from datetime import date

class LexxHelp(QCAlgorithm):

    def Initialize(self):
        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

        self.SetStartDate(2013, 12, 31)  #Set Start Date
        self.SetEndDate(2015, 1, 1)    #Set End Date
        self.SetCash(100000)            #Set Strategy Cash
        
        self.current_month = 12

        # what resolution should the data *added* to the universe be?
        self.UniverseSettings.Resolution = Resolution.Daily
        
        # An indicator(or any rolling window) needs data(updates) to have a value, doesnt help due to monthly selection?
        #self.UniverseSettings.MinimumTimeInUniverse = 10
        #self.SetWarmUp(10+1)

        # this add universe method accepts two parameters:
        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
        
        # Set dictionary of indicators
        self.indicators = {}
        
        # Set a list of the selected universe
        self.universe = []

        self.__numberOfSymbols     = 50
        self.__numberOfSymbolsFine = 10
        
        #self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol

    def OnData(self, data):

        # This updates the indicators at each data step(based on resolution)
        for symbol in self.universe:
            
            # is symbol iin Slice object? (do we even have data on this step for this asset)
            if not data.ContainsKey(symbol):
                continue
            
            
            # 686 | 13:35:43: Runtime Error: Python.Runtime.PythonException: AttributeError : 'NoneType' object has no attribute 'Price'
            if data[symbol] is None:
                continue
            
            # Does this slice have the price data we need at this moment?
            if data[symbol].Price is None:
                continue

            # Either create a new indicator, or update one we already have
            if symbol not in self.indicators:
                self.indicators[symbol] = SymbolData(symbol, self)
            
            self.indicators[symbol].update_value(self.Time, data[symbol].Price)

            # We are warming up the indicators, cannot trade or other stuff
            if self.IsWarmingUp: continue
            
            # now you can use logic to trade, random example:
            lowerband = self.indicators[symbol].bb_10.LowerBand.Current.Value
            upperband = self.indicators[symbol].bb_10.UpperBand.Current.Value
            
            # Log the symbol, price & indicators. 
            self.Log("{0}\tPrice : {1:0.2f}\tUPPERBAND : {2:0.2f}\tLOWERBAND : {3:0.2f}".format(symbol, 
                                                                                                data[symbol].Price, 
                                                                                                upperband, 
                                                                                                lowerband))
                                                                                                
            # SLOW, but used to generate some trades.
            #ma = self.History(symbol, 10).close.mean()
            
            # current price: self.Securities[symbol].Price or data[symbol].Price
            if self.Securities[symbol].Price < lowerband:
                #self.SetHoldings(symbol, -0.99/float(len(self.universe)))
                self.SetHoldings(symbol, 0.1)
            elif self.Securities[symbol].Price > upperband:
                #self.SetHoldings(symbol, 0.99/float(len(self.universe)))
                self.Liquidate(symbol)
                

      
      

    # sort the data by daily dollar volume and take the top 'NumberOfSymbols'
    def CoarseSelectionFunction(self, coarse):
        
        # Attempt to only try rebalance quarterly on the 1st day of the month
        today = self.Time
        if not (today.month == 1 or today.month == 4 or today.month == 7 or today.month == 10) and today.day == 1:
            return self.universe
                
        
        # 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):
        
        # Attempt to only try rebalance quarterly on the 1st day of the month
        today = self.Time
        if not (today.month == 1 or today.month == 4 or today.month == 7 or today.month == 10) and today.day == 1:
            return self.universe
        
        
        # sort descending by P/E ratio
        sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.FCFYield, reverse=True)
        
        # resulting symbols
        result = [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ]
        
        # Only update our universes on a new month? Not sure I like this hack, might work better in coarse to save more resources?
        #if self.current_month != self.Time.month:
            #self.Log(str(self.Time.month)+ " : " +str(len(result)))
        #    self.current_month = self.Time.month
        #    self.universe      = result
        #    return result
        #else:
        return self.universe

        
    # this event fires whenever we have changes to our universe
    def OnSecuritiesChanged(self, changes):
        
        # liquidate removed securities
        for security in changes.RemovedSecurities:
            if security.Invested:
                self.Liquidate(security.Symbol)
                
                # clean up
                del self.indicators[security.Symbol]

class SymbolData(object):
    def __init__(self, symbol, context):
        self.symbol = symbol
        """
        I had to pass ATR from outside object to get it to work, could pass context and use any indica
        var atr = ATR(Symbol symbol, int period, MovingAverageType type = null, Resolution resolution = null, Func`2[Data.IBaseData,Data.Market.IBaseDataBar] selector = null)
        """
        #self.ema = context.EMA(symbol, self.window)
        #self.indicator = context.BB(symbol, self.window)
        #self.bb_10 = context.BB(symbol,12,1,MovingAverageType.Simple,Resolution.Daily)
        self.bb_10 = context.BollingerBands(symbol,10,1,MovingAverageType.Simple)
        #self.indicator2 = context.BB(symbol,20,1,MovingAverageType.Simple,Resolution.Daily)

