xbbg 1.0.0rc1

Intuitive Bloomberg data API

xbbg: An intuitive Bloomberg API for Python

Quick Links: Discord • Documentation • Installation • Quickstart • Examples • Contributing • Changelog

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Latest release: xbbg==1.0.0rc1 (release: notes)

This main branch now tracks the Rust-based v1 beta line, a significant upgrade over 0.x in performance and architecture. Need the legacy pure-Python behavior? Use release/0.x.

Table of Contents

• Overview
• Why Choose xbbg?
• Supported Functionality
• Requirements
• Installation
• Quickstart
  • Basic Usage
  • Common Use Cases
  • Connection Options
  • Async Functions
  • Multi-Backend Support
• Examples
  • 📊 Reference Data
  • 📈 Historical Data
  • ⏱️ Intraday Data
  • 🔍 Screening & Queries
  • 📡 Real-time
  • 🔧 Utilities
• Data Storage
• Troubleshooting
• Development
• Contributing
• Getting Help
• Links
• License

Overview

xbbg is a comprehensive Bloomberg API wrapper for Python, providing a clean, Pythonic interface to Bloomberg data services. This main branch is the v1 beta generation: core request execution is now Rust-powered while preserving the familiar xbbg API for day-to-day workflows.

Key Features

Complete API Coverage Reference data (BDP/BDS) Historical time series (BDH) Intraday bars and tick data Real-time subscriptions BQL, BEQS, and BSRCH queries Technical analysis (BTA)	Production-Grade Features Parquet caching for intraday bars Async/await support for non-blocking operations Multi-backend output (pandas, Polars, PyArrow, DuckDB) Full type hints for IDE integration Comprehensive error handling Exchange-aware market hours
Excel Compatibility Familiar Bloomberg Excel syntax Same field names and date formats Minimal learning curve for Excel users Direct migration path from Excel workflows	Developer Experience Consistent, intuitive API design Extensive documentation and examples Active community support (Discord) Regular updates and maintenance Semantic versioning

Quick Example

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 with sub-minute precision
intraday = blp.bdib('TSLA US Equity', dt='2024-01-15', interval=10, intervalHasSeconds=True)

See examples/xbbg_jupyter_examples.ipynb for comprehensive tutorials and examples.

Why Choose xbbg?

xbbg is the most complete and production-ready Bloomberg API wrapper for Python, trusted by quantitative researchers and financial engineers worldwide. Here's what sets it apart:

🎯 Unmatched Feature Coverage

xbbg is the only Python library that provides:

• Complete Bloomberg API access: All major services (Reference, Historical, Intraday, Real-time, BQL, BEQS, BSRCH)
• Sub-second precision: Down to 10-second intraday bars (unique to xbbg)
• Real-time streaming: Live market data with async support
• Advanced utilities: Futures/CDX contract resolution, currency conversion, market hours

📊 Production-Grade Features

• Intraday caching: Automatic Parquet storage for bdib() bar data
• Async/await support: Non-blocking operations for modern Python applications
• Exchange-aware sessions: Precise market hour handling for 50+ global exchanges
• Type safety: Full type hints for IDE autocomplete and static analysis
• Comprehensive error handling: Clear, actionable error messages

💡 Developer Experience

• Excel-compatible: Use familiar Bloomberg Excel syntax - zero learning curve
• Pythonic API: Consistent, intuitive function names (bdp, bdh, bdib)
• Rich documentation: 100+ examples, Jupyter notebooks, comprehensive guides
• Active community: Discord support, regular updates, responsive maintainers

🚀 Performance & Reliability

• Battle-tested: Used in production by hedge funds, asset managers, and research teams
• Modern Python: Supports Python 3.10-3.14 with latest language features
• CI/CD pipeline: Automated testing across multiple Python versions and platforms
• Semantic versioning: Predictable releases with clear upgrade paths

Comparison with Alternatives

Feature	xbbg	pdblp	blp	polars-bloomberg
Data Services
Reference Data (BDP/BDS)	✅	✅	✅	✅
Historical Data (BDH)	✅	✅	✅	✅
Intraday Bars (BDIB)	✅	❌	❌	✅
Tick-by-Tick Data	✅	❌	❌	❌
Real-time Subscriptions	✅	❌	❌	❌
Advanced Features
Equity Screening (BEQS)	✅	❌	❌	❌
Query Language (BQL)	✅	❌	❌	✅
Quote Request (BQR)	✅	❌	❌	❌
Search (BSRCH)	✅	❌	❌	✅
Technical Analysis (BTA)	✅	❌	❌	❌
Yield & Spread Analysis (YAS)	✅	❌	❌	❌
Developer Features
Excel-compatible syntax	✅	❌	❌	❌
Sub-minute intervals (10s bars)	✅	❌	❌	❌
Async/await support	✅	❌	❌	❌
Intraday bar caching (Parquet)	✅	❌	❌	❌
Multi-backend output	✅	❌	❌	❌
Utilities
Currency conversion	✅	❌	❌	❌
Futures contract resolution	✅	❌	❌	❌
CDX index resolution	✅	❌	❌	❌
Exchange market hours	✅	❌	❌	❌
Project Health
Active development	✅	❌[^1]	✅	✅
Python version support	3.10-3.14	3.8+	3.8+	3.12+
DataFrame library	Multi-backend	pandas	pandas	Polars
Type hints	✅ Full	❌	Partial	✅ Full
CI/CD testing	✅	❌	✅	✅

[^1]: pdblp has been superseded by blp and is no longer under active development.

Bottom line: If you need comprehensive Bloomberg API access with production-grade features, xbbg is the clear choice.

Complete API Reference

Reference Data - Point-in-Time Snapshots

Function	Description	Key Features
bdp()	Get current/reference data	Multiple tickers & fields Excel-style overrides ISIN/CUSIP/SEDOL support
bds()	Bulk/multi-row data	Portfolio holdings Fixed income cash flows Corporate actions
abdp()	Async reference data	Non-blocking operations Concurrent requests Web application friendly
abds()	Async bulk data	Parallel bulk queries Same API as bds()
bflds()	Unified field metadata	Get field info or search by keyword Single function for both use cases
fieldInfo()	Field metadata lookup (alias for bflds)	Data types & descriptions Discover available fields
fieldSearch()	Search Bloomberg fields (alias for bflds)	Find fields by keyword Explore data catalog
blkp()	Find tickers by name	Company name search Asset class filtering
bport()	Portfolio data queries	Dedicated portfolio API Holdings & weights

Fixed Income Analytics

Function	Description	Key Features
yas()	Yield & Spread Analysis	YAS calculator wrapper YTM/YTC yield types Price↔yield conversion Spread calculations

Bond Analytics (via xbbg.ext)

Function	Description	Key Features
bond_info()	Bond reference metadata	Ratings, maturity, coupon
bond_risk()	Duration and risk metrics	Modified/Macaulay duration, convexity, DV01
bond_spreads()	Spread analytics	OAS, Z-spread, I-spread, ASW
bond_cashflows()	Cash flow schedule	Coupon and principal payments
bond_key_rates()	Key rate durations	Key rate DV01s and risks
bond_curve()	Relative value comparison	Multi-bond analytics

Options Analytics (via xbbg.ext)

