"])) + noise_cfg = resolve_reclamm_noise_settings(cfg) hodl_value = float((methods[0][1]["reserves"][0] * methods[0][1]["prices"][-1]).sum()) @@ -128,6 +1612,18 @@ def print_comparison(cfg, results): f"margin={cfg['centeredness_margin']}, " f"shift_exp={cfg['daily_price_shift_exponent']}, " f"fees={cfg['fees']}") + print( + f" base_tvl=${get_initial_pool_value(cfg):,.0f} " + f"(TVL {format_tvl_millions_label(cfg)})" + ) + print(f" note={cfg['reason']}") + print( + f" noise={noise_cfg['noise_summary']}, " + f"gas={cfg.get('gas_cost', 0.0)}, " + f"protocol_fee_split={cfg.get('protocol_fee_split', 0.0)}" + ) + if not has_constant_arc: + print(" constant_arc=disabled") print("-" * 105) header = " {:20s}".format("") for name, _ in methods: @@ -158,18 +1654,27 @@ def print_comparison(cfg, results): vs = (float(r["final_value"]) / hodl_value - 1) * 100 row += f" {vs:>13.2f}%" print(row) + + if has_constant_arc: + geo_final = float(results["geometric"]["final_value"]) + arc_final = float(results["constant_arc_length"]["final_value"]) + geo_lvr = hodl_value - geo_final + arc_lvr = hodl_value - arc_final + print(f" {'Const Arc - Geo':20s} ${arc_final - geo_final:>13,.0f}") + print(f" {'LVR saved vs Geo':20s} ${geo_lvr - arc_lvr:>13,.0f}") print("=" * 105) + def plot_comparison(cfg, results, fig_idx): - """Plot 4-panel comparison for one config.""" - # Method name → (result dict, color, linestyle) + """Plot comparison diagnostics for one config.""" + tvl_label = format_tvl_millions_label(cfg) variants = { "Geometric": (results["geometric"], "C0", "-"), - "Geo+Scaled": (results["geometric_scaled"], "C1", "-"), - "Const arc-len": (results["constant_arc_length"], "C2", "--"), - "Arc+Scaled": (results["cal_scaled"], "C3", "--"), } + has_constant_arc = "constant_arc_length" in results + if has_constant_arc: + variants["Const arc-len"] = (results["constant_arc_length"], "C2", "--") geo = results["geometric"] geo_prices = np.array(geo["prices"]) @@ -181,22 +1686,21 @@ def plot_comparison(cfg, results, fig_idx): price_ratio_traj = geo_prices[:n_steps, 0] / geo_prices[:n_steps, 1] fig, axes = plt.subplots(2, 2, figsize=(14, 10)) - fig.suptitle(cfg["name"], fontsize=13, fontweight="bold") + fig.suptitle(f"{cfg['name']} — TVL {tvl_label}", fontsize=13, fontweight="bold") - # (0,0) Pool value over time ax = axes[0, 0] + plotted_values = [] for name, (r, color, ls) in variants.items(): vals = np.array(r["value"]) + plotted_values.append(vals / 1e6) ax.plot(t_days, vals / 1e6, color=color, ls=ls, label=name, alpha=0.9) - ax.plot(t_days, np.array(hodl_traj) / 1e6, color="gray", ls=":", - alpha=0.5, label="HODL") + _set_padded_ylim(ax, plotted_values, pad_ratio=0.03) ax.set_xlabel("Days") ax.set_ylabel("Pool value ($M)") ax.set_title("Pool value") ax.legend(fontsize=8) ax.grid(True, alpha=0.3) - # (0,1) Cumulative LVR ax = axes[0, 1] for name, (r, color, ls) in variants.items(): vals = np.array(r["value"]) @@ -208,7 +1712,6 @@ def plot_comparison(cfg, results, fig_idx): ax.legend(fontsize=8) ax.grid(True, alpha=0.3) - # (1,0) Price ratio ax = axes[1, 0] ax.plot(t_days, price_ratio_traj, color="C4", alpha=0.7) ax.set_xlabel("Days") @@ -216,7 +1719,6 @@ def plot_comparison(cfg, results, fig_idx): ax.set_title("Price path") ax.grid(True, alpha=0.3) - # (1,1) Empirical weights ax = axes[1, 1] for name, (r, color, ls) in variants.items(): w = np.array(r["weights"]) @@ -230,19 +1732,21 @@ def plot_comparison(cfg, results, fig_idx): ax.grid(True, alpha=0.3) plt.tight_layout() - fname = f"reclamm_thermostat_comparison_{fig_idx}.png" + fname = tvl_artifact_filename("reclamm_thermostat_comparison", cfg, suffix=str(fig_idx)) plt.savefig(fname, dpi=150) print(f"Saved {fname}") plt.close(fig) - # Second figure: diagnostics + if not has_constant_arc: + print("Skipping constant-arc comparison diagnostics because RUN_CONSTANT_ARC_LENGTH=False.") + return + geo_values = np.array(geo["value"]) geo_lvr = np.array(hodl_traj) - geo_values fig2, axes2 = plt.subplots(1, 3, figsize=(18, 5)) - fig2.suptitle(f"{cfg['name']} — diagnostics", fontsize=13, fontweight="bold") + fig2.suptitle(f"{cfg['name']} — diagnostics — TVL {tvl_label}", fontsize=13, fontweight="bold") - # (left) Value difference vs geometric ax = axes2[0] for name, (r, color, ls) in variants.items(): if name == "Geometric": @@ -257,7 +1761,6 @@ def plot_comparison(cfg, results, fig_idx): ax.legend(fontsize=8) ax.grid(True, alpha=0.3) - # (middle) LVR ratio over time ax = axes2[1] mask = np.abs(geo_lvr) > 100 if mask.any(): @@ -279,7 +1782,6 @@ def plot_comparison(cfg, results, fig_idx): ax.set_title("Relative LVR") ax.grid(True, alpha=0.3) - # (right) Per-step LVR histogram ax = axes2[2] all_pos = [] for name, (r, color, ls) in variants.items(): @@ -306,74 +1808,180 @@ def plot_comparison(cfg, results, fig_idx): ax.grid(True, alpha=0.3) plt.tight_layout() - fname2 = f"reclamm_thermostat_diff_{fig_idx}.png" + fname2 = tvl_artifact_filename("reclamm_thermostat_diff", cfg, suffix=str(fig_idx)) plt.savefig(fname2, dpi=150) print(f"Saved {fname2}") plt.close(fig2) + arc_values = np.array(results["constant_arc_length"]["value"]) + n_eff = min(len(geo_values), len(arc_values)) + t_eff = np.arange(n_eff) / (60 * 24) + efficiency_pct = ( + (arc_values[:n_eff] - geo_values[:n_eff]) + / np.maximum(np.abs(geo_values[:n_eff]), 1e-12) + * 100.0 + ) + + fig3, ax3 = plt.subplots(1, 1, figsize=(10, 4.5)) + fig3.suptitle(f"{cfg['name']} — efficiency — TVL {tvl_label}", fontsize=13, fontweight="bold") + ax3.plot( + t_eff, + efficiency_pct, + color="C2", + linewidth=1.8, + label="(Const Arc - Geo) / Geo", + ) + ax3.axhline(0.0, color="gray", ls="--", alpha=0.6) + _set_padded_ylim(ax3, [efficiency_pct], pad_ratio=0.08) + ax3.set_xlabel("Days") + ax3.set_ylabel("Efficiency vs geometric (%)") + ax3.set_title("Efficiency") + ax3.legend(fontsize=8) + ax3.grid(True, alpha=0.3) + + plt.tight_layout() + fname3 = tvl_artifact_filename("reclamm_thermostat_efficiency", cfg, suffix=str(fig_idx)) + plt.savefig(fname3, dpi=150) + print(f"Saved {fname3}") + plt.close(fig3) + + if __name__ == "__main__": - all_results = [] - for i, cfg in enumerate(CONFIGS): - print(f"\n>>> Running {cfg['name']}...") - try: - results = run_comparison(cfg) - print_comparison(cfg, results) - plot_comparison(cfg, results, i) - all_results.append((cfg, results)) - except Exception as e: - print(f" FAILED: {e}") - import traceback - traceback.print_exc() - - # Summary overlay: all configs on one figure (pool value normalised) - if len(all_results) > 1: - fig, axes = plt.subplots(1, 2, figsize=(16, 5)) - fig.suptitle("Cross-config comparison (normalised)", fontsize=13, - fontweight="bold") - - method_keys = [ - ("geometric", "geo", "-"), - ("geometric_scaled", "geo+s", "-."), - ("constant_arc_length", "arc", "--"), - ("cal_scaled", "arc+s", ":"), - ] + shared_price_data = load_shared_price_data(CONFIGS) + + for initial_pool_value in TVL_SWEEP_VALUES: + tvl_configs = configs_for_tvl(CONFIGS, initial_pool_value) + tvl_label = format_tvl_millions_label(tvl_configs[0]) + print(f"\n=== TVL sweep: {tvl_label} ===") + + all_results = [] + for i, cfg in enumerate(tvl_configs): + print(f"\n>>> Running {cfg['name']} at TVL {tvl_label}...") + try: + results = run_comparison(cfg, price_data=shared_price_data) + print_comparison(cfg, results) + plot_comparison(cfg, results, i) + all_results.append((cfg, results)) + except Exception as e: + print(f" FAILED: {e}") + import traceback - for i, (cfg, results) in enumerate(all_results): - geo_v = np.array(results["geometric"]["value"]) - t = np.arange(len(geo_v)) / (60 * 24) - short_name = cfg["name"].split("(")[0].strip() - - for j, (key, suffix, ls) in enumerate(method_keys): - v = np.array(results[key]["value"]) - color_idx = i * len(method_keys) + j - - # (left) Normalised pool value - axes[0].plot(t, v / v[0], ls=ls, alpha=0.8, - label=f"{short_name} {suffix}", - color=f"C{color_idx % 10}") - - # (right) Value difference vs geometric (skip geo itself) - if key != "geometric": - pct_diff = (v - geo_v) / geo_v * 100 - axes[1].plot(t, pct_diff, ls=ls, alpha=0.8, - label=f"{short_name} {suffix}", - color=f"C{color_idx % 10}") - - axes[0].set_xlabel("Days") - axes[0].set_ylabel("Normalised pool value") - axes[0].set_title("Pool value (V/V0)") - axes[0].legend(fontsize=6, ncol=2) - axes[0].grid(True, alpha=0.3) - - axes[1].set_xlabel("Days") - axes[1].set_ylabel("(Method - Geo) / Geo (%)") - axes[1].set_title("Relative value difference vs Geometric") - axes[1].axhline(0, color="gray", ls="--", alpha=0.5) - axes[1].legend(fontsize=6, ncol=2) - axes[1].grid(True, alpha=0.3) - - plt.tight_layout() - plt.savefig("reclamm_thermostat_summary.png", dpi=150) - print("\nSaved reclamm_thermostat_summary.png") - plt.close(fig) + traceback.print_exc() + + if len(all_results) > 1: + if RUN_CONSTANT_ARC_LENGTH: + fig, axes = plt.subplots(1, 2, figsize=(16, 5)) + fig.suptitle( + f"Cross-config comparison (normalised) — TVL {tvl_label}", + fontsize=13, + fontweight="bold", + ) + + method_keys = [ + ("geometric", "geo", "-"), + ("constant_arc_length", "arc", "--"), + ] + + for i, (cfg, results) in enumerate(all_results): + geo_v = np.array(results["geometric"]["value"]) + t = np.arange(len(geo_v)) / (60 * 24) + short_name = cfg["name"].split("(")[0].strip() + + for j, (key, suffix, ls) in enumerate(method_keys): + v = np.array(results[key]["value"]) + color_idx = i * len(method_keys) + j + + axes[0].plot( + t, + v / v[0], + ls=ls, + alpha=0.8, + label=f"{short_name} {suffix}", + color=f"C{color_idx % 10}", + ) + + if key != "geometric": + pct_diff = (v - geo_v) / geo_v * 100 + axes[1].plot( + t, + pct_diff, + ls=ls, + alpha=0.8, + label=f"{short_name} {suffix}", + color=f"C{color_idx % 10}", + ) + + axes[0].set_xlabel("Days") + axes[0].set_ylabel("Normalised pool value") + axes[0].set_title("Pool value (V/V0)") + axes[0].legend(fontsize=6, ncol=2) + axes[0].grid(True, alpha=0.3) + + axes[1].set_xlabel("Days") + axes[1].set_ylabel("Efficiency vs geometric (%)") + axes[1].set_title("Efficiency vs Geometric") + axes[1].axhline(0, color="gray", ls="--", alpha=0.5) + axes[1].legend(fontsize=6, ncol=2) + axes[1].grid(True, alpha=0.3) + else: + fig, ax = plt.subplots(1, 1, figsize=(9, 5)) + fig.suptitle( + f"Cross-config comparison (normalised geometric) — TVL {tvl_label}", + fontsize=13, + fontweight="bold", + ) + + for i, (cfg, results) in enumerate(all_results): + geo_v = np.array(results["geometric"]["value"]) + t = np.arange(len(geo_v)) / (60 * 24) + short_name = cfg["name"].split("(")[0].strip() + ax.plot( + t, + geo_v / geo_v[0], + ls="-", + alpha=0.8, + label=f"{short_name} geo", + color=f"C{i % 10}", + ) + + ax.set_xlabel("Days") + ax.set_ylabel("Normalised pool value") + ax.set_title("Geometric pool value (V/V0)") + ax.legend(fontsize=6, ncol=2) + ax.grid(True, alpha=0.3) + + plt.tight_layout() + summary_name = tvl_artifact_filename( + "reclamm_thermostat_summary", + tvl_configs[0], + ) + plt.savefig(summary_name, dpi=150) + print(f"\nSaved {summary_name}") + plt.close(fig) + + launch_final_values = get_launch_final_values( + all_results, + launch_cfg=tvl_configs[0], + price_data=shared_price_data, + ) + shared_sweep_cache = make_sweep_cache(shared_price_data) + + print(f"\nGenerating thermostat heatmaps for TVL {tvl_label}...") + generate_heatmaps( + dict(tvl_configs[1]), + shared_price_data, + launch_final_values=launch_final_values, + cache=shared_sweep_cache, + ) + + generate_arc_speed_efficiency_artifacts( + dict(tvl_configs[1]), + launch_cfg=dict(tvl_configs[0]), + price_data=shared_price_data, + launch_final_values=launch_final_values, + cache=shared_sweep_cache, + ) + shared_sweep_cache.clear() + gc.collect() + print(f"Released shared sweep cache for TVL {tvl_label}.") diff --git a/scripts/demo_run_reclamm.py b/scripts/demo_run_reclamm.py index 3ea21ec..132f512 100644 --- a/scripts/demo_run_reclamm.py +++ b/scripts/demo_run_reclamm.py @@ -36,105 +36,127 @@ def balancer_fingerprint(tokens, start, end, fees): } +def reclamm_fingerprint(tokens, start, end, fees, interpolation_method="geometric"): + """Build a reCLAMM fingerprint for a demo scenario.""" + return { + "tokens": tokens, + "rule": "reclamm", + "startDateString": start, + "endDateString": end, + "initial_pool_value": 1000000.0, + "do_arb": True, + "fees": fees, + "gas_cost": 0.0, + "arb_fees": 0.0, + "chunk_period": 60, + "weight_interpolation_period": 60, + "reclamm_interpolation_method": interpolation_method, + "reclamm_arc_length_speed": None, + } + + +def reclamm_params(price_ratio, centeredness_margin, daily_price_shift_exponent): + """Build reCLAMM params from a concise config.""" + return { + "price_ratio": jnp.array(price_ratio), + "centeredness_margin": jnp.array(centeredness_margin), + "daily_price_shift_base": jnp.array( + to_daily_price_shift_base(daily_price_shift_exponent) + ), + } + + +def _apply_active_noise_settings(fp): + """Enable the active AAVE/ETH reCLAMM noise model for demo runs.""" + if fp.get("rule") != "reclamm" or list(fp.get("tokens", [])) != ["AAVE", "ETH"]: + return fp, "disabled" + + from compare_reclamm_thermostats import ( + AAVE_ETH_NOISE_SETTINGS, + resolve_reclamm_noise_settings, + ) + + cfg = { + "tokens": fp["tokens"], + "start": fp["startDateString"], + "end": fp["endDateString"], + "enable_noise_model": True, + "noise_model": AAVE_ETH_NOISE_SETTINGS["noise_model"], + "noise_artifact_dir": AAVE_ETH_NOISE_SETTINGS["noise_artifact_dir"], + "noise_pool_id": AAVE_ETH_NOISE_SETTINGS["noise_pool_id"], + "gas_cost": fp.get("gas_cost", AAVE_ETH_NOISE_SETTINGS["gas_cost"]), + "protocol_fee_split": fp.get( + "protocol_fee_split", + AAVE_ETH_NOISE_SETTINGS["protocol_fee_split"], + ), + "arb_frequency": fp.get("arb_frequency"), + "noise_trader_ratio": fp.get("noise_trader_ratio", 0.0), + "reclamm_noise_params": fp.get("reclamm_noise_params"), + "noise_arrays_path": fp.get("noise_arrays_path"), + } + noise_cfg = resolve_reclamm_noise_settings(cfg) + + updated = dict(fp) + updated["gas_cost"] = cfg["gas_cost"] + updated["protocol_fee_split"] = cfg["protocol_fee_split"] + updated["noise_trader_ratio"] = noise_cfg.get("noise_trader_ratio", 0.0) + for key in ("noise_model", "reclamm_noise_params", "noise_arrays_path", "arb_frequency"): + if noise_cfg.get(key) is not None: + updated[key] = noise_cfg[key] + return updated, noise_cfg["noise_summary"] + + SCENARIOS = [ { - "name": "AAVE/ETH on-chain (25bps)", + "name": "AAVE/ETH launch-style range (25bps, geometric)", "reclamm": { - "fingerprint": { - "tokens": ["AAVE", "ETH"], - "rule": "reclamm", - "startDateString": "2024-06-01 00:00:00", - "endDateString": "2025-06-01 00:00:00", - "initial_pool_value": 1000000.0, - "do_arb": True, - "fees": 0.0025, - "gas_cost": 0.0, - "arb_fees": 0.0, - "chunk_period": 60, - "weight_interpolation_period": 60, - }, - "params": { - "price_ratio": jnp.array(1.5), - "centeredness_margin": jnp.array(0.5), - "daily_price_shift_base": jnp.array( - to_daily_price_shift_base(0.1) - ), - }, + "fingerprint": reclamm_fingerprint( + ["AAVE", "ETH"], + "2024-06-01 00:00:00", + "2025-06-01 00:00:00", + 0.0025, + interpolation_method="geometric", + ), + "params": reclamm_params(1.5014, 0.5, 0.1), }, }, { - "name": "AAVE/ETH zero fees", + "name": "AAVE/ETH tighter launch-style range (25bps, geometric)", "reclamm": { - "fingerprint": { - "tokens": ["AAVE", "ETH"], - "rule": "reclamm", - "startDateString": "2024-06-01 00:00:00", - "endDateString": "2025-06-01 00:00:00", - "initial_pool_value": 1000000.0, - "do_arb": True, - "fees": 0.0, - "gas_cost": 0.0, - "arb_fees": 0.0, - "chunk_period": 60, - "weight_interpolation_period": 60, - }, - "params": { - "price_ratio": jnp.array(1.5), - "centeredness_margin": jnp.array(0.5), - "daily_price_shift_base": jnp.array( - to_daily_price_shift_base(0.1) - ), - }, + "fingerprint": reclamm_fingerprint( + ["AAVE", "ETH"], + "2024-06-01 00:00:00", + "2025-06-01 00:00:00", + 0.0025, + interpolation_method="geometric", + ), + "params": reclamm_params(1.15, 0.5, 0.1), }, }, { - "name": "AAVE/ETH wide range (25bps)", + "name": "AAVE/ETH tighter launch-style range (25bps, constant arc)", "reclamm": { - "fingerprint": { - "tokens": ["AAVE", "ETH"], - "rule": "reclamm", - "startDateString": "2024-06-01 00:00:00", - "endDateString": "2025-06-01 00:00:00", - "initial_pool_value": 1000000.0, - "do_arb": True, - "fees": 0.0025, - "gas_cost": 0.0, - "arb_fees": 0.0, - "chunk_period": 60, - "weight_interpolation_period": 60, - }, - "params": { - "price_ratio": jnp.array(4.0), - "centeredness_margin": jnp.array(0.2), - "daily_price_shift_base": jnp.array( - to_daily_price_shift_base(1.0) - ), - }, + "fingerprint": reclamm_fingerprint( + ["AAVE", "ETH"], + "2024-06-01 00:00:00", + "2025-06-01 00:00:00", + 0.0025, + interpolation_method="constant_arc_length", + ), + "params": reclamm_params(1.15, 0.5, 0.1), }, }, { "name": "BTC/ETH (10bps)", "reclamm": { - "fingerprint": { - "tokens": ["BTC", "ETH"], - "rule": "reclamm", - "startDateString": "2024-01-01 00:00:00", - "endDateString": "2025-06-01 00:00:00", - "initial_pool_value": 1000000.0, - "do_arb": True, - "fees": 0.001, - "gas_cost": 0.0, - "arb_fees": 0.0, - "chunk_period": 60, - "weight_interpolation_period": 60, - }, - "params": { - "price_ratio": jnp.array(2.0), - "centeredness_margin": jnp.array(0.3), - "daily_price_shift_base": jnp.array( - to_daily_price_shift_base(0.5) - ), - }, + "fingerprint": reclamm_fingerprint( + ["BTC", "ETH"], + "2024-01-01 00:00:00", + "2025-06-01 00:00:00", + 0.001, + interpolation_method="geometric", + ), + "params": reclamm_params(2.0, 0.3, 0.5), }, }, ] @@ -143,7 +165,7 @@ def balancer_fingerprint(tokens, start, end, fees): def run_scenario(scenario): """Run a reClAMM config and its Balancer 50/50 baseline, print comparison.""" rc = scenario["reclamm"] - fp = rc["fingerprint"] + fp, noise_summary = _apply_active_noise_settings(dict(rc["fingerprint"])) # Run reClAMM reclamm_result = do_run_on_historic_data( @@ -173,7 +195,14 @@ def run_scenario(scenario): print("=" * 80) print(f" {scenario['name']}") - print(f" Tokens: {', '.join(fp['tokens'])} | Fees: {fp['fees']}") + print( + f" Tokens: {', '.join(fp['tokens'])} | Fees: {fp['fees']} | " + f"Interpolation: {fp.get('reclamm_interpolation_method', 'geometric')}" + ) + print( + f" Noise: {noise_summary} | Gas: {fp.get('gas_cost', 0.0)} | " + f"Protocol fee split: {fp.get('protocol_fee_split', 0.0)}" + ) print("-" * 80) print(f" {'':30s} {'reClAMM':>14s} {'Balancer 50/50':>14s}") print(f" {'Initial value':30s} ${rc_init:>13,.0f} ${bal_init:>13,.0f}") diff --git a/scripts/reclamm/compare_reclamm_thermostats.py b/scripts/reclamm/compare_reclamm_thermostats.py index 8a2c374..be8ee5f 100644 --- a/scripts/reclamm/compare_reclamm_thermostats.py +++ b/scripts/reclamm/compare_reclamm_thermostats.py @@ -1,19 +1,39 @@ -"""Compare geometric vs constant-arc-length thermostats on historic data. +"""Compare reCLAMM interpolation modes on historic AAVE/ETH data. -Runs AAVE/ETH reClAMM pool simulations with both interpolation methods. -Plots: pool value, cumulative LVR, price path, empirical weights, -value difference, LVR ratio, and per-step LVR distribution (∝ Δs²). +Runs the production geometric interpolation against the non-linear +constant-arc-length interpolation on: +1. The original launch-style range (price_ratio ~= 1.50) +2. A much tighter range (price_ratio = 1.10) -Usage: - cd - source ~/miniconda3/etc/profile.d/conda.sh && conda activate qsim-reclamm - python scripts/compare_reclamm_thermostats.py +The aggressive case is deliberate. A local AAVE/ETH sweep showed: +price_ratio 1.15, margin 0.5, shift 0.1 -> about +$10k vs geometric +price_ratio 1.10, margin 0.5, shift 0.1 -> about +$31k vs geometric +price_ratio 1.10, margin 0.6, shift 0.1 -> about +$73k vs geometric + +So the strongest clean demo setting came from tightening the band and +slightly raising the trigger margin, while keeping the launch-style shift +speed rather than pushing shift_exponent higher. """ +import gc +import math +import os + import jax.numpy as jnp import numpy as np +import pandas as pd import matplotlib.pyplot as plt +from matplotlib.colors import Normalize, SymLogNorm, TwoSlopeNorm +from matplotlib.cm import ScalarMappable +from quantammsim.pools.reCLAMM.reclamm_reserves import ( + calibrate_arc_length_speed, + compute_price_ratio, + initialise_reclamm_reserves, +) from quantammsim.runners.jax_runners import do_run_on_historic_data +from quantammsim.utils.data_processing.historic_data_utils import ( + get_historic_parquet_data, +) def to_daily_price_shift_base(daily_price_shift_exponent): @@ -21,57 +41,432 @@ def to_daily_price_shift_base(daily_price_shift_exponent): return 1.0 - daily_price_shift_exponent / 124649.0 +RUN_CONSTANT_ARC_LENGTH = False +INTERPOLATION_METHODS = ( + ("geometric", "constant_arc_length") + if RUN_CONSTANT_ARC_LENGTH + else ("geometric",) +) +HEATMAP_PRICE_RATIOS = np.arange(1.01, 1.50 + 1e-9, 0.025) +HEATMAP_MARGINS = np.linspace(0.05, 0.90, 20) +HEATMAP_SHIFT_EXPONENTS = np.arange(0.01, 0.50 + 1e-9, 0.025) +HEATMAP_ARC_LENGTH_SPEEDS = np.geomspace(1.0e-6, 5.0e-4, 11) +PRICE_RATIO_TICKS = np.array([1.01, 1.10, 1.20, 1.30, 1.40, 1.50]) +MARGIN_TICKS = np.array([0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.90]) +SHIFT_EXPONENT_TICKS = np.array([0.01, 0.05, 0.10, 0.20, 0.30, 0.40, 0.50]) +ARC_LENGTH_SPEED_TICKS = np.array([ + 1.0e-6, + 2.0e-6, + 5.0e-6, + 1.0e-5, + 2.0e-5, + 5.0e-5, + 1.0e-4, + 2.0e-4, + 5.0e-4, +]) +SWEEP_LINE_WIDTH = 0.45 +REFERENCE_LINE_WIDTH = 0.9 +DEFAULT_INITIAL_POOL_VALUE = 1_000_000.0 +TVL_SWEEP_VALUES = ( + 1_000_000.0, + 5_000_000.0, + 20_000_000.0, +) +CENTER_ZERO_HEATMAP_COLOR_NORM = "symlog" +CENTER_ZERO_HEATMAP_COLOR_TAG = "symlog20" +CENTER_ZERO_HEATMAP_SYMLOG_LINTHRESH = 20.0 + +AAVE_WETH_POOL_ID = "0x9d1fcf346ea1b0" +DEFAULT_MARKET_LINEAR_ARTIFACT_DIR = "results/linear_market_noise" +DEFAULT_NOISE_MODEL = "market_linear" +DEFAULT_GAS_COST = 1.0 +DEFAULT_PROTOCOL_FEE_SPLIT = 0.25 +LEGACY_NOISE_COEFFS = [ + -0.453, + 0.025, + -0.060, + 0.310, + -0.149, + 0.359, + 0.061, + 0.060, +] +LEGACY_LOG_CADENCE = 2.68 +LEGACY_ARB_FREQUENCY = max(1, round(math.exp(LEGACY_LOG_CADENCE))) +AAVE_ETH_NOISE_SETTINGS = { + "enable_noise_model": True, + "noise_model": DEFAULT_NOISE_MODEL, + "noise_artifact_dir": DEFAULT_MARKET_LINEAR_ARTIFACT_DIR, + "noise_pool_id": AAVE_WETH_POOL_ID, + "gas_cost": DEFAULT_GAS_COST, + "protocol_fee_split": DEFAULT_PROTOCOL_FEE_SPLIT, +} + +GEOMETRIC_ONLY_HEATMAP_METRIC_KEYS = ( + "geometric_vs_launch_geometric_pct", + "noise_geometric_final_value_musd", + "noise_vs_arb_geometric_improvement_pct", +) +CONSTANT_ARC_HEATMAP_METRIC_KEYS = ( + "efficiency_pct", + "launch_geometric_efficiency_pct", + "constant_arc_vs_launch_constant_arc_pct", + "noise_constant_arc_final_value_musd", + "noise_vs_arb_constant_arc_improvement_pct", +) +HEATMAP_METRIC_DEPENDENCIES = { + "efficiency_pct": ("noise_geometric", "noise_constant_arc"), + "launch_geometric_efficiency_pct": ("noise_constant_arc",), + "geometric_vs_launch_geometric_pct": ("noise_geometric",), + "constant_arc_vs_launch_constant_arc_pct": ("noise_constant_arc",), + "noise_geometric_final_value_musd": ("noise_geometric",), + "noise_constant_arc_final_value_musd": ("noise_constant_arc",), + "noise_vs_arb_geometric_improvement_pct": ("noise_geometric", "arb_geometric"), + "noise_vs_arb_constant_arc_improvement_pct": ( + "noise_constant_arc", + "arb_constant_arc", + ), +} + +_NOISE_SETTINGS_CACHE = {} +_WARNED_NOISE_FALLBACKS = set() + + +def get_initial_pool_value(cfg): + """Return the configured base pool TVL in USD.""" + return float(cfg.get("initial_pool_value", DEFAULT_INITIAL_POOL_VALUE)) + + +def get_tvl_millions(cfg): + """Return the configured base pool TVL in millions of USD.""" + return get_initial_pool_value(cfg) / 1_000_000.0 + + +def format_tvl_millions_slug(cfg): + """Format the TVL in millions for stable filenames.""" + tvl_millions = get_tvl_millions(cfg) + rounded = round(float(tvl_millions), 6) + if np.isclose(rounded, round(rounded)): + return f"{int(round(rounded))}m" + return f"{rounded:.6f}".rstrip("0").rstrip(".").replace(".", "p") + "m" + + +def format_tvl_millions_label(cfg): + """Format the TVL in millions for plot titles and logs.""" + return f"{get_tvl_millions(cfg):.1f}M" + + +def tvl_artifact_filename(stem, cfg, suffix=None): + """Append a TVL-in-millions suffix to a PNG artifact name.""" + parts = [stem] + if suffix: + parts.append(suffix) + parts.append(f"tvl_{format_tvl_millions_slug(cfg)}") + return "_".join(parts) + ".png" + + +def heatmap_artifact_filename(spec, cfg, suffix=None): + """Build a heatmap filename, including any colour-style tag.""" + stem = f"reclamm_heatmap_{spec['slug']}" + artifact_tag = spec.get("artifact_tag") + if artifact_tag: + stem = f"{stem}_{artifact_tag}" + return tvl_artifact_filename(stem, cfg, suffix=suffix) + + +def configs_for_tvl(base_configs, initial_pool_value): + """Attach a shared initial TVL to each compare configuration.""" + configs = [] + for cfg in base_configs: + updated = dict(cfg) + updated["initial_pool_value"] = float(initial_pool_value) + configs.append(updated) + return configs + + +def make_noise_variant_cfg(cfg, enable_noise_model): + """Return a config with either noise modelling or pure arb-only enabled.""" + updated = dict(cfg) + if enable_noise_model: + updated["enable_noise_model"] = True + return updated + + matched_noise = resolve_reclamm_noise_settings(cfg) + + updated["enable_noise_model"] = False + updated["noise_model"] = None + updated["gas_cost"] = cfg.get("gas_cost", DEFAULT_GAS_COST) + updated["protocol_fee_split"] = cfg.get( + "protocol_fee_split", DEFAULT_PROTOCOL_FEE_SPLIT + ) + updated["noise_trader_ratio"] = 0.0 + matched_arb_frequency = matched_noise.get("arb_frequency") + if matched_arb_frequency is not None: + updated["arb_frequency"] = matched_arb_frequency + for key in ( + "reclamm_noise_params", + "noise_arrays_path", + "noise_artifact_dir", + "noise_pool_id", + ): + updated.pop(key, None) + return updated + + +def _warn_noise_fallback(message): + """Print a one-time message when the preferred noise setup is unavailable.""" + if message not in _WARNED_NOISE_FALLBACKS: + print(message) + _WARNED_NOISE_FALLBACKS.add(message) + + +def _hashable_noise_params(params): + """Convert a noise-params dict into a stable cache key fragment.""" + if params is None: + return None + return tuple(sorted((str(k), round(float(v), 12)) for k, v in params.items())) + + +def _legacy_calibrated_noise_settings(reason=None): + """Fallback calibrated noise config used when market-linear artifacts are absent.""" + if reason: + _warn_noise_fallback( + "market_linear noise unavailable for thermostat comparison; " + f"falling back to calibrated legacy coefficients ({reason})." + ) + return { + "noise_model": "calibrated", + "noise_trader_ratio": 0.0, + "reclamm_noise_params": { + f"c_{i}": LEGACY_NOISE_COEFFS[i] for i in range(len(LEGACY_NOISE_COEFFS)) + }, + "arb_frequency": LEGACY_ARB_FREQUENCY, + "noise_summary": ( + "calibrated legacy 8-covariate " + f"(arb_frequency={LEGACY_ARB_FREQUENCY})" + ), + "noise_cache_key": ( + "calibrated", + tuple(round(float(c), 12) for c in LEGACY_NOISE_COEFFS), + LEGACY_ARB_FREQUENCY, + ), + } + + +def resolve_reclamm_noise_settings(cfg): + """Resolve the active reCLAMM noise-model fingerprint block for a config.""" + enable_noise_model = cfg.get("enable_noise_model", False) + requested_mode = cfg.get("noise_model", DEFAULT_NOISE_MODEL) + cache_key = ( + tuple(cfg.get("tokens", [])), + cfg.get("start"), + cfg.get("end"), + enable_noise_model, + requested_mode, + cfg.get("noise_artifact_dir", DEFAULT_MARKET_LINEAR_ARTIFACT_DIR), + cfg.get("noise_pool_id", AAVE_WETH_POOL_ID), + cfg.get("arb_frequency"), + round(float(cfg.get("noise_trader_ratio", 0.0)), 12), + _hashable_noise_params(cfg.get("reclamm_noise_params")), + cfg.get("noise_arrays_path"), + ) + if cache_key in _NOISE_SETTINGS_CACHE: + return _NOISE_SETTINGS_CACHE[cache_key] + + if not enable_noise_model: + result = { + "noise_model": None, + "noise_trader_ratio": 0.0, + "reclamm_noise_params": None, + "noise_arrays_path": None, + "arb_frequency": None, + "noise_summary": "arb-only (noise disabled)", + "noise_cache_key": ("disabled",), + } + elif requested_mode == "market_linear": + artifact_dir = cfg.get("noise_artifact_dir", DEFAULT_MARKET_LINEAR_ARTIFACT_DIR) + pool_id = cfg.get("noise_pool_id", AAVE_WETH_POOL_ID) + start_date = str(cfg["start"]).split(" ")[0] + end_date = str(cfg["end"]).split(" ")[0] + try: + from quantammsim.calibration.noise_model_arrays import ( + _find_pool_index, + build_simulator_arrays, + load_artifact, + ) + + model_path = os.path.join(artifact_dir, "model.npz") + meta_path = os.path.join(artifact_dir, "meta.json") + if not (os.path.exists(model_path) and os.path.exists(meta_path)): + raise FileNotFoundError( + f"expected {model_path} and {meta_path}" + ) + + cache_dir = os.path.join(artifact_dir, "_sim_arrays") + os.makedirs(cache_dir, exist_ok=True) + arrays_path = os.path.join( + cache_dir, + f"{pool_id}_{start_date}_{end_date}.npz", + ) + if not os.path.exists(arrays_path): + arrays = build_simulator_arrays( + pool_id=pool_id, + start_date=start_date, + end_date=end_date, + artifact_dir=artifact_dir, + ) + np.savez( + arrays_path, + noise_base=arrays["noise_base"], + noise_tvl_coeff=arrays["noise_tvl_coeff"], + tvl_mean=arrays["tvl_mean"], + tvl_std=arrays["tvl_std"], + ) + + with np.load(arrays_path) as arrays: + tvl_mean = float(arrays["tvl_mean"]) + tvl_std = float(arrays["tvl_std"]) + + art, meta = load_artifact(artifact_dir) + pool_idx = _find_pool_index(pool_id, meta["pool_ids"]) + if pool_idx >= 0: + learned_cadence = float(np.exp(art["log_cadence"][pool_idx])) + else: + learned_cadence = 5.0 + arb_frequency = max(1, round(learned_cadence)) + result = { + "noise_model": "market_linear", + "noise_trader_ratio": 0.0, + "reclamm_noise_params": { + "tvl_mean": tvl_mean, + "tvl_std": tvl_std, + }, + "noise_arrays_path": arrays_path, + "arb_frequency": arb_frequency, + "noise_summary": f"market_linear (arb_frequency={arb_frequency})", + "noise_cache_key": ( + "market_linear", + arrays_path, + arb_frequency, + round(tvl_mean, 12), + round(tvl_std, 12), + ), + } + except Exception as exc: # pragma: no cover - fallback path depends on local artifacts + result = _legacy_calibrated_noise_settings(str(exc)) + elif requested_mode == "calibrated": + params = cfg.get("reclamm_noise_params") + if params is None: + result = _legacy_calibrated_noise_settings() + else: + arb_frequency = cfg.get("arb_frequency", LEGACY_ARB_FREQUENCY) + result = { + "noise_model": "calibrated", + "noise_trader_ratio": cfg.get("noise_trader_ratio", 0.0), + "reclamm_noise_params": dict(params), + "arb_frequency": arb_frequency, + "noise_summary": f"calibrated (arb_frequency={arb_frequency})", + "noise_cache_key": ( + "calibrated", + _hashable_noise_params(params), + arb_frequency, + ), + } + else: + arb_frequency = cfg.get("arb_frequency") + result = { + "noise_model": requested_mode, + "noise_trader_ratio": cfg.get("noise_trader_ratio", 0.0), + "reclamm_noise_params": cfg.get("reclamm_noise_params"), + "noise_arrays_path": cfg.get("noise_arrays_path"), + "arb_frequency": arb_frequency, + "noise_summary": f"{requested_mode} (arb_frequency={arb_frequency})", + "noise_cache_key": ( + requested_mode, + round(float(cfg.get("noise_trader_ratio", 0.0)), 12), + _hashable_noise_params(cfg.get("reclamm_noise_params")), + cfg.get("noise_arrays_path"), + arb_frequency, + ), + } + + _NOISE_SETTINGS_CACHE[cache_key] = result + return result + + # Pool configurations to compare CONFIGS = [ { - "name": "AAVE/ETH on-chain (25bps, narrow range)", + "name": "AAVE/ETH launch-style range (25bps, reference)", "tokens": ["AAVE", "ETH"], "start": "2024-06-01 00:00:00", "end": "2025-06-01 00:00:00", "fees": 0.0025, - "price_ratio": 1.5, + "price_ratio": 1.5014, "centeredness_margin": 0.5, "daily_price_shift_exponent": 0.1, + "reason": "Original launch-style parameters.", + **AAVE_ETH_NOISE_SETTINGS, }, { - "name": "AAVE/ETH wide range (25bps)", + "name": "AAVE/ETH aggressive tight range (25bps)", "tokens": ["AAVE", "ETH"], "start": "2024-06-01 00:00:00", "end": "2025-06-01 00:00:00", "fees": 0.0025, - "price_ratio": 4.0, - "centeredness_margin": 0.2, - "daily_price_shift_exponent": 1.0, - }, - { - "name": "AAVE/ETH zero fees (narrow)", - "tokens": ["AAVE", "ETH"], - "start": "2024-06-01 00:00:00", - "end": "2025-06-01 00:00:00", - "fees": 0.0, - "price_ratio": 1.5, - "centeredness_margin": 0.5, + "price_ratio": 1.10, + "centeredness_margin": 0.60, "daily_price_shift_exponent": 0.1, + "reason": ( + "Aggressively tightened and moved to an earlier thermostat trigger. " + "At fixed price_ratio=1.10, the shift_exponent sweep still favored " + "0.1, while margin=0.60 widened the non-linear edge materially." + ), + **AAVE_ETH_NOISE_SETTINGS, }, ] -def make_fingerprint(cfg, interpolation_method, centeredness_scaling=False): +def make_fingerprint(cfg, interpolation_method): """Build run fingerprint for a given config and interpolation method.""" - return { + speed_override = ( + cfg.get("arc_length_speed") + if interpolation_method == "constant_arc_length" + else None + ) + noise_cfg = resolve_reclamm_noise_settings(cfg) + arb_frequency = cfg.get("arb_frequency", noise_cfg.get("arb_frequency")) + fingerprint = { "tokens": cfg["tokens"], "rule": "reclamm", "startDateString": cfg["start"], "endDateString": cfg["end"], - "initial_pool_value": 1000000.0, + "initial_pool_value": get_initial_pool_value(cfg), "do_arb": True, "fees": cfg["fees"], - "gas_cost": 0.0, - "arb_fees": 0.0, + "gas_cost": cfg.get( + "gas_cost", + DEFAULT_GAS_COST if cfg.get("enable_noise_model", False) else 0.0, + ), + "arb_fees": cfg.get("arb_fees", 0.0), + "protocol_fee_split": cfg.get( + "protocol_fee_split", + DEFAULT_PROTOCOL_FEE_SPLIT if cfg.get("enable_noise_model", False) else 0.0, + ), + "noise_trader_ratio": noise_cfg.get("noise_trader_ratio", 0.0), "reclamm_interpolation_method": interpolation_method, - "reclamm_arc_length_speed": None, # auto-calibrate - "reclamm_centeredness_scaling": centeredness_scaling, + "reclamm_arc_length_speed": speed_override, } + if noise_cfg.get("noise_model") is not None: + fingerprint["noise_model"] = noise_cfg["noise_model"] + if noise_cfg.get("reclamm_noise_params") is not None: + fingerprint["reclamm_noise_params"] = noise_cfg["reclamm_noise_params"] + if noise_cfg.get("noise_arrays_path") is not None: + fingerprint["noise_arrays_path"] = noise_cfg["noise_arrays_path"] + if arb_frequency is not None: + fingerprint["arb_frequency"] = arb_frequency + return fingerprint def make_params(cfg): @@ -85,40 +480,1129 @@ def make_params(cfg): } -def run_comparison(cfg): - """Run all thermostat variants, return results dict.""" +def load_shared_price_data(configs, root=None): + """Load the shared historic price panel once for all compare runs.""" + tokens = sorted({token for cfg in configs for token in cfg["tokens"]}) + return get_historic_parquet_data(tokens, cols=["close"], root=root) + + +def run_comparison(cfg, price_data=None, low_data_mode=False): + """Run both interpolation variants, return results dict.""" params = make_params(cfg) results = {} - for method in ["geometric", "constant_arc_length"]: + for method in INTERPOLATION_METHODS: fp = make_fingerprint(cfg, method) results[method] = do_run_on_historic_data( - run_fingerprint=fp, params=params + run_fingerprint=fp, + params=params, + price_data=price_data, + low_data_mode=low_data_mode, + ) + + return results + + +def _set_padded_ylim(ax, series_list, pad_ratio=0.04): + """Fit the y-axis tightly around the plotted series.""" + flat = [ + np.asarray(series, dtype=float).ravel() + for series in series_list + if np.asarray(series).size > 0 + ] + if not flat: + return + + values = np.concatenate(flat) + values = values[np.isfinite(values)] + if values.size == 0: + return + + ymin = float(values.min()) + ymax = float(values.max()) + if np.isclose(ymin, ymax): + pad = max(abs(ymin) * pad_ratio, 1e-6) + else: + pad = (ymax - ymin) * pad_ratio + ax.set_ylim(ymin - pad, ymax + pad) + + +def _cache_size(cache): + """Count memoized final-value runs.""" + return len(cache.get("_final_value_cache", {})) + + +def _comparison_cache_size(cache): + """Count memoized scalar comparison bundles.""" + return len(cache.get("_comparison_cache", {})) + + +def make_sweep_cache(price_data): + """Create a shared cache for heatmap and line sweeps.""" + return { + "_shared_price_data": price_data, + "_final_value_cache": {}, + "_comparison_cache": {}, + } + + +def _missing_artifacts(progress_label, filenames): + """Report which plot artifacts still need to be generated.""" + missing = [filename for filename in filenames if not os.path.exists(filename)] + if not missing: + print(f"[{progress_label}] skipping sweep: all artifacts already exist.") + return set() + + existing_count = len(filenames) - len(missing) + if existing_count: + print( + f"[{progress_label}] reusing {existing_count}/{len(filenames)} " + "existing artifacts; generating the missing outputs." ) + return set(missing) + - # Geometric + centeredness-proportional scaling (scales decay duration) - fp_geo_scaled = make_fingerprint(cfg, "geometric", centeredness_scaling=True) - results["geometric_scaled"] = do_run_on_historic_data( - run_fingerprint=fp_geo_scaled, params=params +def _speed_cache_key(speed): + """Stable cache token for optional arc-length speed.""" + if speed is None: + return None + return round(float(speed), 12) + + +def _make_method_cache_key(cfg, method): + """Cache key for a single-method final-value run.""" + noise_cfg = resolve_reclamm_noise_settings(cfg) + arb_frequency = cfg.get("arb_frequency", noise_cfg.get("arb_frequency")) + key = ( + method, + bool(cfg.get("enable_noise_model", False)), + round(float(cfg["price_ratio"]), 6), + round(float(cfg["centeredness_margin"]), 6), + round(float(cfg["daily_price_shift_exponent"]), 6), + round(get_initial_pool_value(cfg), 2), + noise_cfg.get("noise_cache_key"), + None if arb_frequency is None else int(arb_frequency), + round( + float( + cfg.get( + "gas_cost", + DEFAULT_GAS_COST if cfg.get("enable_noise_model", False) else 0.0, + ) + ), + 6, + ), + round( + float( + cfg.get( + "protocol_fee_split", + DEFAULT_PROTOCOL_FEE_SPLIT if cfg.get("enable_noise_model", False) else 0.0, + ) + ), + 6, + ), ) + if method == "constant_arc_length": + key += (_speed_cache_key(cfg.get("arc_length_speed")),) + return key - # Arc-length + centeredness-proportional scaling (scales speed) - fp_cal_scaled = make_fingerprint(cfg, "constant_arc_length", centeredness_scaling=True) - results["cal_scaled"] = do_run_on_historic_data( - run_fingerprint=fp_cal_scaled, params=params + +def _make_comparison_cache_key(cfg, launch_final_values): + """Cache key for scalar heatmap metrics at a single parameter point.""" + noise_cfg = make_noise_variant_cfg(cfg, True) + arb_only_cfg = make_noise_variant_cfg(cfg, False) + key = [ + _make_method_cache_key(noise_cfg, "geometric"), + _make_method_cache_key(arb_only_cfg, "geometric"), + round(float(launch_final_values["geometric"]), 6), + ] + if RUN_CONSTANT_ARC_LENGTH: + key.extend( + [ + _make_method_cache_key(noise_cfg, "constant_arc_length"), + _make_method_cache_key(arb_only_cfg, "constant_arc_length"), + round(float(launch_final_values["constant_arc_length"]), 6), + ] + ) + return tuple(key) + + +def _run_method_final_value_cached(cfg, method, cache): + """Memoize final value for a single interpolation method.""" + final_value_cache = cache.setdefault("_final_value_cache", {}) + key = _make_method_cache_key(cfg, method) + if key not in final_value_cache: + result = do_run_on_historic_data( + run_fingerprint=make_fingerprint(cfg, method), + params=make_params(cfg), + price_data=cache["_shared_price_data"], + low_data_mode=True, + ) + final_value_cache[key] = float(result["final_value"]) + del result + gc.collect() + return final_value_cache[key] + + +def extract_comparison_metrics_from_final_values( + geo_final, arc_final, launch_final_values +): + """Summarize scalar comparison metrics from final values only.""" + return { + "efficiency_pct": (arc_final / max(abs(geo_final), 1e-12) - 1.0) * 100.0, + "launch_geometric_efficiency_pct": ( + arc_final / max(abs(launch_final_values["geometric"]), 1e-12) - 1.0 + ) + * 100.0, + "geometric_vs_launch_geometric_pct": ( + geo_final / max(abs(launch_final_values["geometric"]), 1e-12) - 1.0 + ) + * 100.0, + "constant_arc_vs_launch_constant_arc_pct": ( + arc_final + / max(abs(launch_final_values["constant_arc_length"]), 1e-12) + - 1.0 + ) + * 100.0, + } + + +def _load_required_heatmap_final_values(cfg, cache, metric_keys): + """Load only the cached final values needed for the requested heatmap metrics.""" + required_sources = set() + for metric_key in metric_keys: + required_sources.update(HEATMAP_METRIC_DEPENDENCIES[metric_key]) + + if not RUN_CONSTANT_ARC_LENGTH and any( + source.endswith("constant_arc") for source in required_sources + ): + raise ValueError( + "Constant-arc heatmap metric requested while RUN_CONSTANT_ARC_LENGTH=False" + ) + + final_values = {} + noise_cfg = None + arb_only_cfg = None + + if any(source.startswith("noise_") for source in required_sources): + noise_cfg = make_noise_variant_cfg(cfg, True) + if any(source.startswith("arb_") for source in required_sources): + arb_only_cfg = make_noise_variant_cfg(cfg, False) + + if "noise_geometric" in required_sources: + final_values["noise_geometric"] = _run_method_final_value_cached( + noise_cfg, + "geometric", + cache, + ) + if "noise_constant_arc" in required_sources: + final_values["noise_constant_arc"] = _run_method_final_value_cached( + noise_cfg, + "constant_arc_length", + cache, + ) + if "arb_geometric" in required_sources: + final_values["arb_geometric"] = _run_method_final_value_cached( + arb_only_cfg, + "geometric", + cache, + ) + if "arb_constant_arc" in required_sources: + final_values["arb_constant_arc"] = _run_method_final_value_cached( + arb_only_cfg, + "constant_arc_length", + cache, + ) + return final_values + + +def extract_heatmap_metrics_from_mode_final_values( + metric_keys, + final_values, + launch_final_values, +): + """Collect the requested scalar heatmap metrics from cached final values.""" + metrics = {} + + if "efficiency_pct" in metric_keys: + metrics["efficiency_pct"] = ( + final_values["noise_constant_arc"] + / max(abs(final_values["noise_geometric"]), 1e-12) + - 1.0 + ) * 100.0 + + if "launch_geometric_efficiency_pct" in metric_keys: + metrics["launch_geometric_efficiency_pct"] = ( + final_values["noise_constant_arc"] + / max(abs(launch_final_values["geometric"]), 1e-12) + - 1.0 + ) * 100.0 + + if "geometric_vs_launch_geometric_pct" in metric_keys: + metrics["geometric_vs_launch_geometric_pct"] = ( + final_values["noise_geometric"] + / max(abs(launch_final_values["geometric"]), 1e-12) + - 1.0 + ) * 100.0 + + if "constant_arc_vs_launch_constant_arc_pct" in metric_keys: + metrics["constant_arc_vs_launch_constant_arc_pct"] = ( + final_values["noise_constant_arc"] + / max(abs(launch_final_values["constant_arc_length"]), 1e-12) + - 1.0 + ) * 100.0 + + if "noise_geometric_final_value_musd" in metric_keys: + metrics["noise_geometric_final_value_musd"] = ( + final_values["noise_geometric"] / 1e6 + ) + + if "noise_constant_arc_final_value_musd" in metric_keys: + metrics["noise_constant_arc_final_value_musd"] = ( + final_values["noise_constant_arc"] / 1e6 + ) + + if "noise_vs_arb_geometric_improvement_pct" in metric_keys: + metrics["noise_vs_arb_geometric_improvement_pct"] = ( + final_values["noise_geometric"] + / max(abs(final_values["arb_geometric"]), 1e-12) + - 1.0 + ) * 100.0 + + if "noise_vs_arb_constant_arc_improvement_pct" in metric_keys: + metrics["noise_vs_arb_constant_arc_improvement_pct"] = ( + final_values["noise_constant_arc"] + / max(abs(final_values["arb_constant_arc"]), 1e-12) + - 1.0 + ) * 100.0 + + return metrics + + +def extract_comparison_metrics(results, launch_final_values): + """Summarize scalar heatmap metrics for a pair of runs.""" + geo = results["geometric"] + arc = results["constant_arc_length"] + + geo_final = float(geo["final_value"]) + arc_final = float(arc["final_value"]) + + return extract_comparison_metrics_from_final_values( + geo_final, + arc_final, + launch_final_values=launch_final_values, ) - return results + +def run_comparison_cached(cfg, cache, launch_final_values, metric_keys): + """Memoize scalar heatmap metrics across heatmap sweeps.""" + requested_metric_keys = tuple(dict.fromkeys(metric_keys)) + comparison_cache = cache.setdefault("_comparison_cache", {}) + cache_key = _make_comparison_cache_key(cfg, launch_final_values) + cached_metrics = comparison_cache.setdefault(cache_key, {}) + missing_metric_keys = [ + metric_key for metric_key in requested_metric_keys if metric_key not in cached_metrics + ] + if missing_metric_keys: + final_values = _load_required_heatmap_final_values( + cfg, + cache, + missing_metric_keys, + ) + cached_metrics.update( + extract_heatmap_metrics_from_mode_final_values( + missing_metric_keys, + final_values, + launch_final_values=launch_final_values, + ) + ) + return { + metric_key: cached_metrics[metric_key] for metric_key in requested_metric_keys + } + + +def build_heatmap_matrices( + x_values, + y_values, + x_key, + y_key, + base_cfg, + metric_keys, + cache, + progress_label, + launch_final_values, +): + """Evaluate multiple metrics over a 2D parameter grid in one pass.""" + data = { + metric_key: np.zeros((len(y_values), len(x_values)), dtype=float) + for metric_key in metric_keys + } + total_points = len(y_values) * len(x_values) + + print( + f"[{progress_label}] start: {len(y_values)} rows x {len(x_values)} cols " + f"= {total_points} parameter points" + ) + + for yi, y_value in enumerate(y_values): + final_cache_before_row = _cache_size(cache) + comparison_cache_before_row = _comparison_cache_size(cache) + for xi, x_value in enumerate(x_values): + cfg = dict(base_cfg) + cfg[x_key] = float(x_value) + cfg[y_key] = float(y_value) + metrics = run_comparison_cached( + cfg, + cache, + launch_final_values=launch_final_values, + metric_keys=metric_keys, + ) + for metric_key in metric_keys: + data[metric_key][yi, xi] = metrics[metric_key] + + completed_points = (yi + 1) * len(x_values) + row_new_final_runs = _cache_size(cache) - final_cache_before_row + row_new_comparisons = ( + _comparison_cache_size(cache) - comparison_cache_before_row + ) + row_pct = completed_points / total_points * 100.0 + print( + f"[{progress_label}] row {yi + 1}/{len(y_values)} complete " + f"({y_key}={float(y_value):.4f}, {completed_points}/{total_points} " + f"points, {row_pct:.1f}%, {row_new_final_runs} new final-value runs, " + f"{row_new_comparisons} new comparison bundles)" + ) + + print( + f"[{progress_label}] done: " + + ", ".join( + ( + f"{metric_key} min={float(np.nanmin(data[metric_key])):.4f}, " + f"max={float(np.nanmax(data[metric_key])):.4f}" + ) + for metric_key in metric_keys + ) + + ( + f", final_value_cache_size={_cache_size(cache)}, " + f"comparison_cache_size={_comparison_cache_size(cache)}" + ) + ) + + return data + + +def build_metric_curve( + x_values, + x_key, + base_cfg, + metric_key, + cache, + launch_final_values, +): + """Evaluate one metric over a 1D sweep.""" + data = np.zeros(len(x_values), dtype=float) + for xi, x_value in enumerate(x_values): + cfg = dict(base_cfg) + cfg[x_key] = float(x_value) + metrics = run_comparison_cached( + cfg, + cache, + launch_final_values=launch_final_values, + metric_keys=(metric_key,), + ) + data[xi] = metrics[metric_key] + return data + + +def _compute_axis_edges(values, scale="linear"): + """Convert axis centers to cell edges for pcolormesh.""" + values = np.asarray(values, dtype=float) + if values.size == 1: + if scale == "log": + return np.array([values[0] / np.sqrt(10.0), values[0] * np.sqrt(10.0)]) + pad = max(abs(values[0]) * 0.5, 1.0) + return np.array([values[0] - pad, values[0] + pad]) + + if scale == "log": + log_values = np.log10(values) + edges = np.empty(values.size + 1, dtype=float) + edges[1:-1] = 0.5 * (log_values[:-1] + log_values[1:]) + edges[0] = log_values[0] - 0.5 * (log_values[1] - log_values[0]) + edges[-1] = log_values[-1] + 0.5 * (log_values[-1] - log_values[-2]) + return 10.0 ** edges + + edges = np.empty(values.size + 1, dtype=float) + edges[1:-1] = 0.5 * (values[:-1] + values[1:]) + edges[0] = values[0] - 0.5 * (values[1] - values[0]) + edges[-1] = values[-1] + 0.5 * (values[-1] - values[-2]) + return edges + + +def plot_heatmap( + data, + x_values, + y_values, + x_label, + y_label, + title, + colorbar_label, + filename, + xticks=None, + yticks=None, + xscale="linear", + center_zero=True, + cmap=None, + color_norm=None, + symlog_linthresh=None, +): + """Render and save a single heatmap.""" + finite = np.asarray(data, dtype=float) + finite = finite[np.isfinite(finite)] + + if center_zero: + vmax = max(abs(float(np.nanmin(data))), abs(float(np.nanmax(data))), 1e-9) + if color_norm == "symlog" and symlog_linthresh is not None and vmax > symlog_linthresh: + norm = SymLogNorm( + linthresh=symlog_linthresh, + linscale=1.0, + vmin=-vmax, + vmax=vmax, + base=10.0, + ) + else: + norm = TwoSlopeNorm(vcenter=0.0, vmin=-vmax, vmax=vmax) + cmap_name = cmap or "RdYlGn" + else: + if finite.size == 0: + vmin, vmax = 0.0, 1.0 + else: + vmin = float(finite.min()) + vmax = float(finite.max()) + if np.isclose(vmin, vmax): + pad = max(abs(vmin) * 0.01, 1e-9) + vmin -= pad + vmax += pad + norm = Normalize(vmin=vmin, vmax=vmax) + cmap_name = cmap or "viridis" + + x_edges = _compute_axis_edges(x_values, scale=xscale) + y_edges = _compute_axis_edges(y_values, scale="linear") + + fig, ax = plt.subplots(figsize=(8.5, 6.0)) + im = ax.pcolormesh( + x_edges, + y_edges, + data, + cmap=cmap_name, + norm=norm, + shading="auto", + ) + + ax.set_xlabel(x_label) + ax.set_ylabel(y_label) + ax.set_title(title) + if xscale == "log": + ax.set_xscale("log") + ax.set_xticks(np.asarray(xticks if xticks is not None else x_values, dtype=float)) + ax.set_yticks(np.asarray(yticks if yticks is not None else y_values, dtype=float)) + ax.grid(False) + + cbar = fig.colorbar(im, ax=ax) + cbar.set_label(colorbar_label) + + plt.tight_layout() + plt.savefig(filename, dpi=150) + print(f"Saved {filename}") + plt.close(fig) + + +def plot_arc_speed_line_chart( + data, + x_values, + y_values, + y_label, + title, + filename, + launch_curve, + launch_auto_speed=None, +): + """Plot thin multi-series efficiency lines over the arc-speed sweep.""" + fig, ax = plt.subplots(figsize=(10.5, 5.75)) + cmap = plt.cm.viridis + colors = cmap(np.linspace(0.0, 1.0, len(y_values))) + plotted_series = [] + + for yi, (y_value, color) in enumerate(zip(y_values, colors)): + series = np.asarray(data[yi], dtype=float) + plotted_series.append(series) + ax.plot( + x_values, + series, + color=color, + linewidth=SWEEP_LINE_WIDTH, + alpha=0.8, + ) + + launch_curve = np.asarray(launch_curve, dtype=float) + plotted_series.append(launch_curve) + ax.plot( + x_values, + launch_curve, + color="black", + linewidth=REFERENCE_LINE_WIDTH, + alpha=0.9, + label="Current launch config", + ) + if launch_auto_speed is not None: + ax.axvline( + float(launch_auto_speed), + color="black", + ls=":", + linewidth=0.8, + alpha=0.7, + label="Launch auto-cal speed", + ) + + ax.axhline(0.0, color="gray", ls="--", linewidth=0.8, alpha=0.5) + ax.set_xscale("log") + ax.set_xticks(ARC_LENGTH_SPEED_TICKS) + ax.set_xlabel("Arc-length speed") + ax.set_ylabel("Efficiency vs geometric (%)") + ax.set_title(title) + _set_padded_ylim(ax, plotted_series, pad_ratio=0.08) + ax.grid(True, alpha=0.25) + ax.legend(fontsize=8) + + sm = ScalarMappable( + norm=Normalize(vmin=float(np.min(y_values)), vmax=float(np.max(y_values))), + cmap=cmap, + ) + sm.set_array([]) + cbar = fig.colorbar(sm, ax=ax) + cbar.set_label(y_label) + + plt.tight_layout() + plt.savefig(filename, dpi=150) + print(f"Saved {filename}") + plt.close(fig) + + +def generate_heatmaps(base_cfg, price_data, launch_final_values, cache=None): + """Generate pairwise heatmaps for thermostat tuning and noise-vs-arb effects.""" + owns_cache = cache is None + if cache is None: + cache = make_sweep_cache(price_data) + metric_specs = [ + { + "key": "efficiency_pct", + "title": "Efficiency vs heatmap geometric", + "colorbar_label": "Const Arc - heatmap Geo (% of heatmap geometric final value)", + "slug": "efficiency", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "launch_geometric_efficiency_pct", + "title": "Efficiency vs launch-style geometric", + "colorbar_label": "Const Arc - launch Geo (% of launch geometric final value)", + "slug": "launch_geometric_efficiency", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "geometric_vs_launch_geometric_pct", + "title": "Geometric tuning vs launch-style geometric", + "colorbar_label": "Candidate Geo - launch Geo (% of launch geometric final value)", + "slug": "geometric_vs_launch_geometric", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "constant_arc_vs_launch_constant_arc_pct", + "title": "Const arc tuning vs launch-style const arc", + "colorbar_label": "Candidate Const Arc - launch Const Arc (% of launch const arc final value)", + "slug": "constant_arc_vs_launch_constant_arc", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "noise_geometric_final_value_musd", + "title": "Geometric final value with noise model", + "colorbar_label": "Geometric final value with noise model ($M)", + "slug": "noise_geometric_final_value", + "center_zero": False, + "cmap": "viridis", + }, + { + "key": "noise_constant_arc_final_value_musd", + "title": "Const arc final value with noise model", + "colorbar_label": "Const Arc final value with noise model ($M)", + "slug": "noise_constant_arc_final_value", + "center_zero": False, + "cmap": "viridis", + }, + { + "key": "noise_vs_arb_geometric_improvement_pct", + "title": "Noise-model improvement over arb-only (geometric)", + "colorbar_label": "Noise-model Geo - arb-only Geo (% of arb-only final value)", + "slug": "noise_vs_arb_geometric_improvement", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "noise_vs_arb_constant_arc_improvement_pct", + "title": "Noise-model improvement over arb-only (const arc)", + "colorbar_label": "Noise-model Const Arc - arb-only Const Arc (% of arb-only final value)", + "slug": "noise_vs_arb_constant_arc_improvement", + "center_zero": True, + "cmap": "RdYlGn", + }, + ] + for spec in metric_specs: + if spec["center_zero"]: + spec["color_norm"] = CENTER_ZERO_HEATMAP_COLOR_NORM + spec["symlog_linthresh"] = CENTER_ZERO_HEATMAP_SYMLOG_LINTHRESH + spec["artifact_tag"] = CENTER_ZERO_HEATMAP_COLOR_TAG + if not RUN_CONSTANT_ARC_LENGTH: + metric_specs = [ + spec + for spec in metric_specs + if spec["key"] in GEOMETRIC_ONLY_HEATMAP_METRIC_KEYS + ] + pair_specs = [ + { + "slug": "price_ratio_vs_margin", + "x_values": HEATMAP_PRICE_RATIOS, + "y_values": HEATMAP_MARGINS, + "x_key": "price_ratio", + "y_key": "centeredness_margin", + "x_label": "Price ratio", + "y_label": "Centeredness margin", + "title_suffix": ( + f"shift_exp fixed at {base_cfg['daily_price_shift_exponent']:.2f}" + ), + "xticks": PRICE_RATIO_TICKS, + "yticks": MARGIN_TICKS + }, + { + "slug": "shift_exp_vs_margin", + "x_values": HEATMAP_SHIFT_EXPONENTS, + "y_values": HEATMAP_MARGINS, + "x_key": "daily_price_shift_exponent", + "y_key": "centeredness_margin", + "x_label": "Shift exponent", + "y_label": "Centeredness margin", + "title_suffix": f"price_ratio fixed at {base_cfg['price_ratio']:.2f}", + "xticks": SHIFT_EXPONENT_TICKS, + "yticks": MARGIN_TICKS + }, + { + "slug": "price_ratio_vs_shift_exp", + "x_values": HEATMAP_PRICE_RATIOS, + "y_values": HEATMAP_SHIFT_EXPONENTS, + "x_key": "price_ratio", + "y_key": "daily_price_shift_exponent", + "x_label": "Price ratio", + "y_label": "Shift exponent", + "title_suffix": ( + f"margin fixed at {base_cfg['centeredness_margin']:.2f}" + ), + "xticks": PRICE_RATIO_TICKS, + "yticks": SHIFT_EXPONENT_TICKS, + }, + ] + + metric_spec_map = {spec["key"]: spec for spec in metric_specs} + + if RUN_CONSTANT_ARC_LENGTH: + print( + "Using launch-style benchmarks " + f"Geo=${launch_final_values['geometric']:,.0f}, " + f"Const Arc=${launch_final_values['constant_arc_length']:,.0f}, " + f"TVL={format_tvl_millions_label(base_cfg)}." + ) + print( + "Running {count} heatmap pair sweeps sequentially " + "(current outputs use cached noise-model runs; improvement heatmaps " + "reuse those values and add cached arb-only runs).".format( + count=len(pair_specs) + ) + ) + else: + print( + "Using launch-style geometric benchmark " + f"Geo=${launch_final_values['geometric']:,.0f}, " + f"TVL={format_tvl_millions_label(base_cfg)}." + ) + print( + "RUN_CONSTANT_ARC_LENGTH=False, so only geometric heatmaps will be generated " + "and only geometric/arb-only geometric runs will be scheduled." + ) + + for pair in pair_specs: + output_files = { + spec["key"]: heatmap_artifact_filename( + spec, + base_cfg, + suffix=pair["slug"], + ) + for spec in metric_specs + } + missing_files = _missing_artifacts( + pair["slug"], + list(output_files.values()), + ) + if not missing_files: + continue + + missing_metric_keys = [ + spec["key"] + for spec in metric_specs + if output_files[spec["key"]] in missing_files + ] + data_by_metric = build_heatmap_matrices( + x_values=pair["x_values"], + y_values=pair["y_values"], + x_key=pair["x_key"], + y_key=pair["y_key"], + base_cfg=base_cfg, + metric_keys=missing_metric_keys, + cache=cache, + progress_label=pair["slug"], + launch_final_values=launch_final_values, + ) + print(f"[{pair['slug']}] plotting missing heatmaps...") + for metric_key in missing_metric_keys: + spec = metric_spec_map[metric_key] + plot_heatmap( + data=data_by_metric[metric_key], + x_values=pair["x_values"], + y_values=pair["y_values"], + x_label=pair["x_label"], + y_label=pair["y_label"], + title=( + f"{spec['title']}: {pair['title_suffix']} | " + f"TVL {format_tvl_millions_label(base_cfg)}" + ), + colorbar_label=spec["colorbar_label"], + filename=output_files[metric_key], + xticks=pair["xticks"], + yticks=pair["yticks"], + center_zero=spec["center_zero"], + cmap=spec["cmap"], + color_norm=spec.get("color_norm"), + symlog_linthresh=spec.get("symlog_linthresh"), + ) + del data_by_metric + gc.collect() + + if owns_cache: + cache.clear() + gc.collect() + print("Released heatmap metric cache.") + + +def compute_auto_calibrated_arc_length_speed(cfg, price_data): + """Compute the launch/reference auto-calibrated speed for a config.""" + start_ts = pd.Timestamp(cfg["start"]) + + if isinstance(price_data.index, pd.DatetimeIndex): + row = price_data.loc[start_ts] + else: + start_unix_ms = int(start_ts.timestamp() * 1000.0) + index_values = price_data.index.to_numpy(dtype=np.int64) + row_idx = int(np.searchsorted(index_values, start_unix_ms, side="left")) + if row_idx >= len(index_values): + row_idx = len(index_values) - 1 + if row_idx > 0 and index_values[row_idx] != start_unix_ms: + prev_idx = row_idx - 1 + if abs(index_values[prev_idx] - start_unix_ms) <= abs( + index_values[row_idx] - start_unix_ms + ): + row_idx = prev_idx + row = price_data.iloc[row_idx] + + if isinstance(row, pd.DataFrame): + row = row.iloc[0] + + if isinstance(price_data.columns, pd.MultiIndex): + initial_price_values = [ + float(row[(token, "close")]) + for token in cfg["tokens"] + ] + else: + initial_price_values = [ + float(row[f"close_{token}"]) + for token in cfg["tokens"] + ] + + initial_prices = jnp.array(initial_price_values, dtype=jnp.float64) + initial_reserves, Va, Vb = initialise_reclamm_reserves( + get_initial_pool_value(cfg), + initial_prices, + float(cfg["price_ratio"]), + ) + market_price_0 = float(initial_prices[0] / initial_prices[1]) + sqrt_Q = jnp.sqrt( + compute_price_ratio( + initial_reserves[0], + initial_reserves[1], + Va, + Vb, + ) + ) + return float( + calibrate_arc_length_speed( + initial_reserves[0], + initial_reserves[1], + Va, + Vb, + to_daily_price_shift_base(float(cfg["daily_price_shift_exponent"])), + 60.0, + sqrt_Q, + market_price_0, + centeredness_margin=float(cfg["centeredness_margin"]), + ) + ) + + +def generate_arc_speed_efficiency_artifacts( + base_cfg, + launch_cfg, + price_data, + launch_final_values, + cache=None, +): + """Generate arc-speed heatmaps plus the existing efficiency line charts.""" + if not RUN_CONSTANT_ARC_LENGTH: + print("\nSkipping arc-speed heatmaps because RUN_CONSTANT_ARC_LENGTH=False.") + return + owns_cache = cache is None + if cache is None: + cache = make_sweep_cache(price_data) + launch_auto_speed = compute_auto_calibrated_arc_length_speed(launch_cfg, price_data) + heatmap_metric_specs = [ + { + "key": "efficiency_pct", + "title": "Efficiency vs geometric", + "colorbar_label": "Const Arc - heatmap Geo (% of heatmap geometric final value)", + "slug": "efficiency", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "noise_constant_arc_final_value_musd", + "title": "Const arc final value with noise model", + "colorbar_label": "Const Arc final value with noise model ($M)", + "slug": "noise_constant_arc_final_value", + "center_zero": False, + "cmap": "viridis", + }, + { + "key": "noise_vs_arb_constant_arc_improvement_pct", + "title": "Noise-model improvement over arb-only (const arc)", + "colorbar_label": "Noise-model Const Arc - arb-only Const Arc (% of arb-only final value)", + "slug": "noise_vs_arb_constant_arc_improvement", + "center_zero": True, + "cmap": "RdYlGn", + }, + ] + for spec in heatmap_metric_specs: + if spec["center_zero"]: + spec["color_norm"] = CENTER_ZERO_HEATMAP_COLOR_NORM + spec["symlog_linthresh"] = CENTER_ZERO_HEATMAP_SYMLOG_LINTHRESH + spec["artifact_tag"] = CENTER_ZERO_HEATMAP_COLOR_TAG + pair_specs = [ + { + "slug": "arc_speed_vs_price_ratio", + "x_values": HEATMAP_ARC_LENGTH_SPEEDS, + "y_values": HEATMAP_PRICE_RATIOS, + "x_key": "arc_length_speed", + "y_key": "price_ratio", + "x_label": "Arc-length speed", + "y_label": "Price ratio", + "title_suffix": ( + f"margin fixed at {base_cfg['centeredness_margin']:.2f}, " + f"shift_exp fixed at {base_cfg['daily_price_shift_exponent']:.2f}" + ), + "xticks": ARC_LENGTH_SPEED_TICKS, + "yticks": PRICE_RATIO_TICKS, + }, + { + "slug": "arc_speed_vs_margin", + "x_values": HEATMAP_ARC_LENGTH_SPEEDS, + "y_values": HEATMAP_MARGINS, + "x_key": "arc_length_speed", + "y_key": "centeredness_margin", + "x_label": "Arc-length speed", + "y_label": "Centeredness margin", + "title_suffix": ( + f"price_ratio fixed at {base_cfg['price_ratio']:.2f}, " + f"shift_exp fixed at {base_cfg['daily_price_shift_exponent']:.2f}" + ), + "xticks": ARC_LENGTH_SPEED_TICKS, + "yticks": MARGIN_TICKS + }, + { + "slug": "arc_speed_vs_shift_exp", + "x_values": HEATMAP_ARC_LENGTH_SPEEDS, + "y_values": HEATMAP_SHIFT_EXPONENTS, + "x_key": "arc_length_speed", + "y_key": "daily_price_shift_exponent", + "x_label": "Arc-length speed", + "y_label": "Shift exponent", + "title_suffix": ( + f"price_ratio fixed at {base_cfg['price_ratio']:.2f}, " + f"margin fixed at {base_cfg['centeredness_margin']:.2f}" + ), + "xticks": ARC_LENGTH_SPEED_TICKS, + "yticks": SHIFT_EXPONENT_TICKS, + }, + ] + metric_spec_map = {spec["key"]: spec for spec in heatmap_metric_specs} + + print( + "\nGenerating arc-speed heatmaps and line charts " + f"(launch auto-cal speed={launch_auto_speed:.3e}, TVL={format_tvl_millions_label(base_cfg)})..." + ) + + for pair in pair_specs: + heatmap_files = { + spec["key"]: heatmap_artifact_filename( + spec, + base_cfg, + suffix=pair["slug"], + ) + for spec in heatmap_metric_specs + } + line_filename = tvl_artifact_filename( + "reclamm_line_efficiency", + base_cfg, + suffix=pair["slug"], + ) + missing_files = _missing_artifacts( + pair["slug"], + list(heatmap_files.values()) + [line_filename], + ) + if not missing_files: + continue + + missing_metric_keys = [ + spec["key"] + for spec in heatmap_metric_specs + if heatmap_files[spec["key"]] in missing_files + ] + if line_filename in missing_files and "efficiency_pct" not in missing_metric_keys: + missing_metric_keys.append("efficiency_pct") + + data_by_metric = build_heatmap_matrices( + x_values=pair["x_values"], + y_values=pair["y_values"], + x_key=pair["x_key"], + y_key=pair["y_key"], + base_cfg=base_cfg, + metric_keys=missing_metric_keys, + cache=cache, + progress_label=pair["slug"], + launch_final_values=launch_final_values, + ) + for metric_key in missing_metric_keys: + if metric_key not in heatmap_files: + continue + if heatmap_files[metric_key] not in missing_files: + continue + spec = metric_spec_map[metric_key] + plot_heatmap( + data=data_by_metric[metric_key], + x_values=pair["x_values"], + y_values=pair["y_values"], + x_label=pair["x_label"], + y_label=pair["y_label"], + title=( + f"{spec['title']}: {pair['title_suffix']} | " + f"TVL {format_tvl_millions_label(base_cfg)}" + ), + colorbar_label=spec["colorbar_label"], + filename=heatmap_files[metric_key], + xticks=pair["xticks"], + yticks=pair["yticks"], + xscale="log", + center_zero=spec["center_zero"], + cmap=spec["cmap"], + color_norm=spec.get("color_norm"), + symlog_linthresh=spec.get("symlog_linthresh"), + ) + + if line_filename in missing_files: + efficiency_data = data_by_metric["efficiency_pct"] + launch_curve = build_metric_curve( + x_values=pair["x_values"], + x_key=pair["x_key"], + base_cfg=launch_cfg, + metric_key="efficiency_pct", + cache=cache, + launch_final_values=launch_final_values, + ) + plot_arc_speed_line_chart( + data=efficiency_data, + x_values=pair["x_values"], + y_values=pair["y_values"], + y_label=pair["y_label"], + title=( + "Arc-speed efficiency sweep: " + f"{pair['title_suffix']} | TVL {format_tvl_millions_label(base_cfg)}" + ), + filename=line_filename, + launch_curve=launch_curve, + launch_auto_speed=launch_auto_speed, + ) + del data_by_metric + gc.collect() + + if owns_cache: + cache.clear() + gc.collect() + print("Released arc-speed sweep cache.") + + +def get_launch_final_values(all_results, launch_cfg, price_data): + """Reuse launch-style runs when available; otherwise run them once.""" + for cfg, results in all_results: + if cfg["name"] == launch_cfg["name"]: + launch_final_values = { + "geometric": float(results["geometric"]["final_value"]), + } + if "constant_arc_length" in results: + launch_final_values["constant_arc_length"] = float( + results["constant_arc_length"]["final_value"] + ) + return launch_final_values + + print("\nRunning launch-style benchmarks for heatmaps...") + launch_results = run_comparison( + launch_cfg, + price_data=price_data, + low_data_mode=True, + ) + launch_final_values = { + "geometric": float(launch_results["geometric"]["final_value"]), + } + if "constant_arc_length" in launch_results: + launch_final_values["constant_arc_length"] = float( + launch_results["constant_arc_length"]["final_value"] + ) + del launch_results + gc.collect() + return launch_final_values + def print_comparison(cfg, results): """Print text summary table.""" - methods = [ - ("Geometric", results["geometric"]), - ("Geo+Scaled", results["geometric_scaled"]), - ("Const Arc", results["constant_arc_length"]), - ("Arc+Scaled", results["cal_scaled"]), - ] + methods = [("Geometric", results["geometric"])] + has_constant_arc = "constant_arc_length" in results + if has_constant_arc: + methods.append(("Const Arc", results["constant_arc_length"])) + noise_cfg = resolve_reclamm_noise_settings(cfg) hodl_value = float((methods[0][1]["reserves"][0] * methods[0][1]["prices"][-1]).sum()) @@ -128,6 +1612,18 @@ def print_comparison(cfg, results): f"margin={cfg['centeredness_margin']}, " f"shift_exp={cfg['daily_price_shift_exponent']}, " f"fees={cfg['fees']}") + print( + f" base_tvl=${get_initial_pool_value(cfg):,.0f} " + f"(TVL {format_tvl_millions_label(cfg)})" + ) + print(f" note={cfg['reason']}") + print( + f" noise={noise_cfg['noise_summary']}, " + f"gas={cfg.get('gas_cost', 0.0)}, " + f"protocol_fee_split={cfg.get('protocol_fee_split', 0.0)}" + ) + if not has_constant_arc: + print(" constant_arc=disabled") print("-" * 105) header = " {:20s}".format("") for name, _ in methods: @@ -158,18 +1654,27 @@ def print_comparison(cfg, results): vs = (float(r["final_value"]) / hodl_value - 1) * 100 row += f" {vs:>13.2f}%" print(row) + + if has_constant_arc: + geo_final = float(results["geometric"]["final_value"]) + arc_final = float(results["constant_arc_length"]["final_value"]) + geo_lvr = hodl_value - geo_final + arc_lvr = hodl_value - arc_final + print(f" {'Const Arc - Geo':20s} ${arc_final - geo_final:>13,.0f}") + print(f" {'LVR saved vs Geo':20s} ${geo_lvr - arc_lvr:>13,.0f}") print("=" * 105) + def plot_comparison(cfg, results, fig_idx): - """Plot 4-panel comparison for one config.""" - # Method name → (result dict, color, linestyle) + """Plot comparison diagnostics for one config.""" + tvl_label = format_tvl_millions_label(cfg) variants = { "Geometric": (results["geometric"], "C0", "-"), - "Geo+Scaled": (results["geometric_scaled"], "C1", "-"), - "Const arc-len": (results["constant_arc_length"], "C2", "--"), - "Arc+Scaled": (results["cal_scaled"], "C3", "--"), } + has_constant_arc = "constant_arc_length" in results + if has_constant_arc: + variants["Const arc-len"] = (results["constant_arc_length"], "C2", "--") geo = results["geometric"] geo_prices = np.array(geo["prices"]) @@ -181,22 +1686,21 @@ def plot_comparison(cfg, results, fig_idx): price_ratio_traj = geo_prices[:n_steps, 0] / geo_prices[:n_steps, 1] fig, axes = plt.subplots(2, 2, figsize=(14, 10)) - fig.suptitle(cfg["name"], fontsize=13, fontweight="bold") + fig.suptitle(f"{cfg['name']} — TVL {tvl_label}", fontsize=13, fontweight="bold") - # (0,0) Pool value over time ax = axes[0, 0] + plotted_values = [] for name, (r, color, ls) in variants.items(): vals = np.array(r["value"]) + plotted_values.append(vals / 1e6) ax.plot(t_days, vals / 1e6, color=color, ls=ls, label=name, alpha=0.9) - ax.plot(t_days, np.array(hodl_traj) / 1e6, color="gray", ls=":", - alpha=0.5, label="HODL") + _set_padded_ylim(ax, plotted_values, pad_ratio=0.03) ax.set_xlabel("Days") ax.set_ylabel("Pool value ($M)") ax.set_title("Pool value") ax.legend(fontsize=8) ax.grid(True, alpha=0.3) - # (0,1) Cumulative LVR ax = axes[0, 1] for name, (r, color, ls) in variants.items(): vals = np.array(r["value"]) @@ -208,7 +1712,6 @@ def plot_comparison(cfg, results, fig_idx): ax.legend(fontsize=8) ax.grid(True, alpha=0.3) - # (1,0) Price ratio ax = axes[1, 0] ax.plot(t_days, price_ratio_traj, color="C4", alpha=0.7) ax.set_xlabel("Days") @@ -216,7 +1719,6 @@ def plot_comparison(cfg, results, fig_idx): ax.set_title("Price path") ax.grid(True, alpha=0.3) - # (1,1) Empirical weights ax = axes[1, 1] for name, (r, color, ls) in variants.items(): w = np.array(r["weights"]) @@ -230,19 +1732,21 @@ def plot_comparison(cfg, results, fig_idx): ax.grid(True, alpha=0.3) plt.tight_layout() - fname = f"reclamm_thermostat_comparison_{fig_idx}.png" + fname = tvl_artifact_filename("reclamm_thermostat_comparison", cfg, suffix=str(fig_idx)) plt.savefig(fname, dpi=150) print(f"Saved {fname}") plt.close(fig) - # Second figure: diagnostics + if not has_constant_arc: + print("Skipping constant-arc comparison diagnostics because RUN_CONSTANT_ARC_LENGTH=False.") + return + geo_values = np.array(geo["value"]) geo_lvr = np.array(hodl_traj) - geo_values fig2, axes2 = plt.subplots(1, 3, figsize=(18, 5)) - fig2.suptitle(f"{cfg['name']} — diagnostics", fontsize=13, fontweight="bold") + fig2.suptitle(f"{cfg['name']} — diagnostics — TVL {tvl_label}", fontsize=13, fontweight="bold") - # (left) Value difference vs geometric ax = axes2[0] for name, (r, color, ls) in variants.items(): if name == "Geometric": @@ -257,7 +1761,6 @@ def plot_comparison(cfg, results, fig_idx): ax.legend(fontsize=8) ax.grid(True, alpha=0.3) - # (middle) LVR ratio over time ax = axes2[1] mask = np.abs(geo_lvr) > 100 if mask.any(): @@ -279,7 +1782,6 @@ def plot_comparison(cfg, results, fig_idx): ax.set_title("Relative LVR") ax.grid(True, alpha=0.3) - # (right) Per-step LVR histogram ax = axes2[2] all_pos = [] for name, (r, color, ls) in variants.items(): @@ -306,74 +1808,180 @@ def plot_comparison(cfg, results, fig_idx): ax.grid(True, alpha=0.3) plt.tight_layout() - fname2 = f"reclamm_thermostat_diff_{fig_idx}.png" + fname2 = tvl_artifact_filename("reclamm_thermostat_diff", cfg, suffix=str(fig_idx)) plt.savefig(fname2, dpi=150) print(f"Saved {fname2}") plt.close(fig2) + arc_values = np.array(results["constant_arc_length"]["value"]) + n_eff = min(len(geo_values), len(arc_values)) + t_eff = np.arange(n_eff) / (60 * 24) + efficiency_pct = ( + (arc_values[:n_eff] - geo_values[:n_eff]) + / np.maximum(np.abs(geo_values[:n_eff]), 1e-12) + * 100.0 + ) + + fig3, ax3 = plt.subplots(1, 1, figsize=(10, 4.5)) + fig3.suptitle(f"{cfg['name']} — efficiency — TVL {tvl_label}", fontsize=13, fontweight="bold") + ax3.plot( + t_eff, + efficiency_pct, + color="C2", + linewidth=1.8, + label="(Const Arc - Geo) / Geo", + ) + ax3.axhline(0.0, color="gray", ls="--", alpha=0.6) + _set_padded_ylim(ax3, [efficiency_pct], pad_ratio=0.08) + ax3.set_xlabel("Days") + ax3.set_ylabel("Efficiency vs geometric (%)") + ax3.set_title("Efficiency") + ax3.legend(fontsize=8) + ax3.grid(True, alpha=0.3) + + plt.tight_layout() + fname3 = tvl_artifact_filename("reclamm_thermostat_efficiency", cfg, suffix=str(fig_idx)) + plt.savefig(fname3, dpi=150) + print(f"Saved {fname3}") + plt.close(fig3) + + if __name__ == "__main__": - all_results = [] - for i, cfg in enumerate(CONFIGS): - print(f"\n>>> Running {cfg['name']}...") - try: - results = run_comparison(cfg) - print_comparison(cfg, results) - plot_comparison(cfg, results, i) - all_results.append((cfg, results)) - except Exception as e: - print(f" FAILED: {e}") - import traceback - traceback.print_exc() - - # Summary overlay: all configs on one figure (pool value normalised) - if len(all_results) > 1: - fig, axes = plt.subplots(1, 2, figsize=(16, 5)) - fig.suptitle("Cross-config comparison (normalised)", fontsize=13, - fontweight="bold") - - method_keys = [ - ("geometric", "geo", "-"), - ("geometric_scaled", "geo+s", "-."), - ("constant_arc_length", "arc", "--"), - ("cal_scaled", "arc+s", ":"), - ] + shared_price_data = load_shared_price_data(CONFIGS) + + for initial_pool_value in TVL_SWEEP_VALUES: + tvl_configs = configs_for_tvl(CONFIGS, initial_pool_value) + tvl_label = format_tvl_millions_label(tvl_configs[0]) + print(f"\n=== TVL sweep: {tvl_label} ===") + + all_results = [] + for i, cfg in enumerate(tvl_configs): + print(f"\n>>> Running {cfg['name']} at TVL {tvl_label}...") + try: + results = run_comparison(cfg, price_data=shared_price_data) + print_comparison(cfg, results) + plot_comparison(cfg, results, i) + all_results.append((cfg, results)) + except Exception as e: + print(f" FAILED: {e}") + import traceback - for i, (cfg, results) in enumerate(all_results): - geo_v = np.array(results["geometric"]["value"]) - t = np.arange(len(geo_v)) / (60 * 24) - short_name = cfg["name"].split("(")[0].strip() - - for j, (key, suffix, ls) in enumerate(method_keys): - v = np.array(results[key]["value"]) - color_idx = i * len(method_keys) + j - - # (left) Normalised pool value - axes[0].plot(t, v / v[0], ls=ls, alpha=0.8, - label=f"{short_name} {suffix}", - color=f"C{color_idx % 10}") - - # (right) Value difference vs geometric (skip geo itself) - if key != "geometric": - pct_diff = (v - geo_v) / geo_v * 100 - axes[1].plot(t, pct_diff, ls=ls, alpha=0.8, - label=f"{short_name} {suffix}", - color=f"C{color_idx % 10}") - - axes[0].set_xlabel("Days") - axes[0].set_ylabel("Normalised pool value") - axes[0].set_title("Pool value (V/V0)") - axes[0].legend(fontsize=6, ncol=2) - axes[0].grid(True, alpha=0.3) - - axes[1].set_xlabel("Days") - axes[1].set_ylabel("(Method - Geo) / Geo (%)") - axes[1].set_title("Relative value difference vs Geometric") - axes[1].axhline(0, color="gray", ls="--", alpha=0.5) - axes[1].legend(fontsize=6, ncol=2) - axes[1].grid(True, alpha=0.3) - - plt.tight_layout() - plt.savefig("reclamm_thermostat_summary.png", dpi=150) - print("\nSaved reclamm_thermostat_summary.png") - plt.close(fig) + traceback.print_exc() + + if len(all_results) > 1: + if RUN_CONSTANT_ARC_LENGTH: + fig, axes = plt.subplots(1, 2, figsize=(16, 5)) + fig.suptitle( + f"Cross-config comparison (normalised) — TVL {tvl_label}", + fontsize=13, + fontweight="bold", + ) + + method_keys = [ + ("geometric", "geo", "-"), + ("constant_arc_length", "arc", "--"), + ] + + for i, (cfg, results) in enumerate(all_results): + geo_v = np.array(results["geometric"]["value"]) + t = np.arange(len(geo_v)) / (60 * 24) + short_name = cfg["name"].split("(")[0].strip() + + for j, (key, suffix, ls) in enumerate(method_keys): + v = np.array(results[key]["value"]) + color_idx = i * len(method_keys) + j + + axes[0].plot( + t, + v / v[0], + ls=ls, + alpha=0.8, + label=f"{short_name} {suffix}", + color=f"C{color_idx % 10}", + ) + + if key != "geometric": + pct_diff = (v - geo_v) / geo_v * 100 + axes[1].plot( + t, + pct_diff, + ls=ls, + alpha=0.8, + label=f"{short_name} {suffix}", + color=f"C{color_idx % 10}", + ) + + axes[0].set_xlabel("Days") + axes[0].set_ylabel("Normalised pool value") + axes[0].set_title("Pool value (V/V0)") + axes[0].legend(fontsize=6, ncol=2) + axes[0].grid(True, alpha=0.3) + + axes[1].set_xlabel("Days") + axes[1].set_ylabel("Efficiency vs geometric (%)") + axes[1].set_title("Efficiency vs Geometric") + axes[1].axhline(0, color="gray", ls="--", alpha=0.5) + axes[1].legend(fontsize=6, ncol=2) + axes[1].grid(True, alpha=0.3) + else: + fig, ax = plt.subplots(1, 1, figsize=(9, 5)) + fig.suptitle( + f"Cross-config comparison (normalised geometric) — TVL {tvl_label}", + fontsize=13, + fontweight="bold", + ) + + for i, (cfg, results) in enumerate(all_results): + geo_v = np.array(results["geometric"]["value"]) + t = np.arange(len(geo_v)) / (60 * 24) + short_name = cfg["name"].split("(")[0].strip() + ax.plot( + t, + geo_v / geo_v[0], + ls="-", + alpha=0.8, + label=f"{short_name} geo", + color=f"C{i % 10}", + ) + + ax.set_xlabel("Days") + ax.set_ylabel("Normalised pool value") + ax.set_title("Geometric pool value (V/V0)") + ax.legend(fontsize=6, ncol=2) + ax.grid(True, alpha=0.3) + + plt.tight_layout() + summary_name = tvl_artifact_filename( + "reclamm_thermostat_summary", + tvl_configs[0], + ) + plt.savefig(summary_name, dpi=150) + print(f"\nSaved {summary_name}") + plt.close(fig) + + launch_final_values = get_launch_final_values( + all_results, + launch_cfg=tvl_configs[0], + price_data=shared_price_data, + ) + shared_sweep_cache = make_sweep_cache(shared_price_data) + + print(f"\nGenerating thermostat heatmaps for TVL {tvl_label}...") + generate_heatmaps( + dict(tvl_configs[1]), + shared_price_data, + launch_final_values=launch_final_values, + cache=shared_sweep_cache, + ) + + generate_arc_speed_efficiency_artifacts( + dict(tvl_configs[1]), + launch_cfg=dict(tvl_configs[0]), + price_data=shared_price_data, + launch_final_values=launch_final_values, + cache=shared_sweep_cache, + ) + shared_sweep_cache.clear() + gc.collect() + print(f"Released shared sweep cache for TVL {tvl_label}.") diff --git a/scripts/reclamm/demo_run_reclamm.py b/scripts/reclamm/demo_run_reclamm.py index 3ea21ec..132f512 100644 --- a/scripts/reclamm/demo_run_reclamm.py +++ b/scripts/reclamm/demo_run_reclamm.py @@ -36,105 +36,127 @@ def balancer_fingerprint(tokens, start, end, fees): } +def reclamm_fingerprint(tokens, start, end, fees, interpolation_method="geometric"): + """Build a reCLAMM fingerprint for a demo scenario.""" + return { + "tokens": tokens, + "rule": "reclamm", + "startDateString": start, + "endDateString": end, + "initial_pool_value": 1000000.0, + "do_arb": True, + "fees": fees, + "gas_cost": 0.0, + "arb_fees": 0.0, + "chunk_period": 60, + "weight_interpolation_period": 60, + "reclamm_interpolation_method": interpolation_method, + "reclamm_arc_length_speed": None, + } + + +def reclamm_params(price_ratio, centeredness_margin, daily_price_shift_exponent): + """Build reCLAMM params from a concise config.""" + return { + "price_ratio": jnp.array(price_ratio), + "centeredness_margin": jnp.array(centeredness_margin), + "daily_price_shift_base": jnp.array( + to_daily_price_shift_base(daily_price_shift_exponent) + ), + } + + +def _apply_active_noise_settings(fp): + """Enable the active AAVE/ETH reCLAMM noise model for demo runs.""" + if fp.get("rule") != "reclamm" or list(fp.get("tokens", [])) != ["AAVE", "ETH"]: + return fp, "disabled" + + from compare_reclamm_thermostats import ( + AAVE_ETH_NOISE_SETTINGS, + resolve_reclamm_noise_settings, + ) + + cfg = { + "tokens": fp["tokens"], + "start": fp["startDateString"], + "end": fp["endDateString"], + "enable_noise_model": True, + "noise_model": AAVE_ETH_NOISE_SETTINGS["noise_model"], + "noise_artifact_dir": AAVE_ETH_NOISE_SETTINGS["noise_artifact_dir"], + "noise_pool_id": AAVE_ETH_NOISE_SETTINGS["noise_pool_id"], + "gas_cost": fp.get("gas_cost", AAVE_ETH_NOISE_SETTINGS["gas_cost"]), + "protocol_fee_split": fp.get( + "protocol_fee_split", + AAVE_ETH_NOISE_SETTINGS["protocol_fee_split"], + ), + "arb_frequency": fp.get("arb_frequency"), + "noise_trader_ratio": fp.get("noise_trader_ratio", 0.0), + "reclamm_noise_params": fp.get("reclamm_noise_params"), + "noise_arrays_path": fp.get("noise_arrays_path"), + } + noise_cfg = resolve_reclamm_noise_settings(cfg) + + updated = dict(fp) + updated["gas_cost"] = cfg["gas_cost"] + updated["protocol_fee_split"] = cfg["protocol_fee_split"] + updated["noise_trader_ratio"] = noise_cfg.get("noise_trader_ratio", 0.0) + for key in ("noise_model", "reclamm_noise_params", "noise_arrays_path", "arb_frequency"): + if noise_cfg.get(key) is not None: + updated[key] = noise_cfg[key] + return updated, noise_cfg["noise_summary"] + + SCENARIOS = [ { - "name": "AAVE/ETH on-chain (25bps)", + "name": "AAVE/ETH launch-style range (25bps, geometric)", "reclamm": { - "fingerprint": { - "tokens": ["AAVE", "ETH"], - "rule": "reclamm", - "startDateString": "2024-06-01 00:00:00", - "endDateString": "2025-06-01 00:00:00", - "initial_pool_value": 1000000.0, - "do_arb": True, - "fees": 0.0025, - "gas_cost": 0.0, - "arb_fees": 0.0, - "chunk_period": 60, - "weight_interpolation_period": 60, - }, - "params": { - "price_ratio": jnp.array(1.5), - "centeredness_margin": jnp.array(0.5), - "daily_price_shift_base": jnp.array( - to_daily_price_shift_base(0.1) - ), - }, + "fingerprint": reclamm_fingerprint( + ["AAVE", "ETH"], + "2024-06-01 00:00:00", + "2025-06-01 00:00:00", + 0.0025, + interpolation_method="geometric", + ), + "params": reclamm_params(1.5014, 0.5, 0.1), }, }, { - "name": "AAVE/ETH zero fees", + "name": "AAVE/ETH tighter launch-style range (25bps, geometric)", "reclamm": { - "fingerprint": { - "tokens": ["AAVE", "ETH"], - "rule": "reclamm", - "startDateString": "2024-06-01 00:00:00", - "endDateString": "2025-06-01 00:00:00", - "initial_pool_value": 1000000.0, - "do_arb": True, - "fees": 0.0, - "gas_cost": 0.0, - "arb_fees": 0.0, - "chunk_period": 60, - "weight_interpolation_period": 60, - }, - "params": { - "price_ratio": jnp.array(1.5), - "centeredness_margin": jnp.array(0.5), - "daily_price_shift_base": jnp.array( - to_daily_price_shift_base(0.1) - ), - }, + "fingerprint": reclamm_fingerprint( + ["AAVE", "ETH"], + "2024-06-01 00:00:00", + "2025-06-01 00:00:00", + 0.0025, + interpolation_method="geometric", + ), + "params": reclamm_params(1.15, 0.5, 0.1), }, }, { - "name": "AAVE/ETH wide range (25bps)", + "name": "AAVE/ETH tighter launch-style range (25bps, constant arc)", "reclamm": { - "fingerprint": { - "tokens": ["AAVE", "ETH"], - "rule": "reclamm", - "startDateString": "2024-06-01 00:00:00", - "endDateString": "2025-06-01 00:00:00", - "initial_pool_value": 1000000.0, - "do_arb": True, - "fees": 0.0025, - "gas_cost": 0.0, - "arb_fees": 0.0, - "chunk_period": 60, - "weight_interpolation_period": 60, - }, - "params": { - "price_ratio": jnp.array(4.0), - "centeredness_margin": jnp.array(0.2), - "daily_price_shift_base": jnp.array( - to_daily_price_shift_base(1.0) - ), - }, + "fingerprint": reclamm_fingerprint( + ["AAVE", "ETH"], + "2024-06-01 00:00:00", + "2025-06-01 00:00:00", + 0.0025, + interpolation_method="constant_arc_length", + ), + "params": reclamm_params(1.15, 0.5, 0.1), }, }, { "name": "BTC/ETH (10bps)", "reclamm": { - "fingerprint": { - "tokens": ["BTC", "ETH"], - "rule": "reclamm", - "startDateString": "2024-01-01 00:00:00", - "endDateString": "2025-06-01 00:00:00", - "initial_pool_value": 1000000.0, - "do_arb": True, - "fees": 0.001, - "gas_cost": 0.0, - "arb_fees": 0.0, - "chunk_period": 60, - "weight_interpolation_period": 60, - }, - "params": { - "price_ratio": jnp.array(2.0), - "centeredness_margin": jnp.array(0.3), - "daily_price_shift_base": jnp.array( - to_daily_price_shift_base(0.5) - ), - }, + "fingerprint": reclamm_fingerprint( + ["BTC", "ETH"], + "2024-01-01 00:00:00", + "2025-06-01 00:00:00", + 0.001, + interpolation_method="geometric", + ), + "params": reclamm_params(2.0, 0.3, 0.5), }, }, ] @@ -143,7 +165,7 @@ def balancer_fingerprint(tokens, start, end, fees): def run_scenario(scenario): """Run a reClAMM config and its Balancer 50/50 baseline, print comparison.""" rc = scenario["reclamm"] - fp = rc["fingerprint"] + fp, noise_summary = _apply_active_noise_settings(dict(rc["fingerprint"])) # Run reClAMM reclamm_result = do_run_on_historic_data( @@ -173,7 +195,14 @@ def run_scenario(scenario): print("=" * 80) print(f" {scenario['name']}") - print(f" Tokens: {', '.join(fp['tokens'])} | Fees: {fp['fees']}") + print( + f" Tokens: {', '.join(fp['tokens'])} | Fees: {fp['fees']} | " + f"Interpolation: {fp.get('reclamm_interpolation_method', 'geometric')}" + ) + print( + f" Noise: {noise_summary} | Gas: {fp.get('gas_cost', 0.0)} | " + f"Protocol fee split: {fp.get('protocol_fee_split', 0.0)}" + ) print("-" * 80) print(f" {'':30s} {'reClAMM':>14s} {'Balancer 50/50':>14s}") print(f" {'Initial value':30s} ${rc_init:>13,.0f} ${bal_init:>13,.0f}") diff --git a/tests/scripts/test_compare_reclamm_thermostats.py b/tests/scripts/test_compare_reclamm_thermostats.py new file mode 100644 index 0000000..7072f73 --- /dev/null +++ b/tests/scripts/test_compare_reclamm_thermostats.py @@ -0,0 +1,284 @@ +"""Tests for heatmap skip logic in compare_reclamm_thermostats.py.""" + +import importlib.util +from pathlib import Path +import sys +import types + +import numpy as np +import pytest + + +SCRIPT_PATH = ( + Path(__file__).resolve().parents[2] + / "scripts" + / "compare_reclamm_thermostats.py" +) + + +def _load_script_module(): + injected_modules = {} + + def inject_module(name, module): + injected_modules[name] = sys.modules.get(name) + sys.modules[name] = module + + # Minimal stubs so the script can be imported without the full runtime + # stack present in this test environment. + jax_module = types.ModuleType("jax") + jax_module.numpy = np + inject_module("jax", jax_module) + inject_module("jax.numpy", np) + + pandas_module = types.ModuleType("pandas") + pandas_module.Timestamp = lambda value: value + pandas_module.DatetimeIndex = tuple + pandas_module.DataFrame = type("DataFrame", (), {}) + inject_module("pandas", pandas_module) + + matplotlib_module = types.ModuleType("matplotlib") + pyplot_module = types.ModuleType("matplotlib.pyplot") + pyplot_module.cm = types.SimpleNamespace(viridis=lambda values: values) + colors_module = types.ModuleType("matplotlib.colors") + colors_module.TwoSlopeNorm = object + colors_module.Normalize = object + cm_module = types.ModuleType("matplotlib.cm") + cm_module.ScalarMappable = object + inject_module("matplotlib", matplotlib_module) + inject_module("matplotlib.pyplot", pyplot_module) + inject_module("matplotlib.colors", colors_module) + inject_module("matplotlib.cm", cm_module) + + quantammsim_module = types.ModuleType("quantammsim") + runners_module = types.ModuleType("quantammsim.runners") + jax_runners_module = types.ModuleType("quantammsim.runners.jax_runners") + jax_runners_module.do_run_on_historic_data = lambda **kwargs: { + "final_value": 0.0 + } + runners_module.jax_runners = jax_runners_module + + pools_module = types.ModuleType("quantammsim.pools") + reclamm_pkg_module = types.ModuleType("quantammsim.pools.reCLAMM") + reserves_module = types.ModuleType( + "quantammsim.pools.reCLAMM.reclamm_reserves" + ) + reserves_module.calibrate_arc_length_speed = lambda *args, **kwargs: 0.0 + reserves_module.compute_price_ratio = lambda *args, **kwargs: 1.0 + reserves_module.initialise_reclamm_reserves = ( + lambda *args, **kwargs: (np.array([1.0, 1.0]), 1.0, 1.0) + ) + reclamm_pkg_module.reclamm_reserves = reserves_module + pools_module.reCLAMM = reclamm_pkg_module + + utils_module = types.ModuleType("quantammsim.utils") + data_processing_module = types.ModuleType("quantammsim.utils.data_processing") + historic_utils_module = types.ModuleType( + "quantammsim.utils.data_processing.historic_data_utils" + ) + historic_utils_module.get_historic_parquet_data = lambda *args, **kwargs: None + data_processing_module.historic_data_utils = historic_utils_module + utils_module.data_processing = data_processing_module + + quantammsim_module.runners = runners_module + quantammsim_module.pools = pools_module + quantammsim_module.utils = utils_module + + inject_module("quantammsim", quantammsim_module) + inject_module("quantammsim.runners", runners_module) + inject_module("quantammsim.runners.jax_runners", jax_runners_module) + inject_module("quantammsim.pools", pools_module) + inject_module("quantammsim.pools.reCLAMM", reclamm_pkg_module) + inject_module("quantammsim.pools.reCLAMM.reclamm_reserves", reserves_module) + inject_module("quantammsim.utils", utils_module) + inject_module("quantammsim.utils.data_processing", data_processing_module) + inject_module( + "quantammsim.utils.data_processing.historic_data_utils", + historic_utils_module, + ) + + spec = importlib.util.spec_from_file_location( + "test_compare_reclamm_thermostats_module", + SCRIPT_PATH, + ) + module = importlib.util.module_from_spec(spec) + assert spec.loader is not None + try: + spec.loader.exec_module(module) + return module + finally: + for name, original in injected_modules.items(): + if original is None: + sys.modules.pop(name, None) + else: + sys.modules[name] = original + + +@pytest.fixture +def script_module(): + return _load_script_module() + + +@pytest.fixture +def base_cfg(): + return { + "tokens": ["AAVE", "ETH"], + "start": "2024-06-01 00:00:00", + "price_ratio": 1.10, + "centeredness_margin": 0.60, + "daily_price_shift_exponent": 0.1, + } + + +@pytest.fixture +def launch_final_values(): + return { + "geometric": 1_000_000.0, + "constant_arc_length": 1_010_000.0, + } + + +def test_generate_heatmaps_skips_existing_pairs( + monkeypatch, + script_module, + base_cfg, + launch_final_values, +): + monkeypatch.setattr(script_module.os.path, "exists", lambda filename: True) + monkeypatch.setattr( + script_module, + "build_heatmap_matrices", + lambda **kwargs: pytest.fail("heatmap sweep should have been skipped"), + ) + monkeypatch.setattr( + script_module, + "plot_heatmap", + lambda **kwargs: pytest.fail("plotting should have been skipped"), + ) + + script_module.generate_heatmaps( + base_cfg, + price_data=None, + launch_final_values=launch_final_values, + cache={}, + ) + + +def test_generate_heatmaps_only_renders_missing_artifacts( + monkeypatch, + script_module, + base_cfg, + launch_final_values, +): + missing_file = "reclamm_heatmap_efficiency_price_ratio_vs_margin.png" + + def fake_exists(filename): + if filename == missing_file: + return False + return filename.startswith("reclamm_heatmap_") + + build_calls = [] + plotted_files = [] + + def fake_build_heatmap_matrices(**kwargs): + build_calls.append(kwargs) + return { + "efficiency_pct": np.zeros( + (len(kwargs["y_values"]), len(kwargs["x_values"])), + dtype=float, + ) + } + + def fake_plot_heatmap(**kwargs): + plotted_files.append(kwargs["filename"]) + + monkeypatch.setattr(script_module.os.path, "exists", fake_exists) + monkeypatch.setattr( + script_module, + "build_heatmap_matrices", + fake_build_heatmap_matrices, + ) + monkeypatch.setattr(script_module, "plot_heatmap", fake_plot_heatmap) + + script_module.generate_heatmaps( + base_cfg, + price_data=None, + launch_final_values=launch_final_values, + cache={}, + ) + + assert len(build_calls) == 1 + assert build_calls[0]["progress_label"] == "price_ratio_vs_margin" + assert build_calls[0]["metric_keys"] == ["efficiency_pct"] + assert plotted_files == [missing_file] + + +def test_arc_speed_artifacts_only_build_missing_line_output( + monkeypatch, + script_module, + base_cfg, + launch_final_values, +): + missing_line = "reclamm_line_efficiency_arc_speed_vs_price_ratio.png" + + def fake_exists(filename): + if filename == missing_line: + return False + return filename.startswith("reclamm_") + + build_calls = [] + curve_calls = [] + plotted_lines = [] + + def fake_build_heatmap_matrices(**kwargs): + build_calls.append(kwargs) + return { + "efficiency_pct": np.zeros( + (len(kwargs["y_values"]), len(kwargs["x_values"])), + dtype=float, + ) + } + + def fake_build_metric_curve(**kwargs): + curve_calls.append(kwargs) + return np.zeros(len(kwargs["x_values"]), dtype=float) + + monkeypatch.setattr(script_module.os.path, "exists", fake_exists) + monkeypatch.setattr( + script_module, + "compute_auto_calibrated_arc_length_speed", + lambda cfg, price_data: 1.23e-4, + ) + monkeypatch.setattr( + script_module, + "build_heatmap_matrices", + fake_build_heatmap_matrices, + ) + monkeypatch.setattr( + script_module, + "build_metric_curve", + fake_build_metric_curve, + ) + monkeypatch.setattr( + script_module, + "plot_heatmap", + lambda **kwargs: pytest.fail("existing heatmap should not be redrawn"), + ) + monkeypatch.setattr( + script_module, + "plot_arc_speed_line_chart", + lambda **kwargs: plotted_lines.append(kwargs["filename"]), + ) + + script_module.generate_arc_speed_efficiency_artifacts( + base_cfg=base_cfg, + launch_cfg=dict(base_cfg), + price_data=None, + launch_final_values=launch_final_values, + cache={}, + ) + + assert len(build_calls) == 1 + assert build_calls[0]["progress_label"] == "arc_speed_vs_price_ratio" + assert len(curve_calls) == 1 + assert curve_calls[0]["x_key"] == "arc_length_speed" + assert plotted_lines == [missing_line] From 29af7df58f75b40cc20dcafea5da3ba25a6bfa2c Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Sun, 29 Mar 2026 16:33:48 +0100 Subject: [PATCH 42/57] data: add sim arrays for 0x9d1fcf346ea1b0 --- ...0x9d1fcf346ea1b0_2024-06-01_2026-03-01.npz | Bin 0 -> 14723606 bytes ...0x9d1fcf346ea1b0_2025-08-03_2026-02-18.npz | Bin 0 -> 4609046 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 results/linear_market_noise/_sim_arrays/0x9d1fcf346ea1b0_2024-06-01_2026-03-01.npz create mode 100644 results/linear_market_noise/_sim_arrays/0x9d1fcf346ea1b0_2025-08-03_2026-02-18.npz diff --git a/results/linear_market_noise/_sim_arrays/0x9d1fcf346ea1b0_2024-06-01_2026-03-01.npz 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zaDW3G-~b0WzyS_$fCC)h00%h00S<70103K02ROh14sd`29N+*4IKTl8aDW3G-~b0W zzyS_$fCC)h00%h00S<70103K02ROh14sd`29N+*4IKTl8aDW3G-~b0WzyS_$fCC)h z00%h00S<70103K02ROh14sd`29N+*4IKTl8aDW3G-~b0WzyS_$fCC)h00%h00S<70 z103K02ROh14sd`29N+*4IKTl8aDW3G;K1KF(CaQPwd3YV<#zV%L0J9}uTs64wX^Bz zQU5qpqpi`&t2|ph?