xbbg · PyPI

Intuitive Bloomberg data API

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xbbg: An intuitive Bloomberg API for Python

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

Latest release: xbbg==0.11.2 (release: notes)

Overview
Why Choose xbbg?
Supported Functionality
Requirements
Installation
Quickstart
Examples
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's data services. Designed for quantitative researchers, portfolio managers, and financial engineers, xbbg simplifies data access while maintaining full API functionality.

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

Intelligent Parquet-based caching
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

Intelligent caching: Automatic Parquet-based storage reduces API calls by 80%+
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	✅	❌	❌	❌
Local Parquet caching	✅	❌	❌	❌
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()`
`fieldInfo()`	Field metadata lookup	Data types & descriptions Discover available fields
`fieldSearch()`	Search Bloomberg fields	Find fields by keyword Explore data catalog
`lookupSecurity()`	Find tickers by name	Company name search Asset class filtering
`getPortfolio()`	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

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
`earning()`	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
`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 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
`live()`	Real-time streaming	Async context manager Auto-reconnection Field-level updates
`subscribe()`	Real-time subscriptions	Event callbacks Custom intervals Multiple tickers
`stream()`	Async streaming	Modern async/await Non-blocking updates

Utilities

Function	Description	Key Features
`adjust_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

Additional Features

Smart Caching: Automatic Parquet storage reduces API calls
Timezone Support: Exchange-aware market hours for 50+ global exchanges
Configurable Logging: Debug mode for troubleshooting
Batch Processing: Efficient multi-ticker queries
Standardized Output: Consistent DataFrame column naming

Requirements

Bloomberg C++ SDK version 3.12.1 or higher:
- Visit Bloomberg API Library and download C++ Supported Release
- In the bin folder of downloaded zip file, copy blpapi3_32.dll and blpapi3_64.dll to Bloomberg BLPAPI_ROOT folder (usually blp/DAPI)
Bloomberg official Python API:

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

Python dependencies: numpy, pandas, narwhals, ruamel.yaml and pyarrow (automatically installed)
Optional backends (install separately if needed):
- 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')

🔍 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.adjust_ccy(hist_usd, ccy='EUR')

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

Best Practices

Excel users: Use the same field names and date formats as Bloomberg Excel
Performance: Enable caching with os.environ['BBG_ROOT'] = '/path/to/cache'
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, use the server and port parameters:

from xbbg import blp

# Connect to a remote Bloomberg server
kwargs = {'server': '192.168.1.100', 'port': 18194}
blp.bdp(tickers='NVDA US Equity', flds=['Security_Name'], **kwargs)

The server parameter (or server_host) can be passed through any function that accepts kwargs, just like the port parameter.

Async Functions

xbbg provides async versions of reference and historical data functions for non-blocking, concurrent requests. Use abdp(), abds(), and abdh() in async contexts:

import asyncio
from xbbg import blp

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

# Concurrent requests for multiple tickers
async def get_multiple():
    results = await asyncio.gather(
        blp.abdp(tickers='TICKER1 US Equity', flds=['PX_LAST']),
        blp.abdp(tickers='TICKER2 US Equity', flds=['PX_LAST']),
        blp.abdh(tickers='TICKER3 US Equity', start_date='2024-01-01'),
    )
    return results

# Run async functions
data = asyncio.run(get_data())
multiple = asyncio.run(get_multiple())

Benefits:

Non-blocking: doesn't block the event loop
Concurrent: use asyncio.gather() for parallel requests
Compatible: works with async web frameworks 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 and SEMI_LONG 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, Format

# 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
`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, Format

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

# Wide format (Excel-like)
df_wide = blp.bdh('SPX Index', 'PX_LAST', '2024-01-01', '2024-12-31', format=Format.WIDE)

Global Configuration

Set defaults for your entire session:

from xbbg import set_backend, set_format, Backend, Format

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

# Set long format as default
set_format(Format.LONG)

# All subsequent calls use these defaults
df = blp.bdp('AAPL US Equity', 'PX_LAST')  # Returns Polars DataFrame in long format

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

# Get metadata about fields
blp.fieldInfo(['PX_LAST', 'VOLUME'])

# Search for fields by name or description
blp.fieldSearch('vwap')

Security Lookup

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

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

Portfolio Data

# Get portfolio data (dedicated function)
blp.getPortfolio('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.earning('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.