Function	Description	Key Features
option_info()	Contract metadata	Strike, expiry, exercise type
option_greeks()	Greeks and implied vol	Delta, gamma, theta, vega, IV
option_pricing()	Value decomposition	Intrinsic/time value, activity
option_chain()	Chain via overrides	CHAIN_TICKERS with filtering
option_chain_bql()	Chain via BQL	Rich filtering, expiry/strike ranges
option_screen()	Multi-option comparison	Side-by-side analytics

CDX Analytics (via xbbg.ext)

Function	Description	Key Features
cdx_info()	CDX reference metadata	Series, version, constituents
cdx_defaults()	Default history	Settled defaults in index
cdx_pricing()	Market pricing	Spread, price, recovery rate
cdx_risk()	Risk metrics	DV01, duration, spread sensitivity
cdx_basis()	Basis analytics	CDX vs intrinsics spread
cdx_default_prob()	Default probability	Implied default rates
cdx_cashflows()	Cash flow schedule	Premium and protection legs
cdx_curve()	Term structure	Multi-tenor curve analytics

Historical Data - Time Series Analysis

Function	Description	Key Features
bdh()	End-of-day historical data	Flexible date ranges Multiple frequencies Dividend/split adjustments
abdh()	Async historical data	Non-blocking time series Batch historical queries
dividend()	Dividend & split history	All dividend types Projected dividends Date range filtering
earnings()	Corporate earnings	Geographic breakdowns Product segments Fiscal period analysis
turnover()	Trading volume & turnover	Multi-currency support Automatic FX conversion

Intraday Data - High-Frequency Analysis

Function	Description	Key Features
bdib()	Intraday bar data	Sub-minute bars (10s intervals) Session filtering (open/close) Exchange-aware timing Timezone control (tz parameter)
bdtick()	Tick-by-tick data	Trade & quote events Condition codes Exchange/broker details

Screening & Advanced Queries

Function	Description	Key Features
beqs()	Bloomberg Equity Screening	Custom screening criteria Private & public screens
bql()	Bloomberg Query Language	SQL-like syntax Complex transformations Options chain analysis
bqr()	Bloomberg Quote Request	Tick-level dealer quotes Broker attribution codes Spread price & yield data Date offset support (-2d, -1w)
bsrch()	SRCH (Search) queries	Fixed income searches Commodity screens Weather data
bta()	Technical Analysis	50+ technical indicators Custom studies
etf_holdings()	ETF holdings via BQL	Complete holdings list Weights & positions

Real-Time - Live Market Data

Function	Description	Key Features
subscribe()	Real-time subscriptions	Async iteration Topic failure isolation Health/status metadata
stream()	Simplified streaming	Context manager Non-blocking updates
vwap()	Real-time VWAP	Streaming volume-weighted average price
mktbar()	Real-time market bars	Streaming OHLCV bars
depth()	Market depth	Streaming order book levels
chains()	Option chains	Real-time chain data

Utilities

Function	Description	Key Features
convert_ccy()	Currency conversion	Multi-currency DataFrames Historical FX rates Automatic alignment
fut_ticker()	Futures contract resolution	Generic to specific mapping Date-aware selection
active_futures()	Active futures selection	Volume-based logic Roll date handling
cdx_ticker()	CDX index resolution	Series mapping Index family support
active_cdx()	Active CDX selection	On-the-run detection Lookback windows

Schema Introspection

Function	Description	Key Features
bops()	List service operations	Discover available request types
bschema()	Get operation schema	Field definitions, types, enums
list_valid_elements()	Valid request elements	Check parameter names before sending
get_enum_values()	Enum values for a field	Discover valid override values
generate_stubs()	IDE stub generation	Auto-complete for request parameters

Engine Lifecycle

Function	Description	Key Features
configure()	Engine and session setup	Server host/port, auth, options Replaces connect() / disconnect()
shutdown()	Stop engine and sessions	Graceful cleanup
reset()	Reset engine state	Clear sessions and caches
is_connected()	Check connection status	Boolean connectivity check

Request Middleware

Function	Description	Key Features
add_middleware()	Register request middleware	Logging, caching, instrumentation
remove_middleware()	Unregister middleware	Clean removal
RequestContext	Request metadata	Request ID, request payload, timing, results
RequestEnvironment	Engine/auth snapshot	Host, auth method, validation mode, server list

Additional Features

• Intraday Caching: Automatic Parquet storage for bdib() bar data
• Timezone Support: Exchange-aware market hours for 50+ global exchanges; bdib() / bdtick() support request_tz and output_tz (interpretation and time-column relabeling in the Rust engine; Bloomberg wire format remains UTC)
• Configurable Logging: Debug mode for troubleshooting
• Batch Processing: Efficient multi-ticker queries
• Standardized Output: Consistent DataFrame column naming
• Non-live test helpers: xbbg.testing exposes mock_engine() and TestUtil-backed helpers for unit testing Bloomberg flows without a live terminal

Requirements

• Bloomberg C++ SDK version 3.12.1 or higher:

  • Visit Bloomberg API Library and download C++ Supported Release
  • For local source builds in this repo, install it with bash ./scripts/sdktool.sh on macOS/Linux or .\scripts\sdktool.ps1 on Windows PowerShell
  • If you manage the SDK yourself, set BLPAPI_ROOT to the extracted SDK root

• Bloomberg official Python API:

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

• Python dependencies (core): narwhals>=1.30, pyarrow>=22.0.0

• Optional backends (install separately if needed):

  • pandas - For pandas DataFrame output (pip install pandas)
  • polars - For Polars DataFrame output
  • duckdb - For DuckDB relation output

Installation

pip install xbbg

Supported Python versions: 3.10 – 3.14 (universal wheel).

Quickstart

Basic Usage

from xbbg import blp

# Get current stock prices
prices = blp.bdp(['AAPL US Equity', 'MSFT US Equity'], 'PX_LAST')
print(prices)

Common Workflows

📊 Get Reference Data (Current Snapshot)

# Single ticker, multiple fields
info = blp.bdp('NVDA US Equity', ['Security_Name', 'GICS_Sector_Name', 'PX_LAST'])

# Multiple tickers, single field
prices = blp.bdp(['AAPL US Equity', 'MSFT US Equity', 'GOOGL US Equity'], 'PX_LAST')

# With overrides (e.g., VWAP for specific date)
vwap = blp.bdp('AAPL US Equity', 'Eqy_Weighted_Avg_Px', VWAP_Dt='20240115')

📈 Get Historical Data (Time Series)

# Simple historical query
hist = blp.bdh('SPX Index', 'PX_LAST', '2024-01-01', '2024-12-31')

# Multiple fields
ohlc = blp.bdh('AAPL US Equity', ['open', 'high', 'low', 'close'], '2024-01-01', '2024-01-31')

# With dividend/split adjustments
adjusted = blp.bdh('AAPL US Equity', 'px_last', '2024-01-01', '2024-12-31', adjust='all')

# Weekly data with forward fill
weekly = blp.bdh('SPX Index', 'PX_LAST', '2024-01-01', '2024-12-31', Per='W', Fill='P')

⏱️ Get Intraday Data (High Frequency)

# 5-minute bars
bars_5m = blp.bdib('SPY US Equity', dt='2024-01-15', interval=5)

# 1-minute bars (default)
bars_1m = blp.bdib('TSLA US Equity', dt='2024-01-15')

# Sub-minute bars (10-second intervals) - UNIQUE TO XBBG!
bars_10s = blp.bdib('AAPL US Equity', dt='2024-01-15', interval=10, intervalHasSeconds=True)

# Session filtering (e.g., first 30 minutes)
opening = blp.bdib('SPY US Equity', dt='2024-01-15', session='day_open_30')

# Get data in UTC instead of exchange local time
bars_utc = blp.bdib('SPY US Equity', dt='2024-01-15', tz='UTC')

🔍 Advanced Queries (BQL, Screening)

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

# Equity screening
screen_results = blp.beqs(screen='MyScreen', asof='2024-01-01')

# ETF holdings
holdings = blp.etf_holdings('SPY US Equity')

# Search queries
bonds = blp.bsrch("FI:MYSEARCH")

# Dealer quotes with broker codes (BQR)
quotes = blp.bqr("XYZ 4.5 01/15/30@MSG1 Corp", date_offset="-2d")

🔧 Utilities (Futures, Currency, etc.)

