Intuitive Bloomberg data API
Project description
xbbg
Intuitive Bloomberg data API
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:
-
Visit Bloomberg API Library and download C++ Supported Release
-
In the
binfolder of downloaded zip file, copyblpapi3_32.dllandblpapi3_64.dllto BloombergBLPAPI_ROOTfolder (usuallyblp/DAPI)
-
-
Bloomberg official Python API:
pip install blpapi --index-url=https://bcms.bloomberg.com/pip/simple/
numpy,pandas,ruamel.yamlandpyarrow
Installation
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
In [1]: from xbbg import blp
Basics
BDPexample:
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
BDPwith 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
BDHexample:
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
BDHexample 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
BDHwithout 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
BDHadjusted 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
BDSexample:
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
BDIBexample:
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:
BDHwithout 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
BDHadjusted 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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