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)