# Resolve futures contract
contract = blp.fut_ticker('ES1 Index', '2024-01-15', freq='ME')  # → 'ESH24 Index'

# Get active futures
active = blp.active_futures('ESA Index', '2024-01-15')

# Currency conversion
hist_usd = blp.bdh('BMW GR Equity', 'PX_LAST', '2024-01-01', '2024-01-31')
hist_eur = blp.convert_ccy(hist_usd, ccy='EUR')

# Dividend history
divs = blp.dividend('AAPL US Equity', start_date='2024-01-01', end_date='2024-12-31')

Fixed Income Analytics (Bond, CDX)

from xbbg.ext import bond_info, bond_risk, bond_spreads, cdx_info, cdx_pricing

# Bond reference data
info = bond_info('T 4.5 05/15/38 Govt')

# Bond risk metrics (duration, convexity, DV01)
risk = bond_risk('T 4.5 05/15/38 Govt')

# Bond spreads (OAS, Z-spread, ASW)
spreads = bond_spreads('T 4.5 05/15/38 Govt')

# CDX index info
cdx = cdx_info('CDX IG CDSI GEN 5Y Corp')

# CDX pricing
px = cdx_pricing('CDX IG CDSI GEN 5Y Corp')

Options Analytics

from xbbg.ext import option_info, option_greeks, option_chain_bql

# Option contract metadata
info = option_info('AAPL US 01/17/25 C200 Equity')

# Greeks and implied volatility
greeks = option_greeks('AAPL US 01/17/25 C200 Equity')

# Option chain via BQL (rich filtering)
chain = option_chain_bql('AAPL US Equity', expiry='2025-01-17')

Best Practices

• Excel users: Use the same field names and date formats as Bloomberg Excel
• Performance: bdib() caches intraday bars as Parquet files automatically (see Data Storage)
• Async operations: Use abdp(), abdh(), abds() for non-blocking requests
• Debugging: Set logging.getLogger('xbbg').setLevel(logging.DEBUG) for detailed logs

Connection Options

By default, xbbg connects to localhost on port 8194. To connect to a remote Bloomberg server or configure authentication, use configure():

from xbbg import blp

# Connect to a remote Bloomberg server
blp.configure(server_host='192.168.1.100', server_port=18194)

# With SAPI authentication
blp.configure(
    server_host='192.168.1.100',
    server_port=18194,
    auth_method='app',
    app_name='myapp:SAPI',
)

# All subsequent calls use the configured connection
blp.bdp(tickers='NVDA US Equity', flds=['Security_Name'])

You can also pass server and port as kwargs to individual function calls for ad-hoc connections.

Engine Architecture

xbbg v1 is powered by a Rust async engine with pre-warmed worker pools:

┌─────────────────────────────────────────────────┐
│                   xbbg Engine                   │
│                                                 │
│  ┌──────────────────────┐  ┌─────────────────┐  │
│  │  Request Worker Pool │  │ Subscription    │  │
│  │  (request_pool_size) │  │ Session Pool    │  │
│  │                      │  │ (sub_pool_size) │  │
│  │  Worker 1 ──session  │  │                 │  │
│  │  Worker 2 ──session  │  │  Session 1      │  │
│  │  ...                 │  │  ...            │  │
│  └──────────────────────┘  └─────────────────┘  │
│           │ round-robin          │ isolated      │
│           ▼                      ▼               │
│    bdp/bdh/bds/bdib       subscribe/stream       │
│    bql/bsrch/beqs         vwap/mktbar/depth      │
└─────────────────────────────────────────────────┘

• Request workers each hold an independent Bloomberg session. Concurrent bdp/bdh/bds calls are dispatched round-robin across workers, so request_pool_size=4 allows 4 parallel Bloomberg requests.
• Subscription sessions are isolated per session to avoid cross-contamination between topic streams. Each subscribe() call gets its own Bloomberg session from the pool.
• Workers are pre-warmed at first use — sessions are started and services opened before your first request, eliminating cold-start latency.

EngineConfig Reference

Call configure() before any Bloomberg request to tune the engine. All fields have sensible defaults:

from xbbg import configure, EngineConfig

# Keyword arguments (most common)
configure(request_pool_size=4, subscription_pool_size=2)

# Or use an EngineConfig object
configure(EngineConfig(request_pool_size=4, subscription_pool_size=2))

Connection & Session

Parameter	Default	Description
host	'localhost'	Bloomberg server host. Aliases: server_host, server
port	8194	Bloomberg server port. Alias: server_port
num_start_attempts	3	Retries before giving up on session start. Alias: max_attempt
auto_restart_on_disconnection	True	Auto-reconnect on session disconnect. Alias: auto_restart

Worker Pools

Parameter	Default	Description
request_pool_size	2	Number of pre-warmed request workers (parallel Bloomberg sessions for bdp/bdh/bds/etc.)
subscription_pool_size	1	Number of pre-warmed subscription sessions (isolated sessions for subscribe/stream)
warmup_services	['//blp/refdata', '//blp/apiflds']	Services to pre-open on startup

Subscription Tuning

Parameter	Default	Description
subscription_flush_threshold	1	Ticks buffered before flushing to Python (increase for throughput, decrease for latency)
subscription_stream_capacity	256	Backpressure buffer size per subscription stream
overflow_policy	'drop_newest'	Slow consumer policy: 'drop_newest', 'drop_oldest', or 'block'

Internal Buffers

Parameter	Default	Description
max_event_queue_size	10000	Bloomberg SDK event queue depth
command_queue_size	256	Internal command channel capacity

Validation

Parameter	Default	Description
validation_mode	'disabled'	Field validation: 'disabled', 'strict' (reject unknown fields), or 'lenient' (warn)
field_cache_path	~/.xbbg/field_cache.json	Path for persistent field type cache. Set to customize location

Authentication (SAPI / B-PIPE)

Parameter	Default	Description
auth_method	None	Auth mode: 'user', 'app', 'userapp', 'dir', 'manual', or 'token'
app_name	None	Application name (required for app, userapp, manual)
user_id	None	Bloomberg user ID (required for manual)
ip_address	None	Bloomberg IP address (required for manual)
dir_property	None	Active Directory property (required for dir)
token	None	Auth token (required for token)

Auth mode examples:

# B-PIPE application auth
configure(auth_method='app', app_name='myapp:8888', host='bpipe-host')

# Manual auth (SAPI)
configure(auth_method='manual', app_name='myapp', user_id='12345', ip_address='10.0.0.1')

# Active Directory auth
configure(auth_method='dir', dir_property='mail')

Async Functions

Every sync function has an async counterpart prefixed with a — for example bdp() → abdp(), bdh() → abdh(), bdib() → abdib(). In the v1 architecture, async implementations are the canonical source of truth and sync functions delegate via _run_sync().

