Overall Statistics
from System.Collections.Generic import List
from QuantConnect.Data.UniverseSelection import *
import operator
from math import ceil,floor

class CoarseFineFundamentalComboAlgorithm(QCAlgorithm):
    '''In this algorithm we demonstrate how to define a universe as a combination of use the coarse fundamental data and fine fundamental data'''
    def Initialize(self):

		self.SetStartDate(2012,01,02)  #Set Start Date
		self.SetEndDate(2014,03,02)    #Set End Date
		self.SetCash(50000)            #Set Strategy Cash
		self.flag1 = 1
		self.flag2 = 0
		self.flag3 = 0

		self.UniverseSettings.Resolution = Resolution.Daily        
        # this add universe method accepts two parameters:
        # - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
        # - fine selection function: accepts an IEnumerable<FineFundamental> and returns an IEnumerable<Symbol>
		self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
		self.__numberOfSymbols = 200
		self.__numberOfSymbolsFine = 20
		self.num_portfolios = 5
		self._changes = SecurityChanges.None
		self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), Action(self.Rebalancing))

    # sort the data by daily dollar volume and take the top 'NumberOfSymbols'
    def CoarseSelectionFunction(self, coarse):
		if self.flag1:
			CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData]
			sortedByDollarVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=True) 
	        # return the symbol objects of the top entries from our sorted collection
			top = sortedByDollarVolume[:self.__numberOfSymbols]
	        # we need to return only the symbol objects
			list = List[Symbol]()
			for x in top:
			return list

    def FineSelectionFunction(self, fine):
		if self.flag1:
			self.flag1 = 0
			self.flag2 = 1
			filtered_fine = [x for x in fine if x.ValuationRatios.PERatio 
											and x.ValuationRatios.PriceChange1M 
											and x.ValuationRatios.BookValuePerShare]
			sortedByfactor1 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.PERatio, reverse=False)
			sortedByfactor2 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.PriceChange1M, reverse=False)
			sortedByfactor3 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
			num_stocks = floor(len(filtered_fine)/self.num_portfolios)*self.num_portfolios 

			stock_dict = {}
			for i,ele in enumerate(sortedByfactor1):
				rank1 = i
				rank2 = sortedByfactor2.index(ele)
				rank3 = sortedByfactor3.index(ele)
				score = [ceil(rank1/num_stocks),ceil(rank2/num_stocks),ceil(rank3/num_stocks)]
				score = sum(score)
				stock_dict[ele] = score
			self.sorted_stock = sorted(stock_dict.items(), key=lambda d:d[1],reverse=True)
			sorted_symbol = [self.sorted_stock[i][0] for i in xrange(len(self.sorted_stock))]
	        # take the top entries from our sorted collection
			topFine = sorted_symbol[:self.__numberOfSymbolsFine]
			list = List[Symbol]()
			for x in topFine:
			self.flag3 = self.flag3 + 1	        
			return list
			return (List[Symbol]())

    def OnData(self, data):
		if self.flag3 > 0:
			if self.flag2 == 1:
				self.flag2 = 0

	  			# if we have no changes, do nothing
				if self._changes == SecurityChanges.None: return
				# liquidate removed securities
				for security in self._changes.RemovedSecurities:
					if security.Invested:
				for security in self._changes.AddedSecurities:
					self.SetHoldings(security.Symbol, 1/float(len(self._changes.AddedSecurities)))    
				self._changes = SecurityChanges.None;

    # this event fires whenever we have changes to our universe
    def OnSecuritiesChanged(self, changes):
        self._changes = changes
    def Rebalancing(self):
		self.flag1 = 1