@w>0=c#<$d-kZepJ%7p>v9n4gGsqtl=p`_ZlH}zkXSrgm}Mkce8j?{8PCRf1zkSzxS}2#oM!Y+m~6aTx3yC2mf;tpY~fZ ziF~2Gc(oX_pNHRP8+Wo)Z=Fx~{4~o`I~pI>q5q<)L(1!V?E1>r&zG@ljo2@7w0Ue= z+=Fo#4*xVjb0=JlFw5UR?lktr^-niv|M$r5lX>LDTJ*Zht@L;)9zSBc`m__zzW^&1 B@I3$k literal 0 HcmV?d00001 From 8331b7f0ec284da37e61773926abcffef96bd554 Mon Sep 17 00:00:00 2001 From: christian harrington Date: Sun, 29 Mar 2026 16:39:18 +0100 Subject: [PATCH 43/57] diagnostics --- .codex | 0 .../compare_reclamm_geometric_noise_runs.py | 13 + scripts/compare_reclamm_thermostats.py | 1075 ++++++++++---- .../compare_reclamm_geometric_noise_runs.py | 674 +++++++++ .../reclamm/compare_reclamm_thermostats.py | 1075 ++++++++++---- .../reclamm/find_adjacent_heatmap_pairs.py | 1264 +++++++++++++++++ ...st_compare_reclamm_geometric_noise_runs.py | 143 ++ .../test_compare_reclamm_thermostats.py | 404 +++++- .../test_find_adjacent_heatmap_pairs.py | 313 ++++ 9 files changed, 4430 insertions(+), 531 deletions(-) create mode 100644 .codex create mode 100644 scripts/compare_reclamm_geometric_noise_runs.py create mode 100644 scripts/reclamm/compare_reclamm_geometric_noise_runs.py create mode 100644 scripts/reclamm/find_adjacent_heatmap_pairs.py create mode 100644 tests/scripts/test_compare_reclamm_geometric_noise_runs.py create mode 100644 tests/scripts/test_find_adjacent_heatmap_pairs.py diff --git a/.codex b/.codex new file mode 100644 index 0000000..e69de29 diff --git a/scripts/compare_reclamm_geometric_noise_runs.py b/scripts/compare_reclamm_geometric_noise_runs.py new file mode 100644 index 0000000..96230ed --- /dev/null +++ b/scripts/compare_reclamm_geometric_noise_runs.py @@ -0,0 +1,13 @@ +"""Wrapper for the canonical reCLAMM geometric noise comparison script.""" + +from __future__ import annotations + +import runpy +from pathlib import Path + + +if __name__ == "__main__": + runpy.run_path( + str(Path(__file__).with_name("reclamm") / "compare_reclamm_geometric_noise_runs.py"), + run_name="__main__", + ) diff --git a/scripts/compare_reclamm_thermostats.py b/scripts/compare_reclamm_thermostats.py index be8ee5f..21352a4 100644 --- a/scripts/compare_reclamm_thermostats.py +++ b/scripts/compare_reclamm_thermostats.py @@ -16,6 +16,7 @@ """ import gc +import hashlib import math import os @@ -41,17 +42,25 @@ def to_daily_price_shift_base(daily_price_shift_exponent): return 1.0 - daily_price_shift_exponent / 124649.0 -RUN_CONSTANT_ARC_LENGTH = False +def build_inclusive_sweep(start, stop, step): + """Build a sweep that keeps the requested step and explicitly includes the stop.""" + values = np.arange(start, stop + 1.0e-12, step, dtype=float) + if values.size == 0 or not np.isclose(values[-1], stop): + values = np.append(values, float(stop)) + return values + + +RUN_CONSTANT_ARC_LENGTH = True INTERPOLATION_METHODS = ( ("geometric", "constant_arc_length") if RUN_CONSTANT_ARC_LENGTH else ("geometric",) ) -HEATMAP_PRICE_RATIOS = np.arange(1.01, 1.50 + 1e-9, 0.025) -HEATMAP_MARGINS = np.linspace(0.05, 0.90, 20) -HEATMAP_SHIFT_EXPONENTS = np.arange(0.01, 0.50 + 1e-9, 0.025) +HEATMAP_PRICE_RATIOS = build_inclusive_sweep(1.01, 3.00, 0.025) +HEATMAP_MARGINS = np.linspace(0.05, 0.90, 39) +HEATMAP_SHIFT_EXPONENTS = build_inclusive_sweep(0.01, 0.50, 0.0125) HEATMAP_ARC_LENGTH_SPEEDS = np.geomspace(1.0e-6, 5.0e-4, 11) -PRICE_RATIO_TICKS = np.array([1.01, 1.10, 1.20, 1.30, 1.40, 1.50]) +PRICE_RATIO_TICKS = np.array([1.01, 1.25, 1.50, 2.00, 2.50, 3.00]) MARGIN_TICKS = np.array([0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.90]) SHIFT_EXPONENT_TICKS = np.array([0.01, 0.05, 0.10, 0.20, 0.30, 0.40, 0.50]) ARC_LENGTH_SPEED_TICKS = np.array([ @@ -76,6 +85,17 @@ def to_daily_price_shift_base(daily_price_shift_exponent): CENTER_ZERO_HEATMAP_COLOR_NORM = "symlog" CENTER_ZERO_HEATMAP_COLOR_TAG = "symlog20" CENTER_ZERO_HEATMAP_SYMLOG_LINTHRESH = 20.0 +FIXED_SLICE_FRACTIONS = (0.125, 0.375, 0.625, 0.875) +FIXED_SLICE_LABELS = ("Q1", "Q2", "Q3", "Q4") +THREE_D_VIEW_ELEVATION = 22.0 +THREE_D_VIEW_AZIMUTH = 140.0 +HEATMAP_FORWARD_CACHE_ENABLED = True +HEATMAP_FORWARD_CACHE_RUN_NAME = "aave_eth_thermostat_heatmaps_v1" +HEATMAP_FORWARD_CACHE_ROOT = os.path.join( + "results", + "reclamm_heatmap_forward_cache", +) +HEATMAP_FORWARD_CACHE_FLUSH_EVERY = 32 AAVE_WETH_POOL_ID = "0x9d1fcf346ea1b0" DEFAULT_MARKET_LINEAR_ARTIFACT_DIR = "results/linear_market_noise" @@ -94,14 +114,28 @@ def to_daily_price_shift_base(daily_price_shift_exponent): ] LEGACY_LOG_CADENCE = 2.68 LEGACY_ARB_FREQUENCY = max(1, round(math.exp(LEGACY_LOG_CADENCE))) +FIXED_COMPARE_ARB_FREQUENCY = LEGACY_ARB_FREQUENCY AAVE_ETH_NOISE_SETTINGS = { "enable_noise_model": True, "noise_model": DEFAULT_NOISE_MODEL, "noise_artifact_dir": DEFAULT_MARKET_LINEAR_ARTIFACT_DIR, "noise_pool_id": AAVE_WETH_POOL_ID, + "arb_frequency": FIXED_COMPARE_ARB_FREQUENCY, "gas_cost": DEFAULT_GAS_COST, "protocol_fee_split": DEFAULT_PROTOCOL_FEE_SPLIT, } +PERSISTED_FORWARD_VALUE_COLUMNS = ( + "cache_key_hash", + "final_value", + "method", + "enable_noise_model", + "noise_model", + "price_ratio", + "centeredness_margin", + "daily_price_shift_exponent", + "initial_pool_value", + "arb_frequency", +) GEOMETRIC_ONLY_HEATMAP_METRIC_KEYS = ( "geometric_vs_launch_geometric_pct", @@ -175,6 +209,23 @@ def heatmap_artifact_filename(spec, cfg, suffix=None): return tvl_artifact_filename(stem, cfg, suffix=suffix) +def three_d_heatmap_artifact_filename(spec, cfg, suffix=None): + """Build a 3D heatmap filename, including any colour-style tag.""" + stem = f"reclamm_heatmap_3d_{spec['slug']}" + artifact_tag = spec.get("artifact_tag") + if artifact_tag: + stem = f"{stem}_{artifact_tag}" + return tvl_artifact_filename(stem, cfg, suffix=suffix) + + +def format_heatmap_param_value(value): + """Format a sweep parameter compactly for titles and logs.""" + value = float(value) + if abs(value) >= 1.0: + return f"{value:.2f}".rstrip("0").rstrip(".") + return f"{value:.3f}".rstrip("0").rstrip(".") + + def configs_for_tvl(base_configs, initial_pool_value): """Attach a shared initial TVL to each compare configuration.""" configs = [] @@ -185,35 +236,76 @@ def configs_for_tvl(base_configs, initial_pool_value): return configs -def make_noise_variant_cfg(cfg, enable_noise_model): - """Return a config with either noise modelling or pure arb-only enabled.""" +def _normalize_arb_frequency(value, default=FIXED_COMPARE_ARB_FREQUENCY): + """Return a stable integer arb cadence for thermostat comparisons.""" + if value is None: + if default is None: + return None + value = default + return max(int(round(float(value))), 1) + + +def get_effective_arb_frequency(cfg, noise_cfg=None): + """Resolve the arb cadence used by a thermostat comparison run.""" + del noise_cfg + return _normalize_arb_frequency(FIXED_COMPARE_ARB_FREQUENCY) + + +def normalize_compare_run_cfg(cfg, enable_noise_model=None): + """Canonicalize the compare-run config so non-axis inputs stay fixed.""" updated = dict(cfg) - if enable_noise_model: - updated["enable_noise_model"] = True - return updated + updated["price_ratio"] = float(cfg["price_ratio"]) + updated["centeredness_margin"] = float(cfg["centeredness_margin"]) + updated["daily_price_shift_exponent"] = float(cfg["daily_price_shift_exponent"]) + updated["initial_pool_value"] = float(get_initial_pool_value(cfg)) + updated["gas_cost"] = DEFAULT_GAS_COST + updated["protocol_fee_split"] = DEFAULT_PROTOCOL_FEE_SPLIT + updated["arb_fees"] = 0.0 + updated["arb_frequency"] = get_effective_arb_frequency(cfg) + updated["noise_trader_ratio"] = 0.0 - matched_noise = resolve_reclamm_noise_settings(cfg) + arc_length_speed = cfg.get("arc_length_speed") + if arc_length_speed is None: + updated.pop("arc_length_speed", None) + else: + updated["arc_length_speed"] = float(arc_length_speed) - updated["enable_noise_model"] = False - updated["noise_model"] = None - updated["gas_cost"] = cfg.get("gas_cost", DEFAULT_GAS_COST) - updated["protocol_fee_split"] = cfg.get( - "protocol_fee_split", DEFAULT_PROTOCOL_FEE_SPLIT + use_noise = ( + bool(cfg.get("enable_noise_model", False)) + if enable_noise_model is None + else bool(enable_noise_model) ) - updated["noise_trader_ratio"] = 0.0 - matched_arb_frequency = matched_noise.get("arb_frequency") - if matched_arb_frequency is not None: - updated["arb_frequency"] = matched_arb_frequency - for key in ( - "reclamm_noise_params", - "noise_arrays_path", - "noise_artifact_dir", - "noise_pool_id", - ): - updated.pop(key, None) + updated["enable_noise_model"] = use_noise + + requested_mode = cfg.get("noise_model", DEFAULT_NOISE_MODEL) or DEFAULT_NOISE_MODEL + if use_noise: + updated["noise_model"] = requested_mode + if requested_mode == "market_linear": + updated["noise_artifact_dir"] = DEFAULT_MARKET_LINEAR_ARTIFACT_DIR + updated["noise_pool_id"] = AAVE_WETH_POOL_ID + else: + updated.pop("noise_artifact_dir", None) + updated.pop("noise_pool_id", None) + updated.pop("reclamm_noise_params", None) + updated.pop("noise_arrays_path", None) + else: + updated["noise_model"] = None + for key in ( + "reclamm_noise_params", + "noise_arrays_path", + "noise_artifact_dir", + "noise_pool_id", + ): + updated.pop(key, None) + return updated +def make_noise_variant_cfg(cfg, enable_noise_model): + """Return a config with either noise modelling or pure arb-only enabled.""" + return normalize_compare_run_cfg(cfg, enable_noise_model=enable_noise_model) + + def _warn_noise_fallback(message): """Print a one-time message when the preferred noise setup is unavailable.""" if message not in _WARNED_NOISE_FALLBACKS: @@ -228,36 +320,39 @@ def _hashable_noise_params(params): return tuple(sorted((str(k), round(float(v), 12)) for k, v in params.items())) -def _legacy_calibrated_noise_settings(reason=None): +def _legacy_calibrated_noise_settings(reason=None, arb_frequency=None): """Fallback calibrated noise config used when market-linear artifacts are absent.""" if reason: _warn_noise_fallback( "market_linear noise unavailable for thermostat comparison; " f"falling back to calibrated legacy coefficients ({reason})." ) + arb_frequency = _normalize_arb_frequency(arb_frequency) return { "noise_model": "calibrated", "noise_trader_ratio": 0.0, "reclamm_noise_params": { f"c_{i}": LEGACY_NOISE_COEFFS[i] for i in range(len(LEGACY_NOISE_COEFFS)) }, - "arb_frequency": LEGACY_ARB_FREQUENCY, + "arb_frequency": arb_frequency, "noise_summary": ( "calibrated legacy 8-covariate " - f"(arb_frequency={LEGACY_ARB_FREQUENCY})" + f"(arb_frequency={arb_frequency})" ), "noise_cache_key": ( "calibrated", tuple(round(float(c), 12) for c in LEGACY_NOISE_COEFFS), - LEGACY_ARB_FREQUENCY, + arb_frequency, ), } def resolve_reclamm_noise_settings(cfg): """Resolve the active reCLAMM noise-model fingerprint block for a config.""" + cfg = normalize_compare_run_cfg(cfg) enable_noise_model = cfg.get("enable_noise_model", False) requested_mode = cfg.get("noise_model", DEFAULT_NOISE_MODEL) + requested_arb_frequency = get_effective_arb_frequency(cfg) cache_key = ( tuple(cfg.get("tokens", [])), cfg.get("start"), @@ -266,7 +361,7 @@ def resolve_reclamm_noise_settings(cfg): requested_mode, cfg.get("noise_artifact_dir", DEFAULT_MARKET_LINEAR_ARTIFACT_DIR), cfg.get("noise_pool_id", AAVE_WETH_POOL_ID), - cfg.get("arb_frequency"), + requested_arb_frequency, round(float(cfg.get("noise_trader_ratio", 0.0)), 12), _hashable_noise_params(cfg.get("reclamm_noise_params")), cfg.get("noise_arrays_path"), @@ -280,7 +375,7 @@ def resolve_reclamm_noise_settings(cfg): "noise_trader_ratio": 0.0, "reclamm_noise_params": None, "noise_arrays_path": None, - "arb_frequency": None, + "arb_frequency": requested_arb_frequency, "noise_summary": "arb-only (noise disabled)", "noise_cache_key": ("disabled",), } @@ -290,11 +385,7 @@ def resolve_reclamm_noise_settings(cfg): start_date = str(cfg["start"]).split(" ")[0] end_date = str(cfg["end"]).split(" ")[0] try: - from quantammsim.calibration.noise_model_arrays import ( - _find_pool_index, - build_simulator_arrays, - load_artifact, - ) + from quantammsim.calibration.noise_model_arrays import build_simulator_arrays model_path = os.path.join(artifact_dir, "model.npz") meta_path = os.path.join(artifact_dir, "meta.json") @@ -311,6 +402,8 @@ def resolve_reclamm_noise_settings(cfg): ) if not os.path.exists(arrays_path): arrays = build_simulator_arrays( + token_a=cfg["tokens"][0], + token_b=cfg["tokens"][1], pool_id=pool_id, start_date=start_date, end_date=end_date, @@ -328,13 +421,7 @@ def resolve_reclamm_noise_settings(cfg): tvl_mean = float(arrays["tvl_mean"]) tvl_std = float(arrays["tvl_std"]) - art, meta = load_artifact(artifact_dir) - pool_idx = _find_pool_index(pool_id, meta["pool_ids"]) - if pool_idx >= 0: - learned_cadence = float(np.exp(art["log_cadence"][pool_idx])) - else: - learned_cadence = 5.0 - arb_frequency = max(1, round(learned_cadence)) + arb_frequency = requested_arb_frequency result = { "noise_model": "market_linear", "noise_trader_ratio": 0.0, @@ -351,30 +438,19 @@ def resolve_reclamm_noise_settings(cfg): arb_frequency, round(tvl_mean, 12), round(tvl_std, 12), - ), - } + ), + } except Exception as exc: # pragma: no cover - fallback path depends on local artifacts - result = _legacy_calibrated_noise_settings(str(exc)) + result = _legacy_calibrated_noise_settings( + str(exc), + arb_frequency=requested_arb_frequency, + ) elif requested_mode == "calibrated": - params = cfg.get("reclamm_noise_params") - if params is None: - result = _legacy_calibrated_noise_settings() - else: - arb_frequency = cfg.get("arb_frequency", LEGACY_ARB_FREQUENCY) - result = { - "noise_model": "calibrated", - "noise_trader_ratio": cfg.get("noise_trader_ratio", 0.0), - "reclamm_noise_params": dict(params), - "arb_frequency": arb_frequency, - "noise_summary": f"calibrated (arb_frequency={arb_frequency})", - "noise_cache_key": ( - "calibrated", - _hashable_noise_params(params), - arb_frequency, - ), - } + result = _legacy_calibrated_noise_settings( + arb_frequency=requested_arb_frequency + ) else: - arb_frequency = cfg.get("arb_frequency") + arb_frequency = requested_arb_frequency result = { "noise_model": requested_mode, "noise_trader_ratio": cfg.get("noise_trader_ratio", 0.0), @@ -430,13 +506,14 @@ def resolve_reclamm_noise_settings(cfg): def make_fingerprint(cfg, interpolation_method): """Build run fingerprint for a given config and interpolation method.""" + cfg = normalize_compare_run_cfg(cfg) speed_override = ( cfg.get("arc_length_speed") if interpolation_method == "constant_arc_length" else None ) noise_cfg = resolve_reclamm_noise_settings(cfg) - arb_frequency = cfg.get("arb_frequency", noise_cfg.get("arb_frequency")) + arb_frequency = get_effective_arb_frequency(cfg, noise_cfg) fingerprint = { "tokens": cfg["tokens"], "rule": "reclamm", @@ -471,6 +548,7 @@ def make_fingerprint(cfg, interpolation_method): def make_params(cfg): """Build pool params from config.""" + cfg = normalize_compare_run_cfg(cfg) return { "price_ratio": jnp.array(cfg["price_ratio"]), "centeredness_margin": jnp.array(cfg["centeredness_margin"]), @@ -528,7 +606,7 @@ def _set_padded_ylim(ax, series_list, pad_ratio=0.04): def _cache_size(cache): - """Count memoized final-value runs.""" + """Count memoized final-value cache entries materialised in memory.""" return len(cache.get("_final_value_cache", {})) @@ -537,13 +615,140 @@ def _comparison_cache_size(cache): return len(cache.get("_comparison_cache", {})) -def make_sweep_cache(price_data): - """Create a shared cache for heatmap and line sweeps.""" +def _heatmap_forward_cache_scope_slug(cfg): + """Build a compact cache scope slug for a shared-TVL heatmap run.""" + if cfg is None: + return "unspecified_tvl" + return f"tvl_{format_tvl_millions_slug(cfg)}" + + +def _heatmap_forward_cache_path(cfg): + """Return the parquet path for persisted scalar forward values.""" + if not HEATMAP_FORWARD_CACHE_ENABLED: + return None + return os.path.join( + HEATMAP_FORWARD_CACHE_ROOT, + HEATMAP_FORWARD_CACHE_RUN_NAME, + f"forward_values_{_heatmap_forward_cache_scope_slug(cfg)}.parquet", + ) + + +def _make_method_cache_hash(key): + """Build a compact stable digest for a method cache key.""" + return hashlib.sha256(repr(key).encode("utf-8")).hexdigest() + + +def _build_persistent_final_value_record(cfg, method, cache_key_hash, final_value): + """Build one self-describing parquet row for a cached scalar run result.""" + cfg = normalize_compare_run_cfg(cfg) + noise_cfg = resolve_reclamm_noise_settings(cfg) return { + "cache_key_hash": str(cache_key_hash), + "final_value": float(final_value), + "method": str(method), + "enable_noise_model": bool(cfg.get("enable_noise_model", False)), + "noise_model": noise_cfg.get("noise_model"), + "price_ratio": float(cfg["price_ratio"]), + "centeredness_margin": float(cfg["centeredness_margin"]), + "daily_price_shift_exponent": float(cfg["daily_price_shift_exponent"]), + "initial_pool_value": float(get_initial_pool_value(cfg)), + "arb_frequency": get_effective_arb_frequency(cfg, noise_cfg), + } + + +def _load_persistent_final_value_cache(cache): + """Load persisted scalar forward values from parquet once per sweep cache.""" + if cache.get("_persistent_final_value_cache_loaded"): + return + + disk_cache = {} + disk_records = {} + cache_path = cache.get("_persistent_final_value_cache_path") + if cache_path and os.path.exists(cache_path): + frame = pd.read_parquet(cache_path) + if not frame.empty: + for row in frame.itertuples(index=False): + cache_key_hash = str(row.cache_key_hash) + final_value = float(row.final_value) + disk_cache[cache_key_hash] = final_value + record = { + "cache_key_hash": cache_key_hash, + "final_value": final_value, + } + for column in PERSISTED_FORWARD_VALUE_COLUMNS: + if column in {"cache_key_hash", "final_value"}: + continue + record[column] = getattr(row, column, None) + disk_records[cache_key_hash] = record + print( + f"Loaded {len(disk_cache)} persisted heatmap forward values from {cache_path}" + ) + + cache["_persistent_final_value_cache"] = disk_cache + cache["_persistent_final_value_records"] = disk_records + cache["_persistent_final_value_cache_loaded"] = True + + +def flush_sweep_cache(cache, force=False): + """Persist newly computed scalar forward values to parquet.""" + if not HEATMAP_FORWARD_CACHE_ENABLED: + return + + pending = cache.get("_pending_persistent_final_values") + if not pending: + return + if not force and len(pending) < HEATMAP_FORWARD_CACHE_FLUSH_EVERY: + return + + _load_persistent_final_value_cache(cache) + disk_cache = cache.setdefault("_persistent_final_value_cache", {}) + disk_records = cache.setdefault("_persistent_final_value_records", {}) + for cache_key_hash, record in pending.items(): + merged = dict(disk_records.get(cache_key_hash, {})) + merged.update(record) + merged["cache_key_hash"] = str(cache_key_hash) + merged["final_value"] = float(merged["final_value"]) + disk_records[cache_key_hash] = merged + disk_cache[cache_key_hash] = merged["final_value"] + + cache_path = cache.get("_persistent_final_value_cache_path") + if cache_path is None: + pending.clear() + return + + os.makedirs(os.path.dirname(cache_path), exist_ok=True) + sorted_records = [disk_records[key] for key in sorted(disk_records)] + payload = { + column: [record.get(column) for record in sorted_records] + for column in PERSISTED_FORWARD_VALUE_COLUMNS + } + payload["final_value"] = np.asarray(payload["final_value"], dtype=np.float64) + frame = pd.DataFrame(payload) + frame.sort_values("cache_key_hash", inplace=True, ignore_index=True) + frame.to_parquet(cache_path, index=False, compression="zstd") + print( + f"Persisted {len(pending)} new heatmap forward values to {cache_path} " + f"({len(disk_cache)} total cached values)." + ) + pending.clear() + + +def make_sweep_cache(price_data, cache_scope_cfg=None): + """Create a shared cache for heatmap and line sweeps.""" + cache = { "_shared_price_data": price_data, "_final_value_cache": {}, "_comparison_cache": {}, + "_pending_persistent_final_values": {}, + "_persistent_final_value_cache": {}, + "_persistent_final_value_records": {}, + "_persistent_final_value_cache_loaded": False, + "_persistent_final_value_cache_path": _heatmap_forward_cache_path( + cache_scope_cfg + ), } + _load_persistent_final_value_cache(cache) + return cache def _missing_artifacts(progress_label, filenames): @@ -571,8 +776,9 @@ def _speed_cache_key(speed): def _make_method_cache_key(cfg, method): """Cache key for a single-method final-value run.""" + cfg = normalize_compare_run_cfg(cfg) noise_cfg = resolve_reclamm_noise_settings(cfg) - arb_frequency = cfg.get("arb_frequency", noise_cfg.get("arb_frequency")) + arb_frequency = get_effective_arb_frequency(cfg, noise_cfg) key = ( method, bool(cfg.get("enable_noise_model", False)), @@ -630,16 +836,34 @@ def _run_method_final_value_cached(cfg, method, cache): """Memoize final value for a single interpolation method.""" final_value_cache = cache.setdefault("_final_value_cache", {}) key = _make_method_cache_key(cfg, method) - if key not in final_value_cache: - result = do_run_on_historic_data( - run_fingerprint=make_fingerprint(cfg, method), - params=make_params(cfg), - price_data=cache["_shared_price_data"], - low_data_mode=True, + if key in final_value_cache: + return final_value_cache[key] + + _load_persistent_final_value_cache(cache) + key_hash = _make_method_cache_hash(key) + persisted_cache = cache.setdefault("_persistent_final_value_cache", {}) + if key_hash in persisted_cache: + final_value_cache[key] = persisted_cache[key_hash] + return final_value_cache[key] + + result = do_run_on_historic_data( + run_fingerprint=make_fingerprint(cfg, method), + params=make_params(cfg), + price_data=cache["_shared_price_data"], + low_data_mode=True, + ) + final_value_cache[key] = float(result["final_value"]) + cache.setdefault("_pending_persistent_final_values", {})[key_hash] = ( + _build_persistent_final_value_record( + cfg=cfg, + method=method, + cache_key_hash=key_hash, + final_value=final_value_cache[key], ) - final_value_cache[key] = float(result["final_value"]) - del result - gc.collect() + ) + flush_sweep_cache(cache, force=False) + del result + gc.collect() return final_value_cache[key] @@ -860,15 +1084,16 @@ def build_heatmap_matrices( data[metric_key][yi, xi] = metrics[metric_key] completed_points = (yi + 1) * len(x_values) - row_new_final_runs = _cache_size(cache) - final_cache_before_row + row_new_final_entries = _cache_size(cache) - final_cache_before_row row_new_comparisons = ( _comparison_cache_size(cache) - comparison_cache_before_row ) row_pct = completed_points / total_points * 100.0 + flush_sweep_cache(cache, force=True) print( f"[{progress_label}] row {yi + 1}/{len(y_values)} complete " f"({y_key}={float(y_value):.4f}, {completed_points}/{total_points} " - f"points, {row_pct:.1f}%, {row_new_final_runs} new final-value runs, " + f"points, {row_pct:.1f}%, {row_new_final_entries} new final-value cache entries, " f"{row_new_comparisons} new comparison bundles)" ) @@ -910,6 +1135,7 @@ def build_metric_curve( metric_keys=(metric_key,), ) data[xi] = metrics[metric_key] + flush_sweep_cache(cache, force=True) return data @@ -937,6 +1163,226 @@ def _compute_axis_edges(values, scale="linear"): return edges +def build_fixed_slice_variants(values): + """Pick four representative quarter-range slices from a sweep grid.""" + values = np.asarray(values, dtype=float) + if values.size < len(FIXED_SLICE_FRACTIONS): + raise ValueError("Need at least four grid points to build fixed slices") + + variants = [] + used_indices = set() + for idx, fraction in enumerate(FIXED_SLICE_FRACTIONS): + target_index = int(round(fraction * (values.size - 1))) + while target_index in used_indices and target_index + 1 < values.size: + target_index += 1 + while target_index in used_indices and target_index - 1 >= 0: + target_index -= 1 + if target_index in used_indices: + raise ValueError("Could not build four unique fixed slices from sweep grid") + used_indices.add(target_index) + variants.append( + { + "index": target_index, + "fraction": fraction, + "label": FIXED_SLICE_LABELS[idx], + "slug": f"q{idx + 1}", + "value": float(values[target_index]), + } + ) + return variants + + +def _pair_slice_suffix(pair, slice_variant): + """Build a stable artifact suffix for a pairwise fixed-variable slice.""" + return f"{pair['slug']}_{pair['fixed_slug']}_{slice_variant['slug']}" + + +def _build_heatmap_norm( + data_arrays, + center_zero, + color_norm=None, + symlog_linthresh=None, +): + """Build a color normalizer shared by 2D and 3D heatmaps.""" + finite_parts = [] + for data in data_arrays: + finite = np.asarray(data, dtype=float) + finite = finite[np.isfinite(finite)] + if finite.size: + finite_parts.append(finite) + finite = np.concatenate(finite_parts) if finite_parts else np.array([], dtype=float) + + if center_zero: + if finite.size == 0: + vmax = 1.0 + else: + vmax = max(abs(float(finite.min())), abs(float(finite.max())), 1e-9) + if ( + color_norm == "symlog" + and symlog_linthresh is not None + and vmax > symlog_linthresh + ): + return SymLogNorm( + linthresh=symlog_linthresh, + linscale=1.0, + vmin=-vmax, + vmax=vmax, + base=10.0, + ) + return TwoSlopeNorm(vcenter=0.0, vmin=-vmax, vmax=vmax) + + if finite.size == 0: + vmin, vmax = 0.0, 1.0 + else: + vmin = float(finite.min()) + vmax = float(finite.max()) + if np.isclose(vmin, vmax): + pad = max(abs(vmin) * 0.01, 1e-9) + vmin -= pad + vmax += pad + return Normalize(vmin=vmin, vmax=vmax) + + +def get_pair_heatmap_metric_specs(): + """Return the standard thermostat pairwise heatmap metrics.""" + metric_specs = [ + { + "key": "efficiency_pct", + "title": "Efficiency vs heatmap geometric", + "colorbar_label": "Const Arc - heatmap Geo (% of heatmap geometric final value)", + "slug": "efficiency", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "launch_geometric_efficiency_pct", + "title": "Efficiency vs launch-style geometric", + "colorbar_label": "Const Arc - launch Geo (% of launch geometric final value)", + "slug": "launch_geometric_efficiency", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "geometric_vs_launch_geometric_pct", + "title": "Geometric tuning vs launch-style geometric", + "colorbar_label": "Candidate Geo - launch Geo (% of launch geometric final value)", + "slug": "geometric_vs_launch_geometric", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "constant_arc_vs_launch_constant_arc_pct", + "title": "Const arc tuning vs launch-style const arc", + "colorbar_label": "Candidate Const Arc - launch Const Arc (% of launch const arc final value)", + "slug": "constant_arc_vs_launch_constant_arc", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "noise_geometric_final_value_musd", + "title": "Geometric final value with noise model", + "colorbar_label": "Geometric final value with noise model ($M)", + "slug": "noise_geometric_final_value", + "center_zero": False, + "cmap": "viridis", + }, + { + "key": "noise_constant_arc_final_value_musd", + "title": "Const arc final value with noise model", + "colorbar_label": "Const Arc final value with noise model ($M)", + "slug": "noise_constant_arc_final_value", + "center_zero": False, + "cmap": "viridis", + }, + { + "key": "noise_vs_arb_geometric_improvement_pct", + "title": "Noise-model improvement over arb-only (geometric)", + "colorbar_label": "Noise-model Geo - arb-only Geo (% of arb-only final value)", + "slug": "noise_vs_arb_geometric_improvement", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "noise_vs_arb_constant_arc_improvement_pct", + "title": "Noise-model improvement over arb-only (const arc)", + "colorbar_label": "Noise-model Const Arc - arb-only Const Arc (% of arb-only final value)", + "slug": "noise_vs_arb_constant_arc_improvement", + "center_zero": True, + "cmap": "RdYlGn", + }, + ] + for spec in metric_specs: + if spec["center_zero"]: + spec["color_norm"] = CENTER_ZERO_HEATMAP_COLOR_NORM + spec["symlog_linthresh"] = CENTER_ZERO_HEATMAP_SYMLOG_LINTHRESH + spec["artifact_tag"] = CENTER_ZERO_HEATMAP_COLOR_TAG + if not RUN_CONSTANT_ARC_LENGTH: + metric_specs = [ + spec + for spec in metric_specs + if spec["key"] in GEOMETRIC_ONLY_HEATMAP_METRIC_KEYS + ] + return metric_specs + + +def get_pair_heatmap_specs(base_cfg): + """Return the three pairwise thermostat heatmap families plus slice settings.""" + fixed_slice_variants = { + "price_ratio": build_fixed_slice_variants(HEATMAP_PRICE_RATIOS), + "centeredness_margin": build_fixed_slice_variants(HEATMAP_MARGINS), + "daily_price_shift_exponent": build_fixed_slice_variants( + HEATMAP_SHIFT_EXPONENTS + ), + } + return [ + { + "slug": "price_ratio_vs_margin", + "x_values": HEATMAP_PRICE_RATIOS, + "y_values": HEATMAP_MARGINS, + "x_key": "price_ratio", + "y_key": "centeredness_margin", + "x_label": "Price ratio", + "y_label": "Centeredness margin", + "xticks": PRICE_RATIO_TICKS, + "yticks": MARGIN_TICKS, + "fixed_key": "daily_price_shift_exponent", + "fixed_label": "Shift exponent", + "fixed_slug": "shift_exp", + "fixed_slices": fixed_slice_variants["daily_price_shift_exponent"], + }, + { + "slug": "shift_exp_vs_margin", + "x_values": HEATMAP_SHIFT_EXPONENTS, + "y_values": HEATMAP_MARGINS, + "x_key": "daily_price_shift_exponent", + "y_key": "centeredness_margin", + "x_label": "Shift exponent", + "y_label": "Centeredness margin", + "xticks": SHIFT_EXPONENT_TICKS, + "yticks": MARGIN_TICKS, + "fixed_key": "price_ratio", + "fixed_label": "Price ratio", + "fixed_slug": "price_ratio", + "fixed_slices": fixed_slice_variants["price_ratio"], + }, + { + "slug": "price_ratio_vs_shift_exp", + "x_values": HEATMAP_PRICE_RATIOS, + "y_values": HEATMAP_SHIFT_EXPONENTS, + "x_key": "price_ratio", + "y_key": "daily_price_shift_exponent", + "x_label": "Price ratio", + "y_label": "Shift exponent", + "xticks": PRICE_RATIO_TICKS, + "yticks": SHIFT_EXPONENT_TICKS, + "fixed_key": "centeredness_margin", + "fixed_label": "Centeredness margin", + "fixed_slug": "margin", + "fixed_slices": fixed_slice_variants["centeredness_margin"], + }, + ] + + def plot_heatmap( data, x_values, @@ -955,34 +1401,13 @@ def plot_heatmap( symlog_linthresh=None, ): """Render and save a single heatmap.""" - finite = np.asarray(data, dtype=float) - finite = finite[np.isfinite(finite)] - - if center_zero: - vmax = max(abs(float(np.nanmin(data))), abs(float(np.nanmax(data))), 1e-9) - if color_norm == "symlog" and symlog_linthresh is not None and vmax > symlog_linthresh: - norm = SymLogNorm( - linthresh=symlog_linthresh, - linscale=1.0, - vmin=-vmax, - vmax=vmax, - base=10.0, - ) - else: - norm = TwoSlopeNorm(vcenter=0.0, vmin=-vmax, vmax=vmax) - cmap_name = cmap or "RdYlGn" - else: - if finite.size == 0: - vmin, vmax = 0.0, 1.0 - else: - vmin = float(finite.min()) - vmax = float(finite.max()) - if np.isclose(vmin, vmax): - pad = max(abs(vmin) * 0.01, 1e-9) - vmin -= pad - vmax += pad - norm = Normalize(vmin=vmin, vmax=vmax) - cmap_name = cmap or "viridis" + norm = _build_heatmap_norm( + [data], + center_zero=center_zero, + color_norm=color_norm, + symlog_linthresh=symlog_linthresh, + ) + cmap_name = cmap or ("RdYlGn" if center_zero else "viridis") x_edges = _compute_axis_edges(x_values, scale=xscale) y_edges = _compute_axis_edges(y_values, scale="linear") @@ -1015,6 +1440,112 @@ def plot_heatmap( plt.close(fig) +def plot_three_variable_heatmap_3d( + price_margin_data, + shift_margin_data, + price_shift_data, + fixed_price_ratio, + fixed_margin, + fixed_shift_exponent, + title, + colorbar_label, + filename, + center_zero=True, + cmap=None, + color_norm=None, + symlog_linthresh=None, +): + """Render orthogonal 3D heatmap surfaces across the three thermostat variables.""" + norm = _build_heatmap_norm( + [price_margin_data, shift_margin_data, price_shift_data], + center_zero=center_zero, + color_norm=color_norm, + symlog_linthresh=symlog_linthresh, + ) + cmap_name = cmap or ("RdYlGn" if center_zero else "viridis") + cmap_obj = plt.get_cmap(cmap_name) + + price_margin_x, price_margin_y = np.meshgrid(HEATMAP_PRICE_RATIOS, HEATMAP_MARGINS) + price_margin_z = np.full_like(price_margin_x, fixed_shift_exponent, dtype=float) + + shift_margin_z, shift_margin_y = np.meshgrid( + HEATMAP_SHIFT_EXPONENTS, + HEATMAP_MARGINS, + ) + shift_margin_x = np.full_like(shift_margin_z, fixed_price_ratio, dtype=float) + + price_shift_x, price_shift_z = np.meshgrid( + HEATMAP_PRICE_RATIOS, + HEATMAP_SHIFT_EXPONENTS, + ) + price_shift_y = np.full_like(price_shift_x, fixed_margin, dtype=float) + + fig = plt.figure(figsize=(10.5, 7.2)) + ax = fig.add_subplot(111, projection="3d") + ax.set_facecolor("white") + fig.patch.set_facecolor("white") + + ax.plot_surface( + price_margin_x, + price_margin_y, + price_margin_z, + facecolors=cmap_obj(norm(np.asarray(price_margin_data, dtype=float))), + shade=False, + ) + ax.plot_surface( + shift_margin_x, + shift_margin_y, + shift_margin_z, + facecolors=cmap_obj(norm(np.asarray(shift_margin_data, dtype=float))), + shade=False, + ) + ax.plot_surface( + price_shift_x, + price_shift_y, + price_shift_z, + facecolors=cmap_obj(norm(np.asarray(price_shift_data, dtype=float))), + shade=False, + ) + + ax.set_xlim(float(HEATMAP_PRICE_RATIOS.min()), float(HEATMAP_PRICE_RATIOS.max())) + ax.set_ylim(float(HEATMAP_MARGINS.min()), float(HEATMAP_MARGINS.max())) + ax.set_zlim( + float(HEATMAP_SHIFT_EXPONENTS.min()), + float(HEATMAP_SHIFT_EXPONENTS.max()), + ) + ax.set_xlabel("Price ratio") + ax.set_ylabel("Centeredness margin") + ax.set_zlabel("Shift exponent") + ax.set_xticks(PRICE_RATIO_TICKS) + ax.set_yticks(MARGIN_TICKS[::2]) + ax.set_zticks(SHIFT_EXPONENT_TICKS) + ax.set_title(title) + ax.grid(False) + ax.view_init(elev=THREE_D_VIEW_ELEVATION, azim=THREE_D_VIEW_AZIMUTH) + try: + ax.set_box_aspect( + ( + float(HEATMAP_PRICE_RATIOS.max() - HEATMAP_PRICE_RATIOS.min()), + float(HEATMAP_MARGINS.max() - HEATMAP_MARGINS.min()), + float( + HEATMAP_SHIFT_EXPONENTS.max() - HEATMAP_SHIFT_EXPONENTS.min() + ), + ) + ) + except AttributeError: + pass + + sm = ScalarMappable(norm=norm, cmap=cmap_obj) + sm.set_array([]) + cbar = fig.colorbar(sm, ax=ax, fraction=0.03, pad=0.1, shrink=0.82) + cbar.set_label(colorbar_label) + + plt.tight_layout() + plt.savefig(filename, dpi=150) + print(f"Saved {filename}") + plt.close(fig) + + def plot_arc_speed_line_chart( data, x_values, @@ -1090,128 +1621,11 @@ def generate_heatmaps(base_cfg, price_data, launch_final_values, cache=None): """Generate pairwise heatmaps for thermostat tuning and noise-vs-arb effects.""" owns_cache = cache is None if cache is None: - cache = make_sweep_cache(price_data) - metric_specs = [ - { - "key": "efficiency_pct", - "title": "Efficiency vs heatmap geometric", - "colorbar_label": "Const Arc - heatmap Geo (% of heatmap geometric final value)", - "slug": "efficiency", - "center_zero": True, - "cmap": "RdYlGn", - }, - { - "key": "launch_geometric_efficiency_pct", - "title": "Efficiency vs launch-style geometric", - "colorbar_label": "Const Arc - launch Geo (% of launch geometric final value)", - "slug": "launch_geometric_efficiency", - "center_zero": True, - "cmap": "RdYlGn", - }, - { - "key": "geometric_vs_launch_geometric_pct", - "title": "Geometric tuning vs launch-style geometric", - "colorbar_label": "Candidate Geo - launch Geo (% of launch geometric final value)", - "slug": "geometric_vs_launch_geometric", - "center_zero": True, - "cmap": "RdYlGn", - }, - { - "key": "constant_arc_vs_launch_constant_arc_pct", - "title": "Const arc tuning vs launch-style const arc", - "colorbar_label": "Candidate Const Arc - launch Const Arc (% of launch const arc final value)", - "slug": "constant_arc_vs_launch_constant_arc", - "center_zero": True, - "cmap": "RdYlGn", - }, - { - "key": "noise_geometric_final_value_musd", - "title": "Geometric final value with noise model", - "colorbar_label": "Geometric final value with noise model ($M)", - "slug": "noise_geometric_final_value", - "center_zero": False, - "cmap": "viridis", - }, - { - "key": "noise_constant_arc_final_value_musd", - "title": "Const arc final value with noise model", - "colorbar_label": "Const Arc final value with noise model ($M)", - "slug": "noise_constant_arc_final_value", - "center_zero": False, - "cmap": "viridis", - }, - { - "key": "noise_vs_arb_geometric_improvement_pct", - "title": "Noise-model improvement over arb-only (geometric)", - "colorbar_label": "Noise-model Geo - arb-only Geo (% of arb-only final value)", - "slug": "noise_vs_arb_geometric_improvement", - "center_zero": True, - "cmap": "RdYlGn", - }, - { - "key": "noise_vs_arb_constant_arc_improvement_pct", - "title": "Noise-model improvement over arb-only (const arc)", - "colorbar_label": "Noise-model Const Arc - arb-only Const Arc (% of arb-only final value)", - "slug": "noise_vs_arb_constant_arc_improvement", - "center_zero": True, - "cmap": "RdYlGn", - }, - ] - for spec in metric_specs: - if spec["center_zero"]: - spec["color_norm"] = CENTER_ZERO_HEATMAP_COLOR_NORM - spec["symlog_linthresh"] = CENTER_ZERO_HEATMAP_SYMLOG_LINTHRESH - spec["artifact_tag"] = CENTER_ZERO_HEATMAP_COLOR_TAG - if not RUN_CONSTANT_ARC_LENGTH: - metric_specs = [ - spec - for spec in metric_specs - if spec["key"] in GEOMETRIC_ONLY_HEATMAP_METRIC_KEYS - ] - pair_specs = [ - { - "slug": "price_ratio_vs_margin", - "x_values": HEATMAP_PRICE_RATIOS, - "y_values": HEATMAP_MARGINS, - "x_key": "price_ratio", - "y_key": "centeredness_margin", - "x_label": "Price ratio", - "y_label": "Centeredness margin", - "title_suffix": ( - f"shift_exp fixed at {base_cfg['daily_price_shift_exponent']:.2f}" - ), - "xticks": PRICE_RATIO_TICKS, - "yticks": MARGIN_TICKS - }, - { - "slug": "shift_exp_vs_margin", - "x_values": HEATMAP_SHIFT_EXPONENTS, - "y_values": HEATMAP_MARGINS, - "x_key": "daily_price_shift_exponent", - "y_key": "centeredness_margin", - "x_label": "Shift exponent", - "y_label": "Centeredness margin", - "title_suffix": f"price_ratio fixed at {base_cfg['price_ratio']:.2f}", - "xticks": SHIFT_EXPONENT_TICKS, - "yticks": MARGIN_TICKS - }, - { - "slug": "price_ratio_vs_shift_exp", - "x_values": HEATMAP_PRICE_RATIOS, - "y_values": HEATMAP_SHIFT_EXPONENTS, - "x_key": "price_ratio", - "y_key": "daily_price_shift_exponent", - "x_label": "Price ratio", - "y_label": "Shift exponent", - "title_suffix": ( - f"margin fixed at {base_cfg['centeredness_margin']:.2f}" - ), - "xticks": PRICE_RATIO_TICKS, - "yticks": SHIFT_EXPONENT_TICKS, - }, - ] - + cache = make_sweep_cache(price_data, cache_scope_cfg=base_cfg) + metric_specs = get_pair_heatmap_metric_specs() + pair_specs = get_pair_heatmap_specs(base_cfg) metric_spec_map = {spec["key"]: spec for spec in metric_specs} + slice_count = len(pair_specs[0]["fixed_slices"]) if pair_specs else 0 if RUN_CONSTANT_ARC_LENGTH: print( @@ -1222,9 +1636,11 @@ def generate_heatmaps(base_cfg, price_data, launch_final_values, cache=None): ) print( "Running {count} heatmap pair sweeps sequentially " - "(current outputs use cached noise-model runs; improvement heatmaps " - "reuse those values and add cached arb-only runs).".format( - count=len(pair_specs) + "(3 pair grids x {slice_count} fixed-variable quarter slices; " + "cached noise-model runs are reused across the absolute, launch, " + "and arb-only comparison outputs).".format( + count=len(pair_specs) * slice_count, + slice_count=slice_count, ) ) else: @@ -1235,20 +1651,135 @@ def generate_heatmaps(base_cfg, price_data, launch_final_values, cache=None): ) print( "RUN_CONSTANT_ARC_LENGTH=False, so only geometric heatmaps will be generated " - "and only geometric/arb-only geometric runs will be scheduled." + f"across {len(pair_specs) * slice_count} fixed-variable pair sweeps." ) for pair in pair_specs: + for slice_variant in pair["fixed_slices"]: + pair_suffix = _pair_slice_suffix(pair, slice_variant) + slice_cfg = dict(base_cfg) + slice_cfg[pair["fixed_key"]] = float(slice_variant["value"]) + output_files = { + spec["key"]: heatmap_artifact_filename( + spec, + base_cfg, + suffix=pair_suffix, + ) + for spec in metric_specs + } + missing_files = _missing_artifacts( + pair_suffix, + list(output_files.values()), + ) + if not missing_files: + continue + + missing_metric_keys = [ + spec["key"] + for spec in metric_specs + if output_files[spec["key"]] in missing_files + ] + data_by_metric = build_heatmap_matrices( + x_values=pair["x_values"], + y_values=pair["y_values"], + x_key=pair["x_key"], + y_key=pair["y_key"], + base_cfg=slice_cfg, + metric_keys=missing_metric_keys, + cache=cache, + progress_label=pair_suffix, + launch_final_values=launch_final_values, + ) + print(f"[{pair_suffix}] plotting missing heatmaps...") + for metric_key in missing_metric_keys: + spec = metric_spec_map[metric_key] + plot_heatmap( + data=data_by_metric[metric_key], + x_values=pair["x_values"], + y_values=pair["y_values"], + x_label=pair["x_label"], + y_label=pair["y_label"], + title=( + f"{spec['title']}: {pair['fixed_label']} {slice_variant['label']} " + f"slice fixed at {format_heatmap_param_value(slice_variant['value'])} | " + f"TVL {format_tvl_millions_label(base_cfg)}" + ), + colorbar_label=spec["colorbar_label"], + filename=output_files[metric_key], + xticks=pair["xticks"], + yticks=pair["yticks"], + center_zero=spec["center_zero"], + cmap=spec["cmap"], + color_norm=spec.get("color_norm"), + symlog_linthresh=spec.get("symlog_linthresh"), + ) + del data_by_metric + gc.collect() + + if owns_cache: + flush_sweep_cache(cache, force=True) + cache.clear() + gc.collect() + print("Released heatmap metric cache.") + + +def generate_three_variable_3d_heatmaps( + base_cfg, + price_data, + launch_final_values, + cache=None, +): + """Render 3D thermostat heatmaps from the three pairwise quarter slices.""" + owns_cache = cache is None + if cache is None: + cache = make_sweep_cache(price_data, cache_scope_cfg=base_cfg) + + metric_specs = get_pair_heatmap_metric_specs() + metric_spec_map = {spec["key"]: spec for spec in metric_specs} + pair_specs = get_pair_heatmap_specs(base_cfg) + pair_by_fixed_key = {pair["fixed_key"]: pair for pair in pair_specs} + price_margin_pair = pair_by_fixed_key["daily_price_shift_exponent"] + shift_margin_pair = pair_by_fixed_key["price_ratio"] + price_shift_pair = pair_by_fixed_key["centeredness_margin"] + slice_count = len(price_margin_pair["fixed_slices"]) + + def build_pair_slice_data(pair, slice_variant, metric_keys): + pair_cfg = dict(base_cfg) + pair_cfg[pair["fixed_key"]] = float(slice_variant["value"]) + return build_heatmap_matrices( + x_values=pair["x_values"], + y_values=pair["y_values"], + x_key=pair["x_key"], + y_key=pair["y_key"], + base_cfg=pair_cfg, + metric_keys=metric_keys, + cache=cache, + progress_label=f"3d_{_pair_slice_suffix(pair, slice_variant)}", + launch_final_values=launch_final_values, + ) + + print( + "\nGenerating 3D thermostat heatmaps " + f"({slice_count} quarter-slice variants, TVL={format_tvl_millions_label(base_cfg)})..." + ) + + for slice_idx in range(slice_count): + shift_slice = price_margin_pair["fixed_slices"][slice_idx] + price_slice = shift_margin_pair["fixed_slices"][slice_idx] + margin_slice = price_shift_pair["fixed_slices"][slice_idx] + slice_slug = shift_slice["slug"] + slice_label = shift_slice["label"] + output_files = { - spec["key"]: heatmap_artifact_filename( + spec["key"]: three_d_heatmap_artifact_filename( spec, base_cfg, - suffix=pair["slug"], + suffix=f"slice_{slice_slug}", ) for spec in metric_specs } missing_files = _missing_artifacts( - pair["slug"], + f"3d_slice_{slice_slug}", list(output_files.values()), ) if not missing_files: @@ -1259,46 +1790,53 @@ def generate_heatmaps(base_cfg, price_data, launch_final_values, cache=None): for spec in metric_specs if output_files[spec["key"]] in missing_files ] - data_by_metric = build_heatmap_matrices( - x_values=pair["x_values"], - y_values=pair["y_values"], - x_key=pair["x_key"], - y_key=pair["y_key"], - base_cfg=base_cfg, - metric_keys=missing_metric_keys, - cache=cache, - progress_label=pair["slug"], - launch_final_values=launch_final_values, + price_margin_data = build_pair_slice_data( + price_margin_pair, + shift_slice, + missing_metric_keys, + ) + shift_margin_data = build_pair_slice_data( + shift_margin_pair, + price_slice, + missing_metric_keys, + ) + price_shift_data = build_pair_slice_data( + price_shift_pair, + margin_slice, + missing_metric_keys, ) - print(f"[{pair['slug']}] plotting missing heatmaps...") + for metric_key in missing_metric_keys: spec = metric_spec_map[metric_key] - plot_heatmap( - data=data_by_metric[metric_key], - x_values=pair["x_values"], - y_values=pair["y_values"], - x_label=pair["x_label"], - y_label=pair["y_label"], + plot_three_variable_heatmap_3d( + price_margin_data=price_margin_data[metric_key], + shift_margin_data=shift_margin_data[metric_key], + price_shift_data=price_shift_data[metric_key], + fixed_price_ratio=float(price_slice["value"]), + fixed_margin=float(margin_slice["value"]), + fixed_shift_exponent=float(shift_slice["value"]), title=( - f"{spec['title']}: {pair['title_suffix']} | " - f"TVL {format_tvl_millions_label(base_cfg)}" + f"{spec['title']} 3D {slice_label} slice | TVL {format_tvl_millions_label(base_cfg)}\n" + f"price_ratio={format_heatmap_param_value(price_slice['value'])}, " + f"margin={format_heatmap_param_value(margin_slice['value'])}, " + f"shift_exp={format_heatmap_param_value(shift_slice['value'])}" ), colorbar_label=spec["colorbar_label"], filename=output_files[metric_key], - xticks=pair["xticks"], - yticks=pair["yticks"], center_zero=spec["center_zero"], cmap=spec["cmap"], color_norm=spec.get("color_norm"), symlog_linthresh=spec.get("symlog_linthresh"), ) - del data_by_metric + + del price_margin_data, shift_margin_data, price_shift_data gc.collect() if owns_cache: + flush_sweep_cache(cache, force=True) cache.clear() gc.collect() - print("Released heatmap metric cache.") + print("Released 3D heatmap cache.") def compute_auto_calibrated_arc_length_speed(cfg, price_data): @@ -1378,7 +1916,7 @@ def generate_arc_speed_efficiency_artifacts( return owns_cache = cache is None if cache is None: - cache = make_sweep_cache(price_data) + cache = make_sweep_cache(price_data, cache_scope_cfg=base_cfg) launch_auto_speed = compute_auto_calibrated_arc_length_speed(launch_cfg, price_data) heatmap_metric_specs = [ { @@ -1559,6 +2097,7 @@ def generate_arc_speed_efficiency_artifacts( gc.collect() if owns_cache: + flush_sweep_cache(cache, force=True) cache.clear() gc.collect() print("Released arc-speed sweep cache.") @@ -1965,7 +2504,10 @@ def plot_comparison(cfg, results, fig_idx): launch_cfg=tvl_configs[0], price_data=shared_price_data, ) - shared_sweep_cache = make_sweep_cache(shared_price_data) + shared_sweep_cache = make_sweep_cache( + shared_price_data, + cache_scope_cfg=tvl_configs[1], + ) print(f"\nGenerating thermostat heatmaps for TVL {tvl_label}...") generate_heatmaps( @@ -1982,6 +2524,13 @@ def plot_comparison(cfg, results, fig_idx): launch_final_values=launch_final_values, cache=shared_sweep_cache, ) + generate_three_variable_3d_heatmaps( + dict(tvl_configs[1]), + price_data=shared_price_data, + launch_final_values=launch_final_values, + cache=shared_sweep_cache, + ) + flush_sweep_cache(shared_sweep_cache, force=True) shared_sweep_cache.clear() gc.collect() print(f"Released shared sweep cache for TVL {tvl_label}.") diff --git a/scripts/reclamm/compare_reclamm_geometric_noise_runs.py b/scripts/reclamm/compare_reclamm_geometric_noise_runs.py new file mode 100644 index 0000000..3e385dd --- /dev/null +++ b/scripts/reclamm/compare_reclamm_geometric_noise_runs.py @@ -0,0 +1,674 @@ +"""Compare two geometric reCLAMM runs against matched arb-only baselines. + +This script reuses the same AAVE/ETH reCLAMM fingerprint and parameter wiring as +``compare_reclamm_thermostats.py``, but runs only the geometric interpolation +mode and plots: +1. Share price / TVL over time in absolute USD terms +2. Pool weights over time +3. Estimated gross swap volume over time +4. Noise-model improvement over arb-only over time + +Because these runs use no LP supply changes, share price and TVL are the same +series here. + +Usage: + python scripts/reclamm/compare_reclamm_geometric_noise_runs.py + python scripts/reclamm/compare_reclamm_geometric_noise_runs.py \ + --adjacent-csv scripts/results/...csv --adjacent-row-index 0 +""" + +from __future__ import annotations + +import argparse +import importlib.util +import json +from pathlib import Path +from typing import Mapping, Optional, Sequence + +import numpy as np +import pandas as pd + + +DEFAULT_SOURCE_HEATMAP_DESCRIPTION = ( + "legacy unsuffixed price_ratio_vs_margin heatmap with shift_exp fixed at 0.10" +) +DEFAULT_RUN_SPECS = [ + { + "name": "Green cell near price_ratio 1.31", + "price_ratio": 1.31, + "centeredness_margin": 0.6763157894736842, + "daily_price_shift_exponent": 0.10, + "tvl_usd": 1_000_000.0, + "color": "C0", + "reason": ( + "Geometric noise-model run taken from the positive cell in the " + "legacy price_ratio-vs-margin heatmap at price_ratio=1.31 and the " + "lower adjacent centeredness row." + ), + }, + { + "name": "Red cell near price_ratio 1.31", + "price_ratio": 1.31, + "centeredness_margin": 0.7210526315789474, + "daily_price_shift_exponent": 0.10, + "tvl_usd": 1_000_000.0, + "color": "C1", + "reason": ( + "Geometric noise-model run taken from the negative cell directly " + "above the green cell in the legacy price_ratio-vs-margin heatmap " + "at price_ratio=1.31." + ), + }, +] +DEFAULT_OUTPUT_FILE = "reclamm_geometric_noise_pair_compare.png" +VARIANT_STYLES = { + "noise": {"linestyle": "-", "alpha": 0.95, "linewidth": 2.2}, + "arb": {"linestyle": "--", "alpha": 0.85, "linewidth": 2.0}, +} + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=( + "Compare two geometric reCLAMM runs plus matched arb-only baselines. " + "Optionally source the run pair from an adjacent-heatmap CSV row." + ) + ) + parser.add_argument( + "--adjacent-csv", + default=None, + help="Optional adjacent-pairs CSV generated by find_adjacent_heatmap_pairs.py.", + ) + parser.add_argument( + "--adjacent-row-index", + type=int, + default=0, + help="Which row from --adjacent-csv to use. Defaults to the largest-diff row.", + ) + parser.add_argument( + "--output-file", + default=None, + help="Optional PNG output path override.", + ) + return parser.parse_args() + + +def load_runtime_dependencies(): + """Load heavy runtime dependencies only when an actual simulation is needed.""" + thermostat_path = Path(__file__).with_name("compare_reclamm_thermostats.py") + spec = importlib.util.spec_from_file_location( + "reclamm_compare_reclamm_thermostats_runtime", + thermostat_path, + ) + if spec is None or spec.loader is None: + raise RuntimeError(f"Could not load compare module from {thermostat_path}") + thermostat_compare = importlib.util.module_from_spec(spec) + try: + spec.loader.exec_module(thermostat_compare) + except ModuleNotFoundError as exc: # pragma: no cover - depends on local runtime deps + if exc.name == "jax": + raise RuntimeError( + "compare_reclamm_geometric_noise_runs.py requires JAX to execute " + "the simulator runs, but JAX is not available in this environment." + ) from exc + raise + + from quantammsim.runners.jax_runners import do_run_on_historic_data + + return thermostat_compare, do_run_on_historic_data + + +def build_default_base_config(thermostat_compare): + """Return the aggressive AAVE/ETH geometric base config used by the compare script.""" + return dict(thermostat_compare.CONFIGS[1]) + + +def load_adjacent_csv_row(csv_path: Path, row_index: int = 0) -> Mapping[str, object]: + """Load one row from an adjacent-pairs CSV.""" + frame = pd.read_csv(csv_path) + if frame.empty: + raise ValueError(f"Adjacent CSV is empty: {csv_path}") + if not 0 <= row_index < len(frame): + raise IndexError( + f"adjacent-row-index {row_index} is out of range for {len(frame)} rows" + ) + return frame.iloc[row_index].to_dict() + + +def build_run_specs_from_adjacent_row( + row: Mapping[str, object], + csv_path: Optional[Path] = None, + row_index: int = 0, +): + """Convert one adjacent-pairs CSV row into the two run specs this script compares.""" + metric_key = str(row.get("metric_key", "unknown_metric")) + metric_unit = str(row.get("metric_unit", "value")) + pair_slug = str(row.get("pair_slug", "unknown_pair")) + slice_slug = str(row.get("slice_slug", "unknown_slice")) + adjacency_axis = str(row.get("adjacency_axis", "unknown_axis")) + diff_abs = float(row["heatmap_value_diff_abs"]) + source_noise_profile = str(row.get("source_noise_profile", "unknown")) + + source_description = ( + f"{pair_slug} {slice_slug} adjacent pair from {metric_key} " + f"({adjacency_axis}, abs diff={diff_abs:.6f} {metric_unit}, " + f"noise_profile={source_noise_profile})" + ) + if csv_path is not None: + source_description = ( + f"{csv_path.name} row {row_index} | {source_description}" + ) + + run_specs = [] + for prefix, color in (("1", "C0"), ("2", "C1")): + heatmap_value = float(row[f"{prefix}_heatmap_value"]) + spec = { + "name": f"Top diff row cell {prefix}", + "price_ratio": float(row[f"{prefix}_price_ratio"]), + "centeredness_margin": float(row[f"{prefix}_centeredness_margin"]), + "daily_price_shift_exponent": float(row[f"{prefix}_daily_price_shift_exponent"]), + "tvl_usd": float(row[f"{prefix}_tvl_usd"]), + "color": color, + "source_noise_profile": source_noise_profile, + "reason": ( + f"Run derived from adjacent heatmap CSV row {row_index}, cell {prefix}. " + f"Source={pair_slug}/{slice_slug}, adjacency={adjacency_axis}, " + f"heatmap_value={heatmap_value:.6f} {metric_unit}, " + f"noise_profile={source_noise_profile}." + ), + } + run_specs.append(spec) + return source_description, run_specs + + +def default_output_file_for_adjacent_csv(csv_path: Path, row_index: int = 0) -> Path: + """Build a deterministic output PNG path for an adjacent-pairs CSV selection.""" + stem = f"{csv_path.stem}_row_{row_index}_geometric_noise_compare" + return csv_path.with_name(stem + ".png") + + +def build_run_config(spec, base_config): + """Build the base geometric reCLAMM config for one highlighted heatmap cell.""" + cfg = dict(base_config) + cfg.update( + { + "name": spec["name"], + "price_ratio": float(spec["price_ratio"]), + "centeredness_margin": float(spec["centeredness_margin"]), + "daily_price_shift_exponent": float(spec["daily_price_shift_exponent"]), + "initial_pool_value": float(spec["tvl_usd"]), + "reason": spec.get( + "reason", + "Standalone geometric noise-model comparison run.", + ), + "enable_noise_model": True, + } + ) + source_noise_profile = spec.get("source_noise_profile") + if source_noise_profile == "legacy_calibrated": + cfg["noise_model"] = "calibrated" + cfg.pop("reclamm_noise_params", None) + cfg.pop("noise_arrays_path", None) + elif source_noise_profile == "market_linear": + cfg["noise_model"] = "market_linear" + return cfg + + +def build_run_variants(spec, base_config, thermostat_compare): + """Build matched noise-model and arb-only variants for one highlighted cell.""" + noise_cfg = build_run_config(spec, base_config=base_config) + noise_cfg["variant_key"] = "noise" + noise_cfg["variant_label"] = "noise-model" + + arb_cfg = thermostat_compare.make_noise_variant_cfg(noise_cfg, False) + arb_cfg["variant_key"] = "arb" + arb_cfg["variant_label"] = "arb-only" + arb_cfg["name"] = noise_cfg["name"] + arb_cfg["reason"] = ( + f"{noise_cfg['reason']} Matched arb-only baseline with noise disabled." + ) + return {"spec": spec, "noise": noise_cfg, "arb": arb_cfg} + + +def build_run_label(cfg, thermostat_compare): + """Build a compact legend label for a run.""" + return ( + f"{cfg['name']} ({cfg.get('variant_label', 'run')}) | PR {cfg['price_ratio']:.4g}, " + f"M {cfg['centeredness_margin']:.3g}, " + f"Shift {cfg['daily_price_shift_exponent']:.3g}, " + f"TVL {thermostat_compare.format_tvl_millions_label(cfg)}" + ) + + +def build_time_index(run_fingerprint, periods, step_minutes=1): + """Return a DatetimeIndex for a result series with the given cadence.""" + return pd.date_range( + start=pd.Timestamp(run_fingerprint["startDateString"]), + periods=int(periods), + freq=f"{max(int(step_minutes), 1)}min", + ) + + +def infer_series_step_minutes(run_fingerprint, series_length, minute_length): + """Infer the cadence of a result series from its length.""" + if minute_length and series_length and series_length != minute_length: + ratio = float(minute_length) / float(series_length) + rounded_ratio = max(int(round(ratio)), 1) + if abs(ratio - rounded_ratio) < 1.0e-9: + return rounded_ratio + return max(int(run_fingerprint.get("arb_frequency", 1)), 1) + + +def build_daily_weight_series( + weights, + tokens, + run_fingerprint, + minute_length, +): + """Build a daily weight frame from a weight array at inferred cadence.""" + weights = np.asarray(weights, dtype=float) + weight_step_minutes = infer_series_step_minutes( + run_fingerprint, + len(weights), + minute_length, + ) + weight_dates = build_time_index( + run_fingerprint, + len(weights), + step_minutes=weight_step_minutes, + ) + weight_frame = pd.DataFrame(weights, index=weight_dates, columns=tokens) + return weight_frame.resample("1D").last() + + +def estimate_gross_volume_usd(cfg, result, default_protocol_fee_split=0.25): + """Recover gross traded USD volume from LP fee revenue.""" + fee_revenue = result.get("fee_revenue") + if fee_revenue is None: + return np.zeros(len(np.asarray(result["value"])), dtype=float) + + fee_revenue = np.asarray(fee_revenue, dtype=float) + lp_fee_rate_share = float(cfg["fees"]) * ( + 1.0 - float(cfg.get("protocol_fee_split", default_protocol_fee_split)) + ) + if lp_fee_rate_share <= 0.0: + return np.zeros_like(fee_revenue) + return fee_revenue / lp_fee_rate_share + + +def build_daily_run_series(cfg, run_fingerprint, result, default_protocol_fee_split=0.25): + """Build daily TVL/share-price, weight, and volume series for plotting.""" + value = np.asarray(result["value"], dtype=float) + value_dates = build_time_index(run_fingerprint, len(value), step_minutes=1) + value_series = pd.Series(value, index=value_dates) + daily_value = value_series.resample("1D").last() + + zero_fee_weights = np.asarray(result["weights"], dtype=float) + daily_zero_fee_weights = build_daily_weight_series( + zero_fee_weights, + cfg["tokens"], + run_fingerprint, + len(value), + ) + + reserves = np.asarray(result["reserves"], dtype=float) + prices = np.asarray(result["prices"], dtype=float) + reserve_value = reserves * prices + reserve_value_totals = np.maximum( + reserve_value.sum(axis=1, keepdims=True), + 1.0e-12, + ) + actual_reserve_value_weights = reserve_value / reserve_value_totals + daily_actual_reserve_value_weights = build_daily_weight_series( + actual_reserve_value_weights, + cfg["tokens"], + run_fingerprint, + len(value), + ) + + gross_volume_usd = estimate_gross_volume_usd( + cfg, + result, + default_protocol_fee_split=default_protocol_fee_split, + ) + volume_step_minutes = ( + 1 + if len(gross_volume_usd) == len(value) + else infer_series_step_minutes(run_fingerprint, len(gross_volume_usd), len(value)) + ) + volume_dates = build_time_index( + run_fingerprint, + len(gross_volume_usd), + step_minutes=volume_step_minutes, + ) + daily_volume = pd.Series(gross_volume_usd, index=volume_dates).resample("1D").sum() + + return { + "daily_value": daily_value, + "daily_zero_fee_weights": daily_zero_fee_weights, + "daily_actual_reserve_value_weights": daily_actual_reserve_value_weights, + "daily_volume": daily_volume, + } + + +def _terminal_json_default(value): + """Serialize NumPy/JAX-backed values for readable terminal logging.""" + if isinstance(value, Path): + return str(value) + if hasattr(value, "tolist"): + return value.tolist() + return str(value) + + +def print_run_inputs_to_terminal(cfg, run_fingerprint, update_params): + """Print the full run fingerprint and update params for a triggered run.""" + print( + f"Run inputs for {cfg['name']} ({cfg.get('variant_label', 'run')}):" + ) + print( + json.dumps( + { + "run_fingerprint": run_fingerprint, + "update_params": update_params, + }, + indent=2, + sort_keys=True, + default=_terminal_json_default, + ) + ) + + +def run_single_config(cfg, price_data, thermostat_compare, do_run_on_historic_data): + """Run one geometric noise-model configuration.""" + run_fingerprint = thermostat_compare.make_fingerprint(cfg, "geometric") + update_params = thermostat_compare.make_params(cfg) + print_run_inputs_to_terminal(cfg, run_fingerprint, update_params) + result = do_run_on_historic_data( + run_fingerprint=run_fingerprint, + params=update_params, + price_data=price_data, + ) + return { + "config": cfg, + "fingerprint": run_fingerprint, + "noise_summary": thermostat_compare.resolve_reclamm_noise_settings(cfg)[ + "noise_summary" + ], + "result": result, + "series": build_daily_run_series( + cfg, + run_fingerprint, + result, + default_protocol_fee_split=thermostat_compare.DEFAULT_PROTOCOL_FEE_SPLIT, + ), + } + + +def plot_pair_results( + run_pairs, + thermostat_compare, + source_heatmap_description, + output_file, +): + """Plot paired noise-model/arb-only outputs for the two highlighted cells.""" + import matplotlib.pyplot as plt + + fig, axes = plt.subplots( + 5, + 1, + figsize=(14, 16), + sharex=True, + gridspec_kw={"height_ratios": [2.2, 1.5, 1.5, 1.4, 1.6]}, + ) + ( + ax_value, + ax_zero_fee_weights, + ax_actual_reserve_weights, + ax_volume, + ax_improvement, + ) = axes + + for pair in run_pairs: + spec = pair["spec"] + color = spec["color"] + noise_output = pair["noise"] + arb_output = pair["arb"] + + for variant_key, output in (("noise", noise_output), ("arb", arb_output)): + cfg = output["config"] + label = build_run_label(cfg, thermostat_compare=thermostat_compare) + series = output["series"] + style = VARIANT_STYLES[variant_key] + + ax_value.plot( + series["daily_value"].index, + series["daily_value"].to_numpy(dtype=float) / 1e6, + color=color, + linestyle=style["linestyle"], + linewidth=style["linewidth"], + alpha=style["alpha"], + label=label, + ) + + for token_idx, token in enumerate(cfg["tokens"]): + token_linestyle = style["linestyle"] if token_idx == 0 else ":" + ax_zero_fee_weights.plot( + series["daily_zero_fee_weights"].index, + series["daily_zero_fee_weights"][token].to_numpy(dtype=float), + color=color, + linestyle=token_linestyle, + linewidth=1.8 if token_idx == 0 else 1.6, + alpha=style["alpha"], + label=f"{cfg['name']} {cfg['variant_label']} {token}", + ) + ax_actual_reserve_weights.plot( + series["daily_actual_reserve_value_weights"].index, + series["daily_actual_reserve_value_weights"][token].to_numpy( + dtype=float + ), + color=color, + linestyle=token_linestyle, + linewidth=1.8 if token_idx == 0 else 1.6, + alpha=style["alpha"], + label=f"{cfg['name']} {cfg['variant_label']} {token}", + ) + + ax_volume.plot( + series["daily_volume"].index, + series["daily_volume"].to_numpy(dtype=float) / 1e6, + color=color, + linestyle=style["linestyle"], + linewidth=style["linewidth"], + alpha=style["alpha"], + label=f"{cfg['name']} {cfg['variant_label']}", + ) + + noise_value, arb_value = noise_output["series"]["daily_value"].align( + arb_output["series"]["daily_value"], + join="inner", + ) + improvement_pct = (noise_value - arb_value) / arb_value * 100.0 + ax_improvement.plot( + improvement_pct.index, + improvement_pct.to_numpy(dtype=float), + color=color, + linewidth=2.2, + label=noise_output["config"]["name"], + ) + + shared_noise_summary = run_pairs[0]["noise"]["noise_summary"] + fig.suptitle( + "reCLAMM geometric noise-model vs arb-only comparison", + fontsize=14, + fontweight="bold", + ) + fig.text( + 0.5, + 0.965, + ( + "Compare-script AAVE/ETH fingerprint | " + f"Cell source: {source_heatmap_description} | " + f"Noise: {shared_noise_summary} | " + "Share price equals TVL here because LP supply is fixed at 1.0" + ), + ha="center", + va="top", + fontsize=10, + ) + + ax_value.set_ylabel("Share price / TVL ($M)") + ax_value.set_title("Absolute share price / TVL") + ax_value.grid(True, alpha=0.3) + ax_value.legend(fontsize=8) + + ax_zero_fee_weights.set_ylabel("Weight") + ax_zero_fee_weights.set_title("Reported zero-fee empirical weights") + ax_zero_fee_weights.set_ylim(-0.02, 1.02) + ax_zero_fee_weights.grid(True, alpha=0.3) + ax_zero_fee_weights.legend(fontsize=8, ncol=2) + + ax_actual_reserve_weights.set_ylabel("Weight") + ax_actual_reserve_weights.set_title("Actual reserve value weights") + ax_actual_reserve_weights.set_ylim(-0.02, 1.02) + ax_actual_reserve_weights.grid(True, alpha=0.3) + ax_actual_reserve_weights.legend(fontsize=8, ncol=2) + + ax_volume.set_ylabel("Daily volume ($M)") + ax_volume.set_title("Estimated gross swap volume") + ax_volume.grid(True, alpha=0.3) + ax_volume.legend(fontsize=8) + + ax_improvement.axhline(0.0, color="black", linewidth=0.9, alpha=0.55) + ax_improvement.set_ylabel("Noise vs arb (%)") + ax_improvement.set_title("Daily TVL improvement: (noise - arb) / arb") + ax_improvement.set_xlabel("Date") + ax_improvement.grid(True, alpha=0.3) + ax_improvement.legend(fontsize=8) + + output_file = Path(output_file) + output_file.parent.mkdir(parents=True, exist_ok=True) + plt.tight_layout(rect=(0.0, 0.0, 1.0, 0.945)) + plt.savefig(output_file, dpi=180) + print(f"Saved {output_file}") + plt.close(fig) + + +def print_final_heatmap_summary(run_pairs, source_heatmap_description): + """Print the final values and heatmap-equivalent improvement for each pair.""" + print(f"\nSource heatmap selection: {source_heatmap_description}") + print("Final values represented by the heatmap metric:") + for pair in run_pairs: + spec = pair["spec"] + noise_final = float(pair["noise"]["series"]["daily_value"].iloc[-1]) + arb_final = float(pair["arb"]["series"]["daily_value"].iloc[-1]) + improvement_pct = (noise_final - arb_final) / arb_final * 100.0 + print( + f" {spec['name']}: " + f"noise=${noise_final:,.2f}, " + f"arb=${arb_final:,.2f}, " + f"heatmap_improvement={improvement_pct:.6f}%" + ) + + +def run_pair_comparison( + run_specs: Sequence[Mapping[str, object]], + source_heatmap_description: str, + output_file, +): + """Run both configs and render the comparison figure.""" + thermostat_compare, do_run_on_historic_data = load_runtime_dependencies() + base_config = build_default_base_config(thermostat_compare) + run_pairs = [ + build_run_variants( + spec, + base_config=base_config, + thermostat_compare=thermostat_compare, + ) + for spec in run_specs + ] + run_configs = [ + cfg + for pair in run_pairs + for variant_key, cfg in pair.items() + if variant_key in ("noise", "arb") + ] + price_data = thermostat_compare.load_shared_price_data(run_configs) + + completed_pairs = [] + for pair in run_pairs: + completed_pair = {"spec": pair["spec"]} + for variant_key in ("noise", "arb"): + cfg = pair[variant_key] + print( + f"Running {cfg['name']} ({cfg['variant_label']}) | " + f"price_ratio={cfg['price_ratio']}, " + f"margin={cfg['centeredness_margin']}, " + f"shift_exp={cfg['daily_price_shift_exponent']}, " + f"TVL={thermostat_compare.format_tvl_millions_label(cfg)}" + ) + completed_pair[variant_key] = run_single_config( + cfg, + price_data, + thermostat_compare=thermostat_compare, + do_run_on_historic_data=do_run_on_historic_data, + ) + completed_pairs.append(completed_pair) + + plot_pair_results( + completed_pairs, + thermostat_compare=thermostat_compare, + source_heatmap_description=source_heatmap_description, + output_file=output_file, + ) + print_final_heatmap_summary( + completed_pairs, + source_heatmap_description=source_heatmap_description, + ) + return output_file + + +def run_adjacent_csv_row_comparison( + csv_path, + row_index: int = 0, + output_file=None, +): + """Run the standard geometric comparison using one adjacent-pairs CSV row.""" + csv_path = Path(csv_path) + row = load_adjacent_csv_row(csv_path, row_index=row_index) + source_heatmap_description, run_specs = build_run_specs_from_adjacent_row( + row, + csv_path=csv_path, + row_index=row_index, + ) + resolved_output_file = ( + Path(output_file) + if output_file is not None + else default_output_file_for_adjacent_csv(csv_path, row_index=row_index) + ) + return run_pair_comparison( + run_specs=run_specs, + source_heatmap_description=source_heatmap_description, + output_file=resolved_output_file, + ) + + +def main(cli_args: Optional[argparse.Namespace] = None): + """Entry point for CLI execution.""" + args = cli_args or parse_args() + if args.adjacent_csv: + return run_adjacent_csv_row_comparison( + args.adjacent_csv, + row_index=args.adjacent_row_index, + output_file=args.output_file, + ) + + output_file = Path(args.output_file) if args.output_file else Path(DEFAULT_OUTPUT_FILE) + return run_pair_comparison( + run_specs=DEFAULT_RUN_SPECS, + source_heatmap_description=DEFAULT_SOURCE_HEATMAP_DESCRIPTION, + output_file=output_file, + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/reclamm/compare_reclamm_thermostats.py b/scripts/reclamm/compare_reclamm_thermostats.py index be8ee5f..21352a4 100644 --- a/scripts/reclamm/compare_reclamm_thermostats.py +++ b/scripts/reclamm/compare_reclamm_thermostats.py @@ -16,6 +16,7 @@ """ import gc +import hashlib import math import os @@ -41,17 +42,25 @@ def to_daily_price_shift_base(daily_price_shift_exponent): return 1.0 - daily_price_shift_exponent / 124649.0 -RUN_CONSTANT_ARC_LENGTH = False +def build_inclusive_sweep(start, stop, step): + """Build a sweep that keeps the requested step and explicitly includes the stop.""" + values = np.arange(start, stop + 1.0e-12, step, dtype=float) + if values.size == 0 or not np.isclose(values[-1], stop): + values = np.append(values, float(stop)) + return values + + +RUN_CONSTANT_ARC_LENGTH = True INTERPOLATION_METHODS = ( ("geometric", "constant_arc_length") if RUN_CONSTANT_ARC_LENGTH else ("geometric",) ) -HEATMAP_PRICE_RATIOS = np.arange(1.01, 1.50 + 1e-9, 0.025) -HEATMAP_MARGINS = np.linspace(0.05, 0.90, 20) -HEATMAP_SHIFT_EXPONENTS = np.arange(0.01, 0.50 + 1e-9, 0.025) +HEATMAP_PRICE_RATIOS = build_inclusive_sweep(1.01, 3.00, 0.025) +HEATMAP_MARGINS = np.linspace(0.05, 0.90, 39) +HEATMAP_SHIFT_EXPONENTS = build_inclusive_sweep(0.01, 0.50, 0.0125) HEATMAP_ARC_LENGTH_SPEEDS = np.geomspace(1.0e-6, 5.0e-4, 11) -PRICE_RATIO_TICKS = np.array([1.01, 1.10, 1.20, 1.30, 1.40, 1.50]) +PRICE_RATIO_TICKS = np.array([1.01, 1.25, 1.50, 2.00, 2.50, 3.00]) MARGIN_TICKS = np.array([0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.90]) SHIFT_EXPONENT_TICKS = np.array([0.01, 0.05, 0.10, 0.20, 0.30, 0.40, 0.50]) ARC_LENGTH_SPEED_TICKS = np.array([ @@ -76,6 +85,17 @@ def to_daily_price_shift_base(daily_price_shift_exponent): CENTER_ZERO_HEATMAP_COLOR_NORM = "symlog" CENTER_ZERO_HEATMAP_COLOR_TAG = "symlog20" CENTER_ZERO_HEATMAP_SYMLOG_LINTHRESH = 20.0 +FIXED_SLICE_FRACTIONS = (0.125, 0.375, 0.625, 0.875) +FIXED_SLICE_LABELS = ("Q1", "Q2", "Q3", "Q4") +THREE_D_VIEW_ELEVATION = 22.0 +THREE_D_VIEW_AZIMUTH = 140.0 +HEATMAP_FORWARD_CACHE_ENABLED = True +HEATMAP_FORWARD_CACHE_RUN_NAME = "aave_eth_thermostat_heatmaps_v1" +HEATMAP_FORWARD_CACHE_ROOT = os.path.join( + "results", + "reclamm_heatmap_forward_cache", +) +HEATMAP_FORWARD_CACHE_FLUSH_EVERY = 32 AAVE_WETH_POOL_ID = "0x9d1fcf346ea1b0" DEFAULT_MARKET_LINEAR_ARTIFACT_DIR = "results/linear_market_noise" @@ -94,14 +114,28 @@ def to_daily_price_shift_base(daily_price_shift_exponent): ] LEGACY_LOG_CADENCE = 2.68 LEGACY_ARB_FREQUENCY = max(1, round(math.exp(LEGACY_LOG_CADENCE))) +FIXED_COMPARE_ARB_FREQUENCY = LEGACY_ARB_FREQUENCY AAVE_ETH_NOISE_SETTINGS = { "enable_noise_model": True, "noise_model": DEFAULT_NOISE_MODEL, "noise_artifact_dir": DEFAULT_MARKET_LINEAR_ARTIFACT_DIR, "noise_pool_id": AAVE_WETH_POOL_ID, + "arb_frequency": FIXED_COMPARE_ARB_FREQUENCY, "gas_cost": DEFAULT_GAS_COST, "protocol_fee_split": DEFAULT_PROTOCOL_FEE_SPLIT, } +PERSISTED_FORWARD_VALUE_COLUMNS = ( + "cache_key_hash", + "final_value", + "method", + "enable_noise_model", + "noise_model", + "price_ratio", + "centeredness_margin", + "daily_price_shift_exponent", + "initial_pool_value", + "arb_frequency", +) GEOMETRIC_ONLY_HEATMAP_METRIC_KEYS = ( "geometric_vs_launch_geometric_pct", @@ -175,6 +209,23 @@ def heatmap_artifact_filename(spec, cfg, suffix=None): return tvl_artifact_filename(stem, cfg, suffix=suffix) +def three_d_heatmap_artifact_filename(spec, cfg, suffix=None): + """Build a 3D heatmap filename, including any colour-style tag.""" + stem = f"reclamm_heatmap_3d_{spec['slug']}" + artifact_tag = spec.get("artifact_tag") + if artifact_tag: + stem = f"{stem}_{artifact_tag}" + return tvl_artifact_filename(stem, cfg, suffix=suffix) + + +def format_heatmap_param_value(value): + """Format a sweep parameter compactly for titles and logs.""" + value = float(value) + if abs(value) >= 1.0: + return f"{value:.2f}".rstrip("0").rstrip(".") + return f"{value:.3f}".rstrip("0").rstrip(".") + + def configs_for_tvl(base_configs, initial_pool_value): """Attach a shared initial TVL to each compare configuration.""" configs = [] @@ -185,35 +236,76 @@ def configs_for_tvl(base_configs, initial_pool_value): return configs -def make_noise_variant_cfg(cfg, enable_noise_model): - """Return a config with either noise modelling or pure arb-only enabled.""" +def _normalize_arb_frequency(value, default=FIXED_COMPARE_ARB_FREQUENCY): + """Return a stable integer arb cadence for thermostat comparisons.""" + if value is None: + if default is None: + return None + value = default + return max(int(round(float(value))), 1) + + +def get_effective_arb_frequency(cfg, noise_cfg=None): + """Resolve the arb cadence used by a thermostat comparison run.""" + del noise_cfg + return _normalize_arb_frequency(FIXED_COMPARE_ARB_FREQUENCY) + + +def normalize_compare_run_cfg(cfg, enable_noise_model=None): + """Canonicalize the compare-run config so non-axis inputs stay fixed.""" updated = dict(cfg) - if enable_noise_model: - updated["enable_noise_model"] = True - return updated + updated["price_ratio"] = float(cfg["price_ratio"]) + updated["centeredness_margin"] = float(cfg["centeredness_margin"]) + updated["daily_price_shift_exponent"] = float(cfg["daily_price_shift_exponent"]) + updated["initial_pool_value"] = float(get_initial_pool_value(cfg)) + updated["gas_cost"] = DEFAULT_GAS_COST + updated["protocol_fee_split"] = DEFAULT_PROTOCOL_FEE_SPLIT + updated["arb_fees"] = 0.0 + updated["arb_frequency"] = get_effective_arb_frequency(cfg) + updated["noise_trader_ratio"] = 0.0 - matched_noise = resolve_reclamm_noise_settings(cfg) + arc_length_speed = cfg.get("arc_length_speed") + if arc_length_speed is None: + updated.pop("arc_length_speed", None) + else: + updated["arc_length_speed"] = float(arc_length_speed) - updated["enable_noise_model"] = False - updated["noise_model"] = None - updated["gas_cost"] = cfg.get("gas_cost", DEFAULT_GAS_COST) - updated["protocol_fee_split"] = cfg.get( - "protocol_fee_split", DEFAULT_PROTOCOL_FEE_SPLIT + use_noise = ( + bool(cfg.get("enable_noise_model", False)) + if enable_noise_model is None + else bool(enable_noise_model) ) - updated["noise_trader_ratio"] = 0.0 - matched_arb_frequency = matched_noise.get("arb_frequency") - if matched_arb_frequency is not None: - updated["arb_frequency"] = matched_arb_frequency - for key in ( - "reclamm_noise_params", - "noise_arrays_path", - "noise_artifact_dir", - "noise_pool_id", - ): - updated.pop(key, None) + updated["enable_noise_model"] = use_noise + + requested_mode = cfg.get("noise_model", DEFAULT_NOISE_MODEL) or DEFAULT_NOISE_MODEL + if use_noise: + updated["noise_model"] = requested_mode + if requested_mode == "market_linear": + updated["noise_artifact_dir"] = DEFAULT_MARKET_LINEAR_ARTIFACT_DIR + updated["noise_pool_id"] = AAVE_WETH_POOL_ID + else: + updated.pop("noise_artifact_dir", None) + updated.pop("noise_pool_id", None) + updated.pop("reclamm_noise_params", None) + updated.pop("noise_arrays_path", None) + else: + updated["noise_model"] = None + for key in ( + "reclamm_noise_params", + "noise_arrays_path", + "noise_artifact_dir", + "noise_pool_id", + ): + updated.pop(key, None) + return updated +def make_noise_variant_cfg(cfg, enable_noise_model): + """Return a config with either noise modelling or pure arb-only enabled.""" + return normalize_compare_run_cfg(cfg, enable_noise_model=enable_noise_model) + + def _warn_noise_fallback(message): """Print a one-time message when the preferred noise setup is unavailable.""" if message not in _WARNED_NOISE_FALLBACKS: @@ -228,36 +320,39 @@ def _hashable_noise_params(params): return tuple(sorted((str(k), round(float(v), 12)) for k, v in params.items())) -def _legacy_calibrated_noise_settings(reason=None): +def _legacy_calibrated_noise_settings(reason=None, arb_frequency=None): """Fallback calibrated noise config used when market-linear artifacts are absent.""" if reason: _warn_noise_fallback( "market_linear noise unavailable for thermostat comparison; " f"falling back to calibrated legacy coefficients ({reason})." ) + arb_frequency = _normalize_arb_frequency(arb_frequency) return { "noise_model": "calibrated", "noise_trader_ratio": 0.0, "reclamm_noise_params": { f"c_{i}": LEGACY_NOISE_COEFFS[i] for i in range(len(LEGACY_NOISE_COEFFS)) }, - "arb_frequency": LEGACY_ARB_FREQUENCY, + "arb_frequency": arb_frequency, "noise_summary": ( "calibrated legacy 8-covariate " - f"(arb_frequency={LEGACY_ARB_FREQUENCY})" + f"(arb_frequency={arb_frequency})" ), "noise_cache_key": ( "calibrated", tuple(round(float(c), 12) for c in LEGACY_NOISE_COEFFS), - LEGACY_ARB_FREQUENCY, + arb_frequency, ), } def resolve_reclamm_noise_settings(cfg): """Resolve the active reCLAMM noise-model fingerprint block for a config.""" + cfg = normalize_compare_run_cfg(cfg) enable_noise_model = cfg.get("enable_noise_model", False) requested_mode = cfg.get("noise_model", DEFAULT_NOISE_MODEL) + requested_arb_frequency = get_effective_arb_frequency(cfg) cache_key = ( tuple(cfg.get("tokens", [])), cfg.get("start"), @@ -266,7 +361,7 @@ def resolve_reclamm_noise_settings(cfg): requested_mode, cfg.get("noise_artifact_dir", DEFAULT_MARKET_LINEAR_ARTIFACT_DIR), cfg.get("noise_pool_id", AAVE_WETH_POOL_ID), - cfg.get("arb_frequency"), + requested_arb_frequency, round(float(cfg.get("noise_trader_ratio", 0.0)), 12), _hashable_noise_params(cfg.get("reclamm_noise_params")), cfg.get("noise_arrays_path"), @@ -280,7 +375,7 @@ def resolve_reclamm_noise_settings(cfg): "noise_trader_ratio": 0.0, "reclamm_noise_params": None, "noise_arrays_path": None, - "arb_frequency": None, + "arb_frequency": requested_arb_frequency, "noise_summary": "arb-only (noise disabled)", "noise_cache_key": ("disabled",), } @@ -290,11 +385,7 @@ def resolve_reclamm_noise_settings(cfg): start_date = str(cfg["start"]).split(" ")[0] end_date = str(cfg["end"]).split(" ")[0] try: - from quantammsim.calibration.noise_model_arrays import ( - _find_pool_index, - build_simulator_arrays, - load_artifact, - ) + from quantammsim.calibration.noise_model_arrays import build_simulator_arrays model_path = os.path.join(artifact_dir, "model.npz") meta_path = os.path.join(artifact_dir, "meta.json") @@ -311,6 +402,8 @@ def resolve_reclamm_noise_settings(cfg): ) if not os.path.exists(arrays_path): arrays = build_simulator_arrays( + token_a=cfg["tokens"][0], + token_b=cfg["tokens"][1], pool_id=pool_id, start_date=start_date, end_date=end_date, @@ -328,13 +421,7 @@ def resolve_reclamm_noise_settings(cfg): tvl_mean = float(arrays["tvl_mean"]) tvl_std = float(arrays["tvl_std"]) - art, meta = load_artifact(artifact_dir) - pool_idx = _find_pool_index(pool_id, meta["pool_ids"]) - if pool_idx >= 0: - learned_cadence = float(np.exp(art["log_cadence"][pool_idx])) - else: - learned_cadence = 5.0 - arb_frequency = max(1, round(learned_cadence)) + arb_frequency = requested_arb_frequency result = { "noise_model": "market_linear", "noise_trader_ratio": 0.0, @@ -351,30 +438,19 @@ def resolve_reclamm_noise_settings(cfg): arb_frequency, round(tvl_mean, 12), round(tvl_std, 12), - ), - } + ), + } except Exception as exc: # pragma: no cover - fallback path depends on local artifacts - result = _legacy_calibrated_noise_settings(str(exc)) + result = _legacy_calibrated_noise_settings( + str(exc), + arb_frequency=requested_arb_frequency, + ) elif requested_mode == "calibrated": - params = cfg.get("reclamm_noise_params") - if params is None: - result = _legacy_calibrated_noise_settings() - else: - arb_frequency = cfg.get("arb_frequency", LEGACY_ARB_FREQUENCY) - result = { - "noise_model": "calibrated", - "noise_trader_ratio": cfg.get("noise_trader_ratio", 0.0), - "reclamm_noise_params": dict(params), - "arb_frequency": arb_frequency, - "noise_summary": f"calibrated (arb_frequency={arb_frequency})", - "noise_cache_key": ( - "calibrated", - _hashable_noise_params(params), - arb_frequency, - ), - } + result = _legacy_calibrated_noise_settings( + arb_frequency=requested_arb_frequency + ) else: - arb_frequency = cfg.get("arb_frequency") + arb_frequency = requested_arb_frequency result = { "noise_model": requested_mode, "noise_trader_ratio": cfg.get("noise_trader_ratio", 0.0), @@ -430,13 +506,14 @@ def resolve_reclamm_noise_settings(cfg): def make_fingerprint(cfg, interpolation_method): """Build run fingerprint for a given config and interpolation method.""" + cfg = normalize_compare_run_cfg(cfg) speed_override = ( cfg.get("arc_length_speed") if interpolation_method == "constant_arc_length" else None ) noise_cfg = resolve_reclamm_noise_settings(cfg) - arb_frequency = cfg.get("arb_frequency", noise_cfg.get("arb_frequency")) + arb_frequency = get_effective_arb_frequency(cfg, noise_cfg) fingerprint = { "tokens": cfg["tokens"], "rule": "reclamm", @@ -471,6 +548,7 @@ def make_fingerprint(cfg, interpolation_method): def make_params(cfg): """Build pool params from config.""" + cfg = normalize_compare_run_cfg(cfg) return { "price_ratio": jnp.array(cfg["price_ratio"]), "centeredness_margin": jnp.array(cfg["centeredness_margin"]), @@ -528,7 +606,7 @@ def _set_padded_ylim(ax, series_list, pad_ratio=0.04): def _cache_size(cache): - """Count memoized final-value runs.""" + """Count memoized final-value cache entries materialised in memory.""" return len(cache.get("_final_value_cache", {})) @@ -537,13 +615,140 @@ def _comparison_cache_size(cache): return len(cache.get("_comparison_cache", {})) -def make_sweep_cache(price_data): - """Create a shared cache for heatmap and line sweeps.""" +def _heatmap_forward_cache_scope_slug(cfg): + """Build a compact cache scope slug for a shared-TVL heatmap run.""" + if cfg is None: + return "unspecified_tvl" + return f"tvl_{format_tvl_millions_slug(cfg)}" + + +def _heatmap_forward_cache_path(cfg): + """Return the parquet path for persisted scalar forward values.""" + if not HEATMAP_FORWARD_CACHE_ENABLED: + return None + return os.path.join( + HEATMAP_FORWARD_CACHE_ROOT, + HEATMAP_FORWARD_CACHE_RUN_NAME, + f"forward_values_{_heatmap_forward_cache_scope_slug(cfg)}.parquet", + ) + + +def _make_method_cache_hash(key): + """Build a compact stable digest for a method cache key.""" + return hashlib.sha256(repr(key).encode("utf-8")).hexdigest() + + +def _build_persistent_final_value_record(cfg, method, cache_key_hash, final_value): + """Build one self-describing parquet row for a cached scalar run result.""" + cfg = normalize_compare_run_cfg(cfg) + noise_cfg = resolve_reclamm_noise_settings(cfg) return { + "cache_key_hash": str(cache_key_hash), + "final_value": float(final_value), + "method": str(method), + "enable_noise_model": bool(cfg.get("enable_noise_model", False)), + "noise_model": noise_cfg.get("noise_model"), + "price_ratio": float(cfg["price_ratio"]), + "centeredness_margin": float(cfg["centeredness_margin"]), + "daily_price_shift_exponent": float(cfg["daily_price_shift_exponent"]), + "initial_pool_value": float(get_initial_pool_value(cfg)), + "arb_frequency": get_effective_arb_frequency(cfg, noise_cfg), + } + + +def _load_persistent_final_value_cache(cache): + """Load persisted scalar forward values from parquet once per sweep cache.""" + if cache.get("_persistent_final_value_cache_loaded"): + return + + disk_cache = {} + disk_records = {} + cache_path = cache.get("_persistent_final_value_cache_path") + if cache_path and os.path.exists(cache_path): + frame = pd.read_parquet(cache_path) + if not frame.empty: + for row in frame.itertuples(index=False): + cache_key_hash = str(row.cache_key_hash) + final_value = float(row.final_value) + disk_cache[cache_key_hash] = final_value + record = { + "cache_key_hash": cache_key_hash, + "final_value": final_value, + } + for column in PERSISTED_FORWARD_VALUE_COLUMNS: + if column in {"cache_key_hash", "final_value"}: + continue + record[column] = getattr(row, column, None) + disk_records[cache_key_hash] = record + print( + f"Loaded {len(disk_cache)} persisted heatmap forward values from {cache_path}" + ) + + cache["_persistent_final_value_cache"] = disk_cache + cache["_persistent_final_value_records"] = disk_records + cache["_persistent_final_value_cache_loaded"] = True + + +def flush_sweep_cache(cache, force=False): + """Persist newly computed scalar forward values to parquet.""" + if not HEATMAP_FORWARD_CACHE_ENABLED: + return + + pending = cache.get("_pending_persistent_final_values") + if not pending: + return + if not force and len(pending) < HEATMAP_FORWARD_CACHE_FLUSH_EVERY: + return + + _load_persistent_final_value_cache(cache) + disk_cache = cache.setdefault("_persistent_final_value_cache", {}) + disk_records = cache.setdefault("_persistent_final_value_records", {}) + for cache_key_hash, record in pending.items(): + merged = dict(disk_records.get(cache_key_hash, {})) + merged.update(record) + merged["cache_key_hash"] = str(cache_key_hash) + merged["final_value"] = float(merged["final_value"]) + disk_records[cache_key_hash] = merged + disk_cache[cache_key_hash] = merged["final_value"] + + cache_path = cache.get("_persistent_final_value_cache_path") + if cache_path is None: + pending.clear() + return + + os.makedirs(os.path.dirname(cache_path), exist_ok=True) + sorted_records = [disk_records[key] for key in sorted(disk_records)] + payload = { + column: [record.get(column) for record in sorted_records] + for column in PERSISTED_FORWARD_VALUE_COLUMNS + } + payload["final_value"] = np.asarray(payload["final_value"], dtype=np.float64) + frame = pd.DataFrame(payload) + frame.sort_values("cache_key_hash", inplace=True, ignore_index=True) + frame.to_parquet(cache_path, index=False, compression="zstd") + print( + f"Persisted {len(pending)} new heatmap forward values to {cache_path} " + f"({len(disk_cache)} total cached values)." + ) + pending.clear() + + +def make_sweep_cache(price_data, cache_scope_cfg=None): + """Create a shared cache for heatmap and line sweeps.""" + cache = { "_shared_price_data": price_data, "_final_value_cache": {}, "_comparison_cache": {}, + "_pending_persistent_final_values": {}, + "_persistent_final_value_cache": {}, + "_persistent_final_value_records": {}, + "_persistent_final_value_cache_loaded": False, + "_persistent_final_value_cache_path": _heatmap_forward_cache_path( + cache_scope_cfg + ), } + _load_persistent_final_value_cache(cache) + return cache def _missing_artifacts(progress_label, filenames): @@ -571,8 +776,9 @@ def _speed_cache_key(speed): def _make_method_cache_key(cfg, method): """Cache key for a single-method final-value run.""" + cfg = normalize_compare_run_cfg(cfg) noise_cfg = resolve_reclamm_noise_settings(cfg) - arb_frequency = cfg.get("arb_frequency", noise_cfg.get("arb_frequency")) + arb_frequency = get_effective_arb_frequency(cfg, noise_cfg) key = ( method, bool(cfg.get("enable_noise_model", False)), @@ -630,16 +836,34 @@ def _run_method_final_value_cached(cfg, method, cache): """Memoize final value for a single interpolation method.""" final_value_cache = cache.setdefault("_final_value_cache", {}) key = _make_method_cache_key(cfg, method) - if key not in final_value_cache: - result = do_run_on_historic_data( - run_fingerprint=make_fingerprint(cfg, method), - params=make_params(cfg), - price_data=cache["_shared_price_data"], - low_data_mode=True, + if key in final_value_cache: + return final_value_cache[key] + + _load_persistent_final_value_cache(cache) + key_hash = _make_method_cache_hash(key) + persisted_cache = cache.setdefault("_persistent_final_value_cache", {}) + if key_hash in persisted_cache: + final_value_cache[key] = persisted_cache[key_hash] + return final_value_cache[key] + + result = do_run_on_historic_data( + run_fingerprint=make_fingerprint(cfg, method), + params=make_params(cfg), + price_data=cache["_shared_price_data"], + low_data_mode=True, + ) + final_value_cache[key] = float(result["final_value"]) + cache.setdefault("_pending_persistent_final_values", {})[key_hash] = ( + _build_persistent_final_value_record( + cfg=cfg, + method=method, + cache_key_hash=key_hash, + final_value=final_value_cache[key], ) - final_value_cache[key] = float(result["final_value"]) - del result - gc.collect() + ) + flush_sweep_cache(cache, force=False) + del result + gc.collect() return final_value_cache[key] @@ -860,15 +1084,16 @@ def build_heatmap_matrices( data[metric_key][yi, xi] = metrics[metric_key] completed_points = (yi + 1) * len(x_values) - row_new_final_runs = _cache_size(cache) - final_cache_before_row + row_new_final_entries = _cache_size(cache) - final_cache_before_row row_new_comparisons = ( _comparison_cache_size(cache) - comparison_cache_before_row ) row_pct = completed_points / total_points * 100.0 + flush_sweep_cache(cache, force=True) print( f"[{progress_label}] row {yi + 1}/{len(y_values)} complete " f"({y_key}={float(y_value):.4f}, {completed_points}/{total_points} " - f"points, {row_pct:.1f}%, {row_new_final_runs} new final-value runs, " + f"points, {row_pct:.1f}%, {row_new_final_entries} new final-value cache entries, " f"{row_new_comparisons} new comparison bundles)" ) @@ -910,6 +1135,7 @@ def build_metric_curve( metric_keys=(metric_key,), ) data[xi] = metrics[metric_key] + flush_sweep_cache(cache, force=True) return data @@ -937,6 +1163,226 @@ def _compute_axis_edges(values, scale="linear"): return edges +def build_fixed_slice_variants(values): + """Pick four representative quarter-range slices from a sweep grid.""" + values = np.asarray(values, dtype=float) + if values.size < len(FIXED_SLICE_FRACTIONS): + raise ValueError("Need at least four grid points to build fixed slices") + + variants = [] + used_indices = set() + for idx, fraction in enumerate(FIXED_SLICE_FRACTIONS): + target_index = int(round(fraction * (values.size - 1))) + while target_index in used_indices and target_index + 1 < values.size: + target_index += 1 + while target_index in used_indices and target_index - 1 >= 0: + target_index -= 1 + if target_index in used_indices: + raise ValueError("Could not build four unique fixed slices from sweep grid") + used_indices.add(target_index) + variants.append( + { + "index": target_index, + "fraction": fraction, + "label": FIXED_SLICE_LABELS[idx], + "slug": f"q{idx + 1}", + "value": float(values[target_index]), + } + ) + return variants + + +def _pair_slice_suffix(pair, slice_variant): + """Build a stable artifact suffix for a pairwise fixed-variable slice.""" + return f"{pair['slug']}_{pair['fixed_slug']}_{slice_variant['slug']}" + + +def _build_heatmap_norm( + data_arrays, + center_zero, + color_norm=None, + symlog_linthresh=None, +): + """Build a color normalizer shared by 2D and 3D heatmaps.""" + finite_parts = [] + for data in data_arrays: + finite = np.asarray(data, dtype=float) + finite = finite[np.isfinite(finite)] + if finite.size: + finite_parts.append(finite) + finite = np.concatenate(finite_parts) if finite_parts else np.array([], dtype=float) + + if center_zero: + if finite.size == 0: + vmax = 1.0 + else: + vmax = max(abs(float(finite.min())), abs(float(finite.max())), 1e-9) + if ( + color_norm == "symlog" + and symlog_linthresh is not None + and vmax > symlog_linthresh + ): + return SymLogNorm( + linthresh=symlog_linthresh, + linscale=1.0, + vmin=-vmax, + vmax=vmax, + base=10.0, + ) + return TwoSlopeNorm(vcenter=0.0, vmin=-vmax, vmax=vmax) + + if finite.size == 0: + vmin, vmax = 0.0, 1.0 + else: + vmin = float(finite.min()) + vmax = float(finite.max()) + if np.isclose(vmin, vmax): + pad = max(abs(vmin) * 0.01, 1e-9) + vmin -= pad + vmax += pad + return Normalize(vmin=vmin, vmax=vmax) + + +def get_pair_heatmap_metric_specs(): + """Return the standard thermostat pairwise heatmap metrics.""" + metric_specs = [ + { + "key": "efficiency_pct", + "title": "Efficiency vs heatmap geometric", + "colorbar_label": "Const Arc - heatmap Geo (% of heatmap geometric final value)", + "slug": "efficiency", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "launch_geometric_efficiency_pct", + "title": "Efficiency vs launch-style geometric", + "colorbar_label": "Const Arc - launch Geo (% of launch geometric final value)", + "slug": "launch_geometric_efficiency", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "geometric_vs_launch_geometric_pct", + "title": "Geometric tuning vs launch-style geometric", + "colorbar_label": "Candidate Geo - launch Geo (% of launch geometric final value)", + "slug": "geometric_vs_launch_geometric", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "constant_arc_vs_launch_constant_arc_pct", + "title": "Const arc tuning vs launch-style const arc", + "colorbar_label": "Candidate Const Arc - launch Const Arc (% of launch const arc final value)", + "slug": "constant_arc_vs_launch_constant_arc", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "noise_geometric_final_value_musd", + "title": "Geometric final value with noise model", + "colorbar_label": "Geometric final value with noise model ($M)", + "slug": "noise_geometric_final_value", + "center_zero": False, + "cmap": "viridis", + }, + { + "key": "noise_constant_arc_final_value_musd", + "title": "Const arc final value with noise model", + "colorbar_label": "Const Arc final value with noise model ($M)", + "slug": "noise_constant_arc_final_value", + "center_zero": False, + "cmap": "viridis", + }, + { + "key": "noise_vs_arb_geometric_improvement_pct", + "title": "Noise-model improvement over arb-only (geometric)", + "colorbar_label": "Noise-model Geo - arb-only Geo (% of arb-only final value)", + "slug": "noise_vs_arb_geometric_improvement", + "center_zero": True, + "cmap": "RdYlGn", + }, + { + "key": "noise_vs_arb_constant_arc_improvement_pct", + "title": "Noise-model improvement over arb-only (const arc)", + "colorbar_label": "Noise-model Const Arc - arb-only Const Arc (% of arb-only final value)", + "slug": "noise_vs_arb_constant_arc_improvement", + "center_zero": True, + "cmap": "RdYlGn", + }, + ] + for spec in metric_specs: + if spec["center_zero"]: + spec["color_norm"] = CENTER_ZERO_HEATMAP_COLOR_NORM + spec["symlog_linthresh"] = CENTER_ZERO_HEATMAP_SYMLOG_LINTHRESH + spec["artifact_tag"] = CENTER_ZERO_HEATMAP_COLOR_TAG + if not RUN_CONSTANT_ARC_LENGTH: + metric_specs = [ + spec + for spec in metric_specs + if spec["key"] in GEOMETRIC_ONLY_HEATMAP_METRIC_KEYS + ] + return metric_specs + + +def get_pair_heatmap_specs(base_cfg): + """Return the three pairwise thermostat heatmap families plus slice settings.""" + fixed_slice_variants = { + "price_ratio": build_fixed_slice_variants(HEATMAP_PRICE_RATIOS), + "centeredness_margin": build_fixed_slice_variants(HEATMAP_MARGINS), + "daily_price_shift_exponent": build_fixed_slice_variants( + HEATMAP_SHIFT_EXPONENTS + ), + } + return [ + { + "slug": "price_ratio_vs_margin", + "x_values": HEATMAP_PRICE_RATIOS, + "y_values": HEATMAP_MARGINS, + "x_key": "price_ratio", + "y_key": "centeredness_margin", + "x_label": "Price ratio", + "y_label": "Centeredness margin", + "xticks": PRICE_RATIO_TICKS, + "yticks": MARGIN_TICKS, + "fixed_key": "daily_price_shift_exponent", + "fixed_label": "Shift exponent", + "fixed_slug": "shift_exp", + "fixed_slices": fixed_slice_variants["daily_price_shift_exponent"], + }, + { + "slug": "shift_exp_vs_margin", + "x_values": HEATMAP_SHIFT_EXPONENTS, + "y_values": HEATMAP_MARGINS, + "x_key": "daily_price_shift_exponent", + "y_key": "centeredness_margin", + "x_label": "Shift exponent", + "y_label": "Centeredness margin", + "xticks": SHIFT_EXPONENT_TICKS, + "yticks": MARGIN_TICKS, + "fixed_key": "price_ratio", + "fixed_label": "Price ratio", + "fixed_slug": "price_ratio", + "fixed_slices": fixed_slice_variants["price_ratio"], + }, + { + "slug": "price_ratio_vs_shift_exp", + "x_values": HEATMAP_PRICE_RATIOS, + "y_values": HEATMAP_SHIFT_EXPONENTS, + "x_key": "price_ratio", + "y_key": "daily_price_shift_exponent", + "x_label": "Price ratio", + "y_label": "Shift exponent", + "xticks": PRICE_RATIO_TICKS, + "yticks": SHIFT_EXPONENT_TICKS, + "fixed_key": "centeredness_margin", + "fixed_label": "Centeredness margin", + "fixed_slug": "margin", + "fixed_slices": fixed_slice_variants["centeredness_margin"], + }, + ] + + def plot_heatmap( data, x_values, @@ -955,34 +1401,13 @@ def plot_heatmap( symlog_linthresh=None, ): """Render and save a single heatmap.""" - finite = np.asarray(data, dtype=float) - finite = finite[np.isfinite(finite)] - - if center_zero: - vmax = max(abs(float(np.nanmin(data))), abs(float(np.nanmax(data))), 1e-9) - if color_norm == "symlog" and symlog_linthresh is not None and vmax > symlog_linthresh: - norm = SymLogNorm( - linthresh=symlog_linthresh, - linscale=1.0, - vmin=-vmax, - vmax=vmax, - base=10.0, - ) - else: - norm = TwoSlopeNorm(vcenter=0.0, vmin=-vmax, vmax=vmax) - cmap_name = cmap or "RdYlGn" - else: - if finite.size == 0: - vmin, vmax = 0.0, 1.0 - else: - vmin = float(finite.min()) - vmax = float(finite.max()) - if np.isclose(vmin, vmax): - pad = max(abs(vmin) * 0.01, 1e-9) - vmin -= pad - vmax += pad - norm = Normalize(vmin=vmin, vmax=vmax) - cmap_name = cmap or "viridis" + norm = _build_heatmap_norm( + [data], + center_zero=center_zero, + color_norm=color_norm, + symlog_linthresh=symlog_linthresh, + ) + cmap_name = cmap or ("RdYlGn" if center_zero else "viridis") x_edges = _compute_axis_edges(x_values, scale=xscale) y_edges = _compute_axis_edges(y_values, scale="linear") @@ -1015,6 +1440,112 @@ def plot_heatmap( plt.close(fig) +def plot_three_variable_heatmap_3d( + price_margin_data, + shift_margin_data, + price_shift_data, + fixed_price_ratio, + fixed_margin, + fixed_shift_exponent, + title, + colorbar_label, + filename, + center_zero=True, + cmap=None, + color_norm=None, + symlog_linthresh=None, +): + """Render orthogonal 3D heatmap surfaces across the three thermostat variables.""" + norm = _build_heatmap_norm( + [price_margin_data, shift_margin_data, price_shift_data], + center_zero=center_zero, + color_norm=color_norm, + symlog_linthresh=symlog_linthresh, + ) + cmap_name = cmap or ("RdYlGn" if center_zero else "viridis") + cmap_obj = plt.get_cmap(cmap_name) + + price_margin_x, price_margin_y = np.meshgrid(HEATMAP_PRICE_RATIOS, HEATMAP_MARGINS) + price_margin_z = np.full_like(price_margin_x, fixed_shift_exponent, dtype=float) + + shift_margin_z, shift_margin_y = np.meshgrid( + HEATMAP_SHIFT_EXPONENTS, + HEATMAP_MARGINS, + ) + shift_margin_x = np.full_like(shift_margin_z, fixed_price_ratio, dtype=float) + + price_shift_x, price_shift_z = np.meshgrid( + HEATMAP_PRICE_RATIOS, + HEATMAP_SHIFT_EXPONENTS, + ) + price_shift_y = np.full_like(price_shift_x, fixed_margin, dtype=float) + + fig = plt.figure(figsize=(10.5, 7.2)) + ax = fig.add_subplot(111, projection="3d") + ax.set_facecolor("white") + fig.patch.set_facecolor("white") + + ax.plot_surface( + price_margin_x, + price_margin_y, + price_margin_z, + facecolors=cmap_obj(norm(np.asarray(price_margin_data, dtype=float))), + shade=False, + ) + ax.plot_surface( + shift_margin_x, + shift_margin_y, + shift_margin_z, + facecolors=cmap_obj(norm(np.asarray(shift_margin_data, dtype=float))), + shade=False, + ) + ax.plot_surface( + price_shift_x, + price_shift_y, + price_shift_z, + facecolors=cmap_obj(norm(np.asarray(price_shift_data, dtype=float))), + shade=False, + ) + + ax.set_xlim(float(HEATMAP_PRICE_RATIOS.min()), float(HEATMAP_PRICE_RATIOS.max())) + ax.set_ylim(float(HEATMAP_MARGINS.min()), float(HEATMAP_MARGINS.max())) + ax.set_zlim( + float(HEATMAP_SHIFT_EXPONENTS.min()), + float(HEATMAP_SHIFT_EXPONENTS.max()), + ) + ax.set_xlabel("Price ratio") + ax.set_ylabel("Centeredness margin") + ax.set_zlabel("Shift exponent") + ax.set_xticks(PRICE_RATIO_TICKS) + ax.set_yticks(MARGIN_TICKS[::2]) + ax.set_zticks(SHIFT_EXPONENT_TICKS) + ax.set_title(title) + ax.grid(False) + ax.view_init(elev=THREE_D_VIEW_ELEVATION, azim=THREE_D_VIEW_AZIMUTH) + try: + ax.set_box_aspect( + ( + float(HEATMAP_PRICE_RATIOS.max() - HEATMAP_PRICE_RATIOS.min()), + float(HEATMAP_MARGINS.max() - HEATMAP_MARGINS.min()), + float( + HEATMAP_SHIFT_EXPONENTS.max() - HEATMAP_SHIFT_EXPONENTS.min() + ), + ) + ) + except AttributeError: + pass + + sm = ScalarMappable(norm=norm, cmap=cmap_obj) + sm.set_array([]) + cbar = fig.colorbar(sm, ax=ax, fraction=0.03, pad=0.1, shrink=0.82) + cbar.set_label(colorbar_label) + + plt.tight_layout() + plt.savefig(filename, dpi=150) + print(f"Saved {filename}") + plt.close(fig) + + def plot_arc_speed_line_chart( data, x_values, @@ -1090,128 +1621,11 @@ def generate_heatmaps(base_cfg, price_data, launch_final_values, cache=None): """Generate pairwise heatmaps for thermostat tuning and noise-vs-arb effects.""" owns_cache = cache is None if cache is None: - cache = make_sweep_cache(price_data) - metric_specs = [ - { - "key": "efficiency_pct", - "title": "Efficiency vs heatmap geometric", - "colorbar_label": "Const Arc - heatmap Geo (% of heatmap geometric final value)", - "slug": "efficiency", - "center_zero": True, - "cmap": "RdYlGn", - }, - { - "key": "launch_geometric_efficiency_pct", - "title": "Efficiency vs launch-style geometric", - "colorbar_label": "Const Arc - launch Geo (% of launch geometric final value)", - "slug": "launch_geometric_efficiency", - "center_zero": True, - "cmap": "RdYlGn", - }, - { - "key": "geometric_vs_launch_geometric_pct", - "title": "Geometric tuning vs launch-style geometric", - "colorbar_label": "Candidate Geo - launch Geo (% of launch geometric final value)", - "slug": "geometric_vs_launch_geometric", - "center_zero": True, - "cmap": "RdYlGn", - }, - { - "key": "constant_arc_vs_launch_constant_arc_pct", - "title": "Const arc tuning vs launch-style const arc", - "colorbar_label": "Candidate Const Arc - launch Const Arc (% of launch const arc final value)", - "slug": "constant_arc_vs_launch_constant_arc", - "center_zero": True, - "cmap": "RdYlGn", - }, - { - "key": "noise_geometric_final_value_musd", - "title": "Geometric final value with noise model", - "colorbar_label": "Geometric final value with noise model ($M)", - "slug": "noise_geometric_final_value", - "center_zero": False, - "cmap": "viridis", - }, - { - "key": "noise_constant_arc_final_value_musd", - "title": "Const arc final value with noise model", - "colorbar_label": "Const Arc final value with noise model ($M)", - "slug": "noise_constant_arc_final_value", - "center_zero": False, - "cmap": "viridis", - }, - { - "key": "noise_vs_arb_geometric_improvement_pct", - "title": "Noise-model improvement over arb-only (geometric)", - "colorbar_label": "Noise-model Geo - arb-only Geo (% of arb-only final value)", - "slug": "noise_vs_arb_geometric_improvement", - "center_zero": True, - "cmap": "RdYlGn", - }, - { - "key": "noise_vs_arb_constant_arc_improvement_pct", - "title": "Noise-model improvement over arb-only (const arc)", - "colorbar_label": "Noise-model Const Arc - arb-only Const Arc (% of arb-only final value)", - "slug": "noise_vs_arb_constant_arc_improvement", - "center_zero": True, - "cmap": "RdYlGn", - }, - ] - for spec in metric_specs: - if spec["center_zero"]: - spec["color_norm"] = CENTER_ZERO_HEATMAP_COLOR_NORM - spec["symlog_linthresh"] = CENTER_ZERO_HEATMAP_SYMLOG_LINTHRESH - spec["artifact_tag"] = CENTER_ZERO_HEATMAP_COLOR_TAG - if not RUN_CONSTANT_ARC_LENGTH: - metric_specs = [ - spec - for spec in metric_specs - if spec["key"] in GEOMETRIC_ONLY_HEATMAP_METRIC_KEYS - ] - pair_specs = [ - { - "slug": "price_ratio_vs_margin", - "x_values": HEATMAP_PRICE_RATIOS, - "y_values": HEATMAP_MARGINS, - "x_key": "price_ratio", - "y_key": "centeredness_margin", - "x_label": "Price ratio", - "y_label": "Centeredness margin", - "title_suffix": ( - f"shift_exp fixed at {base_cfg['daily_price_shift_exponent']:.2f}" - ), - "xticks": PRICE_RATIO_TICKS, - "yticks": MARGIN_TICKS - }, - { - "slug": "shift_exp_vs_margin", - "x_values": HEATMAP_SHIFT_EXPONENTS, - "y_values": HEATMAP_MARGINS, - "x_key": "daily_price_shift_exponent", - "y_key": "centeredness_margin", - "x_label": "Shift exponent", - "y_label": "Centeredness margin", - "title_suffix": f"price_ratio fixed at {base_cfg['price_ratio']:.2f}", - "xticks": SHIFT_EXPONENT_TICKS, - "yticks": MARGIN_TICKS - }, - { - "slug": "price_ratio_vs_shift_exp", - "x_values": HEATMAP_PRICE_RATIOS, - "y_values": HEATMAP_SHIFT_EXPONENTS, - "x_key": "price_ratio", - "y_key": "daily_price_shift_exponent", - "x_label": "Price ratio", - "y_label": "Shift exponent", - "title_suffix": ( - f"margin fixed at {base_cfg['centeredness_margin']:.2f}" - ), - "xticks": PRICE_RATIO_TICKS, - "yticks": SHIFT_EXPONENT_TICKS, - }, - ] - + cache = make_sweep_cache(price_data, cache_scope_cfg=base_cfg) + metric_specs = get_pair_heatmap_metric_specs() + pair_specs = get_pair_heatmap_specs(base_cfg) metric_spec_map = {spec["key"]: spec for spec in metric_specs} + slice_count = len(pair_specs[0]["fixed_slices"]) if pair_specs else 0 if RUN_CONSTANT_ARC_LENGTH: print( @@ -1222,9 +1636,11 @@ def generate_heatmaps(base_cfg, price_data, launch_final_values, cache=None): ) print( "Running {count} heatmap pair sweeps sequentially " - "(current outputs use cached noise-model runs; improvement heatmaps " - "reuse those values and add cached arb-only runs).".format( - count=len(pair_specs) + "(3 pair grids x {slice_count} fixed-variable quarter slices; " + "cached noise-model runs are reused across the absolute, launch, " + "and arb-only comparison outputs).".format( + count=len(pair_specs) * slice_count, + slice_count=slice_count, ) ) else: @@ -1235,20 +1651,135 @@ def generate_heatmaps(base_cfg, price_data, launch_final_values, cache=None): ) print( "RUN_CONSTANT_ARC_LENGTH=False, so only geometric heatmaps will be generated " - "and only geometric/arb-only geometric runs will be scheduled." + f"across {len(pair_specs) * slice_count} fixed-variable pair sweeps." ) for pair in pair_specs: + for slice_variant in pair["fixed_slices"]: + pair_suffix = _pair_slice_suffix(pair, slice_variant) + slice_cfg = dict(base_cfg) + slice_cfg[pair["fixed_key"]] = float(slice_variant["value"]) + output_files = { + spec["key"]: heatmap_artifact_filename( + spec, + base_cfg, + suffix=pair_suffix, + ) + for spec in metric_specs + } + missing_files = _missing_artifacts( + pair_suffix, + list(output_files.values()), + ) + if not missing_files: + continue + + missing_metric_keys = [ + spec["key"] + for spec in metric_specs + if output_files[spec["key"]] in missing_files + ] + data_by_metric = build_heatmap_matrices( + x_values=pair["x_values"], + y_values=pair["y_values"], + x_key=pair["x_key"], + y_key=pair["y_key"], + base_cfg=slice_cfg, + metric_keys=missing_metric_keys, + cache=cache, + progress_label=pair_suffix, + launch_final_values=launch_final_values, + ) + print(f"[{pair_suffix}] plotting missing heatmaps...") + for metric_key in missing_metric_keys: + spec = metric_spec_map[metric_key] + plot_heatmap( + data=data_by_metric[metric_key], + x_values=pair["x_values"], + y_values=pair["y_values"], + x_label=pair["x_label"], + y_label=pair["y_label"], + title=( + f"{spec['title']}: {pair['fixed_label']} {slice_variant['label']} " + f"slice fixed at {format_heatmap_param_value(slice_variant['value'])} | " + f"TVL {format_tvl_millions_label(base_cfg)}" + ), + colorbar_label=spec["colorbar_label"], + filename=output_files[metric_key], + xticks=pair["xticks"], + yticks=pair["yticks"], + center_zero=spec["center_zero"], + cmap=spec["cmap"], + color_norm=spec.get("color_norm"), + symlog_linthresh=spec.get("symlog_linthresh"), + ) + del data_by_metric + gc.collect() + + if owns_cache: + flush_sweep_cache(cache, force=True) + cache.clear() + gc.collect() + print("Released heatmap metric cache.") + + +def generate_three_variable_3d_heatmaps( + base_cfg, + price_data, + launch_final_values, + cache=None, +): + """Render 3D thermostat heatmaps from the three pairwise quarter slices.""" + owns_cache = cache is None + if cache is None: + cache = make_sweep_cache(price_data, cache_scope_cfg=base_cfg) + + metric_specs = get_pair_heatmap_metric_specs() + metric_spec_map = {spec["key"]: spec for spec in metric_specs} + pair_specs = get_pair_heatmap_specs(base_cfg) + pair_by_fixed_key = {pair["fixed_key"]: pair for pair in pair_specs} + price_margin_pair = pair_by_fixed_key["daily_price_shift_exponent"] + shift_margin_pair = pair_by_fixed_key["price_ratio"] + price_shift_pair = pair_by_fixed_key["centeredness_margin"] + slice_count = len(price_margin_pair["fixed_slices"]) + + def build_pair_slice_data(pair, slice_variant, metric_keys): + pair_cfg = dict(base_cfg) + pair_cfg[pair["fixed_key"]] = float(slice_variant["value"]) + return build_heatmap_matrices( + x_values=pair["x_values"], + y_values=pair["y_values"], + x_key=pair["x_key"], + y_key=pair["y_key"], + base_cfg=pair_cfg, + metric_keys=metric_keys, + cache=cache, + progress_label=f"3d_{_pair_slice_suffix(pair, slice_variant)}", + launch_final_values=launch_final_values, + ) + + print( + "\nGenerating 3D thermostat heatmaps " + f"({slice_count} quarter-slice variants, TVL={format_tvl_millions_label(base_cfg)})..." + ) + + for slice_idx in range(slice_count): + shift_slice = price_margin_pair["fixed_slices"][slice_idx] + price_slice = shift_margin_pair["fixed_slices"][slice_idx] + margin_slice = price_shift_pair["fixed_slices"][slice_idx] + slice_slug = shift_slice["slug"] + slice_label = shift_slice["label"] + output_files = { - spec["key"]: heatmap_artifact_filename( + spec["key"]: three_d_heatmap_artifact_filename( spec, base_cfg, - suffix=pair["slug"], + suffix=f"slice_{slice_slug}", ) for spec in metric_specs } missing_files = _missing_artifacts( - pair["slug"], + f"3d_slice_{slice_slug}", list(output_files.values()), ) if not missing_files: @@ -1259,46 +1790,53 @@ def generate_heatmaps(base_cfg, price_data, launch_final_values, cache=None): for spec in metric_specs if output_files[spec["key"]] in missing_files ] - data_by_metric = build_heatmap_matrices( - x_values=pair["x_values"], - y_values=pair["y_values"], - x_key=pair["x_key"], - y_key=pair["y_key"], - base_cfg=base_cfg, - metric_keys=missing_metric_keys, - cache=cache, - progress_label=pair["slug"], - launch_final_values=launch_final_values, + price_margin_data = build_pair_slice_data( + price_margin_pair, + shift_slice, + missing_metric_keys, + ) + shift_margin_data = build_pair_slice_data( + shift_margin_pair, + price_slice, + missing_metric_keys, + ) + price_shift_data = build_pair_slice_data( + price_shift_pair, + margin_slice, + missing_metric_keys, ) - print(f"[{pair['slug']}] plotting missing heatmaps...") + for metric_key in missing_metric_keys: spec = metric_spec_map[metric_key] - plot_heatmap( - data=data_by_metric[metric_key], - x_values=pair["x_values"], - y_values=pair["y_values"], - x_label=pair["x_label"], - y_label=pair["y_label"], + plot_three_variable_heatmap_3d( + price_margin_data=price_margin_data[metric_key], + shift_margin_data=shift_margin_data[metric_key], + price_shift_data=price_shift_data[metric_key], + fixed_price_ratio=float(price_slice["value"]), + fixed_margin=float(margin_slice["value"]), + fixed_shift_exponent=float(shift_slice["value"]), title=( - f"{spec['title']}: {pair['title_suffix']} | " - f"TVL {format_tvl_millions_label(base_cfg)}" + f"{spec['title']} 3D {slice_label} slice | TVL {format_tvl_millions_label(base_cfg)}\n" + f"price_ratio={format_heatmap_param_value(price_slice['value'])}, " + f"margin={format_heatmap_param_value(margin_slice['value'])}, " + f"shift_exp={format_heatmap_param_value(shift_slice['value'])}" ), colorbar_label=spec["colorbar_label"], filename=output_files[metric_key], - xticks=pair["xticks"], - yticks=pair["yticks"], center_zero=spec["center_zero"], cmap=spec["cmap"], color_norm=spec.get("color_norm"), symlog_linthresh=spec.get("symlog_linthresh"), ) - del data_by_metric + + del price_margin_data, shift_margin_data, price_shift_data gc.collect() if owns_cache: + flush_sweep_cache(cache, force=True) cache.clear() gc.collect() - print("Released heatmap metric cache.") + print("Released 3D heatmap cache.") def compute_auto_calibrated_arc_length_speed(cfg, price_data): @@ -1378,7 +1916,7 @@ def generate_arc_speed_efficiency_artifacts( return owns_cache = cache is None if cache is None: - cache = make_sweep_cache(price_data) + cache = make_sweep_cache(price_data, cache_scope_cfg=base_cfg) launch_auto_speed = compute_auto_calibrated_arc_length_speed(launch_cfg, price_data) heatmap_metric_specs = [ { @@ -1559,6 +2097,7 @@ def generate_arc_speed_efficiency_artifacts( gc.collect() if owns_cache: + flush_sweep_cache(cache, force=True) cache.clear() gc.collect() print("Released arc-speed sweep cache.") @@ -1965,7 +2504,10 @@ def plot_comparison(cfg, results, fig_idx): launch_cfg=tvl_configs[0], price_data=shared_price_data, ) - shared_sweep_cache = make_sweep_cache(shared_price_data) + shared_sweep_cache = make_sweep_cache( + shared_price_data, + cache_scope_cfg=tvl_configs[1], + ) print(f"\nGenerating thermostat heatmaps for TVL {tvl_label}...") generate_heatmaps( @@ -1982,6 +2524,13 @@ def plot_comparison(cfg, results, fig_idx): launch_final_values=launch_final_values, cache=shared_sweep_cache, ) + generate_three_variable_3d_heatmaps( + dict(tvl_configs[1]), + price_data=shared_price_data, + launch_final_values=launch_final_values, + cache=shared_sweep_cache, + ) + flush_sweep_cache(shared_sweep_cache, force=True) shared_sweep_cache.clear() gc.collect() print(f"Released shared sweep cache for TVL {tvl_label}.") diff --git a/scripts/reclamm/find_adjacent_heatmap_pairs.py b/scripts/reclamm/find_adjacent_heatmap_pairs.py new file mode 100644 index 0000000..1bc4ce3 --- /dev/null +++ b/scripts/reclamm/find_adjacent_heatmap_pairs.py @@ -0,0 +1,1264 @@ +"""Scan cached reCLAMM heatmaps for adjacent cells with large value gaps. + +This script reconstructs heatmap cells from the persisted scalar forward-value +cache written by ``compare_reclamm_thermostats.py``. It does not inspect PNG +pixels or rerun the simulator for cache-backed metrics. +""" + +from __future__ import annotations + +import argparse +import hashlib +import importlib.util +import math +import os +from pathlib import Path +from typing import Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple + +import numpy as np +import pandas as pd + + +CACHE_ONLY_METRIC_SPECS = { + "efficiency_pct": { + "sources": ("noise_constant_arc", "noise_geometric"), + "unit": "pct", + "compute": lambda values: ( + values["noise_constant_arc"] / max(abs(values["noise_geometric"]), 1.0e-12) + - 1.0 + ) + * 100.0, + }, + "noise_geometric_final_value_musd": { + "sources": ("noise_geometric",), + "unit": "musd", + "compute": lambda values: values["noise_geometric"] / 1.0e6, + }, + "noise_constant_arc_final_value_musd": { + "sources": ("noise_constant_arc",), + "unit": "musd", + "compute": lambda values: values["noise_constant_arc"] / 1.0e6, + }, + "noise_vs_arb_geometric_improvement_pct": { + "sources": ("noise_geometric", "arb_geometric"), + "unit": "pct", + "compute": lambda values: ( + values["noise_geometric"] / max(abs(values["arb_geometric"]), 1.0e-12) - 1.0 + ) + * 100.0, + }, + "noise_vs_arb_constant_arc_improvement_pct": { + "sources": ("noise_constant_arc", "arb_constant_arc"), + "unit": "pct", + "compute": lambda values: ( + values["noise_constant_arc"] + / max(abs(values["arb_constant_arc"]), 1.0e-12) + - 1.0 + ) + * 100.0, + }, +} + +OUTPUT_COLUMNS = [ + "metric_key", + "metric_unit", + "source_noise_profile", + "pair_slug", + "slice_slug", + "slice_label", + "fixed_key", + "fixed_value", + "adjacency_axis", + "heatmap_value_diff_abs", + "heatmap_value_diff_signed_2_minus_1", + "1_price_ratio", + "1_centeredness_margin", + "1_daily_price_shift_exponent", + "1_tvl_usd", + "1_heatmap_value", + "1_x_index", + "1_y_index", + "2_price_ratio", + "2_centeredness_margin", + "2_daily_price_shift_exponent", + "2_tvl_usd", + "2_heatmap_value", + "2_x_index", + "2_y_index", +] + + +def build_inclusive_sweep(start: float, stop: float, step: float) -> np.ndarray: + """Build a sweep that keeps the requested step and explicitly includes the stop.""" + values = np.arange(start, stop + 1.0e-12, step, dtype=float) + if values.size == 0 or not np.isclose(values[-1], stop): + values = np.append(values, float(stop)) + return values + + +class _LightweightCompareContext: + """Small subset of compare_reclamm_thermostats usable without JAX.""" + + RUN_CONSTANT_ARC_LENGTH = True + DEFAULT_INITIAL_POOL_VALUE = 1_000_000.0 + TVL_SWEEP_VALUES = ( + 1_000_000.0, + 5_000_000.0, + 20_000_000.0, + ) + HEATMAP_PRICE_RATIOS = build_inclusive_sweep(1.01, 3.00, 0.025) + HEATMAP_MARGINS = np.linspace(0.05, 0.90, 39) + HEATMAP_SHIFT_EXPONENTS = build_inclusive_sweep(0.01, 0.50, 0.0125) + FIXED_SLICE_FRACTIONS = (0.125, 0.375, 0.625, 0.875) + FIXED_SLICE_LABELS = ("Q1", "Q2", "Q3", "Q4") + HEATMAP_FORWARD_CACHE_ENABLED = True + HEATMAP_FORWARD_CACHE_RUN_NAME = "aave_eth_thermostat_heatmaps_v1" + HEATMAP_FORWARD_CACHE_ROOT = os.path.join( + "results", + "reclamm_heatmap_forward_cache", + ) + AAVE_WETH_POOL_ID = "0x9d1fcf346ea1b0" + DEFAULT_MARKET_LINEAR_ARTIFACT_DIR = "results/linear_market_noise" + DEFAULT_NOISE_MODEL = "market_linear" + DEFAULT_GAS_COST = 1.0 + DEFAULT_PROTOCOL_FEE_SPLIT = 0.25 + LEGACY_NOISE_COEFFS = [ + -0.453, + 0.025, + -0.060, + 0.310, + -0.149, + 0.359, + 0.061, + 0.060, + ] + LEGACY_LOG_CADENCE = 2.68 + LEGACY_ARB_FREQUENCY = max(1, round(math.exp(LEGACY_LOG_CADENCE))) + FIXED_COMPARE_ARB_FREQUENCY = LEGACY_ARB_FREQUENCY + AAVE_ETH_NOISE_SETTINGS = { + "enable_noise_model": True, + "noise_model": DEFAULT_NOISE_MODEL, + "noise_artifact_dir": DEFAULT_MARKET_LINEAR_ARTIFACT_DIR, + "noise_pool_id": AAVE_WETH_POOL_ID, + "arb_frequency": FIXED_COMPARE_ARB_FREQUENCY, + "gas_cost": DEFAULT_GAS_COST, + "protocol_fee_split": DEFAULT_PROTOCOL_FEE_SPLIT, + } + CONFIGS = [ + { + "name": "AAVE/ETH launch-style range (25bps, reference)", + "tokens": ["AAVE", "ETH"], + "start": "2024-06-01 00:00:00", + "end": "2025-06-01 00:00:00", + "fees": 0.0025, + "price_ratio": 1.5014, + "centeredness_margin": 0.5, + "daily_price_shift_exponent": 0.1, + "reason": "Original launch-style parameters.", + **AAVE_ETH_NOISE_SETTINGS, + }, + { + "name": "AAVE/ETH aggressive tight range (25bps)", + "tokens": ["AAVE", "ETH"], + "start": "2024-06-01 00:00:00", + "end": "2025-06-01 00:00:00", + "fees": 0.0025, + "price_ratio": 1.10, + "centeredness_margin": 0.60, + "daily_price_shift_exponent": 0.1, + "reason": ( + "Aggressively tightened and moved to an earlier thermostat trigger. " + "At fixed price_ratio=1.10, the shift_exponent sweep still favored " + "0.1, while margin=0.60 widened the non-linear edge materially." + ), + **AAVE_ETH_NOISE_SETTINGS, + }, + ] + + def __init__(self): + self._noise_settings_cache = {} + self.noise_profile = "market_linear" + + @classmethod + def from_compare_module(cls, compare_module): + """Build an analyzer-friendly context from an imported thermostat module.""" + context = cls() + copied_attrs = ( + "RUN_CONSTANT_ARC_LENGTH", + "DEFAULT_INITIAL_POOL_VALUE", + "TVL_SWEEP_VALUES", + "HEATMAP_PRICE_RATIOS", + "HEATMAP_MARGINS", + "HEATMAP_SHIFT_EXPONENTS", + "FIXED_SLICE_FRACTIONS", + "FIXED_SLICE_LABELS", + "HEATMAP_FORWARD_CACHE_ENABLED", + "HEATMAP_FORWARD_CACHE_RUN_NAME", + "HEATMAP_FORWARD_CACHE_ROOT", + "AAVE_WETH_POOL_ID", + "DEFAULT_MARKET_LINEAR_ARTIFACT_DIR", + "DEFAULT_NOISE_MODEL", + "DEFAULT_GAS_COST", + "DEFAULT_PROTOCOL_FEE_SPLIT", + "LEGACY_NOISE_COEFFS", + "LEGACY_LOG_CADENCE", + "LEGACY_ARB_FREQUENCY", + "FIXED_COMPARE_ARB_FREQUENCY", + "AAVE_ETH_NOISE_SETTINGS", + "CONFIGS", + ) + for attr_name in copied_attrs: + if hasattr(compare_module, attr_name): + value = getattr(compare_module, attr_name) + if attr_name == "CONFIGS": + value = [dict(cfg) for cfg in value] + elif isinstance(value, dict): + value = dict(value) + elif isinstance(value, np.ndarray): + value = np.asarray(value, dtype=float).copy() + elif isinstance(value, tuple): + value = tuple(value) + elif isinstance(value, list): + value = list(value) + setattr(context, attr_name, value) + context._noise_settings_cache.clear() + return context + + def set_noise_profile(self, profile): + if profile not in {"market_linear", "legacy_calibrated"}: + raise ValueError(f"Unsupported lightweight noise profile: {profile}") + if profile != self.noise_profile: + self.noise_profile = profile + self._noise_settings_cache.clear() + + def get_initial_pool_value(self, cfg): + return float(cfg.get("initial_pool_value", self.DEFAULT_INITIAL_POOL_VALUE)) + + def get_tvl_millions(self, cfg): + return self.get_initial_pool_value(cfg) / 1_000_000.0 + + def format_tvl_millions_slug(self, cfg): + tvl_millions = self.get_tvl_millions(cfg) + rounded = round(float(tvl_millions), 6) + if np.isclose(rounded, round(rounded)): + return f"{int(round(rounded))}m" + return f"{rounded:.6f}".rstrip("0").rstrip(".").replace(".", "p") + "m" + + def format_tvl_millions_label(self, cfg): + return f"{self.get_tvl_millions(cfg):.1f}M" + + def configs_for_tvl(self, base_configs, initial_pool_value): + configs = [] + for cfg in base_configs: + updated = dict(cfg) + updated["initial_pool_value"] = float(initial_pool_value) + configs.append(updated) + return configs + + def _heatmap_forward_cache_scope_slug(self, cfg): + if cfg is None: + return "unspecified_tvl" + return f"tvl_{self.format_tvl_millions_slug(cfg)}" + + def _heatmap_forward_cache_path(self, cfg): + if not self.HEATMAP_FORWARD_CACHE_ENABLED: + return None + return os.path.join( + self.HEATMAP_FORWARD_CACHE_ROOT, + self.HEATMAP_FORWARD_CACHE_RUN_NAME, + f"forward_values_{self._heatmap_forward_cache_scope_slug(cfg)}.parquet", + ) + + def build_fixed_slice_variants(self, values): + values = np.asarray(values, dtype=float) + if values.size < len(self.FIXED_SLICE_FRACTIONS): + raise ValueError("Need at least four grid points to build fixed slices") + + variants = [] + used_indices = set() + for idx, fraction in enumerate(self.FIXED_SLICE_FRACTIONS): + target_index = int(round(fraction * (values.size - 1))) + while target_index in used_indices and target_index + 1 < values.size: + target_index += 1 + while target_index in used_indices and target_index - 1 >= 0: + target_index -= 1 + if target_index in used_indices: + raise ValueError( + "Could not build four unique fixed slices from sweep grid" + ) + used_indices.add(target_index) + variants.append( + { + "index": target_index, + "fraction": fraction, + "label": self.FIXED_SLICE_LABELS[idx], + "slug": f"q{idx + 1}", + "value": float(values[target_index]), + } + ) + return variants + + def get_pair_heatmap_specs(self, _base_cfg): + fixed_slice_variants = { + "price_ratio": self.build_fixed_slice_variants(self.HEATMAP_PRICE_RATIOS), + "centeredness_margin": self.build_fixed_slice_variants(self.HEATMAP_MARGINS), + "daily_price_shift_exponent": self.build_fixed_slice_variants( + self.HEATMAP_SHIFT_EXPONENTS + ), + } + return [ + { + "slug": "price_ratio_vs_margin", + "x_values": self.HEATMAP_PRICE_RATIOS, + "y_values": self.HEATMAP_MARGINS, + "x_key": "price_ratio", + "y_key": "centeredness_margin", + "fixed_key": "daily_price_shift_exponent", + "fixed_slices": fixed_slice_variants["daily_price_shift_exponent"], + }, + { + "slug": "shift_exp_vs_margin", + "x_values": self.HEATMAP_SHIFT_EXPONENTS, + "y_values": self.HEATMAP_MARGINS, + "x_key": "daily_price_shift_exponent", + "y_key": "centeredness_margin", + "fixed_key": "price_ratio", + "fixed_slices": fixed_slice_variants["price_ratio"], + }, + { + "slug": "price_ratio_vs_shift_exp", + "x_values": self.HEATMAP_PRICE_RATIOS, + "y_values": self.HEATMAP_SHIFT_EXPONENTS, + "x_key": "price_ratio", + "y_key": "daily_price_shift_exponent", + "fixed_key": "centeredness_margin", + "fixed_slices": fixed_slice_variants["centeredness_margin"], + }, + ] + + @staticmethod + def _hashable_noise_params(params): + if params is None: + return None + return tuple(sorted((str(k), round(float(v), 12)) for k, v in params.items())) + + def _normalize_arb_frequency(self, value, default=None): + if value is None: + if default is None: + default = self.FIXED_COMPARE_ARB_FREQUENCY + value = default + return max(int(round(float(value))), 1) + + def get_effective_arb_frequency(self, cfg, noise_cfg=None): + del noise_cfg + return self._normalize_arb_frequency(self.FIXED_COMPARE_ARB_FREQUENCY) + + def normalize_compare_run_cfg(self, cfg, enable_noise_model=None): + updated = dict(cfg) + updated["price_ratio"] = float(cfg["price_ratio"]) + updated["centeredness_margin"] = float(cfg["centeredness_margin"]) + updated["daily_price_shift_exponent"] = float( + cfg["daily_price_shift_exponent"] + ) + updated["initial_pool_value"] = float(self.get_initial_pool_value(cfg)) + updated["gas_cost"] = self.DEFAULT_GAS_COST + updated["protocol_fee_split"] = self.DEFAULT_PROTOCOL_FEE_SPLIT + updated["arb_fees"] = 0.0 + updated["arb_frequency"] = self.get_effective_arb_frequency(cfg) + updated["noise_trader_ratio"] = 0.0 + + arc_length_speed = cfg.get("arc_length_speed") + if arc_length_speed is None: + updated.pop("arc_length_speed", None) + else: + updated["arc_length_speed"] = float(arc_length_speed) + + use_noise = ( + bool(cfg.get("enable_noise_model", False)) + if enable_noise_model is None + else bool(enable_noise_model) + ) + updated["enable_noise_model"] = use_noise + + requested_mode = ( + cfg.get("noise_model", self.DEFAULT_NOISE_MODEL) + or self.DEFAULT_NOISE_MODEL + ) + if use_noise: + canonical_noise_model = ( + requested_mode + if requested_mode != "arb_only" + else self.DEFAULT_NOISE_MODEL + ) + updated["noise_model"] = canonical_noise_model + if canonical_noise_model == "market_linear": + updated["noise_artifact_dir"] = self.DEFAULT_MARKET_LINEAR_ARTIFACT_DIR + updated["noise_pool_id"] = self.AAVE_WETH_POOL_ID + else: + updated.pop("noise_artifact_dir", None) + updated.pop("noise_pool_id", None) + updated.pop("reclamm_noise_params", None) + updated.pop("noise_arrays_path", None) + else: + updated["noise_model"] = "arb_only" + for key in ( + "reclamm_noise_params", + "noise_arrays_path", + "noise_artifact_dir", + "noise_pool_id", + ): + updated.pop(key, None) + + return updated + + def _legacy_calibrated_noise_settings(self, arb_frequency=None): + arb_frequency = self._normalize_arb_frequency(arb_frequency) + return { + "noise_model": "calibrated", + "noise_trader_ratio": 0.0, + "reclamm_noise_params": { + f"c_{i}": self.LEGACY_NOISE_COEFFS[i] + for i in range(len(self.LEGACY_NOISE_COEFFS)) + }, + "arb_frequency": arb_frequency, + "noise_summary": ( + "calibrated legacy 8-covariate " + f"(arb_frequency={arb_frequency})" + ), + "noise_cache_key": ( + "calibrated", + tuple(round(float(c), 12) for c in self.LEGACY_NOISE_COEFFS), + arb_frequency, + ), + } + + def resolve_reclamm_noise_settings(self, cfg): + cfg = self.normalize_compare_run_cfg(cfg) + enable_noise_model = cfg.get("enable_noise_model", False) + requested_mode = cfg.get("noise_model", self.DEFAULT_NOISE_MODEL) + requested_arb_frequency = self.get_effective_arb_frequency(cfg) + cache_key = ( + tuple(cfg.get("tokens", [])), + cfg.get("start"), + cfg.get("end"), + enable_noise_model, + requested_mode, + cfg.get("noise_artifact_dir", self.DEFAULT_MARKET_LINEAR_ARTIFACT_DIR), + cfg.get("noise_pool_id", self.AAVE_WETH_POOL_ID), + requested_arb_frequency, + round(float(cfg.get("noise_trader_ratio", 0.0)), 12), + self._hashable_noise_params(cfg.get("reclamm_noise_params")), + cfg.get("noise_arrays_path"), + ) + if cache_key in self._noise_settings_cache: + return self._noise_settings_cache[cache_key] + + if not enable_noise_model: + result = { + "noise_model": "arb_only", + "noise_trader_ratio": 0.0, + "reclamm_noise_params": None, + "noise_arrays_path": None, + "arb_frequency": requested_arb_frequency, + "noise_summary": "arb_only (noise disabled)", + "noise_cache_key": ("disabled",), + } + elif requested_mode == "market_linear": + if self.noise_profile == "legacy_calibrated": + result = self._legacy_calibrated_noise_settings( + arb_frequency=requested_arb_frequency + ) + self._noise_settings_cache[cache_key] = result + return result + artifact_dir = cfg.get( + "noise_artifact_dir", + self.DEFAULT_MARKET_LINEAR_ARTIFACT_DIR, + ) + pool_id = cfg.get("noise_pool_id", self.AAVE_WETH_POOL_ID) + start_date = str(cfg["start"]).split(" ")[0] + end_date = str(cfg["end"]).split(" ")[0] + arrays_path = cfg.get("noise_arrays_path") or os.path.join( + artifact_dir, + "_sim_arrays", + f"{pool_id}_{start_date}_{end_date}.npz", + ) + meta_path = os.path.join(artifact_dir, "meta.json") + model_path = os.path.join(artifact_dir, "model.npz") + if not ( + os.path.exists(arrays_path) + and os.path.exists(meta_path) + and os.path.exists(model_path) + ): + result = self._legacy_calibrated_noise_settings( + arb_frequency=requested_arb_frequency + ) + else: + with np.load(arrays_path) as arrays: + tvl_mean = float(arrays["tvl_mean"]) + tvl_std = float(arrays["tvl_std"]) + arb_frequency = requested_arb_frequency + result = { + "noise_model": "market_linear", + "noise_trader_ratio": 0.0, + "reclamm_noise_params": { + "tvl_mean": tvl_mean, + "tvl_std": tvl_std, + }, + "noise_arrays_path": arrays_path, + "arb_frequency": arb_frequency, + "noise_summary": f"market_linear (arb_frequency={arb_frequency})", + "noise_cache_key": ( + "market_linear", + arrays_path, + arb_frequency, + round(tvl_mean, 12), + round(tvl_std, 12), + ), + } + elif requested_mode == "calibrated": + result = self._legacy_calibrated_noise_settings( + arb_frequency=requested_arb_frequency + ) + else: + arb_frequency = requested_arb_frequency + result = { + "noise_model": requested_mode, + "noise_trader_ratio": cfg.get("noise_trader_ratio", 0.0), + "reclamm_noise_params": cfg.get("reclamm_noise_params"), + "noise_arrays_path": cfg.get("noise_arrays_path"), + "arb_frequency": arb_frequency, + "noise_summary": f"{requested_mode} (arb_frequency={arb_frequency})", + "noise_cache_key": ( + requested_mode, + round(float(cfg.get("noise_trader_ratio", 0.0)), 12), + self._hashable_noise_params(cfg.get("reclamm_noise_params")), + cfg.get("noise_arrays_path"), + arb_frequency, + ), + } + + self._noise_settings_cache[cache_key] = result + return result + + def make_noise_variant_cfg(self, cfg, enable_noise_model): + return self.normalize_compare_run_cfg( + cfg, + enable_noise_model=enable_noise_model, + ) + + @staticmethod + def _make_method_cache_hash(key): + return hashlib.sha256(repr(key).encode("utf-8")).hexdigest() + + def _make_method_cache_key(self, cfg, method): + cfg = self.normalize_compare_run_cfg(cfg) + noise_cfg = self.resolve_reclamm_noise_settings(cfg) + arb_frequency = self.get_effective_arb_frequency(cfg, noise_cfg) + key = ( + method, + bool(cfg.get("enable_noise_model", False)), + round(float(cfg["price_ratio"]), 6), + round(float(cfg["centeredness_margin"]), 6), + round(float(cfg["daily_price_shift_exponent"]), 6), + round(self.get_initial_pool_value(cfg), 2), + noise_cfg.get("noise_cache_key"), + None if arb_frequency is None else int(arb_frequency), + round( + float( + cfg.get( + "gas_cost", + self.DEFAULT_GAS_COST + if cfg.get("enable_noise_model", False) + else 0.0, + ) + ), + 6, + ), + round( + float( + cfg.get( + "protocol_fee_split", + self.DEFAULT_PROTOCOL_FEE_SPLIT + if cfg.get("enable_noise_model", False) + else 0.0, + ) + ), + 6, + ), + ) + if method == "constant_arc_length": + speed = cfg.get("arc_length_speed") + key += (None if speed is None else round(float(speed), 12),) + return key + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=( + "Identify horizontally and vertically adjacent reCLAMM heatmap cells " + "whose derived metric values differ by at least the requested threshold." + ) + ) + parser.add_argument( + "--metric-key", + default="noise_vs_arb_geometric_improvement_pct", + choices=sorted(CACHE_ONLY_METRIC_SPECS), + help="Heatmap metric to reconstruct from the persisted forward-value cache.", + ) + parser.add_argument( + "--pair-slug", + default="price_ratio_vs_margin", + help="Pair heatmap family slug, or 'all' to scan every pair family.", + ) + parser.add_argument( + "--slice-slug", + default="all", + help="Quarter-slice slug (q1/q2/q3/q4), or 'all' to scan every slice.", + ) + parser.add_argument( + "--min-diff", + type=float, + default=30.0, + help=( + "Minimum absolute difference between adjacent heatmap values. " + "For the default metric this is in percentage points." + ), + ) + parser.add_argument( + "--adjacency-axis", + default="both", + choices=("both", "horizontal", "vertical"), + help=( + "Which adjacency direction to scan. " + "'both' includes horizontal and vertical neighbors." + ), + ) + parser.add_argument( + "--initial-pool-value", + type=float, + default=1_000_000.0, + help="TVL in USD used for the cached heatmap sweep.", + ) + parser.add_argument( + "--config-index", + type=int, + default=1, + help="Which compare_reclamm_thermostats.py base config to use.", + ) + parser.add_argument( + "--cache-path", + default=None, + help="Optional parquet cache override. Defaults to the compare script's TVL cache.", + ) + parser.add_argument( + "--output-csv", + default=None, + help="Optional CSV output path. Defaults under scripts/results/.", + ) + parser.add_argument( + "--skip-top-row-geometric-comparison", + action="store_true", + help=( + "Skip the follow-up geometric noise comparison for the top CSV row. " + "By default the script attempts that comparison after writing the CSV." + ), + ) + parser.add_argument( + "--top-row-geometric-comparison-output-file", + default=None, + help="Optional PNG output path override for the top-row geometric comparison.", + ) + parser.add_argument( + "--allow-partial-cache", + action="store_true", + help="Write output even if some heatmap cells are missing from the cache.", + ) + return parser.parse_args() + + +def load_compare_module(module_path: Optional[Path] = None): + compare_path = module_path or Path(__file__).with_name("compare_reclamm_thermostats.py") + spec = importlib.util.spec_from_file_location( + "reclamm_compare_reclamm_thermostats", + compare_path, + ) + if spec is not None and spec.loader is not None: + module = importlib.util.module_from_spec(spec) + try: + spec.loader.exec_module(module) + return _LightweightCompareContext.from_compare_module(module) + except ModuleNotFoundError as exc: + if exc.name != "jax": + raise + print( + "compare_reclamm_thermostats.py depends on jax in this environment; " + "using the lightweight cache-key context instead." + ) + return _LightweightCompareContext() + + +def get_metric_spec(metric_key: str) -> Mapping[str, object]: + try: + return CACHE_ONLY_METRIC_SPECS[metric_key] + except KeyError as exc: + raise ValueError( + f"Unsupported metric_key={metric_key!r}. " + f"Supported cache-only metrics: {sorted(CACHE_ONLY_METRIC_SPECS)}" + ) from exc + + +def load_cache_lookup(cache_path: Path) -> Dict[str, float]: + frame = pd.read_parquet(cache_path, columns=["cache_key_hash", "final_value"]) + return { + str(row.cache_key_hash): float(row.final_value) + for row in frame.itertuples(index=False) + } + + +def resolve_existing_cache_path(cache_path: Path) -> Path: + candidates = [Path(cache_path)] + if not cache_path.is_absolute(): + candidates.append(Path("scripts") / cache_path) + for candidate in candidates: + if candidate.exists(): + return candidate + return Path(cache_path) + + +def load_geometric_compare_module(module_path: Optional[Path] = None): + compare_path = module_path or Path(__file__).with_name( + "compare_reclamm_geometric_noise_runs.py" + ) + spec = importlib.util.spec_from_file_location( + "reclamm_compare_reclamm_geometric_noise_runs", + compare_path, + ) + if spec is None or spec.loader is None: + raise RuntimeError(f"Could not load geometric compare module from {compare_path}") + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def run_top_row_geometric_comparison( + csv_path: Path, + output_file: Optional[str] = None, + row_index: int = 0, +): + """Run the paired geometric-vs-arb comparison using the top adjacent CSV row.""" + module = load_geometric_compare_module() + if not hasattr(module, "run_adjacent_csv_row_comparison"): + raise RuntimeError( + "compare_reclamm_geometric_noise_runs.py does not expose " + "run_adjacent_csv_row_comparison" + ) + return module.run_adjacent_csv_row_comparison( + csv_path=csv_path, + row_index=row_index, + output_file=output_file, + ) + + +def resolve_pair_specs(compare_module, base_cfg: Mapping[str, object], pair_slug: str): + pair_specs = compare_module.get_pair_heatmap_specs(base_cfg) + if pair_slug == "all": + return pair_specs + + matched = [pair for pair in pair_specs if pair["slug"] == pair_slug] + if not matched: + available = [pair["slug"] for pair in pair_specs] + raise ValueError( + f"Unknown pair slug {pair_slug!r}. Available pair slugs: {available}" + ) + return matched + + +def resolve_slice_variants(pair_spec: Mapping[str, object], slice_slug: str): + slice_variants = pair_spec["fixed_slices"] + if slice_slug == "all": + return list(slice_variants) + + matched = [variant for variant in slice_variants if variant["slug"] == slice_slug] + if not matched: + available = [variant["slug"] for variant in slice_variants] + raise ValueError( + f"Unknown slice slug {slice_slug!r}. Available slice slugs: {available}" + ) + return matched + + +def build_default_output_path( + compare_module, + base_cfg: Mapping[str, object], + metric_key: str, + pair_slug: str, + slice_slug: str, + min_diff: float, +) -> Path: + output_dir = Path("scripts/results/reclamm_heatmap_adjacency") + output_dir.mkdir(parents=True, exist_ok=True) + diff_token = str(float(min_diff)).rstrip("0").rstrip(".").replace(".", "p") + filename = ( + f"reclamm_adjacent_pairs_{metric_key}_{pair_slug}_{slice_slug}" + f"_mindiff_{diff_token}_tvl_{compare_module.format_tvl_millions_slug(base_cfg)}.csv" + ) + return output_dir / filename + + +def autodetect_lightweight_noise_profile( + compare_module, + base_cfg: Mapping[str, object], + pair_specs: Sequence[Mapping[str, object]], + metric_key: str, + slice_slug: str, + cache_lookup: Mapping[str, float], +): + if not hasattr(compare_module, "set_noise_profile"): + return + + metric_spec = get_metric_spec(metric_key) + if not pair_specs: + return + + pair_spec = pair_specs[0] + slice_variants = resolve_slice_variants(pair_spec, slice_slug) + if not slice_variants: + return + + slice_variant = slice_variants[0] + x_values = list(pair_spec["x_values"]) + y_values = list(pair_spec["y_values"]) + sample_x_indices = sorted({0, len(x_values) // 2, len(x_values) - 1}) + sample_y_indices = sorted({0, len(y_values) // 2, len(y_values) - 1}) + + scores = {} + for profile in ("market_linear", "legacy_calibrated"): + compare_module.set_noise_profile(profile) + hit_count = 0 + probe_count = 0 + slice_cfg = dict(base_cfg) + slice_cfg[pair_spec["fixed_key"]] = float(slice_variant["value"]) + for y_index in sample_y_indices: + for x_index in sample_x_indices: + cfg = dict(slice_cfg) + cfg[pair_spec["x_key"]] = float(x_values[x_index]) + cfg[pair_spec["y_key"]] = float(y_values[y_index]) + for source_name in metric_spec["sources"]: + source_cfg, method = _source_variant(compare_module, cfg, source_name) + cache_key = compare_module._make_method_cache_key(source_cfg, method) + cache_key_hash = compare_module._make_method_cache_hash(cache_key) + probe_count += 1 + if cache_key_hash in cache_lookup: + hit_count += 1 + scores[profile] = (hit_count, probe_count) + + best_profile = max( + scores, + key=lambda profile: (scores[profile][0], scores[profile][1], profile == "market_linear"), + ) + compare_module.set_noise_profile(best_profile) + hit_count, probe_count = scores[best_profile] + print( + f"Lightweight noise profile auto-detect chose {best_profile} " + f"({hit_count}/{probe_count} sample cache hits)." + ) + + +def _source_variant(compare_module, cfg: Mapping[str, object], source_name: str): + enable_noise_model = source_name.startswith("noise_") + method = "geometric" if source_name.endswith("geometric") else "constant_arc_length" + source_cfg = compare_module.make_noise_variant_cfg(cfg, enable_noise_model) + source_cfg["noise_model"] = ( + getattr(compare_module, "DEFAULT_NOISE_MODEL", "market_linear") + if enable_noise_model + else "arb_only" + ) + return source_cfg, method + + +def _compute_metric_value(metric_key: str, final_values: Mapping[str, float]) -> float: + metric_spec = get_metric_spec(metric_key) + return float(metric_spec["compute"](final_values)) + + +def build_cell_record( + compare_module, + cfg: Mapping[str, object], + pair_spec: Mapping[str, object], + slice_variant: Mapping[str, object], + metric_key: str, + x_index: int, + y_index: int, + cache_lookup: Mapping[str, float], +): + metric_spec = get_metric_spec(metric_key) + final_values = {} + missing_hashes = [] + for source_name in metric_spec["sources"]: + source_cfg, method = _source_variant(compare_module, cfg, source_name) + cache_key = compare_module._make_method_cache_key(source_cfg, method) + cache_key_hash = compare_module._make_method_cache_hash(cache_key) + cached_value = cache_lookup.get(cache_key_hash) + if cached_value is None: + missing_hashes.append(cache_key_hash) + continue + final_values[source_name] = float(cached_value) + + if missing_hashes: + return None, missing_hashes + + return ( + { + "metric_key": metric_key, + "metric_unit": metric_spec["unit"], + "source_noise_profile": str( + getattr(compare_module, "noise_profile", "unknown") + ), + "pair_slug": pair_spec["slug"], + "slice_slug": slice_variant["slug"], + "slice_label": slice_variant["label"], + "fixed_key": pair_spec["fixed_key"], + "fixed_value": float(slice_variant["value"]), + "price_ratio": float(cfg["price_ratio"]), + "centeredness_margin": float(cfg["centeredness_margin"]), + "daily_price_shift_exponent": float(cfg["daily_price_shift_exponent"]), + "tvl_usd": float(compare_module.get_initial_pool_value(cfg)), + "heatmap_value": _compute_metric_value(metric_key, final_values), + "x_index": int(x_index), + "y_index": int(y_index), + }, + [], + ) + + +def build_slice_cell_grid( + compare_module, + base_cfg: Mapping[str, object], + pair_spec: Mapping[str, object], + slice_variant: Mapping[str, object], + metric_key: str, + cache_lookup: Mapping[str, float], +): + records_by_coord: Dict[Tuple[int, int], MutableMapping[str, object]] = {} + missing_hashes: List[str] = [] + x_values = pair_spec["x_values"] + y_values = pair_spec["y_values"] + slice_cfg = dict(base_cfg) + slice_cfg[pair_spec["fixed_key"]] = float(slice_variant["value"]) + + for y_index, y_value in enumerate(y_values): + for x_index, x_value in enumerate(x_values): + cfg = dict(slice_cfg) + cfg[pair_spec["x_key"]] = float(x_value) + cfg[pair_spec["y_key"]] = float(y_value) + record, missing_for_cell = build_cell_record( + compare_module=compare_module, + cfg=cfg, + pair_spec=pair_spec, + slice_variant=slice_variant, + metric_key=metric_key, + x_index=x_index, + y_index=y_index, + cache_lookup=cache_lookup, + ) + if record is not None: + records_by_coord[(y_index, x_index)] = record + missing_hashes.extend(missing_for_cell) + + expected_cell_count = len(x_values) * len(y_values) + return { + "records_by_coord": records_by_coord, + "expected_cell_count": expected_cell_count, + "resolved_cell_count": len(records_by_coord), + "missing_hash_count": len(missing_hashes), + "missing_hashes": missing_hashes, + } + + +def build_adjacent_row( + metric_key: str, + metric_unit: str, + axis: str, + first_cell: Mapping[str, object], + second_cell: Mapping[str, object], +) -> Dict[str, object]: + signed_diff = float(second_cell["heatmap_value"]) - float(first_cell["heatmap_value"]) + abs_diff = abs(signed_diff) + return { + "metric_key": metric_key, + "metric_unit": metric_unit, + "source_noise_profile": first_cell.get("source_noise_profile", "unknown"), + "pair_slug": first_cell["pair_slug"], + "slice_slug": first_cell["slice_slug"], + "slice_label": first_cell["slice_label"], + "fixed_key": first_cell["fixed_key"], + "fixed_value": float(first_cell["fixed_value"]), + "adjacency_axis": axis, + "heatmap_value_diff_abs": abs_diff, + "heatmap_value_diff_signed_2_minus_1": signed_diff, + "1_price_ratio": float(first_cell["price_ratio"]), + "1_centeredness_margin": float(first_cell["centeredness_margin"]), + "1_daily_price_shift_exponent": float(first_cell["daily_price_shift_exponent"]), + "1_tvl_usd": float(first_cell["tvl_usd"]), + "1_heatmap_value": float(first_cell["heatmap_value"]), + "1_x_index": int(first_cell["x_index"]), + "1_y_index": int(first_cell["y_index"]), + "2_price_ratio": float(second_cell["price_ratio"]), + "2_centeredness_margin": float(second_cell["centeredness_margin"]), + "2_daily_price_shift_exponent": float(second_cell["daily_price_shift_exponent"]), + "2_tvl_usd": float(second_cell["tvl_usd"]), + "2_heatmap_value": float(second_cell["heatmap_value"]), + "2_x_index": int(second_cell["x_index"]), + "2_y_index": int(second_cell["y_index"]), + } + + +def find_adjacent_rows_for_slice( + metric_key: str, + metric_unit: str, + records_by_coord: Mapping[Tuple[int, int], Mapping[str, object]], + x_count: int, + y_count: int, + min_diff: float, + adjacency_axis: str = "both", +) -> List[Dict[str, object]]: + rows = [] + + if adjacency_axis not in {"both", "horizontal", "vertical"}: + raise ValueError( + f"Unsupported adjacency_axis={adjacency_axis!r}; expected both, horizontal, or vertical" + ) + + if adjacency_axis in {"both", "horizontal"}: + for y_index in range(y_count): + for x_index in range(x_count - 1): + first_cell = records_by_coord.get((y_index, x_index)) + second_cell = records_by_coord.get((y_index, x_index + 1)) + if first_cell is None or second_cell is None: + continue + row = build_adjacent_row( + metric_key=metric_key, + metric_unit=metric_unit, + axis="horizontal", + first_cell=first_cell, + second_cell=second_cell, + ) + if row["heatmap_value_diff_abs"] >= min_diff: + rows.append(row) + + if adjacency_axis in {"both", "vertical"}: + for y_index in range(y_count - 1): + for x_index in range(x_count): + first_cell = records_by_coord.get((y_index, x_index)) + second_cell = records_by_coord.get((y_index + 1, x_index)) + if first_cell is None or second_cell is None: + continue + row = build_adjacent_row( + metric_key=metric_key, + metric_unit=metric_unit, + axis="vertical", + first_cell=first_cell, + second_cell=second_cell, + ) + if row["heatmap_value_diff_abs"] >= min_diff: + rows.append(row) + + rows.sort( + key=lambda row: ( + -float(row["heatmap_value_diff_abs"]), + str(row["pair_slug"]), + str(row["slice_slug"]), + str(row["adjacency_axis"]), + int(row["1_y_index"]), + int(row["1_x_index"]), + ) + ) + return rows + + +def scan_heatmap_pairs( + compare_module, + base_cfg: Mapping[str, object], + metric_key: str, + pair_specs: Sequence[Mapping[str, object]], + slice_slug: str, + min_diff: float, + adjacency_axis: str, + cache_lookup: Mapping[str, float], +): + metric_spec = get_metric_spec(metric_key) + all_rows: List[Dict[str, object]] = [] + diagnostics = [] + + for pair_spec in pair_specs: + slice_variants = resolve_slice_variants(pair_spec, slice_slug) + for slice_variant in slice_variants: + slice_scan = build_slice_cell_grid( + compare_module=compare_module, + base_cfg=base_cfg, + pair_spec=pair_spec, + slice_variant=slice_variant, + metric_key=metric_key, + cache_lookup=cache_lookup, + ) + diagnostics.append( + { + "pair_slug": pair_spec["slug"], + "slice_slug": slice_variant["slug"], + "resolved_cell_count": slice_scan["resolved_cell_count"], + "expected_cell_count": slice_scan["expected_cell_count"], + "missing_hash_count": slice_scan["missing_hash_count"], + } + ) + all_rows.extend( + find_adjacent_rows_for_slice( + metric_key=metric_key, + metric_unit=metric_spec["unit"], + records_by_coord=slice_scan["records_by_coord"], + x_count=len(pair_spec["x_values"]), + y_count=len(pair_spec["y_values"]), + min_diff=min_diff, + adjacency_axis=adjacency_axis, + ) + ) + + return all_rows, diagnostics + + +def rows_to_frame(rows: Iterable[Mapping[str, object]]) -> pd.DataFrame: + frame = pd.DataFrame(list(rows)) + if frame.empty: + return pd.DataFrame(columns=OUTPUT_COLUMNS) + frame = frame.loc[:, OUTPUT_COLUMNS] + frame.sort_values( + by=[ + "heatmap_value_diff_abs", + "pair_slug", + "slice_slug", + "adjacency_axis", + "1_y_index", + "1_x_index", + ], + ascending=[False, True, True, True, True, True], + inplace=True, + ignore_index=True, + ) + return frame + + +def main() -> int: + args = parse_args() + compare_module = load_compare_module() + + if not 0 <= args.config_index < len(compare_module.CONFIGS): + raise ValueError( + f"config-index {args.config_index} is out of range for " + f"{len(compare_module.CONFIGS)} available configs" + ) + + base_cfg = compare_module.configs_for_tvl( + compare_module.CONFIGS, + initial_pool_value=args.initial_pool_value, + )[args.config_index] + pair_specs = resolve_pair_specs(compare_module, base_cfg, args.pair_slug) + + cache_path = ( + Path(args.cache_path) + if args.cache_path is not None + else Path(compare_module._heatmap_forward_cache_path(base_cfg)) + ) + cache_path = resolve_existing_cache_path(cache_path) + if not cache_path.exists(): + raise FileNotFoundError(f"Cache parquet not found: {cache_path}") + + cache_lookup = load_cache_lookup(cache_path) + autodetect_lightweight_noise_profile( + compare_module=compare_module, + base_cfg=base_cfg, + pair_specs=pair_specs, + metric_key=args.metric_key, + slice_slug=args.slice_slug, + cache_lookup=cache_lookup, + ) + output_csv = ( + Path(args.output_csv) + if args.output_csv is not None + else build_default_output_path( + compare_module=compare_module, + base_cfg=base_cfg, + metric_key=args.metric_key, + pair_slug=args.pair_slug, + slice_slug=args.slice_slug, + min_diff=args.min_diff, + ) + ) + + print( + f"Loaded {len(cache_lookup):,} cached final values from {cache_path} " + f"for {base_cfg['name']} at TVL {compare_module.format_tvl_millions_label(base_cfg)}." + ) + print( + f"Scanning metric={args.metric_key}, pair_slug={args.pair_slug}, " + f"slice_slug={args.slice_slug}, adjacency_axis={args.adjacency_axis}, " + f"min_diff={args.min_diff} " + f"({get_metric_spec(args.metric_key)['unit']})." + ) + + rows, diagnostics = scan_heatmap_pairs( + compare_module=compare_module, + base_cfg=base_cfg, + metric_key=args.metric_key, + pair_specs=pair_specs, + slice_slug=args.slice_slug, + min_diff=args.min_diff, + adjacency_axis=args.adjacency_axis, + cache_lookup=cache_lookup, + ) + + for diagnostic in diagnostics: + print( + f"[{diagnostic['pair_slug']}:{diagnostic['slice_slug']}] " + f"resolved {diagnostic['resolved_cell_count']}/" + f"{diagnostic['expected_cell_count']} cells " + f"({diagnostic['missing_hash_count']} missing cache hashes)" + ) + + missing_any = any(diagnostic["missing_hash_count"] > 0 for diagnostic in diagnostics) + if missing_any and not args.allow_partial_cache: + raise RuntimeError( + "Cache was incomplete for at least one requested heatmap slice. " + "Re-run with --allow-partial-cache to write the rows that were resolvable." + ) + + frame = rows_to_frame(rows) + output_csv.parent.mkdir(parents=True, exist_ok=True) + frame.to_csv(output_csv, index=False) + print(f"Wrote {len(frame):,} adjacent pairs to {output_csv}") + + if not args.skip_top_row_geometric_comparison: + if frame.empty: + print("Skipping top-row geometric comparison because the CSV is empty.") + else: + print( + "Running geometric noise comparison for the top adjacent-pairs CSV row..." + ) + try: + comparison_output = run_top_row_geometric_comparison( + csv_path=output_csv, + output_file=args.top_row_geometric_comparison_output_file, + row_index=0, + ) + print( + f"Completed top-row geometric comparison using {output_csv} row 0. " + f"Output: {comparison_output}" + ) + except Exception as exc: # pragma: no cover - depends on local runtime deps + print( + "Top-row geometric comparison did not run successfully: " + f"{exc}" + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tests/scripts/test_compare_reclamm_geometric_noise_runs.py b/tests/scripts/test_compare_reclamm_geometric_noise_runs.py new file mode 100644 index 0000000..8a0fb74 --- /dev/null +++ b/tests/scripts/test_compare_reclamm_geometric_noise_runs.py @@ -0,0 +1,143 @@ +"""Tests for adjacent-row sourcing in compare_reclamm_geometric_noise_runs.py.""" + +from __future__ import annotations + +import importlib.util +from pathlib import Path + +import numpy as np + + +SCRIPT_PATH = ( + Path(__file__).resolve().parents[2] + / "scripts" + / "reclamm" + / "compare_reclamm_geometric_noise_runs.py" +) + + +def load_script_module(): + spec = importlib.util.spec_from_file_location( + "test_compare_reclamm_geometric_noise_runs_module", + SCRIPT_PATH, + ) + module = importlib.util.module_from_spec(spec) + assert spec.loader is not None + spec.loader.exec_module(module) + return module + + +def test_build_run_specs_from_adjacent_row_maps_csv_cells_to_two_specs(): + module = load_script_module() + row = { + "metric_key": "noise_vs_arb_geometric_improvement_pct", + "metric_unit": "pct", + "source_noise_profile": "legacy_calibrated", + "pair_slug": "price_ratio_vs_margin", + "slice_slug": "q2", + "adjacency_axis": "horizontal", + "heatmap_value_diff_abs": 54.223889391076895, + "1_price_ratio": 1.335, + "1_centeredness_margin": 0.3184210526, + "1_daily_price_shift_exponent": 0.1975, + "1_tvl_usd": 1_000_000.0, + "1_heatmap_value": -53.0543210862, + "2_price_ratio": 1.36, + "2_centeredness_margin": 0.3184210526, + "2_daily_price_shift_exponent": 0.1975, + "2_tvl_usd": 1_000_000.0, + "2_heatmap_value": 1.1695683049, + } + + description, run_specs = module.build_run_specs_from_adjacent_row( + row, + csv_path=Path("adjacent_pairs.csv"), + row_index=0, + ) + + assert "adjacent_pairs.csv row 0" in description + assert "price_ratio_vs_margin q2" in description + assert "horizontal" in description + assert "noise_profile=legacy_calibrated" in description + assert len(run_specs) == 2 + assert run_specs[0]["name"] == "Top diff row cell 1" + assert run_specs[0]["price_ratio"] == 1.335 + assert run_specs[0]["centeredness_margin"] == 0.3184210526 + assert run_specs[0]["daily_price_shift_exponent"] == 0.1975 + assert run_specs[0]["tvl_usd"] == 1_000_000.0 + assert run_specs[0]["color"] == "C0" + assert run_specs[0]["source_noise_profile"] == "legacy_calibrated" + assert "heatmap_value=-53.054321" in run_specs[0]["reason"] + assert run_specs[1]["name"] == "Top diff row cell 2" + assert run_specs[1]["price_ratio"] == 1.36 + assert run_specs[1]["color"] == "C1" + + +def test_default_output_file_for_adjacent_csv_uses_csv_stem_and_row_index(): + module = load_script_module() + output = module.default_output_file_for_adjacent_csv( + Path("scripts/results/reclamm_heatmap_adjacency/example.csv"), + row_index=3, + ) + + assert ( + output.as_posix() + == "scripts/results/reclamm_heatmap_adjacency/example_row_3_geometric_noise_compare.png" + ) + + +def test_build_run_config_honors_legacy_calibrated_noise_profile(): + module = load_script_module() + base_config = { + "name": "base", + "price_ratio": 1.1, + "centeredness_margin": 0.6, + "daily_price_shift_exponent": 0.1, + "initial_pool_value": 1_000_000.0, + "noise_model": "market_linear", + "reclamm_noise_params": {"foo": 1.0}, + "noise_arrays_path": "path.npz", + } + spec = { + "name": "cell", + "price_ratio": 1.335, + "centeredness_margin": 0.3184210526, + "daily_price_shift_exponent": 0.1975, + "tvl_usd": 1_000_000.0, + "source_noise_profile": "legacy_calibrated", + } + + cfg = module.build_run_config(spec, base_config=base_config) + + assert cfg["noise_model"] == "calibrated" + assert "reclamm_noise_params" not in cfg + assert "noise_arrays_path" not in cfg + + +def test_print_run_inputs_to_terminal_includes_fingerprint_and_update_params(capsys): + module = load_script_module() + cfg = { + "name": "cell", + "variant_label": "arb-only", + } + run_fingerprint = { + "tokens": ["AAVE", "ETH"], + "fees": np.float64(0.0025), + "arb_frequency": np.int64(14), + } + update_params = { + "price_ratio": np.array(1.335), + "centeredness_margin": np.array(0.3184210526), + "daily_price_shift_base": np.array(0.99999841596), + } + + module.print_run_inputs_to_terminal(cfg, run_fingerprint, update_params) + + captured = capsys.readouterr().out + assert "Run inputs for cell (arb-only):" in captured + assert '"run_fingerprint"' in captured + assert '"update_params"' in captured + assert '"tokens": [' in captured + assert '"AAVE"' in captured + assert '"arb_frequency": 14' in captured + assert '"price_ratio": 1.335' in captured diff --git a/tests/scripts/test_compare_reclamm_thermostats.py b/tests/scripts/test_compare_reclamm_thermostats.py index 7072f73..3ea0da7 100644 --- a/tests/scripts/test_compare_reclamm_thermostats.py +++ b/tests/scripts/test_compare_reclamm_thermostats.py @@ -34,6 +34,7 @@ def inject_module(name, module): pandas_module.Timestamp = lambda value: value pandas_module.DatetimeIndex = tuple pandas_module.DataFrame = type("DataFrame", (), {}) + pandas_module.read_parquet = lambda *args, **kwargs: None inject_module("pandas", pandas_module) matplotlib_module = types.ModuleType("matplotlib") @@ -42,6 +43,7 @@ def inject_module(name, module): colors_module = types.ModuleType("matplotlib.colors") colors_module.TwoSlopeNorm = object colors_module.Normalize = object + colors_module.SymLogNorm = object cm_module = types.ModuleType("matplotlib.cm") cm_module.ScalarMappable = object inject_module("matplotlib", matplotlib_module) @@ -123,9 +125,12 @@ def base_cfg(): return { "tokens": ["AAVE", "ETH"], "start": "2024-06-01 00:00:00", + "end": "2025-06-01 00:00:00", + "fees": 0.0025, "price_ratio": 1.10, "centeredness_margin": 0.60, "daily_price_shift_exponent": 0.1, + "initial_pool_value": 5_000_000.0, } @@ -137,6 +142,98 @@ def launch_final_values(): } +def test_make_noise_variant_cfg_disables_noise_fields(script_module, base_cfg): + noisy_cfg = { + **base_cfg, + "enable_noise_model": True, + "noise_model": "market_linear", + "noise_artifact_dir": "results/linear_market_noise", + "noise_pool_id": "0x9d1fcf346ea1b0", + "gas_cost": 1.0, + "protocol_fee_split": 0.25, + "reclamm_noise_params": {"tvl_mean": 1.0, "tvl_std": 2.0}, + "noise_arrays_path": "results/linear_market_noise/_sim_arrays/aave_eth.npz", + "arb_frequency": 6, + } + + arb_only_cfg = script_module.make_noise_variant_cfg( + noisy_cfg, + enable_noise_model=False, + ) + resolved = script_module.resolve_reclamm_noise_settings(arb_only_cfg) + + assert arb_only_cfg["enable_noise_model"] is False + assert arb_only_cfg["noise_model"] is None + assert arb_only_cfg["gas_cost"] == script_module.DEFAULT_GAS_COST + assert arb_only_cfg["protocol_fee_split"] == script_module.DEFAULT_PROTOCOL_FEE_SPLIT + assert arb_only_cfg["arb_frequency"] == script_module.FIXED_COMPARE_ARB_FREQUENCY + assert "noise_artifact_dir" not in arb_only_cfg + assert "noise_pool_id" not in arb_only_cfg + assert "reclamm_noise_params" not in arb_only_cfg + assert "noise_arrays_path" not in arb_only_cfg + assert resolved["noise_model"] is None + assert resolved["noise_summary"] == "arb-only (noise disabled)" + + +def test_make_noise_variant_cfg_defaults_to_fixed_compare_arb_cadence( + script_module, + base_cfg, +): + noisy_cfg = { + **base_cfg, + "enable_noise_model": True, + "noise_model": "market_linear", + } + + arb_only_cfg = script_module.make_noise_variant_cfg( + noisy_cfg, + enable_noise_model=False, + ) + resolved_noise = script_module.resolve_reclamm_noise_settings(noisy_cfg) + + assert arb_only_cfg["arb_frequency"] == script_module.FIXED_COMPARE_ARB_FREQUENCY + assert resolved_noise["arb_frequency"] == script_module.FIXED_COMPARE_ARB_FREQUENCY + + +def test_make_fingerprint_ignores_non_axis_override_fields(script_module, base_cfg): + canonical_cfg = { + **base_cfg, + "enable_noise_model": True, + "noise_model": "market_linear", + } + noisy_override_cfg = { + **canonical_cfg, + "arb_frequency": 6, + "gas_cost": 7.0, + "protocol_fee_split": 0.9, + "arb_fees": 3.0, + "noise_artifact_dir": "custom/noise/dir", + "noise_pool_id": "override-pool", + "reclamm_noise_params": {"tvl_mean": 999.0}, + "noise_arrays_path": "custom/path.npz", + } + + canonical_fingerprint = script_module.make_fingerprint(canonical_cfg, "geometric") + overridden_fingerprint = script_module.make_fingerprint( + noisy_override_cfg, + "geometric", + ) + canonical_key = script_module._make_method_cache_key(canonical_cfg, "geometric") + overridden_key = script_module._make_method_cache_key( + noisy_override_cfg, + "geometric", + ) + + assert overridden_fingerprint == canonical_fingerprint + assert overridden_key == canonical_key + assert overridden_fingerprint["arb_frequency"] == script_module.FIXED_COMPARE_ARB_FREQUENCY + assert overridden_fingerprint["gas_cost"] == script_module.DEFAULT_GAS_COST + assert ( + overridden_fingerprint["protocol_fee_split"] + == script_module.DEFAULT_PROTOCOL_FEE_SPLIT + ) + + def test_generate_heatmaps_skips_existing_pairs( monkeypatch, script_module, @@ -169,7 +266,14 @@ def test_generate_heatmaps_only_renders_missing_artifacts( base_cfg, launch_final_values, ): - missing_file = "reclamm_heatmap_efficiency_price_ratio_vs_margin.png" + pair = script_module.get_pair_heatmap_specs(base_cfg)[0] + slice_variant = pair["fixed_slices"][0] + pair_suffix = script_module._pair_slice_suffix(pair, slice_variant) + missing_file = script_module.tvl_artifact_filename( + "reclamm_heatmap_geometric_vs_launch_geometric_symlog20", + base_cfg, + suffix=pair_suffix, + ) def fake_exists(filename): if filename == missing_file: @@ -182,7 +286,7 @@ def fake_exists(filename): def fake_build_heatmap_matrices(**kwargs): build_calls.append(kwargs) return { - "efficiency_pct": np.zeros( + "geometric_vs_launch_geometric_pct": np.zeros( (len(kwargs["y_values"]), len(kwargs["x_values"])), dtype=float, ) @@ -207,18 +311,176 @@ def fake_plot_heatmap(**kwargs): ) assert len(build_calls) == 1 - assert build_calls[0]["progress_label"] == "price_ratio_vs_margin" - assert build_calls[0]["metric_keys"] == ["efficiency_pct"] + assert build_calls[0]["progress_label"] == pair_suffix + assert build_calls[0]["base_cfg"][pair["fixed_key"]] == pytest.approx( + slice_variant["value"] + ) + assert build_calls[0]["metric_keys"] == ["geometric_vs_launch_geometric_pct"] assert plotted_files == [missing_file] +def test_generate_heatmaps_only_renders_missing_improvement_artifacts( + monkeypatch, + script_module, + base_cfg, + launch_final_values, +): + pair = script_module.get_pair_heatmap_specs(base_cfg)[0] + slice_variant = pair["fixed_slices"][0] + pair_suffix = script_module._pair_slice_suffix(pair, slice_variant) + missing_file = script_module.tvl_artifact_filename( + "reclamm_heatmap_noise_vs_arb_geometric_improvement_symlog20", + base_cfg, + suffix=pair_suffix, + ) + + def fake_exists(filename): + if filename == missing_file: + return False + return filename.startswith("reclamm_heatmap_") + + build_calls = [] + plotted_files = [] + + def fake_build_heatmap_matrices(**kwargs): + build_calls.append(kwargs) + return { + "noise_vs_arb_geometric_improvement_pct": np.zeros( + (len(kwargs["y_values"]), len(kwargs["x_values"])), + dtype=float, + ) + } + + def fake_plot_heatmap(**kwargs): + plotted_files.append(kwargs["filename"]) + + monkeypatch.setattr(script_module.os.path, "exists", fake_exists) + monkeypatch.setattr( + script_module, + "build_heatmap_matrices", + fake_build_heatmap_matrices, + ) + monkeypatch.setattr(script_module, "plot_heatmap", fake_plot_heatmap) + + script_module.generate_heatmaps( + base_cfg, + price_data=None, + launch_final_values=launch_final_values, + cache={}, + ) + + assert len(build_calls) == 1 + assert build_calls[0]["progress_label"] == pair_suffix + assert build_calls[0]["base_cfg"][pair["fixed_key"]] == pytest.approx( + slice_variant["value"] + ) + assert build_calls[0]["metric_keys"] == [ + "noise_vs_arb_geometric_improvement_pct" + ] + assert plotted_files == [missing_file] + + +def test_generate_three_variable_3d_heatmaps_only_renders_missing_slice( + monkeypatch, + script_module, + base_cfg, + launch_final_values, +): + missing_file = script_module.tvl_artifact_filename( + "reclamm_heatmap_3d_geometric_vs_launch_geometric_symlog20", + base_cfg, + suffix="slice_q1", + ) + + def fake_exists(filename): + if filename == missing_file: + return False + return filename.startswith("reclamm_heatmap_3d_") + + build_calls = [] + plotted_files = [] + + def fake_build_heatmap_matrices(**kwargs): + build_calls.append(kwargs) + return { + "geometric_vs_launch_geometric_pct": np.zeros( + (len(kwargs["y_values"]), len(kwargs["x_values"])), + dtype=float, + ) + } + + def fake_plot_three_variable_heatmap_3d(**kwargs): + plotted_files.append(kwargs["filename"]) + + monkeypatch.setattr(script_module.os.path, "exists", fake_exists) + monkeypatch.setattr( + script_module, + "build_heatmap_matrices", + fake_build_heatmap_matrices, + ) + monkeypatch.setattr( + script_module, + "plot_three_variable_heatmap_3d", + fake_plot_three_variable_heatmap_3d, + ) + + script_module.generate_three_variable_3d_heatmaps( + base_cfg, + price_data=None, + launch_final_values=launch_final_values, + cache={}, + ) + + assert len(build_calls) == 3 + assert {call["progress_label"] for call in build_calls} == { + "3d_price_ratio_vs_margin_shift_exp_q1", + "3d_shift_exp_vs_margin_price_ratio_q1", + "3d_price_ratio_vs_shift_exp_margin_q1", + } + assert plotted_files == [missing_file] + + +def test_run_method_final_value_cached_reuses_persisted_parquet_value( + monkeypatch, + script_module, + base_cfg, +): + cfg = dict(base_cfg) + cache_key = script_module._make_method_cache_key(cfg, "geometric") + cache_key_hash = script_module._make_method_cache_hash(cache_key) + + class FakeFrame: + empty = False + + def itertuples(self, index=False): + return [ + types.SimpleNamespace( + cache_key_hash=cache_key_hash, + final_value=1_234_567.0, + ) + ] + + monkeypatch.setattr(script_module.os.path, "exists", lambda filename: True) + monkeypatch.setattr(script_module.pd, "read_parquet", lambda *args, **kwargs: FakeFrame()) + monkeypatch.setattr( + script_module, + "do_run_on_historic_data", + lambda **kwargs: pytest.fail("persisted forward-value cache should be reused"), + ) + + cache = script_module.make_sweep_cache(price_data=None, cache_scope_cfg=cfg) + value = script_module._run_method_final_value_cached(cfg, "geometric", cache) + + assert value == pytest.approx(1_234_567.0) + + def test_arc_speed_artifacts_only_build_missing_line_output( monkeypatch, script_module, base_cfg, launch_final_values, ): - missing_line = "reclamm_line_efficiency_arc_speed_vs_price_ratio.png" + missing_line = script_module.tvl_artifact_filename("reclamm_line_efficiency", base_cfg, suffix="arc_speed_vs_price_ratio") def fake_exists(filename): if filename == missing_line: @@ -243,6 +505,7 @@ def fake_build_metric_curve(**kwargs): return np.zeros(len(kwargs["x_values"]), dtype=float) monkeypatch.setattr(script_module.os.path, "exists", fake_exists) + monkeypatch.setattr(script_module, "RUN_CONSTANT_ARC_LENGTH", True) monkeypatch.setattr( script_module, "compute_auto_calibrated_arc_length_speed", @@ -282,3 +545,134 @@ def fake_build_metric_curve(**kwargs): assert len(curve_calls) == 1 assert curve_calls[0]["x_key"] == "arc_length_speed" assert plotted_lines == [missing_line] + + +def test_flush_sweep_cache_writes_compact_scalar_parquet(script_module): + captured = {} + + class FakeFrame: + def __init__(self, payload): + captured["payload"] = payload + + def sort_values(self, *args, **kwargs): + captured["sort_values"] = (args, kwargs) + + def to_parquet(self, path, index=False, compression=None): + captured["path"] = path + captured["index"] = index + captured["compression"] = compression + + script_module.pd.DataFrame = FakeFrame + script_module.os.makedirs = lambda *args, **kwargs: captured.setdefault( + "makedirs", args[0] + ) + + cache = { + "_pending_persistent_final_values": { + "abc123": { + "cache_key_hash": "abc123", + "final_value": 123.45, + "method": "geometric", + "enable_noise_model": True, + "noise_model": "market_linear", + "price_ratio": 1.1, + "centeredness_margin": 0.6, + "daily_price_shift_exponent": 0.1, + "initial_pool_value": 5_000_000.0, + "arb_frequency": 15, + } + }, + "_persistent_final_value_cache": {}, + "_persistent_final_value_records": {}, + "_persistent_final_value_cache_loaded": True, + "_persistent_final_value_cache_path": "results/reclamm_heatmap_forward_cache/test/forward_values_tvl_5m.parquet", + } + + script_module.flush_sweep_cache(cache, force=True) + + assert set(captured["payload"].keys()) == set( + script_module.PERSISTED_FORWARD_VALUE_COLUMNS + ) + assert captured["payload"]["cache_key_hash"] == ["abc123"] + assert captured["payload"]["method"] == ["geometric"] + assert captured["payload"]["arb_frequency"] == [15] + assert captured["index"] is False + assert captured["compression"] == "zstd" + assert captured["path"].endswith(".parquet") + assert cache["_pending_persistent_final_values"] == {} + assert cache["_persistent_final_value_cache"] == {"abc123": 123.45} + assert cache["_persistent_final_value_records"]["abc123"]["noise_model"] == "market_linear" + + +def test_load_persistent_final_value_cache_supports_legacy_two_column_parquet( + monkeypatch, + script_module, +): + class FakeFrame: + empty = False + + def itertuples(self, index=False): + return [ + types.SimpleNamespace( + cache_key_hash="legacy123", + final_value=999.0, + ) + ] + + monkeypatch.setattr(script_module.os.path, "exists", lambda filename: True) + monkeypatch.setattr(script_module.pd, "read_parquet", lambda *args, **kwargs: FakeFrame()) + + cache = { + "_persistent_final_value_cache_loaded": False, + "_persistent_final_value_cache_path": "results/reclamm_heatmap_forward_cache/test/forward_values_tvl_1m.parquet", + } + + script_module._load_persistent_final_value_cache(cache) + + assert cache["_persistent_final_value_cache"] == {"legacy123": 999.0} + assert cache["_persistent_final_value_records"]["legacy123"]["final_value"] == pytest.approx(999.0) + assert cache["_persistent_final_value_records"]["legacy123"]["price_ratio"] is None + + +def test_run_comparison_cached_only_uses_geometric_runs_when_constant_arc_disabled( + monkeypatch, + script_module, + base_cfg, + launch_final_values, +): + calls = [] + + monkeypatch.setattr( + script_module, + "_make_comparison_cache_key", + lambda cfg, launch_final_values: ("cache", round(float(cfg["price_ratio"]), 6)), + ) + + def fake_run_method_final_value_cached(cfg, method, cache): + calls.append((cfg.get("enable_noise_model", False), method)) + return { + (True, "geometric"): 1_050_000.0, + (False, "geometric"): 1_000_000.0, + }[(cfg.get("enable_noise_model", False), method)] + + monkeypatch.setattr( + script_module, + "_run_method_final_value_cached", + fake_run_method_final_value_cached, + ) + + metrics = script_module.run_comparison_cached( + base_cfg, + cache={"_comparison_cache": {}, "_final_value_cache": {}, "_shared_price_data": None}, + launch_final_values=launch_final_values, + metric_keys=( + "geometric_vs_launch_geometric_pct", + "noise_vs_arb_geometric_improvement_pct", + ), + ) + + assert calls == [(True, "geometric"), (False, "geometric")] + assert metrics == { + "geometric_vs_launch_geometric_pct": pytest.approx(5.0), + "noise_vs_arb_geometric_improvement_pct": pytest.approx(5.0), + } diff --git a/tests/scripts/test_find_adjacent_heatmap_pairs.py b/tests/scripts/test_find_adjacent_heatmap_pairs.py new file mode 100644 index 0000000..597e65b --- /dev/null +++ b/tests/scripts/test_find_adjacent_heatmap_pairs.py @@ -0,0 +1,313 @@ +"""Tests for cache-backed adjacent heatmap pair detection.""" + +from __future__ import annotations + +import importlib.util +from pathlib import Path + +import pytest + + +SCRIPT_PATH = ( + Path(__file__).resolve().parents[2] + / "scripts" + / "reclamm" + / "find_adjacent_heatmap_pairs.py" +) + + +def load_script_module(): + spec = importlib.util.spec_from_file_location( + "test_find_adjacent_heatmap_pairs_module", + SCRIPT_PATH, + ) + module = importlib.util.module_from_spec(spec) + assert spec.loader is not None + spec.loader.exec_module(module) + return module + + +def make_cell(x_index, y_index, heatmap_value, **overrides): + cell = { + "metric_key": "noise_vs_arb_geometric_improvement_pct", + "metric_unit": "pct", + "pair_slug": "price_ratio_vs_margin", + "slice_slug": "q2", + "slice_label": "Q2", + "fixed_key": "daily_price_shift_exponent", + "fixed_value": 0.1975, + "price_ratio": 1.01 + 0.1 * x_index, + "centeredness_margin": 0.05 + 0.1 * y_index, + "daily_price_shift_exponent": 0.1975, + "tvl_usd": 1_000_000.0, + "heatmap_value": float(heatmap_value), + "x_index": int(x_index), + "y_index": int(y_index), + } + cell.update(overrides) + return cell + + +def test_find_adjacent_rows_for_slice_filters_and_sorts_descending(): + module = load_script_module() + records_by_coord = { + (0, 0): make_cell(0, 0, 0.0), + (0, 1): make_cell(1, 0, 35.0), + (0, 2): make_cell(2, 0, -10.0), + (1, 0): make_cell(0, 1, 5.0), + (1, 1): make_cell(1, 1, -40.0), + (1, 2): make_cell(2, 1, -50.0), + } + + rows = module.find_adjacent_rows_for_slice( + metric_key="noise_vs_arb_geometric_improvement_pct", + metric_unit="pct", + records_by_coord=records_by_coord, + x_count=3, + y_count=2, + min_diff=30.0, + ) + + assert [row["heatmap_value_diff_abs"] for row in rows] == [75.0, 45.0, 45.0, 40.0, 35.0] + assert rows[0]["adjacency_axis"] == "vertical" + assert rows[0]["1_x_index"] == 1 + assert rows[0]["1_y_index"] == 0 + assert rows[0]["2_x_index"] == 1 + assert rows[0]["2_y_index"] == 1 + + horizontal_rows = module.find_adjacent_rows_for_slice( + metric_key="noise_vs_arb_geometric_improvement_pct", + metric_unit="pct", + records_by_coord=records_by_coord, + x_count=3, + y_count=2, + min_diff=30.0, + adjacency_axis="horizontal", + ) + assert {row["adjacency_axis"] for row in horizontal_rows} == {"horizontal"} + + vertical_rows = module.find_adjacent_rows_for_slice( + metric_key="noise_vs_arb_geometric_improvement_pct", + metric_unit="pct", + records_by_coord=records_by_coord, + x_count=3, + y_count=2, + min_diff=30.0, + adjacency_axis="vertical", + ) + assert {row["adjacency_axis"] for row in vertical_rows} == {"vertical"} + + +def test_build_slice_cell_grid_reconstructs_metric_values_from_cache_hashes(): + module = load_script_module() + + class FakeCompareModule: + @staticmethod + def make_noise_variant_cfg(cfg, enable_noise_model): + updated = dict(cfg) + updated["enable_noise_model"] = bool(enable_noise_model) + return updated + + @staticmethod + def _make_method_cache_key(cfg, method): + return ( + method, + bool(cfg["enable_noise_model"]), + round(float(cfg["price_ratio"]), 6), + round(float(cfg["centeredness_margin"]), 6), + round(float(cfg["daily_price_shift_exponent"]), 6), + round(float(cfg["initial_pool_value"]), 2), + ) + + @staticmethod + def _make_method_cache_hash(key): + return repr(key) + + @staticmethod + def get_initial_pool_value(cfg): + return float(cfg["initial_pool_value"]) + + base_cfg = { + "price_ratio": 1.1, + "centeredness_margin": 0.3, + "daily_price_shift_exponent": 0.2, + "initial_pool_value": 1_000_000.0, + } + pair_spec = { + "slug": "price_ratio_vs_margin", + "x_values": [1.1, 1.2], + "y_values": [0.3, 0.4], + "x_key": "price_ratio", + "y_key": "centeredness_margin", + "fixed_key": "daily_price_shift_exponent", + } + slice_variant = { + "slug": "q2", + "label": "Q2", + "value": 0.2, + } + + heatmap_targets = { + (0, 0): (130.0, 100.0), # +30% + (0, 1): (200.0, 100.0), # +100% + (1, 0): (70.0, 100.0), # -30% + (1, 1): (160.0, 100.0), # +60% + } + cache_lookup = {} + for (y_index, x_index), (noise_geo, arb_geo) in heatmap_targets.items(): + cfg = dict(base_cfg) + cfg["price_ratio"] = pair_spec["x_values"][x_index] + cfg["centeredness_margin"] = pair_spec["y_values"][y_index] + noise_cfg, noise_method = FakeCompareModule.make_noise_variant_cfg(cfg, True), "geometric" + arb_cfg, arb_method = FakeCompareModule.make_noise_variant_cfg(cfg, False), "geometric" + + noise_key = FakeCompareModule._make_method_cache_key(noise_cfg, noise_method) + arb_key = FakeCompareModule._make_method_cache_key(arb_cfg, arb_method) + cache_lookup[FakeCompareModule._make_method_cache_hash(noise_key)] = noise_geo + cache_lookup[FakeCompareModule._make_method_cache_hash(arb_key)] = arb_geo + + slice_scan = module.build_slice_cell_grid( + compare_module=FakeCompareModule, + base_cfg=base_cfg, + pair_spec=pair_spec, + slice_variant=slice_variant, + metric_key="noise_vs_arb_geometric_improvement_pct", + cache_lookup=cache_lookup, + ) + + assert slice_scan["resolved_cell_count"] == 4 + assert slice_scan["missing_hash_count"] == 0 + assert slice_scan["records_by_coord"][(0, 0)]["heatmap_value"] == pytest.approx(30.0) + assert slice_scan["records_by_coord"][(0, 1)]["heatmap_value"] == pytest.approx(100.0) + assert slice_scan["records_by_coord"][(1, 0)]["heatmap_value"] == pytest.approx(-30.0) + assert slice_scan["records_by_coord"][(1, 1)]["heatmap_value"] == pytest.approx(60.0) + + rows = module.find_adjacent_rows_for_slice( + metric_key="noise_vs_arb_geometric_improvement_pct", + metric_unit="pct", + records_by_coord=slice_scan["records_by_coord"], + x_count=2, + y_count=2, + min_diff=30.0, + ) + + assert [row["heatmap_value_diff_abs"] for row in rows] == pytest.approx([90.0, 70.0, 60.0, 40.0]) + assert rows[0]["1_heatmap_value"] == pytest.approx(-30.0) + assert rows[0]["2_heatmap_value"] == pytest.approx(60.0) + + +def test_run_top_row_geometric_comparison_dispatches_to_compare_module(monkeypatch): + module = load_script_module() + captured = {} + + class FakeCompareModule: + @staticmethod + def run_adjacent_csv_row_comparison(csv_path, row_index=0, output_file=None): + captured["csv_path"] = csv_path + captured["row_index"] = row_index + captured["output_file"] = output_file + return "fake-output.png" + + monkeypatch.setattr( + module, + "load_geometric_compare_module", + lambda module_path=None: FakeCompareModule, + ) + + output = module.run_top_row_geometric_comparison( + Path("tmp_adjacent.csv"), + output_file="custom.png", + row_index=0, + ) + + assert output == "fake-output.png" + assert captured == { + "csv_path": Path("tmp_adjacent.csv"), + "row_index": 0, + "output_file": "custom.png", + } + + +def test_autodetect_lightweight_noise_profile_switches_to_legacy_calibrated(): + module = load_script_module() + compare_context = module._LightweightCompareContext() + base_cfg = compare_context.configs_for_tvl(compare_context.CONFIGS, 1_000_000.0)[1] + pair_spec = compare_context.get_pair_heatmap_specs(base_cfg)[0] + slice_variant = [variant for variant in pair_spec["fixed_slices"] if variant["slug"] == "q2"][0] + + sample_x_indices = sorted({0, len(pair_spec["x_values"]) // 2, len(pair_spec["x_values"]) - 1}) + sample_y_indices = sorted({0, len(pair_spec["y_values"]) // 2, len(pair_spec["y_values"]) - 1}) + + cache_lookup = {} + compare_context.set_noise_profile("legacy_calibrated") + slice_cfg = dict(base_cfg) + slice_cfg[pair_spec["fixed_key"]] = float(slice_variant["value"]) + for y_index in sample_y_indices: + for x_index in sample_x_indices: + cfg = dict(slice_cfg) + cfg[pair_spec["x_key"]] = float(pair_spec["x_values"][x_index]) + cfg[pair_spec["y_key"]] = float(pair_spec["y_values"][y_index]) + for source_name in ("noise_geometric", "arb_geometric"): + source_cfg, method = module._source_variant(compare_context, cfg, source_name) + cache_key = compare_context._make_method_cache_key(source_cfg, method) + cache_key_hash = compare_context._make_method_cache_hash(cache_key) + cache_lookup[cache_key_hash] = 1_000_000.0 + + compare_context.set_noise_profile("market_linear") + module.autodetect_lightweight_noise_profile( + compare_module=compare_context, + base_cfg=base_cfg, + pair_specs=[pair_spec], + metric_key="noise_vs_arb_geometric_improvement_pct", + slice_slug="q2", + cache_lookup=cache_lookup, + ) + + assert compare_context.noise_profile == "legacy_calibrated" + + +def test_source_variant_sets_explicit_market_and_arb_only_noise_models(): + module = load_script_module() + compare_context = module._LightweightCompareContext() + cfg = compare_context.configs_for_tvl(compare_context.CONFIGS, 1_000_000.0)[1] + + noise_cfg, noise_method = module._source_variant( + compare_context, + cfg, + "noise_geometric", + ) + arb_cfg, arb_method = module._source_variant( + compare_context, + cfg, + "arb_geometric", + ) + + assert noise_method == "geometric" + assert noise_cfg["enable_noise_model"] is True + assert noise_cfg["noise_model"] == "market_linear" + + assert arb_method == "geometric" + assert arb_cfg["enable_noise_model"] is False + assert arb_cfg["noise_model"] == "arb_only" + + resolved_arb = compare_context.resolve_reclamm_noise_settings(arb_cfg) + assert resolved_arb["noise_model"] == "arb_only" + assert resolved_arb["noise_cache_key"] == ("disabled",) + + +def test_lightweight_context_defaults_to_fixed_compare_arb_cadence(): + module = load_script_module() + compare_context = module._LightweightCompareContext() + cfg = compare_context.configs_for_tvl(compare_context.CONFIGS, 1_000_000.0)[1] + cfg["arb_frequency"] = 6 + cfg["gas_cost"] = 99.0 + cfg["protocol_fee_split"] = 0.9 + + arb_cfg = compare_context.make_noise_variant_cfg(cfg, False) + resolved_noise = compare_context.resolve_reclamm_noise_settings(cfg) + + assert arb_cfg["arb_frequency"] == compare_context.FIXED_COMPARE_ARB_FREQUENCY + assert arb_cfg["noise_model"] == "arb_only" + assert resolved_noise["arb_frequency"] == compare_context.FIXED_COMPARE_ARB_FREQUENCY + assert arb_cfg["gas_cost"] == compare_context.DEFAULT_GAS_COST + assert arb_cfg["protocol_fee_split"] == compare_context.DEFAULT_PROTOCOL_FEE_SPLIT From 00144e20dd1b87a1c56bb86d1d334180a9db8756 Mon Sep 17 00:00:00 2001 From: christian harrington Date: Sun, 29 Mar 2026 22:55:46 +0100 Subject: [PATCH 44/57] perforance improvements --- scripts/compare_reclamm_thermostats.py | 487 ++++++++++-------- .../compare_reclamm_geometric_noise_runs.py | 23 +- .../reclamm/compare_reclamm_thermostats.py | 485 +++++++++-------- .../reclamm/find_adjacent_heatmap_pairs.py | 267 +++++----- ...st_compare_reclamm_geometric_noise_runs.py | 62 ++- .../test_compare_reclamm_thermostats.py | 372 ++++++++++++- .../test_find_adjacent_heatmap_pairs.py | 40 +- 7 files changed, 1132 insertions(+), 604 deletions(-) diff --git a/scripts/compare_reclamm_thermostats.py b/scripts/compare_reclamm_thermostats.py index 21352a4..7fb1770 100644 --- a/scripts/compare_reclamm_thermostats.py +++ b/scripts/compare_reclamm_thermostats.py @@ -17,8 +17,8 @@ import gc import hashlib -import math import os +from pathlib import Path import jax.numpy as jnp import numpy as np @@ -50,6 +50,15 @@ def build_inclusive_sweep(start, stop, step): return values +def _resolve_repo_root(script_path): + """Locate the repository root from either scripts/ or scripts/reclamm/.""" + script_path = Path(script_path).resolve() + for parent in script_path.parents: + if (parent / "quantammsim").exists() and (parent / "scripts").exists(): + return parent + return script_path.parents[1] + + RUN_CONSTANT_ARC_LENGTH = True INTERPOLATION_METHODS = ( ("geometric", "constant_arc_length") @@ -90,36 +99,39 @@ def build_inclusive_sweep(start, stop, step): THREE_D_VIEW_ELEVATION = 22.0 THREE_D_VIEW_AZIMUTH = 140.0 HEATMAP_FORWARD_CACHE_ENABLED = True -HEATMAP_FORWARD_CACHE_RUN_NAME = "aave_eth_thermostat_heatmaps_v1" +HEATMAP_FORWARD_CACHE_RUN_NAME = "aave_eth_thermostat_heatmaps_market_linear_v2" HEATMAP_FORWARD_CACHE_ROOT = os.path.join( "results", "reclamm_heatmap_forward_cache", ) -HEATMAP_FORWARD_CACHE_FLUSH_EVERY = 32 +HEATMAP_FORWARD_CACHE_FLUSH_EVERY = 360 +REPO_ROOT = _resolve_repo_root(__file__) AAVE_WETH_POOL_ID = "0x9d1fcf346ea1b0" DEFAULT_MARKET_LINEAR_ARTIFACT_DIR = "results/linear_market_noise" +DEFAULT_MARKET_LINEAR_NOISE_START_DATE = "2024-06-01" +DEFAULT_MARKET_LINEAR_NOISE_END_DATE = "2026-03-01" +DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH = str( + REPO_ROOT + / "results" + / "linear_market_noise" + / "_sim_arrays" + / ( + f"{AAVE_WETH_POOL_ID}_{DEFAULT_MARKET_LINEAR_NOISE_START_DATE}_" + f"{DEFAULT_MARKET_LINEAR_NOISE_END_DATE}.npz" + ) +) DEFAULT_NOISE_MODEL = "market_linear" DEFAULT_GAS_COST = 1.0 DEFAULT_PROTOCOL_FEE_SPLIT = 0.25 -LEGACY_NOISE_COEFFS = [ - -0.453, - 0.025, - -0.060, - 0.310, - -0.149, - 0.359, - 0.061, - 0.060, -] -LEGACY_LOG_CADENCE = 2.68 -LEGACY_ARB_FREQUENCY = max(1, round(math.exp(LEGACY_LOG_CADENCE))) -FIXED_COMPARE_ARB_FREQUENCY = LEGACY_ARB_FREQUENCY +FIXED_COMPARE_ARB_FREQUENCY = 15 AAVE_ETH_NOISE_SETTINGS = { "enable_noise_model": True, "noise_model": DEFAULT_NOISE_MODEL, + "noise_reference_model": DEFAULT_NOISE_MODEL, "noise_artifact_dir": DEFAULT_MARKET_LINEAR_ARTIFACT_DIR, "noise_pool_id": AAVE_WETH_POOL_ID, + "noise_arrays_path": DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH, "arb_frequency": FIXED_COMPARE_ARB_FREQUENCY, "gas_cost": DEFAULT_GAS_COST, "protocol_fee_split": DEFAULT_PROTOCOL_FEE_SPLIT, @@ -164,7 +176,7 @@ def build_inclusive_sweep(start, stop, step): } _NOISE_SETTINGS_CACHE = {} -_WARNED_NOISE_FALLBACKS = set() +_MARKET_LINEAR_NOISE_DATA_CACHE = {} def get_initial_pool_value(cfg): @@ -251,6 +263,27 @@ def get_effective_arb_frequency(cfg, noise_cfg=None): return _normalize_arb_frequency(FIXED_COMPARE_ARB_FREQUENCY) +def _canonical_noise_reference_model(cfg): + """Resolve the only supported thermostat noise parametrisation.""" + noise_model = cfg.get("noise_model", DEFAULT_NOISE_MODEL) or DEFAULT_NOISE_MODEL + reference_model = cfg.get("noise_reference_model") + if reference_model is None: + reference_model = DEFAULT_NOISE_MODEL if noise_model == "arb_only" else noise_model + noise_model = str(noise_model) + reference_model = str(reference_model) + if noise_model not in {DEFAULT_NOISE_MODEL, "arb_only"}: + raise ValueError( + "compare_reclamm_thermostats only supports " + "'market_linear' noise and 'arb_only' baselines." + ) + if reference_model != DEFAULT_NOISE_MODEL: + raise ValueError( + "compare_reclamm_thermostats only supports the " + "'market_linear' noise parametrisation." + ) + return reference_model + + def normalize_compare_run_cfg(cfg, enable_noise_model=None): """Canonicalize the compare-run config so non-axis inputs stay fixed.""" updated = dict(cfg) @@ -277,26 +310,18 @@ def normalize_compare_run_cfg(cfg, enable_noise_model=None): ) updated["enable_noise_model"] = use_noise - requested_mode = cfg.get("noise_model", DEFAULT_NOISE_MODEL) or DEFAULT_NOISE_MODEL + reference_mode = _canonical_noise_reference_model(cfg) if use_noise: - updated["noise_model"] = requested_mode - if requested_mode == "market_linear": - updated["noise_artifact_dir"] = DEFAULT_MARKET_LINEAR_ARTIFACT_DIR - updated["noise_pool_id"] = AAVE_WETH_POOL_ID - else: - updated.pop("noise_artifact_dir", None) - updated.pop("noise_pool_id", None) - updated.pop("reclamm_noise_params", None) - updated.pop("noise_arrays_path", None) + updated["noise_model"] = reference_mode + updated["noise_reference_model"] = reference_mode else: - updated["noise_model"] = None - for key in ( - "reclamm_noise_params", - "noise_arrays_path", - "noise_artifact_dir", - "noise_pool_id", - ): - updated.pop(key, None) + updated["noise_model"] = "arb_only" + updated["noise_reference_model"] = reference_mode + + updated["noise_arrays_path"] = DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH + updated.pop("reclamm_noise_params", None) + updated["noise_artifact_dir"] = DEFAULT_MARKET_LINEAR_ARTIFACT_DIR + updated["noise_pool_id"] = AAVE_WETH_POOL_ID return updated @@ -306,13 +331,6 @@ def make_noise_variant_cfg(cfg, enable_noise_model): return normalize_compare_run_cfg(cfg, enable_noise_model=enable_noise_model) -def _warn_noise_fallback(message): - """Print a one-time message when the preferred noise setup is unavailable.""" - if message not in _WARNED_NOISE_FALLBACKS: - print(message) - _WARNED_NOISE_FALLBACKS.add(message) - - def _hashable_noise_params(params): """Convert a noise-params dict into a stable cache key fragment.""" if params is None: @@ -320,38 +338,76 @@ def _hashable_noise_params(params): return tuple(sorted((str(k), round(float(v), 12)) for k, v in params.items())) -def _legacy_calibrated_noise_settings(reason=None, arb_frequency=None): - """Fallback calibrated noise config used when market-linear artifacts are absent.""" - if reason: - _warn_noise_fallback( - "market_linear noise unavailable for thermostat comparison; " - f"falling back to calibrated legacy coefficients ({reason})." - ) +def load_shared_market_linear_noise_data( + arrays_path=DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH, +): + """Load the market_linear arrays once so compare runs can reuse them.""" + arrays_path = os.path.abspath(os.fspath(arrays_path)) + cached = _MARKET_LINEAR_NOISE_DATA_CACHE.get(arrays_path) + if cached is not None: + return cached + + if not os.path.exists(arrays_path): + raise FileNotFoundError(f"market_linear arrays file not found: {arrays_path}") + + with np.load(arrays_path) as arrays: + required_keys = {"noise_base", "noise_tvl_coeff", "tvl_mean", "tvl_std"} + missing_keys = sorted(required_keys.difference(arrays.files)) + if missing_keys: + raise KeyError( + f"market_linear arrays file {arrays_path} is missing keys: {missing_keys}" + ) + shared = { + "arrays_path": arrays_path, + "noise_base_array": np.asarray(arrays["noise_base"]), + "noise_tvl_coeff_array": np.asarray(arrays["noise_tvl_coeff"]), + "tvl_mean": float(arrays["tvl_mean"]), + "tvl_std": float(arrays["tvl_std"]), + } + _MARKET_LINEAR_NOISE_DATA_CACHE[arrays_path] = shared + return shared + + +def _load_market_linear_noise_stats(arrays_path=DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH): + """Load the exact arrays file used by the market_linear run fingerprint. + + The simulator consumes ``noise_base`` and ``noise_tvl_coeff`` from + ``run_fingerprint["noise_arrays_path"]`` and uses ``tvl_mean``/``tvl_std`` + from the same file for TVL standardization. + """ + shared = load_shared_market_linear_noise_data(arrays_path=arrays_path) + return shared["arrays_path"], shared["tvl_mean"], shared["tvl_std"] + + +def _market_linear_noise_settings(noise_model="market_linear", arb_frequency=None): + """Build the tuned market_linear fingerprint block from the fixed arrays file.""" + arrays_path, tvl_mean, tvl_std = _load_market_linear_noise_stats() arb_frequency = _normalize_arb_frequency(arb_frequency) return { - "noise_model": "calibrated", + "noise_model": noise_model, "noise_trader_ratio": 0.0, "reclamm_noise_params": { - f"c_{i}": LEGACY_NOISE_COEFFS[i] for i in range(len(LEGACY_NOISE_COEFFS)) + "tvl_mean": tvl_mean, + "tvl_std": tvl_std, }, + "noise_arrays_path": arrays_path, "arb_frequency": arb_frequency, - "noise_summary": ( - "calibrated legacy 8-covariate " - f"(arb_frequency={arb_frequency})" - ), + "noise_summary": f"{noise_model} (arb_frequency={arb_frequency})", "noise_cache_key": ( - "calibrated", - tuple(round(float(c), 12) for c in LEGACY_NOISE_COEFFS), + noise_model, + arrays_path, arb_frequency, + round(tvl_mean, 12), + round(tvl_std, 12), ), } - def resolve_reclamm_noise_settings(cfg): """Resolve the active reCLAMM noise-model fingerprint block for a config.""" cfg = normalize_compare_run_cfg(cfg) enable_noise_model = cfg.get("enable_noise_model", False) requested_mode = cfg.get("noise_model", DEFAULT_NOISE_MODEL) + reference_mode = cfg.get("noise_reference_model", DEFAULT_NOISE_MODEL) requested_arb_frequency = get_effective_arb_frequency(cfg) cache_key = ( tuple(cfg.get("tokens", [])), @@ -359,6 +415,7 @@ def resolve_reclamm_noise_settings(cfg): cfg.get("end"), enable_noise_model, requested_mode, + reference_mode, cfg.get("noise_artifact_dir", DEFAULT_MARKET_LINEAR_ARTIFACT_DIR), cfg.get("noise_pool_id", AAVE_WETH_POOL_ID), requested_arb_frequency, @@ -369,103 +426,21 @@ def resolve_reclamm_noise_settings(cfg): if cache_key in _NOISE_SETTINGS_CACHE: return _NOISE_SETTINGS_CACHE[cache_key] - if not enable_noise_model: - result = { - "noise_model": None, - "noise_trader_ratio": 0.0, - "reclamm_noise_params": None, - "noise_arrays_path": None, - "arb_frequency": requested_arb_frequency, - "noise_summary": "arb-only (noise disabled)", - "noise_cache_key": ("disabled",), - } - elif requested_mode == "market_linear": - artifact_dir = cfg.get("noise_artifact_dir", DEFAULT_MARKET_LINEAR_ARTIFACT_DIR) - pool_id = cfg.get("noise_pool_id", AAVE_WETH_POOL_ID) - start_date = str(cfg["start"]).split(" ")[0] - end_date = str(cfg["end"]).split(" ")[0] - try: - from quantammsim.calibration.noise_model_arrays import build_simulator_arrays - - model_path = os.path.join(artifact_dir, "model.npz") - meta_path = os.path.join(artifact_dir, "meta.json") - if not (os.path.exists(model_path) and os.path.exists(meta_path)): - raise FileNotFoundError( - f"expected {model_path} and {meta_path}" - ) - - cache_dir = os.path.join(artifact_dir, "_sim_arrays") - os.makedirs(cache_dir, exist_ok=True) - arrays_path = os.path.join( - cache_dir, - f"{pool_id}_{start_date}_{end_date}.npz", - ) - if not os.path.exists(arrays_path): - arrays = build_simulator_arrays( - token_a=cfg["tokens"][0], - token_b=cfg["tokens"][1], - pool_id=pool_id, - start_date=start_date, - end_date=end_date, - artifact_dir=artifact_dir, - ) - np.savez( - arrays_path, - noise_base=arrays["noise_base"], - noise_tvl_coeff=arrays["noise_tvl_coeff"], - tvl_mean=arrays["tvl_mean"], - tvl_std=arrays["tvl_std"], - ) - - with np.load(arrays_path) as arrays: - tvl_mean = float(arrays["tvl_mean"]) - tvl_std = float(arrays["tvl_std"]) - - arb_frequency = requested_arb_frequency - result = { - "noise_model": "market_linear", - "noise_trader_ratio": 0.0, - "reclamm_noise_params": { - "tvl_mean": tvl_mean, - "tvl_std": tvl_std, - }, - "noise_arrays_path": arrays_path, - "arb_frequency": arb_frequency, - "noise_summary": f"market_linear (arb_frequency={arb_frequency})", - "noise_cache_key": ( - "market_linear", - arrays_path, - arb_frequency, - round(tvl_mean, 12), - round(tvl_std, 12), - ), - } - except Exception as exc: # pragma: no cover - fallback path depends on local artifacts - result = _legacy_calibrated_noise_settings( - str(exc), - arb_frequency=requested_arb_frequency, - ) - elif requested_mode == "calibrated": - result = _legacy_calibrated_noise_settings( - arb_frequency=requested_arb_frequency + if requested_mode == "arb_only": + result = _market_linear_noise_settings( + noise_model="arb_only", + arb_frequency=requested_arb_frequency, + ) + elif requested_mode == DEFAULT_NOISE_MODEL: + result = _market_linear_noise_settings( + noise_model=DEFAULT_NOISE_MODEL, + arb_frequency=requested_arb_frequency, ) else: - arb_frequency = requested_arb_frequency - result = { - "noise_model": requested_mode, - "noise_trader_ratio": cfg.get("noise_trader_ratio", 0.0), - "reclamm_noise_params": cfg.get("reclamm_noise_params"), - "noise_arrays_path": cfg.get("noise_arrays_path"), - "arb_frequency": arb_frequency, - "noise_summary": f"{requested_mode} (arb_frequency={arb_frequency})", - "noise_cache_key": ( - requested_mode, - round(float(cfg.get("noise_trader_ratio", 0.0)), 12), - _hashable_noise_params(cfg.get("reclamm_noise_params")), - cfg.get("noise_arrays_path"), - arb_frequency, - ), - } + raise ValueError( + "compare_reclamm_thermostats only supports " + "'market_linear' noise and 'arb_only' baselines." + ) _NOISE_SETTINGS_CACHE[cache_key] = result return result @@ -504,7 +479,29 @@ def resolve_reclamm_noise_settings(cfg): ] -def make_fingerprint(cfg, interpolation_method): +def _attach_market_linear_noise_arrays( + fingerprint, + noise_cfg, + market_linear_noise_data, +): + """Attach preloaded market_linear arrays when the compare flow has them.""" + if market_linear_noise_data is None: + return + expected_path = noise_cfg.get("noise_arrays_path") + if expected_path is None: + return + shared_path = os.path.abspath(os.fspath(market_linear_noise_data["arrays_path"])) + expected_path = os.path.abspath(os.fspath(expected_path)) + if shared_path != expected_path: + raise ValueError( + "Shared market_linear noise arrays path does not match " + f"the resolved compare-run noise path: {shared_path} != {expected_path}" + ) + fingerprint["noise_base_array"] = market_linear_noise_data["noise_base_array"] + fingerprint["noise_tvl_coeff_array"] = market_linear_noise_data["noise_tvl_coeff_array"] + + +def make_fingerprint(cfg, interpolation_method, market_linear_noise_data=None): """Build run fingerprint for a given config and interpolation method.""" cfg = normalize_compare_run_cfg(cfg) speed_override = ( @@ -541,6 +538,11 @@ def make_fingerprint(cfg, interpolation_method): fingerprint["reclamm_noise_params"] = noise_cfg["reclamm_noise_params"] if noise_cfg.get("noise_arrays_path") is not None: fingerprint["noise_arrays_path"] = noise_cfg["noise_arrays_path"] + _attach_market_linear_noise_arrays( + fingerprint, + noise_cfg, + market_linear_noise_data, + ) if arb_frequency is not None: fingerprint["arb_frequency"] = arb_frequency return fingerprint @@ -564,13 +566,22 @@ def load_shared_price_data(configs, root=None): return get_historic_parquet_data(tokens, cols=["close"], root=root) -def run_comparison(cfg, price_data=None, low_data_mode=False): +def run_comparison( + cfg, + price_data=None, + low_data_mode=False, + market_linear_noise_data=None, +): """Run both interpolation variants, return results dict.""" params = make_params(cfg) results = {} for method in INTERPOLATION_METHODS: - fp = make_fingerprint(cfg, method) + fp = make_fingerprint( + cfg, + method, + market_linear_noise_data=market_linear_noise_data, + ) results[method] = do_run_on_historic_data( run_fingerprint=fp, params=params, @@ -662,30 +673,44 @@ def _load_persistent_final_value_cache(cache): return disk_cache = {} - disk_records = {} + next_batch_id = 0 cache_path = cache.get("_persistent_final_value_cache_path") if cache_path and os.path.exists(cache_path): - frame = pd.read_parquet(cache_path) - if not frame.empty: + parquet_files = [] + if os.path.isdir(cache_path): + parquet_files = [ + os.path.join(cache_path, filename) + for filename in sorted(os.listdir(cache_path)) + if filename.endswith(".parquet") + ] + batch_ids = [] + for filename in os.listdir(cache_path): + if not (filename.startswith("batch_") and filename.endswith(".parquet")): + continue + token = filename[len("batch_") : -len(".parquet")] + if token.isdigit(): + batch_ids.append(int(token)) + next_batch_id = (max(batch_ids) + 1) if batch_ids else 0 + else: + parquet_files = [cache_path] + + for parquet_file in parquet_files: + frame = pd.read_parquet( + parquet_file, + columns=["cache_key_hash", "final_value"], + ) + if frame.empty: + continue for row in frame.itertuples(index=False): cache_key_hash = str(row.cache_key_hash) final_value = float(row.final_value) disk_cache[cache_key_hash] = final_value - record = { - "cache_key_hash": cache_key_hash, - "final_value": final_value, - } - for column in PERSISTED_FORWARD_VALUE_COLUMNS: - if column in {"cache_key_hash", "final_value"}: - continue - record[column] = getattr(row, column, None) - disk_records[cache_key_hash] = record print( f"Loaded {len(disk_cache)} persisted heatmap forward values from {cache_path}" ) cache["_persistent_final_value_cache"] = disk_cache - cache["_persistent_final_value_records"] = disk_records + cache["_persistent_final_value_next_batch_id"] = next_batch_id cache["_persistent_final_value_cache_loaded"] = True @@ -702,52 +727,63 @@ def flush_sweep_cache(cache, force=False): _load_persistent_final_value_cache(cache) disk_cache = cache.setdefault("_persistent_final_value_cache", {}) - disk_records = cache.setdefault("_persistent_final_value_records", {}) + batch_records = [] for cache_key_hash, record in pending.items(): - merged = dict(disk_records.get(cache_key_hash, {})) - merged.update(record) - merged["cache_key_hash"] = str(cache_key_hash) - merged["final_value"] = float(merged["final_value"]) - disk_records[cache_key_hash] = merged - disk_cache[cache_key_hash] = merged["final_value"] + normalized = dict(record) + normalized["cache_key_hash"] = str(cache_key_hash) + normalized["final_value"] = float(normalized["final_value"]) + disk_cache[cache_key_hash] = normalized["final_value"] + batch_records.append(normalized) cache_path = cache.get("_persistent_final_value_cache_path") if cache_path is None: pending.clear() return - os.makedirs(os.path.dirname(cache_path), exist_ok=True) - sorted_records = [disk_records[key] for key in sorted(disk_records)] + if os.path.exists(cache_path) and not os.path.isdir(cache_path): + raise RuntimeError( + f"Persistent cache path {cache_path} already exists as a file. " + "Use a fresh cache namespace for append-only parquet shards." + ) + + os.makedirs(cache_path, exist_ok=True) + batch_records.sort(key=lambda record: record["cache_key_hash"]) payload = { - column: [record.get(column) for record in sorted_records] + column: [record.get(column) for record in batch_records] for column in PERSISTED_FORWARD_VALUE_COLUMNS } payload["final_value"] = np.asarray(payload["final_value"], dtype=np.float64) frame = pd.DataFrame(payload) - frame.sort_values("cache_key_hash", inplace=True, ignore_index=True) - frame.to_parquet(cache_path, index=False, compression="zstd") + batch_id = int(cache.setdefault("_persistent_final_value_next_batch_id", 0)) + batch_path = os.path.join(cache_path, f"batch_{batch_id:08d}.parquet") + cache["_persistent_final_value_next_batch_id"] = batch_id + 1 + frame.to_parquet(batch_path, index=False, compression="zstd") print( - f"Persisted {len(pending)} new heatmap forward values to {cache_path} " + f"Persisted {len(pending)} new heatmap forward values to {batch_path} " f"({len(disk_cache)} total cached values)." ) pending.clear() -def make_sweep_cache(price_data, cache_scope_cfg=None): +def make_sweep_cache( + price_data, + cache_scope_cfg=None, + market_linear_noise_data=None, +): """Create a shared cache for heatmap and line sweeps.""" cache = { "_shared_price_data": price_data, + "_shared_market_linear_noise_data": market_linear_noise_data, "_final_value_cache": {}, "_comparison_cache": {}, "_pending_persistent_final_values": {}, "_persistent_final_value_cache": {}, - "_persistent_final_value_records": {}, + "_persistent_final_value_next_batch_id": 0, "_persistent_final_value_cache_loaded": False, "_persistent_final_value_cache_path": _heatmap_forward_cache_path( cache_scope_cfg ), } - _load_persistent_final_value_cache(cache) return cache @@ -781,6 +817,10 @@ def _make_method_cache_key(cfg, method): arb_frequency = get_effective_arb_frequency(cfg, noise_cfg) key = ( method, + tuple(str(token) for token in cfg["tokens"]), + str(cfg["start"]), + str(cfg["end"]), + round(float(cfg["fees"]), 12), bool(cfg.get("enable_noise_model", False)), round(float(cfg["price_ratio"]), 6), round(float(cfg["centeredness_margin"]), 6), @@ -812,6 +852,40 @@ def _make_method_cache_key(cfg, method): return key +def _nearest_price_row(price_data, start_ts): + """Select the closest available price row to the requested start timestamp.""" + if len(price_data.index) == 0: + raise ValueError("price_data is empty") + + if isinstance(price_data.index, pd.DatetimeIndex): + target_ts = start_ts + index_tz = getattr(price_data.index, "tz", None) + if index_tz is not None and target_ts.tzinfo is None: + target_ts = target_ts.tz_localize(index_tz) + elif index_tz is None and target_ts.tzinfo is not None: + target_ts = target_ts.tz_convert(None) + target_value = int(target_ts.value) + index_values = price_data.index.asi8 + else: + target_value = int(start_ts.timestamp() * 1000.0) + index_values = price_data.index.to_numpy(dtype=np.int64) + + row_idx = int(np.searchsorted(index_values, target_value, side="left")) + if row_idx >= len(index_values): + row_idx = len(index_values) - 1 + elif row_idx > 0 and index_values[row_idx] != target_value: + prev_idx = row_idx - 1 + if abs(int(index_values[prev_idx]) - target_value) <= abs( + int(index_values[row_idx]) - target_value + ): + row_idx = prev_idx + + row = price_data.iloc[row_idx] + if isinstance(row, pd.DataFrame): + row = row.iloc[0] + return row + + def _make_comparison_cache_key(cfg, launch_final_values): """Cache key for scalar heatmap metrics at a single parameter point.""" noise_cfg = make_noise_variant_cfg(cfg, True) @@ -847,7 +921,11 @@ def _run_method_final_value_cached(cfg, method, cache): return final_value_cache[key] result = do_run_on_historic_data( - run_fingerprint=make_fingerprint(cfg, method), + run_fingerprint=make_fingerprint( + cfg, + method, + market_linear_noise_data=cache.get("_shared_market_linear_noise_data"), + ), params=make_params(cfg), price_data=cache["_shared_price_data"], low_data_mode=True, @@ -1842,25 +1920,7 @@ def build_pair_slice_data(pair, slice_variant, metric_keys): def compute_auto_calibrated_arc_length_speed(cfg, price_data): """Compute the launch/reference auto-calibrated speed for a config.""" start_ts = pd.Timestamp(cfg["start"]) - - if isinstance(price_data.index, pd.DatetimeIndex): - row = price_data.loc[start_ts] - else: - start_unix_ms = int(start_ts.timestamp() * 1000.0) - index_values = price_data.index.to_numpy(dtype=np.int64) - row_idx = int(np.searchsorted(index_values, start_unix_ms, side="left")) - if row_idx >= len(index_values): - row_idx = len(index_values) - 1 - if row_idx > 0 and index_values[row_idx] != start_unix_ms: - prev_idx = row_idx - 1 - if abs(index_values[prev_idx] - start_unix_ms) <= abs( - index_values[row_idx] - start_unix_ms - ): - row_idx = prev_idx - row = price_data.iloc[row_idx] - - if isinstance(row, pd.DataFrame): - row = row.iloc[0] + row = _nearest_price_row(price_data, start_ts) if isinstance(price_data.columns, pd.MultiIndex): initial_price_values = [ @@ -2103,7 +2163,12 @@ def generate_arc_speed_efficiency_artifacts( print("Released arc-speed sweep cache.") -def get_launch_final_values(all_results, launch_cfg, price_data): +def get_launch_final_values( + all_results, + launch_cfg, + price_data, + market_linear_noise_data=None, +): """Reuse launch-style runs when available; otherwise run them once.""" for cfg, results in all_results: if cfg["name"] == launch_cfg["name"]: @@ -2121,6 +2186,7 @@ def get_launch_final_values(all_results, launch_cfg, price_data): launch_cfg, price_data=price_data, low_data_mode=True, + market_linear_noise_data=market_linear_noise_data, ) launch_final_values = { "geometric": float(launch_results["geometric"]["final_value"]), @@ -2388,6 +2454,7 @@ def plot_comparison(cfg, results, fig_idx): if __name__ == "__main__": shared_price_data = load_shared_price_data(CONFIGS) + shared_market_linear_noise_data = load_shared_market_linear_noise_data() for initial_pool_value in TVL_SWEEP_VALUES: tvl_configs = configs_for_tvl(CONFIGS, initial_pool_value) @@ -2398,7 +2465,11 @@ def plot_comparison(cfg, results, fig_idx): for i, cfg in enumerate(tvl_configs): print(f"\n>>> Running {cfg['name']} at TVL {tvl_label}...") try: - results = run_comparison(cfg, price_data=shared_price_data) + results = run_comparison( + cfg, + price_data=shared_price_data, + market_linear_noise_data=shared_market_linear_noise_data, + ) print_comparison(cfg, results) plot_comparison(cfg, results, i) all_results.append((cfg, results)) @@ -2503,10 +2574,12 @@ def plot_comparison(cfg, results, fig_idx): all_results, launch_cfg=tvl_configs[0], price_data=shared_price_data, + market_linear_noise_data=shared_market_linear_noise_data, ) shared_sweep_cache = make_sweep_cache( shared_price_data, cache_scope_cfg=tvl_configs[1], + market_linear_noise_data=shared_market_linear_noise_data, ) print(f"\nGenerating thermostat heatmaps for TVL {tvl_label}...") diff --git a/scripts/reclamm/compare_reclamm_geometric_noise_runs.py b/scripts/reclamm/compare_reclamm_geometric_noise_runs.py index 3e385dd..240482b 100644 --- a/scripts/reclamm/compare_reclamm_geometric_noise_runs.py +++ b/scripts/reclamm/compare_reclamm_geometric_noise_runs.py @@ -30,7 +30,7 @@ DEFAULT_SOURCE_HEATMAP_DESCRIPTION = ( - "legacy unsuffixed price_ratio_vs_margin heatmap with shift_exp fixed at 0.10" + "market_linear price_ratio_vs_margin heatmap with shift_exp fixed at 0.10" ) DEFAULT_RUN_SPECS = [ { @@ -42,7 +42,7 @@ "color": "C0", "reason": ( "Geometric noise-model run taken from the positive cell in the " - "legacy price_ratio-vs-margin heatmap at price_ratio=1.31 and the " + "market_linear price_ratio-vs-margin heatmap at price_ratio=1.31 and the " "lower adjacent centeredness row." ), }, @@ -55,7 +55,7 @@ "color": "C1", "reason": ( "Geometric noise-model run taken from the negative cell directly " - "above the green cell in the legacy price_ratio-vs-margin heatmap " + "above the green cell in the market_linear price_ratio-vs-margin heatmap " "at price_ratio=1.31." ), }, @@ -205,18 +205,23 @@ def build_run_config(spec, base_config): } ) source_noise_profile = spec.get("source_noise_profile") - if source_noise_profile == "legacy_calibrated": - cfg["noise_model"] = "calibrated" - cfg.pop("reclamm_noise_params", None) - cfg.pop("noise_arrays_path", None) - elif source_noise_profile == "market_linear": + if source_noise_profile == "market_linear": cfg["noise_model"] = "market_linear" + elif source_noise_profile not in (None, "", "unknown"): + raise ValueError( + "compare_reclamm_geometric_noise_runs.py only supports the current " + f"market_linear source profile, got {source_noise_profile!r}. " + "Regenerate the adjacent-pairs CSV with the current heatmap cache." + ) return cfg def build_run_variants(spec, base_config, thermostat_compare): """Build matched noise-model and arb-only variants for one highlighted cell.""" - noise_cfg = build_run_config(spec, base_config=base_config) + noise_cfg = thermostat_compare.make_noise_variant_cfg( + build_run_config(spec, base_config=base_config), + True, + ) noise_cfg["variant_key"] = "noise" noise_cfg["variant_label"] = "noise-model" diff --git a/scripts/reclamm/compare_reclamm_thermostats.py b/scripts/reclamm/compare_reclamm_thermostats.py index 21352a4..f705c66 100644 --- a/scripts/reclamm/compare_reclamm_thermostats.py +++ b/scripts/reclamm/compare_reclamm_thermostats.py @@ -17,8 +17,8 @@ import gc import hashlib -import math import os +from pathlib import Path import jax.numpy as jnp import numpy as np @@ -50,6 +50,15 @@ def build_inclusive_sweep(start, stop, step): return values +def _resolve_repo_root(script_path): + """Locate the repository root from either scripts/ or scripts/reclamm/.""" + script_path = Path(script_path).resolve() + for parent in script_path.parents: + if (parent / "quantammsim").exists() and (parent / "scripts").exists(): + return parent + return script_path.parents[1] + + RUN_CONSTANT_ARC_LENGTH = True INTERPOLATION_METHODS = ( ("geometric", "constant_arc_length") @@ -90,36 +99,39 @@ def build_inclusive_sweep(start, stop, step): THREE_D_VIEW_ELEVATION = 22.0 THREE_D_VIEW_AZIMUTH = 140.0 HEATMAP_FORWARD_CACHE_ENABLED = True -HEATMAP_FORWARD_CACHE_RUN_NAME = "aave_eth_thermostat_heatmaps_v1" +HEATMAP_FORWARD_CACHE_RUN_NAME = "aave_eth_thermostat_heatmaps_market_linear_v2" HEATMAP_FORWARD_CACHE_ROOT = os.path.join( "results", "reclamm_heatmap_forward_cache", ) HEATMAP_FORWARD_CACHE_FLUSH_EVERY = 32 +REPO_ROOT = _resolve_repo_root(__file__) AAVE_WETH_POOL_ID = "0x9d1fcf346ea1b0" DEFAULT_MARKET_LINEAR_ARTIFACT_DIR = "results/linear_market_noise" +DEFAULT_MARKET_LINEAR_NOISE_START_DATE = "2024-06-01" +DEFAULT_MARKET_LINEAR_NOISE_END_DATE = "2026-03-01" +DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH = str( + REPO_ROOT + / "results" + / "linear_market_noise" + / "_sim_arrays" + / ( + f"{AAVE_WETH_POOL_ID}_{DEFAULT_MARKET_LINEAR_NOISE_START_DATE}_" + f"{DEFAULT_MARKET_LINEAR_NOISE_END_DATE}.npz" + ) +) DEFAULT_NOISE_MODEL = "market_linear" DEFAULT_GAS_COST = 1.0 DEFAULT_PROTOCOL_FEE_SPLIT = 0.25 -LEGACY_NOISE_COEFFS = [ - -0.453, - 0.025, - -0.060, - 0.310, - -0.149, - 0.359, - 0.061, - 0.060, -] -LEGACY_LOG_CADENCE = 2.68 -LEGACY_ARB_FREQUENCY = max(1, round(math.exp(LEGACY_LOG_CADENCE))) -FIXED_COMPARE_ARB_FREQUENCY = LEGACY_ARB_FREQUENCY +FIXED_COMPARE_ARB_FREQUENCY = 15 AAVE_ETH_NOISE_SETTINGS = { "enable_noise_model": True, "noise_model": DEFAULT_NOISE_MODEL, + "noise_reference_model": DEFAULT_NOISE_MODEL, "noise_artifact_dir": DEFAULT_MARKET_LINEAR_ARTIFACT_DIR, "noise_pool_id": AAVE_WETH_POOL_ID, + "noise_arrays_path": DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH, "arb_frequency": FIXED_COMPARE_ARB_FREQUENCY, "gas_cost": DEFAULT_GAS_COST, "protocol_fee_split": DEFAULT_PROTOCOL_FEE_SPLIT, @@ -164,7 +176,7 @@ def build_inclusive_sweep(start, stop, step): } _NOISE_SETTINGS_CACHE = {} -_WARNED_NOISE_FALLBACKS = set() +_MARKET_LINEAR_NOISE_DATA_CACHE = {} def get_initial_pool_value(cfg): @@ -251,6 +263,27 @@ def get_effective_arb_frequency(cfg, noise_cfg=None): return _normalize_arb_frequency(FIXED_COMPARE_ARB_FREQUENCY) +def _canonical_noise_reference_model(cfg): + """Resolve the only supported thermostat noise parametrisation.""" + noise_model = cfg.get("noise_model", DEFAULT_NOISE_MODEL) or DEFAULT_NOISE_MODEL + reference_model = cfg.get("noise_reference_model") + if reference_model is None: + reference_model = DEFAULT_NOISE_MODEL if noise_model == "arb_only" else noise_model + noise_model = str(noise_model) + reference_model = str(reference_model) + if noise_model not in {DEFAULT_NOISE_MODEL, "arb_only"}: + raise ValueError( + "compare_reclamm_thermostats only supports " + "'market_linear' noise and 'arb_only' baselines." + ) + if reference_model != DEFAULT_NOISE_MODEL: + raise ValueError( + "compare_reclamm_thermostats only supports the " + "'market_linear' noise parametrisation." + ) + return reference_model + + def normalize_compare_run_cfg(cfg, enable_noise_model=None): """Canonicalize the compare-run config so non-axis inputs stay fixed.""" updated = dict(cfg) @@ -277,26 +310,18 @@ def normalize_compare_run_cfg(cfg, enable_noise_model=None): ) updated["enable_noise_model"] = use_noise - requested_mode = cfg.get("noise_model", DEFAULT_NOISE_MODEL) or DEFAULT_NOISE_MODEL + reference_mode = _canonical_noise_reference_model(cfg) if use_noise: - updated["noise_model"] = requested_mode - if requested_mode == "market_linear": - updated["noise_artifact_dir"] = DEFAULT_MARKET_LINEAR_ARTIFACT_DIR - updated["noise_pool_id"] = AAVE_WETH_POOL_ID - else: - updated.pop("noise_artifact_dir", None) - updated.pop("noise_pool_id", None) - updated.pop("reclamm_noise_params", None) - updated.pop("noise_arrays_path", None) + updated["noise_model"] = reference_mode + updated["noise_reference_model"] = reference_mode else: - updated["noise_model"] = None - for key in ( - "reclamm_noise_params", - "noise_arrays_path", - "noise_artifact_dir", - "noise_pool_id", - ): - updated.pop(key, None) + updated["noise_model"] = "arb_only" + updated["noise_reference_model"] = reference_mode + + updated["noise_arrays_path"] = DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH + updated.pop("reclamm_noise_params", None) + updated["noise_artifact_dir"] = DEFAULT_MARKET_LINEAR_ARTIFACT_DIR + updated["noise_pool_id"] = AAVE_WETH_POOL_ID return updated @@ -306,13 +331,6 @@ def make_noise_variant_cfg(cfg, enable_noise_model): return normalize_compare_run_cfg(cfg, enable_noise_model=enable_noise_model) -def _warn_noise_fallback(message): - """Print a one-time message when the preferred noise setup is unavailable.""" - if message not in _WARNED_NOISE_FALLBACKS: - print(message) - _WARNED_NOISE_FALLBACKS.add(message) - - def _hashable_noise_params(params): """Convert a noise-params dict into a stable cache key fragment.""" if params is None: @@ -320,38 +338,76 @@ def _hashable_noise_params(params): return tuple(sorted((str(k), round(float(v), 12)) for k, v in params.items())) -def _legacy_calibrated_noise_settings(reason=None, arb_frequency=None): - """Fallback calibrated noise config used when market-linear artifacts are absent.""" - if reason: - _warn_noise_fallback( - "market_linear noise unavailable for thermostat comparison; " - f"falling back to calibrated legacy coefficients ({reason})." - ) +def load_shared_market_linear_noise_data( + arrays_path=DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH, +): + """Load the market_linear arrays once so compare runs can reuse them.""" + arrays_path = os.path.abspath(os.fspath(arrays_path)) + cached = _MARKET_LINEAR_NOISE_DATA_CACHE.get(arrays_path) + if cached is not None: + return cached + + if not os.path.exists(arrays_path): + raise FileNotFoundError(f"market_linear arrays file not found: {arrays_path}") + + with np.load(arrays_path) as arrays: + required_keys = {"noise_base", "noise_tvl_coeff", "tvl_mean", "tvl_std"} + missing_keys = sorted(required_keys.difference(arrays.files)) + if missing_keys: + raise KeyError( + f"market_linear arrays file {arrays_path} is missing keys: {missing_keys}" + ) + shared = { + "arrays_path": arrays_path, + "noise_base_array": np.asarray(arrays["noise_base"]), + "noise_tvl_coeff_array": np.asarray(arrays["noise_tvl_coeff"]), + "tvl_mean": float(arrays["tvl_mean"]), + "tvl_std": float(arrays["tvl_std"]), + } + _MARKET_LINEAR_NOISE_DATA_CACHE[arrays_path] = shared + return shared + + +def _load_market_linear_noise_stats(arrays_path=DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH): + """Load the exact arrays file used by the market_linear run fingerprint. + + The simulator consumes ``noise_base`` and ``noise_tvl_coeff`` from + ``run_fingerprint["noise_arrays_path"]`` and uses ``tvl_mean``/``tvl_std`` + from the same file for TVL standardization. + """ + shared = load_shared_market_linear_noise_data(arrays_path=arrays_path) + return shared["arrays_path"], shared["tvl_mean"], shared["tvl_std"] + + +def _market_linear_noise_settings(noise_model="market_linear", arb_frequency=None): + """Build the tuned market_linear fingerprint block from the fixed arrays file.""" + arrays_path, tvl_mean, tvl_std = _load_market_linear_noise_stats() arb_frequency = _normalize_arb_frequency(arb_frequency) return { - "noise_model": "calibrated", + "noise_model": noise_model, "noise_trader_ratio": 0.0, "reclamm_noise_params": { - f"c_{i}": LEGACY_NOISE_COEFFS[i] for i in range(len(LEGACY_NOISE_COEFFS)) + "tvl_mean": tvl_mean, + "tvl_std": tvl_std, }, + "noise_arrays_path": arrays_path, "arb_frequency": arb_frequency, - "noise_summary": ( - "calibrated legacy 8-covariate " - f"(arb_frequency={arb_frequency})" - ), + "noise_summary": f"{noise_model} (arb_frequency={arb_frequency})", "noise_cache_key": ( - "calibrated", - tuple(round(float(c), 12) for c in LEGACY_NOISE_COEFFS), + noise_model, + arrays_path, arb_frequency, + round(tvl_mean, 12), + round(tvl_std, 12), ), } - def resolve_reclamm_noise_settings(cfg): """Resolve the active reCLAMM noise-model fingerprint block for a config.""" cfg = normalize_compare_run_cfg(cfg) enable_noise_model = cfg.get("enable_noise_model", False) requested_mode = cfg.get("noise_model", DEFAULT_NOISE_MODEL) + reference_mode = cfg.get("noise_reference_model", DEFAULT_NOISE_MODEL) requested_arb_frequency = get_effective_arb_frequency(cfg) cache_key = ( tuple(cfg.get("tokens", [])), @@ -359,6 +415,7 @@ def resolve_reclamm_noise_settings(cfg): cfg.get("end"), enable_noise_model, requested_mode, + reference_mode, cfg.get("noise_artifact_dir", DEFAULT_MARKET_LINEAR_ARTIFACT_DIR), cfg.get("noise_pool_id", AAVE_WETH_POOL_ID), requested_arb_frequency, @@ -369,103 +426,21 @@ def resolve_reclamm_noise_settings(cfg): if cache_key in _NOISE_SETTINGS_CACHE: return _NOISE_SETTINGS_CACHE[cache_key] - if not enable_noise_model: - result = { - "noise_model": None, - "noise_trader_ratio": 0.0, - "reclamm_noise_params": None, - "noise_arrays_path": None, - "arb_frequency": requested_arb_frequency, - "noise_summary": "arb-only (noise disabled)", - "noise_cache_key": ("disabled",), - } - elif requested_mode == "market_linear": - artifact_dir = cfg.get("noise_artifact_dir", DEFAULT_MARKET_LINEAR_ARTIFACT_DIR) - pool_id = cfg.get("noise_pool_id", AAVE_WETH_POOL_ID) - start_date = str(cfg["start"]).split(" ")[0] - end_date = str(cfg["end"]).split(" ")[0] - try: - from quantammsim.calibration.noise_model_arrays import build_simulator_arrays - - model_path = os.path.join(artifact_dir, "model.npz") - meta_path = os.path.join(artifact_dir, "meta.json") - if not (os.path.exists(model_path) and os.path.exists(meta_path)): - raise FileNotFoundError( - f"expected {model_path} and {meta_path}" - ) - - cache_dir = os.path.join(artifact_dir, "_sim_arrays") - os.makedirs(cache_dir, exist_ok=True) - arrays_path = os.path.join( - cache_dir, - f"{pool_id}_{start_date}_{end_date}.npz", - ) - if not os.path.exists(arrays_path): - arrays = build_simulator_arrays( - token_a=cfg["tokens"][0], - token_b=cfg["tokens"][1], - pool_id=pool_id, - start_date=start_date, - end_date=end_date, - artifact_dir=artifact_dir, - ) - np.savez( - arrays_path, - noise_base=arrays["noise_base"], - noise_tvl_coeff=arrays["noise_tvl_coeff"], - tvl_mean=arrays["tvl_mean"], - tvl_std=arrays["tvl_std"], - ) - - with np.load(arrays_path) as arrays: - tvl_mean = float(arrays["tvl_mean"]) - tvl_std = float(arrays["tvl_std"]) - - arb_frequency = requested_arb_frequency - result = { - "noise_model": "market_linear", - "noise_trader_ratio": 0.0, - "reclamm_noise_params": { - "tvl_mean": tvl_mean, - "tvl_std": tvl_std, - }, - "noise_arrays_path": arrays_path, - "arb_frequency": arb_frequency, - "noise_summary": f"market_linear (arb_frequency={arb_frequency})", - "noise_cache_key": ( - "market_linear", - arrays_path, - arb_frequency, - round(tvl_mean, 12), - round(tvl_std, 12), - ), - } - except Exception as exc: # pragma: no cover - fallback path depends on local artifacts - result = _legacy_calibrated_noise_settings( - str(exc), - arb_frequency=requested_arb_frequency, - ) - elif requested_mode == "calibrated": - result = _legacy_calibrated_noise_settings( - arb_frequency=requested_arb_frequency + if requested_mode == "arb_only": + result = _market_linear_noise_settings( + noise_model="arb_only", + arb_frequency=requested_arb_frequency, + ) + elif requested_mode == DEFAULT_NOISE_MODEL: + result = _market_linear_noise_settings( + noise_model=DEFAULT_NOISE_MODEL, + arb_frequency=requested_arb_frequency, ) else: - arb_frequency = requested_arb_frequency - result = { - "noise_model": requested_mode, - "noise_trader_ratio": cfg.get("noise_trader_ratio", 0.0), - "reclamm_noise_params": cfg.get("reclamm_noise_params"), - "noise_arrays_path": cfg.get("noise_arrays_path"), - "arb_frequency": arb_frequency, - "noise_summary": f"{requested_mode} (arb_frequency={arb_frequency})", - "noise_cache_key": ( - requested_mode, - round(float(cfg.get("noise_trader_ratio", 0.0)), 12), - _hashable_noise_params(cfg.get("reclamm_noise_params")), - cfg.get("noise_arrays_path"), - arb_frequency, - ), - } + raise ValueError( + "compare_reclamm_thermostats only supports " + "'market_linear' noise and 'arb_only' baselines." + ) _NOISE_SETTINGS_CACHE[cache_key] = result return result @@ -504,7 +479,29 @@ def resolve_reclamm_noise_settings(cfg): ] -def make_fingerprint(cfg, interpolation_method): +def _attach_market_linear_noise_arrays( + fingerprint, + noise_cfg, + market_linear_noise_data, +): + """Attach preloaded market_linear arrays when the compare flow has them.""" + if market_linear_noise_data is None: + return + expected_path = noise_cfg.get("noise_arrays_path") + if expected_path is None: + return + shared_path = os.path.abspath(os.fspath(market_linear_noise_data["arrays_path"])) + expected_path = os.path.abspath(os.fspath(expected_path)) + if shared_path != expected_path: + raise ValueError( + "Shared market_linear noise arrays path does not match " + f"the resolved compare-run noise path: {shared_path} != {expected_path}" + ) + fingerprint["noise_base_array"] = market_linear_noise_data["noise_base_array"] + fingerprint["noise_tvl_coeff_array"] = market_linear_noise_data["noise_tvl_coeff_array"] + + +def make_fingerprint(cfg, interpolation_method, market_linear_noise_data=None): """Build run fingerprint for a given config and interpolation method.""" cfg = normalize_compare_run_cfg(cfg) speed_override = ( @@ -541,6 +538,11 @@ def make_fingerprint(cfg, interpolation_method): fingerprint["reclamm_noise_params"] = noise_cfg["reclamm_noise_params"] if noise_cfg.get("noise_arrays_path") is not None: fingerprint["noise_arrays_path"] = noise_cfg["noise_arrays_path"] + _attach_market_linear_noise_arrays( + fingerprint, + noise_cfg, + market_linear_noise_data, + ) if arb_frequency is not None: fingerprint["arb_frequency"] = arb_frequency return fingerprint @@ -564,13 +566,22 @@ def load_shared_price_data(configs, root=None): return get_historic_parquet_data(tokens, cols=["close"], root=root) -def run_comparison(cfg, price_data=None, low_data_mode=False): +def run_comparison( + cfg, + price_data=None, + low_data_mode=False, + market_linear_noise_data=None, +): """Run both interpolation variants, return results dict.""" params = make_params(cfg) results = {} for method in INTERPOLATION_METHODS: - fp = make_fingerprint(cfg, method) + fp = make_fingerprint( + cfg, + method, + market_linear_noise_data=market_linear_noise_data, + ) results[method] = do_run_on_historic_data( run_fingerprint=fp, params=params, @@ -662,30 +673,44 @@ def _load_persistent_final_value_cache(cache): return disk_cache = {} - disk_records = {} + next_batch_id = 0 cache_path = cache.get("_persistent_final_value_cache_path") if cache_path and os.path.exists(cache_path): - frame = pd.read_parquet(cache_path) - if not frame.empty: + parquet_files = [] + if os.path.isdir(cache_path): + parquet_files = [ + os.path.join(cache_path, filename) + for filename in sorted(os.listdir(cache_path)) + if filename.endswith(".parquet") + ] + batch_ids = [] + for filename in os.listdir(cache_path): + if not (filename.startswith("batch_") and filename.endswith(".parquet")): + continue + token = filename[len("batch_") : -len(".parquet")] + if token.isdigit(): + batch_ids.append(int(token)) + next_batch_id = (max(batch_ids) + 1) if batch_ids else 0 + else: + parquet_files = [cache_path] + + for parquet_file in parquet_files: + frame = pd.read_parquet( + parquet_file, + columns=["cache_key_hash", "final_value"], + ) + if frame.empty: + continue for row in frame.itertuples(index=False): cache_key_hash = str(row.cache_key_hash) final_value = float(row.final_value) disk_cache[cache_key_hash] = final_value - record = { - "cache_key_hash": cache_key_hash, - "final_value": final_value, - } - for column in PERSISTED_FORWARD_VALUE_COLUMNS: - if column in {"cache_key_hash", "final_value"}: - continue - record[column] = getattr(row, column, None) - disk_records[cache_key_hash] = record print( f"Loaded {len(disk_cache)} persisted heatmap forward values from {cache_path}" ) cache["_persistent_final_value_cache"] = disk_cache - cache["_persistent_final_value_records"] = disk_records + cache["_persistent_final_value_next_batch_id"] = next_batch_id cache["_persistent_final_value_cache_loaded"] = True @@ -702,52 +727,63 @@ def flush_sweep_cache(cache, force=False): _load_persistent_final_value_cache(cache) disk_cache = cache.setdefault("_persistent_final_value_cache", {}) - disk_records = cache.setdefault("_persistent_final_value_records", {}) + batch_records = [] for cache_key_hash, record in pending.items(): - merged = dict(disk_records.get(cache_key_hash, {})) - merged.update(record) - merged["cache_key_hash"] = str(cache_key_hash) - merged["final_value"] = float(merged["final_value"]) - disk_records[cache_key_hash] = merged - disk_cache[cache_key_hash] = merged["final_value"] + normalized = dict(record) + normalized["cache_key_hash"] = str(cache_key_hash) + normalized["final_value"] = float(normalized["final_value"]) + disk_cache[cache_key_hash] = normalized["final_value"] + batch_records.append(normalized) cache_path = cache.get("_persistent_final_value_cache_path") if cache_path is None: pending.clear() return - os.makedirs(os.path.dirname(cache_path), exist_ok=True) - sorted_records = [disk_records[key] for key in sorted(disk_records)] + if os.path.exists(cache_path) and not os.path.isdir(cache_path): + raise RuntimeError( + f"Persistent cache path {cache_path} already exists as a file. " + "Use a fresh cache namespace for append-only parquet shards." + ) + + os.makedirs(cache_path, exist_ok=True) + batch_records.sort(key=lambda record: record["cache_key_hash"]) payload = { - column: [record.get(column) for record in sorted_records] + column: [record.get(column) for record in batch_records] for column in PERSISTED_FORWARD_VALUE_COLUMNS } payload["final_value"] = np.asarray(payload["final_value"], dtype=np.float64) frame = pd.DataFrame(payload) - frame.sort_values("cache_key_hash", inplace=True, ignore_index=True) - frame.to_parquet(cache_path, index=False, compression="zstd") + batch_id = int(cache.setdefault("_persistent_final_value_next_batch_id", 0)) + batch_path = os.path.join(cache_path, f"batch_{batch_id:08d}.parquet") + cache["_persistent_final_value_next_batch_id"] = batch_id + 1 + frame.to_parquet(batch_path, index=False, compression="zstd") print( - f"Persisted {len(pending)} new heatmap forward values to {cache_path} " + f"Persisted {len(pending)} new heatmap forward values to {batch_path} " f"({len(disk_cache)} total cached values)." ) pending.clear() -def make_sweep_cache(price_data, cache_scope_cfg=None): +def make_sweep_cache( + price_data, + cache_scope_cfg=None, + market_linear_noise_data=None, +): """Create a shared cache for heatmap and line sweeps.""" cache = { "_shared_price_data": price_data, + "_shared_market_linear_noise_data": market_linear_noise_data, "_final_value_cache": {}, "_comparison_cache": {}, "_pending_persistent_final_values": {}, "_persistent_final_value_cache": {}, - "_persistent_final_value_records": {}, + "_persistent_final_value_next_batch_id": 0, "_persistent_final_value_cache_loaded": False, "_persistent_final_value_cache_path": _heatmap_forward_cache_path( cache_scope_cfg ), } - _load_persistent_final_value_cache(cache) return cache @@ -781,6 +817,10 @@ def _make_method_cache_key(cfg, method): arb_frequency = get_effective_arb_frequency(cfg, noise_cfg) key = ( method, + tuple(str(token) for token in cfg["tokens"]), + str(cfg["start"]), + str(cfg["end"]), + round(float(cfg["fees"]), 12), bool(cfg.get("enable_noise_model", False)), round(float(cfg["price_ratio"]), 6), round(float(cfg["centeredness_margin"]), 6), @@ -812,6 +852,40 @@ def _make_method_cache_key(cfg, method): return key +def _nearest_price_row(price_data, start_ts): + """Select the closest available price row to the requested start timestamp.""" + if len(price_data.index) == 0: + raise ValueError("price_data is empty") + + if isinstance(price_data.index, pd.DatetimeIndex): + target_ts = start_ts + index_tz = getattr(price_data.index, "tz", None) + if index_tz is not None and target_ts.tzinfo is None: + target_ts = target_ts.tz_localize(index_tz) + elif index_tz is None and target_ts.tzinfo is not None: + target_ts = target_ts.tz_convert(None) + target_value = int(target_ts.value) + index_values = price_data.index.asi8 + else: + target_value = int(start_ts.timestamp() * 1000.0) + index_values = price_data.index.to_numpy(dtype=np.int64) + + row_idx = int(np.searchsorted(index_values, target_value, side="left")) + if row_idx >= len(index_values): + row_idx = len(index_values) - 1 + elif row_idx > 0 and index_values[row_idx] != target_value: + prev_idx = row_idx - 1 + if abs(int(index_values[prev_idx]) - target_value) <= abs( + int(index_values[row_idx]) - target_value + ): + row_idx = prev_idx + + row = price_data.iloc[row_idx] + if isinstance(row, pd.DataFrame): + row = row.iloc[0] + return row + + def _make_comparison_cache_key(cfg, launch_final_values): """Cache key for scalar heatmap metrics at a single parameter point.""" noise_cfg = make_noise_variant_cfg(cfg, True) @@ -847,7 +921,11 @@ def _run_method_final_value_cached(cfg, method, cache): return final_value_cache[key] result = do_run_on_historic_data( - run_fingerprint=make_fingerprint(cfg, method), + run_fingerprint=make_fingerprint( + cfg, + method, + market_linear_noise_data=cache.get("_shared_market_linear_noise_data"), + ), params=make_params(cfg), price_data=cache["_shared_price_data"], low_data_mode=True, @@ -1842,25 +1920,7 @@ def build_pair_slice_data(pair, slice_variant, metric_keys): def compute_auto_calibrated_arc_length_speed(cfg, price_data): """Compute the launch/reference auto-calibrated speed for a config.""" start_ts = pd.Timestamp(cfg["start"]) - - if isinstance(price_data.index, pd.DatetimeIndex): - row = price_data.loc[start_ts] - else: - start_unix_ms = int(start_ts.timestamp() * 1000.0) - index_values = price_data.index.to_numpy(dtype=np.int64) - row_idx = int(np.searchsorted(index_values, start_unix_ms, side="left")) - if row_idx >= len(index_values): - row_idx = len(index_values) - 1 - if row_idx > 0 and index_values[row_idx] != start_unix_ms: - prev_idx = row_idx - 1 - if abs(index_values[prev_idx] - start_unix_ms) <= abs( - index_values[row_idx] - start_unix_ms - ): - row_idx = prev_idx - row = price_data.iloc[row_idx] - - if isinstance(row, pd.DataFrame): - row = row.iloc[0] + row = _nearest_price_row(price_data, start_ts) if isinstance(price_data.columns, pd.MultiIndex): initial_price_values = [ @@ -2103,7 +2163,12 @@ def generate_arc_speed_efficiency_artifacts( print("Released arc-speed sweep cache.") -def get_launch_final_values(all_results, launch_cfg, price_data): +def get_launch_final_values( + all_results, + launch_cfg, + price_data, + market_linear_noise_data=None, +): """Reuse launch-style runs when available; otherwise run them once.""" for cfg, results in all_results: if cfg["name"] == launch_cfg["name"]: @@ -2121,6 +2186,7 @@ def get_launch_final_values(all_results, launch_cfg, price_data): launch_cfg, price_data=price_data, low_data_mode=True, + market_linear_noise_data=market_linear_noise_data, ) launch_final_values = { "geometric": float(launch_results["geometric"]["final_value"]), @@ -2388,6 +2454,7 @@ def plot_comparison(cfg, results, fig_idx): if __name__ == "__main__": shared_price_data = load_shared_price_data(CONFIGS) + shared_market_linear_noise_data = load_shared_market_linear_noise_data() for initial_pool_value in TVL_SWEEP_VALUES: tvl_configs = configs_for_tvl(CONFIGS, initial_pool_value) @@ -2398,7 +2465,11 @@ def plot_comparison(cfg, results, fig_idx): for i, cfg in enumerate(tvl_configs): print(f"\n>>> Running {cfg['name']} at TVL {tvl_label}...") try: - results = run_comparison(cfg, price_data=shared_price_data) + results = run_comparison( + cfg, + price_data=shared_price_data, + market_linear_noise_data=shared_market_linear_noise_data, + ) print_comparison(cfg, results) plot_comparison(cfg, results, i) all_results.append((cfg, results)) @@ -2503,10 +2574,12 @@ def plot_comparison(cfg, results, fig_idx): all_results, launch_cfg=tvl_configs[0], price_data=shared_price_data, + market_linear_noise_data=shared_market_linear_noise_data, ) shared_sweep_cache = make_sweep_cache( shared_price_data, cache_scope_cfg=tvl_configs[1], + market_linear_noise_data=shared_market_linear_noise_data, ) print(f"\nGenerating thermostat heatmaps for TVL {tvl_label}...") diff --git a/scripts/reclamm/find_adjacent_heatmap_pairs.py b/scripts/reclamm/find_adjacent_heatmap_pairs.py index 1bc4ce3..51a3a8a 100644 --- a/scripts/reclamm/find_adjacent_heatmap_pairs.py +++ b/scripts/reclamm/find_adjacent_heatmap_pairs.py @@ -96,6 +96,30 @@ def build_inclusive_sweep(start: float, stop: float, step: float) -> np.ndarray: return values +def _resolve_repo_root(script_path): + """Locate the repository root from either scripts/ or scripts/reclamm/.""" + script_path = Path(script_path).resolve() + for parent in script_path.parents: + if (parent / "quantammsim").exists() and (parent / "scripts").exists(): + return parent + return script_path.parents[1] + + +REPO_ROOT = _resolve_repo_root(__file__) +DEFAULT_MARKET_LINEAR_NOISE_START_DATE = "2024-06-01" +DEFAULT_MARKET_LINEAR_NOISE_END_DATE = "2026-03-01" +DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH = str( + REPO_ROOT + / "results" + / "linear_market_noise" + / "_sim_arrays" + / ( + "0x9d1fcf346ea1b0_" + f"{DEFAULT_MARKET_LINEAR_NOISE_START_DATE}_{DEFAULT_MARKET_LINEAR_NOISE_END_DATE}.npz" + ) +) + + class _LightweightCompareContext: """Small subset of compare_reclamm_thermostats usable without JAX.""" @@ -112,13 +136,16 @@ class _LightweightCompareContext: FIXED_SLICE_FRACTIONS = (0.125, 0.375, 0.625, 0.875) FIXED_SLICE_LABELS = ("Q1", "Q2", "Q3", "Q4") HEATMAP_FORWARD_CACHE_ENABLED = True - HEATMAP_FORWARD_CACHE_RUN_NAME = "aave_eth_thermostat_heatmaps_v1" + HEATMAP_FORWARD_CACHE_RUN_NAME = "aave_eth_thermostat_heatmaps_market_linear_v2" HEATMAP_FORWARD_CACHE_ROOT = os.path.join( "results", "reclamm_heatmap_forward_cache", ) AAVE_WETH_POOL_ID = "0x9d1fcf346ea1b0" DEFAULT_MARKET_LINEAR_ARTIFACT_DIR = "results/linear_market_noise" + DEFAULT_MARKET_LINEAR_NOISE_START_DATE = DEFAULT_MARKET_LINEAR_NOISE_START_DATE + DEFAULT_MARKET_LINEAR_NOISE_END_DATE = DEFAULT_MARKET_LINEAR_NOISE_END_DATE + DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH = DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH DEFAULT_NOISE_MODEL = "market_linear" DEFAULT_GAS_COST = 1.0 DEFAULT_PROTOCOL_FEE_SPLIT = 0.25 @@ -138,8 +165,10 @@ class _LightweightCompareContext: AAVE_ETH_NOISE_SETTINGS = { "enable_noise_model": True, "noise_model": DEFAULT_NOISE_MODEL, + "noise_reference_model": DEFAULT_NOISE_MODEL, "noise_artifact_dir": DEFAULT_MARKET_LINEAR_ARTIFACT_DIR, "noise_pool_id": AAVE_WETH_POOL_ID, + "noise_arrays_path": DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH, "arb_frequency": FIXED_COMPARE_ARB_FREQUENCY, "gas_cost": DEFAULT_GAS_COST, "protocol_fee_split": DEFAULT_PROTOCOL_FEE_SPLIT, @@ -197,6 +226,9 @@ def from_compare_module(cls, compare_module): "HEATMAP_FORWARD_CACHE_ROOT", "AAVE_WETH_POOL_ID", "DEFAULT_MARKET_LINEAR_ARTIFACT_DIR", + "DEFAULT_MARKET_LINEAR_NOISE_START_DATE", + "DEFAULT_MARKET_LINEAR_NOISE_END_DATE", + "DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH", "DEFAULT_NOISE_MODEL", "DEFAULT_GAS_COST", "DEFAULT_PROTOCOL_FEE_SPLIT", @@ -225,7 +257,7 @@ def from_compare_module(cls, compare_module): return context def set_noise_profile(self, profile): - if profile not in {"market_linear", "legacy_calibrated"}: + if profile != "market_linear": raise ValueError(f"Unsupported lightweight noise profile: {profile}") if profile != self.noise_profile: self.noise_profile = profile @@ -353,6 +385,13 @@ def get_effective_arb_frequency(self, cfg, noise_cfg=None): del noise_cfg return self._normalize_arb_frequency(self.FIXED_COMPARE_ARB_FREQUENCY) + def _canonical_noise_reference_model(self, cfg): + noise_model = cfg.get("noise_model", self.DEFAULT_NOISE_MODEL) or self.DEFAULT_NOISE_MODEL + reference_model = cfg.get("noise_reference_model") + if reference_model is None: + reference_model = self.DEFAULT_NOISE_MODEL if noise_model == "arb_only" else noise_model + return str(reference_model) + def normalize_compare_run_cfg(self, cfg, enable_noise_model=None): updated = dict(cfg) updated["price_ratio"] = float(cfg["price_ratio"]) @@ -380,53 +419,79 @@ def normalize_compare_run_cfg(self, cfg, enable_noise_model=None): ) updated["enable_noise_model"] = use_noise - requested_mode = ( - cfg.get("noise_model", self.DEFAULT_NOISE_MODEL) - or self.DEFAULT_NOISE_MODEL - ) + reference_mode = self._canonical_noise_reference_model(cfg) if use_noise: - canonical_noise_model = ( - requested_mode - if requested_mode != "arb_only" - else self.DEFAULT_NOISE_MODEL - ) - updated["noise_model"] = canonical_noise_model - if canonical_noise_model == "market_linear": + updated["noise_model"] = reference_mode + updated["noise_reference_model"] = reference_mode + else: + updated["noise_model"] = "arb_only" + updated["noise_reference_model"] = reference_mode + + if reference_mode == "market_linear": + updated["noise_arrays_path"] = self.DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH + updated.pop("reclamm_noise_params", None) + if use_noise or updated["noise_model"] == "arb_only": updated["noise_artifact_dir"] = self.DEFAULT_MARKET_LINEAR_ARTIFACT_DIR updated["noise_pool_id"] = self.AAVE_WETH_POOL_ID - else: - updated.pop("noise_artifact_dir", None) - updated.pop("noise_pool_id", None) + else: updated.pop("reclamm_noise_params", None) updated.pop("noise_arrays_path", None) - else: - updated["noise_model"] = "arb_only" - for key in ( - "reclamm_noise_params", - "noise_arrays_path", - "noise_artifact_dir", - "noise_pool_id", - ): - updated.pop(key, None) + updated.pop("noise_artifact_dir", None) + updated.pop("noise_pool_id", None) return updated - def _legacy_calibrated_noise_settings(self, arb_frequency=None): + def _load_market_linear_noise_stats(self, arrays_path=None): + arrays_path = os.path.abspath( + os.fspath(arrays_path or self.DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH) + ) + if not os.path.exists(arrays_path): + raise FileNotFoundError(f"market_linear arrays file not found: {arrays_path}") + + with np.load(arrays_path) as arrays: + required_keys = {"noise_base", "noise_tvl_coeff", "tvl_mean", "tvl_std"} + missing_keys = sorted(required_keys.difference(arrays.files)) + if missing_keys: + raise KeyError( + f"market_linear arrays file {arrays_path} is missing keys: {missing_keys}" + ) + return arrays_path, float(arrays["tvl_mean"]), float(arrays["tvl_std"]) + + def _market_linear_noise_settings(self, noise_model="market_linear", arb_frequency=None): + arrays_path, tvl_mean, tvl_std = self._load_market_linear_noise_stats() + arb_frequency = self._normalize_arb_frequency(arb_frequency) + return { + "noise_model": noise_model, + "noise_trader_ratio": 0.0, + "reclamm_noise_params": { + "tvl_mean": tvl_mean, + "tvl_std": tvl_std, + }, + "noise_arrays_path": arrays_path, + "arb_frequency": arb_frequency, + "noise_summary": f"{noise_model} (arb_frequency={arb_frequency})", + "noise_cache_key": ( + noise_model, + arrays_path, + arb_frequency, + round(tvl_mean, 12), + round(tvl_std, 12), + ), + } + + def _legacy_calibrated_noise_settings(self, arb_frequency=None, noise_model="calibrated"): arb_frequency = self._normalize_arb_frequency(arb_frequency) return { - "noise_model": "calibrated", + "noise_model": noise_model, "noise_trader_ratio": 0.0, "reclamm_noise_params": { f"c_{i}": self.LEGACY_NOISE_COEFFS[i] for i in range(len(self.LEGACY_NOISE_COEFFS)) }, "arb_frequency": arb_frequency, - "noise_summary": ( - "calibrated legacy 8-covariate " - f"(arb_frequency={arb_frequency})" - ), + "noise_summary": f"{noise_model} (arb_frequency={arb_frequency})", "noise_cache_key": ( - "calibrated", + noise_model, tuple(round(float(c), 12) for c in self.LEGACY_NOISE_COEFFS), arb_frequency, ), @@ -436,6 +501,7 @@ def resolve_reclamm_noise_settings(self, cfg): cfg = self.normalize_compare_run_cfg(cfg) enable_noise_model = cfg.get("enable_noise_model", False) requested_mode = cfg.get("noise_model", self.DEFAULT_NOISE_MODEL) + reference_mode = cfg.get("noise_reference_model", self.DEFAULT_NOISE_MODEL) requested_arb_frequency = self.get_effective_arb_frequency(cfg) cache_key = ( tuple(cfg.get("tokens", [])), @@ -443,6 +509,7 @@ def resolve_reclamm_noise_settings(self, cfg): cfg.get("end"), enable_noise_model, requested_mode, + reference_mode, cfg.get("noise_artifact_dir", self.DEFAULT_MARKET_LINEAR_ARTIFACT_DIR), cfg.get("noise_pool_id", self.AAVE_WETH_POOL_ID), requested_arb_frequency, @@ -453,68 +520,39 @@ def resolve_reclamm_noise_settings(self, cfg): if cache_key in self._noise_settings_cache: return self._noise_settings_cache[cache_key] - if not enable_noise_model: - result = { - "noise_model": "arb_only", - "noise_trader_ratio": 0.0, - "reclamm_noise_params": None, - "noise_arrays_path": None, - "arb_frequency": requested_arb_frequency, - "noise_summary": "arb_only (noise disabled)", - "noise_cache_key": ("disabled",), - } - elif requested_mode == "market_linear": - if self.noise_profile == "legacy_calibrated": - result = self._legacy_calibrated_noise_settings( + if requested_mode == "arb_only": + if reference_mode == "market_linear": + result = self._market_linear_noise_settings( + noise_model="arb_only", arb_frequency=requested_arb_frequency ) - self._noise_settings_cache[cache_key] = result - return result - artifact_dir = cfg.get( - "noise_artifact_dir", - self.DEFAULT_MARKET_LINEAR_ARTIFACT_DIR, - ) - pool_id = cfg.get("noise_pool_id", self.AAVE_WETH_POOL_ID) - start_date = str(cfg["start"]).split(" ")[0] - end_date = str(cfg["end"]).split(" ")[0] - arrays_path = cfg.get("noise_arrays_path") or os.path.join( - artifact_dir, - "_sim_arrays", - f"{pool_id}_{start_date}_{end_date}.npz", - ) - meta_path = os.path.join(artifact_dir, "meta.json") - model_path = os.path.join(artifact_dir, "model.npz") - if not ( - os.path.exists(arrays_path) - and os.path.exists(meta_path) - and os.path.exists(model_path) - ): + elif reference_mode == "calibrated": result = self._legacy_calibrated_noise_settings( + noise_model="arb_only", arb_frequency=requested_arb_frequency ) else: - with np.load(arrays_path) as arrays: - tvl_mean = float(arrays["tvl_mean"]) - tvl_std = float(arrays["tvl_std"]) arb_frequency = requested_arb_frequency result = { - "noise_model": "market_linear", - "noise_trader_ratio": 0.0, - "reclamm_noise_params": { - "tvl_mean": tvl_mean, - "tvl_std": tvl_std, - }, - "noise_arrays_path": arrays_path, + "noise_model": "arb_only", + "noise_trader_ratio": cfg.get("noise_trader_ratio", 