from quantopian.pipeline import Pipeline
from quantopian.research import run_pipeline
from quantopian.pipeline.filters.morningstar import Q500US
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline import CustomFactor
from quantopian.pipeline.data import Fundamentals

import alphalens

#import anything else as need be

def make_pipeline():
    #put alpha to test here
    testingFactor = Fundamentals.peg_ratio.latest
    sectorInfo = Fundamentals.morningstar_sector_code.latest
    
    #specify any filters here into universe. Make sure alphas have .notnull() in universe.    
    universe = (testingFactor.notnull() &
                Q500US())
    
    #Use print statements to make sure the BETTER one is ranked HIGHER. Eg Rank 150 is BETTER than rank 149.
    #first do it without ranking them, so you can ensure raw numbers make sense.
    testingFactor = testingFactor.rank(method='average', 
                    ascending=False,
                    mask = universe,
                    groupby = sectorInfo)
    
    testingFactor = testingFactor.zscore(groupby = sectorInfo)

    
    #Seperate into the desired quantiles, top X%, whatever you want here.
    
    #if long and short strategy, specify which sections you would long/short. Returns a pipeline object.
    pipe = Pipeline(columns = {'testingFactor':testingFactor,
                              'sector': sectorInfo},
                   screen = universe)
    
    return pipe

#Makes a dataframe with the given universe, ranked by the testing factor.
results = run_pipeline(make_pipeline(), start_date = '2015-01-01', end_date = '2016-01-01')

#print the results to make sure it fuckin worked. Print once without rankings to verify.
results.head(15)

#Gets each unique stock ticker and puts it in assets
assets = results.index.levels[1].unique()

#gets pricing data. Needs a month before and after. Dunno why.
pricing = get_pricing(assets, start_date='2014-12-01', end_date='2016-02-01', fields='open_price')