bbg-fetch

Bloomberg data in DataFrames. No boilerplate. No bloat.

bbg-fetch gives you clean, production-ready functions for the Bloomberg data you actually need — prices, fundamentals, vol surfaces, futures curves, bond analytics, index constituents — without writing event loops or managing sessions.

from bbg_fetch import fetch_field_timeseries_per_tickers

prices = fetch_field_timeseries_per_tickers(
    tickers={'ES1 Index': 'SPX', 'TY1 Comdty': '10yUST'},
    field='PX_LAST',
    start_date=pd.Timestamp('2020-01-01')
)
# Returns a clean DataFrame with renamed columns, sorted index, split/div adjusted

Why bbg-fetch?

vs. raw blpapi

Writing Bloomberg queries with blpapi directly means 40–60 lines of session management, request construction, event-loop iteration, and response parsing — for every single query. You end up writing the same boilerplate wrapper in every project.

With blpapi:

import blpapi

opts = blpapi.SessionOptions()
opts.setServerHost('localhost')
opts.setServerPort(8194)
session = blpapi.Session(opts)
session.start()
session.openService('//blp/refdata')
service = session.getService('//blp/refdata')
request = service.createRequest('HistoricalDataRequest')
request.getElement('securities').appendValue('AAPL US Equity')
request.getElement('fields').appendValue('PX_LAST')
request.set('startDate', '20200101')
request.set('endDate', '20241231')
request.set('adjustmentNormal', True)
request.set('adjustmentAbnormal', True)
request.set('adjustmentSplit', True)
session.sendRequest(request)
# ... then 30 more lines to parse the event loop into a DataFrame

With bbg-fetch:

from bbg_fetch import fetch_field_timeseries_per_tickers

prices = fetch_field_timeseries_per_tickers(
    tickers=['AAPL US Equity'],
    field='PX_LAST',
    start_date=pd.Timestamp('2020-01-01')
)

Same result. One line.

vs. xbbg

xbbg is a capable library, but its v1 rewrite introduced a Rust core, narwhals, and pyarrow>=22 as hard dependencies — over 30MB of transitive installs for what most quant teams use: bdp, bdh, and bds.

	bbg-fetch	xbbg v1
Dependencies	`numpy` + `pandas`	`narwhals` + `pyarrow` + Rust binary
Install size	~50KB (plus blpapi)	~30MB+ transitive
Python support	3.9–3.12	3.10–3.14
Intraday bars / streaming	No	Yes
Exchange-aware market hours	No	Yes
Session management	Automatic singleton	Configurable pool
Debug Bloomberg errors	Your code, 400 lines	Third-party internals

bbg-fetch is for teams that need historical, reference, and bulk data — reliably, with minimal dependencies. If you need intraday bars or real-time streaming, use xbbg.

The sweet spot

bbg-fetch sits between raw blpapi (too low-level) and xbbg (too many dependencies). It wraps the three Bloomberg services that cover 95% of quant workflows — BDP, BDH, BDS — into high-level functions that return clean DataFrames with proper column naming, corporate action adjustments, and index alignment.

What you get

Multi-asset coverage

Equities: Historical prices with split/dividend adjustments, fundamentals, dividend history
Futures: Contract tables with carry analysis, active contract series, roll handling
Options: Implied volatility surfaces (moneyness and delta), option chains
Fixed Income: Bond pricing and analytics by ISIN, yield curves, CDS spreads
FX: Currency rates and volatility
Indices: Constituent weights, ISIN-to-ticker resolution

Production-ready details

Dict-based ticker renaming: {'ES1 Index': 'SPX'} → DataFrame columns named SPX
Automatic retry on Bloomberg connection flakes
Corporate action adjustments on by default (normal, abnormal, splits)
Predefined field mappings for vol surfaces (30d/60d/3m/6m/12m moneyness, delta)
Carry computation built into futures contract tables

Installation

1. Install blpapi

pip install --index-url=https://blpapi.bloomberg.com/repository/releases/python/simple blpapi

Pre-built wheels for Python 3.8–3.12 on Windows/macOS/Linux bundle the C++ SDK automatically.

