# https://quantpedia.com/strategies/momentum-factor-effect-in-stocks/
#
# The investment universe consists of NYSE, AMEX, and NASDAQ stocks. We define momentum as the past 12-month return, skipping the most 
# recent month’s return (to avoid microstructure and liquidity biases). To capture “momentum”, UMD portfolio goes long stocks that have 
# high relative past one-year returns and short stocks that have low relative past one-year returns.
#
# QC implementation changes:
#   - Instead of all listed stock, we select top 500 stocks by market cap from QC stock universe.
    
class MomentumFactorEffectinStocks(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2000, 1, 1)  
        self.SetCash(100000)

        self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
        
        self.long = []
        self.short = []
        
        self.data = {}
        self.period = 12 * 21
        
        self.coarse_count = 500
        self.selection_flag = False
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
        
        self.Schedule.On(self.DateRules.MonthStart(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)

    def OnSecuritiesChanged(self, changes):
        for security in changes.AddedSecurities:
            security.SetFeeModel(CustomFeeModel(self))
            security.SetLeverage(10)
        
    def CoarseSelectionFunction(self, coarse):
        # Update the rolling window every day.
        for stock in coarse:
            symbol = stock.Symbol

            # Store monthly price.
            if symbol in self.data:
                self.data[symbol].update(stock.AdjustedPrice)

        if not self.selection_flag:
            return Universe.Unchanged
        
        # selected = [x.Symbol for x in coarse if x.HasFundamentalData and x.Market == 'usa' and x.Price > 5]
        selected = [x.Symbol
            for x in sorted([x for x in coarse if x.HasFundamentalData and x.Market == 'usa'],
                key = lambda x: x.DollarVolume, reverse = True)[:self.coarse_count]]
   
        # Warmup price rolling windows.
        for symbol in selected:
            if symbol in self.data:
                continue
            
            self.data[symbol] = SymbolData(symbol, self.period)
            history = self.History(symbol, self.period, Resolution.Daily)
            if history.empty:
                self.Log(f"Not enough data for {symbol} yet")
                continue
            closes = history.loc[symbol].close
            for time, close in closes.iteritems():
                self.data[symbol].update(close)
                
        return [x for x in selected if self.data[x].is_ready()]
    
    def FineSelectionFunction(self, fine):
        fine = [x for x in fine if x.MarketCap != 0 and \
                    ((x.SecurityReference.ExchangeId == "NYS") or (x.SecurityReference.ExchangeId == "NAS") or (x.SecurityReference.ExchangeId == "ASE"))]
                    
        # if len(fine) > self.coarse_count:
        #     sorted_by_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)
        #     top_by_market_cap = [x for x in sorted_by_market_cap[:self.coarse_count]]
        # else:
        #     top_by_market_cap = fine

        perf = {x.Symbol : self.data[x.Symbol].performance() for x in fine}

        sorted_by_perf = sorted(perf.items(), key = lambda x:x[1], reverse=True)
        quintile = int(len(sorted_by_perf) / 5)
        self.long = [x[0] for x in sorted_by_perf[:quintile]]
        self.short = [x[0] for x in sorted_by_perf[-quintile:]]
        
        return self.long + self.short
        
    def OnData(self, data):
        if not self.selection_flag:
            return
        self.selection_flag = False

        # Trade execution.
        long_count = len(self.long)
        short_count = len(self.short)

        stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
        for symbol in stocks_invested:
            if symbol not in self.long + self.short:
                self.Liquidate(symbol)
        
        for symbol in self.long:
            if self.Securities[symbol].Price != 0 and self.Securities[symbol].IsTradable:
                self.SetHoldings(symbol, 1 / long_count)

        for symbol in self.short:
            if self.Securities[symbol].Price != 0 and self.Securities[symbol].IsTradable:
                self.SetHoldings(symbol, -1 / short_count)

        self.long.clear()
        self.short.clear()
        
    def Selection(self):
        self.selection_flag = True

class SymbolData():
    def __init__(self, symbol, period):
        self.Symbol = symbol
        self.Price = RollingWindow[float](period)
    
    def update(self, value):
        self.Price.Add(value)
    
    def is_ready(self):
        return self.Price.IsReady
        
    # Yearly performance, one month skipped.
    def performance(self):
        closes = [x for x in self.Price][21:]
        return (closes[0] / closes[-1] - 1)
    
# Custom fee model.
class CustomFeeModel(FeeModel):
    def GetOrderFee(self, parameters):
        fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
        return OrderFee(CashAmount(fee, "USD"))