# xbbg
Intuitive Bloomberg data API
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## Features
Below are main features. Jupyter notebook examples can be found [here](https://colab.research.google.com/drive/1YVVS5AiJAQGGEECmOFAb7DNQZMOHdXLR).
- Excel compatible inputs
- Straightforward intraday bar requests
- Subscriptions
## 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://bcms.bloomberg.com/pip/simple/
```
- `numpy`, `pandas`, `ruamel.yaml` and `pyarrow`
## Installation
```cmd
pip install xbbg
```
## What's New
_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
## Tutorial
```python
In [1]: from xbbg import blp
```
### Basics
- ``BDP`` example:
```python
In [2]: 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
```
- ``BDP`` with overrides:
```python
In [3]: blp.bdp('AAPL US Equity', 'Eqy_Weighted_Avg_Px', VWAP_Dt='20181224')
```
```pydocstring
Out[3]:
eqy_weighted_avg_px
AAPL US Equity 148.75
```
- ``BDH`` example:
```python
In [4]: 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
```
- ``BDH`` example with Excel compatible inputs:
```python
In [5]: 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
```
- ``BDH`` without adjustment for dividends and splits:
```python
In [6]: blp.bdh(
...: 'AAPL US Equity', 'px_last', '20140605', '20140610',
...: CshAdjNormal=False, CshAdjAbnormal=False, CapChg=False
...: )
```
```pydocstring
Out[6]:
AAPL US Equity
px_last
2014-06-05 647.35
2014-06-06 645.57
2014-06-09 93.70
2014-06-10 94.25
```
- ``BDH`` adjusted for dividends and splits:
```python
In [7]: blp.bdh(
...: 'AAPL US Equity', 'px_last', '20140605', '20140610',
...: CshAdjNormal=True, CshAdjAbnormal=True, CapChg=True
...: )
```
```pydocstring
Out[7]:
AAPL US Equity
px_last
2014-06-05 85.45
2014-06-06 85.22
2014-06-09 86.58
2014-06-10 87.09
```
- ``BDS`` example:
```python
In [8]: 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
```
- Intraday bars ``BDIB`` example:
```python
In [9]: 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
```
Above example works because 1) `AU` in equity ticker is mapped to `EquityAustralia` in
`markets/assets.yml`, and 2) `EquityAustralia` is defined in `markets/exch.yml`.
To add new mappings, define `BBG_ROOT` in sys path and add `assets.yml` and
`exch.yml` under `BBG_ROOT/markets`.
*New in 0.6.6* - if exchange is defined in `/xbbg/markets/exch.yml`, can use `ref` to look for
relevant exchange market hours. Both `ref='ES1 Index'` and `ref='CME'` work for this example:
```python
In [10]: 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
```
- Intraday bars within market session:
```python
In [11]: 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
```
- Corporate earnings:
```python
In [12]: 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
```
- Dividends:
```python
In [13]: 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
```
-----
*New in 0.1.17* - Dividend adjustment can be simplified to one parameter `adjust`:
- ``BDH`` without adjustment for dividends and splits:
```python
In [14]: blp.bdh('AAPL US Equity', 'px_last', '20140606', '20140609', adjust='-')
```
```pydocstring
Out[14]:
AAPL US Equity
px_last
2014-06-06 645.57
2014-06-09 93.70
```
- ``BDH`` adjusted for dividends and splits:
```python
In [15]: 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
```
### Data Storage
If `BBG_ROOT` is provided in `os.environ`, data can be saved locally.
By default, local storage is preferred than Bloomberg for all queries.
Noted that 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.