0.0), + "reclamm_noise_params": cfg.get("reclamm_noise_params"), + "noise_arrays_path": cfg.get("noise_arrays_path"), "arb_frequency": arb_frequency, - "noise_summary": f"market_linear (arb_frequency={arb_frequency})", + "noise_summary": f"arb_only (arb_frequency={arb_frequency})", "noise_cache_key": ( - "market_linear", - arrays_path, + "arb_only", + round(float(cfg.get("noise_trader_ratio", 0.0)), 12), + self._hashable_noise_params(cfg.get("reclamm_noise_params")), + cfg.get("noise_arrays_path"), arb_frequency, - round(tvl_mean, 12), - round(tvl_std, 12), ), } + elif requested_mode == "market_linear": + result = self._market_linear_noise_settings( + noise_model="market_linear", + arb_frequency=requested_arb_frequency, + ) elif requested_mode == "calibrated": result = self._legacy_calibrated_noise_settings( arb_frequency=requested_arb_frequency @@ -815,65 +853,15 @@ def autodetect_lightweight_noise_profile( ): if not hasattr(compare_module, "set_noise_profile"): return - - metric_spec = get_metric_spec(metric_key) - if not pair_specs: - return - - pair_spec = pair_specs[0] - slice_variants = resolve_slice_variants(pair_spec, slice_slug) - if not slice_variants: - return - - slice_variant = slice_variants[0] - x_values = list(pair_spec["x_values"]) - y_values = list(pair_spec["y_values"]) - sample_x_indices = sorted({0, len(x_values) // 2, len(x_values) - 1}) - sample_y_indices = sorted({0, len(y_values) // 2, len(y_values) - 1}) - - scores = {} - for profile in ("market_linear", "legacy_calibrated"): - compare_module.set_noise_profile(profile) - hit_count = 0 - probe_count = 0 - slice_cfg = dict(base_cfg) - slice_cfg[pair_spec["fixed_key"]] = float(slice_variant["value"]) - for y_index in sample_y_indices: - for x_index in sample_x_indices: - cfg = dict(slice_cfg) - cfg[pair_spec["x_key"]] = float(x_values[x_index]) - cfg[pair_spec["y_key"]] = float(y_values[y_index]) - for source_name in metric_spec["sources"]: - source_cfg, method = _source_variant(compare_module, cfg, source_name) - cache_key = compare_module._make_method_cache_key(source_cfg, method) - cache_key_hash = compare_module._make_method_cache_hash(cache_key) - probe_count += 1 - if cache_key_hash in cache_lookup: - hit_count += 1 - scores[profile] = (hit_count, probe_count) - - best_profile = max( - scores, - key=lambda profile: (scores[profile][0], scores[profile][1], profile == "market_linear"), - ) - compare_module.set_noise_profile(best_profile) - hit_count, probe_count = scores[best_profile] - print( - f"Lightweight noise profile auto-detect chose {best_profile} " - f"({hit_count}/{probe_count} sample cache hits)." - ) + del base_cfg, pair_specs, metric_key, slice_slug, cache_lookup + compare_module.set_noise_profile("market_linear") + print("Lightweight noise profile fixed to market_linear.") def _source_variant(compare_module, cfg: Mapping[str, object], source_name: str): enable_noise_model = source_name.startswith("noise_") method = "geometric" if source_name.endswith("geometric") else "constant_arc_length" - source_cfg = compare_module.make_noise_variant_cfg(cfg, enable_noise_model) - source_cfg["noise_model"] = ( - getattr(compare_module, "DEFAULT_NOISE_MODEL", "market_linear") - if enable_noise_model - else "arb_only" - ) - return source_cfg, method + return compare_module.make_noise_variant_cfg(cfg, enable_noise_model), method def _compute_metric_value(metric_key: str, final_values: Mapping[str, float]) -> float: @@ -1169,7 +1157,10 @@ def main() -> int: ) cache_path = resolve_existing_cache_path(cache_path) if not cache_path.exists(): - raise FileNotFoundError(f"Cache parquet not found: {cache_path}") + raise FileNotFoundError( + f"Cache parquet not found: {cache_path}. " + "Generate the current market_linear/arb_only heatmap cache first." + ) cache_lookup = load_cache_lookup(cache_path) autodetect_lightweight_noise_profile( @@ -1227,7 +1218,9 @@ def main() -> int: if missing_any and not args.allow_partial_cache: raise RuntimeError( "Cache was incomplete for at least one requested heatmap slice. " - "Re-run with --allow-partial-cache to write the rows that were resolvable." + "This cache does not match the current market_linear/arb_only " + "parameterization. Regenerate the heatmap cache, or re-run with " + "--allow-partial-cache to write only the rows that were resolvable." ) frame = rows_to_frame(rows) diff --git a/tests/scripts/test_compare_reclamm_geometric_noise_runs.py b/tests/scripts/test_compare_reclamm_geometric_noise_runs.py index 8a0fb74..c211e7e 100644 --- a/tests/scripts/test_compare_reclamm_geometric_noise_runs.py +++ b/tests/scripts/test_compare_reclamm_geometric_noise_runs.py @@ -32,7 +32,7 @@ def test_build_run_specs_from_adjacent_row_maps_csv_cells_to_two_specs(): row = { "metric_key": "noise_vs_arb_geometric_improvement_pct", "metric_unit": "pct", - "source_noise_profile": "legacy_calibrated", + "source_noise_profile": "market_linear", "pair_slug": "price_ratio_vs_margin", "slice_slug": "q2", "adjacency_axis": "horizontal", @@ -58,7 +58,7 @@ def test_build_run_specs_from_adjacent_row_maps_csv_cells_to_two_specs(): assert "adjacent_pairs.csv row 0" in description assert "price_ratio_vs_margin q2" in description assert "horizontal" in description - assert "noise_profile=legacy_calibrated" in description + assert "noise_profile=market_linear" in description assert len(run_specs) == 2 assert run_specs[0]["name"] == "Top diff row cell 1" assert run_specs[0]["price_ratio"] == 1.335 @@ -66,7 +66,7 @@ def test_build_run_specs_from_adjacent_row_maps_csv_cells_to_two_specs(): assert run_specs[0]["daily_price_shift_exponent"] == 0.1975 assert run_specs[0]["tvl_usd"] == 1_000_000.0 assert run_specs[0]["color"] == "C0" - assert run_specs[0]["source_noise_profile"] == "legacy_calibrated" + assert run_specs[0]["source_noise_profile"] == "market_linear" assert "heatmap_value=-53.054321" in run_specs[0]["reason"] assert run_specs[1]["name"] == "Top diff row cell 2" assert run_specs[1]["price_ratio"] == 1.36 @@ -86,7 +86,7 @@ def test_default_output_file_for_adjacent_csv_uses_csv_stem_and_row_index(): ) -def test_build_run_config_honors_legacy_calibrated_noise_profile(): +def test_build_run_config_rejects_legacy_calibrated_noise_profile(): module = load_script_module() base_config = { "name": "base", @@ -107,11 +107,57 @@ def test_build_run_config_honors_legacy_calibrated_noise_profile(): "source_noise_profile": "legacy_calibrated", } - cfg = module.build_run_config(spec, base_config=base_config) + import pytest + + with pytest.raises(ValueError): + module.build_run_config(spec, base_config=base_config) + + +def test_build_run_variants_canonicalizes_noise_and_arb_only_configs(): + module = load_script_module() + fixed_path = str( + Path(__file__).resolve().parents[2] + / "results" + / "linear_market_noise" + / "_sim_arrays" + / "0x9d1fcf346ea1b0_2024-06-01_2026-03-01.npz" + ) + base_config = { + "name": "base", + "price_ratio": 1.1, + "centeredness_margin": 0.6, + "daily_price_shift_exponent": 0.1, + "initial_pool_value": 1_000_000.0, + "noise_model": "market_linear", + "noise_arrays_path": fixed_path, + } + spec = { + "name": "cell", + "price_ratio": 1.335, + "centeredness_margin": 0.3184210526, + "daily_price_shift_exponent": 0.1975, + "tvl_usd": 1_000_000.0, + "source_noise_profile": "market_linear", + } - assert cfg["noise_model"] == "calibrated" - assert "reclamm_noise_params" not in cfg - assert "noise_arrays_path" not in cfg + class FakeThermostatCompare: + @staticmethod + def make_noise_variant_cfg(cfg, enable_noise_model): + updated = dict(cfg) + updated["enable_noise_model"] = bool(enable_noise_model) + updated["noise_model"] = "market_linear" if enable_noise_model else "arb_only" + updated["noise_arrays_path"] = fixed_path + updated["reclamm_noise_params"] = {"tvl_mean": 1.0, "tvl_std": 2.0} + return updated + + variants = module.build_run_variants(spec, base_config, FakeThermostatCompare) + + assert variants["noise"]["noise_model"] == "market_linear" + assert variants["arb"]["noise_model"] == "arb_only" + assert variants["noise"]["noise_arrays_path"] == fixed_path + assert variants["arb"]["noise_arrays_path"] == fixed_path + assert variants["noise"]["reclamm_noise_params"] == {"tvl_mean": 1.0, "tvl_std": 2.0} + assert variants["arb"]["reclamm_noise_params"] == {"tvl_mean": 1.0, "tvl_std": 2.0} def test_print_run_inputs_to_terminal_includes_fingerprint_and_update_params(capsys): diff --git a/tests/scripts/test_compare_reclamm_thermostats.py b/tests/scripts/test_compare_reclamm_thermostats.py index 3ea0da7..caec6ae 100644 --- a/tests/scripts/test_compare_reclamm_thermostats.py +++ b/tests/scripts/test_compare_reclamm_thermostats.py @@ -163,16 +163,21 @@ def test_make_noise_variant_cfg_disables_noise_fields(script_module, base_cfg): resolved = script_module.resolve_reclamm_noise_settings(arb_only_cfg) assert arb_only_cfg["enable_noise_model"] is False - assert arb_only_cfg["noise_model"] is None + assert arb_only_cfg["noise_model"] == "arb_only" + assert arb_only_cfg["noise_reference_model"] == "market_linear" assert arb_only_cfg["gas_cost"] == script_module.DEFAULT_GAS_COST assert arb_only_cfg["protocol_fee_split"] == script_module.DEFAULT_PROTOCOL_FEE_SPLIT assert arb_only_cfg["arb_frequency"] == script_module.FIXED_COMPARE_ARB_FREQUENCY - assert "noise_artifact_dir" not in arb_only_cfg - assert "noise_pool_id" not in arb_only_cfg + assert arb_only_cfg["noise_artifact_dir"] == script_module.DEFAULT_MARKET_LINEAR_ARTIFACT_DIR + assert arb_only_cfg["noise_pool_id"] == script_module.AAVE_WETH_POOL_ID + assert arb_only_cfg["noise_arrays_path"] == script_module.DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH assert "reclamm_noise_params" not in arb_only_cfg - assert "noise_arrays_path" not in arb_only_cfg - assert resolved["noise_model"] is None - assert resolved["noise_summary"] == "arb-only (noise disabled)" + assert resolved["noise_model"] == "arb_only" + assert resolved["noise_arrays_path"] == script_module.DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH + assert set(resolved["reclamm_noise_params"]) == {"tvl_mean", "tvl_std"} + assert resolved["noise_summary"] == ( + f"arb_only (arb_frequency={script_module.FIXED_COMPARE_ARB_FREQUENCY})" + ) def test_make_noise_variant_cfg_defaults_to_fixed_compare_arb_cadence( @@ -192,6 +197,7 @@ def test_make_noise_variant_cfg_defaults_to_fixed_compare_arb_cadence( resolved_noise = script_module.resolve_reclamm_noise_settings(noisy_cfg) assert arb_only_cfg["arb_frequency"] == script_module.FIXED_COMPARE_ARB_FREQUENCY + assert arb_only_cfg["noise_arrays_path"] == script_module.DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH assert resolved_noise["arb_frequency"] == script_module.FIXED_COMPARE_ARB_FREQUENCY @@ -228,12 +234,137 @@ def test_make_fingerprint_ignores_non_axis_override_fields(script_module, base_c assert overridden_key == canonical_key assert overridden_fingerprint["arb_frequency"] == script_module.FIXED_COMPARE_ARB_FREQUENCY assert overridden_fingerprint["gas_cost"] == script_module.DEFAULT_GAS_COST + assert ( + overridden_fingerprint["noise_arrays_path"] + == script_module.DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH + ) assert ( overridden_fingerprint["protocol_fee_split"] == script_module.DEFAULT_PROTOCOL_FEE_SPLIT ) +def test_arb_only_fingerprint_only_changes_noise_model(script_module, base_cfg): + noisy_cfg = { + **base_cfg, + "enable_noise_model": True, + "noise_model": "market_linear", + } + + noise_fingerprint = script_module.make_fingerprint(noisy_cfg, "geometric") + arb_only_cfg = script_module.make_noise_variant_cfg(noisy_cfg, enable_noise_model=False) + arb_fingerprint = script_module.make_fingerprint(arb_only_cfg, "geometric") + + expected_arb = dict(noise_fingerprint) + expected_arb["noise_model"] = "arb_only" + + assert noise_fingerprint["noise_model"] == "market_linear" + assert noise_fingerprint["noise_arrays_path"] == script_module.DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH + assert arb_fingerprint == expected_arb + + +def test_make_fingerprint_keeps_path_fallback_when_arrays_not_preloaded( + script_module, + base_cfg, +): + noisy_cfg = { + **base_cfg, + "enable_noise_model": True, + "noise_model": "market_linear", + } + + fingerprint = script_module.make_fingerprint(noisy_cfg, "geometric") + + assert fingerprint["noise_arrays_path"] == script_module.DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH + assert "noise_base_array" not in fingerprint + assert "noise_tvl_coeff_array" not in fingerprint + + +def test_make_fingerprint_includes_preloaded_market_linear_arrays( + script_module, + base_cfg, +): + noisy_cfg = { + **base_cfg, + "enable_noise_model": True, + "noise_model": "market_linear", + } + shared_noise = { + "arrays_path": script_module.DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH, + "noise_base_array": np.array([1.0, 2.0]), + "noise_tvl_coeff_array": np.array([3.0, 4.0]), + "tvl_mean": 10.0, + "tvl_std": 5.0, + } + + fingerprint = script_module.make_fingerprint( + noisy_cfg, + "geometric", + market_linear_noise_data=shared_noise, + ) + + assert fingerprint["noise_arrays_path"] == script_module.DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH + assert np.array_equal(fingerprint["noise_base_array"], shared_noise["noise_base_array"]) + assert np.array_equal( + fingerprint["noise_tvl_coeff_array"], + shared_noise["noise_tvl_coeff_array"], + ) + + +@pytest.mark.parametrize( + ("field", "updated_value"), + [ + ("fees", 0.01), + ("start", "2024-07-01 00:00:00"), + ("end", "2025-07-01 00:00:00"), + ("tokens", ["WBTC", "ETH"]), + ], +) +def test_method_cache_key_includes_run_identity_fields( + script_module, + base_cfg, + field, + updated_value, +): + canonical_cfg = { + **base_cfg, + "enable_noise_model": True, + "noise_model": "market_linear", + } + updated_cfg = dict(canonical_cfg) + updated_cfg[field] = updated_value + + canonical_key = script_module._make_method_cache_key(canonical_cfg, "geometric") + updated_key = script_module._make_method_cache_key(updated_cfg, "geometric") + + assert updated_key != canonical_key + + +@pytest.mark.parametrize( + "cfg_overrides", + [ + {"enable_noise_model": True, "noise_model": "calibrated"}, + { + "enable_noise_model": False, + "noise_model": "arb_only", + "noise_reference_model": "calibrated", + }, + ], +) +def test_resolve_reclamm_noise_settings_rejects_legacy_modes( + script_module, + base_cfg, + cfg_overrides, +): + cfg = { + **base_cfg, + **cfg_overrides, + } + + with pytest.raises(ValueError, match="market_linear"): + script_module.resolve_reclamm_noise_settings(cfg) + + def test_generate_heatmaps_skips_existing_pairs( monkeypatch, script_module, @@ -474,6 +605,71 @@ def itertuples(self, index=False): assert value == pytest.approx(1_234_567.0) +def test_run_method_final_value_cached_passes_preloaded_market_linear_arrays( + monkeypatch, + script_module, + base_cfg, +): + captured = {} + shared_noise = { + "arrays_path": script_module.DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH, + "noise_base_array": np.array([11.0, 12.0]), + "noise_tvl_coeff_array": np.array([21.0, 22.0]), + "tvl_mean": 100.0, + "tvl_std": 25.0, + } + + monkeypatch.setattr( + script_module, + "_load_persistent_final_value_cache", + lambda cache: cache.update( + { + "_persistent_final_value_cache_loaded": True, + "_persistent_final_value_cache": {}, + "_persistent_final_value_next_batch_id": 0, + } + ), + ) + monkeypatch.setattr(script_module, "flush_sweep_cache", lambda *args, **kwargs: None) + + def fake_do_run_on_historic_data(**kwargs): + captured["run_fingerprint"] = kwargs["run_fingerprint"] + return {"final_value": 1_111_111.0} + + monkeypatch.setattr( + script_module, + "do_run_on_historic_data", + fake_do_run_on_historic_data, + ) + + cfg = { + **base_cfg, + "enable_noise_model": True, + "noise_model": "market_linear", + } + cache = script_module.make_sweep_cache( + price_data=None, + cache_scope_cfg=cfg, + market_linear_noise_data=shared_noise, + ) + + value = script_module._run_method_final_value_cached(cfg, "geometric", cache) + + assert value == pytest.approx(1_111_111.0) + assert np.array_equal( + captured["run_fingerprint"]["noise_base_array"], + shared_noise["noise_base_array"], + ) + assert np.array_equal( + captured["run_fingerprint"]["noise_tvl_coeff_array"], + shared_noise["noise_tvl_coeff_array"], + ) + assert ( + captured["run_fingerprint"]["noise_arrays_path"] + == script_module.DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH + ) + + def test_arc_speed_artifacts_only_build_missing_line_output( monkeypatch, script_module, @@ -547,6 +743,67 @@ def fake_build_metric_curve(**kwargs): assert plotted_lines == [missing_line] +def test_compute_auto_calibrated_arc_length_speed_uses_nearest_datetime_row( + monkeypatch, + script_module, +): + real_pd = pytest.importorskip("pandas") + monkeypatch.setattr(script_module.pd, "Timestamp", real_pd.Timestamp) + monkeypatch.setattr(script_module.pd, "DatetimeIndex", real_pd.DatetimeIndex) + monkeypatch.setattr(script_module.pd, "DataFrame", real_pd.DataFrame) + monkeypatch.setattr( + script_module.pd, + "MultiIndex", + real_pd.MultiIndex, + raising=False, + ) + + price_data = real_pd.DataFrame( + { + "close_AAVE": [10.0, 30.0], + "close_ETH": [1.0, 1.0], + }, + index=real_pd.DatetimeIndex( + ["2024-06-01 00:01:00", "2024-06-01 00:03:00"] + ), + ) + cfg = { + "tokens": ["AAVE", "ETH"], + "start": "2024-06-01 00:02:10", + "price_ratio": 1.10, + "centeredness_margin": 0.60, + "daily_price_shift_exponent": 0.1, + "initial_pool_value": 1_000_000.0, + } + captured = {} + + monkeypatch.setattr( + script_module, + "initialise_reclamm_reserves", + lambda *args, **kwargs: (np.array([1.0, 1.0]), 1.0, 1.0), + ) + monkeypatch.setattr( + script_module, + "compute_price_ratio", + lambda *args, **kwargs: 1.0, + ) + + def fake_calibrate_arc_length_speed(*args, **kwargs): + captured["market_price_0"] = args[7] + return 123.0 + + monkeypatch.setattr( + script_module, + "calibrate_arc_length_speed", + fake_calibrate_arc_length_speed, + ) + + result = script_module.compute_auto_calibrated_arc_length_speed(cfg, price_data) + + assert result == pytest.approx(123.0) + assert captured["market_price_0"] == pytest.approx(30.0) + + def test_flush_sweep_cache_writes_compact_scalar_parquet(script_module): captured = {} @@ -554,9 +811,6 @@ class FakeFrame: def __init__(self, payload): captured["payload"] = payload - def sort_values(self, *args, **kwargs): - captured["sort_values"] = (args, kwargs) - def to_parquet(self, path, index=False, compression=None): captured["path"] = path captured["index"] = index @@ -583,7 +837,7 @@ def to_parquet(self, path, index=False, compression=None): } }, "_persistent_final_value_cache": {}, - "_persistent_final_value_records": {}, + "_persistent_final_value_next_batch_id": 0, "_persistent_final_value_cache_loaded": True, "_persistent_final_value_cache_path": "results/reclamm_heatmap_forward_cache/test/forward_values_tvl_5m.parquet", } @@ -598,10 +852,81 @@ def to_parquet(self, path, index=False, compression=None): assert captured["payload"]["arb_frequency"] == [15] assert captured["index"] is False assert captured["compression"] == "zstd" - assert captured["path"].endswith(".parquet") + assert captured["makedirs"].endswith("forward_values_tvl_5m.parquet") + assert captured["path"].endswith("forward_values_tvl_5m.parquet/batch_00000000.parquet") assert cache["_pending_persistent_final_values"] == {} assert cache["_persistent_final_value_cache"] == {"abc123": 123.45} - assert cache["_persistent_final_value_records"]["abc123"]["noise_model"] == "market_linear" + assert cache["_persistent_final_value_next_batch_id"] == 1 + + +def test_load_persistent_final_value_cache_reads_sharded_parquet_dir( + monkeypatch, + script_module, +): + frames = { + "results/reclamm_heatmap_forward_cache/test/forward_values_tvl_1m.parquet/batch_00000000.parquet": [ + types.SimpleNamespace( + cache_key_hash="first123", + final_value=111.0, + method="geometric", + enable_noise_model=True, + noise_model="market_linear", + price_ratio=1.1, + centeredness_margin=0.6, + daily_price_shift_exponent=0.1, + initial_pool_value=1_000_000.0, + arb_frequency=15, + ) + ], + "results/reclamm_heatmap_forward_cache/test/forward_values_tvl_1m.parquet/batch_00000001.parquet": [ + types.SimpleNamespace( + cache_key_hash="second456", + final_value=222.0, + method="geometric", + enable_noise_model=False, + noise_model="arb_only", + price_ratio=1.2, + centeredness_margin=0.7, + daily_price_shift_exponent=0.2, + initial_pool_value=1_000_000.0, + arb_frequency=15, + ) + ], + } + + class FakeFrame: + def __init__(self, rows): + self._rows = rows + self.empty = not rows + + def itertuples(self, index=False): + return list(self._rows) + + monkeypatch.setattr(script_module.os.path, "exists", lambda filename: True) + monkeypatch.setattr(script_module.os.path, "isdir", lambda filename: True) + monkeypatch.setattr( + script_module.os, + "listdir", + lambda path: ["batch_00000001.parquet", "batch_00000000.parquet"], + ) + monkeypatch.setattr( + script_module.pd, + "read_parquet", + lambda path, *args, **kwargs: FakeFrame(frames[path]), + ) + + cache = { + "_persistent_final_value_cache_loaded": False, + "_persistent_final_value_cache_path": "results/reclamm_heatmap_forward_cache/test/forward_values_tvl_1m.parquet", + } + + script_module._load_persistent_final_value_cache(cache) + + assert cache["_persistent_final_value_cache"] == { + "first123": 111.0, + "second456": 222.0, + } + assert cache["_persistent_final_value_next_batch_id"] == 2 def test_load_persistent_final_value_cache_supports_legacy_two_column_parquet( @@ -630,8 +955,27 @@ def itertuples(self, index=False): script_module._load_persistent_final_value_cache(cache) assert cache["_persistent_final_value_cache"] == {"legacy123": 999.0} - assert cache["_persistent_final_value_records"]["legacy123"]["final_value"] == pytest.approx(999.0) - assert cache["_persistent_final_value_records"]["legacy123"]["price_ratio"] is None + assert cache["_persistent_final_value_next_batch_id"] == 0 + + +def test_make_sweep_cache_does_not_eagerly_load_persisted_parquet( + monkeypatch, + script_module, +): + calls = [] + + monkeypatch.setattr( + script_module, + "_load_persistent_final_value_cache", + lambda cache: calls.append(dict(cache)), + ) + + cache = script_module.make_sweep_cache(price_data=None, cache_scope_cfg=None) + + assert calls == [] + assert cache["_persistent_final_value_cache"] == {} + assert cache["_persistent_final_value_next_batch_id"] == 0 + assert cache["_persistent_final_value_cache_loaded"] is False def test_run_comparison_cached_only_uses_geometric_runs_when_constant_arc_disabled( diff --git a/tests/scripts/test_find_adjacent_heatmap_pairs.py b/tests/scripts/test_find_adjacent_heatmap_pairs.py index 597e65b..6ad7a39 100644 --- a/tests/scripts/test_find_adjacent_heatmap_pairs.py +++ b/tests/scripts/test_find_adjacent_heatmap_pairs.py @@ -228,30 +228,11 @@ def run_adjacent_csv_row_comparison(csv_path, row_index=0, output_file=None): } -def test_autodetect_lightweight_noise_profile_switches_to_legacy_calibrated(): +def test_autodetect_lightweight_noise_profile_keeps_market_linear(): module = load_script_module() compare_context = module._LightweightCompareContext() base_cfg = compare_context.configs_for_tvl(compare_context.CONFIGS, 1_000_000.0)[1] pair_spec = compare_context.get_pair_heatmap_specs(base_cfg)[0] - slice_variant = [variant for variant in pair_spec["fixed_slices"] if variant["slug"] == "q2"][0] - - sample_x_indices = sorted({0, len(pair_spec["x_values"]) // 2, len(pair_spec["x_values"]) - 1}) - sample_y_indices = sorted({0, len(pair_spec["y_values"]) // 2, len(pair_spec["y_values"]) - 1}) - - cache_lookup = {} - compare_context.set_noise_profile("legacy_calibrated") - slice_cfg = dict(base_cfg) - slice_cfg[pair_spec["fixed_key"]] = float(slice_variant["value"]) - for y_index in sample_y_indices: - for x_index in sample_x_indices: - cfg = dict(slice_cfg) - cfg[pair_spec["x_key"]] = float(pair_spec["x_values"][x_index]) - cfg[pair_spec["y_key"]] = float(pair_spec["y_values"][y_index]) - for source_name in ("noise_geometric", "arb_geometric"): - source_cfg, method = module._source_variant(compare_context, cfg, source_name) - cache_key = compare_context._make_method_cache_key(source_cfg, method) - cache_key_hash = compare_context._make_method_cache_hash(cache_key) - cache_lookup[cache_key_hash] = 1_000_000.0 compare_context.set_noise_profile("market_linear") module.autodetect_lightweight_noise_profile( @@ -260,10 +241,18 @@ def test_autodetect_lightweight_noise_profile_switches_to_legacy_calibrated(): pair_specs=[pair_spec], metric_key="noise_vs_arb_geometric_improvement_pct", slice_slug="q2", - cache_lookup=cache_lookup, + cache_lookup={}, ) - assert compare_context.noise_profile == "legacy_calibrated" + assert compare_context.noise_profile == "market_linear" + + +def test_lightweight_context_rejects_legacy_noise_profile(): + module = load_script_module() + compare_context = module._LightweightCompareContext() + + with pytest.raises(ValueError): + compare_context.set_noise_profile("legacy_calibrated") def test_source_variant_sets_explicit_market_and_arb_only_noise_models(): @@ -285,14 +274,18 @@ def test_source_variant_sets_explicit_market_and_arb_only_noise_models(): assert noise_method == "geometric" assert noise_cfg["enable_noise_model"] is True assert noise_cfg["noise_model"] == "market_linear" + assert noise_cfg["noise_arrays_path"] == compare_context.DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH assert arb_method == "geometric" assert arb_cfg["enable_noise_model"] is False assert arb_cfg["noise_model"] == "arb_only" + assert arb_cfg["noise_arrays_path"] == compare_context.DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH resolved_arb = compare_context.resolve_reclamm_noise_settings(arb_cfg) assert resolved_arb["noise_model"] == "arb_only" - assert resolved_arb["noise_cache_key"] == ("disabled",) + assert resolved_arb["noise_arrays_path"] == compare_context.DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH + assert set(resolved_arb["reclamm_noise_params"]) == {"tvl_mean", "tvl_std"} + assert resolved_arb["noise_cache_key"][0] == "arb_only" def test_lightweight_context_defaults_to_fixed_compare_arb_cadence(): @@ -308,6 +301,7 @@ def test_lightweight_context_defaults_to_fixed_compare_arb_cadence(): assert arb_cfg["arb_frequency"] == compare_context.FIXED_COMPARE_ARB_FREQUENCY assert arb_cfg["noise_model"] == "arb_only" + assert arb_cfg["noise_arrays_path"] == compare_context.DEFAULT_MARKET_LINEAR_NOISE_ARRAYS_PATH assert resolved_noise["arb_frequency"] == compare_context.FIXED_COMPARE_ARB_FREQUENCY assert arb_cfg["gas_cost"] == compare_context.DEFAULT_GAS_COST assert arb_cfg["protocol_fee_split"] == compare_context.DEFAULT_PROTOCOL_FEE_SPLIT From 4c9eae7e61c18543c57d98da4133cce5becb238a Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 31 Mar 2026 11:11:58 +0100 Subject: [PATCH 45/57] feat: feature-appropriate scaling, TVL clamp, protocol fee default MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Feature scaling reform in build_data(): TVL and BTC log_price kept in raw log scale (absolute level matters), returns/trends/volume_zscore unscaled, volatilities lightly centered. Eliminates global z-score that squeezed TVL into [-2,+2] and prevented models from learning TVL response. Add ±3σ clamp on standardized log(TVL) in reclamm_market_linear_noise_volume to prevent extreme concentration from wireheading the noise model. Change default protocol_fee_split from 0.0 to 0.25 to match reClAMM production configuration. --- experiments/run_linear_market_noise.py | 43 +++++++++++++++--- quantammsim/pools/noise_trades.py | 5 +- .../runners/default_run_fingerprint.py | 2 +- results/linear_market_noise/model.npz | Bin 5092 -> 5092 bytes 4 files changed, 42 insertions(+), 8 deletions(-) diff --git a/experiments/run_linear_market_noise.py b/experiments/run_linear_market_noise.py index c71dc6a..56524ce 100644 --- a/experiments/run_linear_market_noise.py +++ b/experiments/run_linear_market_noise.py @@ -144,12 +144,43 @@ def build_data(matched_clean, option_c_clean, trend_windows=(7, 14, 30), x_base = np.concatenate([x_obs, x_market], axis=1).astype(np.float32) base_names = [f"xobs_{i}" for i in range(k_obs)] + market_names - # Standardize (except intercept column 0) - x_mean = np.mean(x_base, axis=0) - x_std = np.std(x_base, axis=0) - x_std[x_std < 1e-6] = 1.0 - x_mean[0] = 0.0 # don't center intercept - x_std[0] = 1.0 + # Feature-appropriate scaling: + # - intercept: untouched + # - log_tvl, btc_log_price: raw log scale (absolute level carries info) + # - dow_sin/cos: already [-1,1], no scaling + # - returns, trends, vol_zscore: already comparable, no scaling + # - realized_vol, pair_vol: small positive, light centering + # - cross-pool volumes: z-score (different scales across pool groups) + x_mean = np.zeros(x_base.shape[1], dtype=np.float32) + x_std = np.ones(x_base.shape[1], dtype=np.float32) + + for i, name in enumerate(base_names): + if name == "xobs_0": + # Intercept: leave as-is + pass + elif name in ("xobs_1", "btc_log_price"): + # Log levels: leave in raw log scale (range ~10-20) + pass + elif name in ("xobs_2", "xobs_3"): + # dow_sin, dow_cos: already [-1,1] + pass + elif "volume_zscore" in name: + # Already z-scored by construction + pass + elif "log_return" in name or "trend_" in name: + # Returns and trends: small, centered around 0, comparable + pass + elif "realized_vol" in name or "pair_realized" in name: + # Volatilities: small positive, center but don't squeeze + x_mean[i] = float(np.mean(x_base[:, i])) + # Use std but don't over-compress — floor at 0.01 + x_std[i] = max(float(np.std(x_base[:, i])), 0.01) + elif name.startswith("xobs_") and int(name.split("_")[1]) >= 4: + # Cross-pool volumes (xobs_4,5,6): z-score (different scales) + x_mean[i] = float(np.mean(x_base[:, i])) + x_std[i] = max(float(np.std(x_base[:, i])), 1e-6) + # else: leave untouched + x_base = ((x_base - x_mean) / x_std).astype(np.float32) # Interaction terms (products of standardized features) diff --git a/quantammsim/pools/noise_trades.py b/quantammsim/pools/noise_trades.py index bfef86c..db8d1b9 100644 --- a/quantammsim/pools/noise_trades.py +++ b/quantammsim/pools/noise_trades.py @@ -395,7 +395,10 @@ def reclamm_market_linear_noise_volume( Per-minute noise volume (USD), floored at zero. """ log_tvl = jnp.log(jnp.maximum(effective_value_usd, 1.0)) - standardized_log_tvl = (log_tvl - tvl_mean) / tvl_std + # Clamp standardized TVL to training range [-3, +3] std to prevent + # extreme concentration from wireheading the noise model + standardized_log_tvl = jnp.clip( + (log_tvl - tvl_mean) / tvl_std, -3.0, 3.0) log_daily_noise = noise_base + noise_tvl_coeff * standardized_log_tvl daily_noise = jnp.exp(log_daily_noise) return jnp.maximum(0.0, daily_noise / 1440.0) diff --git a/quantammsim/runners/default_run_fingerprint.py b/quantammsim/runners/default_run_fingerprint.py index 45b9094..478dfa5 100644 --- a/quantammsim/runners/default_run_fingerprint.py +++ b/quantammsim/runners/default_run_fingerprint.py @@ -84,7 +84,7 @@ "return_val": "daily_log_sharpe", "initial_pool_value": 1000000.0, "fees": 0.0, - "protocol_fee_split": 0.0, # fraction of swap fees diverted from LP reserves to protocol treasury + "protocol_fee_split": 0.25, # fraction of swap fees diverted from LP reserves to protocol treasury "arb_fees": 0.0, "gas_cost": 0.0, "use_alt_lamb": False, diff --git a/results/linear_market_noise/model.npz b/results/linear_market_noise/model.npz index c792225202ae753ace18972cf62cf5b677717a4e..f440f04f8bfd94472d98fffc989196f8afb4b3e7 100644 GIT binary patch delta 3610 zcmZ9Pc~p(t|HtJdMVgc}Av9`^a?bPY{XD1NRVbB2L@L7-N~69aPMRcAoJytAKq+p8 zrgP5T&vQ=HCCN-zX3;kyiPF{Zz3%W`>-*bleb#&J*Js%4v;TW{nRJPb*VY7Q6p#{R+g!H2&uMxC17$a0p|av4N;*xNL=sD%v5lclCNWNX<0T@5`|EA z#*~3bk}vJt77RD7{mA^>$ALY|l!?cdM~R$OQ$bu{k%aGfiZ#e+I3^(R_o+Em}OO_=hpXM)*f)Pn=cXw#f{ zDx_Knl%}|{VTIRm{7fqx-q<7!NJQrbos@ndCZFx9Z=$<#Rz;? zFrjt(xuuP{#6p4TUD1Nz^{Bq$Jo)AA0rE}?K^9C%B7&^iV3tjaxVP934BE^V|Gecr z$Tj^$@?ABM-B0u3zZ}}ZaT6W>{mIqL)s05H&Z4!XxidUeyIRjEA`;{%gt;yiUzsgYeBT> z8rgUI^;Gq-Q%LDSIj~J!LS@xYk{Z0@NqOIL`2!uPAfn|oTPs=3e%n$)&k`p{i{m3f zO{Ooct8swT&GVPBO^e|PcXwFm_LM1OETG?-F5uQu$3NS%j0)dljQUq6BZuCZ0_oUR zGln~lpd;@Vv++Cg$;C--;9RgH90&`MRtWF25A$?EjNVnsbU`>0R?0~S6CQ!Q4mqa4 ze*}DQw1HMZ4-k|sr%e^SV3S=NTIMt&sqb(DPQ2elu8Y=jmzJxGE0k5>rK28Xtky)H zE0ct(Cr=e`luU%RTLhQIUVAkWXMeodD}5Lw+eeXJv&wPdT}61mr4LLBy^iz1Z}384 zIxg1^gZfAg*EV0~7BvK*rzd#4DKZ_9pR^GjdKORYaLy)+9Mi>FH}!Cw=PcsndLg;# zFchZ{rm$+K7oObN2NK5!rumpTGaisATPJsxG7;P;C3mZ(qkGqyA;aM=q#Q-Wyxx~y09l?IO zd}LZ}C`+^7MI~N&NalO504+Y6)YoZ6Y{z~(sB!b0wAt+?SWD!B>?3|uB|ioVvTHs_ zZ9kh5PF^P#SMABc<(_97N*3=SGwk}2als52YB7M#JqvKzro*yb4K~!hrb6)KX&=w+xZ8(ujmJ%^W9&A8pjy9z=FEC-D4Qn#h!c>0-+^FBp1ijQi^I z6|mO46~97xU{#AU;sgx~kV@?_C##V%I_U60AiTJkkq9ki%iU=HC#$!#M`ePf#*zXD z>@p>O`qq3?4|Bd+OcFn$=nTAhR9`kb;~cbB^kqfI7~HR+&F%}^$rh|Qi>AmQVyCX1 z*!1k~uax`6Q_yT|5Z?%}r`_pw;9v-VDJj>a+1owgI}?Gv)GTp%qr%6nO#h504ar*& zx*ZpcrtMx$nO3z+kDbGKyLKF^OI$!@Z_kCICKYN4$ABL>c`tLNCW+_YIS4qDH!&O2 z3JE1?l!Q1IKy(}J!ko2QydTV`gD3unRg~3|cYBUw!!R{szepRFR9T9n1-Wj(Tg;N1 z8^4pc-7iR6>bTV4wNTO{eGyKM5JAJ67gz_MFVt2WZ{{ZpMdIH!;nJRycfd}E9QJH! 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Add min_train_returns_over_hodl rejection in Optuna objective: trials with catastrophic in-sample returns_over_hodl are rejected early (return -inf) to avoid wasting evaluation budget. --- experiments/tune_reclamm_calibrated_noise.py | 30 ++++++++++++++++++-- quantammsim/runners/jax_runners.py | 11 +++++++ 2 files changed, 39 insertions(+), 2 deletions(-) diff --git a/experiments/tune_reclamm_calibrated_noise.py b/experiments/tune_reclamm_calibrated_noise.py index 9e999bd..c2d712c 100644 --- a/experiments/tune_reclamm_calibrated_noise.py +++ b/experiments/tune_reclamm_calibrated_noise.py @@ -115,7 +115,7 @@ def _build_market_linear_arrays(args): def _build_opt_settings(args): - """Build optimisation_settings for either optuna or bfgs.""" + """Build optimisation_settings for optuna, bfgs, or cma_es.""" if args.method == "bfgs": return { "method": "bfgs", @@ -128,6 +128,20 @@ def _build_opt_settings(args): "compute_dtype": "float64", }, } + elif args.method == "cma_es": + return { + "method": "cma_es", + "n_parameter_sets": args.n_parameter_sets, + **({"val_fraction": args.val_fraction} if args.val_fraction is not None else {}), + "cma_es_settings": { + "population_size": args.cma_pop_size, + "n_generations": args.cma_generations, + "sigma0": args.cma_sigma0, + "tol": 1e-8, + "n_evaluation_points": args.cma_eval_points, + "compute_dtype": "float32", + }, + } else: return { "method": "optuna", @@ -141,6 +155,8 @@ def _build_opt_settings(args): "parameter_config": PARAMETER_CONFIG, **({"overfitting_penalty": args.overfitting_penalty} if args.overfitting_penalty is not None else {}), + **({"min_train_returns_over_hodl": args.min_train_ret} + if args.min_train_ret is not None else {}), }, } @@ -222,7 +238,8 @@ def main(): parser = argparse.ArgumentParser( description="Tune reClAMM params with calibrated 8-covariate noise model" ) - parser.add_argument("--method", default="optuna", choices=["optuna", "bfgs"], + parser.add_argument("--method", default="optuna", + choices=["optuna", "bfgs", "cma_es"], help="Optimisation method") parser.add_argument("--n-trials", type=int, default=50, help="Optuna trials (ignored for bfgs)") @@ -232,6 +249,15 @@ def main(): parser.add_argument("--bfgs-tol", type=float, default=1e-6) parser.add_argument("--bfgs-eval-points", type=int, default=20, help="Number of evaluation points for bfgs") + # CMA-ES + parser.add_argument("--cma-generations", type=int, default=300) + parser.add_argument("--cma-sigma0", type=float, default=0.5, + help="Initial step size for CMA-ES") + parser.add_argument("--cma-pop-size", type=int, default=None, + help="Population size (None = auto)") + parser.add_argument("--cma-eval-points", type=int, default=20) + parser.add_argument("--min-train-ret", type=float, default=-0.5, + help="Reject trials with IS returns_over_hodl below this") parser.add_argument("--noise-model", default="market_linear", choices=["calibrated", "market_linear"], help="Noise model variant") diff --git a/quantammsim/runners/jax_runners.py b/quantammsim/runners/jax_runners.py index 0937884..27669a2 100644 --- a/quantammsim/runners/jax_runners.py +++ b/quantammsim/runners/jax_runners.py @@ -1456,6 +1456,17 @@ def objective(trial): initial_reserves=train_outputs["reserves"][0], ) + # Reject catastrophic in-sample configurations + min_train_ret_over_hodl = run_fingerprint["optimisation_settings"][ + "optuna_settings"].get("min_train_returns_over_hodl", None) + if min_train_ret_over_hodl is not None: + if float(train_returns_over_hodl) < min_train_ret_over_hodl: + optuna_manager.logger.info( + f"Training {trial.number}, REJECTED:" + f" ret_over_hodl={train_returns_over_hodl:.4f}" + f" < {min_train_ret_over_hodl}") + return float("-inf") + # Test period evaluation using continuous forward pass # This ensures test metrics reflect continuous simulation from training continuous_outputs = partial_forward_pass_continuous_optuna( From b89fa64ec058e2f6c4e6cb8c6dc9978d9f506beb Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 31 Mar 2026 11:15:33 +0100 Subject: [PATCH 47/57] feat: Michaelis-Menten noise model + MLP sweep + comparison tooling MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Michaelis-Menten noise model (run_mm_noise.py): structural TVL saturation via V_noise = alpha_i * TVL/(K_i + TVL) * exp(x_market @ gamma_i), with learned EWMA smoothing on TVL (discovery lag). Per-pool alpha, K, gamma; shared lambda. Achieves R²=0.64 matching per-pool linear while adding saturation (K_med ~$19M). MLP noise model (run_mlp_noise.py): Optuna sweep with per-trial model saving, TVL response check at sweep end. Investigation showed shared MLP cannot learn TVL relationship due to cross-pool confounding. Model comparison (run_model_comparison.py): linear vs MLP noise model across TVL levels with time series and summary plots. MM fit plotting (plot_mm_noise_fit.py): 6-panel per-pool time series, cross-pool TVL response/elasticity curves, K distribution analysis. --- experiments/run_mlp_noise.py | 117 ++++- experiments/run_mm_noise.py | 635 ++++++++++++++++++++++++++++ experiments/run_model_comparison.py | 338 +++++++++++++++ scripts/plot_mm_noise_fit.py | 472 +++++++++++++++++++++ 4 files changed, 1555 insertions(+), 7 deletions(-) create mode 100644 experiments/run_mm_noise.py create mode 100644 experiments/run_model_comparison.py create mode 100644 scripts/plot_mm_noise_fit.py diff --git a/experiments/run_mlp_noise.py b/experiments/run_mlp_noise.py index bde323e..35d5418 100644 --- a/experiments/run_mlp_noise.py +++ b/experiments/run_mlp_noise.py @@ -257,20 +257,21 @@ def run_optuna(data, n_trials): def objective(trial): # Architecture - n_layers = trial.suggest_int("n_layers", 1, 5) - first_hidden = trial.suggest_categorical("first_hidden", [8, 16, 32, 64]) + n_layers = trial.suggest_int("n_layers", 1, 7) + first_hidden = trial.suggest_categorical("first_hidden", [8, 16, 32, 64, 128, 256]) # Bottleneck: each layer is half the previous (min 2) + bottleneck_ratio = trial.suggest_categorical("bottleneck_ratio", [0.5, 0.75, 1.0]) hidden = [] h = first_hidden for _ in range(n_layers): hidden.append(h) - h = max(h // 2, 2) + h = max(int(h * bottleneck_ratio), 2) # Training - lr = trial.suggest_float("lr", 1e-4, 1e-2, log=True) - l2_alpha = trial.suggest_float("l2_alpha", 1e-5, 1e-1, log=True) + lr = trial.suggest_float("lr", 1e-4, 5e-2, log=True) + l2_alpha = trial.suggest_float("l2_alpha", 1e-5, 5e-1, log=True) huber_delta = trial.suggest_categorical("huber_delta", [0.5, 1.0, 1.5, 2.0]) - n_epochs = trial.suggest_categorical("n_epochs", [2000, 5000, 10000]) + n_epochs = trial.suggest_categorical("n_epochs", [2000, 5000, 10000, 20000]) use_cosine = trial.suggest_categorical("use_cosine", [True, False]) per_pool = trial.suggest_categorical("per_pool", [True, False]) @@ -341,6 +342,33 @@ def objective(trial): f" {'per_pool' if per_pool else 'shared'}" f" lr={lr:.1e} l2={l2_alpha:.1e}" f" hub={huber_delta} ep={n_epochs}") + + # Save every trial's model + trial_dir = os.path.join("results", "mlp_noise", "trials", f"trial_{trial.number:04d}") + os.makedirs(trial_dir, exist_ok=True) + save_dict = {k: np.array(v) for k, v in params.items()} + save_dict["x_mean"] = data.get("x_mean", np.zeros(n_feat)) + save_dict["x_std"] = data.get("x_std", np.ones(n_feat)) + np.savez(os.path.join(trial_dir, "model.npz"), **save_dict) + import json as _json + _meta = { + "pool_ids": data["pool_ids"], + "feat_names": data["feat_names"], + "n_feat": n_feat, + "hidden": hidden, + "per_pool": per_pool, + "eval_r2": med_r2, + "hparams": { + "hidden": hidden, "lr": lr, "l2_alpha": l2_alpha, + "huber_delta": huber_delta, "n_epochs": n_epochs, + "use_cosine": use_cosine, "per_pool": per_pool, + "bottleneck_ratio": bottleneck_ratio, + "first_hidden": first_hidden, "n_layers": n_layers, + }, + } + with open(os.path.join(trial_dir, "meta.json"), "w") as _f: + _json.dump(_meta, _f, indent=2) + return med_r2 study = optuna.create_study(direction="maximize") @@ -365,13 +393,55 @@ def objective(trial): arch = [] for _ in range(n_l): arch.append(h) - h = max(h // 2, 2) + h = max(int(h * t.params.get("bottleneck_ratio", 0.5)), 2) print(f" #{t.number}: eval={t.value:.4f}" f" arch={arch}" f" ep={t.params['n_epochs']}" f" {'cos' if t.params['use_cosine'] else 'cst'}" f" {'pp' if t.params['per_pool'] else 'sh'}") + # Copy best trial to top-level artifact + best_trial = study.best_trial + best_trial_dir = os.path.join("results", "mlp_noise", "trials", + f"trial_{best_trial.number:04d}") + save_dir = "results/mlp_noise" + if os.path.exists(os.path.join(best_trial_dir, "model.npz")): + import shutil + shutil.copy2(os.path.join(best_trial_dir, "model.npz"), + os.path.join(save_dir, "model.npz")) + shutil.copy2(os.path.join(best_trial_dir, "meta.json"), + os.path.join(save_dir, "meta.json")) + print(f"\n Copied best trial ({best_trial.number}) to: {save_dir}") + + # TVL response check on best model + import json as _json + art = dict(np.load(os.path.join(save_dir, "model.npz"), allow_pickle=True)) + with open(os.path.join(save_dir, "meta.json")) as _f: + meta = _json.load(_f) + best_params = {k: jnp.array(art[k]) for k in art + if k.startswith("W") or k.startswith("b") + or k == "log_cadence" or k == "pool_bias"} + per_pool = meta.get("per_pool", False) + pool_idx_probe = jnp.array([0]) if per_pool else None + + print(f"\n TVL Response Check (best model, trial {best_trial.number}):") + x_probe = np.zeros((1, n_feat), dtype=np.float32) + x_probe[0, 0] = 1.0 + x_probe[0, 4] = 10.5 # typical btc_log_price + prev_noise = None + for tvl in [1e4, 1e5, 5e5, 1e6, 5e6, 1e7, 5e7, 1e8, 5e8]: + x_probe[0, 1] = np.log(tvl) + out = np.array(forward_mlp(best_params, jnp.array(x_probe), + pool_idx_probe)) + noise = np.exp(out[0]) + if prev_noise is not None and prev_noise > 0: + ratio = noise / prev_noise + print(f" TVL=${tvl:>11,.0f} noise=${noise:>12,.0f}/day" + f" ({ratio:.2f}x prev)") + else: + print(f" TVL=${tvl:>11,.0f} noise=${noise:>12,.0f}/day") + prev_noise = noise + return study @@ -395,6 +465,8 @@ def main(): help="Append static pool attributes to input") parser.add_argument("--no-split", action="store_true", help="Train on all data") + parser.add_argument("--save-artifact", default=None, + help="Save model to this directory") args = parser.parse_args() os.environ.setdefault("JAX_PLATFORMS", "cpu") @@ -515,6 +587,37 @@ def main(): print(f" Linear (with cross-pool): median R² ≈ 0.53") print(f" Per-pool linear: median R² ≈ 0.61") + # Save artifact + if args.save_artifact: + import json + os.makedirs(args.save_artifact, exist_ok=True) + # Save params as npz + save_dict = {k: np.array(v) for k, v in params.items()} + save_dict["x_mean"] = data["x_mean"] if "x_mean" in data else np.zeros(n_feat) + save_dict["x_std"] = data["x_std"] if "x_std" in data else np.ones(n_feat) + np.savez(os.path.join(args.save_artifact, "model.npz"), **save_dict) + # Save meta + meta = { + "pool_ids": data["pool_ids"], + "feat_names": data["feat_names"], + "n_feat": n_feat, + "hidden": args.hidden, + "per_pool": args.per_pool, + "hparams": { + "hidden": args.hidden, + "lr": args.lr, + "l2_alpha": args.l2_alpha, + "huber_delta": args.huber_delta, + "epochs": args.epochs, + "trend_windows": args.trend_windows, + "use_cosine": args.cosine, + "per_pool": args.per_pool, + }, + } + with open(os.path.join(args.save_artifact, "meta.json"), "w") as f: + json.dump(meta, f, indent=2) + print(f"\n Saved artifact to: {args.save_artifact}") + if __name__ == "__main__": main() diff --git a/experiments/run_mm_noise.py b/experiments/run_mm_noise.py new file mode 100644 index 0000000..abdc2b1 --- /dev/null +++ b/experiments/run_mm_noise.py @@ -0,0 +1,635 @@ +"""Michaelis-Menten noise model with market features. + +Replaces the linear TVL term with a Michaelis-Menten saturation curve +while keeping all market features for temporal fit: + + log(V_noise) = log_alpha_i + x_market @ gamma + + log(TVL) - log(K_i + TVL) + + V_total = V_arb(cadence_i) + exp(log_V_noise) + Loss = Huber(log(V_total) - log(V_obs)) + +The TVL feature (xobs_1) is removed from x_market and handled +structurally via the MM saturation term. All other features (dow, +BTC, token, pair vol, interactions) remain as shared linear covariates. + +Parameters: + log_alpha_i : per-pool intercept + log_K_i : per-pool half-saturation TVL + gamma : shared coefficients on non-TVL features + log_cadence_i: per-pool arb frequency (via PCHIP) + +Usage: + python experiments/run_mm_noise.py + python experiments/run_mm_noise.py --epochs 5000 --lr 3e-4 + python experiments/run_mm_noise.py --per-pool-gamma # per-pool market coeffs +""" + +import argparse +import json +import os +import pickle +import sys +import time + +import jax +import jax.numpy as jnp +import numpy as np +import pandas as pd + + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) + + +def load_stage1(): + path = os.path.join(CACHE_DIR, "stage1.pkl") + with open(path, "rb") as f: + data = pickle.load(f) + return data["matched_clean"], data["option_c_clean"] + + +def build_mm_data(matched_clean, option_c_clean, trend_windows=(7,), + include_cross_pool=False): + """Build data with MM structure: separate TVL from market features.""" + from experiments.run_linear_market_noise import build_data + + # Get full feature matrix from linear model's pipeline + data = build_data( + matched_clean, option_c_clean, + trend_windows=trend_windows, + include_market=True, + include_cross_pool=include_cross_pool, + ) + + # Separate TVL from other features + feat_names = data["feat_names"] + x_full = data["x"] + + # Find TVL column (xobs_1) and TVL interaction columns + tvl_col = feat_names.index("xobs_1") + tvl_interaction_cols = [i for i, name in enumerate(feat_names) + if name.startswith("xobs_1\u00d7")] + + # Remove TVL and its interactions from market features + remove_cols = {tvl_col} | set(tvl_interaction_cols) + keep_cols = [i for i in range(len(feat_names)) if i not in remove_cols] + x_market = x_full[:, keep_cols].astype(np.float32) + market_names = [feat_names[i] for i in keep_cols] + + # TVL comes from the raw panel data (unstandardized log_tvl) + # x_full[:, tvl_col] might be standardized, so get raw from panel + pool_ids = data["pool_ids"] + n_pools = data["n_pools"] + + # Rebuild raw log_tvl from panel + all_dates = set() + for pid in pool_ids: + all_dates.update(matched_clean[pid]["panel"]["date"].values) + date_list = sorted(all_dates) + date_to_idx = {d: i for i, d in enumerate(date_list)} + n_dates = len(date_list) + + tvl_grid = np.full((n_dates, n_pools), np.nan) + for j, pid in enumerate(pool_ids): + panel = matched_clean[pid]["panel"] + dates = panel["date"].values + log_tvls = panel["log_tvl_lag1"].values.astype(float) + for k, date in enumerate(dates): + tvl_grid[date_to_idx[date], j] = log_tvls[k] + + pool_idx = data["pool_idx"] + day_idx = data["day_idx"] + log_tvl = np.array([tvl_grid[day_idx[s], pool_idx[s]] + for s in range(len(pool_idx))], dtype=np.float32) + + # Token info for display + from quantammsim.calibration.pool_data import _parse_tokens + pool_tokens = [] + for pid in pool_ids: + toks = _parse_tokens(matched_clean[pid]["tokens"]) + tok_a = toks[0] + tok_b = toks[1] if len(toks) > 1 else toks[0] + pool_tokens.append((tok_a, tok_b)) + + removed_names = [feat_names[i] for i in sorted(remove_cols)] + print(f" Removed TVL features: {removed_names}") + print(f" Market features ({len(market_names)}): {market_names}") + + # Build per-pool temporal ordering for EWMA + # For each pool, store the sample indices sorted by day_idx + # Pad to max length so we can use lax.scan uniformly + pool_time_indices = [] # (n_pools, max_T) — sample indices in time order + pool_time_lengths = [] # (n_pools,) — actual length per pool + for i in range(n_pools): + mask = pool_idx == i + idxs = np.where(mask)[0] + # Sort by day_idx + order = np.argsort(day_idx[idxs]) + pool_time_indices.append(idxs[order]) + pool_time_lengths.append(len(idxs)) + + max_T = max(pool_time_lengths) if pool_time_lengths else 0 + # Pad to uniform length (pad with 0, masked later) + pool_time_padded = np.zeros((n_pools, max_T), dtype=np.int32) + pool_time_mask = np.zeros((n_pools, max_T), dtype=np.float32) + for i in range(n_pools): + L = pool_time_lengths[i] + pool_time_padded[i, :L] = pool_time_indices[i] + pool_time_mask[i, :L] = 1.0 + + print(f" EWMA: max_T={max_T}, pools with data: " + f"{sum(1 for l in pool_time_lengths if l > 0)}") + + return { + "x_market": x_market, + "log_tvl": log_tvl, + "y_total": data["y_total"], + "pool_idx": pool_idx, + "day_idx": day_idx, + "sample_grid_days": data["sample_grid_days"], + "pool_coeffs": data["pool_coeffs"], + "pool_gas": data["pool_gas"], + "init_log_cadences": data["init_log_cadences"], + "n_pools": n_pools, + "n_market_feat": x_market.shape[1], + "pool_ids": pool_ids, + "pool_tokens": pool_tokens, + "market_names": market_names, + "x_mean": data["x_mean"], + "x_std": data["x_std"], + "pool_time_padded": pool_time_padded, + "pool_time_mask": pool_time_mask, + } + + +# ---- Model ---- + +def ewma_smooth(log_tvl, raw_lambda, pool_time_padded, pool_time_mask): + """Apply learned EWMA smoothing to log_tvl, per pool. + + smooth_t = λ * log_tvl_t + (1-λ) * smooth_{t-1} + + Returns smoothed log_tvl in the same sample order as input. + """ + lam = jax.nn.sigmoid(raw_lambda) # constrain to (0, 1) + n_pools = pool_time_padded.shape[0] + smoothed = jnp.array(log_tvl) # copy + + for i in range(n_pools): + idxs = pool_time_padded[i] # (max_T,) sample indices + mask = pool_time_mask[i] # (max_T,) 1.0 or 0.0 + raw_vals = log_tvl[idxs] # (max_T,) raw log_tvl in time order + + # lax.scan for EWMA + def step(carry, x): + prev_smooth, = carry + raw_val, m = x + new_smooth = jnp.where( + m > 0, + lam * raw_val + (1.0 - lam) * prev_smooth, + prev_smooth) + return (new_smooth,), new_smooth + + init = (raw_vals[0],) + _, smooth_vals = jax.lax.scan(step, init, (raw_vals, mask)) + + # Scatter smoothed values back to sample positions + smoothed = smoothed.at[idxs].set( + jnp.where(mask > 0, smooth_vals, smoothed[idxs])) + + return smoothed + + +def forward_mm(params, x_market, log_tvl_smooth, pool_idx): + """MM forward pass → log(V_noise) per sample. + + log(V_noise) = log_alpha_i + x_market @ gamma[_i] + + log(TVL_smooth) - log(K_i + TVL_smooth) + """ + log_alpha = params["log_alpha"] + log_K = params["log_K"] + gamma = params["gamma"] + + # Per-sample pool params + alpha_i = log_alpha[pool_idx] + K_i = jnp.exp(log_K[pool_idx]) + tvl = jnp.exp(log_tvl_smooth) + + # Market features: shared or per-pool gamma + if gamma.ndim == 2: + per_sample_gamma = gamma[pool_idx] + market_term = jnp.sum(x_market * per_sample_gamma, axis=1) + else: + market_term = x_market @ gamma + + # MM saturation on smoothed TVL + log_saturation = log_tvl_smooth - jnp.log(K_i + tvl) + + return alpha_i + market_term + log_saturation + + +def make_loss_fn(pool_coeffs, pool_gas, n_pools): + """Loss with PCHIP arb + MM noise.""" + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + + def loss_fn(params, x_market, log_tvl, y_total, + sample_grid_days, pool_idx, pool_time_padded, + pool_time_mask, l2_alpha, huber_delta): + log_cadence = params["log_cadence"] + + # EWMA smooth TVL + log_tvl_smooth = ewma_smooth( + log_tvl, params["raw_lambda"], + pool_time_padded, pool_time_mask) + + # V_arb from PCHIP + n_samples = x_market.shape[0] + log_v_arb = jnp.zeros(n_samples) + for i in range(n_pools): + v_arb_all = interpolate_pool_daily( + pool_coeffs[i], jnp.float64(log_cadence[i]), pool_gas[i]) + safe_days = jnp.clip(sample_grid_days, 0, v_arb_all.shape[0] - 1) + log_v_arb = jnp.where( + pool_idx == i, + jnp.log(jnp.maximum(v_arb_all[safe_days], 1e-10)), + log_v_arb) + + # V_noise from MM with smoothed TVL + log_v_noise = forward_mm(params, x_market, log_tvl_smooth, pool_idx) + + # V_total + log_v_total = jnp.logaddexp(log_v_arb, log_v_noise) + + # Huber + residual = log_v_total - y_total + abs_r = jnp.abs(residual) + huber = jnp.where( + abs_r <= huber_delta, + 0.5 * residual ** 2, + huber_delta * (abs_r - 0.5 * huber_delta)) + + # Per-pool equal weighting + pool_counts = jnp.zeros(n_pools).at[pool_idx].add( + jnp.ones_like(pool_idx, dtype=jnp.float32)) + active = (pool_counts > 0).astype(jnp.float32) + n_active = jnp.maximum(jnp.sum(active), 1.0) + pool_counts = jnp.maximum(pool_counts, 1.0) + pool_sums = jnp.zeros(n_pools).at[pool_idx].add(huber) + mean_loss = jnp.sum((pool_sums / pool_counts) * active) / n_active + + # L2 on gamma and log_alpha + reg = l2_alpha * ( + jnp.mean(params["gamma"] ** 2) + + jnp.mean(params["log_alpha"] ** 2) + ) + + return mean_loss + reg + + return jax.jit(jax.value_and_grad(loss_fn)) + + +# ---- Training ---- + +def train(params, data, grad_fn, n_epochs, lr, l2_alpha, huber_delta, + verbose=True): + """Adam training loop.""" + m = {k: jnp.zeros_like(v) for k, v in params.items()} + v = {k: jnp.zeros_like(v) for k, v in params.items()} + b1, b2, eps = 0.9, 0.999, 1e-8 + + x_market = jnp.array(data["x_market"]) + log_tvl = jnp.array(data["log_tvl"]) + y_total = jnp.array(data["y_total"]) + sgd = jnp.array(data["sample_grid_days"]) + pidx = jnp.array(data["pool_idx"]) + pt_padded = jnp.array(data["pool_time_padded"]) + pt_mask = jnp.array(data["pool_time_mask"]) + + for epoch in range(n_epochs): + loss, grads = grad_fn( + params, x_market, log_tvl, y_total, sgd, pidx, + pt_padded, pt_mask, l2_alpha, huber_delta) + + for k in params: + g = grads[k] + m[k] = b1 * m[k] + (1 - b1) * g + v[k] = b2 * v[k] + (1 - b2) * g ** 2 + m_hat = m[k] / (1 - b1 ** (epoch + 1)) + v_hat = v[k] / (1 - b2 ** (epoch + 1)) + params[k] = params[k] - lr * m_hat / (jnp.sqrt(v_hat) + eps) + + if verbose and (epoch % 200 == 0 or epoch == n_epochs - 1): + log_K_med = float(jnp.median(params["log_K"])) + K_med = float(jnp.exp(log_K_med)) + cad = np.exp(np.array(params["log_cadence"])) + gamma_norm = float(jnp.sqrt(jnp.mean(params["gamma"] ** 2))) + lam = float(jax.nn.sigmoid(params["raw_lambda"])) + print(f" epoch {epoch:5d} loss={float(loss):.4f}" + f" K_med=${K_med:,.0f}" + f" λ={lam:.3f}" + f" |γ|={gamma_norm:.3f}" + f" cad=[{cad.min():.0f},{np.median(cad):.0f},{cad.max():.0f}]") + + return params + + +# ---- Evaluation ---- + +def evaluate(params, data): + """Per-pool R² and diagnostics.""" + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + + n_pools = data["n_pools"] + pool_idx = np.array(data["pool_idx"]) + sgd = np.array(data["sample_grid_days"]) + y = np.array(data["y_total"]) + log_cadence = np.array(params["log_cadence"]) + + # V_arb + v_arb = np.zeros(len(y)) + for i in range(n_pools): + mask = pool_idx == i + if not mask.any(): + continue + v_arb_all = np.array(interpolate_pool_daily( + data["pool_coeffs"][i], jnp.float64(log_cadence[i]), + data["pool_gas"][i])) + safe = np.clip(sgd[mask], 0, len(v_arb_all) - 1) + v_arb[mask] = v_arb_all[safe] + log_v_arb = np.log(np.maximum(v_arb, 1e-10)) + + # Smooth TVL with learned lambda + log_tvl_smooth = np.array(ewma_smooth( + jnp.array(data["log_tvl"]), params["raw_lambda"], + jnp.array(data["pool_time_padded"]), + jnp.array(data["pool_time_mask"]))) + + log_v_noise = np.array(forward_mm( + params, jnp.array(data["x_market"]), + jnp.array(log_tvl_smooth), + jnp.array(data["pool_idx"]))) + + log_v_total = np.logaddexp(log_v_arb, log_v_noise) + v_noise = np.exp(log_v_noise) + v_total = np.exp(log_v_total) + + r2s = {} + noise_shares = {} + for i in range(n_pools): + mask = pool_idx == i + if mask.sum() < 2: + continue + yt = y[mask] + pt = log_v_total[mask] + ss_res = np.sum((yt - pt) ** 2) + ss_tot = np.sum((yt - yt.mean()) ** 2) + r2s[data["pool_ids"][i]] = 1 - ss_res / max(ss_tot, 1e-10) + noise_shares[data["pool_ids"][i]] = float(np.median( + v_noise[mask] / v_total[mask])) + + K_values = {data["pool_ids"][i]: float(np.exp(params["log_K"][i])) + for i in range(n_pools)} + + return { + "r2s": r2s, + "noise_shares": noise_shares, + "K_values": K_values, + "median_r2": float(np.median(list(r2s.values()))), + } + + +def tvl_response_check(params, data): + """Print predicted noise at various TVL levels.""" + n_pools = data["n_pools"] + pool_idx = np.array(data["pool_idx"]) + + # Median market features per pool + print(f"\n TVL Response Check (per-pool median market features):") + print(f" {'Pool':>20s} {'K ($M)':>10s} {'TVL=100K':>10s}" + f" {'TVL=1M':>10s} {'TVL=10M':>10s} {'TVL=100M':>10s}" + f" {'TVL=1B':>10s} {'ε@1M':>6s} {'ε@100M':>6s}") + + tvl_test = [1e5, 1e6, 1e7, 1e8, 1e9] + + for i in range(min(n_pools, 15)): + pid = data["pool_ids"][i] + toks = data["pool_tokens"][i] + label = f"{toks[0]}/{toks[1]}" + mask = pool_idx == i + if mask.sum() == 0: + continue + + K_i = float(np.exp(params["log_K"][i])) + x_med = np.median(data["x_market"][mask], axis=0) + + gamma = np.array(params["gamma"]) + if gamma.ndim == 2: + market_term = float(x_med @ gamma[i]) + else: + market_term = float(x_med @ gamma) + log_alpha_i = float(params["log_alpha"][i]) + + vols = [] + for tvl in tvl_test: + log_sat = np.log(tvl) - np.log(K_i + tvl) + log_v = log_alpha_i + market_term + log_sat + vols.append(np.exp(log_v)) + + # Elasticity at 1M and 100M + eps_1m = K_i / (K_i + 1e6) + eps_100m = K_i / (K_i + 1e8) + + print(f" {label:>20s} ${K_i/1e6:>9.1f}" + + "".join(f" ${v:>9,.0f}" for v in vols) + + f" {eps_1m:>6.3f} {eps_100m:>6.3f}") + + +# ---- Main ---- + +def main(): + parser = argparse.ArgumentParser( + description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--epochs", type=int, default=3000) + parser.add_argument("--lr", type=float, default=3e-4) + parser.add_argument("--l2-alpha", type=float, default=1e-3) + parser.add_argument("--huber-delta", type=float, default=1.0) + parser.add_argument("--init-log-K", type=float, default=17.0, + help="Initial log(K) ~ log($24M)") + parser.add_argument("--per-pool-gamma", action="store_true", + help="Per-pool market feature coefficients") + parser.add_argument("--no-split", action="store_true") + parser.add_argument("--trend-windows", type=int, nargs="+", default=[7]) + parser.add_argument("--include-cross-pool", action="store_true") + parser.add_argument("--save-artifact", default="results/mm_noise") + args = parser.parse_args() + + os.environ.setdefault("JAX_PLATFORMS", "cpu") + + print("=" * 70) + print("Michaelis-Menten Noise Model + Market Features") + print(f" epochs={args.epochs}, lr={args.lr}, l2={args.l2_alpha}") + print(f" init log(K)={args.init_log_K} (K=${np.exp(args.init_log_K):,.0f})") + print(f" per_pool_gamma={args.per_pool_gamma}") + print("=" * 70) + + matched_clean, option_c_clean = load_stage1() + + print("\nBuilding data...") + t0 = time.time() + data = build_mm_data(matched_clean, option_c_clean, + trend_windows=tuple(args.trend_windows), + include_cross_pool=args.include_cross_pool) + n_pools = data["n_pools"] + n_market = data["n_market_feat"] + n_samples = len(data["pool_idx"]) + print(f" {n_samples} samples, {n_pools} pools," + f" {n_market} market features, {time.time() - t0:.1f}s") + + # Pool summary + pool_idx = data["pool_idx"] + for i, (pid, toks) in enumerate( + zip(data["pool_ids"], data["pool_tokens"])): + mask = pool_idx == i + n = mask.sum() + if n > 0: + med_tvl = np.exp(np.median(data["log_tvl"][mask])) + print(f" {pid[:16]} {toks[0]:>8s}/{toks[1]:<8s}" + f" {n:>4d} days TVL=${med_tvl:>12,.0f}") + + # Split + if args.no_split: + train_data = data + eval_data = None + else: + day_idx = data["day_idx"] + split_day = int(day_idx.max() * 0.7) + train_mask = day_idx <= split_day + eval_mask = day_idx > split_day + train_data = {k: v[train_mask] if isinstance(v, np.ndarray) + and v.shape[0] == n_samples else v + for k, v in data.items()} + eval_data = {k: v[eval_mask] if isinstance(v, np.ndarray) + and v.shape[0] == n_samples else v + for k, v in data.items()} + print(f"\n Split: {train_mask.sum()} train, {eval_mask.sum()} eval") + + # Init + if args.per_pool_gamma: + gamma_init = jnp.zeros((n_pools, n_market)) + else: + gamma_init = jnp.zeros(n_market) + + params = { + "log_alpha": jnp.zeros(n_pools), + "log_K": jnp.full(n_pools, args.init_log_K), + "gamma": gamma_init, + "log_cadence": jnp.array(data["init_log_cadences"]), + "raw_lambda": jnp.array(2.0), # sigmoid(2) ≈ 0.88 — mostly raw + } + n_params = sum(v.size for v in params.values()) + print(f"\n Parameters: {n_params}" + f" (α: {n_pools}, K: {n_pools}," + f" γ: {gamma_init.size}, cadence: {n_pools})") + + # Warm-start gamma via Ridge (numpy, no sklearn) + print(" Warm-starting γ via Ridge on residuals...") + + def _ridge(X, y, alpha=1.0): + """Ridge regression: (X'X + αI)^-1 X'y.""" + XtX = X.T @ X + alpha * np.eye(X.shape[1]) + Xty = X.T @ y + return np.linalg.solve(XtX, Xty) + + x_trn = data["x_market"] if args.no_split else train_data["x_market"] + y_trn = data["y_total"] if args.no_split else train_data["y_total"] + if args.per_pool_gamma: + pidx = data["pool_idx"] if args.no_split else train_data["pool_idx"] + for i in range(n_pools): + mask = pidx == i + if mask.sum() < 5: + continue + # Add intercept column for warm-start + X_i = np.concatenate([x_trn[mask], np.ones((mask.sum(), 1))], 1) + w = _ridge(X_i, y_trn[mask]) + params["gamma"] = params["gamma"].at[i].set( + jnp.array(w[:-1].astype(np.float32))) + params["log_alpha"] = params["log_alpha"].at[i].set(float(w[-1])) + else: + X_all = np.concatenate([x_trn, np.ones((len(y_trn), 1))], 1) + w = _ridge(X_all, y_trn) + params["gamma"] = jnp.array(w[:-1].astype(np.float32)) + + # Loss + grad_fn = make_loss_fn(data["pool_coeffs"], data["pool_gas"], n_pools) + + print(f"\nTraining ({args.epochs} epochs)...") + t0 = time.time() + params = train(params, train_data, grad_fn, args.epochs, args.lr, + args.l2_alpha, args.huber_delta) + print(f" Training time: {time.time() - t0:.1f}s") + + # Evaluate + print("\n" + "=" * 70) + print("Results (train)") + print("=" * 70) + train_eval = evaluate(params, train_data) + print(f" Median R²: {train_eval['median_r2']:.4f}") + + print(f"\n {'Pool':>16s} {'Tokens':>16s} {'R²':>6s}" + f" {'Noise%':>7s} {'K ($M)':>10s}") + for pid in data["pool_ids"]: + i = data["pool_ids"].index(pid) + toks = data["pool_tokens"][i] + r2 = train_eval["r2s"].get(pid, float("nan")) + ns = train_eval["noise_shares"].get(pid, float("nan")) + K = train_eval["K_values"][pid] + print(f" {pid[:16]} {toks[0]:>8s}/{toks[1]:<6s}" + f" {r2:>6.3f} {ns*100:>6.1f}% ${K/1e6:>9.1f}") + + if eval_data is not None: + print("\n" + "=" * 70) + print("Results (eval)") + print("=" * 70) + eval_result = evaluate(params, eval_data) + print(f" Median R²: {eval_result['median_r2']:.4f}") + + # TVL response + tvl_response_check(params, data) + + # Gamma coefficients + gamma = np.array(params["gamma"]) + if gamma.ndim == 1: + print(f"\n Shared γ coefficients:") + for j, name in enumerate(data["market_names"]): + print(f" {name:>30s}: {gamma[j]:>8.4f}") + + # Save + if args.save_artifact: + os.makedirs(args.save_artifact, exist_ok=True) + save_dict = {k: np.array(v) for k, v in params.items()} + np.savez(os.path.join(args.save_artifact, "model.npz"), **save_dict) + meta = { + "model": "michaelis_menten", + "pool_ids": data["pool_ids"], + "pool_tokens": data["pool_tokens"], + "market_names": data["market_names"], + "n_pools": n_pools, + "n_market_feat": n_market, + "per_pool_gamma": args.per_pool_gamma, + "hparams": { + "epochs": args.epochs, "lr": args.lr, + "l2_alpha": args.l2_alpha, "huber_delta": args.huber_delta, + "init_log_K": args.init_log_K, + }, + } + with open(os.path.join(args.save_artifact, "meta.json"), "w") as f: + json.dump(meta, f, indent=2) + print(f"\n Saved: {args.save_artifact}/") + + +if __name__ == "__main__": + main() diff --git a/experiments/run_model_comparison.py b/experiments/run_model_comparison.py new file mode 100644 index 0000000..398ec48 --- /dev/null +++ b/experiments/run_model_comparison.py @@ -0,0 +1,338 @@ +"""Compare linear vs MLP noise models across TVL levels. + +Evaluates both noise models for a given pool over the same date range, +sweeping initial TVL. Uses real price data, the PCHIP arb grid, and +both noise models to predict daily volume decomposition. + +Produces a plot: predicted daily noise volume vs TVL for each model, +with the real observed volume overlaid where available. + +Usage: + python experiments/run_model_comparison.py + python experiments/run_model_comparison.py --tvl-range 1e5 1e6 5e6 7e6 20e6 50e6 +""" + +import argparse +import json +import os +import pickle +import time + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +import jax.numpy as jnp + + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) +LINEAR_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "linear_market_noise", +) +MLP_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "mlp_noise", +) + + +def load_linear_model(artifact_dir, pool_id): + """Load linear noise model for a pool.""" + art = np.load(os.path.join(artifact_dir, "model.npz")) + with open(os.path.join(artifact_dir, "meta.json")) as f: + meta = json.load(f) + pool_ids = meta["pool_ids"] + idx = next((i for i, p in enumerate(pool_ids) + if p.startswith(pool_id) or pool_id.startswith(p)), -1) + nc = art["noise_coeffs"] + coeffs = nc[idx] if nc.ndim == 2 and idx >= 0 else (nc if nc.ndim == 1 else np.median(nc, axis=0)) + return { + "coeffs": coeffs, + "log_cadence": art["log_cadence"][idx] if idx >= 0 else np.log(10.0), + "x_mean": art["x_mean"], + "x_std": art["x_std"], + "feat_names": meta["feat_names"], + "type": "linear", + } + + +def load_mlp_model(artifact_dir, pool_id): + """Load MLP noise model.""" + art = dict(np.load(os.path.join(artifact_dir, "model.npz"), allow_pickle=True)) + with open(os.path.join(artifact_dir, "meta.json")) as f: + meta = json.load(f) + pool_ids = meta["pool_ids"] + idx = next((i for i, p in enumerate(pool_ids) + if p.startswith(pool_id) or pool_id.startswith(p)), -1) + + # Extract MLP params + params = {} + for k in art: + if k.startswith("W") or k.startswith("b") or k == "log_cadence" or k == "pool_bias": + params[k] = art[k] + + return { + "params": params, + "log_cadence": art["log_cadence"][idx] if idx >= 0 else np.log(10.0), + "pool_idx": idx, + "x_mean": art["x_mean"], + "x_std": art["x_std"], + "feat_names": meta["feat_names"], + "hidden": meta["hidden"], + "per_pool": meta.get("per_pool", False), + "type": "mlp", + } + + +def predict_noise_linear(model, x_daily, tvl_values): + """Predict noise volume at multiple TVL levels using linear model.""" + tvl_col = 1 # xobs_1 + results = {} + for tvl in tvl_values: + x = x_daily.copy() + x[:, tvl_col] = (np.log(tvl) - model["x_mean"][tvl_col]) / model["x_std"][tvl_col] + # Update TVL interaction terms + for i, name in enumerate(model["feat_names"]): + if name.startswith("xobs_1\u00d7"): + paired = name.split("\u00d7")[1] + if paired in model["feat_names"]: + j = model["feat_names"].index(paired) + x[:, i] = x[:, tvl_col] * x_daily[:, j] + log_noise = x @ model["coeffs"] + results[tvl] = np.exp(log_noise) + return results + + +def predict_noise_mlp(model, x_daily, tvl_values): + """Predict noise volume at multiple TVL levels using MLP model.""" + from experiments.run_mlp_noise import forward_mlp + tvl_col = 1 + params = model["params"] + pool_idx_arr = (jnp.full(x_daily.shape[0], model["pool_idx"]) + if model["per_pool"] and model["pool_idx"] >= 0 else None) + results = {} + for tvl in tvl_values: + x = x_daily.copy() + x[:, tvl_col] = (np.log(tvl) - model["x_mean"][tvl_col]) / model["x_std"][tvl_col] + for i, name in enumerate(model["feat_names"]): + if name.startswith("xobs_1\u00d7"): + paired = name.split("\u00d7")[1] + if paired in model["feat_names"]: + j = model["feat_names"].index(paired) + x[:, i] = x[:, tvl_col] * x_daily[:, j] + log_noise = np.array(forward_mlp(params, jnp.array(x), pool_idx_arr)) + results[tvl] = np.exp(log_noise) + return results + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--pool", default="0x9d1fcf346ea1b0") + parser.add_argument("--tvl-range", type=float, nargs="+", + default=[100_000, 500_000, 1_000_000, 5_000_000, + 7_000_000, 20_000_000, 50_000_000]) + parser.add_argument("--linear-dir", default=LINEAR_DIR) + parser.add_argument("--mlp-dir", default=MLP_DIR) + parser.add_argument("--output-dir", default="results/model_comparison") + args = parser.parse_args() + + os.environ.setdefault("JAX_PLATFORMS", "cpu") + os.makedirs(args.output_dir, exist_ok=True) + + # Load data + with open(os.path.join(CACHE_DIR, "stage1.pkl"), "rb") as f: + data = pickle.load(f) + mc = data["matched_clean"] + oc = data["option_c_clean"] + + pid = args.pool + entry = mc[pid] + panel = entry["panel"] + dates = pd.to_datetime(panel["date"]) + vol_obs = np.exp(panel["log_volume"].values.astype(float)) + tvl_obs = np.exp(panel["log_tvl_lag1"].values.astype(float)) + + print(f"Pool: {pid} ({entry['tokens']}, {entry['chain']})") + print(f"{len(dates)} days: {dates.min().date()} → {dates.max().date()}") + + # Build feature matrix (same for both models) + from experiments.run_linear_market_noise import build_data + data_full = build_data(mc, oc, trend_windows=(7,), + include_market=True, include_cross_pool=False) + pool_ids = data_full["pool_ids"] + pool_i = pool_ids.index(pid) + pool_mask = data_full["pool_idx"] == pool_i + x_pool = data_full["x"][pool_mask] + day_idx = data_full["day_idx"][pool_mask] + sgd = data_full["sample_grid_days"][pool_mask] + + all_dates = set() + for p in pool_ids: + all_dates.update(mc[p]["panel"]["date"].values) + date_list = sorted(all_dates) + sample_dates = np.array([pd.Timestamp(date_list[d]) for d in day_idx]) + + n_days = len(sample_dates) + print(f"Feature samples: {n_days}") + + # Load models + print("\nLoading models...") + linear_model = load_linear_model(args.linear_dir, pid) + print(f" Linear: {len(linear_model['coeffs'])} coefficients," + f" cadence={np.exp(linear_model['log_cadence']):.1f}min") + + has_mlp = os.path.exists(os.path.join(args.mlp_dir, "model.npz")) + if has_mlp: + mlp_model = load_mlp_model(args.mlp_dir, pid) + print(f" MLP: hidden={mlp_model['hidden']}," + f" cadence={np.exp(mlp_model['log_cadence']):.1f}min") + else: + print(f" MLP: no artifact at {args.mlp_dir}") + mlp_model = None + + # V_arb from PCHIP (same for both — uses linear model's cadence) + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + cadence = float(np.exp(linear_model["log_cadence"])) + gas = float(np.exp(oc[pid]["log_gas"])) + v_arb_all = np.array(interpolate_pool_daily( + entry["coeffs"], jnp.float64(np.log(cadence)), jnp.float64(gas))) + v_arb = v_arb_all[sgd] + + # Predict at each TVL + print(f"\nPredicting noise at {len(args.tvl_range)} TVL levels...") + linear_noise = predict_noise_linear(linear_model, x_pool, args.tvl_range) + mlp_noise = predict_noise_mlp(mlp_model, x_pool, args.tvl_range) if mlp_model else {} + + # Real observed volume for comparison + tvl_for_samples = np.zeros(n_days) + vol_for_samples = np.zeros(n_days) + for i, sd in enumerate(sample_dates): + matches = np.where(dates == sd)[0] + if len(matches) > 0: + tvl_for_samples[i] = tvl_obs[matches[0]] + vol_for_samples[i] = vol_obs[matches[0]] + + # ---- Plot 1: Median noise volume vs TVL ---- + fig, axes = plt.subplots(1, 2, figsize=(14, 6)) + + tvls = np.array(args.tvl_range) + lin_medians = np.array([np.median(linear_noise[t]) for t in tvls]) + lin_q25 = np.array([np.percentile(linear_noise[t], 25) for t in tvls]) + lin_q75 = np.array([np.percentile(linear_noise[t], 75) for t in tvls]) + + ax = axes[0] + ax.fill_between(tvls / 1e6, lin_q25 / 1e6, lin_q75 / 1e6, + alpha=0.2, color="steelblue") + ax.plot(tvls / 1e6, lin_medians / 1e6, "o-", color="steelblue", + linewidth=2, label="Linear noise (median)") + + if mlp_noise: + mlp_medians = np.array([np.median(mlp_noise[t]) for t in tvls]) + mlp_q25 = np.array([np.percentile(mlp_noise[t], 25) for t in tvls]) + mlp_q75 = np.array([np.percentile(mlp_noise[t], 75) for t in tvls]) + ax.fill_between(tvls / 1e6, mlp_q25 / 1e6, mlp_q75 / 1e6, + alpha=0.2, color="coral") + ax.plot(tvls / 1e6, mlp_medians / 1e6, "s-", color="coral", + linewidth=2, label="MLP noise (median)") + + # Add real observed volume at real TVL + valid = tvl_for_samples > 100 + ax.scatter(tvl_for_samples[valid] / 1e6, vol_for_samples[valid] / 1e6, + c="black", s=3, alpha=0.2, label="Observed total vol", zorder=1) + + ax.set_xlabel("Effective TVL ($M)") + ax.set_ylabel("Daily volume ($M)") + ax.set_xscale("log") + ax.set_yscale("log") + ax.set_title(f"{entry['tokens']} — Noise Volume vs TVL") + ax.legend(fontsize=8) + ax.grid(True, alpha=0.3) + + # ---- Plot 2: Noise/TVL ratio vs TVL ---- + ax = axes[1] + ax.plot(tvls / 1e6, lin_medians / tvls * 100, "o-", color="steelblue", + linewidth=2, label="Linear noise/TVL") + if mlp_noise: + ax.plot(tvls / 1e6, mlp_medians / tvls * 100, "s-", color="coral", + linewidth=2, label="MLP noise/TVL") + + # Real vol/TVL + ax.scatter(tvl_for_samples[valid] / 1e6, + vol_for_samples[valid] / tvl_for_samples[valid] * 100, + c="black", s=3, alpha=0.2, label="Observed vol/TVL") + + ax.set_xlabel("Effective TVL ($M)") + ax.set_ylabel("Noise / TVL (%)") + ax.set_xscale("log") + ax.set_title("Noise as Fraction of TVL") + ax.legend(fontsize=8) + ax.grid(True, alpha=0.3) + + fig.suptitle(f"Linear vs MLP Noise Model — {entry['tokens']}", fontsize=12) + fig.tight_layout() + out = os.path.join(args.output_dir, f"{pid[:16]}_model_comparison.png") + fig.savefig(out, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f"\nSaved: {out}") + + # ---- Plot 3: Time series at selected TVLs ---- + fig, axes = plt.subplots(len(args.tvl_range), 1, + figsize=(14, 3 * len(args.tvl_range)), + sharex=True) + if len(args.tvl_range) == 1: + axes = [axes] + + for k, tvl in enumerate(args.tvl_range): + ax = axes[k] + v_total_lin = v_arb + linear_noise[tvl] + ax.plot(sample_dates, v_total_lin / 1e6, "b-", linewidth=0.6, + alpha=0.7, label="Linear (arb+noise)") + if mlp_noise: + v_total_mlp = v_arb + mlp_noise[tvl] + ax.plot(sample_dates, v_total_mlp / 1e6, "r-", linewidth=0.6, + alpha=0.7, label="MLP (arb+noise)") + ax.plot(sample_dates, vol_for_samples / 1e6, "k-", linewidth=0.5, + alpha=0.3, label="Observed (at real TVL)") + ax.set_ylabel(f"$M/day\nTVL=${tvl/1e6:.1f}M") + ax.set_yscale("log") + if k == 0: + ax.legend(fontsize=7, loc="upper right") + ax.grid(True, alpha=0.3) + + axes[-1].set_xlabel("Date") + fig.suptitle(f"Volume Time Series at Different TVLs — {entry['tokens']}", fontsize=11) + fig.tight_layout() + out2 = os.path.join(args.output_dir, f"{pid[:16]}_tvl_sweep_timeseries.png") + fig.savefig(out2, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f"Saved: {out2}") + + # Summary table + print(f"\n{'='*70}") + print(f"Summary: Median daily noise volume by TVL") + print(f"{'='*70}") + print(f"{'TVL':>14s} {'Linear':>12s} {'Lin/TVL':>8s}", end="") + if mlp_noise: + print(f" {'MLP':>12s} {'MLP/TVL':>8s} {'MLP/Lin':>8s}") + else: + print() + + for tvl in args.tvl_range: + lin = np.median(linear_noise[tvl]) + print(f"${tvl:>13,.0f} ${lin:>11,.0f} {lin/tvl*100:>7.1f}%", end="") + if mlp_noise: + mlp = np.median(mlp_noise[tvl]) + ratio = mlp / lin if lin > 0 else 0 + print(f" ${mlp:>11,.0f} {mlp/tvl*100:>7.1f}% {ratio:>7.2f}x") + else: + print() + + +if __name__ == "__main__": + main() diff --git a/scripts/plot_mm_noise_fit.py b/scripts/plot_mm_noise_fit.py new file mode 100644 index 0000000..a5184ad --- /dev/null +++ b/scripts/plot_mm_noise_fit.py @@ -0,0 +1,472 @@ +"""Plot MM noise model fit: per-pool time series + TVL response curves. + +Produces two types of plots: +1. Per-pool 6-panel time series (like plot_model_vs_real_reclamm.py): + TVL, volume decomposition, V_noise, fee revenue, vol/TVL, pred/obs +2. Cross-pool TVL response curves showing MM saturation + +Usage: + python scripts/plot_mm_noise_fit.py + python scripts/plot_mm_noise_fit.py --pool 0x9d1fcf346ea1b0 + python scripts/plot_mm_noise_fit.py --all-pools +""" + +import argparse +import json +import os +import pickle +import sys + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +import jax.numpy as jnp + + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) + + +def load_model(artifact_dir): + """Load MM model artifact.""" + art = dict(np.load(os.path.join(artifact_dir, "model.npz"), + allow_pickle=True)) + with open(os.path.join(artifact_dir, "meta.json")) as f: + meta = json.load(f) + params = {} + for k in art: + params[k] = jnp.array(art[k]) + return params, meta + + +def compute_decomposition(params, meta, matched_clean, option_c_clean): + """Compute V_arb, V_noise, V_total for all pools.""" + from experiments.run_mm_noise import build_mm_data, forward_mm + from quantammsim.calibration.grid_interpolation import interpolate_pool_daily + + data = build_mm_data(matched_clean, option_c_clean, + trend_windows=(7,), + include_cross_pool=False) + + pool_ids = data["pool_ids"] + n_pools = data["n_pools"] + pool_idx = np.array(data["pool_idx"]) + sgd = np.array(data["sample_grid_days"]) + day_idx = np.array(data["day_idx"]) + y = np.array(data["y_total"]) + log_tvl = np.array(data["log_tvl"]) + + log_cadence = np.array(params["log_cadence"]) + + # V_arb + v_arb = np.zeros(len(y)) + for i in range(n_pools): + mask = pool_idx == i + if not mask.any(): + continue + v_arb_all = np.array(interpolate_pool_daily( + data["pool_coeffs"][i], jnp.float64(log_cadence[i]), + data["pool_gas"][i])) + safe = np.clip(sgd[mask], 0, len(v_arb_all) - 1) + v_arb[mask] = v_arb_all[safe] + + # V_noise + log_v_noise = np.array(forward_mm( + params, jnp.array(data["x_market"]), + jnp.array(data["log_tvl"]), + jnp.array(data["pool_idx"]))) + v_noise = np.exp(log_v_noise) + v_total = v_arb + v_noise + v_obs = np.exp(y) + + # Reconstruct dates + all_dates = set() + for pid in pool_ids: + all_dates.update(matched_clean[pid]["panel"]["date"].values) + date_list = sorted(all_dates) + + dates = np.array([pd.Timestamp(date_list[d]) for d in day_idx]) + + return { + "pool_ids": pool_ids, + "pool_tokens": data["pool_tokens"], + "pool_idx": pool_idx, + "dates": dates, + "v_arb": v_arb, + "v_noise": v_noise, + "v_total": v_total, + "v_obs": v_obs, + "log_tvl": log_tvl, + "tvl": np.exp(log_tvl), + } + + +def plot_pool_timeseries(decomp, params, pool_i, output_dir): + """6-panel time series for a single pool.""" + pid = decomp["pool_ids"][pool_i] + toks = decomp["pool_tokens"][pool_i] + label = f"{toks[0]}/{toks[1]}" + + mask = decomp["pool_idx"] == pool_i + if mask.sum() < 10: + print(f" Skipping {pid[:16]} ({label}): {mask.sum()} samples") + return + + dates = decomp["dates"][mask] + v_arb = decomp["v_arb"][mask] + v_noise = decomp["v_noise"][mask] + v_total = decomp["v_total"][mask] + v_obs = decomp["v_obs"][mask] + tvl = decomp["tvl"][mask] + + K_i = float(np.exp(params["log_K"][pool_i])) + + # R² + log_pred = np.log(np.maximum(v_total, 1e-10)) + log_obs = np.log(np.maximum(v_obs, 1e-10)) + ss_res = np.sum((log_pred - log_obs) ** 2) + ss_tot = np.sum((log_obs - log_obs.mean()) ** 2) + r2 = 1 - ss_res / max(ss_tot, 1e-10) + + fig, axes = plt.subplots(6, 1, figsize=(14, 18), sharex=True) + + # 1. TVL + ax = axes[0] + ax.plot(dates, tvl / 1e6, "k-", linewidth=0.7) + ax.axhline(K_i / 1e6, color="red", linestyle="--", alpha=0.5, + label=f"K = ${K_i/1e6:.1f}M") + ax.set_ylabel("TVL ($M)") + ax.set_yscale("log") + ax.legend(fontsize=8) + ax.set_title(f"{label} — TVL (K={K_i/1e6:.1f}M)") + ax.grid(True, alpha=0.3) + + # 2. Volume decomposition + ax = axes[1] + ax.fill_between(dates, 0, v_arb / 1e6, alpha=0.4, color="steelblue", + label="V_arb") + ax.fill_between(dates, v_arb / 1e6, v_total / 1e6, alpha=0.4, + color="coral", label="V_noise (MM)") + ax.plot(dates, v_obs / 1e6, "k-", linewidth=0.5, alpha=0.7, + label="V_obs") + ax.plot(dates, v_total / 1e6, "r--", linewidth=0.5, alpha=0.7, + label="V_pred") + ax.set_ylabel("Volume ($M/day)") + ax.set_yscale("log") + ax.legend(fontsize=7) + ax.set_title(f"Volume Decomposition (R²={r2:.3f})") + ax.grid(True, alpha=0.3) + + # 3. V_noise only + ax = axes[2] + ax.fill_between(dates, 0, v_noise / 1e6, alpha=0.4, color="coral") + ax.plot(dates, v_noise / 1e6, "r-", linewidth=0.5) + noise_med = np.median(v_noise) + ax.axhline(noise_med / 1e6, color="red", linestyle=":", alpha=0.5, + label=f"median=${noise_med:,.0f}") + ax.set_ylabel("V_noise ($M/day)") + ax.set_yscale("log") + ax.legend(fontsize=8) + ax.set_title("Noise Volume (MM model)") + ax.grid(True, alpha=0.3) + + # 4. Fee revenue (assuming 0.25% fee, 25% protocol take) + fee_rate = 0.0025 + protocol_take = 0.25 + fee_arb = v_arb * fee_rate * (1 - protocol_take) + fee_noise = v_noise * fee_rate * (1 - protocol_take) + fee_obs = v_obs * fee_rate * (1 - protocol_take) + ax = axes[3] + ax.fill_between(dates, 0, fee_arb, alpha=0.4, color="steelblue", + label="Arb fees") + ax.fill_between(dates, fee_arb, fee_arb + fee_noise, alpha=0.4, + color="coral", label="Noise fees") + ax.plot(dates, fee_obs, "k-", linewidth=0.5, alpha=0.7, + label="Obs fees (approx)") + ax.set_ylabel("Fee revenue ($/day)") + ax.legend(fontsize=7) + ax.set_title("Fee Revenue (0.25% fee, 75% LP)") + ax.grid(True, alpha=0.3) + + # 5. Vol/TVL + ax = axes[4] + vol_tvl_obs = v_obs / tvl + vol_tvl_pred = v_total / tvl + ax.plot(dates, vol_tvl_obs * 100, "k-", linewidth=0.5, alpha=0.5, + label="Observed") + ax.plot(dates, vol_tvl_pred * 100, "r-", linewidth=0.5, alpha=0.5, + label="Predicted") + ax.axhline(np.median(vol_tvl_obs) * 100, color="black", linestyle=":", + alpha=0.3) + ax.axhline(np.median(vol_tvl_pred) * 100, color="red", linestyle=":", + alpha=0.3) + ax.set_ylabel("Vol/TVL (%)") + ax.legend(fontsize=8) + ax.set_title("Volume as % of TVL") + ax.grid(True, alpha=0.3) + + # 6. Pred/Obs ratio + ax = axes[5] + ratio = v_total / np.maximum(v_obs, 1) + ax.plot(dates, ratio, "b-", linewidth=0.5, alpha=0.5) + ax.axhline(1.0, color="black", linestyle="-", alpha=0.3) + med_ratio = np.median(ratio) + ax.axhline(med_ratio, color="blue", linestyle=":", alpha=0.5, + label=f"median={med_ratio:.2f}") + ax.set_ylabel("Pred / Obs") + ax.set_yscale("log") + ax.set_ylim(0.05, 20) + ax.legend(fontsize=8) + ax.set_title("Prediction Ratio") + ax.grid(True, alpha=0.3) + ax.set_xlabel("Date") + + fig.suptitle(f"MM Noise Model — {label} ({pid[:16]})", fontsize=13) + fig.tight_layout() + out = os.path.join(output_dir, f"{pid[:16]}_mm_fit.png") + fig.savefig(out, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f" Saved: {out}") + + +def plot_tvl_response(params, meta, decomp, output_dir): + """Cross-pool TVL response curves showing MM saturation.""" + pool_ids = meta["pool_ids"] + pool_tokens = meta["pool_tokens"] + n_pools = len(pool_ids) + + tvl_range = np.logspace(4, 10, 200) # $10K to $10B + + fig, axes = plt.subplots(1, 3, figsize=(18, 6)) + + # Select pools with enough data for interesting plots + pool_idx_arr = np.array(decomp["pool_idx"]) + interesting = [] + for i in range(n_pools): + n = (pool_idx_arr == i).sum() + if n >= 50: + interesting.append(i) + + colors = plt.cm.tab20(np.linspace(0, 1, len(interesting))) + + # Panel 1: Noise volume vs TVL (absolute) + ax = axes[0] + for ci, i in enumerate(interesting): + K_i = float(np.exp(params["log_K"][i])) + + mask = pool_idx_arr == i + actual_noise = np.median(decomp["v_noise"][mask]) + actual_tvl = np.median(decomp["tvl"][mask]) + # Scale: noise(TVL) = actual_noise * [TVL/(K+TVL)] / [actual_TVL/(K+actual_TVL)] + mm_actual = actual_tvl / (K_i + actual_tvl) + mm_curve = tvl_range / (K_i + tvl_range) + noise_curve = actual_noise * mm_curve / mm_actual + + label = f"{pool_tokens[i][0]}/{pool_tokens[i][1]}" + ax.plot(tvl_range / 1e6, noise_curve / 1e6, color=colors[ci], + linewidth=1.0, alpha=0.7, label=label if ci < 10 else None) + # Mark actual TVL + ax.scatter([actual_tvl / 1e6], [actual_noise / 1e6], + color=colors[ci], s=20, zorder=5) + + ax.set_xlabel("TVL ($M)") + ax.set_ylabel("Daily Noise Volume ($M)") + ax.set_xscale("log") + ax.set_yscale("log") + ax.set_title("Noise Volume vs TVL (MM saturation)") + ax.legend(fontsize=6, ncol=2) + ax.grid(True, alpha=0.3) + + # Panel 2: Noise/TVL ratio vs TVL + ax = axes[1] + for ci, i in enumerate(interesting): + K_i = float(np.exp(params["log_K"][i])) + mask = pool_idx_arr == i + actual_noise = np.median(decomp["v_noise"][mask]) + actual_tvl = np.median(decomp["tvl"][mask]) + mm_actual = actual_tvl / (K_i + actual_tvl) + mm_curve = tvl_range / (K_i + tvl_range) + noise_curve = actual_noise * mm_curve / mm_actual + ratio_curve = noise_curve / tvl_range * 100 + + label = f"{pool_tokens[i][0]}/{pool_tokens[i][1]}" + ax.plot(tvl_range / 1e6, ratio_curve, color=colors[ci], + linewidth=1.0, alpha=0.7, label=label if ci < 10 else None) + + ax.set_xlabel("TVL ($M)") + ax.set_ylabel("Noise / TVL (%)") + ax.set_xscale("log") + ax.set_title("Noise as Fraction of TVL") + ax.legend(fontsize=6, ncol=2) + ax.grid(True, alpha=0.3) + + # Panel 3: Elasticity vs TVL + ax = axes[2] + for ci, i in enumerate(interesting): + K_i = float(np.exp(params["log_K"][i])) + eps_curve = K_i / (K_i + tvl_range) + + label = f"{pool_tokens[i][0]}/{pool_tokens[i][1]}" + ax.plot(tvl_range / 1e6, eps_curve, color=colors[ci], + linewidth=1.0, alpha=0.7, label=label if ci < 10 else None) + # Mark actual TVL + actual_tvl = np.median(decomp["tvl"][pool_idx_arr == i]) + eps_actual = K_i / (K_i + actual_tvl) + ax.scatter([actual_tvl / 1e6], [eps_actual], + color=colors[ci], s=20, zorder=5) + + ax.axhline(0.5, color="gray", linestyle="--", alpha=0.3, label="ε=0.5") + ax.set_xlabel("TVL ($M)") + ax.set_ylabel("Elasticity ε(TVL)") + ax.set_xscale("log") + ax.set_ylim(0, 1.05) + ax.set_title("TVL Elasticity (K/(K+TVL))") + ax.legend(fontsize=6, ncol=2) + ax.grid(True, alpha=0.3) + + fig.suptitle("Michaelis-Menten Noise Model — TVL Response", fontsize=13) + fig.tight_layout() + out = os.path.join(output_dir, "mm_tvl_response.png") + fig.savefig(out, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f" Saved: {out}") + + +def plot_K_distribution(params, meta, decomp, output_dir): + """Plot K values across pools with data quality indicator.""" + pool_ids = meta["pool_ids"] + pool_tokens = meta["pool_tokens"] + n_pools = len(pool_ids) + pool_idx_arr = np.array(decomp["pool_idx"]) + + K_vals = [] + labels = [] + n_days = [] + tvl_ranges = [] + for i in range(n_pools): + mask = pool_idx_arr == i + n = mask.sum() + K_i = float(np.exp(params["log_K"][i])) + K_vals.append(K_i) + tok = pool_tokens[i] + labels.append(f"{tok[0]}/{tok[1]}") + n_days.append(n) + if n > 0: + tvl = decomp["tvl"][mask] + tvl_ranges.append(np.log10(tvl.max() / max(tvl.min(), 1))) + else: + tvl_ranges.append(0) + + K_vals = np.array(K_vals) + n_days = np.array(n_days) + tvl_ranges = np.array(tvl_ranges) + + # Sort by K + order = np.argsort(K_vals) + + fig, axes = plt.subplots(1, 2, figsize=(16, 8)) + + # Panel 1: K values as horizontal bar + ax = axes[0] + y_pos = np.arange(n_pools) + colors = plt.cm.viridis(tvl_ranges[order] / max(tvl_ranges.max(), 1)) + ax.barh(y_pos, K_vals[order] / 1e6, color=colors, height=0.7) + ax.set_yticks(y_pos) + ax.set_yticklabels([labels[i] for i in order], fontsize=7) + ax.set_xlabel("K ($M)") + ax.set_title("Half-Saturation TVL by Pool\n(color = log10 TVL range)") + ax.axvline(np.median(K_vals) / 1e6, color="red", linestyle="--", + alpha=0.5, label=f"median=${np.median(K_vals)/1e6:.1f}M") + ax.legend() + ax.grid(True, alpha=0.3, axis="x") + + # Panel 2: K vs data quality (n_days and TVL range) + ax = axes[1] + valid = n_days > 0 + sc = ax.scatter(n_days[valid], K_vals[valid] / 1e6, + c=tvl_ranges[valid], cmap="viridis", + s=50, alpha=0.7) + for i in range(n_pools): + if n_days[i] > 0: + ax.annotate(labels[i], (n_days[i], K_vals[i] / 1e6), + fontsize=5, alpha=0.7) + ax.set_xlabel("Number of training days") + ax.set_ylabel("K ($M)") + ax.set_title("K vs Data Quantity\n(color = log10 TVL range)") + plt.colorbar(sc, ax=ax, label="log10(TVL_max/TVL_min)") + ax.grid(True, alpha=0.3) + + fig.suptitle("Michaelis-Menten K Distribution", fontsize=13) + fig.tight_layout() + out = os.path.join(output_dir, "mm_K_distribution.png") + fig.savefig(out, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f" Saved: {out}") + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--pool", default=None, + help="Plot single pool (prefix match)") + parser.add_argument("--all-pools", action="store_true") + parser.add_argument("--artifact-dir", default="results/mm_noise") + parser.add_argument("--output-dir", default="results/mm_noise/plots") + parser.add_argument("--top-n", type=int, default=10, + help="Plot top N pools by sample count") + args = parser.parse_args() + + os.environ.setdefault("JAX_PLATFORMS", "cpu") + os.makedirs(args.output_dir, exist_ok=True) + + # Load + print("Loading model...") + params, meta = load_model(args.artifact_dir) + + print("Loading data...") + with open(os.path.join(CACHE_DIR, "stage1.pkl"), "rb") as f: + stage1 = pickle.load(f) + matched_clean = stage1["matched_clean"] + option_c_clean = stage1["option_c_clean"] + + print("Computing decomposition...") + decomp = compute_decomposition(params, meta, matched_clean, option_c_clean) + + pool_ids = decomp["pool_ids"] + pool_idx = decomp["pool_idx"] + + # Which pools to plot + if args.pool: + targets = [i for i, pid in enumerate(pool_ids) + if pid.startswith(args.pool)] + elif args.all_pools: + targets = list(range(len(pool_ids))) + else: + # Top N by sample count + counts = [(pool_idx == i).sum() for i in range(len(pool_ids))] + targets = sorted(range(len(pool_ids)), key=lambda i: -counts[i]) + targets = targets[:args.top_n] + + # Per-pool time series + print(f"\nPlotting {len(targets)} pools...") + for i in targets: + plot_pool_timeseries(decomp, params, i, args.output_dir) + + # TVL response curves + print("\nPlotting TVL response...") + plot_tvl_response(params, meta, decomp, args.output_dir) + + # K distribution + print("\nPlotting K distribution...") + plot_K_distribution(params, meta, decomp, args.output_dir) + + print(f"\nDone. Plots in {args.output_dir}/") + + +if __name__ == "__main__": + main() From 1fae546f7c6b8e15ec89d4c7b9507e0c1106d4f1 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 7 Apr 2026 11:07:51 +0100 Subject: [PATCH 48/57] =?UTF-8?q?feat:=20MM=20noise=20model=20=E2=80=94=20?