from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel

class RedGhhost(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2021, 1, 12)
        self.SetEndDate(2021, 1, 15)
        self.SetCash(100000) 
        self.AddUniverseSelection(UniverseModule())
        self.UniverseSettings.Resolution = Resolution.Daily
        
    def OnData(self, slice):
        self.Debug(slice.Bars)
    
   
class UniverseModule(FundamentalUniverseSelectionModel):
    def __init__(self, filterFineData = True, universeSettings = None):
        super().__init__(filterFineData, universeSettings)
        self.averages = {}
        
    '''
    Filter by price
    '''
    def SelectCoarse(self, algorithm, coarse):
        return [
            x.Symbol for x in coarse if (
                x.Price > 10
                and x.HasFundamentalData
            )
        ]
        
    '''
    Filter by min market cap and share count
    '''
    def SelectFine(self, algorithm, fine):
        MIN_MKT_CAP = 1 * 10 ^ 9
        MIN_SHARE_COUNT = 1 * 10 ^ 9
        MAX_BARS_BACK = 200
        
        initialFilter = [
            x.Symbol for x in fine if (
                x.FinancialStatements.BalanceSheet.OrdinarySharesNumber.Value >= MIN_SHARE_COUNT
                and x.MarketCap >= MIN_MKT_CAP
            )
        ]
        
        historySlices = algorithm.History(initialFilter, MAX_BARS_BACK, Resolution.Daily)
        for (symbol, time), history in historySlices.groupby(level=[0,1]):
            if symbol not in self.averages:
                self.averages[symbol] = SymbolData(symbol, history)

            avg = self.averages[symbol]
            avg.update(time, history.loc[symbol].close.iloc[-1])
        
        upTrending = list(filter(lambda x: x.isUptrend, self.averages.values()))
        
        finalSymbols = [x.symbol for x in upTrending]
        
        return finalSymbols
        
    
class SymbolData(object):
    def __init__(self, symbol, history):
        self.symbol = symbol
        self.longTerm = ExponentialMovingAverage(200)
        self.mediumTerm = ExponentialMovingAverage(100)
        self.isUptrend = False
        
        for time, row in history.loc[self.symbol].iterrows():
            self.longTerm.Update(time, row.close)
            self.mediumTerm.Update(time, row.close)

    def update(self, time, value):
        self.longTerm.Update(time, value)
        self.mediumTerm.Update(time, value)
        
        longTerm = self.longTerm.Current.Value
        mediumTerm = self.mediumTerm.Current.Value
        
        self.isUptrend = (
            mediumTerm > longTerm
        )
        
    
        
    

# Your New Python File