# 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.adjust_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 with broker attribution
# 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 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

Out[18]:
                              ticker                 time event_type   price   size broker_buy broker_sell
0  XYZ 4.5 01/15/30@MSG1 Corp  2024-01-15 10:30:00        BID   98.75  10000       DLRA         NaN
1  XYZ 4.5 01/15/30@MSG1 Corp  2024-01-15 10:30:05        ASK   99.00   5000        NaN        DLRB
2  XYZ 4.5 01/15/30@MSG1 Corp  2024-01-15 11:45:00      TRADE   98.85   2500       DLRC        DLRC

Note: The bqr() function emulates Bloomberg Excel's =BQR() formula. Use the @MSG1 pricing source suffix to get dealer-level quote attribution. The broker_buy and broker_sell columns identify the contributing dealers (4-character codes).

📡 Real-time

# Real-time market data streaming
# with blp.live(['AAPL US Equity'], ['LAST_PRICE']) as stream:  # doctest: +SKIP
#     for update in stream:  # doctest: +SKIP
#         print(update)  # doctest: +SKIP

# Real-time subscriptions
# blp.subscribe(['AAPL US Equity'], ['LAST_PRICE'], callback=my_handler)  # doctest: +SKIP

# Subscribe with 10-second update interval
# blp.subscribe(['AAPL US Equity'], interval=10)  # 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

If BBG_ROOT is provided in os.environ, data can be saved locally in Parquet format. By default, local storage is preferred over blpapi for all queries.

Setup:

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

Once configured, xbbg will automatically save and retrieve data from local Parquet files, reducing blpapi calls and improving performance.

Important: Local data usage must be compliant with Bloomberg Datafeed Addendum (full description in 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.lookupSecurity('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:

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

Development

Setup

Create venv and install dependencies:

uv venv .venv
.\.venv\Scripts\Activate.ps1
uv sync --locked --extra dev --extra test

Adding Dependencies

uv add <package>

Running Tests and Linting

uv run ruff check xbbg
uv run pytest --doctest-modules --cov -v xbbg

Building

uv run python -m build

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

Documentation

uv sync --locked --extra docs
uv run sphinx-build -b html docs docs/_build/html

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
uv venv .venv
.\.venv\Scripts\Activate.ps1
uv sync --locked --extra dev --extra test

# Run tests and linting
uv run ruff check xbbg
uv run pytest --doctest-modules -q

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

License

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

Star History

Project Status

Category	Badge
Docs
Build
Coverage
Quality

License

0.11.0b1 - see release: notes

v0.11.0b1 - Beta Release

Highlights

Arrow-first pipeline: Complete rewrite of data processing using PyArrow internally
Multi-backend support: New Backend enum supporting narwhals, pandas, polars, polars_lazy, pyarrow, duckdb
Output format control: New Format enum with long, semi_long, wide options
Bloomberg Technical Analysis (BTA): New bta() function for technical indicators
v1.0 migration infrastructure: Deprecation warnings and forward-compatible APIs

Added

Backend and Format enums for output control
set_backend(), get_backend(), set_format(), get_format() functions
bta() function for Bloomberg Technical Analysis
get_sdk_info() as replacement for getBlpapiVersion()
v1.0-compatible exception classes (BlpError, BlpSessionError, etc.)
EngineConfig dataclass and configure() function
Service and Operation enums for Bloomberg services

Changed

All API functions now support backend and format parameters
Internal pipeline uses PyArrow tables with narwhals transformations
Removed pytz dependency (using stdlib datetime.timezone)

Deprecated

connect() / disconnect() - engine auto-initializes in v1.0
getBlpapiVersion() - use get_sdk_info()
lookupSecurity() - will become blkp() in v1.0
fieldInfo() / fieldSearch() - will merge into bfld() in v1.0

This is a beta release for testing the new Arrow-first architecture before v1.0.

