Overall Statistics |
Total Trades 1 Average Win 0% Average Loss 0% Compounding Annual Return 49.195% Drawdown 52.800% Expectancy 0 Net Profit 132.823% Sharpe Ratio 0.886 Probabilistic Sharpe Ratio 42.660% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0.49 Beta -0.245 Annual Standard Deviation 0.522 Annual Variance 0.273 Information Ratio 0.648 Tracking Error 0.547 Treynor Ratio -1.887 Total Fees $1.56 |
class DynamicVentralAntennaArray(QCAlgorithm): def Initialize(self): self.SetStartDate(2018, 1, 1) # Set Start Date self.SetCash(100000) # Set Strategy Cash self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol self.tsla = self.AddEquity("TSLA", Resolution.Daily).Symbol # Set Benchmark self.SetBenchmark("SPY") # Variable to hold the last calculated benchmark value self.lastBenchmarkValue = None # Our inital benchmark value scaled to match our portfolio self.BenchmarkPerformance = self.Portfolio.TotalPortfolioValue def OnData(self, data): # store the current benchmark close price benchmark = self.Securities["SPY"].Close # enter our strategy if not self.Portfolio.Invested: self.SetHoldings("TSLA", 1) # Calculate the performance of our benchmark and update our benchmark value for plotting if self.lastBenchmarkValue is not None: self.BenchmarkPerformance = self.BenchmarkPerformance * (benchmark/self.lastBenchmarkValue) # store today's benchmark close price for use tomorrow self.lastBenchmarkValue = benchmark # make our plots self.Plot("Strategy vs Benchmark", "Portfolio Value", self.Portfolio.TotalPortfolioValue) self.Plot("Strategy vs Benchmark", "Benchmark", self.BenchmarkPerformance)