xbbg 1.2.6

Independent client for Bloomberg-connected data workflows

Links: Documentation · Quickstart · Configuration · Examples notebook · Contributing · Changelog

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Latest release: xbbg==1.2.6 (release: notes)

This main branch is the Rust-powered v1 release. For the legacy pure-Python line, use release/0.x.

Important: xbbg is an independent open-source project. It is not affiliated with, endorsed by, sponsored by, or approved by Bloomberg Finance L.P. or its affiliates. Bloomberg, Bloomberg Terminal, B-PIPE, BQL, and related names are trademarks or service marks of their respective owners. xbbg does not grant access to Bloomberg services, data, software, licenses, credentials, or entitlements; users must obtain and use those separately under their own Bloomberg agreements and applicable policies.

Contents

• What is xbbg?
• Why xbbg?
• Installation
• Quickstart
• JavaScript and Node
• Configuration and engines
• Common API surface
• Output backends
• Async usage
• Subscriptions: raw, tick mode, and all fields
• MCP server
• Troubleshooting
• Development
• Project links

What is xbbg?

xbbg is a Bloomberg client with Python as the primary surface and companion JavaScript/Node bindings, all backed by a shared Rust engine for request execution, response parsing, Arrow-shaped data movement, async workers, typed errors, and diagnostics.

Use xbbg when you already have Bloomberg access and want higher-level helpers for common request patterns, plus an escape hatch for lower-level Bloomberg service requests.

Core scope:

• request helpers for BDP, BDS, BDH, intraday bars, ticks, BQL, BEQS, BSRCH, BQR, BTA, YAS, and related analytics
• local Bloomberg Desktop API / DAPI by default
• configuration for managed Bloomberg environments, including B-PIPE/SAPI, ZFP leased lines, TLS, failover hosts, SOCKS5, and SDK logging
• sync and async Python APIs backed by the same engine
• output as Narwhals, native xbbg Arrow carriers, PyArrow, pandas, Polars, DuckDB, and other optional Narwhals-backed libraries
• JavaScript/Node bindings in js-xbbg

Why xbbg?

xbbg's project goal is direct: be the most complete, technically advanced, and performance-focused open-source Bloomberg client for Python workflows, while staying independent of Bloomberg and requiring users to bring their own authorized Bloomberg access.

The short version: if all you need is a tiny one-off bdp() wrapper, several packages can work. xbbg is built for the path where that notebook later grows into intraday data, BQL, streaming, B-PIPE/SAPI, ZFP, async services, typed errors, diagnostics, and non-pandas data pipelines.

Capability	xbbg	raw blpapi	pdblp / blp	bbg-fetch	polars-bloomberg
BDP/BDS/BDH helpers	yes	manual SDK code	yes	yes	partial
Intraday bars and ticks	yes	manual SDK code	limited / no	no	partial
Streaming subscriptions	yes	manual SDK code	no	no	no
BQL, BEQS, BSRCH, BQR, YAS, BTA	broad helper coverage	manual SDK code	limited	limited	partial
DAPI, SAPI/B-PIPE, ZFP, TLS, failover, SOCKS5	configurable engine support	manual SDK code	limited	limited	limited
Async worker pools and isolated subscription sessions	yes	application-owned	no	no	no
Rust request/parsing engine with Arrow-shaped output	yes	no	no	no	no
Output backends beyond pandas	Narwhals, native, PyArrow, pandas, Polars, DuckDB	application-owned	pandas-first	pandas-first	Polars-first
Typed errors, diagnostics, field cache, testing helpers	yes	application-owned	limited	limited	limited
Usable install footprint (Windows x64, Python 3.14)	xbbg 1.2.2 + narwhals 2.21.0 + blpapi 3.26.3.1 = 22.076 MiB	blpapi 3.26.3.1 = 13.653 MiB	pdblp 0.1.8 + pandas 3.0.3 + blpapi 3.26.3.1 = 88.139 MiB / blp 0.0.4 + pandas 3.0.3 + blpapi 3.26.3.1 = 88.246 MiB	bbg-fetch 2.0.2 + numpy 2.4.4 + pandas 3.0.3 + blpapi 3.26.3.1 = 88.156 MiB	polars-bloomberg 0.5.4 + polars 1.40.1 + blpapi 3.26.3.1 = 191.547 MiB

Install footprints were measured in clean target directories on this workstation with the usable install recipe for each column: xbbg + blpapi, raw blpapi, pdblp + pandas + blpapi, blp + pandas + blpapi, bbg-fetch + blpapi, and polars-bloomberg (which pulls blpapi transitively). That makes xbbg the best fit in this comparison for teams that want one Bloomberg-connected Python client that can start with simple BDP/BDH calls and scale into institutional transport, async, streaming, diagnostics, and multi-backend data workflows.

