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Added support for the Bloomberg equity screening BEQS() function, wit… #9

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Added support for the Bloomberg equity screening BEQS() function, wit… #9

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172 changes: 172 additions & 0 deletions pdblp/pdblp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -424,3 +424,175 @@ def stop(self):
Close the blp session
"""
self.session.stop()

def beqs(self, screen_name, screen_type='PRIVATE', group='General', language_id='ENGLISH', asof_date=None):
"""
The beqs() function allows users to retrieve a table of data for a selected equity screen
that was created using the Equity Screening (EQS) function.
See https://data.bloomberglp.com/professional/sites/4/BLPAPI-Core-Developer-Guide.pdf.
Make a Beqs request, get tickers and fields, return long
pandas Dataframe with columns [ticker, field, value]

Parameters
----------
screen_name: string
String corresponding to name of screen
screen_type: 'GLOBAL' or 'PRIVATE'
Indicates that the screen is a Bloomberg-created sample screen (GLOBAL) or
a saved custom screen that users have created (PRIVATE).
group: string
If the screens are organized into groups, allows users to define the name of the group that
contains the screen. If the users use a Bloomberg sample screen,
they must use this parameter to specify the name of the folder in which the screen appears.
For example, group="Investment Banking" when importing the “Cash/Debt Ratio” screen.
language_id: string
Allows users to override the EQS report header language
asof_date: datetime or None
Allows users to backdate the screen, so they can analyze the historical results on the screen
"""

data = self._beqs(screen_name, screen_type, group, language_id, asof_date)

data = DataFrame(data)
data.columns = ["ticker", "field", "value", "date"]
return data

def _beqs(self, screen_name, screen_type, group, language_id, asof_date):

# flush event queue in case previous call errored out
while (self.session.tryNextEvent()):
pass

request = self._beqs_create_req(screen_name, screen_type, group, language_id)

cid = None
if asof_date is not None:
overrides = request.getElement("overrides")
ovrd = overrides.appendElement()
ovrd.setElement("fieldId", "PiTDate")
ovrd.setElement("value", asof_date.strftime('%Y%m%d'))
cid = blpapi.CorrelationId(asof_date)

logging.debug("Sending Request:\n %s" % request)
# Send the request
self.session.sendRequest(request, correlationId=cid)
data = []
# Process received events
while (True):
# We provide timeout to give the chance for Ctrl+C handling:
ev = self.session.nextEvent(500)
data = self._beqs_parse_event(data, ev)

if ev.eventType() == blpapi.Event.RESPONSE:
# Response completely received, so we could exit
break

return data

def _beqs_parse_event(self, data, ev):
for msg in ev:
logging.debug("Message Received:\n %s" % msg)
corrID = msg.correlationIds()[0].value()
fldData = msg.getElement('data').getElement('securityData')
for i in range(fldData.numValues()):
ticker = (fldData.getValue(i).getElement("security").getValue()) # NOQA
reqFldsData = (fldData.getValue(i).getElement('fieldData'))
for e in reqFldsData.elements():
fld = str(e.name())
# this is for dealing with requests which return arrays
# of values for a single field
if reqFldsData.getElement(fld).isArray():
lrng = reqFldsData.getElement(fld).numValues()
for k in range(lrng):
elms = (reqFldsData.getElement(fld).getValue(k).elements()) # NOQA
# if the elements of the array have multiple
# subelements this will just append them all
# into a list
for elm in elms:
data.append([ticker, fld, elm.getValue(), corrID])
else:
val = reqFldsData.getElement(fld).getValue()
data.append([ticker, fld, val, corrID])
return data

def _beqs_create_req(self, screen_name, screen_type, group, language_id):
request = self.refDataService.createRequest('BeqsRequest')
request.set('screenName', screen_name)
request.set('screenType', screen_type)
request.set('Group', group)
request.set('languageId', language_id)
return request

def beqs_hist(self, screen_name, screen_type='PRIVATE', group='General', language_id='ENGLISH',
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freq parameter here is unused and should be removed. longdata parameter should be documented.

asof_dates=pd.date_range(datetime.date.today(), datetime.date.today(), freq='b'),
timeout=2000, longdata=False):
"""
Get tickers and fields, periodically override ASOF to create
a time series. Return pandas dataframe with column MultiIndex
of tickers and fields, Index otherwise.
If single field, DataFrame is ordered same as tickers,
otherwise MultiIndex is sorted.

