Metadata-Version: 2.1
Name: xbbg
Version: 0.5.6
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
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
Requires-Dist: numpy (>=1.15.0)
Requires-Dist: pandas (>=0.23.4)
Requires-Dist: pyarrow (>=0.11.1)
Requires-Dist: pytz (>=2018.7)
Requires-Dist: ruamel.yaml (>=0.15.0)
Requires-Dist: pytest

# xbbg

Bloomberg data toolkit for humans

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## Features

Below are main features. Jupyter notebook examples can be found [here](https://colab.research.google.com/drive/1YVVS5AiJAQGGEECmOFAb7DNQZMOHdXLR).

- Excel compatible inputs
- Straightforward intraday bar requests
- Subscriptions

## 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 (for latest python API version `3.14.0`, this can only be downloaded thru `WAPI<GO>` in terminal).

    - 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)

- 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

_0.5.0_ - 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

```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
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
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]:
           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:

```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]:
           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:

```python
In[5]: blp.bdh(
  ...:     'AAPL US Equity', 'px_last', '20140605', '20140610',
  ...:     CshAdjNormal=False, CshAdjAbnormal=False, CapChg=False
  ...: )
```

```pydocstring
Out[5]: 
           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:

```python
In[6]: blp.bdh(
  ...:     'AAPL US Equity', 'px_last', '20140605', '20140610',
  ...:     CshAdjNormal=True, CshAdjAbnormal=True, CapChg=True
  ...: )
```

```pydocstring
Out[6]:
           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:

```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
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]:
                          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`.

- Intraday bars within market session:

```python
In[9]: blp.bdib(ticker='7974 JT Equity', dt='2018-10-17', session='am_open_30').tail()
```

```pydocstring
Out[9]:
                          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:

```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
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]:
           AAPL US Equity
                  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]:
           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.


