# https://quantpedia.com/strategies/short-term-reversal-in-stocks/
#
# The investment universe consists of the 100 biggest companies by market capitalization.
# The investor goes long on the ten stocks with the lowest performance in the previous week and
# goes short on the ten stocks with the greatest performance of the prior month. The portfolio is rebalanced weekly.
#
# QC implementation changes:
#   - Instead of all listed stocks, we first select 500 most liquid stock from QC as a first filter due to time complexity issues tied to whole universe filtering.
#   - Then top 100 market cap stocks are used in momentum sorting.

#region imports
from AlgorithmImports import *
from pandas.core.frame import DataFrame
from typing import List, Dict
#endregion

class ShortTermReversalEffectinStocks(QCAlgorithm):

    def Initialize(self) -> None:
        self.SetStartDate(2000, 1, 1)  
        self.SetCash(100000)

        self.symbol:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
        
        self.coarse_count:int = 500
        self.stock_selection:int = 10
        self.top_by_market_cap_count:int = 100
        self.leverage:int = 5
        
        self.period:int = 21
        
        self.long:List[Symbol] = []
        self.short:List[Symbol] = []
        
        # daily close data
        self.data:Dict[Symbol, SymbolData] = {}
        
        self.day:int = 1
        self.selection_flag:bool = False
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
        self.Schedule.On(self.DateRules.EveryDay(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)

    def OnSecuritiesChanged(self, changes:SecurityChanges) -> None:
        for security in changes.AddedSecurities:
            security.SetFeeModel(CustomFeeModel())
            security.SetLeverage(self.leverage)
        
    def CoarseSelectionFunction(self, coarse:List[CoarseFundamental]) -> List[Symbol]:
        # update the rolling window every day
        for stock in coarse:
            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[CoarseFundamental] = sorted([x for x in coarse if x.HasFundamentalData and x.Market == 'usa' and x.Price > 1],
            key=lambda x: x.DollarVolume, reverse=True)
        selected:List[Symbol] = [x.Symbol for x in selected][:self.coarse_count]

        # warmup price rolling windows
        for symbol in selected:
            if symbol in self.data:
                continue

            self.data[symbol] = SymbolData(self.period+1)
            history:DataFrame = self.History(symbol, self.period+1, Resolution.Daily)
            if history.empty:
                self.Log(f"Not enough data for {symbol} yet")
                continue
            closes:pd.Series = history.loc[symbol]
            for time, row in closes.iterrows():
                self.data[symbol].update(row['close'])

        return [x for x in selected if self.data[x].is_ready()]
        
    def FineSelectionFunction(self, fine:List[FineFundamental]) -> List[Symbol]:
        fine:List[FineFundamental] = [x for x in fine if x.MarketCap != 0]
        
        sorted_by_market_cap:List = sorted(fine, key = lambda x:x.MarketCap, reverse = True)
        top_by_market_cap:List[Symbol] = [x.Symbol for x in sorted_by_market_cap[:self.top_by_market_cap_count]]
        
        month_performances:Dict[Symbol, float] = {symbol : self.data[symbol].performance(self.period) for symbol in top_by_market_cap}
        week_performances:Dict[Symbol, float] = {symbol : self.data[symbol].performance(5) for symbol in top_by_market_cap}
            
        sorted_by_month_perf:List[Symbol] = [x[0] for x in sorted(month_performances.items(), key=lambda item: item[1], reverse=True)]
        sorted_by_week_perf:List[Symbol] = [x[0] for x in sorted(week_performances.items(), key=lambda item: item[1])]
        
        self.long = sorted_by_week_perf[:self.stock_selection]
        self.short = sorted_by_month_perf[:self.stock_selection]
        
        return self.long + self.short
    
    def OnData(self, data:Slice) -> None:
        if not self.selection_flag:
            return
        self.selection_flag = False
        
        invested:List[Symbol] = [x.Key for x in self.Portfolio if x.Value.Invested]
        for symbol in invested:
            if symbol not in self.long + self.short:
                self.Liquidate(symbol)
        
        # leveraged portfolio - 100% long, 100% short
        for symbol in self.long:
            if symbol in data and data[symbol]:
                self.SetHoldings(symbol, 1 / len(self.long))

        for symbol in self.short:
            if symbol in data and data[symbol]:
                self.SetHoldings(symbol, -1 / len(self.short))
                
        self.long.clear()
        self.short.clear()
                
    def Selection(self) -> None:
        if self.day == 5:
            self.selection_flag = True
        
        self.day += 1
        if self.day > 5:
            self.day = 1
            
class SymbolData():
    def __init__(self, period:float) -> None:
        self._daily_close = RollingWindow[float](period)

    def update(self, close:float) -> None:
        self._daily_close.Add(close)

    def is_ready(self) -> bool:
        return self._daily_close.IsReady
    
    def performance(self, period:int) -> float:
        return self._daily_close[0] / self._daily_close[period] - 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"))