# 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

# Constants
start_date = '2003-1-1'
end_date = '2020-3-1'

factor_name = 'fcf_yld'

sector_labels, sector_labels[-1] = dict(Sector.SECTOR_NAMES), "Unknown"

group_neutral = True

long_short = True

# 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,
    )

# Get factor data from pipeline
factor_data = run_pipeline(make_pipeline(), start_date, end_date)

factor_data.head()

# Get pricing data
pricing_data = get_pricing(factor_data.index.levels[1], start_date, end_date, fields='open_price')

pricing_data.head()

merged_data.tail()

# Create tear-sheet to analyse
create_full_tear_sheet(merged_data, long_short=long_short, by_group=True, group_neutral=group_neutral)