Overall Statistics
from Alphas.EmaCrossAlphaModel import EmaCrossAlphaModel

class ResistanceNadionThrustAssembly(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2018, 10, 9)  # Set Start Date
        self.SetEndDate(2019,1,31)
        self.SetCash(100000)  # Set Strategy Cash
        self.AddAlpha(EmaCrossAlphaModel(50, 200, Resolution.Minute))

        self.__numberOfSymbols = 100
        self.__numberOfSymbolsFine = 5
        self.lastMonth = -1
        self.symbols = []
        self.SetUniverseSelection(FineFundamentalUniverseSelectionModel(self.CoarseSelectionFunction, self.FineSelectionFunction, None, None))
        
    def OnData(self, data):
        pass
    
    def OnSecuritiesChanged(self, changes):
        self.Log([security.Symbol.Value for security in changes.AddedSecurities])

    def CoarseSelectionFunction(self, coarse):
        if self.Time.month == self.lastMonth: 
            return self.symbols
        self.lastMonth = self.Time.month
        
        # sort descending by daily dollar volume
        self.Log('Refreshing Universe >> ' + str(self.Time))
        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
        self.symbols = [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ]
        # return the symbol objects of the top entries from our sorted collection
        return self.symbols
    
    # sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
    def FineSelectionFunction(self, fine):
        if self.Time.month == self.lastMonth: 
            return self.symbols

        self.Log('Refreshing Universe >> ' + str(self.Time))
        # sort descending by P/E ratio
        sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=True)
        self.symbols = [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ]
    
        # take the top entries from our sorted collection
        return self.symbols