Corporate proxy? Download the .whl from https://blpapi.bloomberg.com/repository/releases/python/simple/blpapi/ via browser, then:

pip install /path/to/blpapi-3.24.6-cp312-cp312-win_amd64.whl

2. Install bbg-fetch

pip install bbg-fetch

Or from source:

git clone https://github.com/ArturSepp/BloombergFetch.git
pip install .

Requirements: Python 3.9+, Bloomberg Terminal running on the same machine.

Examples

Prices across tickers (with renaming)

import pandas as pd
from bbg_fetch import fetch_field_timeseries_per_tickers

# Pass a dict to auto-rename columns: Bloomberg ticker → your label
prices = fetch_field_timeseries_per_tickers(
    tickers={'ES1 Index': 'SPX', 'TY1 Comdty': '10yUST', 'GC1 Comdty': 'Gold'},
    field='PX_LAST',
    start_date=pd.Timestamp('2015-01-01')
)
# DataFrame with columns ['SPX', '10yUST', 'Gold'], DatetimeIndex, sorted, adjusted

# Or pass a list — column names stay as Bloomberg tickers
prices = fetch_field_timeseries_per_tickers(
    tickers=['AAPL US Equity', 'MSFT US Equity'],
    field='PX_LAST',
    start_date=pd.Timestamp('2020-01-01')
)

# Unadjusted prices (for futures, rates, etc.)
raw = fetch_field_timeseries_per_tickers(
    tickers=['TY1 Comdty'], field='PX_LAST',
    CshAdjNormal=False, CshAdjAbnormal=False, CapChg=False
)

Multiple fields for a single ticker

from bbg_fetch import fetch_fields_timeseries_per_ticker

# OHLC data
ohlc = fetch_fields_timeseries_per_ticker(
    ticker='AAPL US Equity',
    fields=['PX_OPEN', 'PX_HIGH', 'PX_LOW', 'PX_LAST'],
    start_date=pd.Timestamp('2023-01-01')
)

# Futures-specific fields
fut_data = fetch_fields_timeseries_per_ticker(
    ticker='ES1 Index',
    fields=['PX_LAST', 'FUT_DAYS_EXP'],
    CshAdjNormal=False, CshAdjAbnormal=False, CapChg=False
)

Company fundamentals

from bbg_fetch import fetch_fundamentals

# Basic security info
info = fetch_fundamentals(
    tickers=['AAPL US Equity', 'GOOGL US Equity'],
    fields=['security_name', 'gics_sector_name', 'crncy', 'market_cap']
)

# Fund-level data
fund_info = fetch_fundamentals(
    tickers=['HAHYIM2 HK Equity'],
    fields=['name', 'front_load', 'back_load', 'fund_mgr_stated_fee', 'fund_min_invest']
)

# Dict-based renaming works for both tickers and fields
info = fetch_fundamentals(
    tickers={'AAPL US Equity': 'Apple', 'MSFT US Equity': 'Microsoft'},
    fields={'security_name': 'Name', 'gics_sector_name': 'Sector'}
)

Balance sheet and credit metrics

from bbg_fetch import fetch_balance_data

credit = fetch_balance_data(
    tickers=['ABI BB Equity', 'T US Equity', 'JPM US Equity'],
    fields=('GICS_SECTOR_NAME', 'TOT_COMMON_EQY', 'BS_LT_BORROW',
            'NET_DEBT_TO_EBITDA', 'INTEREST_COVERAGE_RATIO',
            'FREE_CASH_FLOW_MARGIN', 'EARN_YLD')
)