Installation

pip install xbbg

Conda users can install the conda-forge build:

conda install -c conda-forge xbbg

Most users should also install Bloomberg's official Python package so xbbg can locate the Bloomberg SDK/runtime:

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

Supported Python versions: 3.10 through 3.14.

Requirements and notes:

• You need an authorized Bloomberg environment: local Terminal/DAPI, SAPI/B-PIPE, or ZFP, depending on your setup.
• If you build from source, stage the Bloomberg C++ SDK with bash ./scripts/sdktool.sh on macOS/Linux or .\\scripts\\sdktool.ps1 on Windows PowerShell.
• If you manage the SDK yourself, set BLPAPI_ROOT or use xbbg.set_sdk_path(...).
• Optional dataframe conversions are installed separately: xbbg[pyarrow], xbbg[pandas], xbbg[polars], or xbbg[duckdb].

Verify the install:

import xbbg

print(xbbg.__version__)
print(xbbg.get_sdk_info())

Quickstart

from xbbg import blp

# Reference data
prices = blp.bdp(["AAPL US Equity", "MSFT US Equity"], "PX_LAST")

# Historical data
hist = blp.bdh("SPX Index", "PX_LAST", "2024-01-01", "2024-12-31")

# Intraday bars
bars = blp.bdib("TSLA US Equity", dt="2024-01-15", interval=5)

Common request patterns:

from xbbg import blp

# Multiple fields
info = blp.bdp("NVDA US Equity", ["Security_Name", "GICS_Sector_Name", "PX_LAST"])

# Bloomberg-style overrides
vwap = blp.bdp("AAPL US Equity", "Eqy_Weighted_Avg_Px", VWAP_Dt="20240115")

# Bulk data
holders = blp.bds("AAPL US Equity", "DVD_Hist_All", DVD_Start_Dt="20240101")

# BQL
result = blp.bql("get(px_last) for('AAPL US Equity')")

# Field lookup
fields = blp.bflds(search_spec="vwap")

# Equity screening and constituents
screen = blp.beqs(screen="MyScreen", asof="2024-01-01")
members = blp.index_members("SPX Index", asof="2024-01-02")

# Workflow helpers
active = blp.active_futures("ESA Index", "2024-01-15")
surface = blp.vol_surface("SPX Index", start_date="2024-01-02", end_date="2024-01-05")
resolved = blp.resolve_isins(["US0378331005", "INVALIDISIN000"])

For longer walkthroughs and example output shapes, use the examples notebook or xbbg.org.

JavaScript and Node

xbbg also ships experimental Node bindings in @xbbg/core. The JS layer uses the same Rust engine through a native N-API addon, so Node can use the same Bloomberg connection modes and request surfaces as Python.

npm install @xbbg/core
# or
bun add @xbbg/core

Packaged native addons are currently provided for macOS arm64, Linux x64, and Windows x64. You still need Bloomberg access plus Bloomberg SDK runtime libraries on the target system.

import * as xbbg from '@xbbg/core';

xbbg.configure({ host: 'localhost', port: 8194 });

const hist = await xbbg.blp.abdh(['AAPL US Equity'], ['PX_LAST'], '2024-01-01', '2024-12-31');
const ref = await xbbg.blp.abdp(['AAPL US Equity'], ['PX_LAST', 'SECURITY_NAME']);

See js-xbbg/README.md for platform packaging, runtime prerequisites, and the current alpha API surface.

For LangChain and LangGraph agents, use @xbbg/langgraph. It exposes reusable server-side Bloomberg tools backed by @xbbg/core without making MCP, a chat app, or a browser integration the core path:

npm install @xbbg/langgraph @xbbg/core @langchain/core

import { createAllBloombergTools, BLOOMBERG_TOOL_INSTRUCTIONS } from '@xbbg/langgraph';

const tools = createAllBloombergTools({ maxSecurities: 10, maxFields: 10 });

Use the existing apps/xbbg-mcp package only when you specifically need MCP.