In scripts (no existing event loop)

import asyncio
from xbbg import blp

async def get_data():
    df = await blp.abdp(tickers='AAPL US Equity', flds=['PX_LAST', 'VOLUME'])
    return df

async def get_multiple():
    # Concurrent requests — runs in parallel on a single thread
    results = await asyncio.gather(
        blp.abdp(tickers='AAPL US Equity', flds=['PX_LAST']),
        blp.abdp(tickers='MSFT US Equity', flds=['PX_LAST']),
        blp.abdh(tickers='GOOGL US Equity', start_date='2024-01-01'),
    )
    return results

data = asyncio.run(get_data())
multiple = asyncio.run(get_multiple())

In Jupyter notebooks

Jupyter already runs an event loop, so asyncio.run() will raise RuntimeError: asyncio.run() cannot be called from a running event loop. Use await directly in notebook cells instead:

from xbbg import blp

# Just await directly — Jupyter cells are already async
df = await blp.abdp(tickers='AAPL US Equity', flds=['PX_LAST', 'VOLUME'])

# Concurrent requests work the same way
import asyncio
results = await asyncio.gather(
    blp.abdp(tickers='AAPL US Equity', flds=['PX_LAST']),
    blp.abdp(tickers='MSFT US Equity', flds=['PX_LAST']),
)

Tip: If you don't need async, the sync functions (bdp, bdh, bdib, etc.) work everywhere — scripts, notebooks, and async contexts — without any special handling.

Benefits:

• Non-blocking: doesn't block the event loop
• Concurrent: use asyncio.gather() for parallel requests
• Compatible: works with async web frameworks, Jupyter, and async codebases
• Same API: identical parameters to sync versions (bdp, bds, bdh)

Multi-Backend Support

Starting with v0.11.0, xbbg is DataFrame-library agnostic. You can get output in your preferred format:

Supported Backends

Backend	Type	Output	Best For
Eager Backends
pandas	Eager	pd.DataFrame	Traditional workflows, compatibility
polars	Eager	pl.DataFrame	High performance, large datasets
pyarrow	Eager	pa.Table	Zero-copy interop, memory efficiency
narwhals	Eager	Narwhals DataFrame	Library-agnostic code
modin	Eager	Modin DataFrame	Pandas API with parallel execution
cudf	Eager	cuDF DataFrame	GPU-accelerated processing (NVIDIA)
Lazy Backends
polars_lazy	Lazy	pl.LazyFrame	Deferred execution, query optimization
narwhals_lazy	Lazy	Narwhals LazyFrame	Library-agnostic lazy evaluation
duckdb	Lazy	DuckDB relation	SQL analytics, OLAP queries
dask	Lazy	Dask DataFrame	Out-of-core and distributed computing
ibis	Lazy	Ibis Table	Unified interface to many backends
pyspark	Lazy	Spark DataFrame	Big data processing (requires Java)
sqlframe	Lazy	SQLFrame DataFrame	SQL-first DataFrame operations

Note: Lazy backends only support LONG, SEMI_LONG, LONG_TYPED, and LONG_WITH_METADATA output formats (not WIDE).

Check Backend Availability

from xbbg import get_available_backends, print_backend_status, is_backend_available

# List installed backends
print(get_available_backends())  # ['pandas', 'polars', 'pyarrow', ...]

# Check if a specific backend is available
if is_backend_available('polars'):
    print("Polars is installed!")

# Print detailed status of all backends
print_backend_status()

Usage

from xbbg import blp, Backend

# Get data as Polars DataFrame
df_polars = blp.bdp('AAPL US Equity', 'PX_LAST', backend=Backend.POLARS)

# Get data as PyArrow Table
table = blp.bdh('SPX Index', 'PX_LAST', '2024-01-01', '2024-12-31', backend=Backend.PYARROW)

# Get data as pandas (default)
df_pandas = blp.bdp('MSFT US Equity', 'PX_LAST', backend=Backend.PANDAS)

Output Formats

Control the shape of your data with the format parameter:

Format	Description	Use Case
long	Tidy format with ticker, field, value columns	Analysis, joins, aggregations
long_typed	Typed value columns per data type	Type-safe analysis, no casting needed
long_metadata	String values with dtype column	Serialization, debugging, data catalogs
semi_long	One row per ticker, fields as columns	Quick inspection
wide	Tickers as columns (pandas only)	Time series alignment, Excel-like

from xbbg import blp

# Long format (tidy data, default)
df_long = blp.bdp(['AAPL US Equity', 'MSFT US Equity'], ['PX_LAST', 'VOLUME'], format='long')

# Semi-long format (one row per ticker, fields as columns)
df_semi = blp.bdh('SPX Index', 'PX_LAST', '2024-01-01', '2024-12-31', format='semi_long')

Global Configuration

Set defaults for your entire session:

from xbbg import set_backend, Backend

# Set Polars as default backend
set_backend(Backend.POLARS)

# All subsequent calls use this default
df = blp.bdp('AAPL US Equity', 'PX_LAST')  # Returns Polars DataFrame

Why Multi-Backend?

• Performance: Polars and PyArrow can be 10-100x faster for large datasets
• Memory: Arrow-based backends use zero-copy and columnar storage
• Interoperability: Direct integration with DuckDB, Spark, and other Arrow-compatible tools
• Future-proof: Write library-agnostic code with narwhals backend

Examples

📊 Reference Data

Equity and Index Securities

from xbbg import blp

# Single point-in-time data (BDP)
blp.bdp(tickers='NVDA US Equity', flds=['Security_Name', 'GICS_Sector_Name'])

Out[2]:
               security_name        gics_sector_name
NVDA US Equity   NVIDIA Corp  Information Technology

# With field overrides
blp.bdp('AAPL US Equity', 'Eqy_Weighted_Avg_Px', VWAP_Dt='20181224')

Out[3]:
                eqy_weighted_avg_px
AAPL US Equity               148.75

# Multiple tickers and fields
blp.bdp(
    tickers=['AAPL US Equity', 'MSFT US Equity', 'GOOGL US Equity'],
    flds=['Security_Name', 'GICS_Sector_Name', 'PX_LAST']
)

Out[3a]:
                  security_name        gics_sector_name px_last
AAPL US Equity        Company A  Information Technology  150.25
GOOGL US Equity    Company B  Communication Services  165.30
MSFT US Equity   Company C  Information Technology  180.45

# Bulk/block data (BDS) - multi-row per ticker
blp.bds('AAPL US Equity', 'DVD_Hist_All', DVD_Start_Dt='20180101', DVD_End_Dt='20180531')

Out[8]:
               declared_date     ex_date record_date payable_date  dividend_amount dividend_frequency dividend_type
AAPL US Equity    2018-05-01  2018-05-11  2018-05-14   2018-05-17             0.73            Quarter  Regular Cash
AAPL US Equity    2018-02-01  2018-02-09  2018-02-12   2018-02-15             0.63            Quarter  Regular Cash

Fixed Income Securities

xbbg supports fixed income securities using standard security identifiers (ISIN, CUSIP, SEDOL). Use the /isin/{isin}, /cusip/{cusip}, or /sedol/{sedol} format as the ticker:

# Reference data using ISIN
blp.bdp(tickers='/isin/US1234567890', flds=['SECURITY_NAME', 'MATURITY', 'COUPON', 'PX_LAST'])

Out[9]:
                       security_name    maturity coupon    px_last
/isin/US1234567890  US Treasury Note  2035-05-15   4.25  101.25

# Cash flow schedule using ISIN
blp.bds(tickers='/isin/US1234567890', flds='DES_CASH_FLOW')

Out[10]:
                   payment_date  coupon_amount  principal_amount
/isin/US1234567890   2026-05-15        21250.0               0.0
/isin/US1234567890   2026-11-15        21250.0               0.0
/isin/US1234567890   2027-05-15        21250.0               0.0

Note: Fixed income securities work with bdp(), bds(), and bdh() functions. The identifier format (/isin/, /cusip/, /sedol/) is automatically passed to blpapi.