Current market prices

from bbg_fetch import fetch_last_prices
from bbg_fetch.core import FX_DICT

# FX rates (uses built-in FX_DICT by default: 19 major pairs)
fx = fetch_last_prices()

# Custom tickers
prices = fetch_last_prices(tickers=['AAPL US Equity', 'SPX Index', 'USGG10YR Index'])

# With renaming
prices = fetch_last_prices(
    tickers={'ES1 Index': 'SPX Fut', 'TY1 Comdty': '10y Fut', 'GC1 Comdty': 'Gold Fut'}
)

Implied volatility surface

from bbg_fetch import fetch_vol_timeseries, IMPVOL_FIELDS_DELTA
from bbg_fetch.core import (IMPVOL_FIELDS_MNY_30DAY, IMPVOL_FIELDS_MNY_60DAY,
                             IMPVOL_FIELDS_MNY_3MTH, IMPVOL_FIELDS_MNY_6MTH,
                             IMPVOL_FIELDS_MNY_12M)

# Delta-based vol for FX (1M and 2M, 10Δ to 50Δ puts and calls)
fx_vol = fetch_vol_timeseries(
    ticker='EURUSD Curncy',
    vol_fields=IMPVOL_FIELDS_DELTA,
    start_date=pd.Timestamp('2023-01-01')
)
# Returns: spot_price, div_yield, rf_rate + all vol columns

# Full moneyness surface across 5 tenors (30d, 60d, 3m, 6m, 12m × 9 strikes)
eq_vol = fetch_vol_timeseries(
    ticker='SPX Index',
    vol_fields=[IMPVOL_FIELDS_MNY_30DAY, IMPVOL_FIELDS_MNY_60DAY,
                IMPVOL_FIELDS_MNY_3MTH, IMPVOL_FIELDS_MNY_6MTH,
                IMPVOL_FIELDS_MNY_12M],
    start_date=pd.Timestamp('2010-01-01')
)

# Single tenor with raw field names (no renaming)
vol_30d = fetch_vol_timeseries(
    ticker='SPX Index',
    vol_fields=['30DAY_IMPVOL_100.0%MNY_DF', '30DAY_IMPVOL_90.0%MNY_DF'],
    start_date=pd.Timestamp('2020-01-01')
)

Futures contract table with carry

from bbg_fetch import fetch_futures_contract_table

# Full contract table: prices, bid/ask, volume, OI, days to expiry, annualized carry
curve = fetch_futures_contract_table(ticker="ES1 Index")

# Nikkei futures
nk_curve = fetch_futures_contract_table(ticker="NK1 Index")

Active futures price series

from bbg_fetch import fetch_active_futures

# Front and second month continuous series
front, second = fetch_active_futures(generic_ticker='ES1 Index')

# Start from second generic (e.g., for roll analysis)
gen2, gen3 = fetch_active_futures(generic_ticker='ES1 Index', first_gen=2)

Futures ticker utilities

from bbg_fetch import instrument_to_active_ticker, contract_to_instrument

# ES1 Index → ES
instrument_to_active_ticker('ES1 Index', num=3)   # → 'ES3 Index'
contract_to_instrument('ES1 Index')                 # → 'ES'
contract_to_instrument('TY1 Comdty')                # → 'TY'

Bond analytics by ISIN

from bbg_fetch import fetch_bonds_info

bond_data = fetch_bonds_info(
    isins=['US03522AAJ97', 'US126650CZ11'],
    fields=['id_bb', 'name', 'security_des', 'crncy', 'amt_outstanding',
            'px_last', 'yas_bond_yld', 'yas_oas_sprd', 'yas_mod_dur']
)

# With historical override
bond_hist = fetch_bonds_info(
    isins=['US03522AAJ97'],
    fields=['px_last', 'yas_bond_yld'],
    END_DATE_OVERRIDE='20231231'
)

CDS spreads

from bbg_fetch import fetch_cds_info

cds = fetch_cds_info(
    equity_tickers=['ABI BB Equity', 'CVS US Equity', 'JPM US Equity'],
    field='cds_spread_ticker_5y'
)

