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Intuitive Bloomberg data API

Project description

xbbg

Intuitive Bloomberg data API

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Features

Below are main features. Jupyter notebook examples can be found here.

  • Excel compatible inputs
  • Straightforward intraday bar requests
  • Subscriptions

Requirements

  • Bloomberg C++ SDK version 3.12.1 or higher:

    • Bloomberg API Library

    • Downlaod C++ Experimental Release (for latest python API version 3.14.0, this can only be downloaded thru WAPI<GO> in terminal).

    • Copy blpapi3_32.dll and blpapi3_64.dll under bin folder to Bloomberg BLPAPI_ROOT folder, normally blp/DAPI

  • Bloomberg Open API (need to install manually as shown below)

  • numpy, pandas, ruamel.yaml and pyarrow

Installation

pip install blpapi --index-url=https://bloomberg.bintray.com/pip/simple
pip install xbbg

What's New

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

In [1]: from xbbg import blp

Basics

  • BDP example:
In [2]: 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
  • BDP with overrides:
In [3]: blp.bdp('AAPL US Equity', 'Eqy_Weighted_Avg_Px', VWAP_Dt='20181224')
Out[3]: 
                eqy_weighted_avg_px
AAPL US Equity               148.75
  • BDH example:
In [4]: 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
  • BDH example with Excel compatible inputs:
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',
   ...: )
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:
In [6]: blp.bdh(
   ...:     'AAPL US Equity', 'px_last', '20140605', '20140610',
   ...:     CshAdjNormal=False, CshAdjAbnormal=False, CapChg=False
   ...: )
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:
In [7]: blp.bdh(
   ...:     'AAPL US Equity', 'px_last', '20140605', '20140610',
   ...:     CshAdjNormal=True, CshAdjAbnormal=True, CapChg=True
   ...: )
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:
In [8]: 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
  • Intraday bars BDIB example:
In [9]: 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

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:

In [10]: 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
  • Intraday bars within market session:
In [11]: 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
  • Corporate earnings:
In [12]: 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
  • Dividends:
In [13]: 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

New in 0.1.17 - Dividend adjustment can be simplified to one parameter adjust:

  • BDH without adjustment for dividends and splits:
In [14]: blp.bdh('AAPL US Equity', 'px_last', '20140606', '20140609', adjust='-')
Out[14]:
           AAPL US Equity
                  px_last
2014-06-06         645.57
2014-06-09          93.70
  • BDH adjusted for dividends and splits:
In [15]: 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

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.

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