Yield & Spread Analysis (YAS)

The yas() function provides a convenient wrapper for Bloomberg's YAS calculator:

from xbbg import blp
from xbbg.api.fixed_income import YieldType

# Get yield to maturity
blp.yas('T 4.5 05/15/38 Govt')

Out[11]:
                     YAS_BOND_YLD
ticker
T 4.5 05/15/38 Govt         4.348

# Calculate yield from price
blp.yas('T 4.5 05/15/38 Govt', price=95.0)

Out[12]:
                     YAS_BOND_YLD
ticker
T 4.5 05/15/38 Govt          5.05

# Calculate price from yield
blp.yas('T 4.5 05/15/38 Govt', flds='YAS_BOND_PX', yield_=4.8)

Out[13]:
                     YAS_BOND_PX
ticker
T 4.5 05/15/38 Govt    97.229553

# Yield to call for callable bonds
blp.yas('AAPL 2.65 05/11/50 Corp', yield_type=YieldType.YTC)

Out[14]:
                          YAS_BOND_YLD
ticker
AAPL 2.65 05/11/50 Corp          5.431

# Multiple YAS analytics
blp.yas('T 4.5 05/15/38 Govt', ['YAS_BOND_YLD', 'YAS_MOD_DUR', 'YAS_ASW_SPREAD'])

Out[15]:
                     YAS_ASW_SPREAD  YAS_BOND_YLD  YAS_MOD_DUR
ticker
T 4.5 05/15/38 Govt       33.093531         4.348     9.324928

Available parameters:

• settle_dt: Settlement date (YYYYMMDD or datetime)
• yield_type: YieldType.YTM (default) or YieldType.YTC
• price: Input price to calculate yield
• yield_: Input yield to calculate price
• spread: Spread to benchmark in basis points
• benchmark: Benchmark bond ticker for spread calculations

Field Information and Search

# Unified field lookup (recommended)
blp.bflds(fields=['PX_LAST', 'VOLUME'])  # Get metadata for specific fields
blp.bflds(search_spec='vwap')            # Search for fields by keyword

# Convenience aliases
blp.fieldInfo(['PX_LAST', 'VOLUME'])     # Same as bflds(fields=...)
blp.fieldSearch('vwap')                  # Same as bflds(search_spec=...)

Security Lookup

# Look up securities by company name
blp.blkp('IBM', max_results=10)

# Lookup with asset class filter
blp.blkp('Apple', yellowkey='eqty', max_results=20)

Portfolio Data

# Get portfolio data (dedicated function)
blp.bport('PORTFOLIO_NAME', 'PORTFOLIO_MWEIGHT')

📈 Historical Data

# End-of-day historical data (BDH)
blp.bdh(
    tickers='SPX Index', flds=['high', 'low', 'last_price'],
    start_date='2018-10-10', end_date='2018-10-20',
)

Out[4]:
           SPX Index
                high      low last_price
2018-10-10  2,874.02 2,784.86   2,785.68
2018-10-11  2,795.14 2,710.51   2,728.37
2018-10-12  2,775.77 2,729.44   2,767.13
2018-10-15  2,775.99 2,749.03   2,750.79
2018-10-16  2,813.46 2,766.91   2,809.92
2018-10-17  2,816.94 2,781.81   2,809.21
2018-10-18  2,806.04 2,755.18   2,768.78
2018-10-19  2,797.77 2,760.27   2,767.78

# Multiple tickers and fields
blp.bdh(
    tickers=['AAPL US Equity', 'MSFT US Equity'],
    flds=['px_last', 'volume'],
    start_date='2024-01-01', end_date='2024-01-10',
)

Out[4a]:
           AAPL US Equity             MSFT US Equity            
                  px_last      volume        px_last      volume
2024-01-02         150.25  45000000.0         180.45  25000000.0
2024-01-03         151.30  42000000.0         181.20  23000000.0
2024-01-04         149.80  48000000.0         179.90  24000000.0
2024-01-05         150.10  44000000.0         180.15  22000000.0
2024-01-08         151.50  46000000.0         181.80  26000000.0

# Excel-compatible inputs with periodicity
blp.bdh(
    tickers='SHCOMP Index', flds=['high', 'low', 'last_price'],
    start_date='2018-09-26', end_date='2018-10-20',
    Per='W', Fill='P', Days='A',
)

Out[5]:
           SHCOMP Index
                   high      low last_price
2018-09-28     2,827.34 2,771.16   2,821.35
2018-10-05     2,827.34 2,771.16   2,821.35
2018-10-12     2,771.94 2,536.66   2,606.91
2018-10-19     2,611.97 2,449.20   2,550.47

# Dividend/split adjustments
blp.bdh('AAPL US Equity', 'px_last', '20140606', '20140609', adjust='all')

Out[15]:
           AAPL US Equity
                  px_last
2014-06-06          85.22
2014-06-09          86.58

# Dividend history
blp.dividend(['C US Equity', 'MS US Equity'], start_date='2018-01-01', end_date='2018-05-01')

Out[13]:
                dec_date     ex_date    rec_date    pay_date  dvd_amt dvd_freq      dvd_type
C US Equity   2018-01-18  2018-02-02  2018-02-05  2018-02-23     0.32  Quarter  Regular Cash
MS US Equity  2018-04-18  2018-04-27  2018-04-30  2018-05-15     0.25  Quarter  Regular Cash
MS US Equity  2018-01-18  2018-01-30  2018-01-31  2018-02-15     0.25  Quarter  Regular Cash

# Earnings breakdowns
blp.earnings('AMD US Equity', by='Geo', Eqy_Fund_Year=2017, Number_Of_Periods=1)

Out[12]:
                 level    fy2017  fy2017_pct
Asia-Pacific      1.00  3,540.00       66.43
    China         2.00  1,747.00       49.35
    Japan         2.00  1,242.00       35.08
    Singapore     2.00    551.00       15.56
United States     1.00  1,364.00       25.60
Europe            1.00    263.00        4.94
Other Countries   1.00    162.00        3.04

⏱️ Intraday Data

# Intraday bars (1-minute default)
blp.bdib(ticker='BHP AU Equity', dt='2018-10-17').tail()

Out[9]:
                          BHP AU Equity
                                   open  high   low close   volume num_trds
2018-10-17 15:56:00+11:00         33.62 33.65 33.62 33.64    16660      126
2018-10-17 15:57:00+11:00         33.65 33.65 33.63 33.64    13875      156
2018-10-17 15:58:00+11:00         33.64 33.65 33.62 33.63    16244      159
2018-10-17 15:59:00+11:00         33.63 33.63 33.61 33.62    16507      167
2018-10-17 16:10:00+11:00         33.66 33.66 33.66 33.66  1115523      216

