Overall Statistics |
Total Trades 5954 Average Win 0.22% Average Loss -0.25% Compounding Annual Return 1.216% Drawdown 12.400% Expectancy 0.011 Net Profit 6.227% Sharpe Ratio 0.225 Probabilistic Sharpe Ratio 4.109% Loss Rate 46% Win Rate 54% Profit-Loss Ratio 0.86 Alpha 0.021 Beta -0.046 Annual Standard Deviation 0.068 Annual Variance 0.005 Information Ratio -0.537 Tracking Error 0.212 Treynor Ratio -0.332 Total Fees $0.00 |
from datetime import timedelta, datetime class SMAPairsTrading(QCAlgorithm): def Initialize(self): self.SetStartDate(2015, 7, 13) self.SetCash(10000) symbols = [Symbol.Create("PEP", SecurityType.Equity, Market.USA), Symbol.Create("KO", SecurityType.Equity, Market.USA)] self.AddUniverseSelection(ManualUniverseSelectionModel(symbols)) self.UniverseSettings.Resolution = Resolution.Minute self.SetSecurityInitializer(self.CustomSecurityInitializer) self.AddAlpha(PairsTradingAlphaModel()) self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel()) self.SetExecution(ImmediateExecutionModel()) def OnEndOfDay(self, symbol): self.Log("Taking a position of " + str(self.Portfolio[symbol].Quantity) + " units of symbol " + str(symbol)) def CustomSecurityInitializer(self, security): security.SetFeeModel(CustomFeeModel()) security.SetDataNormalizationMode(DataNormalizationMode.Raw) class PairsTradingAlphaModel(AlphaModel): def __init__(self): self.pair = [ ] self.spreadMean = SimpleMovingAverage(500) self.spreadStd = StandardDeviation(500) self.period = timedelta(hours=2) def Update(self, algorithm, data): spread = self.pair[1].Price - self.pair[0].Price self.spreadMean.Update(algorithm.Time, spread) self.spreadStd.Update(algorithm.Time, spread) upperthreshold = self.spreadMean.Current.Value + self.spreadStd.Current.Value lowerthreshold = self.spreadMean.Current.Value - self.spreadStd.Current.Value if spread > upperthreshold: return Insight.Group( [ Insight.Price(self.pair[0].Symbol, self.period, InsightDirection.Up), Insight.Price(self.pair[1].Symbol, self.period, InsightDirection.Down) ]) if spread < lowerthreshold: return Insight.Group( [ Insight.Price(self.pair[0].Symbol, self.period, InsightDirection.Down), Insight.Price(self.pair[1].Symbol, self.period, InsightDirection.Up) ]) return [] def OnSecuritiesChanged(self, algorithm, changes): self.pair = [x for x in changes.AddedSecurities] #1. Call for 500 bars of history data for each symbol in the pair and save to the variable history history = algorithm.History([x.Symbol for x in self.pair], 500) #2. Unstack the Pandas data frame to reduce it to the history close price history = history.close.unstack(level=0) #3. Iterate through the history tuple and update the mean and standard deviation with historical data for tuple in history.itertuples(): self.spreadMean.Update(tuple[0], tuple[2]-tuple[1]) self.spreadStd.Update(tuple[0], tuple[2]-tuple[1]) class CustomFeeModel: def GetOrderFee(self, parameters): return OrderFee(CashAmount(0, 'USD'))