## Star History
[](https://star-history.com/#alpha-xone/xbbg&Date)
| | |
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Docs | [](https://xbbg.readthedocs.io/) |
| Build | [](https://github.com/alpha-xone/xbbg/actions) |
| | [](https://dev.azure.com/alpha-xone/xbbg/_build) |
| Coverage | [](https://codecov.io/gh/alpha-xone/xbbg) |
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| | [](https://codebeat.co/projects/github-com-alpha-xone-xbbg-main) |
| License | [](https://github.com/alpha-xone/xbbg/blob/main/LICENSE) |
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"description": "\n\n# xbbg\n\nIntuitive Bloomberg data API\n\n[](https://badge.fury.io/py/xbbg)\n[](https://badge.fury.io/py/xbbg)\n[](https://pypistats.org/packages/xbbg)\n[](https://gitter.im/xbbg/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)\n\n[](https://www.buymeacoffee.com/Lntx29Oof)\n\n## Features\n\nBelow are main features. Jupyter notebook examples can be found [here](https://colab.research.google.com/drive/1YVVS5AiJAQGGEECmOFAb7DNQZMOHdXLR).\n\n- Excel compatible inputs\n- Straightforward intraday bar requests\n- Subscriptions\n\n## Requirements\n\n- Bloomberg C++ SDK version 3.12.1 or higher:\n\n - Visit [Bloomberg API Library](https://www.bloomberg.com/professional/support/api-library/) and download C++ Supported Release\n\n - In the `bin` folder of downloaded zip file, copy `blpapi3_32.dll` and `blpapi3_64.dll` to Bloomberg `BLPAPI_ROOT` folder (usually `blp/DAPI`)\n\n- Bloomberg official Python API:\n\n```cmd\npip install blpapi --index-url=https://bcms.bloomberg.com/pip/simple/\n```\n\n- `numpy`, `pandas`, `ruamel.yaml` and `pyarrow`\n\n## Installation\n\n```cmd\npip install xbbg\n```\n\n## What's New\n\n_0.7.7a2_ - Custom `config` and etc. for reference exchange (author `hceh`)\n\n_0.7.6a2_ - Use `blp.connect` for alternative Bloomberg connection (author `anxl2008`)\n\n_0.7.2_ - Use `async` for live data feeds\n\n_0.7.0_ - `bdh` preserves columns orders (both tickers and flds).\n`timeout` argument is available for all queries - `bdtick` usually takes longer to respond -\ncan use `timeout=1000` for example if keep getting empty DataFrame.\n\n_0.6.6_ - Add flexibility to use reference exchange as market hour definition\n(so that it's not necessary to add `.yml` for new tickers, provided that the exchange was defined\nin `/xbbg/markets/exch.yml`). See example of `bdib` below for more details.\n\n_0.6.0_ - Speed improvements and tick data availablity\n\n_0.5.0_ - Rewritten library to add subscription, BEQS, simplify interface and remove dependency of `pdblp`\n\n_0.1.22_ - Remove PyYAML dependency due to security vulnerability\n\n_0.1.17_ - Add `adjust` argument in `bdh` for easier dividend / split adjustments\n\n## Tutorial\n\n```python\nIn [1]: from xbbg import blp\n```\n\n### Basics\n\n- ``BDP`` example:\n\n```python\nIn [2]: blp.bdp(tickers='NVDA US Equity', flds=['Security_Name', 'GICS_Sector_Name'])\n```\n\n```pydocstring\nOut[2]:\n security_name gics_sector_name\nNVDA US Equity NVIDIA Corp Information Technology\n```\n\n- ``BDP`` with overrides:\n\n```python\nIn [3]: blp.bdp('AAPL US Equity', 'Eqy_Weighted_Avg_Px', VWAP_Dt='20181224')\n```\n\n```pydocstring\nOut[3]:\n eqy_weighted_avg_px\nAAPL US Equity 148.75\n```\n\n- ``BDH`` example:\n\n```python\nIn [4]: blp.bdh(\n ...: tickers='SPX Index', flds=['high', 'low', 'last_price'],\n ...: start_date='2018-10-10', end_date='2018-10-20',\n ...: )\n```\n\n```pydocstring\nOut[4]:\n SPX Index\n high low last_price\n2018-10-10 