Bloomberg data toolkit for humans
Project description
xbbg
Bloomberg data toolkit for humans
Requirements
- Bloomberg Open API
pip install --index-url=https://bloomberg.bintray.com/pip/simple blpapi
- pdbdp - 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 - only need to do this when
there are multiple queries in the same scope.
In[1]: from xbbg import blp, conn
In[2]: conn.create_connection()
Out[2]: (<pdblp.pdblp.BCon at 0x1c35cd0e898>, True)
BDP
example:
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:
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:
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
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:
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:
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
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
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.
Specically, _xcon_
in globals()
variable.
Each time when a Bloomberg function is called:
- If there's no live connections, a new connection will be initiated
- But this new connection will be disconnected and deleted before exiting function
- If there's already a live connection, the existing connection will be used
- This live connection will be kept untouched before exiting function
Since initiating connections takes time, if multiple queries are expected, manually initiating a new connection is helpful before calling any queries:
from xbbg.conn import create_connection
create_connection()
This will create Bloomberg connection on the backend to be shared by all queries followed.
Data Storage
If ROOT_DATA_PATH
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
[[source]]
name = "bbg"
url = "https://bloomberg.bintray.com/pip/simple"
verify_ssl = true
Run pipenv
cd ...\xbbg\env
pipenv install blpapi
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