Intuitive Bloomberg data API
Below are main features. Jupyter notebook examples can be found here.
Bloomberg C++ SDK version 3.12.1 or higher:
Visit Bloomberg 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:
pip install blpapi --index-url=https://bcms.bloomberg.com/pip/simple/
numpy, pandas, ruamel.yaml and pyarrowpip install xbbg
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
In [1]: from xbbg import blp
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
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
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
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
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
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.
| Docs | |
| Build |  |
| Coverage |  |
| Quality | |
| License |  |
FAQs
Intuitive Bloomberg data API
We found that xbbg demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
PyPI confirms no security flaws were exploited in the Ultralytics supply chain attack and highlights improvements for safer package publishing.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.