Moving Averages Distance Strategy in Equities

1import numpy as npfrom AlgorithmImports import *from typing import List, Dictclass MovingAveragesDistance(QCAlgorithm):    def Initialize(self):        self.SetStartDate(2000, 1, 1)        self.SetCash(100000)        self.exchange_codes:List[str] = ['NYS', 'NAS', 'ASE']	        self.fundamental_sorting_key = lambda x: x.DollarVolume        self.fundamental_count:int = 500                self.min_share_price:int = 5        self.leverage:int = 10        self.period:int = 200        self.month_period:int = 21                self.data:Dict[Symbol, SymbolData] = {}                self.long:List[Symbol] = []        self.short:List[Symbol] = []                self.symbol:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol                self.selection_flag:bool = False        self.Settings.MinimumOrderMarginPortfolioPercentage = 0.        self.UniverseSettings.Resolution = Resolution.Daily        self.AddUniverse(self.FundamentalSelectionFunction)        self.Schedule.On(self.DateRules.MonthStart(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)        self.settings.daily_precise_end_time = False    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 = stock.Symbol            # Store daily 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.Price > self.min_share_price \            and x.SecurityReference.ExchangeId in self.exchange_codes        ]                    if len(selected) > self.fundamental_count:            selected = [x for x in sorted(selected, key=self.fundamental_sorting_key, reverse=True)[:self.fundamental_count]]        MAD: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(symbol, 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:Series = history.loc[symbol].close                for time, close in closes.items():                    self.data[symbol].update(close)            if not self.data[symbol].is_ready():                continue                        prices:List[float] = self.data[symbol].return_prices()            ma21:float = np.average(prices[:self.month_period])            ma200:float = np.average(prices)                        MAD[symbol] = ma21 / ma200            self.long = [x[0] for x in MAD.items() if x[1] >= 1.2]        self.short = [x[0] for x in MAD.items() if x[1] <= 0.8]                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 Selection(self) -> None:        self.selection_flag = True    class SymbolData():    def __init__(self, symbol:Symbol, period:int):        self.Symbol:Symbol = symbol        self.Prices:RollingWindow = RollingWindow[float](period)            def update(self, price:float):        self.Prices.Add(price)            def is_ready(self) -> bool:        return self.Prices.IsReady            def return_prices(self) -> List[float]:        return [x for x in self.Prices]        class CustomFeeModel(FeeModel):    def GetOrderFee(self, parameters):        fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005        return OrderFee(CashAmount(fee, "USD"))