Selecting bar intervals:

• Minute-based intervals (default): Use the interval parameter to specify minutes. By default, interval=1 (1-minute bars). Common intervals:
  • interval=5 → 5-minute bars
  • interval=15 → 15-minute bars
  • interval=30 → 30-minute bars
  • interval=60 → 1-hour bars

# 5-minute bars
blp.bdib(ticker='AAPL US Equity', dt='2025-11-12', interval=5).head()

# 15-minute bars
blp.bdib(ticker='AAPL US Equity', dt='2025-11-12', interval=15).head()

• Sub-minute intervals (seconds): Set intervalHasSeconds=True and specify seconds:

# 10-second bars
blp.bdib(ticker='AAPL US Equity', dt='2025-11-12', interval=10, intervalHasSeconds=True).head()

Out[9a]:
                          AAPL US Equity
                                   open    high     low   close volume num_trds
2025-11-12 09:31:00-05:00        150.25  150.35  150.20  150.30  25000      150
2025-11-12 09:31:10-05:00        150.30  150.40  150.25  150.35  18000      120
2025-11-12 09:31:20-05:00        150.35  150.45  150.30  150.40  22000      135

Note: By default, interval is interpreted as minutes. Set intervalHasSeconds=True to use seconds-based intervals.

# Market session filtering
blp.bdib(ticker='7974 JT Equity', dt='2018-10-17', session='am_open_30').tail()

Out[11]:
                          7974 JT Equity
                                    open      high       low     close volume num_trds
2018-10-17 09:27:00+09:00      39,970.00 40,020.00 39,970.00 39,990.00  10800       44
2018-10-17 09:28:00+09:00      39,990.00 40,020.00 39,980.00 39,980.00   6300       33
2018-10-17 09:29:00+09:00      39,970.00 40,000.00 39,960.00 39,970.00   3300       21
2018-10-17 09:30:00+09:00      39,960.00 40,010.00 39,950.00 40,000.00   3100       19
2018-10-17 09:31:00+09:00      39,990.00 40,000.00 39,980.00 39,990.00   2000       15

How the session parameter works

The session parameter is resolved by xbbg.core.config.intervals.get_interval() and xbbg.core.process.time_range() using exchange metadata from xbbg/markets/config/exch.yml:

• Base sessions (no underscores) map directly to session windows defined for the ticker's exchange in exch.yml:

  • allday - Full trading day including pre/post market (e.g., [400, 2000] for US equities)
  • day - Regular trading hours (e.g., [0930, 1600] for US equities)
  • am - Morning session (e.g., [901, 1130] for Japanese equities)
  • pm - Afternoon session (e.g., [1230, 1458] for Japanese equities)
  • pre - Pre-market session (e.g., [400, 0930] for US equities)
  • post - Post-market session (e.g., [1601, 2000] for US equities)
  • night - Night trading session (e.g., [1710, 700] for Australian futures)

  Not all exchanges define all sessions. For example, GBP Curncy uses CurrencyGeneric which defines allday and day only.

• Compound sessions (with underscores) allow finer control by combining a base session with modifiers (open, close, normal, exact):

  • Open windows (first N minutes of a session):
    • day_open_30 → first 30 minutes of the day session
    • am_open_30 → first 30 minutes of the am session
    • Note: open is not a base session; use day_open_30, not open_30
  • Close windows (last N minutes of a session):
    • day_close_20 → last 20 minutes of the day session
    • am_close_30 → last 30 minutes of the am session
    • Note: close is not a base session; use day_close_20, not close_20
  • Normal windows (skip open/close buffers):
    • day_normal_30_20 → skips first 30 min and last 20 min of day
    • am_normal_30_30 → skips first 30 min and last 30 min of am
  • Exact clock times (exchange-local HHMM format):
    • day_exact_2130_2230 → [21:30, 22:30] local time (marker session)
    • allday_exact_2130_2230 → [21:30, 22:30] local time (actual window)
    • allday_exact_2130_0230 → [21:30, 02:30 next day] local time

• Resolution order and fallbacks:

  • blp.bdib / blp.bdtick call time_range(), which:
    1. Uses exch.yml + get_interval() and const.exch_info() to resolve local session times and exchange timezone.
    2. Converts that window to UTC and then to your requested tz argument (e.g., 'UTC', 'NY', 'Europe/London').
    3. If exchange metadata is missing for session and the asset, it may fall back to pandas‑market‑calendars (PMC) for simple sessions ('day' / 'allday'), based on the exchange code.

• Errors and diagnostics:

  • If a session name is not defined for the ticker's exchange, get_interval() raises a ValueError listing the available sessions for that exchange and points to xbbg/markets/exch.yml.
  • For compound sessions whose base session doesn't exist (e.g. mis-typed am_open_30 for an exchange that has no am section), get_interval() returns SessNA and time_range() will then try the PMC fallback or ultimately raise a clear ValueError.

In practice:

• Use simple names like session='day' or session='allday' when you just want the main trading hours.
• Use compound names like session='day_open_30' or session='am_normal_30_30' when you need to focus on opening/closing auctions or to exclude "micro" windows (e.g. the first X minutes).
• If you add or customize sessions, update exch.yml and rely on get_interval() to pick them up automatically.

# Using reference exchange for market hours
blp.bdib(ticker='ESM0 Index', dt='2020-03-20', ref='ES1 Index').tail()

out[10]:
                          ESM0 Index
                                open     high      low    close volume num_trds        value
2020-03-20 16:55:00-04:00   2,260.75 2,262.25 2,260.50 2,262.00    412      157   931,767.00
2020-03-20 16:56:00-04:00   2,262.25 2,267.00 2,261.50 2,266.75    812      209 1,838,823.50
2020-03-20 16:57:00-04:00   2,266.75 2,270.00 2,264.50 2,269.00   1136      340 2,576,590.25
2020-03-20 16:58:00-04:00   2,269.25 2,269.50 2,261.25 2,265.75   1077      408 2,439,276.00
2020-03-20 16:59:00-04:00   2,265.25 2,272.00 2,265.00 2,266.50   1271      378 2,882,978.25

# Tick-by-tick data with event types and condition codes
blp.bdtick(ticker='XYZ US Equity', dt='2024-10-15', session='day', types=['TRADE']).head()

Out[12]:
                          XYZ US Equity
                                   volume    typ   cond exch            trd_time
2024-10-15 09:30:15-04:00           1500  TRADE     @  NYSE  2024-10-15 09:30:15
2024-10-15 09:30:23-04:00            800  TRADE     @  NYSE  2024-10-15 09:30:23
2024-10-15 09:30:31-04:00           2200  TRADE     @  NYSE  2024-10-15 09:30:31

# Tick data with timeout (useful for large requests)
blp.bdtick(ticker='XYZ US Equity', dt='2024-10-15', session='day', timeout=1000)

Note: bdtick requests can take longer to respond. Use timeout parameter (in milliseconds) if you encounter empty DataFrames due to timeout.

Timezone handling (bdib / bdtick)

Bloomberg intraday APIs use UTC on the wire. The Rust engine accepts two optional knobs:

• request_tz: How naive start_datetime / end_datetime (and the implicit full-day window when using dt=) are interpreted before the request. Omit or use UTC to keep the previous behavior (naive times treated as UTC wall times, matching older examples that use e.g. 14:30 for US cash open).
• output_tz: Relabel the Arrow/Pandas time column to an IANA zone (same instants; only the timestamp type metadata changes). Omit or UTC leaves UTC.