Bond ISIN → issuer equity ISIN mapping

from bbg_fetch import fetch_issuer_isins_from_bond_isins

# Map bond ISINs to their issuer's equity ISIN
issuer_map = fetch_issuer_isins_from_bond_isins(
    bond_isins=['XS3034073836', 'USY0616GAA14', 'XS3023923314']
)
# Returns: pd.Series with bond ISIN as index, issuer equity ISIN as values

Dividend history and yields

from bbg_fetch import fetch_dividend_history, fetch_div_yields

# Full dividend history
divs = fetch_dividend_history(ticker='AAPL US Equity')
# Columns: declared_date, ex_date, record_date, payable_date,
#          dividend_amount, dividend_frequency, dividend_type

# Trailing 1-year dividend yield for multiple tickers
div_amounts, div_yields_1y = fetch_div_yields(
    tickers=['AHYG SP Equity', 'TIP US Equity'],
    dividend_types=('Income', 'Distribution')
)

# With renaming
div_amounts, div_yields_1y = fetch_div_yields(
    tickers={'TIP US Equity': 'TIPS', 'SDHA LN Equity': 'Asia HY'}
)

Index members and weights

from bbg_fetch import fetch_index_members_weights, fetch_bonds_info

# Index with weights (INDX_MWEIGHT)
members = fetch_index_members_weights('SPCPGN Index')

# Index members only (some indices don't have weights)
members = fetch_index_members_weights('H04064US Index', field='INDX_MEMBERS')

# Historical members
members_hist = fetch_index_members_weights(
    'I31415US Index', END_DATE_OVERRIDE='20200101'
)

# Chain: get index members → fetch bond analytics
members = fetch_index_members_weights('LUACTRUU Index')
bond_data = fetch_bonds_info(
    isins=members.index.to_list(),
    fields=['name', 'px_last', 'yas_bond_yld', 'yas_mod_dur', 'bb_composite']
)

ISIN to Bloomberg ticker resolution

from bbg_fetch import fetch_tickers_from_isins

# Convert ISINs to Bloomberg composite tickers
tickers = fetch_tickers_from_isins(isins=['US88160R1014', 'IL0065100930'])
# Returns: ['TSLA US Equity', ...] (with primary exchange)

Direct BDP / BDH / BDS

For ad-hoc queries not covered by the high-level functions:

from bbg_fetch import bdp, bdh, bds

# Reference data (BDP)
ref = bdp('AAPL US Equity', ['Security_Name', 'GICS_Sector_Name', 'PX_LAST'])

# Historical data with adjustments (BDH)
hist = bdh('SPX Index', 'PX_LAST', '2024-01-01', '2024-12-31',
           CshAdjNormal=True, CshAdjAbnormal=True, CapChg=True)

# Bulk data — option chains (BDS)
chain = bds('TSLA US Equity', 'CHAIN_TICKERS',
            CHAIN_PUT_CALL_TYPE_OVRD='PUT', CHAIN_POINTS_OVRD=1000)

# Yield curve construction
yc_members = bds("YCGT0025 Index", "INDX_MEMBERS")
yc_data = bdp(yc_members.member_ticker_and_exchange_code.tolist(),
              ['YLD_YTM_ASK', 'SECURITY NAME', 'MATURITY'])

# Clean up session explicitly (optional)
from bbg_fetch import disconnect
disconnect()

Function reference

Price data

Function	Description
`fetch_field_timeseries_per_tickers()`	One field across multiple tickers (with optional dict-based renaming)
`fetch_fields_timeseries_per_ticker()`	Multiple fields for a single ticker
`fetch_last_prices()`	Snapshot of current prices

Fundamentals

Function	Description
`fetch_fundamentals()`	Company metadata and fundamentals (dict renaming for tickers and fields)
`fetch_balance_data()`	Balance sheet ratios and credit metrics
`fetch_dividend_history()`	Full dividend history (dates, amounts, types)
`fetch_div_yields()`	Per-ticker dividend amounts and trailing 1-year yield

