python-blpapi

Simple wrapper for blpapi library to Pandas. Resembles BDH, BDP and BDS Bloomberg-Excel functions for historical data. Can only be used in a logged Bloomberg Terminal. Requires blpapi.

To install blpapi, run in cmd

python -m pip install --index-url=https://bcms.bloomberg.com/pip/simple/ blpapi

Examples

BDP

• Securities and fields must be passed as lists.
• Overrides (if any) must be passed as dict.
• For a complete list of fields and overrides, use FLDS when loading a security in Bloomberg.
• Output is a DataFrame. Tickers as index, fields as column names.

from bloomberg import BDP

#Securities and fields must be passed as a list
securities = ['AAPL US Equity','C US Equity']

#For a complete list of available fields, use FLDS when loading a security in Bloomberg.
fields = ['NAME', 'GICS_INDUSTRY_NAME', 'CRNCY_ADJ_PX_LAST']

df_bdp = BDP(securities, fields)

#Example with overrides
override = {'EQY_FUND_CRNCY':'EUR'}
df_bdp_override = BDP(securities, fields, override)

df_bdp

Index	NAME	GICS_INDUSTRY_NAME	CRNCY_ADJ_PX_LAST
AAPL US Equity	APPLE INC	Technology Hardware, Storage &	157.44
C US Equity	CITIGROUP INC	Banks	54.87

df_bdp_override

Index	NAME	GICS_INDUSTRY_NAME	CRNCY_ADJ_PX_LAST
AAPL US Equity	APPLE INC	Technology Hardware, Storage &	144.321
C US Equity	CITIGROUP INC	Banks	50.2979

BDH

• Securities and fields must be passed as lists.
• Overrides (if any) must be passed as dict.
• For a complete list of fields and overrides, use FLDS when loading a security in Bloomberg.
• Output is a multiindex DataFrame. Tickers as first row index, fields as second row index. Column index contains dates.
• Complete list of settings can be found in the "BLPAPI Core Developer Guide", section "15.4. BDH(): HISTORICAL “END-OF-DAY” DATA (STATIC)".

import datetime
from bloomberg import BDH

#Securities and fields must be passed as a list
securities = ['AAPL US Equity','C US Equity']

#For a complete list of available fields, use FLDS when loading a security in Bloomberg.
fields = ['PX_LAST', 'PX_VOLUME']

# Dates are used in %Y%m%d format (e.g. 20220306 for March 6th 2022).
# Suggested use is with datetime:
date_ini = datetime.date(2021,6,1).strftime("%Y%m%d")
date_end = datetime.date(2021,6,30).strftime("%Y%m%d")

#Settings
# Complete list of settings can be found in the "BLPAPI Core 
# Developer Guide", section "15.4. BDH(): HISTORICAL “END-OF-DAY” DATA (STATIC)"
settings = {"periodicityAdjustment":"ACTUAL",
            "periodicitySelection":"DAILY",
            "startDate": date_ini,
            "endDate": date_end,
            "nonTradingDayFillOption":"NON_TRADING_WEEKDAYS",
            "nonTradingDayFillMethod":"PREVIOUS_VALUE",
            } 

df_bdh = BDH(securities, fields, settings)

df_bdh.head(5)

Ticker	AAPL US Equity	AAPL US Equity	C US Equity	C US Equity
Field	PX_LAST	PX_VOLUME	PX_LAST	PX_VOLUME
date
2021-06-01	124.28	67637118.0	79.76	15450506.0
2021-06-02	125.06	59278862.0	79.86	15285588.0
2021-06-03	123.54	76229170.0	79.63	22255785.0
2021-06-04	125.89	75169343.0	79.49	13806958.0
2021-06-07	125.90	71057550.0	79.31	12670074.0

BDS

• Securities must be passed as a list. Only accepts 1 field.
• Overrides (if any) must be passed as dict.
• For a complete list of fields and overrides, use FLDS when loading a security in Bloomberg.
• If a single security is passed, output is a DataFrame. Otherwise, output is a dictionary containing DataFrames. Indices depend on securities and fields consulted.

import datetime
from bloomberg import BDS

#Securities and fields must be passed as a list:
securities = ['YCSW0042 Index', 'YCSW0141 Index']

