Overall Statistics |
Total Trades 37 Average Win 7.76% Average Loss -2.34% Compounding Annual Return -14.964% Drawdown 27.700% Expectancy -0.520 Net Profit -12.694% Sharpe Ratio -0.288 Probabilistic Sharpe Ratio 12.027% Loss Rate 89% Win Rate 11% Profit-Loss Ratio 3.32 Alpha -0.085 Beta 0.25 Annual Standard Deviation 0.344 Annual Variance 0.118 Information Ratio -0.107 Tracking Error 0.414 Treynor Ratio -0.395 Total Fees $671.49 |
class EmaSimple(QCAlgorithm): def Initialize(self): self.SetStartDate(2019, 6, 1) # Set Start Date self.SetEndDate(2020, 4, 1) #Set End Date self.SetCash(100000) #Set Strategy Cash # Find more symbols here: http://quantconnect.com/data self.AddEquity("WORK", Resolution.Hour) self.stock = self.Identity("WORK") # self.spyStock = Identity("SPY") self.__slow = self.EMA("WORK", 200, Resolution.Hour) self.PlotIndicator("WORK", self.__slow, self.stock) stockPlot = Chart("Trade Plot") stockPlot.AddSeries(Series('Buy', SeriesType.Scatter, 0)) stockPlot.AddSeries(Series('Sell', SeriesType.Scatter, 0)) self.AddChart(stockPlot) def OnData(self, data): if not self.__slow.IsReady: return self.slowEMA = self.__slow.Current.Value self.closePrice = data["WORK"].Price tolerance = 0.0025 holdings = self.Portfolio["WORK"].Quantity signalDelta = self.closePrice - self.slowEMA if holdings == 0 and self.closePrice > self.slowEMA: self.SetHoldings("WORK", 1.0) self.Plot("Trade Plot", 'Buy', self.closePrice) elif holdings > 0 and self.closePrice < self.slowEMA: self.Liquidate("WORK") self.Debug("SEll") self.Plot("Trade Plot", 'Sell', self.closePrice)