import pandas as pd

class CoarseFundamentalTop3Algorithm(QCAlgorithm):

    def Initialize(self):

        self.SetStartDate(2019,6,4)
        self.SetEndDate(2019,6,8)
        self.SetCash(50000)

        self.UniverseSettings.Resolution = Resolution.Daily
        
        self.AddUniverse(self.CoarseSelectionFunction, self.SelectFine)
        self.IndustryCode = {}
        self.__numberOfSymbols = 3
        self.__numberOfSymbolsFine = 3
        self._changes = None

    def OnData(self, data):

        if self._changes is None: return

        for security in self._changes.RemovedSecurities:
            if security.Invested:
                self.Liquidate(security.Symbol)

        for security in self._changes.AddedSecurities:
            self.SetHoldings(security.Symbol, 1 / self.__numberOfSymbols)

        self._changes = None


    def OnSecuritiesChanged(self, changes):
        self._changes = changes
    
    def CoarseSelectionFunction(self, coarse):
        CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData]
        self.Log(coarse[-1].Time)
        sortedByVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=True)
        for x in sortedByVolume[:self.__numberOfSymbols]:
            self.Log(x.Symbol.Value)
            self.Log(x.DollarVolume)
        return [ x.Symbol for x in sortedByVolume[:self.__numberOfSymbols] ]
        
    
    
    def SelectFine(self, fine):
        
        IndustryCode = {}
        for x in fine:
            if x.AssetClassification.MorningstarIndustryGroupCode not in IndustryCode:
                IndustryCode[x.AssetClassification.MorningstarIndustryGroupCode] = [x.Symbol]
            else:
                IndustryCode[x.AssetClassification.MorningstarIndustryGroupCode].append(x.Symbol)

                
        history_price = {}
        
        pre_symbols = []
        for value in IndustryCode.values():
            for k in set(value):
                pre_symbols.append(self.AddEquity(str(k.Value), Resolution.Minute).Symbol)
        
        symbols = []
        for value in IndustryCode.values():
            for j in set(value):
                hist = self.History(j, datetime(2019, 6, 4, 0), datetime(2019, 6, 4, 23), Resolution.Minute)
                self.Log(hist)
                if hist.empty: 
                    value.remove(j)
                else:
                    symbols.append(j)
                    history_price[str(j.Value)] = []
                    for tuple in hist.loc[str(j.Value)].itertuples():
                        history_price[str(j.Value)].append(float(tuple.close))
                    if len(history_price[str(j.Value)]) < 390:
                        value.remove(j)
                        symbols.remove(j)
                        history_price.pop(str(j.Value))
        
        df_history_price = pd.DataFrame.from_dict(history_price)
        
 
        
        return symbols