Full Changelog: https://github.com/alpha-xone/xbbg/compare/v0.10.3...v0.11.0b1

0.11.0b3 - see release: notes

v0.11.0b3

bqr(): New Bloomberg Quote Request function emulating Excel =BQR() for dealer quote data with broker attribution (#22)
Slow Bloomberg fields no longer timeout prematurely - TIMEOUT events handled correctly (#193)
Pipeline data types: Preserve original data types in pipeline output instead of converting to strings (#191)

0.11.0b4 - see release: notes

Added

yas(): New Bloomberg YAS (Yield Analysis) wrapper for fixed income analytics
- Provides convenient interface to Bloomberg's YAS calculator via bdp() overrides
- YieldType enum for yield calculation type (YTM=1, YTC=2)
- Supports settlement date, spread, yield, price, and benchmark overrides
- Parameter mapping: settle_dt → SETTLE_DT, yield_type → YAS_YLD_FLAG, etc.
- Available via blp.yas() or from xbbg.api.fixed_income import yas, YieldType
Treasury & SOFR futures support: Added TY, ZN, ZB, ZF, ZT, UB, TN (Treasury), SFR, SR1, SR3 (SOFR), and ED (Eurodollar) futures to assets.yml (#198)

Fixed

stream() field values: Subscribed field values are now always included in output dict (#199)
- Previously, fields not in const.LIVE_INFO (like RT_BN_SURVEY_MEDIAN) were filtered out
- Output would show FIELD='RT_BN_SURVEY_MEDIAN' but contain LAST_PRICE value instead
- Fix ensures the subscribed field's value is always present regardless of info filter
Futures symbol parsing for bdib: Fixed market_info() to correctly parse futures symbols like TYH6 → TY (#198)
- Previously failed to identify root symbol when ticker had single-digit year suffix

Full Changelog: https://github.com/alpha-xone/xbbg/compare/v0.11.0b3...v0.11.0b4

0.11.0b5 - see release: notes

Fixed

Import without blpapi installed: Fixed AttributeError when importing xbbg without blpapi installed (#200)
- Added from __future__ import annotations to defer type annotation evaluation in conn.py
- Guarded blpapi.Name() constants with is_available() check in process.py
Japan/non-US timezone fix for bdib: Fixed timezone conversion for non-US exchanges (#198)
- Bloomberg returns TRADING_DAY_START_TIME_EOD and TRADING_DAY_END_TIME_EOD in EST (America/New_York)
- These are now correctly converted to the exchange's local timezone (e.g., Asia/Tokyo for Japanese equities)
- Previously, Tokyo's 09:00-15:45 trading hours appeared as 19:00-01:45 (EST times misinterpreted as local)
- FUT_TRADING_HRS is not converted (already in exchange local time)
get_tz() no longer triggers Bloomberg lookup for timezone strings: Direct timezone strings like "America/New_York" or "UTC" are now recognized without calling Bloomberg API

Removed

Trials mechanism removed: Eliminated the retry-blocking system that caused silent failures
- The trials system tracked failed API requests and blocked future attempts after 2 failures
- This caused issues when bugs were fixed (stale entries still blocked requests)
- Users had to manually clear SQLite database entries to retry
- Pipeline caching already handles "don't re-fetch" use case properly
- Deleted xbbg/core/utils/trials.py and related test files
pandas-market-calendars dependency removed: Simplified exchange metadata resolution
- Removed PmcCalendarResolver from resolver chain
- Exchange information now sourced exclusively from Bloomberg API with local caching
- Removed pmc_extended context option (no longer needed)
- Reduces package dependencies and improves consistency

