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Bloomberg data toolkit for humans

Project description

xbbg

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Bloomberg data toolkit for humans

Requirements

  • Bloomberg C++ SDK version 3.12.1 or higher:

    • Bloomberg API Library

    • Downlaod C++ Experimental Release

    • 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)

  • pdbdp - pandas wrapper for Bloomberg Open API

  • numpy, pandas, ruamel.yaml and pyarrow

Installation

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

What's New

0.1.22 - 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
ticker
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
ticker
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]:
ticker     SPX Index
field           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[4]: 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[4]:
ticker     SHCOMP Index
field              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[5]: blp.bdh(
  ...:     'AAPL US Equity', 'px_last', '20140605', '20140610',
  ...:     CshAdjNormal=False, CshAdjAbnormal=False, CapChg=False
  ...: )
Out[5]: 
ticker     AAPL US Equity
field             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[6]: blp.bdh(
  ...:     'AAPL US Equity', 'px_last', '20140605', '20140610',
  ...:     CshAdjNormal=True, CshAdjAbnormal=True, CapChg=True
  ...: )
Out[6]:
ticker     AAPL US Equity
field             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[7]: blp.bds('AAPL US Equity', 'DVD_Hist_All', DVD_Start_Dt='20180101', DVD_End_Dt='20180531')
Out[7]:
               declared_date     ex_date record_date payable_date  dividend_amount dividend_frequency dividend_type
ticker
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[8]: blp.bdib(ticker='BHP AU Equity', dt='2018-10-17').tail()
Out[8]:
ticker                    BHP AU Equity
field                              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.

  • Intraday bars within market session:
In[9]: blp.intraday(ticker='7974 JT Equity', dt='2018-10-17', session='am_open_30').tail()
Out[9]:
ticker                    7974 JT Equity
field                               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[10]: blp.earning('AMD US Equity', by='Geo', Eqy_Fund_Year=2017, Number_Of_Periods=1)
Out[10]:
                 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[11]: blp.dividend(['C US Equity', 'MS US Equity'], start_date='2018-01-01', end_date='2018-05-01')
Out[11]:
                dec_date     ex_date    rec_date    pay_date  dvd_amt dvd_freq      dvd_type
ticker
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[12]: blp.bdh('AAPL US Equity', 'px_last', '20140606', '20140609', adjust='-')
Out[12]:
ticker     AAPL US Equity
field             px_last
2014-06-06         645.57
2014-06-09          93.70
  • BDH adjusted for dividends and splits:
In[13]: blp.bdh('AAPL US Equity', 'px_last', '20140606', '20140609', adjust='all')
Out[13]:
ticker     AAPL US Equity
field             px_last
2014-06-06          85.22
2014-06-09          86.58

Optimizations

This library uses a global Bloomberg connection on the backend - more specically, _xcon_ in globals() variable. Since initiation of connections takes time, if multiple queries are expected, manual creation of a new connection (which will be shared by all following queries) is helpful before calling any queries.

  • In command line, below command is helpful:
from xbbg import blp

blp.create_connection()
  • For functions, wrapper function is recommended (connections will be destroyed afterwards):
from xbbg import blp

@blp.with_bloomberg
def query_bbg():
    """
    All queries share the same connection
    """
    blp.bdp(...)
    blp.bdh(...)
    blp.bdib(...)

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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