Derivatives

Function	Description
`fetch_vol_timeseries()`	Implied vol surface with underlying + rates (supports list-of-dicts for multi-tenor)
`fetch_futures_contract_table()`	Contract specs, carry, timestamps
`fetch_active_futures()`	Front + second month price series

Fixed income

Function	Description
`fetch_bonds_info()`	Bond analytics by ISIN (with optional date override)
`fetch_cds_info()`	CDS spread tickers from equity tickers
`fetch_issuer_isins_from_bond_isins()`	Bond ISIN → issuer equity ISIN mapping

Index and resolution

Function	Description
`fetch_index_members_weights()`	Constituents and weights (configurable BDS field)
`fetch_tickers_from_isins()`	ISIN → Bloomberg composite ticker

Futures utilities

Function	Description
`instrument_to_active_ticker()`	`'ES1 Index'` + `num=3` → `'ES3 Index'`
`contract_to_instrument()`	`'ES1 Index'` → `'ES'` (strip generic number)

Low-level blpapi wrappers

Function	Description
`bdp()`	Bloomberg Data Point — reference data (BDP in Excel)
`bdh()`	Bloomberg Data History — historical end-of-day data
`bds()`	Bloomberg Data Set — bulk data (chains, members, dividends)
`disconnect()`	Explicitly stop the shared blpapi session

Predefined field mappings

FX currencies

from bbg_fetch.core import FX_DICT
# 19 major pairs: EUR, GBP, CHF, CAD, JPY, AUD, NZD, MXN, HKD, SEK,
#                 PLN, KRW, TRY, SGD, ZAR, CNY, INR, TWD, NOK

Implied volatility fields

Mapping	Description
`IMPVOL_FIELDS_MNY_30DAY`	30-day moneyness-based vol (80%–120%)
`IMPVOL_FIELDS_MNY_60DAY`	60-day moneyness-based vol
`IMPVOL_FIELDS_MNY_3MTH`	3-month moneyness-based vol
`IMPVOL_FIELDS_MNY_6MTH`	6-month moneyness-based vol
`IMPVOL_FIELDS_MNY_12M`	12-month moneyness-based vol
`IMPVOL_FIELDS_DELTA`	1M/2M delta-based vol (10Δ–50Δ puts and calls)

Configuration

Date defaults

DEFAULT_START_DATE = pd.Timestamp('01Jan1959')  # Historical data
VOLS_START_DATE = pd.Timestamp('03Jan2005')     # Volatility data

Corporate action adjustments

Most price functions support Bloomberg's adjustment flags:

Parameter	Default	Description
`CshAdjNormal`	`True`	Normal cash dividends
`CshAdjAbnormal`	`True`	Special dividends
`CapChg`	`True`	Stock splits and capital changes

Testing

Integration tests require an active Bloomberg Terminal connection:

from bbg_fetch import run_local_test, LocalTests

run_local_test(LocalTests.FIELD_TIMESERIES_PER_TICKERS)
run_local_test(LocalTests.IMPLIED_VOL_TIME_SERIES)
run_local_test(LocalTests.CONTRACT_TABLE)
run_local_test(LocalTests.BOND_INFO)
run_local_test(LocalTests.DIVIDEND)
run_local_test(LocalTests.BOND_MEMBERS)

Available tests: FIELD_TIMESERIES_PER_TICKERS, FIELDS_TIMESERIES_PER_TICKER, FUNDAMENTALS, ACTIVE_FUTURES, CONTRACT_TABLE, IMPLIED_VOL_TIME_SERIES, BOND_INFO, LAST_PRICES, CDS_INFO, BALANCE_DATA, TICKERS_FROM_ISIN, DIVIDEND, BOND_MEMBERS, INDEX_MEMBERS, OPTION_CHAIN, YIELD_CURVE, CHECK, MEMBERS.