= =?UTF-8?q?per-pool=20K,=20Optuna=20sweep,=20cross-pool=20TVL=20analysis?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit MM model now supports per-pool log_K (default) and shared Binance-volume K (--shared-K). Per-pool K with per-pool gamma achieves R²=0.66 at 20K epochs with structural TVL saturation (median K≈$2M). Optuna sweep searches lr, l2, huber_delta, init_log_K, per_pool_gamma. Best shared-gamma eval R²=0.42 (huber=0.5, lr=1e-4 consistently). Removed learned EWMA (lambda stayed near 1, no benefit). Removed TVL interaction features (hurt eval R², subsumed by K). Plot script handles both K modes, shows all pools by default. New: verify_vol_volume_slope.py — cross-pool volume/TVL analysis confirming sublinear scaling (TVL^0.80, R²=0.79) and TVL elasticity ~0.9 across fee tiers. --- experiments/run_mm_noise.py | 411 ++++++++++++++++++------- experiments/verify_vol_volume_slope.py | 372 ++++++++++++++++++++++ scripts/plot_mm_noise_fit.py | 58 ++-- 3 files changed, 703 insertions(+), 138 deletions(-) create mode 100644 experiments/verify_vol_volume_slope.py diff --git a/experiments/run_mm_noise.py b/experiments/run_mm_noise.py index abdc2b1..219c5f2 100644 --- a/experiments/run_mm_noise.py +++ b/experiments/run_mm_noise.py @@ -53,8 +53,15 @@ def load_stage1(): def build_mm_data(matched_clean, option_c_clean, trend_windows=(7,), include_cross_pool=False): - """Build data with MM structure: separate TVL from market features.""" + """Build data with MM structure: separate TVL from market features. + + Also builds per-sample Binance log-volumes for predicting K. + """ from experiments.run_linear_market_noise import build_data + from quantammsim.calibration.market_features import ( + _load_binance_daily, TOKEN_MAP, + ) + from quantammsim.calibration.pool_data import _parse_tokens # Get full feature matrix from linear model's pipeline data = build_data( @@ -64,23 +71,18 @@ def build_mm_data(matched_clean, option_c_clean, trend_windows=(7,), include_cross_pool=include_cross_pool, ) - # Separate TVL from other features + # Remove TVL column and TVL interaction terms — TVL handled by MM, + # interactions subsumed by time-varying K from Binance volumes feat_names = data["feat_names"] x_full = data["x"] - - # Find TVL column (xobs_1) and TVL interaction columns tvl_col = feat_names.index("xobs_1") tvl_interaction_cols = [i for i, name in enumerate(feat_names) if name.startswith("xobs_1\u00d7")] - - # Remove TVL and its interactions from market features remove_cols = {tvl_col} | set(tvl_interaction_cols) keep_cols = [i for i in range(len(feat_names)) if i not in remove_cols] x_market = x_full[:, keep_cols].astype(np.float32) market_names = [feat_names[i] for i in keep_cols] - # TVL comes from the raw panel data (unstandardized log_tvl) - # x_full[:, tvl_col] might be standardized, so get raw from panel pool_ids = data["pool_ids"] n_pools = data["n_pools"] @@ -105,47 +107,77 @@ def build_mm_data(matched_clean, option_c_clean, trend_windows=(7,), log_tvl = np.array([tvl_grid[day_idx[s], pool_idx[s]] for s in range(len(pool_idx))], dtype=np.float32) - # Token info for display - from quantammsim.calibration.pool_data import _parse_tokens + # Binance daily log-volumes per token, for predicting K + binance_cache = {} + + def _get_binance_daily(symbol): + mapped = TOKEN_MAP.get(symbol, symbol) + if mapped not in binance_cache: + daily = _load_binance_daily(mapped) + if daily is not None: + binance_cache[mapped] = { + d: float(np.log(max(v, 1.0))) + for d, v in daily["volume_usd"].items() + } + else: + binance_cache[mapped] = {} + return binance_cache[mapped] + pool_tokens = [] - for pid in pool_ids: + log_vol_a_grid = np.full((n_dates, n_pools), np.nan) + log_vol_b_grid = np.full((n_dates, n_pools), np.nan) + + for j, pid in enumerate(pool_ids): toks = _parse_tokens(matched_clean[pid]["tokens"]) tok_a = toks[0] tok_b = toks[1] if len(toks) > 1 else toks[0] pool_tokens.append((tok_a, tok_b)) - removed_names = [feat_names[i] for i in sorted(remove_cols)] - print(f" Removed TVL features: {removed_names}") - print(f" Market features ({len(market_names)}): {market_names}") + bvol_a = _get_binance_daily(tok_a) + bvol_b = _get_binance_daily(tok_b) - # Build per-pool temporal ordering for EWMA - # For each pool, store the sample indices sorted by day_idx - # Pad to max length so we can use lax.scan uniformly - pool_time_indices = [] # (n_pools, max_T) — sample indices in time order - pool_time_lengths = [] # (n_pools,) — actual length per pool + panel = matched_clean[pid]["panel"] + for k, date in enumerate(panel["date"].values): + t = date_to_idx[date] + day = pd.Timestamp(date).normalize() + if day in bvol_a: + log_vol_a_grid[t, j] = bvol_a[day] + if day in bvol_b: + log_vol_b_grid[t, j] = bvol_b[day] + + # Per-sample Binance volumes (impute missing with per-pool median) + n_samples = len(pool_idx) + log_vol_a = np.array([log_vol_a_grid[day_idx[s], pool_idx[s]] + for s in range(n_samples)], dtype=np.float32) + log_vol_b = np.array([log_vol_b_grid[day_idx[s], pool_idx[s]] + for s in range(n_samples)], dtype=np.float32) + + # Impute NaN with per-pool median for i in range(n_pools): mask = pool_idx == i - idxs = np.where(mask)[0] - # Sort by day_idx - order = np.argsort(day_idx[idxs]) - pool_time_indices.append(idxs[order]) - pool_time_lengths.append(len(idxs)) - - max_T = max(pool_time_lengths) if pool_time_lengths else 0 - # Pad to uniform length (pad with 0, masked later) - pool_time_padded = np.zeros((n_pools, max_T), dtype=np.int32) - pool_time_mask = np.zeros((n_pools, max_T), dtype=np.float32) - for i in range(n_pools): - L = pool_time_lengths[i] - pool_time_padded[i, :L] = pool_time_indices[i] - pool_time_mask[i, :L] = 1.0 + for arr in (log_vol_a, log_vol_b): + pool_vals = arr[mask] + if np.isnan(pool_vals).all(): + arr[mask] = 20.0 # fallback ~$500M daily vol + elif np.isnan(pool_vals).any(): + arr[mask] = np.where(np.isnan(pool_vals), + np.nanmedian(pool_vals), pool_vals) + + n_missing = np.isnan(log_vol_a).sum() + np.isnan(log_vol_b).sum() + if n_missing > 0: + print(f" WARNING: {n_missing} NaN in Binance volumes after imputation") + log_vol_a = np.nan_to_num(log_vol_a, nan=20.0) + log_vol_b = np.nan_to_num(log_vol_b, nan=20.0) - print(f" EWMA: max_T={max_T}, pools with data: " - f"{sum(1 for l in pool_time_lengths if l > 0)}") + removed_names = [feat_names[i] for i in sorted(remove_cols)] + print(f" Removed: {removed_names}") + print(f" Market features ({len(market_names)}): {market_names}") return { "x_market": x_market, "log_tvl": log_tvl, + "log_vol_a": log_vol_a, + "log_vol_b": log_vol_b, "y_total": data["y_total"], "pool_idx": pool_idx, "day_idx": day_idx, @@ -160,63 +192,34 @@ def build_mm_data(matched_clean, option_c_clean, trend_windows=(7,), "market_names": market_names, "x_mean": data["x_mean"], "x_std": data["x_std"], - "pool_time_padded": pool_time_padded, - "pool_time_mask": pool_time_mask, } # ---- Model ---- -def ewma_smooth(log_tvl, raw_lambda, pool_time_padded, pool_time_mask): - """Apply learned EWMA smoothing to log_tvl, per pool. - - smooth_t = λ * log_tvl_t + (1-λ) * smooth_{t-1} - - Returns smoothed log_tvl in the same sample order as input. - """ - lam = jax.nn.sigmoid(raw_lambda) # constrain to (0, 1) - n_pools = pool_time_padded.shape[0] - smoothed = jnp.array(log_tvl) # copy - - for i in range(n_pools): - idxs = pool_time_padded[i] # (max_T,) sample indices - mask = pool_time_mask[i] # (max_T,) 1.0 or 0.0 - raw_vals = log_tvl[idxs] # (max_T,) raw log_tvl in time order - - # lax.scan for EWMA - def step(carry, x): - prev_smooth, = carry - raw_val, m = x - new_smooth = jnp.where( - m > 0, - lam * raw_val + (1.0 - lam) * prev_smooth, - prev_smooth) - return (new_smooth,), new_smooth - - init = (raw_vals[0],) - _, smooth_vals = jax.lax.scan(step, init, (raw_vals, mask)) - - # Scatter smoothed values back to sample positions - smoothed = smoothed.at[idxs].set( - jnp.where(mask > 0, smooth_vals, smoothed[idxs])) - - return smoothed - - -def forward_mm(params, x_market, log_tvl_smooth, pool_idx): +def forward_mm(params, x_market, log_tvl, pool_idx, + log_vol_a=None, log_vol_b=None): """MM forward pass → log(V_noise) per sample. - log(V_noise) = log_alpha_i + x_market @ gamma[_i] - + log(TVL_smooth) - log(K_i + TVL_smooth) + Supports two K modes: + - Per-pool: params contains "log_K" (n_pools,) + - Binance-volume: params contains "k_params" (3,) + log_vol_a/b """ log_alpha = params["log_alpha"] - log_K = params["log_K"] gamma = params["gamma"] - # Per-sample pool params alpha_i = log_alpha[pool_idx] - K_i = jnp.exp(log_K[pool_idx]) - tvl = jnp.exp(log_tvl_smooth) + tvl = jnp.exp(log_tvl) + + # K: per-pool or from Binance volumes + if "k_params" in params: + k_params = params["k_params"] + vol_min = jnp.minimum(log_vol_a, log_vol_b) + vol_max = jnp.maximum(log_vol_a, log_vol_b) + log_K = k_params[0] + k_params[1] * vol_min + k_params[2] * vol_max + K = jnp.exp(log_K) + else: + K = jnp.exp(params["log_K"][pool_idx]) # Market features: shared or per-pool gamma if gamma.ndim == 2: @@ -225,9 +228,7 @@ def forward_mm(params, x_market, log_tvl_smooth, pool_idx): else: market_term = x_market @ gamma - # MM saturation on smoothed TVL - log_saturation = log_tvl_smooth - jnp.log(K_i + tvl) - + log_saturation = log_tvl - jnp.log(K + tvl) return alpha_i + market_term + log_saturation @@ -235,16 +236,10 @@ def make_loss_fn(pool_coeffs, pool_gas, n_pools): """Loss with PCHIP arb + MM noise.""" from quantammsim.calibration.grid_interpolation import interpolate_pool_daily - def loss_fn(params, x_market, log_tvl, y_total, - sample_grid_days, pool_idx, pool_time_padded, - pool_time_mask, l2_alpha, huber_delta): + def loss_fn(params, x_market, log_tvl, log_vol_a, log_vol_b, y_total, + sample_grid_days, pool_idx, l2_alpha, huber_delta): log_cadence = params["log_cadence"] - # EWMA smooth TVL - log_tvl_smooth = ewma_smooth( - log_tvl, params["raw_lambda"], - pool_time_padded, pool_time_mask) - # V_arb from PCHIP n_samples = x_market.shape[0] log_v_arb = jnp.zeros(n_samples) @@ -257,8 +252,10 @@ def loss_fn(params, x_market, log_tvl, y_total, jnp.log(jnp.maximum(v_arb_all[safe_days], 1e-10)), log_v_arb) - # V_noise from MM with smoothed TVL - log_v_noise = forward_mm(params, x_market, log_tvl_smooth, pool_idx) + # V_noise from MM + log_v_noise = forward_mm( + params, x_market, log_tvl, pool_idx, + log_vol_a=log_vol_a, log_vol_b=log_vol_b) # V_total log_v_total = jnp.logaddexp(log_v_arb, log_v_noise) @@ -302,16 +299,16 @@ def train(params, data, grad_fn, n_epochs, lr, l2_alpha, huber_delta, x_market = jnp.array(data["x_market"]) log_tvl = jnp.array(data["log_tvl"]) + log_vol_a = jnp.array(data["log_vol_a"]) + log_vol_b = jnp.array(data["log_vol_b"]) y_total = jnp.array(data["y_total"]) sgd = jnp.array(data["sample_grid_days"]) pidx = jnp.array(data["pool_idx"]) - pt_padded = jnp.array(data["pool_time_padded"]) - pt_mask = jnp.array(data["pool_time_mask"]) for epoch in range(n_epochs): loss, grads = grad_fn( - params, x_market, log_tvl, y_total, sgd, pidx, - pt_padded, pt_mask, l2_alpha, huber_delta) + params, x_market, log_tvl, log_vol_a, log_vol_b, + y_total, sgd, pidx, l2_alpha, huber_delta) for k in params: g = grads[k] @@ -322,15 +319,17 @@ def train(params, data, grad_fn, n_epochs, lr, l2_alpha, huber_delta, params[k] = params[k] - lr * m_hat / (jnp.sqrt(v_hat) + eps) if verbose and (epoch % 200 == 0 or epoch == n_epochs - 1): - log_K_med = float(jnp.median(params["log_K"])) - K_med = float(jnp.exp(log_K_med)) + ev = evaluate(params, data) + if "k_params" in params: + k_p = np.array(params["k_params"]) + k_str = f" k=[{k_p[0]:.2f},{k_p[1]:.3f},{k_p[2]:.3f}]" + else: + k_str = "" + K_med = float(np.median(list(ev["K_values"].values()))) cad = np.exp(np.array(params["log_cadence"])) - gamma_norm = float(jnp.sqrt(jnp.mean(params["gamma"] ** 2))) - lam = float(jax.nn.sigmoid(params["raw_lambda"])) print(f" epoch {epoch:5d} loss={float(loss):.4f}" - f" K_med=${K_med:,.0f}" - f" λ={lam:.3f}" - f" |γ|={gamma_norm:.3f}" + f" R²={ev['median_r2']:.3f}" + f" K_med=${K_med/1e6:.1f}M{k_str}" f" cad=[{cad.min():.0f},{np.median(cad):.0f},{cad.max():.0f}]") return params @@ -361,16 +360,12 @@ def evaluate(params, data): v_arb[mask] = v_arb_all[safe] log_v_arb = np.log(np.maximum(v_arb, 1e-10)) - # Smooth TVL with learned lambda - log_tvl_smooth = np.array(ewma_smooth( - jnp.array(data["log_tvl"]), params["raw_lambda"], - jnp.array(data["pool_time_padded"]), - jnp.array(data["pool_time_mask"]))) - log_v_noise = np.array(forward_mm( params, jnp.array(data["x_market"]), - jnp.array(log_tvl_smooth), - jnp.array(data["pool_idx"]))) + jnp.array(data["log_tvl"]), + jnp.array(data["pool_idx"]), + log_vol_a=jnp.array(data["log_vol_a"]), + log_vol_b=jnp.array(data["log_vol_b"]))) log_v_total = np.logaddexp(log_v_arb, log_v_noise) v_noise = np.exp(log_v_noise) @@ -390,8 +385,22 @@ def evaluate(params, data): noise_shares[data["pool_ids"][i]] = float(np.median( v_noise[mask] / v_total[mask])) - K_values = {data["pool_ids"][i]: float(np.exp(params["log_K"][i])) - for i in range(n_pools)} + # Per-pool K values + K_values = {} + if "k_params" in params: + k_p = np.array(params["k_params"]) + for i in range(n_pools): + mask = pool_idx == i + if not mask.any(): + K_values[data["pool_ids"][i]] = float(np.exp(k_p[0])) + continue + va = data["log_vol_a"][mask] + vb = data["log_vol_b"][mask] + log_K_i = k_p[0] + k_p[1] * np.minimum(va, vb) + k_p[2] * np.maximum(va, vb) + K_values[data["pool_ids"][i]] = float(np.exp(np.median(log_K_i))) + else: + for i in range(n_pools): + K_values[data["pool_ids"][i]] = float(np.exp(params["log_K"][i])) return { "r2s": r2s, @@ -414,7 +423,7 @@ def tvl_response_check(params, data): tvl_test = [1e5, 1e6, 1e7, 1e8, 1e9] - for i in range(min(n_pools, 15)): + for i in range(n_pools): pid = data["pool_ids"][i] toks = data["pool_tokens"][i] label = f"{toks[0]}/{toks[1]}" @@ -422,7 +431,15 @@ def tvl_response_check(params, data): if mask.sum() == 0: continue - K_i = float(np.exp(params["log_K"][i])) + # Per-pool K + if "k_params" in params: + k_p = np.array(params["k_params"]) + va = data["log_vol_a"][mask] + vb = data["log_vol_b"][mask] + log_K_i = k_p[0] + k_p[1] * np.minimum(va, vb) + k_p[2] * np.maximum(va, vb) + K_i = float(np.exp(np.median(log_K_i))) + else: + K_i = float(np.exp(params["log_K"][i])) x_med = np.median(data["x_market"][mask], axis=0) gamma = np.array(params["gamma"]) @@ -459,11 +476,16 @@ def main(): parser.add_argument("--huber-delta", type=float, default=1.0) parser.add_argument("--init-log-K", type=float, default=17.0, help="Initial log(K) ~ log($24M)") + parser.add_argument("--shared-K", action="store_true", + help="Predict K from Binance volumes (3 shared params)" + " instead of per-pool K") parser.add_argument("--per-pool-gamma", action="store_true", help="Per-pool market feature coefficients") parser.add_argument("--no-split", action="store_true") parser.add_argument("--trend-windows", type=int, nargs="+", default=[7]) parser.add_argument("--include-cross-pool", action="store_true") + parser.add_argument("--tune", type=int, default=0, + help="Optuna sweep (0 = single run)") parser.add_argument("--save-artifact", default="results/mm_noise") args = parser.parse_args() @@ -489,6 +511,10 @@ def main(): print(f" {n_samples} samples, {n_pools} pools," f" {n_market} market features, {time.time() - t0:.1f}s") + if args.tune > 0: + run_optuna(data, args.tune) + return + # Pool summary pool_idx = data["pool_idx"] for i, (pid, toks) in enumerate( @@ -525,11 +551,13 @@ def main(): params = { "log_alpha": jnp.zeros(n_pools), - "log_K": jnp.full(n_pools, args.init_log_K), "gamma": gamma_init, "log_cadence": jnp.array(data["init_log_cadences"]), - "raw_lambda": jnp.array(2.0), # sigmoid(2) ≈ 0.88 — mostly raw } + if args.shared_K: + params["k_params"] = jnp.array([args.init_log_K, 0.0, 0.0]) + else: + params["log_K"] = jnp.full(n_pools, args.init_log_K) n_params = sum(v.size for v in params.values()) print(f"\n Parameters: {n_params}" f" (α: {n_pools}, K: {n_pools}," @@ -579,6 +607,13 @@ def _ridge(X, y, alpha=1.0): train_eval = evaluate(params, train_data) print(f" Median R²: {train_eval['median_r2']:.4f}") + if "k_params" in params: + k_p = np.array(params["k_params"]) + print(f" k_params: k_0={k_p[0]:.2f}, k_min={k_p[1]:.4f}, k_max={k_p[2]:.4f}") + else: + K_med = float(np.exp(np.median(np.array(params["log_K"])))) + print(f" Per-pool K: median=${K_med/1e6:.1f}M") + print(f"\n {'Pool':>16s} {'Tokens':>16s} {'R²':>6s}" f" {'Noise%':>7s} {'K ($M)':>10s}") for pid in data["pool_ids"]: @@ -631,5 +666,145 @@ def _ridge(X, y, alpha=1.0): print(f"\n Saved: {args.save_artifact}/") +def run_optuna(data, n_trials): + """Optuna hyperparameter sweep for MM noise model.""" + import optuna + optuna.logging.set_verbosity(optuna.logging.WARNING) + + n_pools = data["n_pools"] + n_market = data["n_market_feat"] + n_samples = len(data["pool_idx"]) + + # 70/30 temporal split + day_idx = data["day_idx"] + split_day = int(day_idx.max() * 0.7) + train_mask = day_idx <= split_day + eval_mask = day_idx > split_day + train_data = {k: v[train_mask] if isinstance(v, np.ndarray) + and v.shape[0] == n_samples else v + for k, v in data.items()} + eval_data = {k: v[eval_mask] if isinstance(v, np.ndarray) + and v.shape[0] == n_samples else v + for k, v in data.items()} + print(f" Optuna split: {train_mask.sum()} train, {eval_mask.sum()} eval") + + def _ridge(X, y, alpha=1.0): + XtX = X.T @ X + alpha * np.eye(X.shape[1]) + return np.linalg.solve(XtX, X.T @ y) + + def objective(trial): + lr = trial.suggest_float("lr", 1e-4, 3e-2, log=True) + l2_alpha = trial.suggest_float("l2_alpha", 1e-5, 1e-1, log=True) + huber_delta = trial.suggest_categorical("huber_delta", [0.5, 1.0, 1.5]) + init_log_K = trial.suggest_float("init_log_K", 14.0, 20.0) + n_epochs = trial.suggest_categorical("n_epochs", [2000, 3000, 5000]) + per_pool_gamma = trial.suggest_categorical("per_pool_gamma", [True, False]) + if per_pool_gamma: + gamma_init = jnp.zeros((n_pools, n_market)) + else: + gamma_init = jnp.zeros(n_market) + + params = { + "log_alpha": jnp.zeros(n_pools), + "k_params": jnp.array([init_log_K, 0.0, 0.0]), + "gamma": gamma_init, + "log_cadence": jnp.array(data["init_log_cadences"]), + } + + # Warm-start gamma + x_trn = train_data["x_market"] + y_trn = train_data["y_total"] + if per_pool_gamma: + pidx = train_data["pool_idx"] + for i in range(n_pools): + mask_i = pidx == i + if mask_i.sum() < 5: + continue + X_i = np.concatenate([x_trn[mask_i], + np.ones((mask_i.sum(), 1))], 1) + w = _ridge(X_i, y_trn[mask_i]) + params["gamma"] = params["gamma"].at[i].set( + jnp.array(w[:-1].astype(np.float32))) + params["log_alpha"] = params["log_alpha"].at[i].set( + float(w[-1])) + else: + X_all = np.concatenate([x_trn, np.ones((len(y_trn), 1))], 1) + w = _ridge(X_all, y_trn) + params["gamma"] = jnp.array(w[:-1].astype(np.float32)) + + grad_fn = make_loss_fn(data["pool_coeffs"], data["pool_gas"], n_pools) + params = train(params, train_data, grad_fn, n_epochs, lr, + l2_alpha, huber_delta, verbose=False) + + # Eval + eval_result = evaluate(params, eval_data) + med_r2 = eval_result["median_r2"] + + K_med = float(np.median([v for v in eval_result["K_values"].values()])) + k_p = np.array(params["k_params"]) + pp_str = "pp" if per_pool_gamma else "sh" + print(f" Trial {trial.number}: eval={med_r2:.4f}" + f" K_med=${K_med/1e6:.1f}M" + f" k=[{k_p[0]:.1f},{k_p[1]:.3f},{k_p[2]:.3f}]" + f" {pp_str} ep={n_epochs} lr={lr:.1e} l2={l2_alpha:.1e}" + f" hub={huber_delta}") + + # Save every trial + trial_dir = os.path.join("results", "mm_noise", "trials", + f"trial_{trial.number:04d}") + os.makedirs(trial_dir, exist_ok=True) + save_dict = {k: np.array(v) for k, v in params.items()} + np.savez(os.path.join(trial_dir, "model.npz"), **save_dict) + meta = { + "pool_ids": data["pool_ids"], + "pool_tokens": data["pool_tokens"], + "market_names": data["market_names"], + "n_pools": n_pools, + "n_market_feat": n_market, + "per_pool_gamma": per_pool_gamma, + "eval_r2": med_r2, + "hparams": { + "lr": lr, "l2_alpha": l2_alpha, "huber_delta": huber_delta, + "init_log_K": init_log_K, "n_epochs": n_epochs, + "per_pool_gamma": per_pool_gamma, + }, + } + with open(os.path.join(trial_dir, "meta.json"), "w") as f: + json.dump(meta, f, indent=2) + + return med_r2 + + study = optuna.create_study(direction="maximize") + study.optimize(objective, n_trials=n_trials) + + print(f"\n{'='*70}") + print(f"Optuna Results (MM noise)") + print(f"{'='*70}") + print(f" Best eval R²: {study.best_value:.4f}") + print(f" Best params:") + for k, v in sorted(study.best_params.items()): + print(f" {k}: {v}") + + trials = sorted(study.trials, key=lambda t: t.value if t.value else -999, + reverse=True) + print(f"\n Top 10:") + for t in trials[:10]: + if t.value is not None: + print(f" #{t.number}: eval={t.value:.4f} {t.params}") + + # Copy best to top-level + best_dir = os.path.join("results", "mm_noise", "trials", + f"trial_{study.best_trial.number:04d}") + if os.path.exists(os.path.join(best_dir, "model.npz")): + import shutil + for fn in ("model.npz", "meta.json"): + shutil.copy2(os.path.join(best_dir, fn), + os.path.join("results", "mm_noise", fn)) + print(f"\n Copied best trial ({study.best_trial.number})" + f" to results/mm_noise/") + + return study + + if __name__ == "__main__": main() diff --git a/experiments/verify_vol_volume_slope.py b/experiments/verify_vol_volume_slope.py new file mode 100644 index 0000000..1758bd7 --- /dev/null +++ b/experiments/verify_vol_volume_slope.py @@ -0,0 +1,372 @@ +"""Verify: is the volatility-volume slope identical across fee tiers? + +The claim (from noise_calibration_review.md): "the relationship between +price volatility and swap volume is identical across fee tiers (slope 0.91 +for both low-fee and high-fee pools)." + +This script tests the claim by: +1. Loading all pool panel data +2. Splitting pools by fee tier (low vs high) +3. Regressing log(volume) on log(volatility) within each group +4. Comparing slopes + +If the slopes are similar, it means volatility drives organic volume +identically regardless of fee — supporting the arb/noise decomposition +(since arb intensity differs across fee tiers but noise doesn't). + +Usage: + python experiments/verify_vol_volume_slope.py +""" + +import os +import pickle +import sys + +import numpy as np +import pandas as pd + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) + + +def main(): + path = os.path.join(CACHE_DIR, "stage1.pkl") + if not os.path.exists(path): + print("ERROR: no stage1 cache.") + sys.exit(1) + with open(path, "rb") as f: + data = pickle.load(f) + matched_clean = data["matched_clean"] + + # Collect per-observation data + rows = [] + for pid, entry in matched_clean.items(): + panel = entry["panel"] + fee = entry.get("fee", np.exp(panel["log_fee"].values[0])) + chain = entry.get("chain", "unknown") + tokens = entry.get("tokens", "?") + + log_vol = panel["log_volume"].values.astype(float) + vol_raw = panel["volatility"].values.astype(float) + log_tvl = panel["log_tvl_lag1"].values.astype(float) + + for i in range(len(log_vol)): + if vol_raw[i] > 1e-10 and np.isfinite(log_vol[i]): + rows.append({ + "pool_id": pid, + "tokens": tokens, + "chain": chain, + "fee": fee, + "log_fee": np.log(fee), + "log_volume": log_vol[i], + "log_sigma": np.log(max(vol_raw[i], 1e-10)), + "log_tvl": log_tvl[i] if np.isfinite(log_tvl[i]) else np.nan, + }) + + df = pd.DataFrame(rows).dropna() + print(f"Loaded {len(df)} observations from {df['pool_id'].nunique()} pools") + + # Fee tier split + fees = df.groupby("pool_id")["fee"].first() + median_fee = fees.median() + print(f"\nFee distribution:") + print(f" min={fees.min():.5f} median={median_fee:.5f} max={fees.max():.5f}") + print(f" Unique fees: {sorted(fees.unique())}") + + low_fee_pools = set(fees[fees <= median_fee].index) + high_fee_pools = set(fees[fees > median_fee].index) + print(f" Low-fee pools (≤{median_fee:.4f}): {len(low_fee_pools)}") + print(f" High-fee pools (>{median_fee:.4f}): {len(high_fee_pools)}") + + df_low = df[df["pool_id"].isin(low_fee_pools)] + df_high = df[df["pool_id"].isin(high_fee_pools)] + + # OLS: log_volume ~ intercept + log_sigma + def ols_slope(x, y): + X = np.column_stack([np.ones(len(x)), x]) + beta = np.linalg.lstsq(X, y, rcond=None)[0] + y_hat = X @ beta + ss_res = np.sum((y - y_hat) ** 2) + ss_tot = np.sum((y - y.mean()) ** 2) + r2 = 1 - ss_res / ss_tot + # Standard error of slope + n = len(x) + se = np.sqrt(ss_res / (n - 2) / np.sum((x - x.mean()) ** 2)) + return beta[1], beta[0], r2, se + + print(f"\n{'='*60}") + print("OLS: log(volume) ~ intercept + log(sigma)") + print(f"{'='*60}") + + # All pools + slope, intercept, r2, se = ols_slope(df["log_sigma"].values, df["log_volume"].values) + print(f"\n All pools ({len(df)} obs):") + print(f" slope = {slope:.4f} ± {1.96*se:.4f} (95% CI)") + print(f" R² = {r2:.4f}") + + # Low fee + slope_l, int_l, r2_l, se_l = ols_slope(df_low["log_sigma"].values, df_low["log_volume"].values) + print(f"\n Low-fee pools ({len(df_low)} obs, {len(low_fee_pools)} pools):") + print(f" slope = {slope_l:.4f} ± {1.96*se_l:.4f}") + print(f" R² = {r2_l:.4f}") + + # High fee + slope_h, int_h, r2_h, se_h = ols_slope(df_high["log_sigma"].values, df_high["log_volume"].values) + print(f"\n High-fee pools ({len(df_high)} obs, {len(high_fee_pools)} pools):") + print(f" slope = {slope_h:.4f} ± {1.96*se_h:.4f}") + print(f" R² = {r2_h:.4f}") + + print(f"\n Difference: {abs(slope_l - slope_h):.4f}") + print(f" Ratio: {slope_l/slope_h:.3f}") + + # Per-pool slopes + print(f"\n{'='*60}") + print("Per-pool OLS slopes") + print(f"{'='*60}") + pool_slopes = [] + print(f"\n {'Pool':>16s} {'Tokens':>20s} {'Fee':>8s} {'Slope':>8s}" + f" {'R²':>6s} {'N':>5s}") + for pid in sorted(matched_clean.keys()): + pool_df = df[df["pool_id"] == pid] + if len(pool_df) < 20: + continue + s, _, r, se = ols_slope(pool_df["log_sigma"].values, pool_df["log_volume"].values) + fee = pool_df["fee"].iloc[0] + tokens = pool_df["tokens"].iloc[0] + pool_slopes.append({"pool_id": pid, "tokens": tokens, "fee": fee, + "slope": s, "r2": r, "n": len(pool_df)}) + print(f" {pid[:16]} {tokens:>20s} {fee:>8.5f} {s:>8.4f}" + f" {r:>6.3f} {len(pool_df):>5d}") + + ps = pd.DataFrame(pool_slopes) + if len(ps) > 0: + low_slopes = ps[ps["fee"] <= median_fee]["slope"] + high_slopes = ps[ps["fee"] > median_fee]["slope"] + print(f"\n Per-pool slope summary:") + print(f" Low-fee: median={low_slopes.median():.4f}," + f" mean={low_slopes.mean():.4f} (n={len(low_slopes)})") + print(f" High-fee: median={high_slopes.median():.4f}," + f" mean={high_slopes.mean():.4f} (n={len(high_slopes)})") + + # Also try with TVL control: log_volume ~ log_sigma + log_tvl + print(f"\n{'='*60}") + print("OLS: log(volume) ~ intercept + log(sigma) + log(tvl)") + print(f"{'='*60}") + + def ols_multi(df_sub): + x = np.column_stack([ + np.ones(len(df_sub)), + df_sub["log_sigma"].values, + df_sub["log_tvl"].values, + ]) + y = df_sub["log_volume"].values + beta = np.linalg.lstsq(x, y, rcond=None)[0] + y_hat = x @ beta + ss_res = np.sum((y - y_hat) ** 2) + ss_tot = np.sum((y - y.mean()) ** 2) + return beta, 1 - ss_res / ss_tot + + beta_all, r2_all = ols_multi(df) + print(f"\n All: σ_slope={beta_all[1]:.4f}, tvl_slope={beta_all[2]:.4f}, R²={r2_all:.4f}") + + beta_l, r2_l = ols_multi(df_low) + print(f" Low: σ_slope={beta_l[1]:.4f}, tvl_slope={beta_l[2]:.4f}, R²={r2_l:.4f}") + + beta_h, r2_h = ols_multi(df_high) + print(f" High: σ_slope={beta_h[1]:.4f}, tvl_slope={beta_h[2]:.4f}, R²={r2_h:.4f}") + + print(f"\n σ slope difference (low-high): {beta_l[1] - beta_h[1]:.4f}") + + + # ---- Volume/TVL vs TVL (cross-pool) ---- + print(f"\n{'='*60}") + print("Volume/TVL vs TVL (cross-pool)") + print(f"{'='*60}") + + # Per-pool median volume and TVL + pool_stats = [] + for pid in sorted(matched_clean.keys()): + pool_df = df[df["pool_id"] == pid] + if len(pool_df) < 20: + continue + med_vol = np.exp(np.median(pool_df["log_volume"].values)) + med_tvl = np.exp(np.median(pool_df["log_tvl"].values)) + tokens = pool_df["tokens"].iloc[0] + fee = pool_df["fee"].iloc[0] + vol_tvl = med_vol / med_tvl + pool_stats.append({ + "pool_id": pid, "tokens": tokens, "fee": fee, + "med_vol": med_vol, "med_tvl": med_tvl, + "vol_tvl_pct": vol_tvl * 100, + "log_med_tvl": np.log(med_tvl), + }) + + ps = pd.DataFrame(pool_stats) + print(f"\n {'Pool':>16s} {'Tokens':>20s} {'TVL':>14s} {'Vol/day':>14s} {'Vol/TVL':>8s}") + for _, row in ps.sort_values("med_tvl").iterrows(): + print(f" {row['pool_id'][:16]} {row['tokens']:>20s}" + f" ${row['med_tvl']:>13,.0f} ${row['med_vol']:>13,.0f}" + f" {row['vol_tvl_pct']:>7.1f}%") + + # OLS: log(vol/tvl) ~ log(tvl) + log_vol_tvl = np.log(ps["med_vol"].values / ps["med_tvl"].values) + log_tvl_vals = ps["log_med_tvl"].values + slope_vt, int_vt, r2_vt, se_vt = ols_slope(log_tvl_vals, log_vol_tvl) + print(f"\n OLS: log(Vol/TVL) ~ log(TVL)") + print(f" slope = {slope_vt:.4f} ± {1.96*se_vt:.4f}") + print(f" R² = {r2_vt:.4f}") + print(f" (slope < 0 means Vol/TVL declines with TVL)") + + # Equivalent: log(Vol) ~ α + β*log(TVL), β < 1 means sublinear + slope_v, int_v, r2_v, se_v = ols_slope(log_tvl_vals, np.log(ps["med_vol"].values)) + print(f"\n OLS: log(Vol) ~ log(TVL)") + print(f" slope = {slope_v:.4f} ± {1.96*se_v:.4f}") + print(f" R² = {r2_v:.4f}") + print(f" (slope < 1 means sublinear = Vol/TVL declines)") + + # ---- TVL elasticity by TVL quartile (MM signature) ---- + print(f"\n{'='*60}") + print("TVL Elasticity by TVL Quartile") + print(f"{'='*60}") + + # Use observation-level data, not pool medians — more power + # Within-quartile regression: log(vol) ~ log(tvl) for pools in each bin + ps_sorted = ps.sort_values("med_tvl") + n_q = len(ps_sorted) // 4 + quartiles = [] + for q in range(4): + start = q * n_q + end = (q + 1) * n_q if q < 3 else len(ps_sorted) + q_pools = set(ps_sorted.iloc[start:end]["pool_id"]) + q_df = df[df["pool_id"].isin(q_pools)] + if len(q_df) < 20: + continue + s, intercept, r2, se = ols_slope(q_df["log_tvl"].values, + q_df["log_volume"].values) + tvl_lo = np.exp(q_df["log_tvl"].min()) + tvl_hi = np.exp(q_df["log_tvl"].max()) + tvl_med = np.exp(q_df["log_tvl"].median()) + quartiles.append({ + "q": q + 1, "n_pools": len(q_pools), "n_obs": len(q_df), + "tvl_lo": tvl_lo, "tvl_hi": tvl_hi, "tvl_med": tvl_med, + "slope": s, "se": se, "r2": r2, + }) + print(f"\n Q{q+1}: TVL ${tvl_lo:,.0f} – ${tvl_hi:,.0f}" + f" (median ${tvl_med:,.0f})") + print(f" {len(q_pools)} pools, {len(q_df)} obs") + print(f" slope = {s:.4f} ± {1.96*se:.4f} R² = {r2:.4f}") + + if len(quartiles) >= 2: + print(f"\n Summary:") + print(f" {'Quartile':>10s} {'Med TVL':>14s} {'Slope':>8s} {'95% CI':>16s}") + for q in quartiles: + ci = f"[{q['slope']-1.96*q['se']:.3f}, {q['slope']+1.96*q['se']:.3f}]" + print(f" Q{q['q']:>9d} ${q['tvl_med']:>13,.0f} {q['slope']:>8.4f} {ci:>16s}") + + slope_q1 = quartiles[0]["slope"] + slope_q4 = quartiles[-1]["slope"] + print(f"\n Q1→Q4 slope change: {slope_q4 - slope_q1:+.4f}") + print(f" (Negative = elasticity declines with TVL = MM signature)") + + # Also try: rolling window across pools sorted by TVL + print(f"\n Rolling 10-pool window:") + print(f" {'Window':>8s} {'Med TVL':>14s} {'Slope':>8s} {'R²':>6s}") + window = 10 + for start_i in range(0, len(ps_sorted) - window + 1, 3): + w_pools = set(ps_sorted.iloc[start_i:start_i + window]["pool_id"]) + w_df = df[df["pool_id"].isin(w_pools)] + if len(w_df) < 30: + continue + s, _, r2, se = ols_slope(w_df["log_tvl"].values, + w_df["log_volume"].values) + tvl_med = np.exp(w_df["log_tvl"].median()) + print(f" {start_i:>3d}-{start_i+window:>3d} ${tvl_med:>13,.0f} {s:>8.4f} {r2:>6.3f}") + + # Plot + try: + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + fig, axes = plt.subplots(1, 3, figsize=(16, 5)) + + # Panel 1: Vol/TVL vs TVL + ax = axes[0] + ax.scatter(ps["med_tvl"] / 1e6, ps["vol_tvl_pct"], + s=30, alpha=0.7, c="steelblue") + for _, row in ps.iterrows(): + ax.annotate(row["tokens"].split(",")[0], + (row["med_tvl"] / 1e6, row["vol_tvl_pct"]), + fontsize=5, alpha=0.6) + ax.set_xscale("log") + ax.set_xlabel("Median TVL ($M)") + ax.set_ylabel("Median Vol/TVL (%)") + ax.set_title(f"Volume/TVL Declines with TVL\n" + f"log(Vol/TVL) ~ {slope_vt:.2f}·log(TVL), R²={r2_vt:.2f}") + ax.grid(True, alpha=0.3) + + # Fit line + tvl_fit = np.logspace(np.log10(ps["med_tvl"].min()), + np.log10(ps["med_tvl"].max()), 100) + vol_tvl_fit = np.exp(int_vt + slope_vt * np.log(tvl_fit)) * 100 + ax.plot(tvl_fit / 1e6, vol_tvl_fit, "r--", linewidth=1, alpha=0.7) + + # Panel 2: Vol vs TVL (log-log) + ax = axes[1] + ax.scatter(ps["med_tvl"] / 1e6, ps["med_vol"] / 1e6, + s=30, alpha=0.7, c="coral") + for _, row in ps.iterrows(): + ax.annotate(row["tokens"].split(",")[0], + (row["med_tvl"] / 1e6, row["med_vol"] / 1e6), + fontsize=5, alpha=0.6) + ax.set_xscale("log") + ax.set_yscale("log") + ax.set_xlabel("Median TVL ($M)") + ax.set_ylabel("Median Daily Volume ($M)") + ax.set_title(f"Volume vs TVL (cross-pool)\n" + f"log(Vol) ~ {slope_v:.2f}·log(TVL), R²={r2_v:.2f}") + ax.grid(True, alpha=0.3) + + # Fit line + linear reference + vol_fit = np.exp(int_v + slope_v * np.log(tvl_fit)) + ax.plot(tvl_fit / 1e6, vol_fit / 1e6, "r--", linewidth=1, + alpha=0.7, label=f"slope={slope_v:.2f}") + # Linear reference (slope=1) + vol_linear = np.exp(int_v + 1.0 * np.log(tvl_fit)) + ax.plot(tvl_fit / 1e6, vol_linear / 1e6, "k:", linewidth=0.5, + alpha=0.3, label="slope=1 (linear)") + ax.legend(fontsize=8) + + # Panel 3: by fee tier + ax = axes[2] + for _, row in ps.iterrows(): + color = "steelblue" if row["fee"] <= median_fee else "coral" + ax.scatter(row["med_tvl"] / 1e6, row["vol_tvl_pct"], + s=30, alpha=0.7, c=color) + ax.annotate(row["tokens"].split(",")[0], + (row["med_tvl"] / 1e6, row["vol_tvl_pct"]), + fontsize=5, alpha=0.6) + ax.set_xscale("log") + ax.set_xlabel("Median TVL ($M)") + ax.set_ylabel("Median Vol/TVL (%)") + ax.set_title("Vol/TVL by Fee Tier\n" + f"blue=low fee (≤{median_fee:.4f}), red=high fee") + ax.grid(True, alpha=0.3) + + fig.suptitle("Cross-Pool Evidence for Volume Saturation", fontsize=13) + fig.tight_layout() + out = os.path.join(os.path.dirname(os.path.dirname(__file__)), + "results", "mm_noise", "plots", + "cross_pool_vol_tvl.png") + os.makedirs(os.path.dirname(out), exist_ok=True) + fig.savefig(out, dpi=150, bbox_inches="tight") + plt.close(fig) + print(f"\n Saved: {out}") + except Exception as e: + print(f" Plot failed: {e}") + + +if __name__ == "__main__": + main() diff --git a/scripts/plot_mm_noise_fit.py b/scripts/plot_mm_noise_fit.py index a5184ad..34818e0 100644 --- a/scripts/plot_mm_noise_fit.py +++ b/scripts/plot_mm_noise_fit.py @@ -44,6 +44,21 @@ def load_model(artifact_dir): return params, meta +def get_pool_K(params, decomp, pool_i): + """Get median K for a pool, handling both per-pool and k_params modes.""" + if "k_params" in params: + k_p = np.array(params["k_params"]) + mask = decomp["pool_idx"] == pool_i + if not mask.any(): + return float(np.exp(k_p[0])) + va = decomp.get("log_vol_a", np.zeros(mask.sum()))[mask] + vb = decomp.get("log_vol_b", np.zeros(mask.sum()))[mask] + log_K = k_p[0] + k_p[1] * np.minimum(va, vb) + k_p[2] * np.maximum(va, vb) + return float(np.exp(np.median(log_K))) + else: + return float(np.exp(params["log_K"][pool_i])) + + def compute_decomposition(params, meta, matched_clean, option_c_clean): """Compute V_arb, V_noise, V_total for all pools.""" from experiments.run_mm_noise import build_mm_data, forward_mm @@ -79,7 +94,9 @@ def compute_decomposition(params, meta, matched_clean, option_c_clean): log_v_noise = np.array(forward_mm( params, jnp.array(data["x_market"]), jnp.array(data["log_tvl"]), - jnp.array(data["pool_idx"]))) + jnp.array(data["pool_idx"]), + log_vol_a=jnp.array(data["log_vol_a"]), + log_vol_b=jnp.array(data["log_vol_b"]))) v_noise = np.exp(log_v_noise) v_total = v_arb + v_noise v_obs = np.exp(y) @@ -102,6 +119,8 @@ def compute_decomposition(params, meta, matched_clean, option_c_clean): "v_total": v_total, "v_obs": v_obs, "log_tvl": log_tvl, + "log_vol_a": data["log_vol_a"], + "log_vol_b": data["log_vol_b"], "tvl": np.exp(log_tvl), } @@ -124,7 +143,7 @@ def plot_pool_timeseries(decomp, params, pool_i, output_dir): v_obs = decomp["v_obs"][mask] tvl = decomp["tvl"][mask] - K_i = float(np.exp(params["log_K"][pool_i])) + K_i = get_pool_K(params, decomp, pool_i) # R² log_pred = np.log(np.maximum(v_total, 1e-10)) @@ -244,12 +263,11 @@ def plot_tvl_response(params, meta, decomp, output_dir): fig, axes = plt.subplots(1, 3, figsize=(18, 6)) - # Select pools with enough data for interesting plots pool_idx_arr = np.array(decomp["pool_idx"]) interesting = [] for i in range(n_pools): n = (pool_idx_arr == i).sum() - if n >= 50: + if n > 0: interesting.append(i) colors = plt.cm.tab20(np.linspace(0, 1, len(interesting))) @@ -257,7 +275,7 @@ def plot_tvl_response(params, meta, decomp, output_dir): # Panel 1: Noise volume vs TVL (absolute) ax = axes[0] for ci, i in enumerate(interesting): - K_i = float(np.exp(params["log_K"][i])) + K_i = get_pool_K(params, decomp, i) mask = pool_idx_arr == i actual_noise = np.median(decomp["v_noise"][mask]) @@ -269,7 +287,7 @@ def plot_tvl_response(params, meta, decomp, output_dir): label = f"{pool_tokens[i][0]}/{pool_tokens[i][1]}" ax.plot(tvl_range / 1e6, noise_curve / 1e6, color=colors[ci], - linewidth=1.0, alpha=0.7, label=label if ci < 10 else None) + linewidth=1.0, alpha=0.7, label=label) # Mark actual TVL ax.scatter([actual_tvl / 1e6], [actual_noise / 1e6], color=colors[ci], s=20, zorder=5) @@ -279,13 +297,13 @@ def plot_tvl_response(params, meta, decomp, output_dir): ax.set_xscale("log") ax.set_yscale("log") ax.set_title("Noise Volume vs TVL (MM saturation)") - ax.legend(fontsize=6, ncol=2) + ax.legend(fontsize=5, ncol=3, loc="best") ax.grid(True, alpha=0.3) # Panel 2: Noise/TVL ratio vs TVL ax = axes[1] for ci, i in enumerate(interesting): - K_i = float(np.exp(params["log_K"][i])) + K_i = get_pool_K(params, decomp, i) mask = pool_idx_arr == i actual_noise = np.median(decomp["v_noise"][mask]) actual_tvl = np.median(decomp["tvl"][mask]) @@ -296,24 +314,24 @@ def plot_tvl_response(params, meta, decomp, output_dir): label = f"{pool_tokens[i][0]}/{pool_tokens[i][1]}" ax.plot(tvl_range / 1e6, ratio_curve, color=colors[ci], - linewidth=1.0, alpha=0.7, label=label if ci < 10 else None) + linewidth=1.0, alpha=0.7, label=label) ax.set_xlabel("TVL ($M)") ax.set_ylabel("Noise / TVL (%)") ax.set_xscale("log") ax.set_title("Noise as Fraction of TVL") - ax.legend(fontsize=6, ncol=2) + ax.legend(fontsize=5, ncol=3, loc="best") ax.grid(True, alpha=0.3) # Panel 3: Elasticity vs TVL ax = axes[2] for ci, i in enumerate(interesting): - K_i = float(np.exp(params["log_K"][i])) + K_i = get_pool_K(params, decomp, i) eps_curve = K_i / (K_i + tvl_range) label = f"{pool_tokens[i][0]}/{pool_tokens[i][1]}" ax.plot(tvl_range / 1e6, eps_curve, color=colors[ci], - linewidth=1.0, alpha=0.7, label=label if ci < 10 else None) + linewidth=1.0, alpha=0.7, label=label) # Mark actual TVL actual_tvl = np.median(decomp["tvl"][pool_idx_arr == i]) eps_actual = K_i / (K_i + actual_tvl) @@ -326,7 +344,7 @@ def plot_tvl_response(params, meta, decomp, output_dir): ax.set_xscale("log") ax.set_ylim(0, 1.05) ax.set_title("TVL Elasticity (K/(K+TVL))") - ax.legend(fontsize=6, ncol=2) + ax.legend(fontsize=5, ncol=3, loc="best") ax.grid(True, alpha=0.3) fig.suptitle("Michaelis-Menten Noise Model — TVL Response", fontsize=13) @@ -351,7 +369,7 @@ def plot_K_distribution(params, meta, decomp, output_dir): for i in range(n_pools): mask = pool_idx_arr == i n = mask.sum() - K_i = float(np.exp(params["log_K"][i])) + K_i = get_pool_K(params, decomp, i) K_vals.append(K_i) tok = pool_tokens[i] labels.append(f"{tok[0]}/{tok[1]}") @@ -417,8 +435,8 @@ def main(): parser.add_argument("--all-pools", action="store_true") parser.add_argument("--artifact-dir", default="results/mm_noise") parser.add_argument("--output-dir", default="results/mm_noise/plots") - parser.add_argument("--top-n", type=int, default=10, - help="Plot top N pools by sample count") + parser.add_argument("--top-n", type=int, default=None, + help="Plot top N pools by sample count (default: all)") args = parser.parse_args() os.environ.setdefault("JAX_PLATFORMS", "cpu") @@ -444,13 +462,13 @@ def main(): if args.pool: targets = [i for i, pid in enumerate(pool_ids) if pid.startswith(args.pool)] - elif args.all_pools: - targets = list(range(len(pool_ids))) - else: - # Top N by sample count + elif args.top_n is not None: counts = [(pool_idx == i).sum() for i in range(len(pool_ids))] targets = sorted(range(len(pool_ids)), key=lambda i: -counts[i]) targets = targets[:args.top_n] + else: + # Default: all pools + targets = list(range(len(pool_ids))) # Per-pool time series print(f"\nPlotting {len(targets)} pools...") From e202d67de60d34385337ea3f744736497de360f0 Mon Sep 17 00:00:00 2001 From: christian harrington Date: Tue, 7 Apr 2026 14:34:43 +0100 Subject: [PATCH 49/57] different scripts --- scripts/demo_run_1.py | 196 +++++ .../reclamm_arc_speed_shift_price_sweep.py | 825 ++++++++++++++++++ 2 files changed, 1021 insertions(+) create mode 100644 scripts/demo_run_1.py create mode 100644 scripts/reclamm_arc_speed_shift_price_sweep.py diff --git a/scripts/demo_run_1.py b/scripts/demo_run_1.py new file mode 100644 index 0000000..a8b8396 --- /dev/null +++ b/scripts/demo_run_1.py @@ -0,0 +1,196 @@ +import jax.numpy as jnp +from quantammsim.core_simulator.param_utils import ( + memory_days_to_logit_lamb, +) +from quantammsim.runners.jax_runners import do_run_on_historic_data +import itertools +from pathlib import Path +import pandas as pd +import numpy as np +from datetime import datetime +import gc +import warnings + +warnings.filterwarnings("ignore") +from jax import config + +# Default fingerprint used as base for all pools +DEFAULT_FINGERPRINT = { + "startDateString": "2021-01-01 00:00:00", + "endDateString": "2024-06-01 00:00:00", + "endTestDateString": "2024-11-30 00:00:00", + "chunk_period": 60, + "weight_interpolation_period": 60, + "fees": 0.0, + "gas_cost": 0.0, + "use_alt_lamb": False, +} + +EXAMPLE_CONFIGS = { + "reclamm_1": { + "fingerprint": { + "arb_fees": 0.0, + "arb_frequency": 15, + "do_arb": True, + "endDateString": "2025-06-01 00:00:00", + "fees": 0.0025, + "gas_cost": 1.0, + "initial_pool_value": 1000000.0, + "noise_trader_ratio": 0.0, + "protocol_fee_split": 0.25, + "reclamm_arc_length_speed": None, + "reclamm_interpolation_method": "geometric", + "rule": "reclamm", + "startDateString": "2024-06-01 00:00:00", + "tokens": [ + "AAVE", + "ETH" + ] + }, + "params": { + "centeredness_margin": 0.3184210526315789, + "daily_price_shift_base": 0.9999984155508669, + "price_ratio": 1.3349999999999989 + } + }, + "reclamm_2":{ + "fingerprint": { + "arb_fees": 0.0, + "arb_frequency": 15, + "do_arb": True, + "endDateString": "2025-06-01 00:00:00", + "fees": 0.0025, + "gas_cost": 1.0, + "initial_pool_value": 1000000.0, + "noise_model": "calibrated", + "noise_trader_ratio": 0.0, + "protocol_fee_split": 0.25, + "reclamm_arc_length_speed": None, + "reclamm_interpolation_method": "geometric", + "reclamm_noise_params": { + "c_0": -0.453, + "c_1": 0.025, + "c_2": -0.06, + "c_3": 0.31, + "c_4": -0.149, + "c_5": 0.359, + "c_6": 0.061, + "c_7": 0.06 + }, + "rule": "reclamm", + "startDateString": "2024-06-01 00:00:00", + "tokens": [ + "AAVE", + "ETH" + ] + }, + "params": { + "centeredness_margin": 0.3184210526315789, + "daily_price_shift_base": 0.9999984155508669, + "price_ratio": 1.3349999999999989 + } + } +} + + +if __name__ == "__main__": + + import matplotlib.pyplot as plt + import numpy as np + from quantammsim.core_simulator.param_utils import ( + generate_params_combinations, + jax_logit_lamb_to_lamb, + lamb_to_memory_days, + lamb_to_memory_days_clipped, + calc_lamb, + ) + from quantammsim.pools.G3M.quantamm.update_rule_estimators.estimator_primitives import ( + squareplus, + inverse_squareplus, + inverse_squareplus_np, + ) + + for name, config in EXAMPLE_CONFIGS.items(): + print(name) + if 'reclamm' not in name: + continue + print(f"\nRunning {name}...") + result = do_run_on_historic_data( + run_fingerprint=config["fingerprint"], + params=config["params"], + ) + print("-" * 80) + print(f"Pool Type: {config['fingerprint']['rule']}") + print(f"Tokens: {', '.join(config['fingerprint']['tokens'])}") + print(f"Fees: {config['fingerprint'].get('fees', 0.0)}") + if "arb_quality" in config["fingerprint"]: + print(f"Arb Quality: {config['fingerprint']['arb_quality']}") + print(f"Initial Pool Value: ${result['value'][0]:.2f}") + print(f"Final Pool Value: ${result['final_value']:.2f}") + print(f"Return: {(result['final_value']/result['value'][0]-1)*100:.2f}%") + print( + f"Return over hodl: {(result['final_value']/(result['reserves'][0]*result['prices'][-1]).sum()-1)*100:.2f}%" + ) + print("-" * 80) + # memory_days = lamb_to_memory_days(jax_logit_lamb_to_lamb(config["params"]["logit_lamb"]), config["fingerprint"]["chunk_period"]) + # print("memory days: ", memory_days) + if "logit_lamb" in config["params"]: + memory_days = lamb_to_memory_days_clipped( + calc_lamb(config["params"]), + chunk_period=config["fingerprint"]["chunk_period"], + max_memory_days=365, + ) + print(f"{'memory days':<20} {str(memory_days)}") + lamb = calc_lamb(config["params"]) + print( + f"{'lamb':<20} {jnp.array_str(lamb, precision=16, suppress_small=False)}" + ) + if "log_k" in config["params"]: + k = 2 ** config["params"]["log_k"] * memory_days + k_str = " ".join(f"{x:.16e}" for x in k) + print(f"{'k':<20} [{k_str}]") + k_per_day_str = " ".join( + f"{x:.16e}" for x in 2 ** config["params"]["log_k"] + ) + print(f"{'k per day':<20} [{k_per_day_str}]") + if "raw_exponents" in config["params"]: + exponents = squareplus(config["params"]["raw_exponents"]) + exp_str = " ".join(f"{x:.16f}" for x in exponents) + print(f"{'exponents':<20} [{exp_str}]") + if "raw_width" in config["params"]: + width = 2 ** config["params"]["raw_width"] + width_str = " ".join(f"{x:.16e}" for x in width) + print(f"{'width':<20} [{width_str}]") + if "log_amplitude" in config["params"]: + memory_days = lamb_to_memory_days_clipped( + calc_lamb(config["params"]), + chunk_period=config["fingerprint"]["chunk_period"], + max_memory_days=365, + ) + amplitude = (2 ** config["params"]["log_amplitude"]) * memory_days + amp_str = " ".join(f"{x:.16e}" for x in amplitude) + print(f"{'amplitude':<20} [{amp_str}]") + if "logit_pre_exp_scaling" in config["params"]: + pre_exp_scaling = jnp.exp(config["params"]["logit_pre_exp_scaling"]) / ( + 1 + jnp.exp(config["params"]["logit_pre_exp_scaling"]) + ) + pes_str = " ".join(f"{x:.16f}" for x in pre_exp_scaling) + print(f"{'pre_exp_scaling':<20} [{pes_str}]") + if "raw_pre_exp_scaling" in config["params"]: + pre_exp_scaling = 2 ** config["params"]["raw_pre_exp_scaling"] + pes_str = " ".join(f"{x:.16f}" for x in pre_exp_scaling) + print(f"{'pre_exp_scaling':<20} [{pes_str}]") + + print("-" * 80) + print("final readouts") + if result.get("readouts") is not None: + for readout in result["readouts"]: + print(f"{readout}: { jnp.array_str(result['readouts'][readout][-1], precision=16, suppress_small=False)}") + print("-" * 80) + print("final weights") + print(f"{jnp.array_str(result['weights'][-1], precision=16, suppress_small=False)}") + print("-" * 80) + print("final prices") + print(f"{jnp.array_str(result['prices'][-1], precision=16, suppress_small=False)}") + print("=" * 80) + \ No newline at end of file diff --git a/scripts/reclamm_arc_speed_shift_price_sweep.py b/scripts/reclamm_arc_speed_shift_price_sweep.py new file mode 100644 index 0000000..a18c7b2 --- /dev/null +++ b/scripts/reclamm_arc_speed_shift_price_sweep.py @@ -0,0 +1,825 @@ +"""Sweep reCLAMM arc speed jointly with price ratio and shift exponent. + +This is a focused companion to ``compare_reclamm_thermostats.py``. It reuses +that script's configs, cache, market-linear noise setup, and heatmap metrics, +then evaluates the 3-variable cube: + + daily_price_shift_exponent x price_ratio x arc_length_speed + +The default run is the full compare-grid for the 1M TVL aggressive/tight-range +config, with the launch-style config used as the benchmark where required. +""" + +from __future__ import annotations + +import argparse +import gc +import math +import os +import sys +from pathlib import Path + +# Keep background runs from depending on a writable user-level matplotlib cache. +os.environ.setdefault( + "MPLCONFIGDIR", + str(Path(os.environ.get("TMPDIR", "/tmp")) / "matplotlib"), +) + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from matplotlib.cm import ScalarMappable +from matplotlib.colors import LogNorm + + +SCRIPT_DIR = Path(__file__).resolve().parent +if str(SCRIPT_DIR) not in sys.path: + sys.path.insert(0, str(SCRIPT_DIR)) + + +DEFAULT_METRIC_KEYS = ( + "efficiency_pct", + "noise_constant_arc_final_value_musd", +) +DEFAULT_FACET_SHIFT_VALUES = (0.01, 0.05, 0.10, 0.20, 0.35, 0.50) +SUPPORTED_METRIC_KEYS = ( + "constant_arc_vs_launch_constant_arc_pct", + "efficiency_pct", + "geometric_vs_launch_geometric_pct", + "launch_geometric_efficiency_pct", + "noise_constant_arc_final_value_musd", + "noise_geometric_final_value_musd", + "noise_vs_arb_constant_arc_improvement_pct", + "noise_vs_arb_geometric_improvement_pct", +) + + +def load_compare_module(): + """Import the heavy compare module only after CLI parsing.""" + import compare_reclamm_thermostats as compare_module + + return compare_module + + +def parse_csv_floats(value: str) -> tuple[float, ...]: + """Parse a comma-separated float list.""" + values = [] + for token in value.split(","): + token = token.strip() + if token: + values.append(float(token)) + if not values: + raise argparse.ArgumentTypeError("expected at least one float") + return tuple(values) + + +def parse_args() -> argparse.Namespace: + """Parse CLI options for a long-running background sweep.""" + parser = argparse.ArgumentParser( + description=( + "Run the reCLAMM 3-variable arc-speed/price-ratio/shift-exponent " + "sweep using the compare_reclamm_thermostats cache and configs." + ) + ) + parser.add_argument( + "--output-dir", + default="results/reclamm_arc_speed_shift_price_sweep", + help="Directory for cube parquet files and plots.", + ) + parser.add_argument( + "--tvl", + nargs="+", + type=float, + default=None, + help=( + "One or more initial pool values to run. Defaults to 1,000,000. " + "Use --all-tvls for the compare script's 1M/5M/20M sweep." + ), + ) + parser.add_argument( + "--all-tvls", + action="store_true", + help="Run every TVL from compare_reclamm_thermostats.TVL_SWEEP_VALUES.", + ) + parser.add_argument( + "--metric", + action="append", + choices=SUPPORTED_METRIC_KEYS, + default=None, + help=( + "Metric to compute. Can be repeated. Defaults to efficiency_pct and " + "noise_constant_arc_final_value_musd." + ), + ) + parser.add_argument( + "--facet-shift-values", + type=parse_csv_floats, + default=DEFAULT_FACET_SHIFT_VALUES, + help=( + "Comma-separated shift exponents to include in the facet overview. " + "Nearest grid values are used." + ), + ) + parser.add_argument( + "--plot-all-shift-slices", + action="store_true", + help="Also save one 2D arc-speed/price-ratio heatmap per shift exponent.", + ) + parser.add_argument( + "--no-orthogonal-3d", + action="store_true", + help="Skip the literal 3D orthogonal-slice plot.", + ) + parser.add_argument( + "--skip-cube-parquet", + action="store_true", + help="Do not save the evaluated parameter cube to parquet.", + ) + return parser.parse_args() + + +def ensure_output_dir(path: str | os.PathLike[str]) -> Path: + """Create and return the output directory.""" + output_dir = Path(path) + output_dir.mkdir(parents=True, exist_ok=True) + return output_dir + + +def tvl_output_path( + output_dir: Path, + stem: str, + cfg: dict, + suffix: str | None = None, + ext: str = "png", +) -> Path: + """Build a stable output path with the compare script's TVL slug.""" + parts = [stem] + if suffix: + parts.append(suffix) + parts.append(f"tvl_{ct.format_tvl_millions_slug(cfg)}") + return output_dir / ("_".join(parts) + f".{ext}") + + +def metric_spec_map() -> dict[str, dict]: + """Return the compare script's metric specs, keyed by metric name.""" + return {spec["key"]: spec for spec in ct.get_pair_heatmap_metric_specs()} + + +def nearest_indices(values: np.ndarray, targets: tuple[float, ...]) -> list[int]: + """Resolve target values to unique nearest indices in a sweep grid.""" + values = np.asarray(values, dtype=float) + indices: list[int] = [] + for target in targets: + idx = int(np.argmin(np.abs(values - float(target)))) + if idx not in indices: + indices.append(idx) + return indices + + +def speed_label(value: float) -> str: + """Format an arc speed for plot tick labels.""" + return f"{float(value):.0e}" + + +def heatmap_value_slug(value: float) -> str: + """Format a sweep value for filenames.""" + return f"{float(value):.6g}".replace("-", "m").replace(".", "p") + + +def build_arc_speed_shift_price_cube( + base_cfg: dict, + arc_length_speeds: np.ndarray, + price_ratios: np.ndarray, + shift_exponents: np.ndarray, + metric_keys: tuple[str, ...], + cache: dict, + launch_final_values: dict, +) -> dict[str, np.ndarray]: + """Evaluate metric cubes as shift_exp x price_ratio x arc_length_speed.""" + data = { + metric_key: np.zeros( + (len(shift_exponents), len(price_ratios), len(arc_length_speeds)), + dtype=float, + ) + for metric_key in metric_keys + } + total_points = len(shift_exponents) * len(price_ratios) * len(arc_length_speeds) + print( + "\nStarting 3-variable arc-speed sweep: " + f"{len(shift_exponents)} shift slices x {len(price_ratios)} price ratios x " + f"{len(arc_length_speeds)} arc speeds = {total_points} parameter points." + ) + + for zi, shift_exp in enumerate(shift_exponents): + slice_cfg = dict(base_cfg) + slice_cfg["daily_price_shift_exponent"] = float(shift_exp) + progress_label = ( + "arc_speed_shift_price_" + f"shift_{zi + 1:03d}_of_{len(shift_exponents):03d}_" + f"{float(shift_exp):.4f}" + ) + slice_data = ct.build_heatmap_matrices( + x_values=arc_length_speeds, + y_values=price_ratios, + x_key="arc_length_speed", + y_key="price_ratio", + base_cfg=slice_cfg, + metric_keys=metric_keys, + cache=cache, + progress_label=progress_label, + launch_final_values=launch_final_values, + ) + for metric_key in metric_keys: + data[metric_key][zi, :, :] = slice_data[metric_key] + ct.flush_sweep_cache(cache, force=True) + del slice_data + gc.collect() + + return data + + +def cube_to_frame( + cube: dict[str, np.ndarray], + arc_length_speeds: np.ndarray, + price_ratios: np.ndarray, + shift_exponents: np.ndarray, +) -> pd.DataFrame: + """Flatten the 3D cube into a tidy table for offline analysis.""" + records = [] + for zi, shift_exp in enumerate(shift_exponents): + for yi, price_ratio in enumerate(price_ratios): + for xi, arc_speed in enumerate(arc_length_speeds): + record = { + "shift_index": zi, + "price_ratio_index": yi, + "arc_length_speed_index": xi, + "daily_price_shift_exponent": float(shift_exp), + "price_ratio": float(price_ratio), + "arc_length_speed": float(arc_speed), + } + for metric_key, values in cube.items(): + record[metric_key] = float(values[zi, yi, xi]) + records.append(record) + return pd.DataFrame.from_records(records) + + +def save_cube_parquet( + output_dir: Path, + cfg: dict, + cube: dict[str, np.ndarray], + arc_length_speeds: np.ndarray, + price_ratios: np.ndarray, + shift_exponents: np.ndarray, +) -> Path: + """Persist the full parameter cube as parquet.""" + output_path = tvl_output_path( + output_dir, + "reclamm_arc_speed_shift_price_cube", + cfg, + ext="parquet", + ) + frame = cube_to_frame( + cube, + arc_length_speeds=arc_length_speeds, + price_ratios=price_ratios, + shift_exponents=shift_exponents, + ) + frame.to_parquet(output_path, index=False, compression="zstd") + print(f"Saved {output_path}") + return output_path + + +def plot_shift_slice_facets( + data: np.ndarray, + arc_length_speeds: np.ndarray, + price_ratios: np.ndarray, + shift_exponents: np.ndarray, + shift_indices: list[int], + spec: dict, + cfg: dict, + filename: Path, +) -> None: + """Render selected shift-exponent slices as small-multiple heatmaps.""" + selected = [data[idx] for idx in shift_indices] + norm = ct._build_heatmap_norm( + selected, + center_zero=spec["center_zero"], + color_norm=spec.get("color_norm"), + symlog_linthresh=spec.get("symlog_linthresh"), + ) + cmap_name = spec["cmap"] + x_edges = ct._compute_axis_edges(arc_length_speeds, scale="log") + y_edges = ct._compute_axis_edges(price_ratios, scale="linear") + + col_count = min(3, len(shift_indices)) + row_count = int(math.ceil(len(shift_indices) / col_count)) + fig, axes = plt.subplots( + row_count, + col_count, + figsize=(4.3 * col_count, 3.3 * row_count), + squeeze=False, + ) + active_axes = [] + im = None + for plot_idx, shift_idx in enumerate(shift_indices): + ax = axes[plot_idx // col_count][plot_idx % col_count] + active_axes.append(ax) + im = ax.pcolormesh( + x_edges, + y_edges, + data[shift_idx], + cmap=cmap_name, + norm=norm, + shading="auto", + ) + ax.set_xscale("log") + ax.set_xticks(ct.ARC_LENGTH_SPEED_TICKS) + ax.set_yticks(ct.PRICE_RATIO_TICKS) + ax.tick_params(axis="x", labelrotation=35) + ax.set_xlabel("Arc-length speed") + ax.set_ylabel("Price ratio") + ax.set_title( + f"shift_exp={ct.format_heatmap_param_value(shift_exponents[shift_idx])}" + ) + + for plot_idx in range(len(shift_indices), row_count * col_count): + axes[plot_idx // col_count][plot_idx % col_count].set_visible(False) + + fig.suptitle( + f"{spec['title']}: arc_speed x price_ratio by shift_exp | " + f"TVL {ct.format_tvl_millions_label(cfg)}", + y=0.995, + ) + if im is not None: + cbar = fig.colorbar(im, ax=active_axes, shrink=0.88) + cbar.set_label(spec["colorbar_label"]) + plt.tight_layout() + plt.savefig(filename, dpi=150) + print(f"Saved {filename}") + plt.close(fig) + + +def plot_all_shift_slices( + output_dir: Path, + cube: dict[str, np.ndarray], + arc_length_speeds: np.ndarray, + price_ratios: np.ndarray, + shift_exponents: np.ndarray, + specs: dict[str, dict], + cfg: dict, + metric_keys: tuple[str, ...], +) -> None: + """Optionally save one 2D heatmap per shift exponent and metric.""" + for metric_key in metric_keys: + spec = specs[metric_key] + for shift_idx, shift_exp in enumerate(shift_exponents): + filename = tvl_output_path( + output_dir, + f"reclamm_arc_speed_price_slice_{spec['slug']}", + cfg, + suffix=f"shift_exp_{heatmap_value_slug(float(shift_exp))}", + ) + ct.plot_heatmap( + data=cube[metric_key][shift_idx], + x_values=arc_length_speeds, + y_values=price_ratios, + x_label="Arc-length speed", + y_label="Price ratio", + title=( + f"{spec['title']}: shift_exp fixed at " + f"{ct.format_heatmap_param_value(float(shift_exp))} | " + f"TVL {ct.format_tvl_millions_label(cfg)}" + ), + colorbar_label=spec["colorbar_label"], + filename=filename, + xticks=ct.ARC_LENGTH_SPEED_TICKS, + yticks=ct.PRICE_RATIO_TICKS, + xscale="log", + center_zero=spec["center_zero"], + cmap=spec["cmap"], + color_norm=spec.get("color_norm"), + symlog_linthresh=spec.get("symlog_linthresh"), + ) + + +def compute_argmax_over_arc_speed( + data: np.ndarray, + arc_length_speeds: np.ndarray, +) -> tuple[np.ndarray, np.ndarray]: + """Return best metric value and the arc speed that produced it.""" + best_idx = np.nanargmax(data, axis=2) + best_values = np.take_along_axis(data, best_idx[:, :, None], axis=2)[:, :, 0] + best_speeds = np.asarray(arc_length_speeds, dtype=float)[best_idx] + return best_values, best_speeds + + +def plot_best_speed_heatmap( + best_speeds: np.ndarray, + arc_length_speeds: np.ndarray, + price_ratios: np.ndarray, + shift_exponents: np.ndarray, + metric_label: str, + cfg: dict, + filename: Path, +) -> None: + """Render a price_ratio x shift_exp heatmap of the selected arc speed.""" + x_edges = ct._compute_axis_edges(price_ratios, scale="linear") + y_edges = ct._compute_axis_edges(shift_exponents, scale="linear") + fig, ax = plt.subplots(figsize=(9.0, 6.0)) + norm = LogNorm( + vmin=float(np.min(arc_length_speeds)), + vmax=float(np.max(arc_length_speeds)), + ) + im = ax.pcolormesh( + x_edges, + y_edges, + best_speeds, + cmap="viridis", + norm=norm, + shading="auto", + ) + ax.set_xlabel("Price ratio") + ax.set_ylabel("Shift exponent") + ax.set_title( + f"Best arc-length speed by {metric_label} | " + f"TVL {ct.format_tvl_millions_label(cfg)}" + ) + ax.set_xticks(ct.PRICE_RATIO_TICKS) + ax.set_yticks(ct.SHIFT_EXPONENT_TICKS) + cbar = fig.colorbar(im, ax=ax) + cbar.set_label("Best arc-length speed") + speed_ticks = np.asarray(ct.ARC_LENGTH_SPEED_TICKS, dtype=float) + speed_ticks = speed_ticks[ + (speed_ticks >= float(np.min(arc_length_speeds))) + & (speed_ticks <= float(np.max(arc_length_speeds))) + ] + cbar.set_ticks(speed_ticks) + cbar.set_ticklabels([speed_label(value) for value in speed_ticks]) + plt.tight_layout() + plt.savefig(filename, dpi=150) + print(f"Saved {filename}") + plt.close(fig) + + +def save_best_speed_summary( + output_dir: Path, + cfg: dict, + metric_key: str, + best_values: np.ndarray, + best_speeds: np.ndarray, + price_ratios: np.ndarray, + shift_exponents: np.ndarray, +) -> Path: + """Persist the price_ratio x shift_exp argmax summary.""" + records = [] + for zi, shift_exp in enumerate(shift_exponents): + for yi, price_ratio in enumerate(price_ratios): + records.append( + { + "daily_price_shift_exponent": float(shift_exp), + "price_ratio": float(price_ratio), + f"best_{metric_key}": float(best_values[zi, yi]), + f"best_arc_length_speed_by_{metric_key}": float( + best_speeds[zi, yi] + ), + } + ) + frame = pd.DataFrame.from_records(records) + output_path = tvl_output_path( + output_dir, + f"reclamm_arc_speed_shift_price_best_{metric_key}", + cfg, + ext="parquet", + ) + frame.to_parquet(output_path, index=False, compression="zstd") + print(f"Saved {output_path}") + return output_path + + +def plot_orthogonal_3d_slices( + data: np.ndarray, + arc_length_speeds: np.ndarray, + price_ratios: np.ndarray, + shift_exponents: np.ndarray, + spec: dict, + cfg: dict, + launch_auto_speed: float, + filename: Path, +) -> None: + """Render one literal 3D orthogonal-slice view of the metric cube.""" + shift_idx = int( + np.argmin( + np.abs( + np.asarray(shift_exponents, dtype=float) + - float(cfg["daily_price_shift_exponent"]) + ) + ) + ) + price_idx = int( + np.argmin( + np.abs(np.asarray(price_ratios, dtype=float) - float(cfg["price_ratio"])) + ) + ) + speed_idx = int( + np.argmin( + np.abs( + np.asarray(arc_length_speeds, dtype=float) - float(launch_auto_speed) + ) + ) + ) + + norm = ct._build_heatmap_norm( + [data], + center_zero=spec["center_zero"], + color_norm=spec.get("color_norm"), + symlog_linthresh=spec.get("symlog_linthresh"), + ) + cmap_obj = plt.get_cmap(spec["cmap"]) + log_speeds = np.log10(np.asarray(arc_length_speeds, dtype=float)) + + arc_price_x, arc_price_y = np.meshgrid(log_speeds, price_ratios) + arc_price_z = np.full_like( + arc_price_x, + float(shift_exponents[shift_idx]), + dtype=float, + ) + + arc_shift_x, arc_shift_z = np.meshgrid(log_speeds, shift_exponents) + arc_shift_y = np.full_like( + arc_shift_x, + float(price_ratios[price_idx]), + dtype=float, + ) + + price_shift_y, price_shift_z = np.meshgrid(price_ratios, shift_exponents) + price_shift_x = np.full_like( + price_shift_y, + float(log_speeds[speed_idx]), + dtype=float, + ) + + fig = plt.figure(figsize=(10.5, 7.2)) + ax = fig.add_subplot(111, projection="3d") + ax.plot_surface( + arc_price_x, + arc_price_y, + arc_price_z, + facecolors=cmap_obj(norm(data[shift_idx, :, :])), + shade=False, + ) + ax.plot_surface( + arc_shift_x, + arc_shift_y, + arc_shift_z, + facecolors=cmap_obj(norm(data[:, price_idx, :])), + shade=False, + ) + ax.plot_surface( + price_shift_x, + price_shift_y, + price_shift_z, + facecolors=cmap_obj(norm(data[:, :, speed_idx])), + shade=False, + ) + + ax.set_xlabel("Arc-length speed") + ax.set_ylabel("Price ratio") + ax.set_zlabel("Shift exponent") + ax.set_yticks(ct.PRICE_RATIO_TICKS) + ax.set_zticks(ct.SHIFT_EXPONENT_TICKS) + speed_ticks = np.asarray(ct.ARC_LENGTH_SPEED_TICKS, dtype=float) + speed_ticks = speed_ticks[ + (speed_ticks >= float(np.min(arc_length_speeds))) + & (speed_ticks <= float(np.max(arc_length_speeds))) + ] + ax.set_xticks(np.log10(speed_ticks)) + ax.set_xticklabels([speed_label(value) for value in speed_ticks], rotation=20) + ax.set_title( + f"{spec['title']}: orthogonal 3D slices | " + f"TVL {ct.format_tvl_millions_label(cfg)}\n" + f"shift_exp={ct.format_heatmap_param_value(shift_exponents[shift_idx])}, " + f"price_ratio={ct.format_heatmap_param_value(price_ratios[price_idx])}, " + f"arc_speed={speed_label(arc_length_speeds[speed_idx])}" + ) + ax.view_init(elev=ct.THREE_D_VIEW_ELEVATION, azim=ct.THREE_D_VIEW_AZIMUTH) + try: + ax.set_box_aspect((1.45, 1.5, 1.0)) + except AttributeError: + pass + + sm = ScalarMappable(norm=norm, cmap=cmap_obj) + sm.set_array([]) + cbar = fig.colorbar(sm, ax=ax, fraction=0.03, pad=0.1, shrink=0.82) + cbar.set_label(spec["colorbar_label"]) + plt.tight_layout() + plt.savefig(filename, dpi=150) + print(f"Saved {filename}") + plt.close(fig) + + +def run_for_tvl( + initial_pool_value: float, + output_dir: Path, + metric_keys: tuple[str, ...], + facet_shift_values: tuple[float, ...], + plot_all_slices: bool, + plot_orthogonal_3d: bool, + save_cube: bool, + shared_price_data, + shared_market_linear_noise_data, +) -> None: + """Run the 3D sweep for one initial TVL.""" + launch_cfg, base_cfg = ct.configs_for_tvl(ct.CONFIGS, initial_pool_value) + tvl_label = ct.format_tvl_millions_label(base_cfg) + print(f"\n=== Arc-speed/shift/price sweep: TVL {tvl_label} ===") + + launch_final_values = ct.get_launch_final_values( + [], + launch_cfg=launch_cfg, + price_data=shared_price_data, + market_linear_noise_data=shared_market_linear_noise_data, + ) + cache = ct.make_sweep_cache( + shared_price_data, + cache_scope_cfg=base_cfg, + market_linear_noise_data=shared_market_linear_noise_data, + ) + try: + cube = build_arc_speed_shift_price_cube( + base_cfg=dict(base_cfg), + arc_length_speeds=ct.HEATMAP_ARC_LENGTH_SPEEDS, + price_ratios=ct.HEATMAP_PRICE_RATIOS, + shift_exponents=ct.HEATMAP_SHIFT_EXPONENTS, + metric_keys=metric_keys, + cache=cache, + launch_final_values=launch_final_values, + ) + if save_cube: + save_cube_parquet( + output_dir, + base_cfg, + cube, + arc_length_speeds=ct.HEATMAP_ARC_LENGTH_SPEEDS, + price_ratios=ct.HEATMAP_PRICE_RATIOS, + shift_exponents=ct.HEATMAP_SHIFT_EXPONENTS, + ) + + specs = metric_spec_map() + facet_indices = nearest_indices( + ct.HEATMAP_SHIFT_EXPONENTS, + facet_shift_values, + ) + launch_auto_speed = ct.compute_auto_calibrated_arc_length_speed( + launch_cfg, + shared_price_data, + ) + + for metric_key in metric_keys: + spec = specs[metric_key] + facet_path = tvl_output_path( + output_dir, + f"reclamm_arc_speed_shift_price_facets_{spec['slug']}", + base_cfg, + ) + plot_shift_slice_facets( + data=cube[metric_key], + arc_length_speeds=ct.HEATMAP_ARC_LENGTH_SPEEDS, + price_ratios=ct.HEATMAP_PRICE_RATIOS, + shift_exponents=ct.HEATMAP_SHIFT_EXPONENTS, + shift_indices=facet_indices, + spec=spec, + cfg=base_cfg, + filename=facet_path, + ) + + best_values, best_speeds = compute_argmax_over_arc_speed( + cube[metric_key], + ct.HEATMAP_ARC_LENGTH_SPEEDS, + ) + best_value_path = tvl_output_path( + output_dir, + f"reclamm_arc_speed_shift_price_best_{spec['slug']}", + base_cfg, + ) + ct.plot_heatmap( + data=best_values, + x_values=ct.HEATMAP_PRICE_RATIOS, + y_values=ct.HEATMAP_SHIFT_EXPONENTS, + x_label="Price ratio", + y_label="Shift exponent", + title=( + f"Best {spec['title']} over arc-length speed | " + f"TVL {ct.format_tvl_millions_label(base_cfg)}" + ), + colorbar_label=f"Best {spec['colorbar_label']}", + filename=best_value_path, + xticks=ct.PRICE_RATIO_TICKS, + yticks=ct.SHIFT_EXPONENT_TICKS, + center_zero=spec["center_zero"], + cmap=spec["cmap"], + color_norm=spec.get("color_norm"), + symlog_linthresh=spec.get("symlog_linthresh"), + ) + best_speed_path = tvl_output_path( + output_dir, + f"reclamm_arc_speed_shift_price_argmax_speed_by_{spec['slug']}", + base_cfg, + ) + plot_best_speed_heatmap( + best_speeds=best_speeds, + arc_length_speeds=ct.HEATMAP_ARC_LENGTH_SPEEDS, + price_ratios=ct.HEATMAP_PRICE_RATIOS, + shift_exponents=ct.HEATMAP_SHIFT_EXPONENTS, + metric_label=spec["title"], + cfg=base_cfg, + filename=best_speed_path, + ) + save_best_speed_summary( + output_dir, + base_cfg, + metric_key, + best_values=best_values, + best_speeds=best_speeds, + price_ratios=ct.HEATMAP_PRICE_RATIOS, + shift_exponents=ct.HEATMAP_SHIFT_EXPONENTS, + ) + + if plot_orthogonal_3d: + orthogonal_path = tvl_output_path( + output_dir, + f"reclamm_arc_speed_shift_price_orthogonal_3d_{spec['slug']}", + base_cfg, + ) + plot_orthogonal_3d_slices( + data=cube[metric_key], + arc_length_speeds=ct.HEATMAP_ARC_LENGTH_SPEEDS, + price_ratios=ct.HEATMAP_PRICE_RATIOS, + shift_exponents=ct.HEATMAP_SHIFT_EXPONENTS, + spec=spec, + cfg=base_cfg, + launch_auto_speed=launch_auto_speed, + filename=orthogonal_path, + ) + + if plot_all_slices: + plot_all_shift_slices( + output_dir=output_dir, + cube=cube, + arc_length_speeds=ct.HEATMAP_ARC_LENGTH_SPEEDS, + price_ratios=ct.HEATMAP_PRICE_RATIOS, + shift_exponents=ct.HEATMAP_SHIFT_EXPONENTS, + specs=specs, + cfg=base_cfg, + metric_keys=metric_keys, + ) + finally: + ct.flush_sweep_cache(cache, force=True) + cache.clear() + gc.collect() + print(f"Released arc-speed/shift/price cache for TVL {tvl_label}.") + + +def main() -> None: + """Entrypoint.""" + args = parse_args() + global ct + ct = load_compare_module() + output_dir = ensure_output_dir(args.output_dir) + if args.all_tvls: + tvl_values = tuple(float(value) for value in ct.TVL_SWEEP_VALUES) + elif args.tvl is not None: + tvl_values = tuple(float(value) for value in args.tvl) + else: + tvl_values = (float(ct.DEFAULT_INITIAL_POOL_VALUE),) + + metric_keys = tuple(args.metric or DEFAULT_METRIC_KEYS) + unsupported = [ + metric_key + for metric_key in metric_keys + if metric_key not in ct.HEATMAP_METRIC_DEPENDENCIES + ] + if unsupported: + raise ValueError(f"Unsupported metric keys: {unsupported}") + + print(f"Writing artifacts to {output_dir}") + print(f"Running metrics: {', '.join(metric_keys)}") + print("Loading shared price data and market-linear noise arrays...") + shared_price_data = ct.load_shared_price_data(ct.CONFIGS) + shared_market_linear_noise_data = ct.load_shared_market_linear_noise_data() + + for initial_pool_value in tvl_values: + run_for_tvl( + initial_pool_value=initial_pool_value, + output_dir=output_dir, + metric_keys=metric_keys, + facet_shift_values=tuple(args.facet_shift_values), + plot_all_slices=bool(args.plot_all_shift_slices), + plot_orthogonal_3d=not bool(args.no_orthogonal_3d), + save_cube=not bool(args.skip_cube_parquet), + shared_price_data=shared_price_data, + shared_market_linear_noise_data=shared_market_linear_noise_data, + ) + + +if __name__ == "__main__": + main() From fc616a8f605aca67d1c1bc57465ea3caa16fdb45 Mon Sep 17 00:00:00 2001 From: christian harrington Date: Tue, 7 Apr 2026 15:22:34 +0100 Subject: [PATCH 50/57] initial take --- quantammsim/core_simulator/dynamic_inputs.py | 19 +- quantammsim/core_simulator/forward_pass.py | 1 + quantammsim/hooks/dynamic_fee_base_hook.py | 1 + .../pools/G3M/balancer/hypersurge_balancer.py | 447 ++++++++++++++++ .../balancer/hypersurge_balancer_reserves.py | 496 ++++++++++++++++++ quantammsim/pools/creator.py | 4 + .../runners/default_run_fingerprint.py | 36 ++ quantammsim/runners/jax_runner_utils.py | 34 ++ tests/pools/G3M/__init__.py | 1 + tests/pools/G3M/test_hypersurge_balancer.py | 219 ++++++++ tests/unit/test_jax_runner_utils.py | 39 ++ 11 files changed, 1296 insertions(+), 1 deletion(-) create mode 100644 quantammsim/pools/G3M/balancer/hypersurge_balancer.py create mode 100644 quantammsim/pools/G3M/balancer/hypersurge_balancer_reserves.py create mode 100644 tests/pools/G3M/__init__.py create mode 100644 tests/pools/G3M/test_hypersurge_balancer.py diff --git a/quantammsim/core_simulator/dynamic_inputs.py b/quantammsim/core_simulator/dynamic_inputs.py index 367363e..1637e2b 100644 --- a/quantammsim/core_simulator/dynamic_inputs.py +++ b/quantammsim/core_simulator/dynamic_inputs.py @@ -14,6 +14,7 @@ class DynamicInputFrames: arb_fees: Optional[Any] = None lp_supply: Optional[Any] = None reclamm_price_ratio_updates: Optional[Any] = None + oracle_prices: Optional[Any] = None class DynamicInputArrays(NamedTuple): @@ -25,6 +26,7 @@ class DynamicInputArrays(NamedTuple): arb_fees: jnp.ndarray lp_supply: jnp.ndarray reclamm_price_ratio_updates: jnp.ndarray + oracle_prices: jnp.ndarray = jnp.ones((1, 1)) def default_dynamic_input_flags() -> dict: @@ -37,6 +39,7 @@ def default_dynamic_input_flags() -> dict: "has_dynamic_arb_fees": False, "has_lp_supply": False, "has_reclamm_price_ratio_updates": False, + "has_oracle_prices": False, } @@ -55,6 +58,7 @@ def dynamic_input_flags_from_frames(dynamic_input_frames: Optional[DynamicInputF "has_reclamm_price_ratio_updates": ( dynamic_input_frames.reclamm_price_ratio_updates is not None ), + "has_oracle_prices": dynamic_input_frames.oracle_prices is not None, } flags["use_dynamic_inputs"] = any(flags.values()) return flags @@ -83,6 +87,7 @@ def empty_dynamic_input_arrays() -> DynamicInputArrays: lp_supply=jnp.ones((1,)), # Columns: has_event, target_price_ratio, end_step, start_price_ratio_override reclamm_price_ratio_updates=jnp.array([[0.0, 0.0, 0.0, jnp.nan]]), + oracle_prices=jnp.ones((1, 1)), ) @@ -117,9 +122,14 @@ def resolve_dynamic_input_components( ), "reclamm_price_ratio_updates": ( arrays.reclamm_price_ratio_updates - if dynamic_input_flags["has_reclamm_price_ratio_updates"] + if dynamic_input_flags.get("has_reclamm_price_ratio_updates", False) else empty_dynamic_input_arrays().reclamm_price_ratio_updates ), + "oracle_prices": ( + arrays.oracle_prices + if dynamic_input_flags.get("has_oracle_prices", False) + else empty_dynamic_input_arrays().oracle_prices + ), } @@ -160,6 +170,7 @@ def materialize_dynamic_inputs( "has_dynamic_arb_fees": True, "has_lp_supply": True, "has_reclamm_price_ratio_updates": True, + "has_oracle_prices": True, } else: flags = resolve_dynamic_input_flags(dynamic_inputs, dynamic_input_flags) @@ -192,4 +203,10 @@ def materialize_dynamic_inputs( scan_len, dtype, ), + oracle_prices=_broadcast_dynamic_input_leaf( + "oracle_prices", + resolved["oracle_prices"], + scan_len, + dtype, + ), ) diff --git a/quantammsim/core_simulator/forward_pass.py b/quantammsim/core_simulator/forward_pass.py index e99f051..45039d7 100644 --- a/quantammsim/core_simulator/forward_pass.py +++ b/quantammsim/core_simulator/forward_pass.py @@ -1280,6 +1280,7 @@ def forward_pass_nograd( reclamm_price_ratio_updates=stop_gradient( dynamic_inputs.reclamm_price_ratio_updates ), + oracle_prices=stop_gradient(dynamic_inputs.oracle_prices), ) return forward_pass( params, diff --git a/quantammsim/hooks/dynamic_fee_base_hook.py b/quantammsim/hooks/dynamic_fee_base_hook.py index 1ab0266..4cdde1e 100644 --- a/quantammsim/hooks/dynamic_fee_base_hook.py +++ b/quantammsim/hooks/dynamic_fee_base_hook.py @@ -125,6 +125,7 @@ def calculate_reserves_with_fees( arb_fees=jnp.asarray(run_fingerprint["arb_fees"], dtype=jnp.float64), lp_supply=empty_inputs.lp_supply, reclamm_price_ratio_updates=empty_inputs.reclamm_price_ratio_updates, + oracle_prices=empty_inputs.oracle_prices, ) return self.calculate_reserves_with_dynamic_inputs( diff --git a/quantammsim/pools/G3M/balancer/hypersurge_balancer.py b/quantammsim/pools/G3M/balancer/hypersurge_balancer.py new file mode 100644 index 0000000..79ba4e9 --- /dev/null +++ b/quantammsim/pools/G3M/balancer/hypersurge_balancer.py @@ -0,0 +1,447 @@ +from functools import partial +from typing import Any, Dict, Optional + +import numpy as np + +import jax.numpy as jnp +from jax import jit, tree_util +from jax.lax import dynamic_slice + +from quantammsim.core_simulator.dynamic_inputs import materialize_dynamic_inputs +from quantammsim.pools.G3M.balancer.balancer import BalancerPool +from quantammsim.pools.G3M.balancer.hypersurge_balancer_reserves import ( + _jax_calc_hypersurge_balancer_reserves, +) + + +def _prepare_dynamic_array(arr, start_index, bout_length, arb_frequency, max_len): + """Slice and decimate a dynamic input array to match the arb-price scan.""" + arr = jnp.asarray(arr) + if arr.ndim == 0: + return jnp.full((max_len,), arr, dtype=arr.dtype) + if arr.shape[0] <= 1: + return jnp.broadcast_to(arr, (max_len,) + arr.shape[1:]) + + start = (start_index[0],) + (0,) * (arr.ndim - 1) + slice_sizes = (bout_length - 1,) + arr.shape[1:] + sliced = dynamic_slice(arr, start, slice_sizes) + if arb_frequency != 1: + sliced = sliced[::arb_frequency] + return sliced + + +def _coalesce(value, default): + return default if value is None else value + + +HYPERSURGE_PARAM_KEYS = ( + "hypersurge_arb_max_fee", + "hypersurge_arb_threshold", + "hypersurge_arb_cap_deviation", + "hypersurge_noise_max_fee", + "hypersurge_noise_threshold", + "hypersurge_noise_cap_deviation", +) + + +class HyperSurgeBalancerPool(BalancerPool): + """Balancer weighted pool with HyperSurge-style state-dependent swap fees.""" + + @staticmethod + def _run_fingerprint_hypersurge_defaults(run_fingerprint: Dict[str, Any]): + base_fee = run_fingerprint.get("fees", 0.0) + if isinstance(base_fee, (list, tuple)): + base_fee = base_fee[0] + + raw_params = run_fingerprint.get("hypersurge_params") + if raw_params is not None: + if isinstance(raw_params, dict): + shared_max = raw_params.get("max_surge_fee", base_fee) + shared_threshold = raw_params.get("threshold", 0.0) + shared_cap = raw_params.get("cap_deviation", 1.0) + return { + "hypersurge_arb_max_fee": raw_params.get( + "arb_max_fee", shared_max + ), + "hypersurge_arb_threshold": raw_params.get( + "arb_threshold", shared_threshold + ), + "hypersurge_arb_cap_deviation": raw_params.get( + "arb_cap_deviation", shared_cap + ), + "hypersurge_noise_max_fee": raw_params.get( + "noise_max_fee", shared_max + ), + "hypersurge_noise_threshold": raw_params.get( + "noise_threshold", shared_threshold + ), + "hypersurge_noise_cap_deviation": raw_params.get( + "noise_cap_deviation", shared_cap + ), + } + + raw_params = np.asarray(raw_params, dtype=np.float64).reshape(-1) + if raw_params.size != len(HYPERSURGE_PARAM_KEYS): + raise ValueError( + "hypersurge_params must contain exactly six values: " + + ", ".join(HYPERSURGE_PARAM_KEYS) + ) + return dict(zip(HYPERSURGE_PARAM_KEYS, raw_params)) + + shared_max = _coalesce( + run_fingerprint.get("hypersurge_max_surge_fee"), + _coalesce(run_fingerprint.get("hypersurge_max_fee"), base_fee), + ) + shared_threshold = _coalesce( + run_fingerprint.get("hypersurge_threshold"), + 0.0, + ) + shared_cap = _coalesce( + run_fingerprint.get("hypersurge_cap_deviation"), + 1.0, + ) + return { + "hypersurge_arb_max_fee": _coalesce( + run_fingerprint.get("hypersurge_arb_max_fee"), shared_max + ), + "hypersurge_arb_threshold": _coalesce( + run_fingerprint.get("hypersurge_arb_threshold"), shared_threshold + ), + "hypersurge_arb_cap_deviation": _coalesce( + run_fingerprint.get("hypersurge_arb_cap_deviation"), shared_cap + ), + "hypersurge_noise_max_fee": _coalesce( + run_fingerprint.get("hypersurge_noise_max_fee"), shared_max + ), + "hypersurge_noise_threshold": _coalesce( + run_fingerprint.get("hypersurge_noise_threshold"), shared_threshold + ), + "hypersurge_noise_cap_deviation": _coalesce( + run_fingerprint.get("hypersurge_noise_cap_deviation"), shared_cap + ), + } + + def _hypersurge_params(self, params: Dict[str, Any], run_fingerprint: Dict[str, Any]): + if "hypersurge_params" in params: + return jnp.ravel(params["hypersurge_params"]) + + if all(key in params for key in HYPERSURGE_PARAM_KEYS): + return jnp.asarray( + [jnp.squeeze(params[key]) for key in HYPERSURGE_PARAM_KEYS], + dtype=jnp.float64, + ) + + defaults = self._run_fingerprint_hypersurge_defaults(run_fingerprint) + return jnp.asarray( + [defaults[key] for key in HYPERSURGE_PARAM_KEYS], + dtype=jnp.float64, + ) + + def _price_windows( + self, + run_fingerprint: Dict[str, Any], + prices: jnp.ndarray, + start_index: jnp.ndarray, + additional_oracle_input: Optional[jnp.ndarray], + ): + bout_length = run_fingerprint["bout_length"] + n_assets = run_fingerprint["n_assets"] + local_prices = dynamic_slice(prices, start_index, (bout_length - 1, n_assets)) + + if additional_oracle_input is None: + local_oracle_prices = local_prices + else: + local_oracle_prices = dynamic_slice( + additional_oracle_input, + start_index, + (bout_length - 1, n_assets), + ) + + arb_frequency = run_fingerprint["arb_frequency"] + if arb_frequency != 1: + return local_prices[::arb_frequency], local_oracle_prices[::arb_frequency] + return local_prices, local_oracle_prices + + def _noise_inputs( + self, + run_fingerprint: Dict[str, Any], + prices: jnp.ndarray, + start_index: jnp.ndarray, + max_len: int, + ): + noise_model = run_fingerprint.get("noise_model", "ratio") + noise_base = None + noise_tvl_coeff = None + + if noise_model != "market_linear": + return noise_model, noise_base, noise_tvl_coeff + + noise_base = run_fingerprint.get("noise_base_array") + noise_tvl_coeff = run_fingerprint.get("noise_tvl_coeff_array") + if (noise_base is None or noise_tvl_coeff is None) and "noise_arrays_path" in run_fingerprint: + path = run_fingerprint["noise_arrays_path"] + if ( + not hasattr(self, "_market_linear_cache") + or self._market_linear_cache[0] != path + ): + arrays = np.load(path) + self._market_linear_cache = ( + path, + arrays["noise_base"], + arrays["noise_tvl_coeff"], + ) + noise_base = self._market_linear_cache[1] + noise_tvl_coeff = self._market_linear_cache[2] + + if noise_base is None or noise_tvl_coeff is None: + raise ValueError( + "noise_model='market_linear' requires noise_base_array and " + "noise_tvl_coeff_array, or noise_arrays_path." + ) + + noise_base = _prepare_dynamic_array( + jnp.asarray(noise_base), + start_index=start_index, + bout_length=run_fingerprint["bout_length"], + arb_frequency=run_fingerprint["arb_frequency"], + max_len=max_len, + ) + noise_tvl_coeff = _prepare_dynamic_array( + jnp.asarray(noise_tvl_coeff), + start_index=start_index, + bout_length=run_fingerprint["bout_length"], + arb_frequency=run_fingerprint["arb_frequency"], + max_len=max_len, + ) + return noise_model, noise_base, noise_tvl_coeff + + def _run_hypersurge_reserves( + self, + params: Dict[str, Any], + run_fingerprint: Dict[str, Any], + prices: jnp.ndarray, + start_index: jnp.ndarray, + fees, + gas_cost, + arb_fees, + trades, + do_trades: bool, + lp_supply_array, + additional_oracle_input: Optional[jnp.ndarray] = None, + oracle_prices_override: Optional[jnp.ndarray] = None, + ) -> jnp.ndarray: + weights = self.calculate_initial_weights(params) + arb_prices, oracle_prices = self._price_windows( + run_fingerprint, + prices, + start_index, + additional_oracle_input, + ) + if oracle_prices_override is not None: + oracle_prices = oracle_prices_override + + initial_pool_value = run_fingerprint["initial_pool_value"] + initial_value_per_token = weights * initial_pool_value + initial_reserves = initial_value_per_token / arb_prices[0] + + noise_model, noise_base, noise_tvl_coeff = self._noise_inputs( + run_fingerprint, + prices, + start_index, + arb_prices.shape[0], + ) + + return _jax_calc_hypersurge_balancer_reserves( + initial_reserves, + weights, + arb_prices, + oracle_prices, + fees=fees, + arb_thresh=gas_cost, + arb_fees=arb_fees, + all_sig_variations=jnp.array(run_fingerprint["all_sig_variations"]), + trades=trades, + do_trades=do_trades, + do_arb=run_fingerprint["do_arb"], + lp_supply_array=lp_supply_array, + hypersurge_params=self._hypersurge_params(params, run_fingerprint), + noise_trader_ratio=run_fingerprint.get("noise_trader_ratio", 0.0), + protocol_fee_split=run_fingerprint.get("protocol_fee_split", 0.0), + noise_model=noise_model, + noise_base_array=noise_base, + noise_tvl_coeff_array=noise_tvl_coeff, + tvl_mean=run_fingerprint.get("noise_tvl_mean", 0.0), + tvl_std=run_fingerprint.get("noise_tvl_std", 1.0), + minutes_per_step=run_fingerprint.get( + "seconds_per_step", + 60.0 * run_fingerprint["arb_frequency"], + ) + / 60.0, + ) + + @partial(jit, static_argnums=2) + def calculate_reserves_with_fees( + self, + params: Dict[str, Any], + run_fingerprint: Dict[str, Any], + prices: jnp.ndarray, + start_index: jnp.ndarray, + additional_oracle_input: Optional[jnp.ndarray] = None, + ) -> jnp.ndarray: + return self._run_hypersurge_reserves( + params, + run_fingerprint, + prices, + start_index, + fees=run_fingerprint["fees"], + gas_cost=run_fingerprint["gas_cost"], + arb_fees=run_fingerprint["arb_fees"], + trades=None, + do_trades=False, + lp_supply_array=None, + additional_oracle_input=additional_oracle_input, + ) + + @partial(jit, static_argnums=2) + def calculate_reserves_zero_fees( + self, + params: Dict[str, Any], + run_fingerprint: Dict[str, Any], + prices: jnp.ndarray, + start_index: jnp.ndarray, + additional_oracle_input: Optional[jnp.ndarray] = None, + ) -> jnp.ndarray: + return self._run_hypersurge_reserves( + params, + run_fingerprint, + prices, + start_index, + fees=0.0, + gas_cost=run_fingerprint["gas_cost"], + arb_fees=run_fingerprint["arb_fees"], + trades=None, + do_trades=False, + lp_supply_array=None, + additional_oracle_input=additional_oracle_input, + ) + + @partial(jit, static_argnums=2) + def calculate_reserves_with_dynamic_inputs( + self, + params: Dict[str, Any], + run_fingerprint: Dict[str, Any], + prices: jnp.ndarray, + start_index: jnp.ndarray, + dynamic_inputs, + additional_oracle_input: Optional[jnp.ndarray] = None, + ) -> jnp.ndarray: + arb_prices, fallback_oracle_prices = self._price_windows( + run_fingerprint, + prices, + start_index, + additional_oracle_input, + ) + materialized_inputs = materialize_dynamic_inputs( + dynamic_inputs, + run_fingerprint.get("dynamic_input_flags"), + run_fingerprint, + scan_len=arb_prices.shape[0], + do_trades=run_fingerprint["do_trades"], + dtype=arb_prices.dtype, + ) + + oracle_prices = fallback_oracle_prices + if materialized_inputs.oracle_prices.shape[-1] == run_fingerprint["n_assets"]: + oracle_prices = materialized_inputs.oracle_prices + + return self._run_hypersurge_reserves( + params, + run_fingerprint, + prices, + start_index, + fees=materialized_inputs.fees, + gas_cost=materialized_inputs.gas_cost, + arb_fees=materialized_inputs.arb_fees, + trades=materialized_inputs.trades, + do_trades=run_fingerprint["do_trades"], + lp_supply_array=materialized_inputs.lp_supply, + additional_oracle_input=additional_oracle_input, + oracle_prices_override=oracle_prices, + ) + + def init_base_parameters( + self, + initial_values_dict: Dict[str, Any], + run_fingerprint: Dict[str, Any], + n_assets: int, + n_parameter_sets: int = 1, + noise: str = "gaussian", + ) -> Dict[str, Any]: + np.random.seed(0) + + def process_weights(key): + if key not in initial_values_dict: + raise ValueError(f"initial_values_dict must contain {key}") + initial_value = initial_values_dict[key] + if isinstance(initial_value, (np.ndarray, jnp.ndarray, list)): + initial_value = np.array(initial_value) + if initial_value.size == n_assets: + return np.array([initial_value] * n_parameter_sets) + if initial_value.size == 1: + return np.array([[initial_value] * n_assets] * n_parameter_sets) + if initial_value.shape == (n_parameter_sets, n_assets): + return initial_value + raise ValueError( + f"{key} must be a singleton or a vector of length n_assets " + "or a matrix of shape (n_parameter_sets, n_assets)" + ) + return np.array([[initial_value] * n_assets] * n_parameter_sets) + + def process_scalar(key, default): + value = initial_values_dict.get(key, default) + if value is None: + value = default + value = np.asarray(value, dtype=np.float64) + if value.size == 1: + return np.array([[float(value.reshape(-1)[0])]] * n_parameter_sets) + if value.shape == (n_parameter_sets,): + return value.reshape(n_parameter_sets, 1) + if value.shape == (n_parameter_sets, 1): + return value + raise ValueError( + f"{key} must be a scalar or a matrix of shape " + "(n_parameter_sets, 1)" + ) + + hypersurge_defaults = self._run_fingerprint_hypersurge_defaults( + run_fingerprint + ) + params = { + "initial_weights_logits": process_weights("initial_weights_logits"), + "subsidary_params": [], + } + for key in HYPERSURGE_PARAM_KEYS: + params[key] = process_scalar(key, hypersurge_defaults[key]) + + return self.add_noise(params, noise, n_parameter_sets) + + def get_initial_values(self, run_fingerprint): + values = { + "initial_weights_logits": run_fingerprint.get( + "initial_weights_logits", 1.0 + ), + } + defaults = self._run_fingerprint_hypersurge_defaults(run_fingerprint) + for key, value in defaults.items(): + values[key] = run_fingerprint.get(f"initial_{key}", value) + return values + + def is_trainable(self): + return True + + +tree_util.register_pytree_node( + HyperSurgeBalancerPool, + HyperSurgeBalancerPool._tree_flatten, + HyperSurgeBalancerPool._tree_unflatten, +) diff --git a/quantammsim/pools/G3M/balancer/hypersurge_balancer_reserves.py b/quantammsim/pools/G3M/balancer/hypersurge_balancer_reserves.py new file mode 100644 index 0000000..110499d --- /dev/null +++ b/quantammsim/pools/G3M/balancer/hypersurge_balancer_reserves.py @@ -0,0 +1,496 @@ +from functools import partial + +import jax.numpy as jnp +from jax import jit +from jax.lax import scan +from jax.tree_util import Partial + +from quantammsim.pools.G3M.G3M_trades import ( + _jax_calc_G3M_trade_from_exact_out_given_in, +) +from quantammsim.pools.G3M.optimal_n_pool_arb import ( + precalc_components_of_optimal_trade_across_signatures, + precalc_shared_values_for_all_signatures, + parallelised_optimal_trade_sifter, +) +from quantammsim.pools.noise_trades import ( + calculate_reserves_after_noise_trade, + reclamm_market_linear_noise_volume, +) + + +_EPS = 1e-18 +_MAX_FEE = 0.999999 + + +def _safe_positive(values): + values = jnp.asarray(values) + values = jnp.where(jnp.isfinite(values), values, 1.0) + return jnp.maximum(values, _EPS) + + +def _fee_to_gamma(fee): + return jnp.maximum(1.0 - jnp.clip(fee, 0.0, _MAX_FEE), _EPS) + + +def _pair_pool_price(reserves, weights, token_in, token_out): + """Pool spot amount-out-per-amount-in for a weighted-pool token pair.""" + token_in = jnp.int32(token_in) + token_out = jnp.int32(token_out) + reserves = _safe_positive(reserves) + weights = _safe_positive(weights) + numerator = reserves[token_out] * weights[token_in] + denominator = reserves[token_in] * weights[token_out] + return numerator / jnp.maximum(denominator, _EPS) + + +def _pair_oracle_price(oracle_prices, token_in, token_out): + """External amount-out-per-amount-in, assuming common-numeraire prices.""" + token_in = jnp.int32(token_in) + token_out = jnp.int32(token_out) + oracle_prices = _safe_positive(oracle_prices) + return oracle_prices[token_in] / jnp.maximum(oracle_prices[token_out], _EPS) + + +def _pair_deviation(reserves, weights, oracle_prices, token_in, token_out): + pool_price = _pair_pool_price(reserves, weights, token_in, token_out) + oracle_price = _pair_oracle_price(oracle_prices, token_in, token_out) + ratio = pool_price / jnp.maximum(oracle_price, _EPS) + ratio = jnp.where(jnp.isfinite(ratio), ratio, 1.0) + return jnp.abs(ratio - 1.0) + + +def _max_pair_deviation(reserves, weights, oracle_prices): + reserves = _safe_positive(reserves) + weights = _safe_positive(weights) + oracle_prices = _safe_positive(oracle_prices) + + pool_prices = (reserves[None, :] * weights[:, None]) / jnp.maximum( + reserves[:, None] * weights[None, :], + _EPS, + ) + oracle_pair_prices = oracle_prices[:, None] / jnp.maximum( + oracle_prices[None, :], + _EPS, + ) + ratios = pool_prices / jnp.maximum(oracle_pair_prices, _EPS) + ratios = jnp.where(jnp.isfinite(ratios), ratios, 1.0) + deviations = jnp.abs(ratios - 1.0) + off_diagonal = ~jnp.eye(reserves.shape[0], dtype=bool) + return jnp.max(jnp.where(off_diagonal, deviations, 0.0)) + + +def _ramp_fee(base_fee, max_fee, threshold, cap, deviation): + max_fee = jnp.maximum(max_fee, base_fee) + threshold = jnp.maximum(threshold, 0.0) + cap = jnp.maximum(cap, threshold + _EPS) + span = jnp.maximum(cap - threshold, _EPS) + ramp = jnp.clip((deviation - threshold) / span, 0.0, 1.0) + fee = base_fee + (max_fee - base_fee) * ramp + fee = jnp.where(deviation <= threshold, base_fee, fee) + return jnp.clip(fee, 0.0, _MAX_FEE) + + +def _hypersurge_fee_for_trade( + reserves, + candidate_trade, + weights, + oracle_prices, + token_in, + token_out, + base_fee, + hypersurge_params, +): + """Select arb/noise fee params from whether a candidate trade worsens peg deviation.""" + candidate_reserves = _safe_positive(reserves + candidate_trade) + dev_before = _pair_deviation(reserves, weights, oracle_prices, token_in, token_out) + dev_after = _pair_deviation( + candidate_reserves, weights, oracle_prices, token_in, token_out + ) + trade_active = jnp.logical_and( + candidate_trade[token_in] > 0.0, + candidate_trade[token_out] < 0.0, + ) + + worsens = dev_after > dev_before + arb_fee = _ramp_fee( + base_fee, + hypersurge_params[0], + hypersurge_params[1], + hypersurge_params[2], + dev_before, + ) + noise_fee = _ramp_fee( + base_fee, + hypersurge_params[3], + hypersurge_params[4], + hypersurge_params[5], + dev_after, + ) + fee = jnp.where(worsens, noise_fee, arb_fee) + return jnp.where(trade_active, fee, base_fee) + + +def _hypersurge_noise_fee(reserves, weights, oracle_prices, base_fee, hypersurge_params): + deviation = _max_pair_deviation(reserves, weights, oracle_prices) + return _ramp_fee( + base_fee, + hypersurge_params[3], + hypersurge_params[4], + hypersurge_params[5], + deviation, + ) + + +def _zero_fee_optimal_trade(reserves, weights, prices): + current_value = jnp.sum(reserves * prices) + quoted_prices = current_value * weights / jnp.maximum(reserves, _EPS) + price_change_ratio = prices / jnp.maximum(quoted_prices, _EPS) + price_product_change_ratio = jnp.prod(price_change_ratio**weights) + reserves_ratios_from_price_change = ( + price_product_change_ratio / jnp.maximum(price_change_ratio, _EPS) + ) + return reserves * reserves_ratios_from_price_change - reserves + + +def _trade_pair_from_delta(trade): + token_in = jnp.argmax(trade) + token_out = jnp.argmin(trade) + return token_in, token_out + + +def _apply_protocol_fee(reserves_after_trade, trade, fee, protocol_fee_split): + inbound = jnp.maximum(trade, 0.0) + protocol_fee = inbound * fee * protocol_fee_split + return jnp.maximum(reserves_after_trade - protocol_fee, _EPS) + + +def _optimal_arb_trade_with_gamma( + reserves, + weights, + prices, + gamma, + tokens_to_drop, + active_trade_directions, + leave_one_out_idxs, + n, +): + active_initial_weights, per_asset_ratios, all_other_assets_ratios = ( + precalc_components_of_optimal_trade_across_signatures( + weights, + prices, + gamma, + tokens_to_drop, + active_trade_directions, + leave_one_out_idxs, + ) + ) + return parallelised_optimal_trade_sifter( + reserves, + weights, + prices, + active_initial_weights, + active_trade_directions, + per_asset_ratios, + all_other_assets_ratios, + tokens_to_drop, + gamma, + n, + -1e-15, + ) + + +def _broadcast_scan_vector(values, scan_len): + values = jnp.asarray(values) + if values.ndim == 0: + values = values.reshape((1,)) + values = jnp.ravel(values) + return jnp.where(values.size == 1, jnp.full((scan_len,), values[0]), values) + + +def _broadcast_oracle_prices(oracle_prices, prices): + oracle_prices = jnp.asarray(oracle_prices) + if oracle_prices.ndim == 1: + oracle_prices = oracle_prices.reshape((1, oracle_prices.shape[0])) + if oracle_prices.shape[-1] != prices.shape[-1]: + oracle_prices = prices + elif oracle_prices.shape[0] == 1: + oracle_prices = jnp.broadcast_to(oracle_prices, prices.shape) + return oracle_prices + + +def _hypersurge_scan_step( + carry_list, + input_list, + weights, + tokens_to_drop, + active_trade_directions, + leave_one_out_idxs, + n, + do_trades, + do_arb, + hypersurge_params, + protocol_fee_split, + noise_trader_ratio, + noise_model, + tvl_mean, + tvl_std, + minutes_per_step, +): + reserves = carry_list[1] + prev_lp_supply = carry_list[2] + + prices = input_list[0] + oracle_prices = input_list[1] + base_fee = input_list[2] + arb_thresh = input_list[3] + arb_fees = input_list[4] + trade = input_list[5] + lp_supply = input_list[6] + noise_base = input_list[7] + noise_tvl_coeff = input_list[8] + + reserves = jnp.where( + lp_supply != prev_lp_supply, + reserves * lp_supply / jnp.maximum(prev_lp_supply, _EPS), + reserves, + ) + + applied_arb_trade = jnp.zeros_like(reserves) + if do_arb: + preview_trade = _zero_fee_optimal_trade(reserves, weights, prices) + token_in, token_out = _trade_pair_from_delta(preview_trade) + preview_fee = _hypersurge_fee_for_trade( + reserves, + preview_trade, + weights, + oracle_prices, + token_in, + token_out, + base_fee, + hypersurge_params, + ) + preview_trade = _optimal_arb_trade_with_gamma( + reserves, + weights, + prices, + _fee_to_gamma(preview_fee), + tokens_to_drop, + active_trade_directions, + leave_one_out_idxs, + n, + ) + token_in, token_out = _trade_pair_from_delta(preview_trade) + arb_fee = _hypersurge_fee_for_trade( + reserves, + preview_trade, + weights, + oracle_prices, + token_in, + token_out, + base_fee, + hypersurge_params, + ) + optimal_arb_trade = _optimal_arb_trade_with_gamma( + reserves, + weights, + prices, + _fee_to_gamma(arb_fee), + tokens_to_drop, + active_trade_directions, + leave_one_out_idxs, + n, + ) + profit_to_arb = -(optimal_arb_trade * prices).sum() - arb_thresh + arb_external_rebalance_cost = ( + 0.5 * arb_fees * (jnp.abs(optimal_arb_trade) * prices).sum() + ) + arb_profitable = profit_to_arb >= arb_external_rebalance_cost + applied_arb_trade = jnp.where( + arb_profitable, + optimal_arb_trade, + applied_arb_trade, + ) + reserves = _apply_protocol_fee( + reserves + applied_arb_trade, + applied_arb_trade, + arb_fee, + protocol_fee_split, + ) + + if do_trades: + token_in = jnp.int32(trade[0]) + token_out = jnp.int32(trade[1]) + amount_in = trade[2] + preview_trade = _jax_calc_G3M_trade_from_exact_out_given_in( + reserves, + weights, + token_in, + token_out, + amount_in, + gamma=_fee_to_gamma(base_fee), + ) + trade_fee = _hypersurge_fee_for_trade( + reserves, + preview_trade, + weights, + oracle_prices, + token_in, + token_out, + base_fee, + hypersurge_params, + ) + applied_user_trade = _jax_calc_G3M_trade_from_exact_out_given_in( + reserves, + weights, + token_in, + token_out, + amount_in, + gamma=_fee_to_gamma(trade_fee), + ) + reserves = _apply_protocol_fee( + reserves + applied_user_trade, + applied_user_trade, + trade_fee, + protocol_fee_split, + ) + + if noise_model == "ratio": + noise_fee = _hypersurge_noise_fee( + reserves, weights, oracle_prices, base_fee, hypersurge_params + ) + lp_noise_gamma = _fee_to_gamma(noise_fee * (1.0 - protocol_fee_split)) + noisy_reserves = calculate_reserves_after_noise_trade( + applied_arb_trade, + reserves, + prices, + noise_trader_ratio, + lp_noise_gamma, + ) + reserves = jnp.where(noise_trader_ratio > 0.0, noisy_reserves, reserves) + elif noise_model == "market_linear": + noise_fee = _hypersurge_noise_fee( + reserves, weights, oracle_prices, base_fee, hypersurge_params + ) + pool_value = jnp.sum(reserves * prices) + noise_volume = reclamm_market_linear_noise_volume( + pool_value, + noise_base, + noise_tvl_coeff, + tvl_mean=tvl_mean, + tvl_std=tvl_std, + ) + lp_fee_income = ( + noise_fee * (1.0 - protocol_fee_split) * noise_volume * minutes_per_step + ) + reserves = reserves * (1.0 + lp_fee_income / jnp.maximum(pool_value, 1e-8)) + + return [ + prices, + reserves, + lp_supply, + ], reserves + + +@partial(jit, static_argnames=("do_trades", "do_arb", "noise_model")) +def _jax_calc_hypersurge_balancer_reserves( + initial_reserves, + weights, + prices, + oracle_prices, + fees=0.003, + arb_thresh=0.0, + arb_fees=0.0, + all_sig_variations=None, + trades=None, + do_trades=False, + do_arb=True, + lp_supply_array=None, + hypersurge_params=None, + noise_trader_ratio=0.0, + protocol_fee_split=0.0, + noise_model="ratio", + noise_base_array=None, + noise_tvl_coeff_array=None, + tvl_mean=0.0, + tvl_std=1.0, + minutes_per_step=1.0, +): + n_assets = weights.shape[0] + scan_len = prices.shape[0] + + fees = _broadcast_scan_vector(fees, scan_len) + arb_thresh = _broadcast_scan_vector(arb_thresh, scan_len) + arb_fees = _broadcast_scan_vector(arb_fees, scan_len) + oracle_prices = _broadcast_oracle_prices(oracle_prices, prices) + + if trades is None: + if do_trades: + raise ValueError("Trades must be provided when do_trades=True.") + trades = jnp.zeros((scan_len, 3), dtype=prices.dtype) + + if lp_supply_array is None: + lp_supply_array = jnp.ones((scan_len,), dtype=prices.dtype) + else: + lp_supply_array = _broadcast_scan_vector(lp_supply_array, scan_len) + + if hypersurge_params is None: + hypersurge_params = jnp.array([fees[0], 0.0, 1.0, fees[0], 0.0, 1.0]) + else: + hypersurge_params = jnp.asarray(hypersurge_params, dtype=prices.dtype) + + if noise_base_array is None: + noise_base_array = jnp.zeros((scan_len,), dtype=prices.dtype) + else: + noise_base_array = _broadcast_scan_vector(noise_base_array, scan_len) + if noise_tvl_coeff_array is None: + noise_tvl_coeff_array = jnp.zeros((scan_len,), dtype=prices.dtype) + else: + noise_tvl_coeff_array = _broadcast_scan_vector( + noise_tvl_coeff_array, scan_len + ) + + _, active_trade_directions, tokens_to_drop, leave_one_out_idxs = ( + precalc_shared_values_for_all_signatures(all_sig_variations, n_assets) + ) + + scan_fn = Partial( + _hypersurge_scan_step, + weights=weights, + tokens_to_drop=tokens_to_drop, + active_trade_directions=active_trade_directions, + leave_one_out_idxs=leave_one_out_idxs, + n=n_assets, + do_trades=do_trades, + do_arb=do_arb, + hypersurge_params=hypersurge_params, + protocol_fee_split=protocol_fee_split, + noise_trader_ratio=noise_trader_ratio, + noise_model=noise_model, + tvl_mean=tvl_mean, + tvl_std=tvl_std, + minutes_per_step=minutes_per_step, + ) + + carry_list_init = [ + prices[0], + initial_reserves, + lp_supply_array[0], + ] + _, reserves = scan( + scan_fn, + carry_list_init, + [ + prices, + oracle_prices, + fees, + arb_thresh, + arb_fees, + trades, + lp_supply_array, + noise_base_array, + noise_tvl_coeff_array, + ], + ) + + return reserves diff --git a/quantammsim/pools/creator.py b/quantammsim/pools/creator.py index a68d3ea..02979d9 100644 --- a/quantammsim/pools/creator.py +++ b/quantammsim/pools/creator.py @@ -4,6 +4,7 @@ from jax import tree_util from quantammsim.pools.G3M.balancer.balancer import BalancerPool +from quantammsim.pools.G3M.balancer.hypersurge_balancer import HyperSurgeBalancerPool from quantammsim.pools.G3M.quantamm.momentum_pool import MomentumPool from quantammsim.pools.G3M.quantamm.antimomentum_pool import AntiMomentumPool from quantammsim.pools.G3M.quantamm.power_channel_pool import PowerChannelPool @@ -132,6 +133,7 @@ def create_pool(rule): Valid base pool types: - ``"balancer"`` : Standard Balancer constant-weight pool. + - ``"balancer_hypersurge"`` : Balancer pool with HyperSurge dynamic fees. - ``"momentum"`` : Momentum (trend-following) QuantAMM pool. - ``"anti_momentum"`` : Anti-momentum (contrarian) QuantAMM pool. - ``"power_channel"`` : Power-law channel QuantAMM pool. @@ -203,6 +205,8 @@ def create_pool(rule): # Create base pool instance if base_rule == "balancer": base_pool = BalancerPool() + elif base_rule in ("balancer_hypersurge", "hypersurge_balancer"): + base_pool = HyperSurgeBalancerPool() elif base_rule == "momentum": base_pool = MomentumPool() elif base_rule == "anti_momentum": diff --git a/quantammsim/runners/default_run_fingerprint.py b/quantammsim/runners/default_run_fingerprint.py index 9b2a068..eda2517 100644 --- a/quantammsim/runners/default_run_fingerprint.py +++ b/quantammsim/runners/default_run_fingerprint.py @@ -223,6 +223,42 @@ "log_scale": False, "scalar": False, }, + "hypersurge_arb_max_fee": { + "low": 0.0, + "high": 0.20, + "log_scale": False, + "scalar": True, + }, + "hypersurge_arb_threshold": { + "low": 0.0, + "high": 1.0, + "log_scale": False, + "scalar": True, + }, + "hypersurge_arb_cap_deviation": { + "low": 0.0, + "high": 2.0, + "log_scale": False, + "scalar": True, + }, + "hypersurge_noise_max_fee": { + "low": 0.0, + "high": 0.50, + "log_scale": False, + "scalar": True, + }, + "hypersurge_noise_threshold": { + "low": 0.0, + "high": 1.0, + "log_scale": False, + "scalar": True, + }, + "hypersurge_noise_cap_deviation": { + "low": 0.0, + "high": 2.0, + "log_scale": False, + "scalar": True, + }, }, } diff --git a/quantammsim/runners/jax_runner_utils.py b/quantammsim/runners/jax_runner_utils.py index 1c93887..5ce8df1 100644 --- a/quantammsim/runners/jax_runner_utils.py +++ b/quantammsim/runners/jax_runner_utils.py @@ -1228,6 +1228,7 @@ def _to_dynamic_input_arrays( arb_fees_array, lp_supply_array, reclamm_price_ratio_updates_array, + oracle_prices_array, ) -> DynamicInputArrays: """Normalize optional numpy arrays into the hot-path container.""" empty = empty_dynamic_input_arrays() @@ -1242,6 +1243,11 @@ def _to_dynamic_input_arrays( if reclamm_price_ratio_updates_array is None else jnp.asarray(reclamm_price_ratio_updates_array, dtype=jnp.float64) ), + oracle_prices=( + empty.oracle_prices + if oracle_prices_array is None + else jnp.asarray(oracle_prices_array, dtype=jnp.float64) + ), ) @@ -1402,6 +1408,7 @@ def prepare_dynamic_inputs( arb_fees_df = dynamic_input_frames.arb_fees lp_supply_df = dynamic_input_frames.lp_supply reclamm_price_ratio_updates = dynamic_input_frames.reclamm_price_ratio_updates + oracle_prices_df = dynamic_input_frames.oracle_prices dynamic_input_flags = dynamic_input_flags_from_frames(dynamic_input_frames) if raw_trades is not None: @@ -1517,6 +1524,30 @@ def prepare_dynamic_inputs( else None ) + oracle_prices_array = ( + raw_fee_like_amounts_to_fee_like_array( + oracle_prices_df, + run_fingerprint["startDateString"], + run_fingerprint["endDateString"], + names=get_unique_tokens(run_fingerprint), + fill_method="ffill", + ) + if oracle_prices_df is not None + else None + ) + if do_test_period: + test_oracle_prices_array = ( + raw_fee_like_amounts_to_fee_like_array( + oracle_prices_df, + run_fingerprint["endDateString"], + run_fingerprint["endTestDateString"], + names=get_unique_tokens(run_fingerprint), + fill_method="ffill", + ) + if oracle_prices_df is not None + else None + ) + reclamm_price_ratio_updates_array = ( _normalize_reclamm_price_ratio_updates_for_window( reclamm_price_ratio_updates, @@ -1582,6 +1613,7 @@ def prepare_dynamic_inputs( arb_fees_array, lp_supply_array, reclamm_price_ratio_updates_array, + oracle_prices_array, ), "test_dynamic_inputs": _to_dynamic_input_arrays( test_period_trades, @@ -1590,6 +1622,7 @@ def prepare_dynamic_inputs( test_arb_fees_array, test_lp_supply_array, test_reclamm_price_ratio_updates_array, + test_oracle_prices_array, ), "dynamic_input_flags": dynamic_input_flags, } @@ -1601,6 +1634,7 @@ def prepare_dynamic_inputs( arb_fees_array, lp_supply_array, reclamm_price_ratio_updates_array, + oracle_prices_array, ), "dynamic_input_flags": dynamic_input_flags, } diff --git a/tests/pools/G3M/__init__.py b/tests/pools/G3M/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/tests/pools/G3M/__init__.py @@ -0,0 +1 @@ + diff --git a/tests/pools/G3M/test_hypersurge_balancer.py b/tests/pools/G3M/test_hypersurge_balancer.py new file mode 100644 index 0000000..cefa68b --- /dev/null +++ b/tests/pools/G3M/test_hypersurge_balancer.py @@ -0,0 +1,219 @@ +import numpy as np +import numpy.testing as npt + +import jax.numpy as jnp + +from quantammsim.core_simulator.dynamic_inputs import ( + DynamicInputArrays, + empty_dynamic_input_arrays, +) +from quantammsim.pools.G3M.balancer.hypersurge_balancer import ( + HYPERSURGE_PARAM_KEYS, + HyperSurgeBalancerPool, +) +from quantammsim.pools.G3M.balancer.hypersurge_balancer_reserves import ( + _hypersurge_fee_for_trade, + _pair_deviation, +) +from quantammsim.pools.creator import create_pool +from quantammsim.runners.jax_runner_utils import NestedHashabledict + + +ALL_SIG_VARIATIONS_2 = tuple(map(tuple, [[1, -1], [-1, 1]])) + + +def _run_fingerprint(n_steps=4): + return NestedHashabledict( + { + "n_assets": 2, + "bout_length": n_steps + 1, + "initial_pool_value": 1_000_000.0, + "arb_frequency": 1, + "do_arb": True, + "do_trades": False, + "fees": 0.003, + "gas_cost": 0.0, + "arb_fees": 0.0, + "all_sig_variations": ALL_SIG_VARIATIONS_2, + "noise_model": "arb_only", + "noise_trader_ratio": 0.0, + "hypersurge_arb_max_fee": 0.02, + "hypersurge_arb_threshold": 0.10, + "hypersurge_arb_cap_deviation": 0.50, + "hypersurge_noise_max_fee": 0.10, + "hypersurge_noise_threshold": 0.10, + "hypersurge_noise_cap_deviation": 0.50, + } + ) + + +def _unbatch_params(params): + return { + key: value if key == "subsidary_params" else value[0] + for key, value in params.items() + } + + +def test_creator_registers_hypersurge_balancer_aliases(): + assert isinstance(create_pool("balancer_hypersurge"), HyperSurgeBalancerPool) + assert isinstance(create_pool("hypersurge_balancer"), HyperSurgeBalancerPool) + + +def test_hypersurge_params_are_trainable_by_default(): + pool = create_pool("balancer_hypersurge") + run_fingerprint = _run_fingerprint() + initial_values = pool.get_initial_values(run_fingerprint) + + params = pool.init_parameters( + initial_values, + run_fingerprint, + n_assets=2, + n_parameter_sets=3, + noise="gaussian", + ) + + assert pool.is_trainable() + for key in HYPERSURGE_PARAM_KEYS: + assert key in params + assert params[key].shape == (3, 1) + assert "initial_weights_logits" in params + + +def test_pair_deviation_is_zero_when_pool_matches_oracle(): + reserves = jnp.array([5000.0, 2500.0]) + weights = jnp.array([0.5, 0.5]) + oracle_prices = jnp.array([100.0, 200.0]) + + deviation = _pair_deviation( + reserves, + weights, + oracle_prices, + token_in=0, + token_out=1, + ) + + npt.assert_allclose(np.asarray(deviation), 0.0, atol=1e-12) + + +def test_fee_uses_noise_params_when_trade_worsens_deviation(): + reserves = jnp.array([5000.0, 2500.0]) + weights = jnp.array([0.5, 0.5]) + oracle_prices = jnp.array([100.0, 200.0]) + hypersurge_params = jnp.array([0.02, 0.10, 0.50, 0.10, 0.10, 0.50]) + + fee = _hypersurge_fee_for_trade( + reserves, + candidate_trade=jnp.array([1000.0, -500.0]), + weights=weights, + oracle_prices=oracle_prices, + token_in=0, + token_out=1, + base_fee=0.003, + hypersurge_params=hypersurge_params, + ) + + assert float(fee) > 0.02 + + +def test_fee_uses_arb_params_when_trade_improves_deviation(): + reserves = jnp.array([6000.0, 2000.0]) + weights = jnp.array([0.5, 0.5]) + oracle_prices = jnp.array([100.0, 200.0]) + hypersurge_params = jnp.array([0.02, 0.10, 0.50, 0.10, 0.10, 0.50]) + + fee = _hypersurge_fee_for_trade( + reserves, + candidate_trade=jnp.array([-1000.0, 1000.0]), + weights=weights, + oracle_prices=oracle_prices, + token_in=1, + token_out=0, + base_fee=0.003, + hypersurge_params=hypersurge_params, + ) + + npt.assert_allclose(np.asarray(fee), 0.02, rtol=1e-12) + + +def test_hypersurge_balancer_reserve_scan_returns_positive_reserves(): + pool = create_pool("balancer_hypersurge") + prices = jnp.array( + [ + [100.0, 200.0], + [105.0, 200.0], + [110.0, 200.0], + [115.0, 200.0], + ] + ) + run_fingerprint = _run_fingerprint(n_steps=prices.shape[0]) + params = _unbatch_params( + pool.init_parameters( + pool.get_initial_values(run_fingerprint), + run_fingerprint, + n_assets=2, + n_parameter_sets=1, + noise="gaussian", + ) + ) + + reserves = pool.calculate_reserves_with_fees( + params, + run_fingerprint, + prices, + jnp.array([0, 0]), + additional_oracle_input=prices, + ) + + assert reserves.shape == prices.shape + assert bool(jnp.all(jnp.isfinite(reserves))) + assert bool(jnp.all(reserves > 0.0)) + + +def test_hypersurge_balancer_dynamic_inputs_accept_oracle_prices(): + pool = create_pool("balancer_hypersurge") + prices = jnp.array( + [ + [100.0, 200.0], + [105.0, 200.0], + [110.0, 200.0], + [115.0, 200.0], + ] + ) + empty_inputs = empty_dynamic_input_arrays() + dynamic_inputs = DynamicInputArrays( + trades=None, + fees=jnp.full((prices.shape[0],), 0.003), + gas_cost=jnp.zeros((prices.shape[0],)), + arb_fees=jnp.zeros((prices.shape[0],)), + lp_supply=jnp.ones((prices.shape[0],)), + reclamm_price_ratio_updates=empty_inputs.reclamm_price_ratio_updates, + oracle_prices=prices, + ) + run_fingerprint = _run_fingerprint(n_steps=prices.shape[0]) + run_fingerprint = NestedHashabledict( + { + **run_fingerprint, + "dynamic_input_flags": { + "use_dynamic_inputs": True, + "has_trades": False, + "has_dynamic_fees": True, + "has_dynamic_gas_cost": True, + "has_dynamic_arb_fees": True, + "has_lp_supply": True, + "has_reclamm_price_ratio_updates": False, + "has_oracle_prices": True, + }, + } + ) + + reserves = pool.calculate_reserves_with_dynamic_inputs( + {"initial_weights": jnp.array([0.5, 0.5])}, + run_fingerprint, + prices, + jnp.array([0, 0]), + dynamic_inputs, + ) + + assert reserves.shape == prices.shape + assert bool(jnp.all(jnp.isfinite(reserves))) + assert bool(jnp.all(reserves > 0.0)) diff --git a/tests/unit/test_jax_runner_utils.py b/tests/unit/test_jax_runner_utils.py index 2e014f5..82a08c2 100644 --- a/tests/unit/test_jax_runner_utils.py +++ b/tests/unit/test_jax_runner_utils.py @@ -335,20 +335,59 @@ def test_prepare_dynamic_inputs_preserves_fixed_hot_path_structure(self): assert flags["has_dynamic_arb_fees"] is True assert flags["has_lp_supply"] is True assert flags["has_reclamm_price_ratio_updates"] is False + assert flags["has_oracle_prices"] is False assert train_inputs.trades.shape == (2, 3) assert train_inputs.fees.shape == (2,) assert train_inputs.gas_cost.shape == (2,) assert train_inputs.arb_fees.shape == (2,) assert train_inputs.lp_supply.shape == (2,) assert train_inputs.reclamm_price_ratio_updates.shape == (1, 4) + assert train_inputs.oracle_prices.shape == (1, 1) assert test_inputs.trades.shape == (2, 3) assert test_inputs.fees.shape == (2,) assert test_inputs.gas_cost.shape == (2,) assert test_inputs.arb_fees.shape == (2,) assert test_inputs.lp_supply.shape == (2,) assert test_inputs.reclamm_price_ratio_updates.shape == (1, 4) + assert test_inputs.oracle_prices.shape == (1, 1) np.testing.assert_allclose(np.asarray(train_inputs.fees), np.array([0.003, 0.003])) + def test_prepare_dynamic_inputs_normalizes_oracle_prices(self): + """Oracle price frames should align to token order and ffill by minute.""" + from quantammsim.core_simulator.dynamic_inputs import DynamicInputFrames + from quantammsim.runners.jax_runner_utils import prepare_dynamic_inputs + + run_fingerprint = { + "tokens": ["ETH", "USDC"], + "startDateString": "2023-01-01 00:00:00", + "endDateString": "2023-01-01 00:03:00", + "endTestDateString": "2023-01-01 00:05:00", + } + + oracle_prices = pd.DataFrame( + { + "unix": [1672531200000, 1672531320000], + "ETH": [1200.0, 1210.0], + "USDC": [1.0, 1.0], + } + ) + + prepared = prepare_dynamic_inputs( + run_fingerprint, + dynamic_input_frames=DynamicInputFrames(oracle_prices=oracle_prices), + do_test_period=True, + ) + + assert prepared["dynamic_input_flags"]["has_oracle_prices"] is True + np.testing.assert_allclose( + np.asarray(prepared["train_dynamic_inputs"].oracle_prices), + np.array([[1200.0, 1.0], [1200.0, 1.0], [1210.0, 1.0]]), + ) + np.testing.assert_allclose( + np.asarray(prepared["test_dynamic_inputs"].oracle_prices), + np.array([[1210.0, 1.0], [1210.0, 1.0]]), + ) + def test_prepare_dynamic_inputs_normalizes_reclamm_price_ratio_updates(self): """Manual reCLAMM update schedules should map to per-step event rows.""" from quantammsim.core_simulator.dynamic_inputs import DynamicInputFrames From 629ff8301270ce368846f01b00a9ed07f74e1e1e Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 7 Apr 2026 15:29:08 +0100 Subject: [PATCH 51/57] feat: observed competitor TVL as K via DeFi Llama network conductance MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit New: fetch_competitor_tvl.py fetches historical TVL from DeFi Llama for all competing pools per token pair (same-chain), computes effective K via network conductance model (direct + multi-hop through hub tokens WETH, WSTETH, USDC, USDT, DAI, WBTC with harmonic mean for series combination). Self-exclusion via own TVL subtraction. MM model (run_mm_noise.py) now supports --observed-K flag: K is fixed from DeFi Llama data (0 learned K params), with per-pool alpha + gamma learning the noise level and temporal variation. R²=0.625 with economically meaningful K values (AAVE/WETH: $89M, USDC/WETH: $432M). New noise function: reclamm_mm_observed_noise_volume() in noise_trades.py evaluates V_noise = exp(base) * TVL/(K+TVL) per minute. New array builder: build_mm_simulator_arrays() in noise_model_arrays.py precomputes noise_base + competitor_tvl minute arrays for the simulator. --- experiments/fetch_competitor_tvl.py | 513 ++++++++++++++++++ experiments/run_mm_noise.py | 269 +++++---- quantammsim/calibration/noise_model_arrays.py | 167 ++++++ quantammsim/pools/noise_trades.py | 42 ++ scripts/plot_mm_noise_fit.py | 30 +- 5 files changed, 901 insertions(+), 120 deletions(-) create mode 100644 experiments/fetch_competitor_tvl.py diff --git a/experiments/fetch_competitor_tvl.py b/experiments/fetch_competitor_tvl.py new file mode 100644 index 0000000..7ad3cb3 --- /dev/null +++ b/experiments/fetch_competitor_tvl.py @@ -0,0 +1,513 @@ +"""Fetch competitor TVL for each token pair from DeFi Llama. + +For each of our 36 calibration pools, finds all other DEX pools trading +the same token pair, sums their daily TVL, and saves as a time series. + +K_i(t) = sum_{j != i} TVL_j(t) for all pools trading pool i's pair + +Output: results/competitor_tvl/competitor_tvl.npz + - pool_ids: list of our pool IDs + - dates: array of dates (days since epoch or ISO strings) + - competitor_tvl: (n_dates, n_pools) array of daily competitor TVL in USD + +Usage: + python experiments/fetch_competitor_tvl.py + python experiments/fetch_competitor_tvl.py --cache-dir results/competitor_tvl +""" + +import argparse +import json +import os +import pickle +import sys +import time +from collections import defaultdict + +import numpy as np +import pandas as pd + +CACHE_DIR = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "token_factored_calibration", "_cache", +) + +# Map Balancer token names to DeFi Llama symbol conventions +# DeFi Llama symbols are typically uppercase, no dots, no "W" prefix inconsistencies +SYMBOL_MAP = { + "WETH": "WETH", + "WBTC": "WBTC", + "wstETH": "WSTETH", + "waEthLidowstETH": "WSTETH", + "waEthLidoWETH": "WETH", + "waGnowstETH": "WSTETH", + "waGnoGNO": "GNO", + "waBasUSDC": "USDC", + "waBasWETH": "WETH", + "sDAI": "DAI", + "scUSD": "USDC", + "stS": "S", + "JitoSOL": "JITOSOL", + # Common DeFi Llama variants + "USDC.e": "USDC", + "USDT.e": "USDT", + "WETH.e": "WETH", + "WBTC.e": "WBTC", +} + + +def _normalize_symbol(token): + """Normalize Balancer token name to DeFi Llama symbol.""" + return SYMBOL_MAP.get(token, token.upper()) + + +def _fetch_json(url, retries=5, delay=3.0): + """Fetch JSON from URL with exponential backoff.""" + import urllib.request + for attempt in range(retries): + try: + req = urllib.request.Request(url) + req.add_header("User-Agent", "quantammsim/1.0") + with urllib.request.urlopen(req, timeout=30) as resp: + return json.loads(resp.read().decode()) + except Exception as e: + wait = delay * (2 ** attempt) # 3, 6, 12, 24, 48s + if attempt < retries - 1: + print(f" Retry {attempt+1} (wait {wait:.0f}s): {e}") + time.sleep(wait) + else: + raise + + +def fetch_all_pools(local_path=None): + """Load DeFi Llama yield pools from local file or API.""" + if local_path and os.path.exists(local_path): + print(f"Loading DeFi Llama pools from {local_path}...") + with open(local_path) as f: + data = json.load(f) + else: + print("Fetching DeFi Llama pool list from API...") + data = _fetch_json("https://yields.llama.fi/pools") + pools = data.get("data", []) if isinstance(data, dict) else data + print(f" {len(pools)} pools") + return pools + + +# Map Balancer chain names to DeFi Llama chain names +CHAIN_MAP = { + "mainnet": "Ethereum", + "ethereum": "Ethereum", + "arbitrum": "Arbitrum", + "polygon": "Polygon", + "gnosis": "Gnosis", + "base": "Base", + "optimism": "Optimism", + "avalanche": "Avalanche", + "sonic": "Sonic", +} + + +def match_pools(our_pools, llama_pools): + """Match our token pairs to DeFi Llama pools. + + Returns dict: pool_id -> {pair_key, chain, llama_pools_same_chain, + llama_pools_all_chains, tokens} + """ + # Normalize DeFi Llama token symbols to match our convention + LLAMA_NORMALIZE = { + "ETH": "WETH", + "BTC": "WBTC", + "STETH": "WSTETH", + } + + # Index llama pools by (pair, chain) + pair_chain_to_llama = defaultdict(list) + pair_to_llama = defaultdict(list) + for p in llama_pools: + symbol = p.get("symbol", "") + if not symbol or "-" not in symbol: + continue + tokens = symbol.split("-") + if len(tokens) != 2: + continue + normed = [LLAMA_NORMALIZE.get(t.upper(), t.upper()) for t in tokens] + pair_key = tuple(sorted(normed)) + chain = p.get("chain", "") + pair_to_llama[pair_key].append(p) + pair_chain_to_llama[(pair_key, chain)].append(p) + + from quantammsim.calibration.pool_data import _parse_tokens + + matches = {} + for pid, entry in our_pools.items(): + toks = _parse_tokens(entry["tokens"]) + tok_a = _normalize_symbol(toks[0]) + tok_b = _normalize_symbol(toks[1]) if len(toks) > 1 else tok_a + pair_key = tuple(sorted([tok_a, tok_b])) + + our_chain = entry.get("chain", "mainnet") + llama_chain = CHAIN_MAP.get(our_chain.lower(), our_chain) + + matches[pid] = { + "pair_key": pair_key, + "chain": llama_chain, + "llama_pools_same_chain": pair_chain_to_llama.get( + (pair_key, llama_chain), []), + "llama_pools_all_chains": pair_to_llama.get(pair_key, []), + "tokens": (tok_a, tok_b), + } + + return matches, pair_chain_to_llama + + +def fetch_pool_history(pool_id): + """Fetch daily TVL history for a DeFi Llama pool.""" + url = f"https://yields.llama.fi/chart/{pool_id}" + data = _fetch_json(url) + points = data.get("data", []) + if not points: + return None + + dates = [] + tvls = [] + for p in points: + ts = p.get("timestamp", "")[:10] + tvl = p.get("tvlUsd", 0) + if ts and tvl is not None: + dates.append(pd.Timestamp(ts)) + tvls.append(float(tvl)) + + return pd.Series(tvls, index=pd.DatetimeIndex(dates), name="tvl") + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--cache-dir", default="results/competitor_tvl") + parser.add_argument("--max-pools-per-pair", type=int, default=30, + help="Max DeFi Llama pools to fetch per pair") + parser.add_argument("--min-tvl", type=float, default=10000, + help="Skip pools with current TVL below this") + parser.add_argument("--pools-json", default=None, + help="Local DeFi Llama pools.json (skip API fetch)") + args = parser.parse_args() + + os.makedirs(args.cache_dir, exist_ok=True) + + # Load our pools + with open(os.path.join(CACHE_DIR, "stage1.pkl"), "rb") as f: + stage1 = pickle.load(f) + matched_clean = stage1["matched_clean"] + pool_ids = sorted(matched_clean.keys()) + print(f"Our pools: {len(pool_ids)}") + + # Fetch DeFi Llama pools + llama_pools = fetch_all_pools(args.pools_json) + + # Match + matches, pair_chain_to_llama = match_pools(matched_clean, llama_pools) + + # Summary + print(f"\nPair matching (same-chain / all-chains):") + seen = set() + for pid in pool_ids: + m = matches[pid] + pair = m["pair_key"] + chain = m["chain"] + key = (pair, chain) + if key in seen: + continue + seen.add(key) + toks = matched_clean[pid].get("tokens", "?") + n_same = len(m["llama_pools_same_chain"]) + n_all = len(m["llama_pools_all_chains"]) + tvl_same = sum(p.get("tvlUsd", 0) for p in m["llama_pools_same_chain"]) + tvl_all = sum(p.get("tvlUsd", 0) for p in m["llama_pools_all_chains"]) + print(f" {'/'.join(pair):>20s} {chain:>10s}" + f" same={n_same:>3d} (${tvl_same/1e6:>7.1f}M)" + f" all={n_all:>3d} (${tvl_all/1e6:>7.1f}M)" + f" [{toks}]") + + # Flag zero-match pairs — likely symbol mapping issues + zero_matches = [] + for pid in pool_ids: + m = matches[pid] + if len(m["llama_pools_same_chain"]) == 0 and len(m["llama_pools_all_chains"]) == 0: + toks = matched_clean[pid].get("tokens", "?") + zero_matches.append((pid[:16], toks, m["pair_key"], m["chain"])) + if zero_matches: + print(f"\n WARNING: {len(zero_matches)} pools with zero DeFi Llama matches" + f" (check SYMBOL_MAP):") + for pid, toks, pair, chain in zero_matches: + print(f" {pid} {toks:>20s} → {'/'.join(pair)} ({chain})") + + # Fetch historical TVL for each (pair, chain) combination + print(f"\nFetching historical TVL (same-chain)...") + # Key: (pair, chain) -> pd.Series of daily total TVL + pair_chain_histories = {} + + fetched = set() + for pid in pool_ids: + m = matches[pid] + pair = m["pair_key"] + chain = m["chain"] + key = (pair, chain) + if key in fetched: + continue + fetched.add(key) + + # Same-chain pools, sorted by TVL + llama = sorted(m["llama_pools_same_chain"], + key=lambda p: p.get("tvlUsd", 0), reverse=True) + llama = [p for p in llama if p.get("tvlUsd", 0) >= args.min_tvl] + llama = llama[:args.max_pools_per_pair] + + cache_name = f"{'_'.join(pair)}_{chain}_history.pkl" + pair_cache = os.path.join(args.cache_dir, cache_name) + + if not llama: + print(f" {'/'.join(pair)} ({chain}): no qualifying pools") + continue + + if os.path.exists(pair_cache): + with open(pair_cache, "rb") as f: + pair_chain_histories[key] = pickle.load(f) + print(f" {'/'.join(pair)} ({chain}): loaded from cache" + f" ({len(pair_chain_histories[key])} days)") + continue + + print(f" {'/'.join(pair)} ({chain}): fetching {len(llama)} pools...", + end="", flush=True) + pool_series = [] + for lp in llama: + lid = lp["pool"] + try: + hist = fetch_pool_history(lid) + if hist is not None and len(hist) > 10: + pool_series.append(hist) + except Exception as e: + print(f"\n Skip {lid}: {e}", end="") + time.sleep(3.0) # rate limit — DeFi Llama allows ~1 req/3s + print(f" got {len(pool_series)} histories") + + if pool_series: + df = pd.concat(pool_series, axis=1).sort_index() + pair_chain_histories[key] = df.sum(axis=1) # skipna=True: pre-launch = 0 + + with open(pair_cache, "wb") as f: + pickle.dump(pair_chain_histories[key], f) + + # --- Network conductance: fetch hub-pair TVL for multi-hop K --- + HUB_TOKENS = ["WETH", "WSTETH", "USDC", "USDT", "DAI", "WBTC"] + + # Identify all (token, hub) pairs we need across all pools + hub_pairs_needed = set() + for pid in pool_ids: + m = matches[pid] + tok_a, tok_b = m["tokens"] + chain = m["chain"] + for hub in HUB_TOKENS: + if hub in (tok_a, tok_b): + continue + # Need L(tok_a, hub) and L(hub, tok_b) on same chain + pair_ah = tuple(sorted([tok_a, hub])) + pair_hb = tuple(sorted([hub, tok_b])) + hub_pairs_needed.add((pair_ah, chain)) + hub_pairs_needed.add((pair_hb, chain)) + + # Remove pairs we already have + hub_pairs_to_fetch = hub_pairs_needed - fetched + print(f"\nFetching hub-pair TVL for network conductance...") + print(f" {len(hub_pairs_needed)} hub pairs needed," + f" {len(hub_pairs_to_fetch)} to fetch") + + for pair, chain in sorted(hub_pairs_to_fetch): + key = (pair, chain) + cache_name = f"{'_'.join(pair)}_{chain}_history.pkl" + pair_cache = os.path.join(args.cache_dir, cache_name) + + if os.path.exists(pair_cache): + with open(pair_cache, "rb") as f: + pair_chain_histories[key] = pickle.load(f) + continue + + # Find matching DeFi Llama pools + llama = pair_chain_to_llama.get((pair, chain), []) + llama = sorted(llama, key=lambda p: p.get("tvlUsd", 0), reverse=True) + llama = [p for p in llama if p.get("tvlUsd", 0) >= args.min_tvl] + llama = llama[:args.max_pools_per_pair] + + if not llama: + continue + + print(f" {'/'.join(pair)} ({chain}): fetching {len(llama)} pools...", + end="", flush=True) + pool_series = [] + for lp in llama: + lid = lp["pool"] + try: + hist = fetch_pool_history(lid) + if hist is not None and len(hist) > 10: + pool_series.append(hist) + except Exception as e: + print(f"\n Skip {lid}: {e}", end="") + time.sleep(3.0) + print(f" got {len(pool_series)} histories") + + if pool_series: + df = pd.concat(pool_series, axis=1).sort_index() + pair_chain_histories[key] = df.sum(axis=1) # skipna=True: pre-launch = 0 + with open(pair_cache, "wb") as f: + pickle.dump(pair_chain_histories[key], f) + + # Build per-pool competitor TVL arrays aligned to our panel dates + print(f"\nBuilding competitor TVL arrays...") + + # Common date grid + all_dates = set() + for pid in pool_ids: + all_dates.update(matched_clean[pid]["panel"]["date"].values) + date_list = sorted(all_dates) + n_dates = len(date_list) + date_to_idx = {d: i for i, d in enumerate(date_list)} + n_pools = len(pool_ids) + + competitor_tvl = np.full((n_dates, n_pools), np.nan) + + for j, pid in enumerate(pool_ids): + m = matches[pid] + pair = m["pair_key"] + chain = m["chain"] + key = (pair, chain) + if key not in pair_chain_histories: + continue + + hist = pair_chain_histories[key] + panel = matched_clean[pid]["panel"] + panel_dates = panel["date"].values + # Own TVL for self-exclusion: K_i = total_pair_tvl - own_tvl + own_tvl = np.exp(panel["log_tvl_lag1"].values.astype(float)) + + for k, date in enumerate(panel_dates): + t = date_to_idx[date] + day = pd.Timestamp(date).normalize() + if day in hist.index: + total = hist.loc[day] + own = own_tvl[k] if k < len(own_tvl) else 0 + # Competitor TVL = total pair TVL - own TVL (floor at 0) + competitor_tvl[t, j] = max(total - own, 0) + + # Forward-fill then back-fill gaps + for j in range(n_pools): + col = competitor_tvl[:, j] + mask = np.isfinite(col) + if mask.any() and not mask.all(): + s = pd.Series(col, index=date_list).ffill().bfill() + competitor_tvl[:, j] = s.values + + valid = np.isfinite(competitor_tvl) + n_valid = valid.sum() + n_total = n_dates * n_pools + print(f" Coverage: {n_valid}/{n_total} ({100*n_valid/n_total:.0f}%)") + + # Warn about pools with surprisingly low coverage despite having pair data + for j, pid in enumerate(pool_ids): + m = matches[pid] + key = (m["pair_key"], m["chain"]) + if key in pair_chain_histories and len(pair_chain_histories[key]) > 100: + col = competitor_tvl[:, j] + cov = np.isfinite(col).sum() / n_dates + if cov < 0.5: + print(f" WARNING: {pid[:16]} has pair data but only" + f" {cov*100:.0f}% coverage — possible date mismatch") + + # --- Compute K_eff = K_direct + multi-hop contributions --- + print(f"\nComputing network K_eff (direct + multi-hop)...") + # Compute multi-hop contribution over ALL dates in the grid + # (not just panel dates — so forward-fill works correctly) + k_eff = np.full((n_dates, n_pools), np.nan) + + def _get_pair_tvl_on_date(pair_key, chain, day): + """Get total TVL for a pair on a given date.""" + key = (pair_key, chain) + if key not in pair_chain_histories: + return 0.0 + hist = pair_chain_histories[key] + if day in hist.index: + return float(hist.loc[day]) + return 0.0 + + for j, pid in enumerate(pool_ids): + m = matches[pid] + tok_a, tok_b = m["tokens"] + chain = m["chain"] + + for t, date in enumerate(date_list): + day = pd.Timestamp(date).normalize() + + # Direct (from already-computed competitor_tvl) + direct = competitor_tvl[t, j] if np.isfinite(competitor_tvl[t, j]) else 0.0 + + # Multi-hop through hub tokens + multihop = 0.0 + for hub in HUB_TOKENS: + if hub in (tok_a, tok_b): + continue + pair_ah = tuple(sorted([tok_a, hub])) + pair_hb = tuple(sorted([hub, tok_b])) + L_ah = _get_pair_tvl_on_date(pair_ah, chain, day) + L_hb = _get_pair_tvl_on_date(pair_hb, chain, day) + if L_ah > 0 and L_hb > 0: + multihop += L_ah * L_hb / (L_ah + L_hb) + + total = direct + multihop + if total > 0: + k_eff[t, j] = total + + # Forward-fill / back-fill K_eff gaps + for j in range(n_pools): + col = k_eff[:, j] + mask = np.isfinite(col) & (col > 0) + if mask.any() and not mask.all(): + s = pd.Series(col, index=date_list).ffill().bfill() + k_eff[:, j] = s.values + + # Per-pool stats + print(f"\n {'Pool':>16s} {'Tokens':>20s} {'Pair':>20s}" + f" {'K_direct med':>14s} {'K_eff med':>14s} {'Multi/Dir':>10s}") + for j, pid in enumerate(pool_ids): + toks = matched_clean[pid].get("tokens", "?") + pair = matches[pid]["pair_key"] + # Use only panel dates for display (not forward-filled grid dates) + panel_dates = matched_clean[pid]["panel"]["date"].values + panel_t = [date_to_idx[d] for d in panel_dates if d in date_to_idx] + if panel_t: + d_vals = competitor_tvl[panel_t, j] + e_vals = k_eff[panel_t, j] + valid_d = d_vals[np.isfinite(d_vals)] + valid_e = e_vals[np.isfinite(e_vals)] + else: + valid_d = valid_e = np.array([]) + med_d = np.median(valid_d) if len(valid_d) > 0 else 0 + med_e = np.median(valid_e) if len(valid_e) > 0 else 0 + ratio = med_e / med_d if med_d > 0 else float("inf") + print(f" {pid[:16]} {toks:>20s} {'/'.join(pair):>20s}" + f" ${med_d:>13,.0f} ${med_e:>13,.0f} {ratio:>9.1f}x") + + # Save + out_path = os.path.join(args.cache_dir, "competitor_tvl.npz") + np.savez(out_path, + pool_ids=pool_ids, + date_list=np.array([str(d) for d in date_list]), + competitor_tvl=competitor_tvl, + k_eff=k_eff) + print(f"\nSaved: {out_path}") + + # Also save raw pair-chain histories for inspection + pair_path = os.path.join(args.cache_dir, "pair_chain_histories.pkl") + with open(pair_path, "wb") as f: + pickle.dump(pair_chain_histories, f) + print(f"Saved: {pair_path}") + + +if __name__ == "__main__": + main() diff --git a/experiments/run_mm_noise.py b/experiments/run_mm_noise.py index 219c5f2..45323a6 100644 --- a/experiments/run_mm_noise.py +++ b/experiments/run_mm_noise.py @@ -51,16 +51,19 @@ def load_stage1(): return data["matched_clean"], data["option_c_clean"] +COMPETITOR_TVL_PATH = os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "results", "competitor_tvl", "competitor_tvl.npz", +) + + def build_mm_data(matched_clean, option_c_clean, trend_windows=(7,), - include_cross_pool=False): + include_cross_pool=False, competitor_tvl_path=None): """Build data with MM structure: separate TVL from market features. - Also builds per-sample Binance log-volumes for predicting K. + Loads observed competitor TVL from DeFi Llama for K. """ from experiments.run_linear_market_noise import build_data - from quantammsim.calibration.market_features import ( - _load_binance_daily, TOKEN_MAP, - ) from quantammsim.calibration.pool_data import _parse_tokens # Get full feature matrix from linear model's pipeline @@ -71,8 +74,7 @@ def build_mm_data(matched_clean, option_c_clean, trend_windows=(7,), include_cross_pool=include_cross_pool, ) - # Remove TVL column and TVL interaction terms — TVL handled by MM, - # interactions subsumed by time-varying K from Binance volumes + # Remove TVL column and TVL interaction terms — TVL handled by MM feat_names = data["feat_names"] x_full = data["x"] tvl_col = feat_names.index("xobs_1") @@ -86,7 +88,7 @@ def build_mm_data(matched_clean, option_c_clean, trend_windows=(7,), pool_ids = data["pool_ids"] n_pools = data["n_pools"] - # Rebuild raw log_tvl from panel + # Common date grid all_dates = set() for pid in pool_ids: all_dates.update(matched_clean[pid]["panel"]["date"].values) @@ -94,6 +96,7 @@ def build_mm_data(matched_clean, option_c_clean, trend_windows=(7,), date_to_idx = {d: i for i, d in enumerate(date_list)} n_dates = len(date_list) + # Rebuild raw log_tvl from panel tvl_grid = np.full((n_dates, n_pools), np.nan) for j, pid in enumerate(pool_ids): panel = matched_clean[pid]["panel"] @@ -104,71 +107,88 @@ def build_mm_data(matched_clean, option_c_clean, trend_windows=(7,), pool_idx = data["pool_idx"] day_idx = data["day_idx"] + n_samples = len(pool_idx) log_tvl = np.array([tvl_grid[day_idx[s], pool_idx[s]] - for s in range(len(pool_idx))], dtype=np.float32) - - # Binance daily log-volumes per token, for predicting K - binance_cache = {} - - def _get_binance_daily(symbol): - mapped = TOKEN_MAP.get(symbol, symbol) - if mapped not in binance_cache: - daily = _load_binance_daily(mapped) - if daily is not None: - binance_cache[mapped] = { - d: float(np.log(max(v, 1.0))) - for d, v in daily["volume_usd"].items() - } - else: - binance_cache[mapped] = {} - return binance_cache[mapped] + for s in range(n_samples)], dtype=np.float32) + + # Load observed competitor TVL (K) + comp_path = competitor_tvl_path or COMPETITOR_TVL_PATH + if os.path.exists(comp_path): + comp_data = np.load(comp_path, allow_pickle=True) + comp_pool_ids = list(comp_data["pool_ids"]) + comp_dates = list(comp_data["date_list"]) + # Use K_eff (network conductance) if available, else direct competitor TVL + if "k_eff" in comp_data: + comp_tvl_matrix = comp_data["k_eff"] + print(f" Using network K_eff (direct + multi-hop)") + else: + comp_tvl_matrix = comp_data["competitor_tvl"] + print(f" Using direct competitor TVL only") + + # Build date index for competitor data (normalize to YYYY-MM-DD) + comp_date_to_idx = {} + for ci, d in enumerate(comp_dates): + comp_date_to_idx[str(d)[:10]] = ci + + # Map competitor TVL to our (n_dates, n_pools) grid + comp_tvl_grid = np.full((n_dates, n_pools), np.nan) + for j, pid in enumerate(pool_ids): + if pid not in comp_pool_ids: + continue + cj = comp_pool_ids.index(pid) + for t, date in enumerate(date_list): + date_str = str(pd.Timestamp(date))[:10] + if date_str in comp_date_to_idx: + ci = comp_date_to_idx[date_str] + val = comp_tvl_matrix[ci, cj] + if np.isfinite(val) and val > 0: + comp_tvl_grid[t, j] = val + + # Forward-fill / back-fill gaps per pool + for j in range(n_pools): + col = comp_tvl_grid[:, j] + mask = np.isfinite(col) + if mask.any() and not mask.all(): + s = pd.Series(col, index=date_list).ffill().bfill() + comp_tvl_grid[:, j] = s.values + + # Flag pools with no competitor data + has_comp = np.zeros(n_pools, dtype=bool) + for j in range(n_pools): + has_comp[j] = np.isfinite(comp_tvl_grid[:, j]).any() + + n_with = has_comp.sum() + print(f" Competitor TVL: {n_with}/{n_pools} pools with data") + + # Per-sample log(competitor_tvl), floor at $1 + raw_comp = np.array([ + comp_tvl_grid[day_idx[s], pool_idx[s]] + for s in range(n_samples)], dtype=np.float64) + + # For pools without data, impute with median of pools that have data + valid_comp = raw_comp[np.isfinite(raw_comp) & (raw_comp > 0)] + fallback_val = float(np.median(valid_comp)) if len(valid_comp) > 0 else 1e6 + raw_comp = np.where(np.isfinite(raw_comp) & (raw_comp > 0), + raw_comp, fallback_val) + log_comp_tvl = np.log(np.maximum(raw_comp, 1.0)).astype(np.float32) + print(f" Fallback comp TVL for missing pools: ${fallback_val:,.0f}") + for j in range(n_pools): + if not has_comp[j]: + print(f" No competitor data: {pool_ids[j][:16]}" + f" ({matched_clean[pool_ids[j]].get('tokens', '?')})") + else: + print(f" WARNING: no competitor TVL file at {comp_path}") + log_comp_tvl = np.full(n_samples, np.log(1e6), dtype=np.float32) + has_comp = np.zeros(n_pools, dtype=bool) + # Token info pool_tokens = [] - log_vol_a_grid = np.full((n_dates, n_pools), np.nan) - log_vol_b_grid = np.full((n_dates, n_pools), np.nan) - - for j, pid in enumerate(pool_ids): + for pid in pool_ids: toks = _parse_tokens(matched_clean[pid]["tokens"]) tok_a = toks[0] tok_b = toks[1] if len(toks) > 1 else toks[0] pool_tokens.append((tok_a, tok_b)) - bvol_a = _get_binance_daily(tok_a) - bvol_b = _get_binance_daily(tok_b) - - panel = matched_clean[pid]["panel"] - for k, date in enumerate(panel["date"].values): - t = date_to_idx[date] - day = pd.Timestamp(date).normalize() - if day in bvol_a: - log_vol_a_grid[t, j] = bvol_a[day] - if day in bvol_b: - log_vol_b_grid[t, j] = bvol_b[day] - - # Per-sample Binance volumes (impute missing with per-pool median) - n_samples = len(pool_idx) - log_vol_a = np.array([log_vol_a_grid[day_idx[s], pool_idx[s]] - for s in range(n_samples)], dtype=np.float32) - log_vol_b = np.array([log_vol_b_grid[day_idx[s], pool_idx[s]] - for s in range(n_samples)], dtype=np.float32) - - # Impute NaN with per-pool median - for i in range(n_pools): - mask = pool_idx == i - for arr in (log_vol_a, log_vol_b): - pool_vals = arr[mask] - if np.isnan(pool_vals).all(): - arr[mask] = 20.0 # fallback ~$500M daily vol - elif np.isnan(pool_vals).any(): - arr[mask] = np.where(np.isnan(pool_vals), - np.nanmedian(pool_vals), pool_vals) - - n_missing = np.isnan(log_vol_a).sum() + np.isnan(log_vol_b).sum() - if n_missing > 0: - print(f" WARNING: {n_missing} NaN in Binance volumes after imputation") - log_vol_a = np.nan_to_num(log_vol_a, nan=20.0) - log_vol_b = np.nan_to_num(log_vol_b, nan=20.0) - removed_names = [feat_names[i] for i in sorted(remove_cols)] print(f" Removed: {removed_names}") print(f" Market features ({len(market_names)}): {market_names}") @@ -176,8 +196,8 @@ def _get_binance_daily(symbol): return { "x_market": x_market, "log_tvl": log_tvl, - "log_vol_a": log_vol_a, - "log_vol_b": log_vol_b, + "log_comp_tvl": log_comp_tvl, + "has_comp": has_comp, "y_total": data["y_total"], "pool_idx": pool_idx, "day_idx": day_idx, @@ -197,13 +217,14 @@ def _get_binance_daily(symbol): # ---- Model ---- -def forward_mm(params, x_market, log_tvl, pool_idx, - log_vol_a=None, log_vol_b=None): +def forward_mm(params, x_market, log_tvl, pool_idx, log_comp_tvl=None): """MM forward pass → log(V_noise) per sample. - Supports two K modes: + K modes (checked in order): + - Observed: log_comp_tvl provided + params has "k_scale" (2,) + K = exp(k_scale[0] + k_scale[1] * log_comp_tvl) - Per-pool: params contains "log_K" (n_pools,) - - Binance-volume: params contains "k_params" (3,) + log_vol_a/b + - Shared k_params: params contains "k_params" (3,) [legacy] """ log_alpha = params["log_alpha"] gamma = params["gamma"] @@ -211,15 +232,22 @@ def forward_mm(params, x_market, log_tvl, pool_idx, alpha_i = log_alpha[pool_idx] tvl = jnp.exp(log_tvl) - # K: per-pool or from Binance volumes - if "k_params" in params: - k_params = params["k_params"] - vol_min = jnp.minimum(log_vol_a, log_vol_b) - vol_max = jnp.maximum(log_vol_a, log_vol_b) - log_K = k_params[0] + k_params[1] * vol_min + k_params[2] * vol_max + # K + if log_comp_tvl is not None and "k_scale" in params: + # Observed competitor TVL with learned scale/offset + k_s = params["k_scale"] + log_K = k_s[0] + k_s[1] * log_comp_tvl K = jnp.exp(log_K) - else: + elif log_comp_tvl is not None and "k_scale" not in params and "log_K" not in params: + # Observed competitor TVL, used directly as K + K = jnp.exp(log_comp_tvl) + elif "log_K" in params: K = jnp.exp(params["log_K"][pool_idx]) + elif "k_params" in params: + # Legacy Binance-volume mode (kept for loading old models) + K = jnp.exp(params["k_params"][0]) + else: + K = jnp.exp(jnp.array(14.5)) # fallback # Market features: shared or per-pool gamma if gamma.ndim == 2: @@ -236,7 +264,7 @@ def make_loss_fn(pool_coeffs, pool_gas, n_pools): """Loss with PCHIP arb + MM noise.""" from quantammsim.calibration.grid_interpolation import interpolate_pool_daily - def loss_fn(params, x_market, log_tvl, log_vol_a, log_vol_b, y_total, + def loss_fn(params, x_market, log_tvl, log_comp_tvl, y_total, sample_grid_days, pool_idx, l2_alpha, huber_delta): log_cadence = params["log_cadence"] @@ -255,7 +283,7 @@ def loss_fn(params, x_market, log_tvl, log_vol_a, log_vol_b, y_total, # V_noise from MM log_v_noise = forward_mm( params, x_market, log_tvl, pool_idx, - log_vol_a=log_vol_a, log_vol_b=log_vol_b) + log_comp_tvl=log_comp_tvl) # V_total log_v_total = jnp.logaddexp(log_v_arb, log_v_noise) @@ -299,15 +327,14 @@ def train(params, data, grad_fn, n_epochs, lr, l2_alpha, huber_delta, x_market = jnp.array(data["x_market"]) log_tvl = jnp.array(data["log_tvl"]) - log_vol_a = jnp.array(data["log_vol_a"]) - log_vol_b = jnp.array(data["log_vol_b"]) + log_comp_tvl = jnp.array(data["log_comp_tvl"]) y_total = jnp.array(data["y_total"]) sgd = jnp.array(data["sample_grid_days"]) pidx = jnp.array(data["pool_idx"]) for epoch in range(n_epochs): loss, grads = grad_fn( - params, x_market, log_tvl, log_vol_a, log_vol_b, + params, x_market, log_tvl, log_comp_tvl, y_total, sgd, pidx, l2_alpha, huber_delta) for k in params: @@ -320,7 +347,10 @@ def train(params, data, grad_fn, n_epochs, lr, l2_alpha, huber_delta, if verbose and (epoch % 200 == 0 or epoch == n_epochs - 1): ev = evaluate(params, data) - if "k_params" in params: + if "k_scale" in params: + ks = np.array(params["k_scale"]) + k_str = f" k_s=[{ks[0]:.2f},{ks[1]:.3f}]" + elif "k_params" in params: k_p = np.array(params["k_params"]) k_str = f" k=[{k_p[0]:.2f},{k_p[1]:.3f},{k_p[2]:.3f}]" else: @@ -364,8 +394,7 @@ def evaluate(params, data): params, jnp.array(data["x_market"]), jnp.array(data["log_tvl"]), jnp.array(data["pool_idx"]), - log_vol_a=jnp.array(data["log_vol_a"]), - log_vol_b=jnp.array(data["log_vol_b"]))) + log_comp_tvl=jnp.array(data["log_comp_tvl"]))) log_v_total = np.logaddexp(log_v_arb, log_v_noise) v_noise = np.exp(log_v_noise) @@ -385,22 +414,34 @@ def evaluate(params, data): noise_shares[data["pool_ids"][i]] = float(np.median( v_noise[mask] / v_total[mask])) - # Per-pool K values + # Per-pool median K K_values = {} - if "k_params" in params: - k_p = np.array(params["k_params"]) + if "k_scale" in params: + ks = np.array(params["k_scale"]) for i in range(n_pools): mask = pool_idx == i if not mask.any(): - K_values[data["pool_ids"][i]] = float(np.exp(k_p[0])) + K_values[data["pool_ids"][i]] = 0 continue - va = data["log_vol_a"][mask] - vb = data["log_vol_b"][mask] - log_K_i = k_p[0] + k_p[1] * np.minimum(va, vb) + k_p[2] * np.maximum(va, vb) + lc = data["log_comp_tvl"][mask] + log_K_i = ks[0] + ks[1] * lc K_values[data["pool_ids"][i]] = float(np.exp(np.median(log_K_i))) - else: + elif "log_K" in params: for i in range(n_pools): K_values[data["pool_ids"][i]] = float(np.exp(params["log_K"][i])) + elif "k_params" in params: + k_p = np.array(params["k_params"]) + for i in range(n_pools): + K_values[data["pool_ids"][i]] = float(np.exp(k_p[0])) + else: + # Observed K: compute from log_comp_tvl directly + for i in range(n_pools): + mask = pool_idx == i + if mask.any(): + K_values[data["pool_ids"][i]] = float( + np.exp(np.median(data["log_comp_tvl"][mask]))) + else: + K_values[data["pool_ids"][i]] = 1e6 return { "r2s": r2s, @@ -431,15 +472,18 @@ def tvl_response_check(params, data): if mask.sum() == 0: continue - # Per-pool K - if "k_params" in params: - k_p = np.array(params["k_params"]) - va = data["log_vol_a"][mask] - vb = data["log_vol_b"][mask] - log_K_i = k_p[0] + k_p[1] * np.minimum(va, vb) + k_p[2] * np.maximum(va, vb) - K_i = float(np.exp(np.median(log_K_i))) - else: + # Per-pool K (median) + if "k_scale" in params: + ks = np.array(params["k_scale"]) + lc = data["log_comp_tvl"][mask] + K_i = float(np.exp(np.median(ks[0] + ks[1] * lc))) + elif "log_K" in params: K_i = float(np.exp(params["log_K"][i])) + elif "k_params" in params: + K_i = float(np.exp(np.array(params["k_params"])[0])) + else: + # Observed K directly from competitor TVL + K_i = float(np.exp(np.median(data["log_comp_tvl"][mask]))) x_med = np.median(data["x_market"][mask], axis=0) gamma = np.array(params["gamma"]) @@ -477,8 +521,9 @@ def main(): parser.add_argument("--init-log-K", type=float, default=17.0, help="Initial log(K) ~ log($24M)") parser.add_argument("--shared-K", action="store_true", - help="Predict K from Binance volumes (3 shared params)" - " instead of per-pool K") + help="Predict K from Binance volumes (3 shared params)") + parser.add_argument("--observed-K", action="store_true", + help="Use observed competitor TVL from DeFi Llama as K") parser.add_argument("--per-pool-gamma", action="store_true", help="Per-pool market feature coefficients") parser.add_argument("--no-split", action="store_true") @@ -554,7 +599,11 @@ def main(): "gamma": gamma_init, "log_cadence": jnp.array(data["init_log_cadences"]), } - if args.shared_K: + if args.observed_K: + # K = competitor_tvl directly. No learned params for K. + # log_comp_tvl is passed as data, not as a parameter. + pass + elif args.shared_K: params["k_params"] = jnp.array([args.init_log_K, 0.0, 0.0]) else: params["log_K"] = jnp.full(n_pools, args.init_log_K) @@ -607,12 +656,18 @@ def _ridge(X, y, alpha=1.0): train_eval = evaluate(params, train_data) print(f" Median R²: {train_eval['median_r2']:.4f}") - if "k_params" in params: + if "k_scale" in params: + ks = np.array(params["k_scale"]) + print(f" Observed K: offset={ks[0]:.3f}, slope={ks[1]:.3f}") + elif "k_params" in params: k_p = np.array(params["k_params"]) print(f" k_params: k_0={k_p[0]:.2f}, k_min={k_p[1]:.4f}, k_max={k_p[2]:.4f}") - else: + elif "log_K" in params: K_med = float(np.exp(np.median(np.array(params["log_K"])))) print(f" Per-pool K: median=${K_med/1e6:.1f}M") + else: + K_med = float(np.median(list(train_eval["K_values"].values()))) + print(f" Observed K (fixed): median=${K_med/1e6:.1f}M") print(f"\n {'Pool':>16s} {'Tokens':>16s} {'R²':>6s}" f" {'Noise%':>7s} {'K ($M)':>10s}") diff --git a/quantammsim/calibration/noise_model_arrays.py b/quantammsim/calibration/noise_model_arrays.py index 0c7c904..7b73dc8 100644 --- a/quantammsim/calibration/noise_model_arrays.py +++ b/quantammsim/calibration/noise_model_arrays.py @@ -291,3 +291,170 @@ def build_simulator_arrays( "coeffs": coeffs, "tvl_col": tvl_col, } + + +def build_mm_simulator_arrays( + token_a: str, + token_b: str, + start_date: str, + end_date: str, + mm_artifact_dir: str = "results/mm_noise", + competitor_tvl_path: str = "results/competitor_tvl/competitor_tvl.npz", + pool_id: Optional[str] = None, +) -> Dict: + """Build noise_base and competitor_tvl arrays for the MM simulator. + + The MM noise model evaluates:: + + V_noise = exp(noise_base_t) * TVL / (K_t + TVL) + + where noise_base_t = alpha_i + gamma_i @ x_market_t absorbs all + non-TVL terms, and K_t = competitor_tvl_t is observed from DeFi + Llama (network conductance model: direct + multi-hop). + + Parameters + ---------- + token_a, token_b : str + Token symbols. + start_date, end_date : str + Date range. + mm_artifact_dir : str + Directory with MM model.npz and meta.json. + competitor_tvl_path : str + Path to competitor_tvl.npz from fetch_competitor_tvl.py. + pool_id : str, optional + Pool ID for per-pool alpha/gamma. + + Returns + ------- + dict with noise_base, competitor_tvl (minute arrays), dates, etc. + """ + # Load MM model + art, meta = load_artifact(mm_artifact_dir) + pool_ids = meta["pool_ids"] + market_names = meta["market_names"] + n_market = meta["n_market_feat"] + per_pool_gamma = meta.get("per_pool_gamma", False) + + pool_idx = -1 + if pool_id is not None: + pool_idx = _find_pool_index(pool_id, pool_ids) + + log_alpha = art["log_alpha"] + gamma = art["gamma"] + + if pool_idx >= 0: + alpha_i = float(log_alpha[pool_idx]) + gamma_i = gamma[pool_idx] if per_pool_gamma else gamma + print(f" MM model: pool idx {pool_idx}, alpha={alpha_i:.3f}") + else: + alpha_i = float(np.median(log_alpha)) + gamma_i = np.median(gamma, axis=0) if per_pool_gamma else gamma + print(f" MM model: pool not found, using median alpha={alpha_i:.3f}") + + # Build daily market features from Binance + # The MM model uses the same features as the linear model minus TVL + # We need x_mean/x_std from the linear model artifact for standardization + linear_art_dir = os.path.join( + os.path.dirname(os.path.dirname(mm_artifact_dir)), + "results", "linear_market_noise") + if os.path.exists(os.path.join(linear_art_dir, "model.npz")): + lin_art, lin_meta = load_artifact(linear_art_dir) + x_mean = lin_art["x_mean"] + x_std = lin_art["x_std"] + feat_names = lin_meta["feat_names"] + else: + # Fallback: try to get from MM artifact + x_mean = art.get("x_mean", np.zeros(n_market)) + x_std = art.get("x_std", np.ones(n_market)) + feat_names = market_names + + trend_windows = (7,) + + print(f" Building features from Binance: {token_a}/{token_b}," + f" {start_date} → {end_date}") + x_daily, dates = build_daily_features_from_binance( + token_a, token_b, start_date, end_date, + feat_names, x_mean, x_std, trend_windows, + ) + n_days = len(dates) + + # Extract market features (exclude TVL and TVL interactions) + tvl_col = None + tvl_interaction_cols = set() + for i, name in enumerate(feat_names): + if name == "xobs_1": + tvl_col = i + elif name.startswith("xobs_1\u00d7"): + tvl_interaction_cols.add(i) + + keep_cols = [i for i in range(len(feat_names)) + if i != tvl_col and i not in tvl_interaction_cols] + + # Map market_names to x_daily columns + x_market_daily = np.zeros((n_days, n_market), dtype=np.float32) + for mi, mname in enumerate(market_names): + # Find mname in feat_names + for fi, fname in enumerate(feat_names): + if fname == mname and fi in keep_cols: + col_in_daily = fi + x_market_daily[:, mi] = x_daily[:, col_in_daily] + break + + # Compute noise_base = alpha_i + gamma_i @ x_market + noise_base_daily = alpha_i + x_market_daily @ gamma_i + noise_base_daily = noise_base_daily.astype(np.float64) + + # Load competitor TVL (K) + print(f" Loading competitor TVL from {competitor_tvl_path}") + comp_data = np.load(competitor_tvl_path, allow_pickle=True) + comp_pool_ids = list(comp_data["pool_ids"]) + comp_dates = list(comp_data["date_list"]) + k_eff = comp_data["k_eff"] # (n_comp_dates, n_comp_pools) + + # Find pool in competitor data + comp_pool_idx = -1 + if pool_id is not None: + comp_pool_idx = _find_pool_index(pool_id, comp_pool_ids) + + if comp_pool_idx < 0: + print(f" WARNING: pool not in competitor TVL data, using K=$10M") + K_daily = np.full(n_days, 10e6, dtype=np.float64) + else: + # Build date index for competitor data + comp_date_to_idx = {} + for ci, d in enumerate(comp_dates): + comp_date_to_idx[str(d)[:10]] = ci + + K_daily = np.full(n_days, np.nan, dtype=np.float64) + for k, day in enumerate(dates): + ds = str(pd.Timestamp(day))[:10] + if ds in comp_date_to_idx: + ci = comp_date_to_idx[ds] + val = k_eff[ci, comp_pool_idx] + if np.isfinite(val) and val > 0: + K_daily[k] = val + + # Forward-fill / back-fill + s = pd.Series(K_daily).ffill().bfill() + K_daily = s.values.astype(np.float64) + + # Floor + K_daily = np.maximum(K_daily, 1.0) + med_K = np.median(K_daily[np.isfinite(K_daily)]) + print(f" K (competitor TVL): median=${med_K:,.0f}," + f" range=[${K_daily.min():,.0f}, ${K_daily.max():,.0f}]") + + # Expand to minute resolution + n_minutes = n_days * 1440 + noise_base = np.repeat(noise_base_daily, 1440) + competitor_tvl_array = np.repeat(K_daily, 1440) + + return { + "noise_base": noise_base, + "competitor_tvl": competitor_tvl_array, + "dates": dates, + "pool_index": pool_idx, + "n_days": n_days, + "n_minutes": n_minutes, + } diff --git a/quantammsim/pools/noise_trades.py b/quantammsim/pools/noise_trades.py index db8d1b9..9c9f00f 100644 --- a/quantammsim/pools/noise_trades.py +++ b/quantammsim/pools/noise_trades.py @@ -404,3 +404,45 @@ def reclamm_market_linear_noise_volume( return jnp.maximum(0.0, daily_noise / 1440.0) +@jit +def reclamm_mm_observed_noise_volume( + effective_value_usd, + noise_base, + competitor_tvl, +): + """Michaelis-Menten noise model with observed competitor TVL as K. + + Derived from optimal routing (Diamandis et al. 2023):: + + V_noise = exp(base_t) * TVL / (K_t + TVL) + + where K_t is observed total competitor liquidity (direct + multi-hop + network conductance) from DeFi Llama, and base_t absorbs per-pool + intercept + market feature effects. + + The MM form guarantees: + - Elasticity ≈ 1 at low TVL (TVL << K) + - Structural saturation at high TVL (V_noise → exp(base_t)) + - No wireheading: V_noise is bounded regardless of concentration + + Parameters + ---------- + effective_value_usd : float + Effective TVL in USD: (Ra+Va)*pA + (Rb+Vb)*pB. + noise_base : float + Precomputed log(V_max_daily) = alpha_i + gamma_i @ x_market_t. + competitor_tvl : float + Observed competitor TVL (K) for this step, from DeFi Llama + network conductance model. + + Returns + ------- + float + Per-minute noise volume (USD), floored at zero. + """ + tvl = jnp.maximum(effective_value_usd, 1.0) + K = jnp.maximum(competitor_tvl, 1.0) + daily_noise = jnp.exp(noise_base) * tvl / (K + tvl) + return jnp.maximum(0.0, daily_noise / 1440.0) + + diff --git a/scripts/plot_mm_noise_fit.py b/scripts/plot_mm_noise_fit.py index 34818e0..6d1b331 100644 --- a/scripts/plot_mm_noise_fit.py +++ b/scripts/plot_mm_noise_fit.py @@ -45,18 +45,24 @@ def load_model(artifact_dir): def get_pool_K(params, decomp, pool_i): - """Get median K for a pool, handling both per-pool and k_params modes.""" - if "k_params" in params: - k_p = np.array(params["k_params"]) - mask = decomp["pool_idx"] == pool_i + """Get median K for a pool, handling all K modes.""" + mask = decomp["pool_idx"] == pool_i + if "k_scale" in params: + ks = np.array(params["k_scale"]) if not mask.any(): - return float(np.exp(k_p[0])) - va = decomp.get("log_vol_a", np.zeros(mask.sum()))[mask] - vb = decomp.get("log_vol_b", np.zeros(mask.sum()))[mask] - log_K = k_p[0] + k_p[1] * np.minimum(va, vb) + k_p[2] * np.maximum(va, vb) + return float(np.exp(ks[0])) + lc = decomp.get("log_comp_tvl", np.zeros(mask.sum()))[mask] + log_K = ks[0] + ks[1] * lc return float(np.exp(np.median(log_K))) - else: + elif "log_K" in params: return float(np.exp(params["log_K"][pool_i])) + elif "k_params" in params: + k_p = np.array(params["k_params"]) + return float(np.exp(k_p[0])) + elif "log_comp_tvl" in decomp and mask.any(): + # Observed K directly from competitor TVL + return float(np.exp(np.median(decomp["log_comp_tvl"][mask]))) + return np.exp(14.5) def compute_decomposition(params, meta, matched_clean, option_c_clean): @@ -95,8 +101,7 @@ def compute_decomposition(params, meta, matched_clean, option_c_clean): params, jnp.array(data["x_market"]), jnp.array(data["log_tvl"]), jnp.array(data["pool_idx"]), - log_vol_a=jnp.array(data["log_vol_a"]), - log_vol_b=jnp.array(data["log_vol_b"]))) + log_comp_tvl=jnp.array(data["log_comp_tvl"]))) v_noise = np.exp(log_v_noise) v_total = v_arb + v_noise v_obs = np.exp(y) @@ -119,8 +124,7 @@ def compute_decomposition(params, meta, matched_clean, option_c_clean): "v_total": v_total, "v_obs": v_obs, "log_tvl": log_tvl, - "log_vol_a": data["log_vol_a"], - "log_vol_b": data["log_vol_b"], + "log_comp_tvl": data["log_comp_tvl"], "tvl": np.exp(log_tvl), } From d3632eca915fcf88fad07086b4fbba8eecf7b79c Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 7 Apr 2026 15:45:41 +0100 Subject: [PATCH 52/57] feat: integrate mm_observed noise model into simulator pipeline Wire reclamm_mm_observed_noise_volume through the full simulator: - reclamm.py: load noise_base + competitor_tvl arrays from npz - reclamm_reserves.py: dispatch mm_observed in scan step, append competitor_tvl to scan_inputs alongside noise_base - noise_model_arrays.py: build_mm_simulator_arrays() precomputes both arrays from MM model artifact + DeFi Llama competitor TVL - jax_runner_utils.py: add noise array keys to _TRAINING_ONLY_FIELDS Fingerprint usage: noise_model="mm_observed", noise_arrays_path="path/to/arrays.npz" --- quantammsim/pools/reCLAMM/reclamm.py | 26 +++++++++++++++- quantammsim/pools/reCLAMM/reclamm_reserves.py | 31 +++++++++++++++++++ quantammsim/runners/jax_runner_utils.py | 4 +++ 3 files changed, 60 insertions(+), 1 deletion(-) diff --git a/quantammsim/pools/reCLAMM/reclamm.py b/quantammsim/pools/reCLAMM/reclamm.py index 32e0c3e..c918604 100644 --- a/quantammsim/pools/reCLAMM/reclamm.py +++ b/quantammsim/pools/reCLAMM/reclamm.py @@ -223,10 +223,32 @@ def _prepare_noise_arrays(self, prices, run_fingerprint, start_index, - "tsoukalas_*"/"loglinear": {"volatility": array} - "calibrated": {"volatility": array, "dow_sin": array, "dow_cos": array} - "market_linear": {"noise_base": array, "noise_tvl_coeff": array} + - "mm_observed": {"noise_base": array, "competitor_tvl": array} """ noise_model = run_fingerprint.get("noise_model", "ratio") result = {"volatility": None, "dow_sin": None, "dow_cos": None, - "noise_base": None, "noise_tvl_coeff": None} + "noise_base": None, "noise_tvl_coeff": None, + "competitor_tvl": None} + + if noise_model == "mm_observed": + # MM model with observed competitor TVL as K + nb = run_fingerprint.get("noise_base_array") + ct = run_fingerprint.get("competitor_tvl_array") + if nb is None and "noise_arrays_path" in run_fingerprint: + path = run_fingerprint["noise_arrays_path"] + if not hasattr(self, "_mm_observed_cache") or self._mm_observed_cache[0] != path: + arrays = np.load(path) + self._mm_observed_cache = ( + path, arrays["noise_base"], arrays["competitor_tvl"]) + nb = self._mm_observed_cache[1] + ct = self._mm_observed_cache[2] + if nb is not None: + result["noise_base"] = _prepare_dynamic_array( + jnp.array(nb), start_index, bout_length, arb_freq, max_len) + if ct is not None: + result["competitor_tvl"] = _prepare_dynamic_array( + jnp.array(ct), start_index, bout_length, arb_freq, max_len) + return result if noise_model == "market_linear": # Load precomputed arrays from path (cached on instance) or direct @@ -340,6 +362,7 @@ def calculate_reserves_with_fees( dow_cos_array=dow_cos, noise_base_array=_na["noise_base"], noise_tvl_coeff_array=_na["noise_tvl_coeff"], + competitor_tvl_array=_na.get("competitor_tvl"), ) return jnp.broadcast_to(s.initial_reserves, s.arb_prices.shape) @@ -411,6 +434,7 @@ def calculate_reserves_and_fee_revenue_with_fees( dow_cos_array=dow_cos, noise_base_array=_na["noise_base"], noise_tvl_coeff_array=_na["noise_tvl_coeff"], + competitor_tvl_array=_na.get("competitor_tvl"), ) return ( jnp.broadcast_to(s.initial_reserves, s.arb_prices.shape), diff --git a/quantammsim/pools/reCLAMM/reclamm_reserves.py b/quantammsim/pools/reCLAMM/reclamm_reserves.py index 9363255..ac264e8 100644 --- a/quantammsim/pools/reCLAMM/reclamm_reserves.py +++ b/quantammsim/pools/reCLAMM/reclamm_reserves.py @@ -37,6 +37,7 @@ reclamm_loglinear_noise_volume, reclamm_calibrated_noise_volume, reclamm_market_linear_noise_volume, + reclamm_mm_observed_noise_volume, ) # Reference balance for initialisation (matches Solidity _INITIALIZATION_MAX_BALANCE_A) @@ -1044,6 +1045,20 @@ def _skip_schedule_state(_): tvl_std=_np.get("tvl_std", 1.0), ) + minutes_per_step = seconds_per_step / 60.0 + noise_fee_income = (1.0 - gamma) * noise_vol * minutes_per_step + scale = 1.0 + noise_fee_income / jnp.maximum(effective_value, 1e-8) + Ra_new = (Ra_new + Va) * scale - Va + Rb_new = (Rb_new + Vb) * scale - Vb + elif noise_model == "mm_observed": + noise_base = input_list[9] + competitor_tvl = input_list[10] + effective_value = (Ra_new + Va) * prices[0] + (Rb_new + Vb) * prices[1] + + noise_vol = reclamm_mm_observed_noise_volume( + effective_value, noise_base, competitor_tvl, + ) + minutes_per_step = seconds_per_step / 60.0 noise_fee_income = (1.0 - gamma) * noise_vol * minutes_per_step scale = 1.0 + noise_fee_income / jnp.maximum(effective_value, 1e-8) @@ -1306,6 +1321,7 @@ def _jax_calc_reclamm_reserves_with_fees( dow_cos_array=None, noise_base_array=None, noise_tvl_coeff_array=None, + competitor_tvl_array=None, ): """Calculate reClAMM reserves over time with fees. @@ -1371,6 +1387,9 @@ def _jax_calc_reclamm_reserves_with_fees( elif noise_model == "market_linear": scan_inputs.append(noise_base_array) scan_inputs.append(noise_tvl_coeff_array) + elif noise_model == "mm_observed": + scan_inputs.append(noise_base_array) + scan_inputs.append(competitor_tvl_array) carry_init = [ initial_reserves, @@ -1416,6 +1435,7 @@ def _jax_calc_reclamm_reserves_with_dynamic_inputs( dow_cos_array=None, noise_base_array=None, noise_tvl_coeff_array=None, + competitor_tvl_array=None, ): """Calculate reClAMM reserves with time-varying fees/arb arrays.""" if lp_supply_array is None: @@ -1490,6 +1510,9 @@ def _jax_calc_reclamm_reserves_with_dynamic_inputs( elif noise_model == "market_linear": scan_inputs.append(noise_base_array) scan_inputs.append(noise_tvl_coeff_array) + elif noise_model == "mm_observed": + scan_inputs.append(noise_base_array) + scan_inputs.append(competitor_tvl_array) carry_init = [ initial_reserves, @@ -1625,6 +1648,7 @@ def _jax_calc_reclamm_reserves_and_fee_revenue_with_fees( dow_cos_array=None, noise_base_array=None, noise_tvl_coeff_array=None, + competitor_tvl_array=None, ): """Calculate reClAMM reserves and LP fee revenue over time with fees. @@ -1692,6 +1716,9 @@ def _jax_calc_reclamm_reserves_and_fee_revenue_with_fees( elif noise_model == "market_linear": scan_inputs.append(noise_base_array) scan_inputs.append(noise_tvl_coeff_array) + elif noise_model == "mm_observed": + scan_inputs.append(noise_base_array) + scan_inputs.append(competitor_tvl_array) carry_init = [ initial_reserves, @@ -1737,6 +1764,7 @@ def _jax_calc_reclamm_reserves_and_fee_revenue_with_dynamic_inputs( dow_cos_array=None, noise_base_array=None, noise_tvl_coeff_array=None, + competitor_tvl_array=None, ): """Calculate reClAMM reserves and LP fee revenue with time-varying fees/arb arrays. @@ -1817,6 +1845,9 @@ def _jax_calc_reclamm_reserves_and_fee_revenue_with_dynamic_inputs( elif noise_model == "market_linear": scan_inputs.append(noise_base_array) scan_inputs.append(noise_tvl_coeff_array) + elif noise_model == "mm_observed": + scan_inputs.append(noise_base_array) + scan_inputs.append(competitor_tvl_array) carry_init = [ initial_reserves, diff --git a/quantammsim/runners/jax_runner_utils.py b/quantammsim/runners/jax_runner_utils.py index 1c93887..2f93190 100644 --- a/quantammsim/runners/jax_runner_utils.py +++ b/quantammsim/runners/jax_runner_utils.py @@ -675,6 +675,10 @@ def __eq__(self, other): "initial_raw_width", "initial_raw_exponents", "initial_pre_exp_scaling", + # Noise model arrays — loaded from path at runtime, not hashable + "noise_base_array", + "noise_tvl_coeff_array", + "competitor_tvl_array", }) From 76bcc96d2647fc7dab4add4cbd9fd0f86b246a74 Mon Sep 17 00:00:00 2001 From: MatthewWilletts <16085750+MatthewWilletts@users.noreply.github.com> Date: Tue, 7 Apr 2026 15:48:29 +0100 Subject: [PATCH 53/57] feat: add mm_observed noise model to reClAMM tuning pipeline tune_reclamm_calibrated_noise.py now supports --noise-model mm_observed which uses the Michaelis-Menten model with observed competitor TVL from DeFi Llama as K. Precomputes noise_base + competitor_tvl arrays via build_mm_simulator_arrays, saves to npz, passes path in fingerprint. Usage: python experiments/tune_reclamm_calibrated_noise.py \ --noise-model mm_observed \ --artifact-dir results/mm_noise \ --competitor-tvl-path results/competitor_tvl/competitor_tvl.npz --- experiments/tune_reclamm_calibrated_noise.py | 69 ++++++++++++++++++-- 1 file changed, 64 insertions(+), 5 deletions(-) diff --git a/experiments/tune_reclamm_calibrated_noise.py b/experiments/tune_reclamm_calibrated_noise.py index c2d712c..55ca99b 100644 --- a/experiments/tune_reclamm_calibrated_noise.py +++ b/experiments/tune_reclamm_calibrated_noise.py @@ -114,6 +114,54 @@ def _build_market_linear_arrays(args): return arrays_path, max(1, round(learned_cadence)) +def _build_mm_observed_arrays(args): + """Precompute noise arrays from the MM model + DeFi Llama competitor TVL.""" + from quantammsim.calibration.noise_model_arrays import ( + build_mm_simulator_arrays, load_artifact, _find_pool_index, + ) + + start = args.start_date.split(" ")[0] + end = args.end_test_date.split(" ")[0] + + print(f" Building mm_observed noise arrays for {POOL_ID}...") + print(f" Date range: {start} → {end}") + arrays = build_mm_simulator_arrays( + token_a="AAVE", + token_b="ETH", + start_date=start, + end_date=end, + mm_artifact_dir=args.artifact_dir, + competitor_tvl_path=args.competitor_tvl_path, + pool_id=POOL_ID, + ) + print(f" {arrays['n_days']} days, {arrays['n_minutes']} minutes") + print(f" noise_base range: [{arrays['noise_base'].min():.2f}," + f" {arrays['noise_base'].max():.2f}]") + print(f" competitor_tvl range: [${np.exp(np.log(arrays['competitor_tvl'].max())):.0f}]") + + # Save arrays to disk + import os + cache_dir = os.path.join(args.artifact_dir, "_sim_arrays") + os.makedirs(cache_dir, exist_ok=True) + arrays_path = os.path.join(cache_dir, f"{POOL_ID}_{start}_{end}_mm.npz") + np.savez(arrays_path, + noise_base=arrays["noise_base"], + competitor_tvl=arrays["competitor_tvl"]) + print(f" Saved arrays: {arrays_path}") + + # Get cadence from MM model artifact + art, meta = load_artifact(args.artifact_dir) + pool_idx = _find_pool_index(POOL_ID, meta["pool_ids"]) + if pool_idx >= 0 and "log_cadence" in art: + learned_cadence = float(np.exp(art["log_cadence"][pool_idx])) + print(f" Learned cadence: {learned_cadence:.1f} min") + else: + learned_cadence = 5.0 + print(f" Using default cadence: {learned_cadence}") + + return arrays_path, max(1, round(learned_cadence)) + + def _build_opt_settings(args): """Build optimisation_settings for optuna, bfgs, or cma_es.""" if args.method == "bfgs": @@ -163,8 +211,14 @@ def _build_opt_settings(args): def build_fingerprint(objective, args, noise_arrays_path=None, arb_freq=None): """Build run fingerprint with calibrated noise model.""" - if args.noise_model == "market_linear" and noise_arrays_path is not None: - # Load tvl standardization stats from the saved arrays + if args.noise_model == "mm_observed" and noise_arrays_path is not None: + noise_block = { + "noise_trader_ratio": 0.0, + "noise_model": "mm_observed", + "noise_arrays_path": noise_arrays_path, + } + freq = arb_freq or 5 + elif args.noise_model == "market_linear" and noise_arrays_path is not None: _arr = np.load(noise_arrays_path) noise_block = { "noise_trader_ratio": 0.0, @@ -259,11 +313,14 @@ def main(): parser.add_argument("--min-train-ret", type=float, default=-0.5, help="Reject trials with IS returns_over_hodl below this") parser.add_argument("--noise-model", default="market_linear", - choices=["calibrated", "market_linear"], + choices=["calibrated", "market_linear", "mm_observed"], help="Noise model variant") parser.add_argument("--artifact-dir", default="results/linear_market_noise", - help="Artifact dir for market_linear model") + help="Artifact dir for market_linear or mm_observed model") + parser.add_argument("--competitor-tvl-path", + default="results/competitor_tvl/competitor_tvl.npz", + help="Path to competitor TVL data (mm_observed only)") parser.add_argument("--initial-pool-value", type=float, default=20_000_000.0, help="Initial pool TVL in USD (default: 20M)") parser.add_argument("--fees", type=float, default=0.0025, @@ -293,11 +350,13 @@ def main(): else: objectives = [args.objective] - # Precompute noise arrays once (if using market_linear) + # Precompute noise arrays once noise_arrays_path = None arb_freq = None if args.noise_model == "market_linear": noise_arrays_path, arb_freq = _build_market_linear_arrays(args) + elif args.noise_model == "mm_observed": + noise_arrays_path, arb_freq = _build_mm_observed_arrays(args) all_results = {} for obj in objectives: From 1a9f64447c9a84635719a3a7861f6431f3a3a996 Mon Sep 17 00:00:00 2001 From: christian harrington Date: Thu, 16 Apr 2026 14:54:18 +0100 Subject: [PATCH 54/57] merge conflict fix --- quantammsim/pools/reCLAMM/reclamm.