In [1]:
# Import Pipeline
from quantopian.pipeline import Pipeline
from quantopian.research import run_pipeline
# Import filter/screen
from quantopian.pipeline.filters import QTradableStocksUS
# Import data
from quantopian.pipeline.data import USEquityPricing
from quantopian.pipeline.data.factset import Fundamentals as ff
from quantopian.pipeline.data.morningstar import Fundamentals as mf
# Import classifier
from quantopian.pipeline.classifiers.fundamentals import Sector
# Import Alphalens
from alphalens.utils import get_clean_factor_and_forward_returns
from alphalens.tears import create_full_tear_sheet
In [2]:
# Constants
start_date = '2003-1-1'
end_date = '2020-2-1'
factor_name = 'fcf_yld'
sector_labels, sector_labels[-1] = dict(Sector.SECTOR_NAMES), "Unknown"
group_neutral = True
long_short = True
In [3]:
# Create pipeline function
def make_pipeline():
# Get our primary data
sector = Sector()
fcf = mf.free_cash_flow.latest
ev = mf.enterprise_value.latest
# Calculate factor
fcf_yld = fcf / ev
#Create screens
factor_screen = fcf_yld.notnull()
sector_screen = sector.notnull()
# Return pipeline
return Pipeline(
columns={
factor_name: fcf_yld,
'sector': sector
},
screen=QTradableStocksUS() & factor_screen & sector_screen,
)
In [4]:
# Get factor data from pipeline
factor_data = run_pipeline(make_pipeline(), start_date, end_date)
In [5]:
factor_data.head()
Out[5]:
In [6]:
# Get pricing data
pricing_data = get_pricing(factor_data.index.levels[1], start_date, end_date, fields='open_price')
In [7]:
pricing_data.head()
Out[7]:
In [8]:
# Get factor and returns data
merged_data = get_clean_factor_and_forward_returns(
factor=factor_data[factor_name],
prices=pricing_data,
periods=(1,5,10),
quantiles=5,
groupby=factor_data['sector'],
groupby_labels = sector_labels,
binning_by_group = group_neutral,
)
In [9]:
merged_data.tail()
Out[9]:
In [10]:
# Create tear-sheet to analyse
create_full_tear_sheet(merged_data, long_short=long_short, by_group=True, group_neutral=group_neutral)
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