class EMABasedStrategy(QCAlgorithm):
    
    def Initialize(self):       #Initialize Dates, Cash, Equities, Fees, Allocation, Parameters, Indicators, Charts
        
        # Set Start Date, End Date, and Cash
        #-------------------------------------------------------
        self.SetTimeZone(TimeZones.NewYork)     #EDIT: Added Timezon
        self.SetStartDate(2011, 1, 1)   # Set Start Date
        self.SetEndDate(2011, 6, 1)      # Set End Date
        self.SetCash(10000)            # Set Strategy Cash
        #-------------------------------------------------------
        
        # Set Custom Universe
        #-------------------------------------------------------
        self.AddUniverse(self.CoarseSelectionFilter, self.FineSelectionFilter)
        self.UniverseSettings.Resolution = Resolution.Hour    #Needs to change to Resolution.Minute once code works, leaving Daily for now to minimize data
        self.UniverseSettings.SetDataNormalizationMode = DataNormalizationMode.SplitAdjusted
        self.UniverseSettings.FeeModel = ConstantFeeModel(0.0)
        self.UniverseSettings.Leverage = 1
        self.SetBrokerageModel(BrokerageName.Alpaca, AccountType.Cash)      #EDIT: Added Brokerage, appears to have set fees to zero
        #-------------------------------------------------------
        
        # Set Contants 
        #-------------------------------------------------------
        self.EMA_Period_Fast = 20
        self.EMA_Period_Slow = 200
        self.EMA_Period_Medium = 50

        self.__numberOfSymbols     = 100
        self.__numberOfSymbolsFine = 5
        #-------------------------------------------------------
        
        # Define Percentage Allocation and variables
        #-------------------------------------------------------
        self.percentagebuy = 0.05
        self.stopLossPercent = 0.9
        self.indicators = {}
        self.highestSymbolPrice = -1
        #-------------------------------------------------------
        
            
    def CoarseSelectionFilter(self, coarse):
        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)   # sort descending by daily dollar volume
        return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ]  # return the symbol objects of the top entries from our sorted collection
    
    def FineSelectionFilter(self, fine):  # sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
        sortedByPeRatio = sorted(fine, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=False)    # sort descending by P/E ratio
        self.universe = [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ]  # take the top entries from our sorted collection
        return self.universe
    
    def OnSecuritiesChanged(self, changes):
        # Create indicator for each new security
        for security in changes.AddedSecurities:
            self.indicators[security.Symbol] = SymbolData(security.Symbol, self, self.EMA_Period_Fast, self.EMA_Period_Slow, self.EMA_Period_Medium)
        
        # for security in changes.RemovedSecurities:
        #     if security.Invested:
        #         self.Liquidate(security.Symbol, "Universe Removed Security")
            
        #     if security in self.indicators:
        #         self.indicators.pop(security.Symbol, None)
    
    
    def OnData(self, data):         #Entry Point for Data and algorithm - Check Data, Define Buy Quantity, Process Volume, Check Portfolio, Check RSI, Execute Buy/Sell orders, Chart Plots
        for symbol in self.universe:
            
            if not data.ContainsKey(symbol):    #Tested and Valid/Necessary
                continue
            
            if data[symbol] is None:            #Tested and Valid/Necessary
                continue
            
            if not symbol in self.indicators:    #Tested and Valid/Necessary
                continue
            
            # Ensure indicators are ready to update rolling windows
            if not self.indicators[symbol].slow_ema.IsReady:
                continue
            
            # Update EMA rolling windows
            self.indicators[symbol].fast_ema_window.Add(self.indicators[symbol].get_fast_EMA())
            self.indicators[symbol].slow_ema_window.Add(self.indicators[symbol].get_slow_EMA())
            self.indicators[symbol].medium_ema_window.Add(self.indicators[symbol].get_medium_EMA())
            
            # Check for Indicator Readiness within Rolling Window
            #-------------------------------------------------------
            if not (self.indicators[symbol].fast_ema_window.IsReady and self.indicators[symbol].slow_ema_window.IsReady and self.indicators[symbol].medium_ema_window.IsReady): 
                continue    #return     #EDIT 
            
            #EXECUTE TRADING LOGIC HERE -
            # if self.Portfolio[symbol].Invested:
            #     # Sell condition
            #     if (self.indicators[symbol].fast_ema_window[1] >= self.indicators[symbol].slow_ema_window[1]) and (self.indicators[symbol].fast_ema_window[4] < self.indicators[symbol].slow_ema_window[4]):
            #         self.Liquidate(symbol)
                    
            #     # Buy conditions
            # elif self.Portfolio.MarginRemaining > 0.9 * self.percentagebuy * self.Portfolio.TotalPortfolioValue:
            #     if self.indicators[symbol].fast_ema_window[1] <= self.indicators[symbol].slow_ema_window[1] and \
            #         (self.indicators[symbol].fast_ema_window[4] > self.indicators[symbol].slow_ema_window[4]):
                        