Configuration and engines

By default, xbbg starts a Rust-backed engine and connects to local Bloomberg Desktop API / DAPI on localhost:8194. Configure the engine before the first request when you need a different transport, authentication mode, worker count, timeout policy, field cache, or logging behavior.

from xbbg import blp, configure

# Equivalent to the default local Terminal / DAPI path
configure(host="localhost", port=8194)

print(blp.bdp("AAPL US Equity", "PX_LAST"))

Common environments:

Environment	Use when	Configuration shape
Desktop API / DAPI	Local Bloomberg Terminal session	no config, or configure(host="localhost", port=8194)
Direct server / SAPI	Firm-managed Bloomberg server	configure(host="bpipe-host", port=8194, auth_method="app", app_name="...")
B-PIPE	Enterprise Bloomberg feed infrastructure	direct host/failover config plus the auth/TLS settings your Bloomberg setup requires
ZFP leased line	Bloomberg zero-footprint leased-line path	configure(zfp_remote="8194", tls_client_credentials="...", tls_trust_material="...")

Example B-PIPE/SAPI-style configuration:

from xbbg import configure

configure(
    host="bpipe-host",
    port=8194,
    auth_method="app",
    app_name="my-app",
    request_pool_size=4,
    subscription_pool_size=2,
    num_start_attempts=5,
)

Example ZFP leased-line configuration:

from xbbg import configure

configure(
    zfp_remote="8194",
    tls_client_credentials="/path/to/client.p12",
    tls_client_credentials_password="<load from your secret store>",
    tls_trust_material="/path/to/trust.pem",
)

The engine uses separate worker pools for request/response calls and subscriptions:

• request workers hold independent Bloomberg sessions and dispatch BDP/BDH/BDS/BQL-style calls across the pool
• subscription sessions are isolated from request workers, so live streams do not share a single blocking session with batch requests
• field validation, field-type caching, SDK logging, retry policy, keep-alive, slow-consumer thresholds, TLS, SOCKS5, and failover servers are configuration options rather than per-call ad hoc code

Use Engine(...) when an application needs a scoped engine with its own connection settings instead of mutating global configuration.

Common API surface

Area	Functions
Reference and bulk data	bdp, bds, bflds, fieldInfo, fieldSearch, blkp, bport
Historical data	bdh, dividend, earnings, turnover, dividend_yield
Intraday data	bdib, bdtick
Query and screening	bql, beqs, bsrch, bqr, bcurves, bgovts, etf_holdings, index_members
Analytics and utilities	yas, bta, ta_studies, ta_study_params, convert_ccy, fut_ticker, active_futures, futures_curve, vol_surface, resolve_isins, issuer_isins, cdx_ticker, active_cdx
Real-time data	subscribe, stream, vwap, mktbar, depth, chains
Generic requests	request, Service, Operation, RequestParams, OutputMode
Schema and diagnostics	bops, bschema, get_sdk_info, enable_sdk_logging, print_backend_status
Testing helpers	xbbg.testing.create_mock_response, xbbg.testing.mock_engine

Most sync helpers have async counterparts with an a prefix: bdp → abdp, bdh → abdh, bdib → abdib, request → arequest.

Output backends

xbbg defaults to a Narwhals DataFrame. When PyArrow is installed, the Narwhals frame is backed by a real pyarrow.Table; otherwise xbbg falls back through available dataframe libraries and finally to its native Arrow carrier.

from xbbg import Backend, blp

# Default Narwhals output
frame = blp.bdh("SPX Index", "PX_LAST", "2024-01-01", "2024-12-31")

# Explicit native xbbg Arrow carrier
table = blp.bdp("AAPL US Equity", "PX_LAST", backend="native")

# Optional conversions
as_pyarrow = blp.bdp("IBM US Equity", "PX_LAST", backend=Backend.PYARROW)
as_pandas = blp.bdp("MSFT US Equity", "PX_LAST", backend=Backend.PANDAS)
as_polars = blp.bdp("AAPL US Equity", "PX_LAST", backend=Backend.POLARS)
as_duckdb = blp.bdh("SPX Index", "PX_LAST", "2024-01-01", "2024-12-31", backend=Backend.DUCKDB)

Output shape is controlled with format=, including long, long_typed, long_metadata, and semi_long.

Async usage

Use async helpers directly in async applications:

import asyncio
from xbbg import blp

async def main():
    aapl, msft = await asyncio.gather(
        blp.abdp("AAPL US Equity", "PX_LAST"),
        blp.abdp("MSFT US Equity", "PX_LAST"),
    )
    return aapl, msft

result = asyncio.run(main())

In Jupyter and VS Code Interactive, one-shot sync calls such as blp.bdp(...) and blp.bdh(...) use a notebook-only bridge when an IPykernel event loop is already running. Generic async applications such as FastAPI or ASGI services should still use the async APIs directly.