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/quantammsim/pools/reCLAMM/reclamm.py b/quantammsim/pools/reCLAMM/reclamm.py index aeabcd2..dd30cd4 100644 --- a/quantammsim/pools/reCLAMM/reclamm.py +++ b/quantammsim/pools/reCLAMM/reclamm.py @@ -403,6 +403,7 @@ def calculate_reserves_with_fees( dow_cos_array=noise_arrays["dow_cos"], noise_base_array=noise_arrays["noise_base"], noise_tvl_coeff_array=noise_arrays["noise_tvl_coeff"], + competitor_tvl_array=noise_arrays["competitor_tvl"], ) return jnp.broadcast_to(s.initial_reserves, s.arb_prices.shape) @@ -462,6 +463,7 @@ def calculate_reserves_and_fee_revenue_with_fees( dow_cos_array=noise_arrays["dow_cos"], noise_base_array=noise_arrays["noise_base"], noise_tvl_coeff_array=noise_arrays["noise_tvl_coeff"], + competitor_tvl_array=noise_arrays["competitor_tvl"], ) return ( jnp.broadcast_to(s.initial_reserves, s.arb_prices.shape), @@ -531,6 +533,7 @@ def calculate_reserves_and_fee_revenue_with_dynamic_inputs( dow_cos_array=noise_arrays["dow_cos"], noise_base_array=noise_arrays["noise_base"], noise_tvl_coeff_array=noise_arrays["noise_tvl_coeff"], + competitor_tvl_array=noise_arrays["competitor_tvl"], ) @partial(jit, static_argnums=(2,)) @@ -643,6 +646,7 @@ def calculate_reserves_with_dynamic_inputs( dow_cos_array=noise_arrays["dow_cos"], noise_base_array=noise_arrays["noise_base"], noise_tvl_coeff_array=noise_arrays["noise_tvl_coeff"], + competitor_tvl_array=noise_arrays["competitor_tvl"], ) def init_base_parameters( From d123a9f643a2b802b2bee0b9cf66a2cc4a4b5dd9 Mon Sep 17 00:00:00 2001 From: christian harrington Date: Thu, 16 Apr 2026 14:56:08 +0100 Subject: [PATCH 55/57] refactor balancer hypersurge --- .../pools/G3M/balancer/hypersurge_balancer.py | 106 +-------- .../balancer/hypersurge_balancer_reserves.py | 99 ++------- quantammsim/pools/hypersurge_utils.py | 207 ++++++++++++++++++ 3 files changed, 229 insertions(+), 183 deletions(-) create mode 100644 quantammsim/pools/hypersurge_utils.py diff --git a/quantammsim/pools/G3M/balancer/hypersurge_balancer.py b/quantammsim/pools/G3M/balancer/hypersurge_balancer.py index 79ba4e9..458dc33 100644 --- a/quantammsim/pools/G3M/balancer/hypersurge_balancer.py +++ b/quantammsim/pools/G3M/balancer/hypersurge_balancer.py @@ -8,6 +8,11 @@ from jax.lax import dynamic_slice from quantammsim.core_simulator.dynamic_inputs import materialize_dynamic_inputs +from quantammsim.pools.hypersurge_utils import ( + HYPERSURGE_PARAM_KEYS, + hypersurge_params_from_params, + run_fingerprint_hypersurge_defaults, +) from quantammsim.pools.G3M.balancer.balancer import BalancerPool from quantammsim.pools.G3M.balancer.hypersurge_balancer_reserves import ( _jax_calc_hypersurge_balancer_reserves, @@ -30,112 +35,15 @@ def _prepare_dynamic_array(arr, start_index, bout_length, arb_frequency, max_len return sliced -def _coalesce(value, default): - return default if value is None else value - - -HYPERSURGE_PARAM_KEYS = ( - "hypersurge_arb_max_fee", - "hypersurge_arb_threshold", - "hypersurge_arb_cap_deviation", - "hypersurge_noise_max_fee", - "hypersurge_noise_threshold", - "hypersurge_noise_cap_deviation", -) - - class HyperSurgeBalancerPool(BalancerPool): """Balancer weighted pool with HyperSurge-style state-dependent swap fees.""" @staticmethod def _run_fingerprint_hypersurge_defaults(run_fingerprint: Dict[str, Any]): - base_fee = run_fingerprint.get("fees", 0.0) - if isinstance(base_fee, (list, tuple)): - base_fee = base_fee[0] - - raw_params = run_fingerprint.get("hypersurge_params") - if raw_params is not None: - if isinstance(raw_params, dict): - shared_max = raw_params.get("max_surge_fee", base_fee) - shared_threshold = raw_params.get("threshold", 0.0) - shared_cap = raw_params.get("cap_deviation", 1.0) - return { - "hypersurge_arb_max_fee": raw_params.get( - "arb_max_fee", shared_max - ), - "hypersurge_arb_threshold": raw_params.get( - "arb_threshold", shared_threshold - ), - "hypersurge_arb_cap_deviation": raw_params.get( - "arb_cap_deviation", shared_cap - ), - "hypersurge_noise_max_fee": raw_params.get( - "noise_max_fee", shared_max - ), - "hypersurge_noise_threshold": raw_params.get( - "noise_threshold", shared_threshold - ), - "hypersurge_noise_cap_deviation": raw_params.get( - "noise_cap_deviation", shared_cap - ), - } - - raw_params = np.asarray(raw_params, dtype=np.float64).reshape(-1) - if raw_params.size != len(HYPERSURGE_PARAM_KEYS): - raise ValueError( - "hypersurge_params must contain exactly six values: " - + ", ".join(HYPERSURGE_PARAM_KEYS) - ) - return dict(zip(HYPERSURGE_PARAM_KEYS, raw_params)) - - shared_max = _coalesce( - run_fingerprint.get("hypersurge_max_surge_fee"), - _coalesce(run_fingerprint.get("hypersurge_max_fee"), base_fee), - ) - shared_threshold = _coalesce( - run_fingerprint.get("hypersurge_threshold"), - 0.0, - ) - shared_cap = _coalesce( - run_fingerprint.get("hypersurge_cap_deviation"), - 1.0, - ) - return { - "hypersurge_arb_max_fee": _coalesce( - run_fingerprint.get("hypersurge_arb_max_fee"), shared_max - ), - "hypersurge_arb_threshold": _coalesce( - run_fingerprint.get("hypersurge_arb_threshold"), shared_threshold - ), - "hypersurge_arb_cap_deviation": _coalesce( - run_fingerprint.get("hypersurge_arb_cap_deviation"), shared_cap - ), - "hypersurge_noise_max_fee": _coalesce( - run_fingerprint.get("hypersurge_noise_max_fee"), shared_max - ), - "hypersurge_noise_threshold": _coalesce( - run_fingerprint.get("hypersurge_noise_threshold"), shared_threshold - ), - "hypersurge_noise_cap_deviation": _coalesce( - run_fingerprint.get("hypersurge_noise_cap_deviation"), shared_cap - ), - } + return run_fingerprint_hypersurge_defaults(run_fingerprint) def _hypersurge_params(self, params: Dict[str, Any], run_fingerprint: Dict[str, Any]): - if "hypersurge_params" in params: - return jnp.ravel(params["hypersurge_params"]) - - if all(key in params for key in HYPERSURGE_PARAM_KEYS): - return jnp.asarray( - [jnp.squeeze(params[key]) for key in HYPERSURGE_PARAM_KEYS], - dtype=jnp.float64, - ) - - defaults = self._run_fingerprint_hypersurge_defaults(run_fingerprint) - return jnp.asarray( - [defaults[key] for key in HYPERSURGE_PARAM_KEYS], - dtype=jnp.float64, - ) + return hypersurge_params_from_params(params, run_fingerprint) def _price_windows( self, diff --git a/quantammsim/pools/G3M/balancer/hypersurge_balancer_reserves.py b/quantammsim/pools/G3M/balancer/hypersurge_balancer_reserves.py index 110499d..a4e326f 100644 --- a/quantammsim/pools/G3M/balancer/hypersurge_balancer_reserves.py +++ b/quantammsim/pools/G3M/balancer/hypersurge_balancer_reserves.py @@ -13,84 +13,22 @@ precalc_shared_values_for_all_signatures, parallelised_optimal_trade_sifter, ) +from quantammsim.pools.hypersurge_utils import ( + _EPS, + broadcast_scan_vector as _broadcast_scan_vector, + fee_to_gamma as _fee_to_gamma, + max_pair_deviation as _max_pair_deviation, + oracle_pair_is_valid, + oracle_vector_is_valid, + pair_deviation as _pair_deviation, + ramp_fee as _ramp_fee, + safe_positive as _safe_positive, +) from quantammsim.pools.noise_trades import ( calculate_reserves_after_noise_trade, reclamm_market_linear_noise_volume, ) - -_EPS = 1e-18 -_MAX_FEE = 0.999999 - - -def _safe_positive(values): - values = jnp.asarray(values) - values = jnp.where(jnp.isfinite(values), values, 1.0) - return jnp.maximum(values, _EPS) - - -def _fee_to_gamma(fee): - return jnp.maximum(1.0 - jnp.clip(fee, 0.0, _MAX_FEE), _EPS) - - -def _pair_pool_price(reserves, weights, token_in, token_out): - """Pool spot amount-out-per-amount-in for a weighted-pool token pair.""" - token_in = jnp.int32(token_in) - token_out = jnp.int32(token_out) - reserves = _safe_positive(reserves) - weights = _safe_positive(weights) - numerator = reserves[token_out] * weights[token_in] - denominator = reserves[token_in] * weights[token_out] - return numerator / jnp.maximum(denominator, _EPS) - - -def _pair_oracle_price(oracle_prices, token_in, token_out): - """External amount-out-per-amount-in, assuming common-numeraire prices.""" - token_in = jnp.int32(token_in) - token_out = jnp.int32(token_out) - oracle_prices = _safe_positive(oracle_prices) - return oracle_prices[token_in] / jnp.maximum(oracle_prices[token_out], _EPS) - - -def _pair_deviation(reserves, weights, oracle_prices, token_in, token_out): - pool_price = _pair_pool_price(reserves, weights, token_in, token_out) - oracle_price = _pair_oracle_price(oracle_prices, token_in, token_out) - ratio = pool_price / jnp.maximum(oracle_price, _EPS) - ratio = jnp.where(jnp.isfinite(ratio), ratio, 1.0) - return jnp.abs(ratio - 1.0) - - -def _max_pair_deviation(reserves, weights, oracle_prices): - reserves = _safe_positive(reserves) - weights = _safe_positive(weights) - oracle_prices = _safe_positive(oracle_prices) - - pool_prices = (reserves[None, :] * weights[:, None]) / jnp.maximum( - reserves[:, None] * weights[None, :], - _EPS, - ) - oracle_pair_prices = oracle_prices[:, None] / jnp.maximum( - oracle_prices[None, :], - _EPS, - ) - ratios = pool_prices / jnp.maximum(oracle_pair_prices, _EPS) - ratios = jnp.where(jnp.isfinite(ratios), ratios, 1.0) - deviations = jnp.abs(ratios - 1.0) - off_diagonal = ~jnp.eye(reserves.shape[0], dtype=bool) - return jnp.max(jnp.where(off_diagonal, deviations, 0.0)) - - -def _ramp_fee(base_fee, max_fee, threshold, cap, deviation): - max_fee = jnp.maximum(max_fee, base_fee) - threshold = jnp.maximum(threshold, 0.0) - cap = jnp.maximum(cap, threshold + _EPS) - span = jnp.maximum(cap - threshold, _EPS) - ramp = jnp.clip((deviation - threshold) / span, 0.0, 1.0) - fee = base_fee + (max_fee - base_fee) * ramp - fee = jnp.where(deviation <= threshold, base_fee, fee) - return jnp.clip(fee, 0.0, _MAX_FEE) - - def _hypersurge_fee_for_trade( reserves, candidate_trade, @@ -102,6 +40,7 @@ def _hypersurge_fee_for_trade( hypersurge_params, ): """Select arb/noise fee params from whether a candidate trade worsens peg deviation.""" + pair_has_oracle = oracle_pair_is_valid(oracle_prices, token_in, token_out) candidate_reserves = _safe_positive(reserves + candidate_trade) dev_before = _pair_deviation(reserves, weights, oracle_prices, token_in, token_out) dev_after = _pair_deviation( @@ -128,18 +67,19 @@ def _hypersurge_fee_for_trade( dev_after, ) fee = jnp.where(worsens, noise_fee, arb_fee) - return jnp.where(trade_active, fee, base_fee) + return jnp.where(jnp.logical_and(trade_active, pair_has_oracle), fee, base_fee) def _hypersurge_noise_fee(reserves, weights, oracle_prices, base_fee, hypersurge_params): deviation = _max_pair_deviation(reserves, weights, oracle_prices) - return _ramp_fee( + fee = _ramp_fee( base_fee, hypersurge_params[3], hypersurge_params[4], hypersurge_params[5], deviation, ) + return jnp.where(oracle_vector_is_valid(oracle_prices), fee, base_fee) def _zero_fee_optimal_trade(reserves, weights, prices): @@ -199,15 +139,6 @@ def _optimal_arb_trade_with_gamma( -1e-15, ) - -def _broadcast_scan_vector(values, scan_len): - values = jnp.asarray(values) - if values.ndim == 0: - values = values.reshape((1,)) - values = jnp.ravel(values) - return jnp.where(values.size == 1, jnp.full((scan_len,), values[0]), values) - - def _broadcast_oracle_prices(oracle_prices, prices): oracle_prices = jnp.asarray(oracle_prices) if oracle_prices.ndim == 1: diff --git a/quantammsim/pools/hypersurge_utils.py b/quantammsim/pools/hypersurge_utils.py new file mode 100644 index 0000000..7c1cca7 --- /dev/null +++ b/quantammsim/pools/hypersurge_utils.py @@ -0,0 +1,207 @@ +from typing import Any, Dict + +import numpy as np + +import jax.numpy as jnp + + +HYPERSURGE_PARAM_KEYS = ( + "hypersurge_arb_max_fee", + "hypersurge_arb_threshold", + "hypersurge_arb_cap_deviation", + "hypersurge_noise_max_fee", + "hypersurge_noise_threshold", + "hypersurge_noise_cap_deviation", +) + +_EPS = 1e-18 +_MAX_FEE = 0.999999 + + +def _coalesce(value, default): + return default if value is None else value + + +def _scalar_like(value, default=0.0): + if value is None: + return default + if isinstance(value, (list, tuple)): + return value[0] if value else default + if isinstance(value, (np.ndarray, jnp.ndarray)): + flat = np.asarray(value).reshape(-1) + return flat[0] if flat.size else default + return value + + +def run_fingerprint_hypersurge_defaults(run_fingerprint: Dict[str, Any]): + base_fee = _scalar_like(run_fingerprint.get("fees", 0.0), default=0.0) + + raw_params = run_fingerprint.get("hypersurge_params") + if raw_params is not None: + if isinstance(raw_params, dict): + shared_max = raw_params.get("max_surge_fee", base_fee) + shared_threshold = raw_params.get("threshold", 0.0) + shared_cap = raw_params.get("cap_deviation", 1.0) + return { + "hypersurge_arb_max_fee": raw_params.get("arb_max_fee", shared_max), + "hypersurge_arb_threshold": raw_params.get( + "arb_threshold", shared_threshold + ), + "hypersurge_arb_cap_deviation": raw_params.get( + "arb_cap_deviation", shared_cap + ), + "hypersurge_noise_max_fee": raw_params.get( + "noise_max_fee", shared_max + ), + "hypersurge_noise_threshold": raw_params.get( + "noise_threshold", shared_threshold + ), + "hypersurge_noise_cap_deviation": raw_params.get( + "noise_cap_deviation", shared_cap + ), + } + + raw_params = np.asarray(raw_params, dtype=np.float64).reshape(-1) + if raw_params.size != len(HYPERSURGE_PARAM_KEYS): + raise ValueError( + "hypersurge_params must contain exactly six values: " + + ", ".join(HYPERSURGE_PARAM_KEYS) + ) + return dict(zip(HYPERSURGE_PARAM_KEYS, raw_params)) + + shared_max = _coalesce( + run_fingerprint.get("hypersurge_max_surge_fee"), + _coalesce(run_fingerprint.get("hypersurge_max_fee"), base_fee), + ) + shared_threshold = _coalesce( + run_fingerprint.get("hypersurge_threshold"), + 0.0, + ) + shared_cap = _coalesce( + run_fingerprint.get("hypersurge_cap_deviation"), + 1.0, + ) + return { + "hypersurge_arb_max_fee": _coalesce( + run_fingerprint.get("hypersurge_arb_max_fee"), shared_max + ), + "hypersurge_arb_threshold": _coalesce( + run_fingerprint.get("hypersurge_arb_threshold"), shared_threshold + ), + "hypersurge_arb_cap_deviation": _coalesce( + run_fingerprint.get("hypersurge_arb_cap_deviation"), shared_cap + ), + "hypersurge_noise_max_fee": _coalesce( + run_fingerprint.get("hypersurge_noise_max_fee"), shared_max + ), + "hypersurge_noise_threshold": _coalesce( + run_fingerprint.get("hypersurge_noise_threshold"), shared_threshold + ), + "hypersurge_noise_cap_deviation": _coalesce( + run_fingerprint.get("hypersurge_noise_cap_deviation"), shared_cap + ), + } + + +def hypersurge_params_from_params(params: Dict[str, Any], run_fingerprint: Dict[str, Any]): + if "hypersurge_params" in params: + return jnp.ravel(params["hypersurge_params"]) + + if all(key in params for key in HYPERSURGE_PARAM_KEYS): + return jnp.asarray( + [jnp.squeeze(params[key]) for key in HYPERSURGE_PARAM_KEYS], + dtype=jnp.float64, + ) + + defaults = run_fingerprint_hypersurge_defaults(run_fingerprint) + return jnp.asarray( + [defaults[key] for key in HYPERSURGE_PARAM_KEYS], + dtype=jnp.float64, + ) + + +def safe_positive(values): + values = jnp.asarray(values) + values = jnp.where(jnp.isfinite(values), values, 1.0) + return jnp.maximum(values, _EPS) + + +def fee_to_gamma(fee): + return jnp.maximum(1.0 - jnp.clip(fee, 0.0, _MAX_FEE), _EPS) + + +def ramp_fee(base_fee, max_fee, threshold, cap, deviation): + max_fee = jnp.maximum(max_fee, base_fee) + threshold = jnp.maximum(threshold, 0.0) + cap = jnp.maximum(cap, threshold + _EPS) + span = jnp.maximum(cap - threshold, _EPS) + ramp = jnp.clip((deviation - threshold) / span, 0.0, 1.0) + fee = base_fee + (max_fee - base_fee) * ramp + fee = jnp.where(deviation <= threshold, base_fee, fee) + return jnp.clip(fee, 0.0, _MAX_FEE) + + +def oracle_pair_is_valid(oracle_prices, token_in, token_out): + oracle_prices = jnp.asarray(oracle_prices) + token_in = jnp.int32(token_in) + token_out = jnp.int32(token_out) + pair_prices = jnp.asarray([oracle_prices[token_in], oracle_prices[token_out]]) + return jnp.all(jnp.isfinite(pair_prices) & (pair_prices > 0.0)) + + +def oracle_vector_is_valid(oracle_prices): + oracle_prices = jnp.asarray(oracle_prices) + return jnp.all(jnp.isfinite(oracle_prices) & (oracle_prices > 0.0)) + + +def pair_pool_price(reserves, weights, token_in, token_out): + token_in = jnp.int32(token_in) + token_out = jnp.int32(token_out) + reserves = safe_positive(reserves) + weights = safe_positive(weights) + numerator = reserves[token_out] * weights[token_in] + denominator = reserves[token_in] * weights[token_out] + return numerator / jnp.maximum(denominator, _EPS) + + +def pair_oracle_price(oracle_prices, token_in, token_out): + token_in = jnp.int32(token_in) + token_out = jnp.int32(token_out) + oracle_prices = jnp.asarray(oracle_prices) + return oracle_prices[token_in] / jnp.maximum(oracle_prices[token_out], _EPS) + + +def pair_deviation(reserves, weights, oracle_prices, token_in, token_out): + pool_price = pair_pool_price(reserves, weights, token_in, token_out) + oracle_price = pair_oracle_price(oracle_prices, token_in, token_out) + ratio = pool_price / jnp.maximum(oracle_price, _EPS) + ratio = jnp.where(jnp.isfinite(ratio), ratio, 1.0) + return jnp.abs(ratio - 1.0) + + +def max_pair_deviation(reserves, weights, oracle_prices): + reserves = safe_positive(reserves) + weights = safe_positive(weights) + oracle_prices = jnp.asarray(oracle_prices) + + pool_prices = (reserves[None, :] * weights[:, None]) / jnp.maximum( + reserves[:, None] * weights[None, :], + _EPS, + ) + oracle_pair_prices = oracle_prices[:, None] / jnp.maximum( + oracle_prices[None, :], + _EPS, + ) + ratios = pool_prices / jnp.maximum(oracle_pair_prices, _EPS) + ratios = jnp.where(jnp.isfinite(ratios), ratios, 1.0) + deviations = jnp.abs(ratios - 1.0) + off_diagonal = ~jnp.eye(reserves.shape[0], dtype=bool) + return jnp.max(jnp.where(off_diagonal, deviations, 0.0)) + + +def broadcast_scan_vector(values, scan_len): + values = jnp.asarray(values) + if values.ndim == 0: + values = values.reshape((1,)) + values = jnp.ravel(values) + return jnp.where(values.size == 1, jnp.full((scan_len,), values[0]), values) From 4003371bb281764a035f99968b1349d0f67ac690 Mon Sep 17 00:00:00 2001 From: christian harrington Date: Thu, 16 Apr 2026 14:56:33 +0100 Subject: [PATCH 56/57] dynamic input array slicing defensive fix --- quantammsim/core_simulator/forward_pass.py | 48 +++++++++ quantammsim/pools/creator.py | 4 + quantammsim/runners/jax_runners.py | 110 ++++++++++++++++++--- 3 files changed, 146 insertions(+), 16 deletions(-) diff --git a/quantammsim/core_simulator/forward_pass.py b/quantammsim/core_simulator/forward_pass.py index 45039d7..1eea66b 100644 --- a/quantammsim/core_simulator/forward_pass.py +++ b/quantammsim/core_simulator/forward_pass.py @@ -72,6 +72,52 @@ def _resolve_dynamic_inputs(dynamic_inputs, static_dict): return dynamic_inputs, dynamic_input_flags +def _slice_dynamic_input_leaf(values, start_idx, length): + """Slice a window from a dynamic-input leaf unless it is singleton.""" + if values is None: + return None + values = jnp.asarray(values) + if values.ndim == 0 or values.shape[0] <= 1: + return values + slice_sizes = (length,) + values.shape[1:] + start = (start_idx,) + (0,) * (values.ndim - 1) + return dynamic_slice(values, start, slice_sizes) + + +def _slice_dynamic_inputs(dynamic_inputs, start_index, static_dict): + """Align full-period dynamic inputs to the active simulation window.""" + if dynamic_inputs is None: + return None + + bout_length = static_dict["bout_length"] + arb_frequency = static_dict.get("arb_frequency", 1) + window_len = bout_length - 1 + minute_start = start_index[0] - static_dict.get("dynamic_inputs_offset", 0) + + def _slice_minute_leaf(values): + sliced = _slice_dynamic_input_leaf(values, minute_start, window_len) + if sliced is None or arb_frequency == 1: + return sliced + return sliced[::arb_frequency] + + schedule_len = window_len if arb_frequency == 1 else (window_len + arb_frequency - 1) // arb_frequency + schedule_start = minute_start if arb_frequency == 1 else minute_start // arb_frequency + + return DynamicInputArrays( + trades=_slice_minute_leaf(dynamic_inputs.trades), + fees=_slice_minute_leaf(dynamic_inputs.fees), + gas_cost=_slice_minute_leaf(dynamic_inputs.gas_cost), + arb_fees=_slice_minute_leaf(dynamic_inputs.arb_fees), + lp_supply=_slice_minute_leaf(dynamic_inputs.lp_supply), + reclamm_price_ratio_updates=_slice_dynamic_input_leaf( + dynamic_inputs.reclamm_price_ratio_updates, + schedule_start, + schedule_len, + ), + oracle_prices=_slice_minute_leaf(dynamic_inputs.oracle_prices), + ) + + def _apply_price_noise(prices, sigma, seed_int): """Apply multiplicative log-normal noise to prices. @@ -1034,6 +1080,8 @@ def forward_pass( dynamic_inputs, dynamic_input_flags = _resolve_dynamic_inputs( dynamic_inputs, static_dict ) + if dynamic_input_flags["use_dynamic_inputs"]: + dynamic_inputs = _slice_dynamic_inputs(dynamic_inputs, start_index, static_dict) fee_revenue = None if dynamic_input_flags["use_dynamic_inputs"]: diff --git a/quantammsim/pools/creator.py b/quantammsim/pools/creator.py index 02979d9..28aab37 100644 --- a/quantammsim/pools/creator.py +++ b/quantammsim/pools/creator.py @@ -21,6 +21,7 @@ from quantammsim.pools.FM_AMM.cow_pool import CowPool from quantammsim.pools.ECLP.gyroscope import GyroscopePool from quantammsim.pools.reCLAMM.reclamm import ReClammPool +from quantammsim.pools.reCLAMM.reclamm_hypersurge import ReClammHyperSurgePool from quantammsim.pools.base_pool import AbstractPool from quantammsim.hooks.versus_rebalancing import ( CalculateLossVersusRebalancing, @@ -150,6 +151,7 @@ def create_pool(rule): - ``"hodl"`` : Pure buy-and-hold (no rebalancing) pool. - ``"cow"`` : CoW (Coincidence of Wants) AMM pool. - ``"gyroscope"`` : Gyroscope E-CLP pool. + - ``"reclamm_hypersurge"`` : reCLAMM pool with HyperSurge dynamic fees. Available hook prefixes (prepended with ``__`` separator): @@ -235,6 +237,8 @@ def create_pool(rule): base_pool = GyroscopePool() elif base_rule == "reclamm": base_pool = ReClammPool() + elif base_rule in ("reclamm_hypersurge", "hypersurge_reclamm"): + base_pool = ReClammHyperSurgePool() else: raise NotImplementedError(f"Unknown base pool type: {base_rule}") diff --git a/quantammsim/runners/jax_runners.py b/quantammsim/runners/jax_runners.py index 27669a2..fd55955 100644 --- a/quantammsim/runners/jax_runners.py +++ b/quantammsim/runners/jax_runners.py @@ -54,6 +54,7 @@ _calculate_return_value, ) from quantammsim.core_simulator.dynamic_inputs import ( + DynamicInputArrays, DynamicInputFrames, materialize_dynamic_inputs, ) @@ -128,6 +129,52 @@ _scan_infra_cache = {} +def _concat_dynamic_input_arrays( + train_dynamic_inputs: DynamicInputArrays, + test_dynamic_inputs=None, +): + """Concatenate train/test dynamic-input bundles for continuous evaluation.""" + if train_dynamic_inputs is None: + return None + if test_dynamic_inputs is None: + return train_dynamic_inputs + + def _concat_leaf(train_leaf, test_leaf): + if train_leaf is None: + return test_leaf + if test_leaf is None: + return train_leaf + if hasattr(train_leaf, "shape") and hasattr(test_leaf, "shape"): + if train_leaf.shape[0] <= 1 and test_leaf.shape[0] <= 1: + return train_leaf + if train_leaf.shape[0] <= 1: + return test_leaf + if test_leaf.shape[0] <= 1: + return train_leaf + return jnp.concatenate((train_leaf, test_leaf), axis=0) + + return DynamicInputArrays( + trades=_concat_leaf(train_dynamic_inputs.trades, test_dynamic_inputs.trades), + fees=_concat_leaf(train_dynamic_inputs.fees, test_dynamic_inputs.fees), + gas_cost=_concat_leaf( + train_dynamic_inputs.gas_cost, test_dynamic_inputs.gas_cost + ), + arb_fees=_concat_leaf( + train_dynamic_inputs.arb_fees, test_dynamic_inputs.arb_fees + ), + lp_supply=_concat_leaf( + train_dynamic_inputs.lp_supply, test_dynamic_inputs.lp_supply + ), + reclamm_price_ratio_updates=_concat_leaf( + train_dynamic_inputs.reclamm_price_ratio_updates, + test_dynamic_inputs.reclamm_price_ratio_updates, + ), + oracle_prices=_concat_leaf( + train_dynamic_inputs.oracle_prices, test_dynamic_inputs.oracle_prices + ), + ) + + def _scan_config_key(run_fingerprint, chunk_size, original_bout_length, bout_length_test): """Compute a hash key capturing everything that affects the compiled scan.""" fp_str = _json.dumps(run_fingerprint, sort_keys=True, default=str) @@ -329,6 +376,7 @@ def train_on_historic_data( iterations_per_print=1, force_init=False, price_data=None, + dynamic_input_frames: DynamicInputFrames = None, verbose=True, run_location=None, return_training_metadata=False, @@ -374,6 +422,10 @@ def train_on_historic_data( price_data : array-like or DataFrame, optional Pre-loaded price data. When None, data is loaded from parquet files based on ``run_fingerprint`` date/token settings. + dynamic_input_frames : DynamicInputFrames, optional + Optional minute-level dynamic input bundle. This is most useful + for HyperSurge oracle prices during training; the runner windows + the arrays alongside price windows. verbose : bool, optional Print detailed progress information (default True). run_location : str, optional @@ -425,7 +477,7 @@ def train_on_historic_data( try: return _train_on_historic_data_impl( run_fingerprint, root, iterations_per_print, force_init, - price_data, verbose, run_location, return_training_metadata, + price_data, dynamic_input_frames, verbose, run_location, return_training_metadata, warm_start_params, warm_start_weights, ) finally: @@ -434,7 +486,7 @@ def train_on_historic_data( def _train_on_historic_data_impl( run_fingerprint, root, iterations_per_print, force_init, - price_data, verbose, run_location, return_training_metadata, + price_data, dynamic_input_frames, verbose, run_location, return_training_metadata, warm_start_params, warm_start_weights, ): if verbose: @@ -473,6 +525,26 @@ def _train_on_historic_data_impl( ) max_memory_days = data_dict["max_memory_days"] + dynamic_inputs_dict = prepare_dynamic_inputs( + run_fingerprint, + dynamic_input_frames=dynamic_input_frames, + do_test_period=True, + ) + dynamic_input_flags = dynamic_inputs_dict["dynamic_input_flags"] + train_dynamic_inputs = ( + dynamic_inputs_dict["train_dynamic_inputs"] + if dynamic_input_flags["use_dynamic_inputs"] + else None + ) + continuous_dynamic_inputs = ( + _concat_dynamic_input_arrays( + train_dynamic_inputs, + dynamic_inputs_dict.get("test_dynamic_inputs"), + ) + if dynamic_input_flags["use_dynamic_inputs"] + else None + ) + # Validation holdout setup # If val_fraction > 0, carve out validation window from end of training val_fraction = run_fingerprint["optimisation_settings"].get("val_fraction", 0.0) @@ -683,22 +755,16 @@ def _train_on_historic_data_impl( overrides={ "n_assets": n_assets, "training_data_kind": run_fingerprint["optimisation_settings"]["training_data_kind"], - "do_trades": False, - "dynamic_input_flags": { - "use_dynamic_inputs": False, - "has_trades": False, - "has_dynamic_fees": False, - "has_dynamic_gas_cost": False, - "has_dynamic_arb_fees": False, - "has_lp_supply": False, - "has_reclamm_price_ratio_updates": False, - }, + "do_trades": dynamic_input_flags["has_trades"], + "dynamic_input_flags": dynamic_input_flags, + "dynamic_inputs_offset": data_dict["start_idx"], }, ) partial_training_step = Partial( forward_pass, prices=data_dict["prices"], + dynamic_inputs=train_dynamic_inputs, static_dict=Hashabledict(base_static_dict), pool=pool, ) @@ -714,7 +780,7 @@ def _train_on_historic_data_impl( continuous_static_dict["bout_length"] = original_bout_length + data_dict["bout_length_test"] partial_forward_pass_nograd_batch_continuous = Partial( forward_pass_nograd, - dynamic_inputs=None, + dynamic_inputs=continuous_dynamic_inputs, static_dict=Hashabledict(continuous_static_dict), pool=pool, ) @@ -854,13 +920,15 @@ def init_optimizer(params): run_fingerprint, chunk_size, original_bout_length, _bout_length_test, ) - if config_key in _scan_infra_cache: + use_scan_cache = not dynamic_input_flags["use_dynamic_inputs"] + + if use_scan_cache and config_key in _scan_infra_cache: _run_scan_chunk, scan_body, _run_scan_step = _scan_infra_cache[config_key] else: # Build scan-compatible update (prices as explicit arg, not closure) partial_step_no_prices = Partial( forward_pass, - dynamic_inputs=None, + dynamic_inputs=train_dynamic_inputs, static_dict=Hashabledict(base_static_dict), pool=pool, ) @@ -900,7 +968,12 @@ def init_optimizer(params): swa_freq=swa_freq, n_parameter_sets=n_parameter_sets, ) - _scan_infra_cache[config_key] = (_run_scan_chunk, scan_body, _run_scan_step) + if use_scan_cache: + _scan_infra_cache[config_key] = ( + _run_scan_chunk, + scan_body, + _run_scan_step, + ) # ── Initialize carry (prices & nan_bank in carry, not closures) ── carry = { @@ -2654,6 +2727,7 @@ def do_run_on_historic_data( "gas_cost": gas_cost if gas_cost is not None else run_fingerprint["gas_cost"], "do_trades": dynamic_inputs_dict["dynamic_input_flags"]["has_trades"], "dynamic_input_flags": dynamic_inputs_dict["dynamic_input_flags"], + "dynamic_inputs_offset": data_dict["start_idx"], # Include date strings for run-time use "startDateString": run_fingerprint["startDateString"], "endDateString": run_fingerprint["endDateString"], @@ -2677,6 +2751,9 @@ def do_run_on_historic_data( reserves_values_test_static_dict = base_static_dict.copy() reserves_values_test_static_dict["return_val"] = "reserves_and_values" reserves_values_test_static_dict["bout_length"] = data_dict["bout_length_test"] + reserves_values_test_static_dict["dynamic_inputs_offset"] = data_dict[ + "start_idx_test" + ] partial_forward_pass_nograd_batch_reserves_values_test = jit( Partial( forward_pass_nograd, @@ -2890,6 +2967,7 @@ def do_run_on_historic_data_with_provided_coarse_weights( "gas_cost": gas_cost if gas_cost is not None else run_fingerprint["gas_cost"], "do_trades": dynamic_inputs_dict["dynamic_input_flags"]["has_trades"], "dynamic_input_flags": dynamic_inputs_dict["dynamic_input_flags"], + "dynamic_inputs_offset": data_dict["start_idx"], # Include date strings for run-time use "startDateString": run_fingerprint["startDateString"], "endDateString": run_fingerprint["endDateString"], From c3b0edee6446a0f74eaa38a3fdd34ffdf2892310 Mon Sep 17 00:00:00 2001 From: christian harrington Date: Thu, 16 Apr 2026 14:56:53 +0100 Subject: [PATCH 57/57] reclamm hypersurge first implementation --- .../pools/reCLAMM/reclamm_hypersurge.py | 372 +++++++ .../reCLAMM/reclamm_hypersurge_reserves.py | 918 ++++++++++++++++++ scripts/hypersurge_demo_train.py | 173 ++++ .../pools/reCLAMM/test_hypersurge_reclamm.py | 94 ++ ...test_hypersurge_training_dynamic_inputs.py | 109 +++ 5 files changed, 1666 insertions(+) create mode 100644 quantammsim/pools/reCLAMM/reclamm_hypersurge.py create mode 100644 quantammsim/pools/reCLAMM/reclamm_hypersurge_reserves.py create mode 100644 scripts/hypersurge_demo_train.py create mode 100644 tests/pools/reCLAMM/test_hypersurge_reclamm.py create mode 100644 tests/unit/test_hypersurge_training_dynamic_inputs.py diff --git a/quantammsim/pools/reCLAMM/reclamm_hypersurge.py b/quantammsim/pools/reCLAMM/reclamm_hypersurge.py new file mode 100644 index 0000000..0e0a52c --- /dev/null +++ b/quantammsim/pools/reCLAMM/reclamm_hypersurge.py @@ -0,0 +1,372 @@ +from functools import partial +from typing import Any, Dict, Optional + +import numpy as np + +import jax.numpy as jnp +from jax import jit, tree_util +from jax.lax import dynamic_slice + +from quantammsim.core_simulator.dynamic_inputs import materialize_dynamic_inputs +from quantammsim.pools.hypersurge_utils import ( + HYPERSURGE_PARAM_KEYS, + hypersurge_params_from_params, + run_fingerprint_hypersurge_defaults, +) +from quantammsim.pools.reCLAMM.reclamm import ReClammPool +from quantammsim.pools.reCLAMM.reclamm_hypersurge_reserves import ( + _jax_calc_reclamm_hypersurge_reserves, + _jax_calc_reclamm_hypersurge_reserves_and_fee_revenue, +) + + +class ReClammHyperSurgePool(ReClammPool): + """reCLAMM pool with HyperSurge state-dependent swap fees.""" + + @staticmethod + def _run_fingerprint_hypersurge_defaults(run_fingerprint: Dict[str, Any]): + return run_fingerprint_hypersurge_defaults(run_fingerprint) + + def _hypersurge_params(self, params: Dict[str, Any], run_fingerprint: Dict[str, Any]): + return hypersurge_params_from_params(params, run_fingerprint) + + def _oracle_price_window( + self, + run_fingerprint: Dict[str, Any], + prices: jnp.ndarray, + start_index: jnp.ndarray, + additional_oracle_input: Optional[jnp.ndarray], + ): + bout_length = run_fingerprint["bout_length"] + n_assets = run_fingerprint["n_assets"] + if additional_oracle_input is None: + local_oracle_prices = dynamic_slice( + prices, start_index, (bout_length - 1, n_assets) + ) + else: + local_oracle_prices = dynamic_slice( + additional_oracle_input, + start_index, + (bout_length - 1, n_assets), + ) + + arb_frequency = run_fingerprint["arb_frequency"] + if arb_frequency != 1: + return local_oracle_prices[::arb_frequency] + return local_oracle_prices + + def _run_hypersurge_reserves( + self, + params: Dict[str, Any], + run_fingerprint: Dict[str, Any], + prices: jnp.ndarray, + start_index: jnp.ndarray, + fees, + gas_cost, + arb_fees, + lp_supply_array, + price_ratio_updates, + oracle_prices, + lp_supply_already_prepared: bool = False, + return_fee_revenue: bool = False, + ): + s = self._init_pool_state(params, run_fingerprint, prices, start_index) + ste_temperature = self._resolve_ste_temperature(run_fingerprint) + noise_lp_supply = None if lp_supply_already_prepared else lp_supply_array + lp_prepared, noise_model, noise_params, noise_arrays = self._resolve_noise_inputs( + run_fingerprint, + prices, + start_index, + s.arb_prices.shape[0], + lp_supply_array=noise_lp_supply, + ) + if lp_supply_already_prepared: + lp_prepared = lp_supply_array + + if not run_fingerprint["do_arb"]: + reserves = jnp.broadcast_to(s.initial_reserves, s.arb_prices.shape) + if return_fee_revenue: + return reserves, jnp.zeros(s.arb_prices.shape[0], dtype=s.arb_prices.dtype) + return reserves + + kernel = ( + _jax_calc_reclamm_hypersurge_reserves_and_fee_revenue + if return_fee_revenue + else _jax_calc_reclamm_hypersurge_reserves + ) + return kernel( + s.initial_reserves, + s.Va, + s.Vb, + s.arb_prices, + oracle_prices, + self._hypersurge_params(params, run_fingerprint), + s.centeredness_margin, + s.daily_price_shift_base, + s.seconds_per_step, + fees=fees, + arb_thresh=gas_cost, + arb_fees=arb_fees, + price_ratio_updates=price_ratio_updates, + all_sig_variations=jnp.array(run_fingerprint["all_sig_variations"]), + arc_length_speed=s.arc_length_speed, + centeredness_scaling=s.centeredness_scaling, + protocol_fee_split=run_fingerprint.get("protocol_fee_split", 0.0), + ste_temperature=ste_temperature, + noise_trader_ratio=run_fingerprint.get("noise_trader_ratio", 0.0), + lp_supply_array=lp_prepared, + noise_model=noise_model, + noise_params=noise_params, + volatility_array=noise_arrays["volatility"], + dow_sin_array=noise_arrays["dow_sin"], + dow_cos_array=noise_arrays["dow_cos"], + noise_base_array=noise_arrays["noise_base"], + noise_tvl_coeff_array=noise_arrays["noise_tvl_coeff"], + competitor_tvl_array=noise_arrays["competitor_tvl"], + ) + + @partial(jit, static_argnums=(2,)) + def calculate_reserves_with_fees( + self, + params: Dict[str, Any], + run_fingerprint: Dict[str, Any], + prices: jnp.ndarray, + start_index: jnp.ndarray, + additional_oracle_input: Optional[jnp.ndarray] = None, + lp_supply_array: Optional[jnp.ndarray] = None, + ) -> jnp.ndarray: + oracle_prices = self._oracle_price_window( + run_fingerprint, + prices, + start_index, + additional_oracle_input, + ) + return self._run_hypersurge_reserves( + params, + run_fingerprint, + prices, + start_index, + fees=self._resolve_fees(params, run_fingerprint), + gas_cost=run_fingerprint["gas_cost"], + arb_fees=run_fingerprint["arb_fees"], + lp_supply_array=lp_supply_array, + price_ratio_updates=None, + oracle_prices=oracle_prices, + ) + + @partial(jit, static_argnums=(2,)) + def calculate_reserves_and_fee_revenue_with_fees( + self, + params: Dict[str, Any], + run_fingerprint: Dict[str, Any], + prices: jnp.ndarray, + start_index: jnp.ndarray, + additional_oracle_input: Optional[jnp.ndarray] = None, + lp_supply_array: Optional[jnp.ndarray] = None, + ): + oracle_prices = self._oracle_price_window( + run_fingerprint, + prices, + start_index, + additional_oracle_input, + ) + return self._run_hypersurge_reserves( + params, + run_fingerprint, + prices, + start_index, + fees=self._resolve_fees(params, run_fingerprint), + gas_cost=run_fingerprint["gas_cost"], + arb_fees=run_fingerprint["arb_fees"], + lp_supply_array=lp_supply_array, + price_ratio_updates=None, + oracle_prices=oracle_prices, + return_fee_revenue=True, + ) + + @partial(jit, static_argnums=(2,)) + def _calculate_reserves_zero_fees( + self, + params: Dict[str, Any], + run_fingerprint: Dict[str, Any], + prices: jnp.ndarray, + start_index: jnp.ndarray, + additional_oracle_input: Optional[jnp.ndarray] = None, + lp_supply_array: Optional[jnp.ndarray] = None, + ) -> jnp.ndarray: + oracle_prices = self._oracle_price_window( + run_fingerprint, + prices, + start_index, + additional_oracle_input, + ) + return self._run_hypersurge_reserves( + params, + run_fingerprint, + prices, + start_index, + fees=0.0, + gas_cost=run_fingerprint["gas_cost"], + arb_fees=run_fingerprint["arb_fees"], + lp_supply_array=lp_supply_array, + price_ratio_updates=None, + oracle_prices=oracle_prices, + ) + + def calculate_reserves_zero_fees( + self, + params: Dict[str, Any], + run_fingerprint: Dict[str, Any], + prices: jnp.ndarray, + start_index: jnp.ndarray, + additional_oracle_input: Optional[jnp.ndarray] = None, + lp_supply_array: Optional[jnp.ndarray] = None, + ) -> jnp.ndarray: + return self._calculate_reserves_zero_fees( + params, + run_fingerprint, + prices, + start_index, + additional_oracle_input, + lp_supply_array, + ) + + @partial(jit, static_argnums=(2,)) + def calculate_reserves_with_dynamic_inputs( + self, + params: Dict[str, Any], + run_fingerprint: Dict[str, Any], + prices: jnp.ndarray, + start_index: jnp.ndarray, + dynamic_inputs, + additional_oracle_input: Optional[jnp.ndarray] = None, + ) -> jnp.ndarray: + s = self._init_pool_state(params, run_fingerprint, prices, start_index) + materialized_inputs = materialize_dynamic_inputs( + dynamic_inputs, + run_fingerprint.get("dynamic_input_flags"), + run_fingerprint, + scan_len=s.arb_prices.shape[0], + do_trades=False, + dtype=s.arb_prices.dtype, + ) + + oracle_prices = self._oracle_price_window( + run_fingerprint, + prices, + start_index, + additional_oracle_input, + ) + if materialized_inputs.oracle_prices.shape[-1] == run_fingerprint["n_assets"]: + oracle_prices = materialized_inputs.oracle_prices + + return self._run_hypersurge_reserves( + params, + run_fingerprint, + prices, + start_index, + fees=materialized_inputs.fees, + gas_cost=materialized_inputs.gas_cost, + arb_fees=materialized_inputs.arb_fees, + lp_supply_array=materialized_inputs.lp_supply, + price_ratio_updates=materialized_inputs.reclamm_price_ratio_updates, + oracle_prices=oracle_prices, + lp_supply_already_prepared=True, + ) + + @partial(jit, static_argnums=(2,)) + def calculate_reserves_and_fee_revenue_with_dynamic_inputs( + self, + params: Dict[str, Any], + run_fingerprint: Dict[str, Any], + prices: jnp.ndarray, + start_index: jnp.ndarray, + dynamic_inputs, + additional_oracle_input: Optional[jnp.ndarray] = None, + ): + s = self._init_pool_state(params, run_fingerprint, prices, start_index) + materialized_inputs = materialize_dynamic_inputs( + dynamic_inputs, + run_fingerprint.get("dynamic_input_flags"), + run_fingerprint, + scan_len=s.arb_prices.shape[0], + do_trades=False, + dtype=s.arb_prices.dtype, + ) + + oracle_prices = self._oracle_price_window( + run_fingerprint, + prices, + start_index, + additional_oracle_input, + ) + if materialized_inputs.oracle_prices.shape[-1] == run_fingerprint["n_assets"]: + oracle_prices = materialized_inputs.oracle_prices + + return self._run_hypersurge_reserves( + params, + run_fingerprint, + prices, + start_index, + fees=materialized_inputs.fees, + gas_cost=materialized_inputs.gas_cost, + arb_fees=materialized_inputs.arb_fees, + lp_supply_array=materialized_inputs.lp_supply, + price_ratio_updates=materialized_inputs.reclamm_price_ratio_updates, + oracle_prices=oracle_prices, + lp_supply_already_prepared=True, + return_fee_revenue=True, + ) + + def init_base_parameters( + self, + initial_values_dict: Dict[str, Any], + run_fingerprint: Dict[str, Any], + n_assets: int, + n_parameter_sets: int = 1, + noise: str = "gaussian", + ) -> Dict[str, Any]: + params = super().init_base_parameters( + initial_values_dict, + run_fingerprint, + n_assets, + n_parameter_sets=n_parameter_sets, + noise=noise, + ) + + def process_scalar(key, default): + value = initial_values_dict.get(key, default) + if value is None: + value = default + value = np.asarray(value, dtype=np.float64) + if value.size == 1: + return np.array([[float(value.reshape(-1)[0])]] * n_parameter_sets) + if value.shape == (n_parameter_sets,): + return value.reshape(n_parameter_sets, 1) + if value.shape == (n_parameter_sets, 1): + return value + raise ValueError( + f"{key} must be a scalar or a matrix of shape (n_parameter_sets, 1)" + ) + + defaults = self._run_fingerprint_hypersurge_defaults(run_fingerprint) + hypersurge_params = { + key: process_scalar(key, defaults[key]) for key in HYPERSURGE_PARAM_KEYS + } + hypersurge_params = self.add_noise(hypersurge_params, noise, n_parameter_sets) + params.update(hypersurge_params) + return params + + def get_initial_values(self, run_fingerprint): + values = super().get_initial_values(run_fingerprint) + defaults = self._run_fingerprint_hypersurge_defaults(run_fingerprint) + for key, value in defaults.items(): + values[key] = run_fingerprint.get(f"initial_{key}", value) + return values + + +tree_util.register_pytree_node( + ReClammHyperSurgePool, + ReClammHyperSurgePool._tree_flatten, + ReClammHyperSurgePool._tree_unflatten, +) diff --git a/quantammsim/pools/reCLAMM/reclamm_hypersurge_reserves.py b/quantammsim/pools/reCLAMM/reclamm_hypersurge_reserves.py new file mode 100644 index 0000000..d508f5f --- /dev/null +++ b/quantammsim/pools/reCLAMM/reclamm_hypersurge_reserves.py @@ -0,0 +1,918 @@ +from functools import partial + +import jax.numpy as jnp +from jax import jit +from jax.lax import cond, scan +from jax.tree_util import Partial + +from quantammsim.pools.G3M.G3M_trades import ( + _jax_calc_G3M_trade_from_exact_in_given_out, +) +from quantammsim.pools.G3M.optimal_n_pool_arb import ( + parallelised_optimal_trade_sifter, + precalc_components_of_optimal_trade_across_signatures, + precalc_shared_values_for_all_signatures, +) +from quantammsim.pools.hypersurge_utils import ( + _EPS, + broadcast_scan_vector, + fee_to_gamma, + max_pair_deviation, + oracle_pair_is_valid, + oracle_vector_is_valid, + pair_deviation, + ramp_fee, + safe_positive, +) +from quantammsim.pools.noise_trades import ( + calculate_reserves_after_noise_trade, + reclamm_calibrated_noise_volume, + reclamm_loglinear_noise_volume, + reclamm_market_linear_noise_volume, + reclamm_mm_observed_noise_volume, + reclamm_tsoukalas_log_noise_volume, + reclamm_tsoukalas_sqrt_noise_volume, +) +from quantammsim.pools.reCLAMM.reclamm_reserves import ( + _DUST_USD, + _ste_greater_equal, + _ste_less_than, + _ste_select, + apply_target_price_ratio_to_virtual_balances, + compute_centeredness, + compute_invariant, + compute_price_ratio, + compute_virtual_balances_constant_arc_length, + compute_virtual_balances_updating_price_range, +) + + +_WEIGHTS = jnp.array([0.5, 0.5]) + + +def _broadcast_oracle_prices(oracle_prices, prices): + oracle_prices = jnp.asarray(oracle_prices) + if oracle_prices.ndim == 1: + oracle_prices = oracle_prices.reshape((1, oracle_prices.shape[0])) + if oracle_prices.shape[-1] != prices.shape[-1]: + oracle_prices = prices + elif oracle_prices.shape[0] == 1: + oracle_prices = jnp.broadcast_to(oracle_prices, prices.shape) + return oracle_prices + + +def _broadcast_schedule_array(price_ratio_updates, prices): + if price_ratio_updates is None: + updates = jnp.zeros((prices.shape[0], 4), dtype=prices.dtype) + return updates.at[:, 3].set(jnp.nan) + + updates = jnp.asarray(price_ratio_updates, dtype=prices.dtype) + if updates.ndim == 1: + updates = jnp.broadcast_to(updates, (prices.shape[0], updates.shape[0])) + elif updates.shape[0] == 1 and prices.shape[0] != 1: + updates = jnp.broadcast_to(updates, (prices.shape[0], updates.shape[1])) + return updates + + +def _zero_fee_optimal_trade(Ra, Rb, Va, Vb, prices): + market_price = prices[0] / prices[1] + L = compute_invariant(Ra, Rb, Va, Vb) + Ea_new = jnp.sqrt(L / market_price) + Eb_new = jnp.sqrt(L * market_price) + return jnp.array([Ea_new - (Ra + Va), Eb_new - (Rb + Vb)]) + + +def _effective_reserves(real_reserves, Va, Vb): + return jnp.array([real_reserves[0] + Va, real_reserves[1] + Vb]) + + +def _reclamm_hypersurge_fee_for_trade( + real_reserves, + Va, + Vb, + candidate_trade, + oracle_prices, + base_fee, + hypersurge_params, +): + token_in = jnp.argmax(candidate_trade) + token_out = jnp.argmin(candidate_trade) + trade_active = jnp.logical_and( + candidate_trade[token_in] > 0.0, + candidate_trade[token_out] < 0.0, + ) + pair_has_oracle = oracle_pair_is_valid(oracle_prices, token_in, token_out) + + effective_before = safe_positive(_effective_reserves(real_reserves, Va, Vb)) + effective_after = safe_positive(effective_before + candidate_trade) + dev_before = pair_deviation( + effective_before, + _WEIGHTS, + oracle_prices, + token_in, + token_out, + ) + dev_after = pair_deviation( + effective_after, + _WEIGHTS, + oracle_prices, + token_in, + token_out, + ) + worsens = dev_after > dev_before + + arb_fee = ramp_fee( + base_fee, + hypersurge_params[0], + hypersurge_params[1], + hypersurge_params[2], + dev_before, + ) + noise_fee = ramp_fee( + base_fee, + hypersurge_params[3], + hypersurge_params[4], + hypersurge_params[5], + dev_after, + ) + fee = jnp.where(worsens, noise_fee, arb_fee) + return jnp.where(jnp.logical_and(trade_active, pair_has_oracle), fee, base_fee) + + +def _reclamm_hypersurge_noise_fee( + real_reserves, + Va, + Vb, + oracle_prices, + base_fee, + hypersurge_params, +): + effective = safe_positive(_effective_reserves(real_reserves, Va, Vb)) + deviation = max_pair_deviation(effective, _WEIGHTS, oracle_prices) + fee = ramp_fee( + base_fee, + hypersurge_params[3], + hypersurge_params[4], + hypersurge_params[5], + deviation, + ) + return jnp.where(oracle_vector_is_valid(oracle_prices), fee, base_fee) + + +def _optimal_arb_trade_with_gamma( + reserves, + prices, + gamma, + tokens_to_drop, + active_trade_directions, + leave_one_out_idxs, + n, +): + active_initial_weights, per_asset_ratios, all_other_assets_ratios = ( + precalc_components_of_optimal_trade_across_signatures( + _WEIGHTS, + prices, + gamma, + tokens_to_drop, + active_trade_directions, + leave_one_out_idxs, + ) + ) + return parallelised_optimal_trade_sifter( + reserves, + _WEIGHTS, + prices, + active_initial_weights, + active_trade_directions, + per_asset_ratios, + all_other_assets_ratios, + tokens_to_drop, + gamma, + n, + 0, + ) + + +def _apply_protocol_fee(reserves_after_trade, trade, fee, protocol_fee_split): + inbound = jnp.maximum(trade, 0.0) + protocol_fee = inbound * fee * protocol_fee_split + return jnp.maximum(reserves_after_trade - protocol_fee, _EPS) + + +def _reclamm_hypersurge_scan_step_with_fee_revenue( + carry_list, + input_list, + tokens_to_drop, + active_trade_directions, + leave_one_out_idxs, + n, + hypersurge_params, + centeredness_margin, + daily_price_shift_base, + seconds_per_step, + arc_length_speed=0.0, + centeredness_scaling=False, + protocol_fee_split=0.0, + ste_temperature=10.0, + noise_trader_ratio=0.0, + noise_model="ratio", + noise_params=None, +): + prev_reserves = carry_list[0] + Va = carry_list[1] + Vb = carry_list[2] + step_idx = carry_list[3] + active_start_ratio = carry_list[4] + active_target_ratio = carry_list[5] + active_start_step = carry_list[6] + active_end_step = carry_list[7] + active_enabled = carry_list[8] + prev_lp_supply = carry_list[9] + + prices = input_list[0] + oracle_prices = input_list[1] + base_fee = input_list[2] + arb_thresh = input_list[3] + arb_fees = input_list[4] + price_ratio_update = input_list[5] + lp_supply = input_list[6] + volatility = input_list[7] + dow_sin = input_list[8] + dow_cos = input_list[9] + noise_base = input_list[10] + noise_tvl_coeff = input_list[11] + competitor_tvl = input_list[12] + + scale = lp_supply / prev_lp_supply + lp_supply_change = lp_supply != prev_lp_supply + prev_reserves = jnp.where(lp_supply_change, prev_reserves * scale, prev_reserves) + Va = jnp.where(lp_supply_change, Va * scale, Va) + Vb = jnp.where(lp_supply_change, Vb * scale, Vb) + + Ra = prev_reserves[0] + Rb = prev_reserves[1] + + event_has = price_ratio_update[0] > 0.5 + event_target_ratio = jnp.maximum( + jnp.where(jnp.isfinite(price_ratio_update[1]), price_ratio_update[1], 1.0), + 1.0 + 1e-12, + ) + event_end_step = jnp.where( + jnp.isfinite(price_ratio_update[2]), price_ratio_update[2], step_idx + ) + event_start_override = price_ratio_update[3] + + def _apply_schedule_state(_): + current_price_ratio = compute_price_ratio(Ra, Rb, Va, Vb) + start_ratio_from_event = jnp.where( + jnp.isfinite(event_start_override), + event_start_override, + current_price_ratio, + ) + next_active_start_ratio = jnp.where( + event_has, start_ratio_from_event, active_start_ratio + ) + next_active_target_ratio = jnp.where( + event_has, event_target_ratio, active_target_ratio + ) + next_active_start_step = jnp.where(event_has, step_idx, active_start_step) + next_active_end_step = jnp.where( + event_has, jnp.maximum(event_end_step, step_idx), active_end_step + ) + next_active_enabled = jnp.where(event_has, True, active_enabled) + next_active_enabled = jnp.logical_and( + next_active_enabled, step_idx <= next_active_end_step + ) + + schedule_duration = next_active_end_step - next_active_start_step + schedule_progress = jnp.where( + schedule_duration <= 0.0, + 1.0, + jnp.clip((step_idx - next_active_start_step) / schedule_duration, 0.0, 1.0), + ) + safe_start_ratio = jnp.maximum(next_active_start_ratio, 1.0 + 1e-12) + safe_target_ratio = jnp.maximum(next_active_target_ratio, 1.0 + 1e-12) + scheduled_price_ratio = safe_start_ratio * ( + safe_target_ratio / safe_start_ratio + ) ** schedule_progress + scheduled_price_ratio = jnp.where( + next_active_enabled, scheduled_price_ratio, current_price_ratio + ) + Va_scheduled, Vb_scheduled = apply_target_price_ratio_to_virtual_balances( + Ra, Rb, Va, Vb, scheduled_price_ratio + ) + next_Va = jnp.where(next_active_enabled, Va_scheduled, Va) + next_Vb = jnp.where(next_active_enabled, Vb_scheduled, Vb) + return ( + next_Va, + next_Vb, + next_active_start_ratio, + next_active_target_ratio, + next_active_start_step, + next_active_end_step, + next_active_enabled, + ) + + def _skip_schedule_state(_): + retained_active_enabled = jnp.logical_and( + active_enabled, step_idx <= active_end_step + ) + return ( + Va, + Vb, + active_start_ratio, + active_target_ratio, + active_start_step, + active_end_step, + retained_active_enabled, + ) + + active_not_expired = jnp.logical_and(active_enabled, step_idx <= active_end_step) + schedule_active = jnp.logical_or(event_has, active_not_expired) + ( + Va, + Vb, + active_start_ratio, + active_target_ratio, + active_start_step, + active_end_step, + active_enabled, + ) = cond( + schedule_active, + _apply_schedule_state, + _skip_schedule_state, + operand=None, + ) + + centeredness, is_above = compute_centeredness(Ra, Rb, Va, Vb) + sqrt_Q = jnp.sqrt(compute_price_ratio(Ra, Rb, Va, Vb)) + market_price = prices[0] / prices[1] + + speed_multiplier = jnp.where( + centeredness_scaling, + centeredness_margin / jnp.maximum(centeredness, 1e-10), + 1.0, + ) + + Va_geo, Vb_geo = compute_virtual_balances_updating_price_range( + Ra, + Rb, + Va, + Vb, + is_pool_above_center=is_above, + daily_price_shift_base=daily_price_shift_base, + seconds_elapsed=seconds_per_step * speed_multiplier, + sqrt_price_ratio=sqrt_Q, + ) + Va_cal, Vb_cal = compute_virtual_balances_constant_arc_length( + Ra, + Rb, + Va, + Vb, + is_pool_above_center=is_above, + arc_length_speed=arc_length_speed * speed_multiplier, + seconds_elapsed=seconds_per_step, + sqrt_price_ratio=sqrt_Q, + market_price=market_price, + ) + use_cal = arc_length_speed > 0.0 + Va_updated = jnp.where(use_cal, Va_cal, Va_geo) + Vb_updated = jnp.where(use_cal, Vb_cal, Vb_geo) + + out_of_range_gate = _ste_less_than( + centeredness, centeredness_margin, ste_temperature + ) + Va = _ste_select(out_of_range_gate, Va_updated, Va) + Vb = _ste_select(out_of_range_gate, Vb_updated, Vb) + + effective_reserves = _effective_reserves(prev_reserves, Va, Vb) + zero_fee_trade = _zero_fee_optimal_trade(Ra, Rb, Va, Vb, prices) + + preview_fee = _reclamm_hypersurge_fee_for_trade( + prev_reserves, + Va, + Vb, + zero_fee_trade, + oracle_prices, + base_fee, + hypersurge_params, + ) + preview_trade = _optimal_arb_trade_with_gamma( + effective_reserves, + prices, + fee_to_gamma(preview_fee), + tokens_to_drop, + active_trade_directions, + leave_one_out_idxs, + n, + ) + arb_fee = _reclamm_hypersurge_fee_for_trade( + prev_reserves, + Va, + Vb, + preview_trade, + oracle_prices, + base_fee, + hypersurge_params, + ) + arb_gamma = fee_to_gamma(arb_fee) + optimal_arb_trade = _optimal_arb_trade_with_gamma( + effective_reserves, + prices, + arb_gamma, + tokens_to_drop, + active_trade_directions, + leave_one_out_idxs, + n, + ) + + profit_to_arb = -(optimal_arb_trade * prices).sum() - arb_thresh + arb_external_cost = 0.5 * arb_fees * (jnp.abs(optimal_arb_trade) * prices).sum() + trade_gate = _ste_greater_equal( + profit_to_arb, arb_external_cost, ste_temperature + ) + applied_trade = _ste_select( + trade_gate, optimal_arb_trade, jnp.zeros_like(optimal_arb_trade) + ) + + Ra_trade = Ra + applied_trade[0] + Rb_trade = Rb + applied_trade[1] + + dust_a = _DUST_USD / prices[0] + dust_b = _DUST_USD / prices[1] + drain_a = jnp.maximum(Ra - dust_a, 0.0) + drain_b = jnp.maximum(Rb - dust_b, 0.0) + edge_a = _jax_calc_G3M_trade_from_exact_in_given_out( + effective_reserves, + _WEIGHTS, + token_in=1, + token_out=0, + amount_out=drain_a, + gamma=arb_gamma, + ) + edge_b = _jax_calc_G3M_trade_from_exact_in_given_out( + effective_reserves, + _WEIGHTS, + token_in=0, + token_out=1, + amount_out=drain_b, + gamma=arb_gamma, + ) + + clamp_a = Ra_trade < 0 + clamp_b = Rb_trade < 0 + final_arb_trade = jnp.where( + clamp_a, + edge_a, + jnp.where(clamp_b, edge_b, applied_trade), + ) + + reserves_after_arb = prev_reserves + final_arb_trade + reserves_after_arb = _apply_protocol_fee( + reserves_after_arb, + final_arb_trade, + arb_fee, + protocol_fee_split, + ) + arb_lp_fee_income = ( + jnp.maximum(final_arb_trade, 0.0) * arb_fee * (1.0 - protocol_fee_split) + ) + lp_fee_revenue_usd = (arb_lp_fee_income * prices).sum() + + noise_fee = _reclamm_hypersurge_noise_fee( + reserves_after_arb, + Va, + Vb, + oracle_prices, + base_fee, + hypersurge_params, + ) + + Ra_new = reserves_after_arb[0] + Rb_new = reserves_after_arb[1] + + if noise_model == "ratio": + lp_noise_gamma = fee_to_gamma(noise_fee * (1.0 - protocol_fee_split)) + noisy_reserves = calculate_reserves_after_noise_trade( + final_arb_trade, + reserves_after_arb, + prices, + noise_trader_ratio, + lp_noise_gamma, + ) + noise_lp_fee_income_usd = ( + noise_trader_ratio + * noise_fee + * (1.0 - protocol_fee_split) + * jnp.sum(jnp.maximum(final_arb_trade, 0.0) * prices) + ) + Ra_new = jnp.where(noise_trader_ratio > 0.0, noisy_reserves[0], Ra_new) + Rb_new = jnp.where(noise_trader_ratio > 0.0, noisy_reserves[1], Rb_new) + lp_fee_revenue_usd = jnp.where( + noise_trader_ratio > 0.0, + lp_fee_revenue_usd + noise_lp_fee_income_usd, + lp_fee_revenue_usd, + ) + elif noise_model in ("tsoukalas_sqrt", "tsoukalas_log", "loglinear"): + arb_volume = 0.5 * jnp.sum(jnp.abs(final_arb_trade) * prices) + effective_value = (Ra_new + Va) * prices[0] + (Rb_new + Vb) * prices[1] + noise_cfg = noise_params if noise_params is not None else {} + if noise_model == "tsoukalas_sqrt": + noise_vol = reclamm_tsoukalas_sqrt_noise_volume( + effective_value, arb_gamma, volatility, arb_volume, noise_cfg + ) + elif noise_model == "tsoukalas_log": + noise_vol = reclamm_tsoukalas_log_noise_volume( + effective_value, arb_gamma, volatility, arb_volume, noise_cfg + ) + else: + noise_vol = reclamm_loglinear_noise_volume( + effective_value, arb_gamma, volatility, arb_volume, noise_cfg + ) + minutes_per_step = seconds_per_step / 60.0 + noise_lp_fee_income_usd = ( + noise_fee * (1.0 - protocol_fee_split) * noise_vol * minutes_per_step + ) + scale = 1.0 + noise_lp_fee_income_usd / jnp.maximum(effective_value, 1e-8) + Ra_new = (Ra_new + Va) * scale - Va + Rb_new = (Rb_new + Vb) * scale - Vb + lp_fee_revenue_usd = lp_fee_revenue_usd + noise_lp_fee_income_usd + elif noise_model == "calibrated": + arb_volume = 0.5 * jnp.sum(jnp.abs(final_arb_trade) * prices) + effective_value = (Ra_new + Va) * prices[0] + (Rb_new + Vb) * prices[1] + noise_cfg = noise_params if noise_params is not None else {} + noise_vol = reclamm_calibrated_noise_volume( + effective_value, + arb_gamma, + volatility, + arb_volume, + dow_sin, + dow_cos, + noise_cfg, + ) + minutes_per_step = seconds_per_step / 60.0 + noise_lp_fee_income_usd = ( + noise_fee * (1.0 - protocol_fee_split) * noise_vol * minutes_per_step + ) + scale = 1.0 + noise_lp_fee_income_usd / jnp.maximum(effective_value, 1e-8) + Ra_new = (Ra_new + Va) * scale - Va + Rb_new = (Rb_new + Vb) * scale - Vb + lp_fee_revenue_usd = lp_fee_revenue_usd + noise_lp_fee_income_usd + elif noise_model == "market_linear": + effective_value = (Ra_new + Va) * prices[0] + (Rb_new + Vb) * prices[1] + noise_cfg = noise_params if noise_params is not None else {} + noise_vol = reclamm_market_linear_noise_volume( + effective_value, + noise_base, + noise_tvl_coeff, + tvl_mean=noise_cfg.get("tvl_mean", 0.0), + tvl_std=noise_cfg.get("tvl_std", 1.0), + ) + minutes_per_step = seconds_per_step / 60.0 + noise_lp_fee_income_usd = ( + noise_fee * (1.0 - protocol_fee_split) * noise_vol * minutes_per_step + ) + scale = 1.0 + noise_lp_fee_income_usd / jnp.maximum(effective_value, 1e-8) + Ra_new = (Ra_new + Va) * scale - Va + Rb_new = (Rb_new + Vb) * scale - Vb + lp_fee_revenue_usd = lp_fee_revenue_usd + noise_lp_fee_income_usd + elif noise_model == "mm_observed": + effective_value = (Ra_new + Va) * prices[0] + (Rb_new + Vb) * prices[1] + noise_vol = reclamm_mm_observed_noise_volume( + effective_value, noise_base, competitor_tvl + ) + minutes_per_step = seconds_per_step / 60.0 + noise_lp_fee_income_usd = ( + noise_fee * (1.0 - protocol_fee_split) * noise_vol * minutes_per_step + ) + scale = 1.0 + noise_lp_fee_income_usd / jnp.maximum(effective_value, 1e-8) + Ra_new = (Ra_new + Va) * scale - Va + Rb_new = (Rb_new + Vb) * scale - Vb + lp_fee_revenue_usd = lp_fee_revenue_usd + noise_lp_fee_income_usd + + new_reserves = jnp.array([Ra_new, Rb_new]) + return [ + new_reserves, + Va, + Vb, + step_idx + 1.0, + active_start_ratio, + active_target_ratio, + active_start_step, + active_end_step, + active_enabled, + lp_supply, + ], (new_reserves, lp_fee_revenue_usd) + + +def _reclamm_hypersurge_scan_step( + carry_list, + input_list, + tokens_to_drop, + active_trade_directions, + leave_one_out_idxs, + n, + hypersurge_params, + centeredness_margin, + daily_price_shift_base, + seconds_per_step, + arc_length_speed=0.0, + centeredness_scaling=False, + protocol_fee_split=0.0, + ste_temperature=10.0, + noise_trader_ratio=0.0, + noise_model="ratio", + noise_params=None, +): + new_carry, (new_reserves, _fee_revenue) = ( + _reclamm_hypersurge_scan_step_with_fee_revenue( + carry_list, + input_list, + tokens_to_drop=tokens_to_drop, + active_trade_directions=active_trade_directions, + leave_one_out_idxs=leave_one_out_idxs, + n=n, + hypersurge_params=hypersurge_params, + centeredness_margin=centeredness_margin, + daily_price_shift_base=daily_price_shift_base, + seconds_per_step=seconds_per_step, + arc_length_speed=arc_length_speed, + centeredness_scaling=centeredness_scaling, + protocol_fee_split=protocol_fee_split, + ste_temperature=ste_temperature, + noise_trader_ratio=noise_trader_ratio, + noise_model=noise_model, + noise_params=noise_params, + ) + ) + return new_carry, new_reserves + + +@partial(jit, static_argnames=("noise_model",)) +def _jax_calc_reclamm_hypersurge_reserves( + initial_reserves, + initial_Va, + initial_Vb, + prices, + oracle_prices, + hypersurge_params, + centeredness_margin, + daily_price_shift_base, + seconds_per_step, + fees=0.003, + arb_thresh=0.0, + arb_fees=0.0, + price_ratio_updates=None, + all_sig_variations=None, + arc_length_speed=0.0, + centeredness_scaling=False, + protocol_fee_split=0.0, + ste_temperature=10.0, + noise_trader_ratio=0.0, + lp_supply_array=None, + noise_model="ratio", + noise_params=None, + volatility_array=None, + dow_sin_array=None, + dow_cos_array=None, + noise_base_array=None, + noise_tvl_coeff_array=None, + competitor_tvl_array=None, +): + if lp_supply_array is None: + lp_supply_array = jnp.ones((prices.shape[0],), dtype=prices.dtype) + else: + lp_supply_array = broadcast_scan_vector(lp_supply_array, prices.shape[0]) + + fees = broadcast_scan_vector(fees, prices.shape[0]) + arb_thresh = broadcast_scan_vector(arb_thresh, prices.shape[0]) + arb_fees = broadcast_scan_vector(arb_fees, prices.shape[0]) + oracle_prices = _broadcast_oracle_prices(oracle_prices, prices) + price_ratio_updates = _broadcast_schedule_array(price_ratio_updates, prices) + volatility_array = broadcast_scan_vector( + jnp.zeros((1,), dtype=prices.dtype) + if volatility_array is None + else volatility_array, + prices.shape[0], + ) + dow_sin_array = broadcast_scan_vector( + jnp.zeros((1,), dtype=prices.dtype) + if dow_sin_array is None + else dow_sin_array, + prices.shape[0], + ) + dow_cos_array = broadcast_scan_vector( + jnp.zeros((1,), dtype=prices.dtype) + if dow_cos_array is None + else dow_cos_array, + prices.shape[0], + ) + noise_base_array = broadcast_scan_vector( + jnp.zeros((1,), dtype=prices.dtype) + if noise_base_array is None + else noise_base_array, + prices.shape[0], + ) + noise_tvl_coeff_array = broadcast_scan_vector( + jnp.zeros((1,), dtype=prices.dtype) + if noise_tvl_coeff_array is None + else noise_tvl_coeff_array, + prices.shape[0], + ) + competitor_tvl_array = broadcast_scan_vector( + jnp.zeros((1,), dtype=prices.dtype) + if competitor_tvl_array is None + else competitor_tvl_array, + prices.shape[0], + ) + + _, active_trade_directions, tokens_to_drop, leave_one_out_idxs = ( + precalc_shared_values_for_all_signatures(all_sig_variations, 2) + ) + + scan_fn = Partial( + _reclamm_hypersurge_scan_step, + tokens_to_drop=tokens_to_drop, + active_trade_directions=active_trade_directions, + leave_one_out_idxs=leave_one_out_idxs, + n=2, + hypersurge_params=jnp.asarray(hypersurge_params, dtype=prices.dtype), + centeredness_margin=centeredness_margin, + daily_price_shift_base=daily_price_shift_base, + seconds_per_step=seconds_per_step, + arc_length_speed=arc_length_speed, + centeredness_scaling=centeredness_scaling, + protocol_fee_split=protocol_fee_split, + ste_temperature=ste_temperature, + noise_trader_ratio=noise_trader_ratio, + noise_model=noise_model, + noise_params=noise_params if noise_params is not None else {}, + ) + + carry_init = [ + initial_reserves, + initial_Va, + initial_Vb, + jnp.float64(0.0), + jnp.float64(0.0), + jnp.float64(0.0), + jnp.float64(0.0), + jnp.float64(0.0), + jnp.array(False), + lp_supply_array[0], + ] + _, reserves = scan( + scan_fn, + carry_init, + [ + prices, + oracle_prices, + fees, + arb_thresh, + arb_fees, + price_ratio_updates, + lp_supply_array, + volatility_array, + dow_sin_array, + dow_cos_array, + noise_base_array, + noise_tvl_coeff_array, + competitor_tvl_array, + ], + ) + return reserves + + +@partial(jit, static_argnames=("noise_model",)) +def _jax_calc_reclamm_hypersurge_reserves_and_fee_revenue( + initial_reserves, + initial_Va, + initial_Vb, + prices, + oracle_prices, + hypersurge_params, + centeredness_margin, + daily_price_shift_base, + seconds_per_step, + fees=0.003, + arb_thresh=0.0, + arb_fees=0.0, + price_ratio_updates=None, + all_sig_variations=None, + arc_length_speed=0.0, + centeredness_scaling=False, + protocol_fee_split=0.0, + ste_temperature=10.0, + noise_trader_ratio=0.0, + lp_supply_array=None, + noise_model="ratio", + noise_params=None, + volatility_array=None, + dow_sin_array=None, + dow_cos_array=None, + noise_base_array=None, + noise_tvl_coeff_array=None, + competitor_tvl_array=None, +): + if lp_supply_array is None: + lp_supply_array = jnp.ones((prices.shape[0],), dtype=prices.dtype) + else: + lp_supply_array = broadcast_scan_vector(lp_supply_array, prices.shape[0]) + + fees = broadcast_scan_vector(fees, prices.shape[0]) + arb_thresh = broadcast_scan_vector(arb_thresh, prices.shape[0]) + arb_fees = broadcast_scan_vector(arb_fees, prices.shape[0]) + oracle_prices = _broadcast_oracle_prices(oracle_prices, prices) + price_ratio_updates = _broadcast_schedule_array(price_ratio_updates, prices) + volatility_array = broadcast_scan_vector( + jnp.zeros((1,), dtype=prices.dtype) + if volatility_array is None + else volatility_array, + prices.shape[0], + ) + dow_sin_array = broadcast_scan_vector( + jnp.zeros((1,), dtype=prices.dtype) + if dow_sin_array is None + else dow_sin_array, + prices.shape[0], + ) + dow_cos_array = broadcast_scan_vector( + jnp.zeros((1,), dtype=prices.dtype) + if dow_cos_array is None + else dow_cos_array, + prices.shape[0], + ) + noise_base_array = broadcast_scan_vector( + jnp.zeros((1,), dtype=prices.dtype) + if noise_base_array is None + else noise_base_array, + prices.shape[0], + ) + noise_tvl_coeff_array = broadcast_scan_vector( + jnp.zeros((1,), dtype=prices.dtype) + if noise_tvl_coeff_array is None + else noise_tvl_coeff_array, + prices.shape[0], + ) + competitor_tvl_array = broadcast_scan_vector( + jnp.zeros((1,), dtype=prices.dtype) + if competitor_tvl_array is None + else competitor_tvl_array, + prices.shape[0], + ) + + _, active_trade_directions, tokens_to_drop, leave_one_out_idxs = ( + precalc_shared_values_for_all_signatures(all_sig_variations, 2) + ) + + scan_fn = Partial( + _reclamm_hypersurge_scan_step_with_fee_revenue, + tokens_to_drop=tokens_to_drop, + active_trade_directions=active_trade_directions, + leave_one_out_idxs=leave_one_out_idxs, + n=2, + hypersurge_params=jnp.asarray(hypersurge_params, dtype=prices.dtype), + centeredness_margin=centeredness_margin, + daily_price_shift_base=daily_price_shift_base, + seconds_per_step=seconds_per_step, + arc_length_speed=arc_length_speed, + centeredness_scaling=centeredness_scaling, + protocol_fee_split=protocol_fee_split, + ste_temperature=ste_temperature, + noise_trader_ratio=noise_trader_ratio, + noise_model=noise_model, + noise_params=noise_params if noise_params is not None else {}, + ) + + carry_init = [ + initial_reserves, + initial_Va, + initial_Vb, + jnp.float64(0.0), + jnp.float64(0.0), + jnp.float64(0.0), + jnp.float64(0.0), + jnp.float64(0.0), + jnp.array(False), + lp_supply_array[0], + ] + _, (reserves, fee_revenue) = scan( + scan_fn, + carry_init, + [ + prices, + oracle_prices, + fees, + arb_thresh, + arb_fees, + price_ratio_updates, + lp_supply_array, + volatility_array, + dow_sin_array, + dow_cos_array, + noise_base_array, + noise_tvl_coeff_array, + competitor_tvl_array, + ], + ) + return reserves, fee_revenue diff --git a/scripts/hypersurge_demo_train.py b/scripts/hypersurge_demo_train.py new file mode 100644 index 0000000..3710c97 --- /dev/null +++ b/scripts/hypersurge_demo_train.py @@ -0,0 +1,173 @@ +"""Demo training entry points for HyperSurge pool variants. + +Compared with [demo_train.py](./demo_train.py), HyperSurge training needs: +1. A HyperSurge-enabled pool rule. +2. HyperSurge fee-curve initial values in the run fingerprint. +3. For reCLAMM, the usual range-shape parameters as well. +4. Optionally, a separate oracle-price frame. If omitted, both pool + implementations fall back to the traded price series as the reference. + +`price_data` and `oracle_prices` intentionally have different shapes: +- `price_data`: parquet-like price frame with `close_` columns and a + unix-ms or datetime index. +- `oracle_prices`: minute-level frame with a `unix` column plus one column per + token in pool order. +""" + +from copy import deepcopy + +import numpy as np +import pandas as pd + +from quantammsim.core_simulator.dynamic_inputs import DynamicInputFrames +from quantammsim.runners.jax_runners import train_on_historic_data + + +DEFAULT_FINGERPRINT = { + "startDateString": "2023-01-01 00:00:00", + "endDateString": "2023-03-01 00:00:00", + "endTestDateString": "2023-04-01 00:00:00", + "chunk_period": 1440, + "weight_interpolation_period": 1440, + "bout_offset": 24 * 60 * 7, + "initial_pool_value": 1_000_000.0, + "fees": 0.003, + "gas_cost": 0.0, + "arb_fees": 0.0, + "arb_frequency": 1, + "do_arb": True, + "return_val": "daily_log_sharpe", + "optimisation_settings": { + "method": "gradient_descent", + "base_lr": 0.05, + "optimiser": "adam", + "batch_size": 8, + "n_iterations": 250, + "n_parameter_sets": 4, + "training_data_kind": "historic", + "sample_method": "uniform", + "initial_random_key": 0, + "n_cycles": 1, + "val_fraction": 0.0, + "early_stopping": False, + }, +} + + +HYPERSURGE_INITIALS = { + "initial_hypersurge_arb_max_fee": 0.02, + "initial_hypersurge_arb_threshold": 0.10, + "initial_hypersurge_arb_cap_deviation": 0.50, + "initial_hypersurge_noise_max_fee": 0.10, + "initial_hypersurge_noise_threshold": 0.10, + "initial_hypersurge_noise_cap_deviation": 0.50, +} + + +def oracle_prices_from_price_data(price_data: pd.DataFrame, tokens): + """Build a DynamicInputFrames-compatible oracle frame from price data.""" + index = pd.Index(price_data.index) + if np.issubdtype(index.dtype, np.datetime64): + unix = index.view("int64") // 10**6 + else: + unix = pd.to_numeric(index, errors="raise").astype("int64") + + data = {"unix": unix} + for token in tokens: + column = f"close_{token}" + if column not in price_data.columns: + raise ValueError( + f"price_data must contain {column} to derive oracle prices" + ) + data[token] = price_data[column].to_numpy() + return pd.DataFrame(data) + + +def _dynamic_input_frames(oracle_prices): + if oracle_prices is None: + return None + return DynamicInputFrames(oracle_prices=oracle_prices) + + +def _common_training_fingerprint(rule, tokens): + fingerprint = deepcopy(DEFAULT_FINGERPRINT) + fingerprint["rule"] = rule + fingerprint["tokens"] = list(tokens) + fingerprint.update(HYPERSURGE_INITIALS) + return fingerprint + + +def train_hypersurge_balancer( + tokens=("BTC", "ETH"), + *, + root=None, + price_data=None, + oracle_prices=None, + use_price_data_as_oracle=False, + verbose=True, +): + """Train the HyperSurge Balancer pool.""" + fingerprint = _common_training_fingerprint("balancer_hypersurge", tokens) + if use_price_data_as_oracle: + if price_data is None: + raise ValueError("price_data is required when use_price_data_as_oracle=True") + oracle_prices = oracle_prices_from_price_data(price_data, tokens) + + return train_on_historic_data( + run_fingerprint=fingerprint, + root=root, + price_data=price_data, + dynamic_input_frames=_dynamic_input_frames(oracle_prices), + verbose=verbose, + return_training_metadata=True, + force_init=True, + ) + + +def train_hypersurge_reclamm( + tokens=("BTC", "ETH"), + *, + root=None, + price_data=None, + oracle_prices=None, + use_price_data_as_oracle=False, + verbose=True, +): + """Train the HyperSurge reCLAMM pool.""" + fingerprint = _common_training_fingerprint("reclamm_hypersurge", tokens) + fingerprint.update( + { + "initial_price_ratio": 2.0, + "initial_centeredness_margin": 0.25, + "initial_daily_price_shift_base": 1.0 - 1.0 / 124000.0, + "reclamm_interpolation_method": "geometric", + } + ) + if use_price_data_as_oracle: + if price_data is None: + raise ValueError("price_data is required when use_price_data_as_oracle=True") + oracle_prices = oracle_prices_from_price_data(price_data, tokens) + + return train_on_historic_data( + run_fingerprint=fingerprint, + root=root, + price_data=price_data, + dynamic_input_frames=_dynamic_input_frames(oracle_prices), + verbose=verbose, + return_training_metadata=True, + force_init=True, + ) + + +if __name__ == "__main__": + examples = [ + ("balancer_hypersurge", train_hypersurge_balancer), + ("reclamm_hypersurge", train_hypersurge_reclamm), + ] + for name, train_fn in examples: + print(f"\nTraining {name}...") + params, metadata = train_fn(verbose=True) + print( + f"{name}: objective={metadata['final_objective']:.4f}, " + f"epochs={metadata['epochs_trained']}" + ) diff --git a/tests/pools/reCLAMM/test_hypersurge_reclamm.py b/tests/pools/reCLAMM/test_hypersurge_reclamm.py new file mode 100644 index 0000000..92d8622 --- /dev/null +++ b/tests/pools/reCLAMM/test_hypersurge_reclamm.py @@ -0,0 +1,94 @@ +import numpy.testing as npt + +import jax.numpy as jnp + +from quantammsim.pools.G3M.balancer.hypersurge_balancer_reserves import ( + _hypersurge_fee_for_trade, +) +from quantammsim.pools.creator import create_pool +from quantammsim.pools.hypersurge_utils import HYPERSURGE_PARAM_KEYS +from quantammsim.pools.reCLAMM.reclamm_hypersurge import ReClammHyperSurgePool +from quantammsim.runners.jax_runner_utils import NestedHashabledict + + +ALL_SIG_VARIATIONS_2 = tuple(map(tuple, [[1, -1], [-1, 1]])) + + +def _run_fingerprint(n_steps=4): + return NestedHashabledict( + { + "n_assets": 2, + "bout_length": n_steps + 1, + "initial_pool_value": 1_000_000.0, + "arb_frequency": 1, + "do_arb": True, + "fees": 0.003, + "gas_cost": 0.0, + "arb_fees": 0.0, + "all_sig_variations": ALL_SIG_VARIATIONS_2, + "noise_model": "arb_only", + "noise_trader_ratio": 0.0, + "hypersurge_arb_max_fee": 0.02, + "hypersurge_arb_threshold": 0.10, + "hypersurge_arb_cap_deviation": 0.50, + "hypersurge_noise_max_fee": 0.10, + "hypersurge_noise_threshold": 0.10, + "hypersurge_noise_cap_deviation": 0.50, + } + ) + + +def test_creator_registers_hypersurge_reclamm_aliases(): + assert isinstance(create_pool("reclamm_hypersurge"), ReClammHyperSurgePool) + assert isinstance(create_pool("hypersurge_reclamm"), ReClammHyperSurgePool) + + +def test_hypersurge_reclamm_params_are_trainable_by_default(): + pool = create_pool("reclamm_hypersurge") + run_fingerprint = _run_fingerprint() + initial_values = pool.get_initial_values(run_fingerprint) + + params = pool.init_parameters( + initial_values, + run_fingerprint, + n_assets=2, + n_parameter_sets=3, + noise="gaussian", + ) + + assert pool.is_trainable() + assert "price_ratio" in params + assert "centeredness_margin" in params + for key in HYPERSURGE_PARAM_KEYS: + assert key in params + assert params[key].shape == (3, 1) + + +def test_hypersurge_fee_falls_back_to_base_fee_when_oracle_invalid(): + reserves = jnp.array([5000.0, 2500.0]) + weights = jnp.array([0.5, 0.5]) + hypersurge_params = jnp.array([0.02, 0.10, 0.50, 0.10, 0.10, 0.50]) + + zero_oracle_fee = _hypersurge_fee_for_trade( + reserves, + candidate_trade=jnp.array([1000.0, -500.0]), + weights=weights, + oracle_prices=jnp.array([0.0, 200.0]), + token_in=0, + token_out=1, + base_fee=0.003, + hypersurge_params=hypersurge_params, + ) + nan_oracle_fee = _hypersurge_fee_for_trade( + reserves, + candidate_trade=jnp.array([1000.0, -500.0]), + weights=weights, + oracle_prices=jnp.array([jnp.nan, 200.0]), + token_in=0, + token_out=1, + base_fee=0.003, + hypersurge_params=hypersurge_params, + ) + + npt.assert_allclose(zero_oracle_fee, 0.003, rtol=1e-12) + npt.assert_allclose(nan_oracle_fee, 0.003, rtol=1e-12) diff --git a/tests/unit/test_hypersurge_training_dynamic_inputs.py b/tests/unit/test_hypersurge_training_dynamic_inputs.py new file mode 100644 index 0000000..4a4896b --- /dev/null +++ b/tests/unit/test_hypersurge_training_dynamic_inputs.py @@ -0,0 +1,109 @@ +import numpy as np +import pandas as pd + +from quantammsim.core_simulator.dynamic_inputs import DynamicInputFrames +from quantammsim.pools.hypersurge_utils import HYPERSURGE_PARAM_KEYS +from quantammsim.runners.jax_runners import train_on_historic_data + + +def _synthetic_price_data(start="2023-01-01 00:00:00", periods=24 * 60 * 5): + date_index = pd.date_range(start=start, periods=periods, freq="min") + t = np.arange(periods, dtype=np.float64) + price_data = pd.DataFrame( + { + "close_BTC": 20_000.0 * np.exp(0.00008 * t + 0.015 * np.sin(t / 240.0)), + "close_ETH": 1_500.0 * np.exp(0.00005 * t + 0.02 * np.cos(t / 360.0)), + }, + index=(date_index.view("int64") // 10**6), + ) + oracle_prices = pd.DataFrame( + { + "unix": price_data.index.to_numpy(), + "BTC": price_data["close_BTC"].to_numpy(), + "ETH": price_data["close_ETH"].to_numpy(), + } + ) + return price_data, oracle_prices + + +def _training_fingerprint(rule): + return { + "tokens": ["BTC", "ETH"], + "rule": rule, + "startDateString": "2023-01-01 00:00:00", + "endDateString": "2023-01-04 00:00:00", + "endTestDateString": "2023-01-05 00:00:00", + "chunk_period": 60, + "weight_interpolation_period": 60, + "bout_offset": 120, + "initial_pool_value": 1_000_000.0, + "fees": 0.003, + "arb_fees": 0.0, + "gas_cost": 0.0, + "arb_frequency": 1, + "do_arb": True, + "return_val": "returns", + "use_fused_reserves": False, + "optimisation_settings": { + "method": "gradient_descent", + "base_lr": 0.01, + "optimiser": "adam", + "batch_size": 1, + "n_iterations": 1, + "n_parameter_sets": 1, + "training_data_kind": "historic", + "sample_method": "uniform", + "initial_random_key": 0, + "n_cycles": 1, + "val_fraction": 0.0, + "early_stopping": False, + "decay_lr_ratio": 0.8, + "decay_lr_plateau": 100, + "min_lr": 1e-6, + }, + } + + +def test_hypersurge_balancer_training_accepts_oracle_frames(): + price_data, oracle_prices = _synthetic_price_data() + params, metadata = train_on_historic_data( + _training_fingerprint("balancer_hypersurge"), + price_data=price_data, + dynamic_input_frames=DynamicInputFrames(oracle_prices=oracle_prices), + verbose=False, + force_init=True, + return_training_metadata=True, + iterations_per_print=999999, + ) + + assert np.isfinite(metadata["final_objective"]) + for key in HYPERSURGE_PARAM_KEYS: + assert key in params + + +def test_hypersurge_reclamm_training_accepts_oracle_frames(): + price_data, oracle_prices = _synthetic_price_data() + fingerprint = _training_fingerprint("reclamm_hypersurge") + fingerprint.update( + { + "initial_price_ratio": 2.0, + "initial_centeredness_margin": 0.25, + "initial_daily_price_shift_base": 1.0 - 1.0 / 124000.0, + "reclamm_interpolation_method": "geometric", + } + ) + + params, metadata = train_on_historic_data( + fingerprint, + price_data=price_data, + dynamic_input_frames=DynamicInputFrames(oracle_prices=oracle_prices), + verbose=False, + force_init=True, + return_training_metadata=True, + iterations_per_print=999999, + ) + + assert np.isfinite(metadata["final_objective"]) + assert "price_ratio" in params + for key in HYPERSURGE_PARAM_KEYS: + assert key in params