2,874.02 2,784.86 2,785.68\n2018-10-11 2,795.14 2,710.51 2,728.37\n2018-10-12 2,775.77 2,729.44 2,767.13\n2018-10-15 2,775.99 2,749.03 2,750.79\n2018-10-16 2,813.46 2,766.91 2,809.92\n2018-10-17 2,816.94 2,781.81 2,809.21\n2018-10-18 2,806.04 2,755.18 2,768.78\n2018-10-19 2,797.77 2,760.27 2,767.78\n```\n\n- ``BDH`` example with Excel compatible inputs:\n\n```python\nIn [5]: blp.bdh(\n ...: tickers='SHCOMP Index', flds=['high', 'low', 'last_price'],\n ...: start_date='2018-09-26', end_date='2018-10-20',\n ...: Per='W', Fill='P', Days='A',\n ...: )\n```\n\n```pydocstring\nOut[5]:\n SHCOMP Index\n high low last_price\n2018-09-28 2,827.34 2,771.16 2,821.35\n2018-10-05 2,827.34 2,771.16 2,821.35\n2018-10-12 2,771.94 2,536.66 2,606.91\n2018-10-19 2,611.97 2,449.20 2,550.47\n```\n\n- ``BDH`` without adjustment for dividends and splits:\n\n```python\nIn [6]: blp.bdh(\n ...: 'AAPL US Equity', 'px_last', '20140605', '20140610',\n ...: CshAdjNormal=False, CshAdjAbnormal=False, CapChg=False\n ...: )\n```\n\n```pydocstring\nOut[6]:\n AAPL US Equity\n px_last\n2014-06-05 647.35\n2014-06-06 645.57\n2014-06-09 93.70\n2014-06-10 94.25\n```\n\n- ``BDH`` adjusted for dividends and splits:\n\n```python\nIn [7]: blp.bdh(\n ...: 'AAPL US Equity', 'px_last', '20140605', '20140610',\n ...: CshAdjNormal=True, CshAdjAbnormal=True, CapChg=True\n ...: )\n```\n\n```pydocstring\nOut[7]:\n AAPL US Equity\n px_last\n2014-06-05 85.45\n2014-06-06 85.22\n2014-06-09 86.58\n2014-06-10 87.09\n```\n\n- ``BDS`` example:\n\n```python\nIn [8]: blp.bds('AAPL US Equity', 'DVD_Hist_All', DVD_Start_Dt='20180101', DVD_End_Dt='20180531')\n```\n\n```pydocstring\nOut[8]:\n declared_date ex_date record_date payable_date dividend_amount dividend_frequency dividend_type\nAAPL US Equity 2018-05-01 2018-05-11 2018-05-14 2018-05-17 0.73 Quarter Regular Cash\nAAPL US Equity 2018-02-01 2018-02-09 2018-02-12 2018-02-15 0.63 Quarter Regular Cash\n```\n\n- Intraday bars ``BDIB`` example:\n\n```python\nIn [9]: blp.bdib(ticker='BHP AU Equity', dt='2018-10-17').tail()\n```\n\n```pydocstring\nOut[9]:\n BHP AU Equity\n open high low close volume num_trds\n2018-10-17 15:56:00+11:00 33.62 33.65 33.62 33.64 16660 126\n2018-10-17 15:57:00+11:00 33.65 33.65 33.63 33.64 13875 156\n2018-10-17 15:58:00+11:00 33.64 33.65 33.62 33.63 16244 159\n2018-10-17 15:59:00+11:00 33.63 33.63 33.61 33.62 16507 167\n2018-10-17 16:10:00+11:00 33.66 33.66 33.66 33.66 1115523 216\n```\n\nAbove example works because 1) `AU` in equity ticker is mapped to `EquityAustralia` in\n`markets/assets.yml`, and 2) `EquityAustralia` is defined in `markets/exch.yml`.\nTo add new mappings, define `BBG_ROOT` in sys path and add `assets.yml` and\n`exch.yml` under `BBG_ROOT/markets`.\n\n*New in 0.6.6* - if exchange is defined in `/xbbg/markets/exch.yml`, can use `ref` to look for\nrelevant exchange market hours. Both `ref='ES1 Index'` and `ref='CME'` work for this example:\n\n```python\nIn [10]: blp.bdib(ticker='ESM0 Index', dt='2020-03-20', ref='ES1 Index').tail()\n```\n\n```pydocstring\nout[10]:\n ESM0 Index\n open high low close volume num_trds value\n2020-03-20 16:55:00-04:00 2,260.75 2,262.25 2,260.50 2,262.00 412 157 931,767.00\n2020-03-20 16:56:00-04:00 2,262.25 2,267.00 2,261.50 2,266.75 812 209 1,838,823.50\n2020-03-20 16:57:00-04:00 2,266.75 2,270.00 2,264.50 2,269.00 1136 340 2,576,590.25\n2020-03-20 16:58:00-04:00 2,269.25 2,269.50 2,261.25 2,265.75 1077 408 2,439,276.00\n2020-03-20 16:59:00-04:00 2,265.25 2,272.00 2,265.00 2,266.50 1271 378 2,882,978.25\n```\n\n- Intraday