xbbg

Latest release: xbbg==1.4.11 (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
• Entitlement IDs
• 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.

Installation

pip install xbbg

Conda users can install the conda-forge build:

conda install -c conda-forge xbbg

`blpapi` is not required as a Python dependency. xbbg only needs Bloomberg's shared runtime library

(`blpapi3_64.dll` on Windows, `libblpapi3_64.so` on macOS/Linux), which can come from Bloomberg

Terminal/DAPI, a managed Bloomberg C++ SDK install, or Bloomberg's official `blpapi` wheel. Installing

the wheel is just the easiest discovery path for many users:

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(...)`.
• On Windows Terminal installs, xbbg automatically probes DAPI runtime roots such as `C:\blp\DAPI` and `C:\Program Files (x86)\Bloomberg\Blp\DAPI` before requiring manual configuration.
• Linux wheels are `manylinux_2_28` (x86_64): any distro with glibc ≥ 2.28 works — RHEL/Alma/Rocky 8+, Debian 10+, Ubuntu 20.04+, Amazon Linux 2023.
• 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, ovr

# 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")
adj = blp.bdp("AAPL US Equity", "CRNCY_ADJ_PX_LAST", overrides=ovr(EQY_FUND_CRNCY="EUR"))
per_sec = blp.bdp(
    ["AAPL US Equity", "MSFT US Equity"],
    "CRNCY_ADJ_PX_LAST",
    overrides=ovr(
        {
            "EQY_FUND_CRNCY": "USD",
            "AAPL US Equity": ovr(EQY_FUND_CRNCY="EUR"),
            "MSFT US Equity": ovr(EQY_FUND_CRNCY="JPY"),
        }
    ),
)

# 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"])

ETF NAV / iNAV workflows live in `xbbg.ext` and resolve Bloomberg's authoritative

`ETF_NAV_TICKER` / `ETF_INAV_TICKER` relationships instead of guessing ticker suffixes:

from xbbg import ext

# Relationship discovery: QQQ US Equity -> QQQNV Index / QXV Index,
# AT1 LN Equity -> null daily NAV / AT1IN Index (independently nullable)
rel = ext.etf_nav_relationships(["QQQ US Equity", "AT1 LN Equity"])

# Daily NAV/iNAV history: mapped Index targets price with PX_LAST; AT1's
# missing daily NAV falls back to the fund's FUND_NET_ASSET_VAL — see the
# nav_source_ticker / nav_source_field columns on every row
hist = ext.etf_nav_history(
    ["QQQ US Equity", "AT1 LN Equity"],
    start_date="2026-06-01",
    end_date="2026-07-01",
)

# Real-time iNAV: validates every mapping first, then subscribes to the
# resolved iNAV topics (here QXV Index) with LAST_PRICE by default
sub = await ext.asubscribe_etf_inav("QQQ US Equity")
async for table in sub:
    print(table.to_pylist())
    break
await sub.unsubscribe()

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

JavaScript and Node

xbbg also ships supported 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

The packages target Node.js 24+ server runtimes. Packaged native addons are provided for macOS arm64, Linux x64 (glibc 2.28+), 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 supported JavaScript API surface.

For LangChain and LangGraph agents, use the supported `@xbbg/langgraph` adapter. 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:

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,
    # Opt-in sharding for wide multi-security BDP/BDH requests:
    # shard_requests=True,
    # shard_threshold=20,
    # shard_chunk_size=16,
    # shard_max_concurrent=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="",
    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

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

Entitlement IDs

Bloomberg can return entitlement IDs only for these four request operations. Opt in with `return_eids=True`:

For example, request EIDs with intraday ticks and check them against the default `//blp/refdata` service:

from xbbg import blp

ticks = blp.bdtick(
    "AAPL US Equity",
    "2024-01-15T09:30:00",
    "2024-01-15T10:00:00",
    return_eids=True,
    backend="native",
)

eid_data = ticks.eid_data or {}
eids = sorted({eid for security_eids in eid_data.values() for eid in security_eids})
if eids:
    print(blp.check_entitlements(eids))

EID metadata remains available through the native `ArrowTable.eid_data` property, pandas `attrs["xbbg_eid_data"]`, or PyArrow schema metadata under `xbbg.eid_data`. Polars and DuckDB do not provide a stable entitlement-metadata side channel; use the native, PyArrow, or pandas backend when EIDs are required.

This opt-in request metadata is separate from a subscription message's top-level `EID` field.

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, VS Code Interactive, and marimo, one-shot sync calls such as `blp.bdp(...)` and `blp.bdh(...)` use a notebook-only bridge when the notebook 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:

• `output` accepts exactly `record_batch`, `backend`, `dict`, or `tick` (case-insensitive); omitting it keeps whatever `raw` and `tick_mode` select
• `raw=True` or `output="record_batch"` yields raw xbbg `ArrowRecordBatch` values for max-performance consumers
• `tick_mode=True`, `output="dict"`, or `output="tick"` returns native dict ticks and implies raw subscription mode
• `output="backend"` returns the configured backend output, the same as default iteration without `raw=True`
• `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, raw GitHub Release tar/zip assets, and the `xbbg-mcp-v.mcpb` local connector artifact. Official MCP Registry publication uses the generated `server.json` metadata after the matching GitHub Release contains the `.mcpb`; no MCP release asset includes 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
• enable opt-in sharding for wide multi-security `bdp`/`bdh` requests with `shard_requests=True`
• 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/xbbg-org/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

Citation

If you use xbbg in research or published work, please cite:

@software{xbbg,
  author = {{xbbg contributors}},
  title = {{xbbg}: Independent client for Bloomberg-connected data workflows},
  year = {2026},
  publisher = {GitHub},
  url = {https://github.com/xbbg-org/xbbg},
  version = {1.3.0}
}