    """
    Runtime Error: Python.Runtime.PythonException: NotSupportedException : AverageTrueRange does not support Update(DateTime, decimal) method overload. Use Update(IBaseDataBar) instead.
    """
    #def update_bar(self, bar):
    #    self.atr.Update(bar)
        
    def update_value(self, time, value):
        self.bb_10.Update(time, value)

class CoarseFineFundamentalATRComboAlgorithm(QCAlgorithm):

    def Initialize(self):
        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

        self.SetStartDate(2014, 1, 1)  #Set Start Date
        self.SetEndDate(2014, 6, 1)    #Set End Date
        self.SetCash(50000)            #Set Strategy Cash

        # what resolution should the data *added* to the universe be?
        self.UniverseSettings.Resolution = Resolution.Daily
        
        # An indicator(or any rolling window) needs data(updates) to have a value
        self.atr_window = 20
        self.UniverseSettings.MinimumTimeInUniverse = self.atr_window
        self.SetWarmUp(self.atr_window)

        # this add universe method accepts two parameters:
        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
        
        #self.flag1 = 1  # variable to control the monthly rebalance of coarse and fine selection function
        
        # Set dictionary of indicators
        self.indicators = {}
        
        # Set a list of the selected universe
        self.universe=[]

        self.__numberOfSymbols     = 200
        self.__numberOfSymbolsFine = 10
        
        self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol
        
        #self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY", 10), Action(self.CoarseSelectionFunction))

                        
    # 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):
        
        # sort descending by P/E ratio
        sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.FCFYield, reverse=True)
        
        # resulting symbols
        self.universe = [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ]

        # take the top entries from our sorted collection
        return self.universe

    def OnData(self, data):
        
        # Return before trying to run a loop on empty list
        #if not self.universe:
        #    return
        
        for symbol in self.universe:
            
            # is symbol in Slice object? (do we even have data on this step for this asset)
            
            #if not data.ContainsKey(symbol):
            #    return
            #self.indicators[symbol].update_value(self.Time, data[symbol].Price)
            
                #continue
            if self.IsWarmingUp: continue
            
            if data.ContainsKey(symbol):
                self.indicators[symbol].update_value(self.Time, data[symbol].Price)
            #else:
            #    continue
            
            # new symbol? setup indicator object. Then update
            if symbol not in self.indicators:
                self.indicators[symbol] = SymbolData(symbol, self, self.atr_window)
                
            # update by bar
            #self.indicators[symbol].update_bar(data[symbol])
            #update by value
    
            
            #self.indicators[symbol].update_value(self.Time, data[symbol].Price)
            
            # now you can use logic to trade, random example:
            lowerband = self.indicators[symbol].get_atr()
            upperband = self.indicators[symbol].get_atr2()
            
            # Log the symbol, price & indicators
            self.Log(str(symbol) + " : " + str(self.indicators[symbol].get_atr()))
            self.Log("PRICE : {}".format(str(self.Securities[symbol].Price)))
            self.Log("UPPERBAND : {}".format(str(lowerband)))
            self.Log("LOWERBAND : {}".format(str(lowerband)))
            
            # Trading Indicators
            if lowerband != 0.0:
                if not self.Portfolio[symbol].HoldStock:
                    if float(self.Securities[symbol].Price) < lowerband:
                        self.SetHoldings(symbol, 0.02)
                elif self.Portfolio[symbol].HoldStock:
                    if float(self.Securities[symbol].Price) > upperband:
                        self.Liquidate(symbol)
            

    # this event fires whenever we have changes to our universe
    def OnSecuritiesChanged(self, changes):
        
        # liquidate removed securities
        for security in changes.RemovedSecurities:
            if security.Invested:
                self.Liquidate(security.Symbol)
                
                # clean up
                del self.indicators[security.Symbol]

class SymbolData(object):
    def __init__(self, symbol, context, window):
        self.symbol = symbol
        """
        I had to pass ATR from outside object to get it to work, could pass context and use any indica
        var atr = ATR(Symbol symbol, int period, MovingAverageType type = null, Resolution resolution = null, Func`2[Data.IBaseData,Data.Market.IBaseDataBar] selector = null)
        """
        self.window    = window
        #self.indicator = context.EMA(symbol, self.window)
        #self.indicator = context.BB(symbol, self.window)
        self.indicator = context.BollingerBands(symbol,12,2,MovingAverageType.Simple)
        self.indicator2 = context.BollingerBands(symbol,12,1,MovingAverageType.Simple)
        self.atr       = 0.0

    """
    Runtime Error: Python.Runtime.PythonException: NotSupportedException : AverageTrueRange does not support Update(DateTime, decimal) method overload. Use Update(IBaseDataBar) instead.
    """
    def update_bar(self, bar):
        self.indicator.Update(bar)
        
    def update_value(self, time, value):
        self.indicator.Update(time, value)
            
    def get_atr(self):
        #return self.indicator.Current.Value
        return self.indicator.LowerBand.Current.Value
        
    def get_atr2(self):
        #return self.indicator.Current.Value
        return self.indicator2.UpperBand.Current.Value