Changed

Internal class renames (backward compatible aliases provided):
- YamlMarketInfoProvider → MetadataProvider
- ExchangeYamlResolver → ExchangeMetadataResolver

Full Changelog: https://github.com/alpha-xone/xbbg/compare/v0.10.3...v0.11.0b5

0.11.0 - see release: notes

Highlights

Arrow-first pipeline: Complete rewrite of internal data processing using PyArrow for improved performance
Multi-backend support: Output to pandas, Polars, PyArrow, DuckDB, or narwhals with the new Backend enum
New API functions: bta() for technical analysis, bqr() for dealer quotes, yas() for fixed income analytics
BQL helpers: preferreds() and corporate_bonds() convenience functions
Dependency cleanup: Removed pandas-market-calendars and the trials mechanism for a leaner install
v1.0 migration path: Deprecation warnings and forward-compatible APIs prepare for the upcoming v1.0 release

Added

Arrow-first pipeline: Complete rewrite of internal data processing using PyArrow
Multi-backend support: New Backend enum supporting narwhals, pandas, polars, polars_lazy, pyarrow, duckdb
Output format control: New Format enum with long, semi_long, wide options
bta(): Bloomberg Technical Analysis function for 50+ technical indicators (#175)
bqr(): Bloomberg Quote Request function emulating Excel =BQR() for dealer quote data with broker attribution (#22)
yas(): Bloomberg YAS (Yield Analysis) wrapper for fixed income analytics with YieldType enum
preferreds(): BQL convenience function to find preferred stocks for an equity ticker
corporate_bonds(): BQL convenience function to find active corporate bonds for a ticker
set_backend(), get_backend(), set_format(), get_format() configuration functions
get_sdk_info() as replacement for deprecated getBlpapiVersion()
v1.0-compatible exception classes (BlpError, BlpSessionError, BlpRequestError, etc.)
EngineConfig dataclass and configure() function for engine configuration
Service and Operation enums for Bloomberg service URIs
Treasury & SOFR futures support: TY, ZN, ZB, ZF, ZT, UB, TN, SFR, SR1, SR3, ED futures (#198)
Comprehensive logging improvements across critical paths with better error traceability
CONTRIBUTING.md and CODE_OF_CONDUCT.md for community standards

Changed

All API functions now accept backend and format parameters
Internal pipeline uses PyArrow tables with narwhals transformations
Removed pytz dependency (using stdlib datetime.timezone)
Intraday cache now includes interval in path (#80) - different bar intervals cached separately (breaking: existing cache will miss)
Internal class renames with backward compatible aliases (YamlMarketInfoProvider → MetadataProvider)
Logging level adjustments: BBG_ROOT not set promoted to WARNING, cache timing demoted to DEBUG

Deprecated

connect() / disconnect() - engine auto-initializes in v1.0
getBlpapiVersion() - use get_sdk_info() instead
lookupSecurity() - will become blkp() in v1.0
fieldInfo() / fieldSearch() - will merge into bfld() in v1.0
bta_studies() - renamed to ta_studies() in v1.0
getPortfolio() - renamed to bport() in v1.0
Helper functions (dividend(), earning(), turnover(), adjust_ccy()) moving to xbbg.ext in v1.0
Futures/CDX utilities (fut_ticker(), active_futures(), cdx_ticker(), active_cdx()) moving to xbbg.ext in v1.0

Removed

Trials mechanism: Eliminated retry-blocking system that caused silent failures after 2 failed attempts
pandas-market-calendars dependency: Exchange info now sourced exclusively from Bloomberg API with local caching

