# https://quantpedia.com/strategies/consistent-momentum-strategy/
#
# The investment universe consists of stocks listed at NYSE, AMEX, and NASDAQ, whose price data (at least for the past 7 months) are available
# at the CRSP database. The investor creates a zero-investment portfolio at the end of the month t, longing stocks that are in the top decile 
# in terms of returns both in the period from t-7 to t-1 and from t-6 to t, while shorting stocks in the bottom decile in both periods (i.e. 
# longing consistent winners and shorting consistent losers). The stocks in the portfolio are weighted equally. The holding period is six months,
# with no rebalancing during the period. There is a one-month skip between the formation and holding period.
#
# QC implementation changes:
#   - The investment universe consists of 1000 most liquid stocks from NASDAQ, Amex, NYSE.

from AlgorithmImports import *

class ConsistentMomentumStrategy(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2010, 1, 1)
        self.SetCash(100000)

        market:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
        
        self.long:List[Symbol] = []
        self.short:List[Symbol] = []
        
        self.data:Dict[Symbol, SymbolData] = {}
        
        self.fundamental_count:int = 1000
        self.fundamental_sorting_key = lambda x: x.DollarVolume
        
        self.period:int = 7 * 21
        self.quantile:int = 10
        self.leverage:int = 5
        self.exchange_codes:List[str] = ['NYS', 'NAS', 'ASE']
        
        self.selection_flag:bool = False
        self.UniverseSettings.Resolution = Resolution.Daily
        self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
        self.AddUniverse(self.FundamentalSelectionFunction)
        self.Schedule.On(self.DateRules.MonthStart(market), self.TimeRules.AfterMarketOpen(market), self.Rebalance)
    
    def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
        for security in changes.AddedSecurities:
            security.SetFeeModel(CustomFeeModel())
            security.SetLeverage(self.leverage)
				
    def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
        # Update the rolling window every day.
        for stock in fundamental:
            symbol: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:List[Fundamental] = [x for x in fundamental if x.HasFundamentalData and x.Market == 'usa' and x.SecurityReference.ExchangeId in self.exchange_codes and \
            x.MarketCap != 0 and x.CompanyReference.IsREIT != 1]
        if len(selected) > self.fundamental_count:
            selected = [x for x in sorted(selected, key=self.fundamental_sorting_key, reverse=True)[:self.fundamental_count]]
        
        momentum_t71_t60:Dict[Symbol, float] = {}

        # Warmup price rolling windows.
        for stock in selected:
            symbol:Symbol = stock.Symbol

            if symbol not in self.data:
                self.data[symbol] = SymbolData(self.period)
                history:DataFrame = self.History(symbol, self.period, Resolution.Daily)
                if history.empty:
                    self.Log(f"Not enough data for {symbol} yet")
                    continue
                closes:pd.Series = history.loc[symbol].close
                for time, close in closes.items():
                    self.data[symbol].update(close)
                
            if self.data[symbol].is_ready():
                momentum_t71_t60[symbol] = (self.data[symbol].performance_t7t1(), self.data[symbol].performance_t6t0())
        
        if len(momentum_t71_t60) >= self.quantile:
            # Momentum t-7 to t-1 sorting
            sorted_by_perf_t71:List = sorted(momentum_t71_t60.items(), key = lambda x: x[1][0], reverse = True)
            quantile:int = int(len(sorted_by_perf_t71) / self.quantile)
            high_by_perf_t71:List[Symbol] = [x[0] for x in sorted_by_perf_t71[:quantile]]
            low_by_perf_t71:List[Symbol] = [x[0] for x in sorted_by_perf_t71[-quantile:]]

            # Momentum t-6 to t sorting
            sorted_by_perf_t60:List = sorted(momentum_t71_t60.items(), key = lambda x: x[1][1], reverse = True)
            quantile = int(len(sorted_by_perf_t60) / self.quantile)
            high_by_perf_t60:List[Symbol] = [x[0] for x in sorted_by_perf_t60[:quantile]]
            low_by_perf_t60:List[Symbol] = [x[0] for x in sorted_by_perf_t60[-quantile:]]
            
            self.long = [x for x in high_by_perf_t71 if x in high_by_perf_t60]
            self.short = [x for x in low_by_perf_t71 if x in low_by_perf_t60]
        
        return self.long + self.short
    
    def OnData(self, data: Slice) -> None:
        if not self.selection_flag:
            return
        self.selection_flag = False

        # order execution
        targets:List[PortfolioTarget] = []
        for i, portfolio in enumerate([self.long, self.short]):
            for symbol in portfolio:
                if symbol in data and data[symbol]:
                    targets.append(PortfolioTarget(symbol, ((-1) ** i) / len(portfolio)))
		
        self.SetHoldings(targets, True)

        self.long.clear()
        self.short.clear()

    def Rebalance(self) -> None:
        if self.Time.month % 6 == 0:
            self.selection_flag = True

class SymbolData():
    def __init__(self, period: int):
        self._price:RollingWindow = RollingWindow[float](period)
    
    def update(self, price: float) -> None:
        self._price.Add(price)
    
    def is_ready(self) -> bool:
        return self._price.IsReady
        
    def performance_t7t1(self) -> float:
        closes:List[float] = [x for x in self._price][21:]
        return (closes[0] / closes[-1] - 1)

    def performance_t6t0(self) -> float:
        closes:List[float] = [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"))