import quantopian.algorithm as algo
from quantopian.pipeline import Pipeline
from quantopian.pipeline.filters import Q3000US
from quantopian.pipeline.data.morningstar import Fundamentals as ms
import quantopian.optimize as opt

import numpy as np
import pandas as pd

def initialize(context):
context.FINE_FILTER = 5
context.stock_weights = pd.Series()
algo.attach_pipeline(make_pipeline(context), 'pipeline')

    schedule_function(
        stocks_weights,
        date_rules.week_start(),
        time_rules.market_open()
    )

    schedule_function(
        trade,
        date_rules.week_start(),
        time_rules.market_open()
    )

def make_pipeline(context):
univ = Q3000US()
factor = ms.free_cash_flow.latest.rank(mask=univ, ascending=False)
top = factor.top(context.FINE_FILTER)
pipe = Pipeline(
columns={'top': top}, screen=univ)
return pipe

def stocks_weights(context, data):
df = algo.pipeline_output('pipeline')
rule = 'top'
stocks_to_hold = df.query(rule).index
stock_weight = 1.0 / context.FINE_FILTER
context.stocks_weights = pd.Series(index=stocks_to_hold, data=stock_weight)

def trade(context, data):
target_weights = opt.TargetWeights(context.stocks_weights)

    constraints = []
    constraints.append(opt.MaxGrossExposure(1.0))

    order_optimal_portfolio(
        objective=target_weights,
        constraints=constraints
    )