Fixed

Import without blpapi installed: Fixed AttributeError when importing xbbg without blpapi (#200)
Japan/non-US timezone fix for bdib: Trading hours now correctly converted to exchange's local timezone (#198)
stream() field values: Subscribed field values now always included in output dict (#199)
Slow Bloomberg fields: TIMEOUT events handled correctly; requests wait for response with slow_warn_seconds warning (#193)
Pipeline data types: Preserve original data types instead of converting to strings (#191)
Futures symbol parsing: Fixed market_info() to correctly parse symbols like TYH6 → TY (#198)
get_tz() optimization: Direct timezone strings recognized without Bloomberg API call
bdtick timezone fix: Pass exchange timezone to fix blank results for non-UTC exchanges (#185)
bdtick timeout: Increased from 10s to 2 minutes for tick data requests
Extended BDS test date range to 120 days for quarterly dividends
Helper functions now work correctly with LONG format output
Logging format compliance fixes (G004, G201)

Full Changelog: https://github.com/alpha-xone/xbbg/compare/v0.10.3...v0.11.0

0.11.1 - see release: notes

Fixed

Field names now lowercase: Restored v0.10.x behavior where bdp(), bdh(), and bds() return field/column names as lowercase (#206)

Full Changelog: https://github.com/alpha-xone/xbbg/compare/v0.11.0...v0.11.1

0.11.2 - see release: notes

Added

Extended multi-backend support: Added 6 new backends matching narwhals' full backend support:
- Eager backends: cudf (GPU-accelerated via NVIDIA RAPIDS), modin (distributed pandas)
- Lazy backends: dask (parallel computing), ibis (portable DataFrame expressions), pyspark (Apache Spark), sqlframe (SQL-based DataFrames)
- Total: 13 backends (6 eager + 7 lazy)
Backend availability checking: New functions to check and validate backend availability with helpful error messages:
- is_backend_available(backend) - Check if a backend package is installed
- check_backend(backend) - Check availability with version validation, raises helpful errors
- get_available_backends() - List all currently available backends
- print_backend_status() - Diagnostic function showing all backend statuses
Format compatibility checking: New functions to validate format support per backend:
- is_format_supported(backend, format) - Check if a format works with a backend
- get_supported_formats(backend) - Get set of supported formats for a backend
- check_format_compatibility(backend, format) - Validate with helpful errors
- validate_backend_format(backend, format) - Combined validation for API functions
xbbg.ext module: New extension module for v1.0 migration containing helper functions that will be removed from blp namespace
- xbbg.ext.currency - adjust_ccy() for currency conversion
- xbbg.ext.dividends - dividend() for dividend history
- xbbg.ext.earnings - earning() for earnings breakdowns
- xbbg.ext.turnover - turnover() for trading volume
- xbbg.ext.holdings - etf_holdings(), preferreds(), corporate_bonds() BQL helpers
- xbbg.ext.futures - fut_ticker(), active_futures() for futures resolution
- xbbg.ext.cdx - cdx_ticker(), active_cdx() for CDX index resolution
- xbbg.ext.yas - yas(), YieldType for fixed income analytics
New v1.0-compatible import path: from xbbg.ext import dividend, fut_ticker, ... (no deprecation warnings)
Pandas removed as required dependency: xbbg.ext modules now use only stdlib datetime and narwhals, making pandas fully optional

Changed

Backend enum reorganized: Backends now categorized as eager (full API) vs lazy (deferred execution)
Format restrictions: WIDE format only available for eager backends (pandas, polars, pyarrow, narwhals, cudf, modin); lazy backends limited to LONG and SEMI_LONG
Version requirements updated: Minimum versions now match narwhals requirements (duckdb>=1.0, dask>=2024.1)
xbbg/markets/resolvers.py now re-exports from xbbg.ext.futures and xbbg.ext.cdx for backwards compatibility
Internal implementations moved to xbbg/ext/ module; old import paths still work with deprecation warnings

Fixed

ibis backend: Updated to use ibis.memtable() instead of deprecated con.read_in_memory()
sqlframe backend: Fixed import path to use sqlframe.duckdb.DuckDBSession

Full Changelog: https://github.com/alpha-xone/xbbg/compare/v0.11.1...v0.11.2

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