Metadata-Version: 2.4
Name: xbbg
Version: 0.7.11
Summary: Intuitive Bloomberg data API
Author-email: Alpha x1 <alpha.xone@outlook.com>
License-Expression: Apache-2.0
Project-URL: Homepage, https://github.com/alpha-xone/xbbg
Project-URL: Documentation, https://xbbg.readthedocs.io/
Project-URL: Source, https://github.com/alpha-xone/xbbg
Project-URL: Issues, https://github.com/alpha-xone/xbbg/issues
Project-URL: Logo, https://raw.githubusercontent.com/alpha-xone/xbbg/main/docs/xbbg.png
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Python: <3.15,>=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=2.2.6
Requires-Dist: pandas<=2.3.0,>=2.0
Requires-Dist: pandas-market-calendars>=5.1.3
Requires-Dist: pyarrow>=22.0.0
Requires-Dist: pytest>=8.4.2
Requires-Dist: python-stdnum>=2.1
Requires-Dist: pytz>=2025.2
Requires-Dist: ruamel.yaml>=0.18.16
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Dynamic: license-file

<!-- markdownlint-disable MD033 MD041 -->
<div align="center">

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<!-- markdownlint-disable MD036 -->
**xbbg: An intuitive Bloomberg API for Python**
<!-- markdownlint-enable MD036 -->

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**Quick Links:** [Documentation](https://xbbg.readthedocs.io/) • [Installation](#installation) • [Quickstart](#quickstart) • [Examples](#examples) • [Source](https://github.com/alpha-xone/xbbg) • [Issues](https://github.com/alpha-xone/xbbg/issues)

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<!-- markdownlint-enable MD033 MD041 -->

---

<!-- xbbg:latest-release-start -->
**Latest release:** xbbg==0.7.10 ([release notes](https://github.com/alpha-xone/xbbg/releases/tag/xbbg%3D%3D0.7.10))
<!-- xbbg:latest-release-end -->

## Overview

xbbg is the **most comprehensive and intuitive Bloomberg API wrapper for Python**, providing a Pythonic interface with Excel-compatible inputs, straightforward intraday bar requests, and real-time subscriptions. All functions return pandas DataFrames for seamless integration with your data workflow.

**Why xbbg?**

- 🎯 **Complete API Coverage**: Reference, historical, intraday bars, tick data, real-time subscriptions, equity screening (BEQS), and BQL support
- 📊 **Excel-Compatible**: Use familiar Excel date formats and field names - no learning curve
- ⚡ **Built-in Caching**: Automatic Parquet-based local storage reduces API calls and speeds up workflows
- 🔧 **Rich Utilities**: Currency conversion, futures/CDX resolvers, exchange-aware market hours, and more
- 🚀 **Modern & Active**: Python 3.10+ support with regular updates and active maintenance
- 💡 **Intuitive Design**: Simple, consistent API (`bdp`, `bdh`, `bdib`, etc.) that feels natural to use

See [`examples/xbbg_jupyter_examples.ipynb`](examples/xbbg_jupyter_examples.ipynb) for interactive tutorials and examples.

## Why Choose xbbg?

xbbg stands out as the most comprehensive and user-friendly Bloomberg API wrapper for Python. Here's how it compares to alternatives:

| Feature | xbbg | pdblp | blp | polars-bloomberg |
|---------|------|-------|-----|------------------|
| Reference Data (BDP/BDS) | ✅ | ✅ | ✅ | ✅ |
| Historical Data (BDH) | ✅ | ✅ | ✅ | ✅ |
| Intraday Bars (BDIB) | ✅ | ❌ | ❌ | ❌ |
| Tick Data | ✅ | ❌ | ❌ | ❌ |
| Real-time Subscriptions | ✅ | ❌ | ❌ | ❌ |
| Equity Screening (BEQS) | ✅ | ❌ | ❌ | ❌ |
| BQL Support | ✅ | ❌ | ❌ | ❌ |
| Excel-compatible inputs | ✅ | ❌ | ❌ | ❌ |
| Sub-minute intervals | ✅ | ❌ | ❌ | ❌ |
| Local Parquet caching | ✅ | ❌ | ❌ | ❌ |
| Currency conversion | ✅ | ❌ | ❌ | ❌ |
| Futures/CDX resolvers | ✅ | ❌ | ❌ | ❌ |
| Active development | ✅ | ❌[^1] | ✅ | ✅ |
| Modern Python (3.10+) | ✅ | ✅ | ✅ | 3.12+ |
| DataFrame Library | pandas | pandas | pandas | Polars |

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

**Key Advantages:**

- 🎯 **Most Complete API**: Covers reference, historical, intraday, tick, real-time, screening, and BQL
- 📊 **Excel Compatibility**: Use familiar Excel date formats and field names
- ⚡ **Performance**: Built-in Parquet caching reduces API calls and speeds up workflows
- 🔧 **Rich Utilities**: Currency conversion, futures resolvers, and more out of the box
- 🚀 **Modern & Active**: Python 3.10+ support with regular updates and active maintenance
- 💡 **Intuitive Design**: Simple, consistent API that feels natural to use

