from quantopian.pipeline import Pipeline
from quantopian.research import run_pipeline
from quantopian.pipeline.factors import CustomFactor
from quantopian.pipeline.data import USEquityPricing
from quantopian.pipeline.filters import QTradableStocksUS
from quantopian.pipeline import CustomFilter

import numpy as np 




class SidInList(CustomFilter):  
    """  
    Filter returns True for any SID included in parameter tuple passed at creation.  
    Usage: my_filter = SidInList(sid_list=(23911, 46631))  
    """  
    inputs = []  
    window_length = 1  
    params = ('sid_list',)

    def compute(self, today, assets, out, sid_list):  
        out[:] = np.in1d(assets, sid_list) 
        

def call_MyFactor(orda,cicle,zfrac, **kwargs):
    step = int(round(((orda)*zfrac))) 
    kwargs['window_length'] += step
    class MyFactor(CustomFactor):
        def compute(self, today, asset_ids, out, high,low):
             out[:] = ((np.nanmax(high[:self.window_length-step],axis=0))+(np.nanmin(low[:self.window_length-step],axis=0)))
    return MyFactor(**kwargs)  


def make_factors():
    factors = {}
    for o in range(1,7):
        for c in range(11,21):
            c=float(c)
            o=float(o)
            z =  float(c/((2*o)+1))  
            window = (int(round((o+1)*z)) - int(round(o*z))) +1
            factors['factor {}'.format(c*o)]  = call_MyFactor(orda=o,cicle=c,zfrac=z ,window_length=window,inputs = [USEquityPricing.high,USEquityPricing.low])
    return factors


def make_pipeline():
    return Pipeline(columns=make_factors(),screen=QTradableStocksUS()) #screen = SidInList(sid_list=(2069)))#screen=QTradableStocksUS())

result = run_pipeline(make_pipeline(), '2018-11-10', '2018-11-10')
#print len(result)
result.head(10)

#result.groupby(level=0).sum()
#result.groupby(level=0).sum().plot()

#def before_trading_start(context, data):  
#    context.output= pipeline_output
#    i = 1
#    for col in pipeline_output:
#        pipeline_output[col] = pipeline_output[col] * i
#        i += 1

#    pipeline_output