Subscriptions: raw, tick mode, and all fields

Use asubscribe() when you need dynamic add/remove, explicit unsubscribe, raw Arrow batches, or subscription health diagnostics. Use stream() when you only want the simple async-iterator wrapper.

from xbbg import asubscribe

sub = await asubscribe(
    ["AAPL US Equity"],
    ["LAST_PRICE", "BID", "ASK"],
    tick_mode=True,
    all_fields=True,
    conflate=True,
)

async for tick in sub:
    print(tick)       # dict ticks in tick_mode
    print(sub.stats)  # messages_received, dropped_batches, data_loss_events, ...
    break

await sub.unsubscribe()

raw_sub = await asubscribe(["AAPL US Equity"], ["LAST_PRICE"], raw=True)

async for batch in raw_sub:
    print(batch.to_table())  # raw xbbg ArrowRecordBatch -> ArrowTable
    break

await raw_sub.unsubscribe()

Key behaviors:

• raw=True or output="record_batch" yields raw xbbg ArrowRecordBatch values for max-performance consumers
• default iteration without raw=True returns the configured backend output instead of raw record batches
• tick_mode=True or output="dict" returns native dict ticks and implies raw subscription mode
• all_fields=True exposes all top-level scalar Bloomberg subscription fields
• filtered mode keeps requested fields plus MKTDATA_EVENT_TYPE and MKTDATA_EVENT_SUBTYPE
• conflate=True requests Bloomberg-conflated quote updates on //blp/mktdata; trades are still delivered as received
• sub.add(...), sub.remove(...), sub.status, sub.events, sub.failed_tickers, and sub.stats expose runtime control and diagnostics

In Node, pass { allFields: true } to stream() / subscribe() helpers for the same top-level field expansion. JS subscriptions use a native zero-copy Arrow path for supported schemas and fail fast with column-level diagnostics when a schema cannot use that path.

MCP server

The repository also includes a local MCP server for coding-agent workflows. It wraps selected xbbg request/response operations and returns bounded JSON results with schema metadata.

See apps/xbbg-mcp/README.md for installation, supported environment variables, and release asset notes. The MCP release assets do not include Bloomberg SDK files or runtime components.

Troubleshooting

Empty results usually mean one of the inputs or entitlements is wrong rather than that the Python call failed:

from xbbg import blp

# Check security lookup and field discovery
print(blp.blkp("Apple", yellowkey="eqty"))
print(blp.fieldSearch("vwap"))

Connection failures:

• confirm Bloomberg Terminal is running and logged in for local DAPI usage
• confirm the host, port, auth method, TLS files, and entitlements for SAPI/B-PIPE/ZFP environments
• run print(xbbg.get_sdk_info()) to see how the SDK/runtime was detected
• enable SDK logging before the first session when debugging low-level connection problems

Timeouts and large responses:

• increase per-request timeout where appropriate
• split large historical/tick requests into smaller date ranges
• tune request_pool_size, subscription_pool_size, queue sizes, and keep-alive settings for managed infrastructure

When reporting issues, include:

1. xbbg version: import xbbg; print(xbbg.__version__)
2. Python version and operating system
3. Bloomberg connection mode: DAPI, SAPI/B-PIPE, ZFP, or other
4. minimal code to reproduce
5. full traceback or error message

Development

Set up the development environment with pixi:

# Stage an authorized Bloomberg SDK locally under vendor/blpapi-sdk/
bash ./scripts/sdktool.sh               # macOS/Linux
# .\scripts\sdktool.ps1                # Windows PowerShell

# Install the environment and compile the Rust extension
pixi install
pixi run install

Common checks:

pixi run test
pixi run lint
pixi run ci

For non-live tests, use xbbg.testing:

from xbbg import blp
from xbbg.testing import create_mock_response, mock_engine

response = create_mock_response(
    service="//blp/refdata",
    operation="ReferenceDataRequest",
    data={"AAPL US Equity": {"PX_LAST": 101.23}},
)

with mock_engine([response]):
    df = blp.bdp("AAPL US Equity", "PX_LAST")

Publishing is handled through GitHub Actions and PyPI Trusted Publishing.

Project links

• Documentation: xbbg.org
• JavaScript/Node bindings: js-xbbg/README.md
• LangChain/LangGraph tools: js-xbbg-langgraph/README.md
• PyPI: pypi.org/project/xbbg
• Source: github.com/alpha-xone/xbbg
• Issues: GitHub Issues
• Discord: Join the community
• Changelog: CHANGELOG.md
• Contributing: CONTRIBUTING.md
• Code of conduct: CODE_OF_CONDUCT.md
• Security: SECURITY.md
• License: LICENSE