Overall Statistics |
Total Trades 465 Average Win 0.13% Average Loss -0.09% Compounding Annual Return 0.302% Drawdown 0.600% Expectancy 0.022 Net Profit 0.225% Sharpe Ratio 0.727 Probabilistic Sharpe Ratio 39.272% Loss Rate 58% Win Rate 42% Profit-Loss Ratio 1.45 Alpha 0.003 Beta -0.003 Annual Standard Deviation 0.004 Annual Variance 0 Information Ratio -0.538 Tracking Error 0.165 Treynor Ratio -0.924 Total Fees $466.15 |
from datetime import timedelta, datetime class SMAPairsTrading(QCAlgorithm): def Initialize(self): self.SetStartDate(2018, 7, 1) self.SetEndDate(2019, 3, 31) self.SetCash(100000) symbols = [Symbol.Create("SPY", SecurityType.Equity, Market.USA), Symbol.Create("VOO", SecurityType.Equity, Market.USA)] self.AddUniverseSelection(ManualUniverseSelectionModel(symbols)) self.UniverseSettings.Resolution = Resolution.Hour self.UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw 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)) 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) ]) elif 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) ]) else: return Insight.Group( [ Insight.Price(self.pair[0].Symbol, self.period, InsightDirection.Flat), Insight.Price(self.pair[1].Symbol, self.period, InsightDirection.Flat) ]) 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])