            #         self.buyquantity = round((self.percentagebuy*self.Portfolio.TotalPortfolioValue)/data[symbol].Close)
            #         self.MarketOrder(symbol, self.buyquantity)
            
            if self.Portfolio[symbol].Invested:
                
                # if self.indicators[symbol].is_buy_signal_liquidate():
                #     self.Debug(str(self.Time) + " Liquidating : " + str(symbol) + 'Quantity -->' + str(self.Portfolio[symbol].Quantity))
                #     self.Liquidate(symbol)
                # el
                if self.Securities[symbol].Close > self.highestSymbolPrice:
                    
                    self.highestSymbolPrice = self.Securities[symbol].Close
                    updateFields = UpdateOrderFields()
                    updateFields.StopPrice = self.highestSymbolPrice * 0.9
                    self.stopMarketTicket.Update(updateFields) 
                    self.Debug('inside trailing SL update OLD-->' + str(self.Securities[symbol].Close) + ' New -->' + str(updateFields.StopPrice))
                # if self.Securities[symbol].Holdings.UnrealizedProfitPercent < 0.1:
                #     self.Debug('Liquidate after 20% loss on trade' + str(symbol))
                #     self.Liquidate(symbol)
            if self.Portfolio.MarginRemaining > self.percentagebuy * self.Portfolio.TotalPortfolioValue:
                if not self.Portfolio[symbol].Invested and self.indicators[symbol].is_buy_signal():
                    self.buyquantity = round((self.percentagebuy*self.Portfolio.TotalPortfolioValue)/data[symbol].Close)
                    ticket = self.MarketOrder(symbol, self.buyquantity)
                     # Put a trailing stop loss order at same time
                    self.stopMarketTicket = self.StopMarketOrder(symbol, self.buyquantity, self.stopLossPercent * data[symbol].Close)
                    updateSettings = UpdateOrderFields()
                    updateSettings.Tag = "Our New Tag for SPY Trade. Medium 0 -->" + str(self.indicators[symbol].medium_ema_window[0]) + 'Slow 0 ->' + str(self.indicators[symbol].slow_ema_window[0]) + 'Medium 1 ->' + str(self.indicators[symbol].medium_ema_window[1]) + 'Slow 1 ->' + str(self.indicators[symbol].slow_ema_window[1])
                    ticket.Update(updateSettings)
                    self.highestSymbolPrice = self.Securities[symbol].Close
                    
            

class SymbolData(object):
    
    rolling_window_length = 2
    
    def __init__(self, symbol, context, fast_ema_period, slow_ema_period, medium_ema_period):
        
        self.symbol = symbol
        self.fast_ema_period = fast_ema_period
        self.slow_ema_period = slow_ema_period
        self.medium_ema_period = medium_ema_period
        self.fast_ema = context.EMA(symbol, self.fast_ema_period, Resolution.Hour)    #, fillDataForward = True, leverage = 1, extendedMarketHours = False)
        self.slow_ema = context.EMA(symbol, self.slow_ema_period, Resolution.Hour)    #, fillDataForward = True, leverage = 1, extendedMarketHours = False)
        self.medium_ema = context.EMA(symbol, self.medium_ema_period, Resolution.Hour)
        self.fast_ema_window = RollingWindow[float](self.rolling_window_length)
        self.slow_ema_window = RollingWindow[float](self.rolling_window_length)
        self.medium_ema_window = RollingWindow[float](self.rolling_window_length)
        
        # Warm up EMA indicators
        history = context.History([symbol], slow_ema_period + self.rolling_window_length, Resolution.Hour)
        for time, row in history.loc[symbol].iterrows():
            self.fast_ema.Update(time, row["close"])
            self.slow_ema.Update(time, row["close"])
            self.medium_ema.Update(time, row["close"])
            
            # Warm up rolling windows
            if self.fast_ema.IsReady:
                self.fast_ema_window.Add(self.fast_ema.Current.Value)
            if self.slow_ema.IsReady:
                self.slow_ema_window.Add(self.slow_ema.Current.Value)
            if self.medium_ema.IsReady:
                self.medium_ema_window.Add(self.medium_ema.Current.Value)
    
    def get_fast_EMA(self):
        return self.fast_ema.Current.Value
        
    def get_slow_EMA(self):
        return self.slow_ema.Current.Value
        
    def get_medium_EMA(self):
        return self.medium_ema.Current.Value
        
    def is_buy_signal(self):
        if self.medium_ema_window[0] > self.slow_ema_window[0] and \
            self.medium_ema_window[1] < self.slow_ema_window[1]:
                if self.fast_ema_window[0] > self.slow_ema_window[0]:
                    return True
    
    def is_buy_signal_liquidate(self):
        return self.fast_ema_window[0] < self.slow_ema_window[0] and \
            self.fast_ema_window[1] > self.slow_ema_window[1]