## Supported Functionality

| Function | Description | Key Features |
|----------|-------------|--------------|
| 📊 **Reference Data** | | |
| `bdp()` | Single point-in-time reference data | Multiple tickers/fields, Excel dates, overrides |
| `bds()` | Bulk/block data (multi-row) | Portfolio data, date filtering, nested structures |
| 📈 **Historical Data** | | |
| `bdh()` | End-of-day historical data | Date ranges, frequencies, dividend/split adjustments |
| `dividend()` | Dividend & split history | Multiple types, date ranges, projected dividends |
| `earning()` | Corporate earnings breakdowns | Geographic/product breakdowns, fiscal periods |
| `turnover()` | Trading volume & turnover | Currency conversion, multi-currency support |
| ⏱️ **Intraday Data** | | |
| `bdib()` | Intraday bar data | Minute/second intervals, sub-minute bars, sessions |
| `bdtick()` | Tick-by-tick data | Event types, condition codes, exchange/broker codes |
| 🔍 **Screening & Queries** | | |
| `beqs()` | Bloomberg Equity Screening | Custom criteria, private/public screens |
| `bql()` | Bloomberg Query Language | SQL-like syntax, complex transformations |
| 📡 **Real-time** | | |
| `live()` | Real-time market data | Async updates, context manager support |
| `subscribe()` | Real-time subscriptions | Field-level subscriptions, event callbacks |
| 🔧 **Utilities** | | |
| `adjust_ccy()` | Currency conversion | Multi-currency, historical FX rates |
| `active_futures()` | Active futures contracts | Volume-based selection, date-aware resolution |
| `fut_ticker()` | Futures ticker resolution | Generic to specific contract mapping |
| `cdx_ticker()` | CDX index ticker resolution | Index series mapping |
| `active_cdx()` | Active CDX contracts | Series resolution, volume-based selection |

**Additional Features**: Local caching (Parquet), configurable logging, timezone support, exchange-aware market hours, batch processing, standardized column mapping

## Requirements

- Bloomberg C++ SDK version 3.12.1 or higher:

  - Visit [Bloomberg API Library](https://www.bloomberg.com/professional/support/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:

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

- `numpy`, `pandas`, `ruamel.yaml` and `pyarrow`

## Installation

```cmd
pip install xbbg
```

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

## Quickstart

```python
from xbbg import blp

# Reference data (BDP)
ref = blp.bdp(tickers='AAPL US Equity', flds=['Security_Name', 'GICS_Sector_Name'])
print(ref)

# Historical data (BDH)
hist = blp.bdh('SPX Index', ['high', 'low', 'last_price'], '2021-01-01', '2021-01-05')
print(hist.tail())
```

## Examples

### 📊 Reference Data

```python
from xbbg import blp

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

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

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

```pydocstring
Out[3]:
                eqy_weighted_avg_px
AAPL US Equity               148.75
```

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

```pydocstring
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
```

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

```pydocstring
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
```

### 📈 Historical Data

```python
# 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',
)
```

```pydocstring
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
```

```python
# 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',
)
```

```pydocstring
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
```

```python
# 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',
)
```

```pydocstring
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
```

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

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

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

```pydocstring
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
```

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

```pydocstring
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

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

```pydocstring
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
```

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

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

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

```pydocstring
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
```

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

```pydocstring
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
```

```python
# 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()
```

```pydocstring
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
```

```python
# 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.

```python
# 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')
```

```pydocstring
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
```

```python
# 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')
```

```pydocstring
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

```python
# Bloomberg Query Language (BQL)
# blp.bql("get(px_last for('AAPL US Equity'))")  # doctest: +SKIP

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

### 📡 Real-time

```python
# 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
```

### 🔧 Utilities

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

```pydocstring
Out[15]:
'ESH24 Index'
```

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

```pydocstring
Out[16]:
'ESH24 Index'
```

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

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

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

```pydocstring
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 Bloomberg for all queries.

**Setup**:

```python
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 Bloomberg API 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.

## Development

### Setup

Create venv and install dependencies:

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

### Adding Dependencies

```cmd
uv add <package>
```

### Running Tests and Linting

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

### Building

```cmd
uv run python -m build
```

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

### Documentation

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

## Contributing

- Issues and feature requests: please open an issue on the repository.
- Pull requests welcome. Run lint and tests locally:

```cmd
uv sync --locked --extra dev --extra test
uv run ruff check xbbg
uv run pytest --doctest-modules -q
```

## Links

- [PyPI](https://pypi.org/project/xbbg/)
- [Documentation](https://xbbg.readthedocs.io/)
- [Source](https://github.com/alpha-xone/xbbg)
- Security policy: see `SECURITY.md`

## What's New

<!-- xbbg:changelog-start -->
_0.7.10_ - see release: [notes](https://github.com/alpha-xone/xbbg/releases/tag/xbbg%3D%3D0.7.10)

What's Changed

- Migrate to uv + PEP 621; modernize CI and blpapi index by @kaijensen55 in #124

New Contributors

- @kaijensen55 made their first contribution in #124

Full Changelog: v0.7.9...xbbg==0.7.10
<!-- xbbg:changelog-end -->

_0.7.7a2_ - Custom `config` and etc. for reference exchange (author `hceh`)

_0.7.6a2_ - Use `blp.connect` for alternative Bloomberg connection (author `anxl2008`)

_0.7.2_ - Use `async` for live data feeds

_0.7.0_ - `bdh` preserves columns orders (both tickers and flds).
`timeout` argument is available for all queries - `bdtick` usually takes longer to respond -
can use `timeout=1000` for example if keep getting empty DataFrame.

_0.6.6_ - Add flexibility to use reference exchange as market hour definition
(so that it's not necessary to add `.yml` for new tickers, provided that the exchange was defined
in `/xbbg/markets/exch.yml`). See example of `bdib` below for more details.

_0.6.0_ - Speed improvements and tick data availablity

_0.5.0_ - Rewritten library to add subscription, BEQS, simplify interface and remove dependency of `pdblp`

_0.1.22_ - Remove PyYAML dependency due to security vulnerability

_0.1.17_ - Add `adjust` argument in `bdh` for easier dividend / split adjustments

## Star History

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