from statistics import mean
class UncoupledNadionSplitter(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2019, 3, 16)  # Set Start Date
        self.SetCash(100000)  # Set Strategy Cash
        self.AddEquity("M", Resolution.Minute)
        self.__numberOfSymbols = 100
        self.__numberOfSymbolsFine = 5
        self.SetUniverseSelection(FineFundamentalUniverseSelectionModel(self.CoarseSelectionFunction, self.FineSelectionFunction, None, None))
        self.average_PE_consumer_cyclical = None
        self.average_PE_consumer_defensive = None

    def OnData(self, data):
        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
            Arguments:
                data: Slice object keyed by symbol containing the stock data
        '''
        self.Log(f'Average Forward PE for Consumer Cyclical Sector: {self.average_PE_consumer_cyclical}')
        self.Log(f'Average Forward PE for Consumer Defensive Sector: {self.average_PE_consumer_defensive}')


    # sort the data by daily dollar volume and take the top 'NumberOfSymbols'
    def CoarseSelectionFunction(self, coarse):
        # sort descending by daily dollar volume
        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
    
        # return the symbol objects of the top entries from our sorted collection
        return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ]
    
    # sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
    def FineSelectionFunction(self, fine):
        # get the average forward PE ratio in Consumer Cyclical sector
        filtered_cyclical = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.ConsumerCyclical]
        pe_cyclical = [x.ValuationRatios.ForwardPERatio for x in filtered_cyclical]
        self.average_PE_consumer_cyclical = sum(pe_cyclical)/len(pe_cyclical)
        
        # get the average forward PE ratio in Consumer Defensive sector
        filtered_defensive = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.ConsumerDefensive]
        pe_defensive = [x.ValuationRatios.ForwardPERatio for x in filtered_defensive]
        self.average_PE_consumer_defensive = sum(pe_defensive)/len(pe_defensive)
        
        return [x.Symbol for x in fine]