Supported labels (case-insensitive where noted): UTC, local (machine IANA zone), exchange (resolve via the request’s security and cached/Bloomberg metadata), short aliases NY, LN, TK, HK, a reference ticker string containing a space (same as exch_info), or any IANA name (e.g. Europe/Zurich).

# Naive times in America/New_York → converted to UTC in the engine before the API call
bars = await blp.abdib(
    "SPY US Equity",
    start_datetime="2024-01-15 09:30",
    end_datetime="2024-01-15 16:00",
    interval=5,
    request_tz="America/New_York",
)

# Present tick times in the listing’s exchange zone (resolved in Rust)
ticks = await blp.abdtick(
    "SPY US Equity",
    "2024-01-15 09:30",
    "2024-01-15 10:00",
    request_tz="exchange",
    output_tz="exchange",
)

# Trading volume & turnover (currency-adjusted, in millions)
blp.turnover(['ABC US Equity', 'DEF US Equity'], start_date='2024-01-01', end_date='2024-01-10', ccy='USD')

Out[13]:
            ABC US Equity  DEF US Equity
2024-01-02        15,304        8,920
2024-01-03        18,450       12,340
2024-01-04        14,890        9,560
2024-01-05        16,720       11,230
2024-01-08        10,905        7,890

# Currency conversion for historical data
hist = blp.bdh(['GHI US Equity'], ['px_last'], '2024-01-01', '2024-01-10')
blp.convert_ccy(hist, ccy='EUR')

Out[14]:
            GHI US Equity
2024-01-02        169.66
2024-01-03        171.23
2024-01-04        170.45
2024-01-05        172.10
2024-01-08        169.46

🔍 Screening & Queries

# Bloomberg Query Language (BQL)
# IMPORTANT: The 'for' clause must be OUTSIDE get(), not inside
# Correct: get(px_last) for('AAPL US Equity')
# Incorrect: get(px_last for('AAPL US Equity'))
# blp.bql("get(px_last) for('AAPL US Equity')")  # doctest: +SKIP

# BQL Options query example - sum open interest
# blp.bql("get(sum(group(open_int))) for(filter(options('SPX Index'), expire_dt=='2025-11-21'))")  # doctest: +SKIP

# BQL Options metadata - get available expiries
# blp.bql("get(expire_dt) for(options('INDEX Ticker'))")  # doctest: +SKIP

# BQL Options metadata - get option tickers for an underlying
# blp.bql("get(id) for(options('INDEX Ticker'))")  # doctest: +SKIP

# BQL Options metadata - get option chain (expiry, strike, put/call)
# blp.bql("get(id, expire_dt, strike_px, PUT_CALL) for(filter(options('INDEX Ticker'), expire_dt=='YYYY-MM-DD'))")  # doctest: +SKIP

# ETF Holdings (BQL)
# blp.etf_holdings('SPY US Equity')  # doctest: +SKIP
# Returns:
#               holding       id_isin SOURCE POSITION_TYPE  weights  position
# 0     MSFT US Equity  US5949181045    ETF             L   0.0725   123456.0
# 1     AAPL US Equity  US0378331005    ETF             L   0.0685   112233.0
# 2     NVDA US Equity  US67066G1040    ETF             L   0.0450    88776.0

# Bloomberg Equity Screening (BEQS)
# blp.beqs(screen='MyScreen', asof='2023-01-01')  # doctest: +SKIP

# SRCH (Search) - Fixed Income example
# blp.bsrch("FI:YOURSRCH")  # doctest: +SKIP

Out[16]:
              id
0  !!ABC123 Mtge
1  !!DEF456 Mtge
2  !!GHI789 Mtge
3  !!JKL012 Mtge
4  !!MNO345 Mtge

# SRCH - Weather data with parameters
blp.bsrch(  # doctest: +SKIP
    "comdty:weather",
    overrides={
        "provider": "wsi",
        "location": "US_XX",
        "model": "ACTUALS",
        "frequency": "DAILY",
        "target_start_date": "2021-01-01",
        "target_end_date": "2021-01-05",
        "location_time": "false",
        "fields": "WIND_SPEED|TEMPERATURE|HDD_65F|CDD_65F|HDD_18C|CDD_18C|PRECIPITATION_24HR|CLOUD_COVER|FEELS_LIKE_TEMPERATURE|MSL_PRESSURE|TEMPERATURE_MAX_24HR|TEMPERATURE_MIN_24HR"
    }
)

Out[17]:
              Reported Time  Wind Speed (m/s)  Temperature (°C)  Heating Degree Days (°F)  Cooling Degree Days (°F)
0 2021-01-01 06:00:00+00:00              3.45              -2.15                   38.25                     0.0
1 2021-01-02 06:00:00+00:00              2.10              -1.85                   36.50                     0.0
2 2021-01-03 06:00:00+00:00              1.95              -2.30                   37.80                     0.0
3 2021-01-04 06:00:00+00:00              2.40              -2.65                   38.10                     0.0
4 2021-01-05 06:00:00+00:00              2.15              -1.20                   35.75                     0.0

Note: The bsrch() function uses the blpapi Excel service (//blp/exrsvc) and supports user-defined SRCH screens, commodity screens, and blpapi example screens. For weather data and other specialized searches, use the overrides parameter to pass search-specific parameters.

# Bloomberg Quote Request (BQR) - Dealer quotes with broker codes
# Emulates Excel =BQR() function for fixed income dealer quotes

# Get quotes from last 2 days
# blp.bqr('XYZ 4.5 01/15/30@MSG1 Corp', date_offset='-2d')  # doctest: +SKIP

# Using ISIN with MSG1 pricing source
# blp.bqr('/isin/US123456789@MSG1', date_offset='-2d')  # doctest: +SKIP

# With broker codes and spread data
# blp.bqr(  # doctest: +SKIP
#     'XYZ 4.5 01/15/30@MSG1 Corp',
#     date_offset='-2d',
#     include_broker_codes=True,
#     include_spread_price=True,
# )

# With explicit date range
# blp.bqr('XYZ 4.5 01/15/30@MSG1 Corp', start_date='2024-01-15', end_date='2024-01-17')  # doctest: +SKIP

