15-strategy algorithmic paper trading platform on AWS EC2 — systemd-supervised Python services, risk engine with kill-lines, market regime detection, and automated analytics pipeline

Quantitative strategy validation pipeline HMM regimes, walk forward cost aware backtesting

A Python framework for testing trading strategies against the ways backtests mislead: look-ahead audits, matched-exposure controls, and block-bootstrap significance tests. The tester is itself tested - a property fuzzer plus mutation testing (4 planted engine bugs, all caught). Includes three case studies of rejected ideas.

Personal research project combining software development, behavioural analysis and quantitative review to transform discretionary trading decisions into an auditable dataset.

End-to-end automated crypto trading workflow featuring market scanning, signal generation, paper trading, risk management, Telegram alerts, PostgreSQL analytics, and Google Sheets reporting.

AI-powered multi-agent quant signal generation engine. Uses LangGraph to orchestrate 4 LLM agents (News Analyst, Trading Analyst, Risk Analyst, Manager) that collaborate to generate risk-adjusted BUY/SELL/HOLD signals using real-time news, vector memory, and backtesting.

Small-account systematic trading bot for Alpaca — built live, diagnosed a losing strategy with real backtests, and rebuilt it.

Cost-aware time-series momentum on a $20 IBKR account

Algorithmic trading framework with pluggable strategy

AI multi-agent system for stock market signal generation using LangGraph, GPT-4, and Qdrant vector search. Achieved 42.8% backtest return vs. 24.5% buy-and-hold, 78% win rate on high-consensus signals. 🥇 Best Use of AI/ML, UB Hacking 2024.

Advanced IDX Market Intelligence & Screener Platform featuring AI-powered Reasoning, Deep Broker Flow Detection, and Automated Trading Journal.

Automated multi-asset mispricing bot for Kalshi BTC/ETH price-level markets — log-normal pricing, adaptive vol calibration, Kelly risk sizing, full replay/audit trail.

Event-driven trading backend — NestJS microservices over TCP + Redis, BullMQ scheduling, Postgres persistence. Sentiment scoring → risk gating → execution, running on a simulated market. docker compose up to run.

Sanitized public case study of AlphaQuant V12: systematic trading architecture, risk governance, QMS testing, safe demo code, and CI.

High-performance C++20 order book engine with REST API, React web terminal, LOBSTER replay, and online ML pipeline.

Public docs for Coil (coil.trade) — an agent-native, long-only trading system: scanner + dashboard + engine, run in your own AI agent (built for Claude Code) against your own broker's MCP. Docs only — not an MCP server.

Quantitative AI hedge fund platform: Flask backend, ML/RL trading models, React web and React Native mobile clients.

Cross-sectional momentum backtest (12-1, monthly rebalance) on the 9 original SPDR sector ETFs — hand-rolled engine, net-of-cost results, factor regression + deflated Sharpe + block bootstrap.

C++ limit order book with price-time priority matching, thread-safe concurrent access (ASan-verified), and full latency percentile analysis (p50-p99.9). 12/12 correctness + 5/5 concurrency gates.