bars within market session:\n\n```python\nIn [11]: blp.bdib(ticker='7974 JT Equity', dt='2018-10-17', session='am_open_30').tail()\n```\n\n```pydocstring\nOut[11]:\n 7974 JT Equity\n open high low close volume num_trds\n2018-10-17 09:27:00+09:00 39,970.00 40,020.00 39,970.00 39,990.00 10800 44\n2018-10-17 09:28:00+09:00 39,990.00 40,020.00 39,980.00 39,980.00 6300 33\n2018-10-17 09:29:00+09:00 39,970.00 40,000.00 39,960.00 39,970.00 3300 21\n2018-10-17 09:30:00+09:00 39,960.00 40,010.00 39,950.00 40,000.00 3100 19\n2018-10-17 09:31:00+09:00 39,990.00 40,000.00 39,980.00 39,990.00 2000 15\n```\n\n- Corporate earnings:\n\n```python\nIn [12]: blp.earning('AMD US Equity', by='Geo', Eqy_Fund_Year=2017, Number_Of_Periods=1)\n```\n\n```pydocstring\nOut[12]:\n level fy2017 fy2017_pct\nAsia-Pacific 1.00 3,540.00 66.43\n China 2.00 1,747.00 49.35\n Japan 2.00 1,242.00 35.08\n Singapore 2.00 551.00 15.56\nUnited States 1.00 1,364.00 25.60\nEurope 1.00 263.00 4.94\nOther Countries 1.00 162.00 3.04\n```\n\n- Dividends:\n\n```python\nIn [13]: blp.dividend(['C US Equity', 'MS US Equity'], start_date='2018-01-01', end_date='2018-05-01')\n```\n\n```pydocstring\nOut[13]:\n dec_date ex_date rec_date pay_date dvd_amt dvd_freq dvd_type\nC US Equity 2018-01-18 2018-02-02 2018-02-05 2018-02-23 0.32 Quarter Regular Cash\nMS US Equity 2018-04-18 2018-04-27 2018-04-30 2018-05-15 0.25 Quarter Regular Cash\nMS US Equity 2018-01-18 2018-01-30 2018-01-31 2018-02-15 0.25 Quarter Regular Cash\n```\n\n-----\n\n*New in 0.1.17* - Dividend adjustment can be simplified to one parameter `adjust`:\n\n- ``BDH`` without adjustment for dividends and splits:\n\n```python\nIn [14]: blp.bdh('AAPL US Equity', 'px_last', '20140606', '20140609', adjust='-')\n```\n\n```pydocstring\nOut[14]:\n AAPL US Equity\n px_last\n2014-06-06 645.57\n2014-06-09 93.70\n```\n\n- ``BDH`` adjusted for dividends and splits:\n\n```python\nIn [15]: blp.bdh('AAPL US Equity', 'px_last', '20140606', '20140609', adjust='all')\n```\n\n```pydocstring\nOut[15]:\n AAPL US Equity\n px_last\n2014-06-06 85.22\n2014-06-09 86.58\n```\n\n### Data Storage\n\nIf `BBG_ROOT` is provided in `os.environ`, data can be saved locally.\nBy default, local storage is preferred than Bloomberg for all queries.\n\nNoted that local data usage must be compliant with Bloomberg Datafeed Addendum\n(full description in `DAPI<GO>`):\n\n> To access Bloomberg data via the API (and use that data in Microsoft Excel),\n> your company must sign the 'Datafeed Addendum' to the Bloomberg Agreement.\n> This legally binding contract describes the terms and conditions of your use\n> of the data and information available via the API (the \"Data\").\n> The most fundamental requirement regarding your use of Data is that it cannot\n> leave the local PC you use to access the BLOOMBERG PROFESSIONAL service.\n\n## Star History\n\n[](https://star-history.com/#alpha-xone/xbbg&Date)\n\n| | |\n| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Docs | [](https://xbbg.readthedocs.io/) |\n| Build | [](https://github.com/alpha-xone/xbbg/actions) |\n| | [](https://dev.azure.com/alpha-xone/xbbg/_build) |\n| Coverage | [](https://codecov.io/gh/alpha-xone/xbbg) |\n| Quality | [](https://www.codacy.com/gh/alpha-xone/xbbg/) |\n| | [](https://www.codefactor.io/repository/github/alpha-xone/xbbg) |\n| | [](https://codebeat.co/projects/github-com-alpha-xone-xbbg-main) |\n| License | [](https://github.com/alpha-xone/xbbg/blob/main/LICENSE) |\n\n\n",
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