# Only accepts 1 field.
# For a complete list of available fields, use FLDS when loading a security in Bloomberg.
fields = ['CURVE_TENOR_RATES']

df_bds = BDS(securities, fields)

#Example with overrides:
# Dates are used in %Y%m%d format (e.g. 20220306 for March 6th 2022).
# Suggested use is with datetime:
date = datetime.date(2021,6,12).strftime("%Y%m%d")
override = {'CURVE_DATE':date}
df_bds_override = BDS(securities, fields, override) 

df_bds['YCSW0042 Index'].head(5)

	Tenor	Ticker	Ask Yield	Mid Yield	Bid Yield	Last Update
0	1D	FEDL01 Index	0.080	0.080	0.080	2022-03-08
1	1W	USSO1Z BGN Curncy	0.155	0.133	0.111	2022-03-08
2	2W	USSO2Z BGN Curncy	0.237	0.230	0.224	2022-03-08
3	3W	USSO3Z BGN Curncy	0.275	0.265	0.255	2022-03-08
4	1M	USSOA BGN Curncy	0.291	0.287	0.284	2022-03-08

df_bds_override['YCSW0042 Index'].head(5)

	Tenor	Ticker	Ask Yield	Mid Yield	Bid Yield	Last Update
0	1D	FEDL01 Index	0.060	0.060	0.060	2021-06-11
1	1W	USSO1Z BGN Curncy	0.071	0.067	0.063	2021-06-11
2	2W	USSO2Z BGN Curncy	0.073	0.070	0.067	2021-06-11
3	3W	USSO3Z BGN Curncy	0.075	0.067	0.059	2021-06-11
4	1M	USSOA BGN Curncy	0.074	0.070	0.066	2021-06-11

BDHIB: BDH Intraday Bar

• Security and event must be passed as strings. Only accepts 1 security and event.
• Outputs a DataFrame object. Index are dates and times.
• Columns are:
  • OPEN: Open price of bar.
  • HIGH: Highest price of bar.
  • LOW: Lowest price of bar.
  • CLOSE: Last price of bar.
  • TICKS: Number of ticks in bar.
  • VOLUME: Volume traded in bar.
• Settings (optional): Complete list and descripion can be found in the "BLPAPI Core Developer Guide", section "15.6. BDH()/BRB(): INTRADAY BAR DATA (STATIC/SUBSCRIPTION)".

import datetime
from bloomberg import BDHIB

#Security and Event must be passed as strings.
security = "USDPEN Curncy"

#Event can be TRADE, BID or ASK.
eventType = "TRADE"

#lenght of each time-bar in minutes. Must be an integer between 1 and 1,440 (24 hours).
interval = 5

#Start and end points must be passed as datetime objects.
startDateTime = datetime.datetime(2022, 5, 25, 0, 0, 0)
endDateTime = datetime.datetime(2022, 5, 28, 0, 0, 0)

#gapFillInitialBar (optional): If true, forces an initial bar on startDateTime with last price avaiable. Default is False.
gapFillInitialBar = True

#settings (optional) must be passed as dict. Complete list and descripion of settings can be found in the BLPAPI Core Developer Guide,
#section 15.6. BDH()/BRB(): INTRADAY BAR DATA (STATIC/SUBSCRIPTION).
# settings = {'adjustmentSplit':True,
#             'adjustmentAbnormal':True,
#             'adjustmentNormal': True,
#             'adjustmentFollowDPDF': True}

df_bdhib = BDHIB(security, eventType, interval, startDateTime, endDateTime, gapFillInitialBar)

df_bdhib.head(5)

	OPEN	HIGH	LOW	CLOSE	TICKS	VOLUME
2022-05-25 00:00:00	3.7039	3.7039	3.7039	3.7039	0	0
2022-05-25 09:05:00	3.7075	3.7075	3.7030	3.7030	5	0
2022-05-25 09:10:00	3.7018	3.7051	3.7000	3.7025	12	0
2022-05-25 09:15:00	3.7025	3.7025	3.7001	3.7001	3	0
2022-05-25 09:20:00	3.7001	3.7026	3.6986	3.7026	11	0