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.equity_per_share_growth.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
  )