Parameters
----------
screen_name: string
String corresponding to name of screen
screen_type: 'GLOBAL' or 'PRIVATE'
Indicates that the screen is a Bloomberg-created sample screen (GLOBAL) or
a saved custom screen that users have created (PRIVATE).
group: string
If the screens are organized into groups, allows users to define the name of the group that
contains the screen. If the users use a Bloomberg sample screen,
they must use this parameter to specify the name of the folder in which the screen appears.
For example, group="Investment Banking" when importing the “Cash/Debt Ratio” screen.
language_id: string
Allows users to override the EQS report header language
asof_dates: DatetimeIndex
Dates on which to get the screen's historical results
timeout: int
Passed into nextEvent(timeout), number of milliseconds before
timeout occurs
"""
# correlationIDs should be unique to a session so rather than
# managing unique IDs for the duration of the session just restart
# a session for each call
self.restart()

request = self._beqs_create_req(screen_name, screen_type, group, language_id)
overrides = request.getElement("overrides")
ovrd = overrides.appendElement()
for dt in asof_dates:
ovrd.setElement("fieldId", "PiTDate")
ovrd.setElement("value", dt.strftime('%Y%m%d'))
# CorrelationID used to keep track of which response coincides with which request
cid = blpapi.CorrelationId(dt)
logging.debug("Sending Request:\n %s" % request)
self.session.sendRequest(request, correlationId=cid)
data = []
# Process received events
ev_counter = 0
while (True):
ev = self.session.nextEvent(timeout)
data = self._beqs_parse_event(data, ev)
if ev.eventType() == blpapi.Event.RESPONSE:
ev_counter += 1
if ev_counter == len(asof_dates):
break
# if ev.eventType() == blpapi.Event.TIMEOUT:
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This commented out section should be removed

# All events processed
# if (len(data) / len(set(data[0])) / len(set(data[1]))) == len(asof_dates):
# break
# else:
# raise(RuntimeError("Timeout, increase timeout parameter"))
data = pd.DataFrame(data)
data.columns = ['ticker', 'field', 'value', 'date']
data = data.sort_values(by=['date', 'ticker', 'field'])
data = data.reset_index(drop=True)
data = data.loc[:, ['date', 'ticker', 'field', 'value']]

if not longdata:
cols = ['ticker', 'field']
data = data.set_index(['date'] + cols).unstack('field')
data.columns = data.columns.droplevel(0)

return data
24 changes: 20 additions & 4 deletions pdblp/tests/test_pdblp.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
import datetime
import unittest
import pandas as pd
from pandas.util.testing import assert_frame_equal
from pandas.util.testing import assertIsInstance
from pdblp import pdblp

IP_PORT = 8194
Expand Down Expand Up @@ -94,18 +94,18 @@ def test_ref_one_ticker_one_field(self):
def test_ref_one_ticker_one_field_many_output(self):
df = self.con.ref('CL1 Comdty', 'FUT_CHAIN')
# unknown / changing data returned so just assert right type
assertIsInstance(df, pd.DataFrame)
assert isinstance(df, pd.DataFrame)

def test_ref_two_ticker_one_field_many_output(self):
df = self.con.ref(['CL1 Comdty', 'CO1 Comdty'], 'FUT_CHAIN')
# unknown / changing data returned so just assert right type
assertIsInstance(df, pd.DataFrame)
assert isinstance(df, pd.DataFrame)

def test_ref_two_ticker_two_field_many_output(self):
df = self.con.ref(['CL1 Comdty', 'CO1 Comdty'],
['FUT_CHAIN', 'FUT_CUR_GEN_TICKER'])
# unknown / changing data returned so just assert right type
assertIsInstance(df, pd.DataFrame)
assert isinstance(df, pd.DataFrame)

def test_ref_one_ticker_one_field_override(self):
df = self.con.ref('AUD Curncy', 'SETTLE_DT',
Expand Down Expand Up @@ -164,3 +164,19 @@ def test_hist_ref_one_ticker_one_field_pivoted_non_numeric(self):
)
df_expect.index.names = ["date"]
assert_frame_equal(df, df_expect)

def test_beqs_current(self):
df = self.con.beqs('Insider Buyers', 'GLOBAL', 'Popular')
assert len(df) > 0

def test_beqs_asof(self):
asof_date = datetime.datetime(2016, 5, 4)
df = self.con.beqs('Insider Buyers', 'GLOBAL', 'Popular', asof_date=asof_date)
assert len(df) == 1145

def test_beqs_hist(self):
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Test is failing for me since I am getting a DataFrame of length 488. Bloombergs exact data returned is not always reliable, a test which just checks the known properties of the returned DataFrame would be better (e.g. a DataFrame with specific columns)

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Hello Matthew,

I have read your codes and also the codes from blpapi. I found it is impossible to get the data of backdates. Actually I want to realize the lookup function that use the market cap rank in 2019 to select stock. But I cannot find any api methods to do that. I have found yours codes is working, but it is now not accurate?

Beth

asof_dates = pd.DatetimeIndex([datetime.datetime(2016, 5, 4),
datetime.datetime(2017, 5, 10)])
df = self.con.beqs_hist('Insider Buyers', 'GLOBAL', 'Popular',
asof_dates=asof_dates, longdata=False)
assert len(df) == 487