# Get only trade events
# blp.bqr('XYZ 4.5 01/15/30@MSG1 Corp', date_offset='-1d', event_types=['TRADE'])  # doctest: +SKIP

```pydocstring
Out[18]:
                              ticker                 time event_type   price      size spread_price broker_buy broker_sell
0  XYZ 4.5 01/15/30@MSG1 Corp  2024-01-15 10:30:00        BID   98.75  10000000         29.0       DLRA         NaN
1  XYZ 4.5 01/15/30@MSG1 Corp  2024-01-15 10:30:05        ASK   99.00   5000000         24.1        NaN        DLRB
2  XYZ 4.5 01/15/30@MSG1 Corp  2024-01-15 11:45:00      TRADE   98.85   2500000          NaN       DLRC        DLRC

Note: The bqr() function emulates Bloomberg Excel's =BQR() formula. Use the @MSG1 pricing source suffix to get dealer-level quote attribution. Optional parameters include include_broker_codes, include_spread_price, include_yield, include_condition_codes, and include_exchange_codes.

📡 Real-time

# Real-time market data streaming (async)
# async for tick in blp.astream(['AAPL US Equity'], ['LAST_PRICE']):  # doctest: +SKIP
#     print(tick)  # doctest: +SKIP

# Subscriptions with failure isolation and health metadata
# sub = await blp.asubscribe(['AAPL US Equity'], ['LAST_PRICE'])  # doctest: +SKIP
# async for update in sub:  # doctest: +SKIP
#     print(update)  # doctest: +SKIP

# Real-time VWAP streaming
# async for bar in blp.avwap(['AAPL US Equity']):  # doctest: +SKIP
#     print(bar)  # doctest: +SKIP

🔧 Utilities

# Futures ticker resolution (generic to specific contract)
blp.fut_ticker('ES1 Index', '2024-01-15', freq='ME')

Out[15]:
'ESH24 Index'

# Active futures contract selection (volume-based)
blp.active_futures('ESA Index', '2024-01-15')

Out[16]:
'ESH24 Index'

# CDX index ticker resolution (series mapping)
blp.cdx_ticker('CDX IG CDSI GEN 5Y Corp', '2024-01-15')

Out[17]:
'CDX IG CDSI S45 5Y Corp'

# Active CDX contract selection
blp.active_cdx('CDX IG CDSI GEN 5Y Corp', '2024-01-15', lookback_days=10)

Out[18]:
'CDX IG CDSI S45 5Y Corp'

Data Storage

What gets cached

Currently, only bdib() intraday bar data is cached as local Parquet files. Other functions (bdp, bds, bdh, bql, beqs, bsrch, bta, bqr) always make live Bloomberg API calls — they are not cached.

When bdib() fetches intraday bars, it will:

1. Check the cache first — if a Parquet file exists for that ticker/date/interval, return it instead of calling Bloomberg.
2. Save results to cache — after a successful Bloomberg fetch, save the bars as a Parquet file (only once the trading session has ended, to avoid saving incomplete data).

Exchange metadata (timezone, session hours) is also cached locally to avoid repeated lookups.

Cache location

By default, xbbg uses a platform-specific cache directory:

Platform	Default location
Windows	%APPDATA%\xbbg
Linux/macOS	~/.cache/xbbg or ~/.xbbg

To use a custom location, set BBG_ROOT before importing xbbg:

import os
os.environ['BBG_ROOT'] = '/path/to/your/cache/directory'

Cache structure

Intraday bar files are organized as:

{BBG_ROOT}/{asset_class}/{ticker}/{event_type}/{interval}/{date}.parq

For example, 1-minute TRADE bars for AAPL on 2024-01-15:

/path/to/cache/Equity/AAPL US Equity/TRADE/1m/2024-01-15.parq

Controlling cache behavior

# Disable cache for a specific call (always fetch from Bloomberg)
blp.bdib('AAPL US Equity', dt='2024-01-15', cache=False)

# Force reload (fetch from Bloomberg even if cached, then overwrite cache)
blp.bdib('AAPL US Equity', dt='2024-01-15', reload=True)

Bloomberg data license compliance

Local data usage must be compliant with the Bloomberg Datafeed Addendum (see DAPI<GO>):

To access Bloomberg data via the API (and use that data in Microsoft Excel), your company must sign the 'Datafeed Addendum' to the Bloomberg Agreement. This legally binding contract describes the terms and conditions of your use of the data and information available via the API (the "Data"). The most fundamental requirement regarding your use of Data is that it cannot leave the local PC you use to access the BLOOMBERG PROFESSIONAL service.

🔧 Troubleshooting

❌ Empty DataFrame Returned

Possible causes:

• ✅ Bloomberg Terminal not running → Start Bloomberg Terminal
• ✅ Wrong ticker format → Use 'AAPL US Equity' not 'AAPL'
• ✅ Data not available for date/time → Check Bloomberg Terminal
• ✅ Timeout too short → Increase: blp.bdtick(..., timeout=1000)

Quick fix:

# Verify ticker exists
blp.blkp('Apple', yellowkey='eqty')

# Check field availability
blp.fieldSearch('price')

🔌 Connection Errors

Checklist:

• ✅ Bloomberg Terminal is running and logged in
• ✅ Default connection is localhost:8194
• ✅ For remote: blp.bdp(..., server='192.168.1.100', port=18194)
• ✅ Bloomberg API (blpapi) is installed

Test connection:

from xbbg import blp
blp.bdp('AAPL US Equity', 'PX_LAST')  # Should return data

⏱️ Timeout Errors

Solutions:

# Increase timeout (milliseconds)
blp.bdtick('AAPL US Equity', dt='2024-01-15', timeout=5000)

# Break large requests into chunks
dates = pd.date_range('2024-01-01', '2024-12-31', freq='MS')
chunks = [blp.bdh('SPX Index', 'PX_LAST', start, end) for start, end in zip(dates[:-1], dates[1:])]
result = pd.concat(chunks)

🔍 Field Not Found

Find the right field:

# Search for fields
blp.fieldSearch('vwap')  # Find VWAP-related fields

# Get field info
blp.fieldInfo(['PX_LAST', 'VOLUME'])  # See data types & descriptions

# Check in Bloomberg Terminal
# Type FLDS<GO> to browse all fields

🐛 Still Stuck?

Get help fast:

• 💬 Discord: Join our community - Usually get answers within hours
• 🐛 GitHub Issues: Report bugs - Include error messages & code
• 📚 Documentation: ReadTheDocs - Comprehensive guides
• 📓 Examples: xbbg_jupyter_examples.ipynb - 100+ working examples

When reporting issues, include:

1. xbbg version: import xbbg; print(xbbg.__version__)
2. Python version: python --version
3. Error message (full traceback)
4. Minimal code to reproduce

Development

Setup

Set up the development environment with pixi:

# Install the Bloomberg SDK into vendor/blpapi-sdk/ and let xbbg discover it
bash ./scripts/sdktool.sh               # macOS/Linux
# .\scripts\sdktool.ps1                # Windows PowerShell

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

If you already manage the SDK yourself, you can still set BLPAPI_ROOT manually.

Running Tests and Linting

pixi run test                  # run tests
pixi run lint                  # lint Python + Rust
pixi run ci                    # full sweep: fmt-check + lint + typecheck + test

For non-live application tests, xbbg.testing can mock Bloomberg-style responses:

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": 254.23}},
)

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

Building

pixi run build

Publishing is handled via GitHub Actions using PyPI Trusted Publishing (OIDC).

Documentation

The docs site uses Astro:

pixi run -e docs docs-install  # install npm deps
pixi run -e docs docs-dev      # local dev server
pixi run -e docs docs-build    # production build

Contributing

We welcome contributions! Please see CONTRIBUTING.md for detailed guidelines on:

• Setting up your development environment
• Code style and standards
• Testing requirements
• Pull request process
• Community guidelines

Quick start for contributors:

# Fork and clone the repository
git clone https://github.com/YOUR-USERNAME/xbbg.git
cd xbbg

# Set up development environment
pixi install && pixi run install

# Run tests and linting
pixi run ci

Getting Help

Community Support

• Discord: Join our community for discussions, questions, and help
• GitHub Issues: Report bugs or request features
• GitHub Discussions: Share ideas and ask questions

Resources

• Documentation: ReadTheDocs
• Examples: examples/xbbg_jupyter_examples.ipynb
• Changelog: CHANGELOG.md
• Security: SECURITY.md

Links

• PyPI Package
• Documentation
• Source Code
• Issue Tracker
• Discord Community
• Changelog
• Contributing Guidelines
• Code of Conduct
• Security Policy

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

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