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popularity
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maintenance
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Popularity

Recognized

GitHub Stars: 251
Forks: 51
Contributors: 9

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package xbbg, we found that it has been starred 251 times.

Security

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Release Date: Jun 19, 2022
Direct Vulnerabilities: 0
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License

Apache-2.0

Security Policy

Yes

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Maintenance

Inactive

Commit Frequency

Open Issues: 24
Open PR: 1
Last Release: 2 years ago
Last Commit: 8 months ago

Further analysis of the maintenance status of xbbg based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for xbbg is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Sustainable

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 9
Funding: No

This project has seen only 10 or less contributors.

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Package

Python Versions Compatibility: ==3.6.*, ==3.7.*, ==3.8.*

Age: 6 years
Latest Release: 2 years ago
Dependencies: 6 Direct / 17 Total
Versions: 125
Maintainers: 1
Wheels: OS Independent

Readme

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 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 pyarrow

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

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):

> 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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xbbg dependencies

numpy pandas pyarrow pytest pytz ruamel-yaml

FAQs

What is xbbg?

Intuitive Bloomberg data API. Visit Snyk Advisor to see a full health score report for xbbg, including popularity, security, maintenance & community analysis.

Is xbbg popular?

The python package xbbg receives a total of 53,964 weekly downloads. As such, xbbg popularity was classified as a recognized. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is xbbg well maintained?

We found indications that xbbg is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is xbbg safe to use?

While scanning the latest version of xbbg, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 21 November-2024, at 11:00 (UTC).

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Intuitive Bloomberg data API For more information about how to use this package see README

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xbbg

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