Package structure

bbg_fetch/
    __init__.py       # Public API
    _blp_api.py       # Direct blpapi shim (bdp, bdh, bds) — 400 lines, zero dependencies
    core.py           # High-level fetch functions
    local_tests.py    # Integration tests (requires Bloomberg Terminal)

Troubleshooting

"No module named blpapi"

Install from Bloomberg's package index — see Installation above.

"UnboundLocalError: cannot access local variable 'toPy'"

The C++ DLLs bundled with blpapi failed to load. Reinstall: pip uninstall blpapi -y then reinstall. If on Python 3.13+, downgrade to 3.12.

Corporate proxy blocks Bloomberg's pip index

Download the .whl file manually from https://blpapi.bloomberg.com/repository/releases/python/simple/blpapi/ via browser and install locally with pip install /path/to/blpapi-*.whl.

Empty DataFrames returned

Ensure the Bloomberg Terminal is running (blpapi connects to localhost:8194). Verify field names using Bloomberg's FLDS function and instrument formatting (e.g., "AAPL US Equity", "ES1 Index", "EURUSD Curncy"). Some indices support INDX_MWEIGHT (with weights) while others only support INDX_MEMBERS — use the field parameter in fetch_index_members_weights() accordingly.

"No module named pip" in venv

Bootstrap pip first: python -m ensurepip --upgrade, then install.

PowerShell path errors

Use .\ prefix for relative paths: .\.venv\Scripts\python.exe, not .venv\Scripts\python.exe.

What's new in v2.0.0

xbbg dependency removed — direct blpapi interface, no transitive Rust/pyarrow/narwhals
field parameter added to fetch_index_members_weights() — supports INDX_MWEIGHT, INDX_MEMBERS, INDX_MEMBERS3
Test code extracted to local_tests.py with run_local_test() (renamed from run_unit_test)
bdp(), bdh(), bds() exported for direct low-level access
Robust field name handling — Bloomberg's inconsistent casing/spacing/hyphens normalized automatically
Migration from v1.x: replace run_unit_test → run_local_test, all other imports unchanged

License

MIT. See LICENSE.txt.

Citation

@software{bloombergfetch,
  author = {Sepp, Artur},
  title = {{BloombergFetch}: A Python Package for Bloomberg Terminal Data Access},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/ArturSepp/BloombergFetch},
  version = {2.0.0}
}

Name		Name	Last commit message	Last commit date
Latest commit History 29 Commits
bbg_fetch		bbg_fetch
.gitignore		.gitignore
LICENSE.txt		LICENSE.txt
README.md		README.md
pyproject.toml		pyproject.toml

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bbg-fetch

Why bbg-fetch?

vs. raw blpapi

vs. xbbg

The sweet spot

What you get

Multi-asset coverage

Production-ready details

Installation

1. Install blpapi

2. Install bbg-fetch

Examples

Prices across tickers (with renaming)

Multiple fields for a single ticker

Company fundamentals

Balance sheet and credit metrics

Current market prices

Implied volatility surface

Futures contract table with carry

Active futures price series

Futures ticker utilities

Bond analytics by ISIN

CDS spreads

Bond ISIN → issuer equity ISIN mapping

Dividend history and yields

Index members and weights

ISIN to Bloomberg ticker resolution

Direct BDP / BDH / BDS

Function reference

Price data

Fundamentals

Derivatives

Fixed income

Index and resolution

Futures utilities

Low-level blpapi wrappers

Predefined field mappings

FX currencies

Implied volatility fields

Configuration

Date defaults

Corporate action adjustments

Testing

Package structure

Troubleshooting

"No module named blpapi"

"UnboundLocalError: cannot access local variable 'toPy'"

Corporate proxy blocks Bloomberg's pip index

Empty DataFrames returned

"No module named pip" in venv

PowerShell path errors

What's new in v2.0.0

License

Citation

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