Metadata-Version: 2.1
Name: xbbg
Version: 0.1.27
Summary: Bloomberg data toolkit for humans
Home-page: https://github.com/alpha-xone/xbbg
Author: Alpha x1
Author-email: alpha.xone@outlook.com
License: Apache
Description: # xbbg
        
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        Bloomberg data toolkit for humans
        
        ## Requirements
        
        - Bloomberg C++ SDK version 3.12.1 or higher:
        
            - [Bloomberg API Library](https://www.bloomberg.com/professional/support/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](https://github.com/matthewgilbert/pdblp) - pandas wrapper for Bloomberg Open API
        
        - numpy, pandas, ruamel.yaml and pyarrow
        
        ## Installation
        
        ```cmd
        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
        
        ```python
        In[1]: from xbbg import blp
        ```
        
        ### Basics
        
        - ``BDP`` example:
        
        ```python
        In[2]: blp.bdp(tickers='NVDA US Equity', flds=['Security_Name', 'GICS_Sector_Name'])
        ```
        
        ```pydocstring
        Out[2]:
                       security_name        gics_sector_name
        ticker
        NVDA US Equity   NVIDIA Corp  Information Technology
        ```
        
        - ``BDP`` with overrides:
        
        ```python
        In[3]: blp.bdp('AAPL US Equity', 'Eqy_Weighted_Avg_Px', VWAP_Dt='20181224')
        ```
        
        ```pydocstring
        Out[3]: 
                        eqy_weighted_avg_px
        ticker
        AAPL US Equity               148.75
        ```
        
        - ``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',
          ...: )
        ```
        
        ```pydocstring
        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:
        
        ```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',
          ...: )
        ```
        
        ```pydocstring
        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:
        
        ```python
        In[5]: blp.bdh(
          ...:     'AAPL US Equity', 'px_last', '20140605', '20140610',
          ...:     CshAdjNormal=False, CshAdjAbnormal=False, CapChg=False
          ...: )
        ```
        
        ```pydocstring
        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:
        
        ```python
        In[6]: blp.bdh(
          ...:     'AAPL US Equity', 'px_last', '20140605', '20140610',
          ...:     CshAdjNormal=True, CshAdjAbnormal=True, CapChg=True
          ...: )
        ```
        
        ```pydocstring
        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:
        
        ```python
        In[7]: blp.bds('AAPL US Equity', 'DVD_Hist_All', DVD_Start_Dt='20180101', DVD_End_Dt='20180531')
        ```
        
        ```pydocstring
        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:
        
        ```python
        In[8]: blp.bdib(ticker='BHP AU Equity', dt='2018-10-17').tail()
        ```
        
        ```pydocstring
        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:
        
        ```python
        In[9]: blp.intraday(ticker='7974 JT Equity', dt='2018-10-17', session='am_open_30').tail()
        ```
        
        ```pydocstring
        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:
        
        ```python
        In[10]: blp.earning('AMD US Equity', by='Geo', Eqy_Fund_Year=2017, Number_Of_Periods=1)
        ```
        
        ```pydocstring
        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:
        
        ```python
        In[11]: blp.dividend(['C US Equity', 'MS US Equity'], start_date='2018-01-01', end_date='2018-05-01')
        ```
        
        ```pydocstring
        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:
        
        ```python
        In[12]: blp.bdh('AAPL US Equity', 'px_last', '20140606', '20140609', adjust='-')
        ```
        
        ```pydocstring
        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:
        
        ```python
        In[13]: blp.bdh('AAPL US Equity', 'px_last', '20140606', '20140609', adjust='all')
        ```
        
        ```pydocstring
        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:
        
        ```python
        from xbbg import blp
        
        blp.create_connection()
        ```
        
        - For functions, wrapper function is recommended (connections will be destroyed afterwards):
        
        ```python
        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.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
