# Research environment functions imports
from quantopian.research import run_pipeline
# Pipeline imports
from quantopian.pipeline.data import EquityPricing # OHLCV data for Global equity
from quantopian.pipeline.data import factset # FactSet data set
from quantopian.pipeline.domain import IN_EQUITIES # Indian equities
from quantopian.pipeline import Pipeline
# Other libraries imports
import matplotlib.pyplot as plt
import pandas as pd
# Select time period to inspect
start_period = '2015-01-15'
end_period= '2016-01-15'
# Pipeline definition
pipe=Pipeline(
columns={ 'price':EquityPricing.close.latest,
'volume':EquityPricing.volume.latest,
'mkt_cap':factset.Fundamentals.mkt_val.latest,
'economy':factset.RBICSFocus.l1_name.latest,
'sector':factset.RBICSFocus.l2_name.latest,
'subsector':factset.RBICSFocus.l3_name.latest,
'listing exchange':factset.EquityMetadata.primary_fsym_security_id.latest,
'listing currency':factset.EquityMetadata.listing_currency.latest,
'security type':factset.EquityMetadata.security_type.latest
},
domain=IN_EQUITIES)
# Pipeline execution
result= run_pipeline(
pipe,
start_period,
end_period)
result.loc['2016-01-15',:][result['security type']=='ETF_ETF']