PyPI 官网下载 | xbbg-0.1.6.tar.gz

# xbbg [](https://badge.fury.io/py/xbbg) [](https://xbbg.readthedocs.io/en/latest) Bloomberg data toolkit for humans ## Requirements - Bloomberg Open API ``` pip install --index-url=https://bloomberg.bintray.com/pip/simple blpapi ``` - [pdbdp](https://github.com/matthewgilbert/pdblp) - pandas wrapper for Bloomberg Open API - numpy, pandas, pyarrow, xone, etc. ## Installation ``` pip install xbbg ``` ## Tutorial Creation of connection `conn.create_connection()` is not necessary. Quries will create new connections without live connections on the backend. Since each initiation of connection takes time, we can manually connect before we want to do multiple queries - just like examples below. ```python In[1]: from xbbg import blp, conn In[2]: conn.create_connection() ``` ``` Out[2]: (<pdblp.pdblp.BCon at 0x1c35cd0e898>, True) ``` - ``BDP`` example: ```python In[3]: blp.bdp(tickers='NVDA US Equity', flds=['Security_Name', 'GICS_Sector_Name']) ``` ``` Out[3]: ticker field value 0 NVDA US Equity Security_Name NVIDIA Corp 1 NVDA US Equity GICS_Sector_Name Information Technology ``` - ``BDH`` example: ```python 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 date 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: ```python 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 date 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 ``` - ``BDS`` example ```python In[5]: blp.bds('AAPL US Equity', 'DVD_Hist_All', DVD_Start_Dt='20180101', DVD_End_Dt='20180531') ``` ``` Out[5]: ticker field name value position 0 AAPL US Equity DVD_Hist_All Declared Date 2018-05-01 0 1 AAPL US Equity DVD_Hist_All Ex-Date 2018-05-11 0 2 AAPL US Equity DVD_Hist_All Record Date 2018-05-14 0 3 AAPL US Equity DVD_Hist_All Payable Date 2018-05-17 0 4 AAPL US Equity DVD_Hist_All Dividend Amount 0.73 0 5 AAPL US Equity DVD_Hist_All Dividend Frequency Quarter 0 6 AAPL US Equity DVD_Hist_All Dividend Type Regular Cash 0 7 AAPL US Equity DVD_Hist_All Declared Date 2018-02-01 1 8 AAPL US Equity DVD_Hist_All Ex-Date 2018-02-09 1 9 AAPL US Equity DVD_Hist_All Record Date 2018-02-12 1 10 AAPL US Equity DVD_Hist_All Payable Date 2018-02-15 1 11 AAPL US Equity DVD_Hist_All Dividend Amount 0.63 1 12 AAPL US Equity DVD_Hist_All Dividend Frequency Quarter 1 13 AAPL US Equity DVD_Hist_All Dividend Type Regular Cash 1 ``` - Intraday bars ``BDIB`` example: ```python In[6]: blp.bdib(ticker='BHP AU Equity', dt='2018-10-17').tail() ``` ``` Out[6]: open high low close volume numEvents 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 ``` - Intraday bars within market session: ```python In[7]: blp.intraday(ticker='7974 JT Equity', dt='2018-10-17', session='am_open_30').tail() ``` ``` Out[7]: open high low close volume numEvents 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 ``` - Earnings ```python In[8]: blp.earnings('AMD US Equity', Eqy_Fund_Year=2017, Number_Of_Periods=1) ``` ``` Out[8]: Level FY_2017 FY_2017_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 ```python In[9]: blp.dividend(['C US Equity', 'MS US Equity'], start_date='2018-01-01', end_date='2018-05-01') ``` ``` Out[9]: declared_date ex_date record_date payable_date dividend_amount dividend_frequency dividend_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 ``` ## 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, manually create a new connection (which will be shared by all following queries) is helpful before calling any queries: ```python from xbbg import conn conn.create_connection() ``` ## 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. ## Bloomberg Binaries - Reference: https://www.bloomberg.com/professional/support/api-library/ - Install with `pip` ``` python -m pip install --index-url=https://bloomberg.bintray.com/pip/simple blpapi ``` - Install in `pipenv` Add `source` in `Pipfile` ```toml [[source]] name = "bbg" url = "https://bloomberg.bintray.com/pip/simple" verify_ssl = true ``` Run `pipenv` ``` cd ...\xbbg\env pipenv install blpapi ```