Overall Statistics |
Total Trades 35158 Average Win 0.37% Average Loss -0.27% Compounding Annual Return 21.791% Drawdown 39.600% Expectancy 0.090 Net Profit 6850.044% Sharpe Ratio 0.892 Probabilistic Sharpe Ratio 16.077% Loss Rate 54% Win Rate 46% Profit-Loss Ratio 1.35 Alpha 0.208 Beta 0.001 Annual Standard Deviation 0.234 Annual Variance 0.055 Information Ratio 0.459 Tracking Error 0.293 Treynor Ratio 261.115 Total Fees $587038.73 Estimated Strategy Capacity $35000000.00 Lowest Capacity Asset RTP R735QTJ8XC9X |
# 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. class ShortTermReversalEffectinStocks(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol self.coarse_count = 500 self.stock_selection = 10 self.top_by_market_cap_count = 100 self.period = 21 self.long = [] self.short = [] # Daily close data self.data = {} self.day = 1 self.selection_flag = 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): for security in changes.AddedSecurities: security.SetFeeModel(CustomFeeModel(self)) security.SetLeverage(5) 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 = 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 = [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) 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] sorted_by_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse = True) top_by_market_cap = [x.Symbol for x in sorted_by_market_cap[:self.top_by_market_cap_count]] month_performances = {symbol : self.data[symbol].monthly_return() for symbol in top_by_market_cap} week_performances = {symbol : self.data[symbol].weekly_return() for symbol in top_by_market_cap} sorted_by_month_perf = [x[0] for x in sorted(month_performances.items(), key=lambda item: item[1], reverse=True)] sorted_by_week_perf = [x[0] for x in sorted(week_performances.items(), key=lambda item: item[1])] self.long = sorted_by_week_perf[:self.stock_selection] for symbol in sorted_by_month_perf: # Month performances are sorted descending if symbol not in self.long: self.short.append(symbol) if len(self.short) == 10: break return self.long + self.short def OnData(self, data): if not self.selection_flag: return self.selection_flag = False invested = [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 self.Securities[symbol].Price != 0 and self.Securities[symbol].IsTradable: self.SetHoldings(symbol, 1 / len(self.long)) for symbol in self.short: if self.Securities[symbol].Price != 0 and self.Securities[symbol].IsTradable: self.SetHoldings(symbol, -1 / len(self.short)) self.long.clear() self.short.clear() def Selection(self): if self.day == 5: self.selection_flag = True self.day += 1 if self.day > 5: self.day = 1 class SymbolData(): def __init__(self, period): self.closes = RollingWindow[float](period) self.period = period def update(self, close): self.closes.Add(close) def is_ready(self) -> bool: return self.closes.IsReady def weekly_return(self) -> float: return self.closes[0] / self.closes[5] - 1 def monthly_return(self) -> float: return self.closes[0] / self.closes[self.period-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"))