Overall Statistics
Total Trades
1
Average Win
0%
Average Loss
0%
Compounding Annual Return
10.884%
Drawdown
38.500%
Expectancy
0
Net Profit
14.552%
Sharpe Ratio
0.46
Probabilistic Sharpe Ratio
23.631%
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0.174
Beta
-0.275
Annual Standard Deviation
0.304
Annual Variance
0.093
Information Ratio
0.035
Tracking Error
0.443
Treynor Ratio
-0.51
Total Fees
$46.48
 
class BenchmarkPlotTemplateAlgorithm(QCAlgorithm):

    def Initialize(self):

        self.SetStartDate(2019, 1, 1)
        self.SetCash(1000000)
        
        # select a ticker as benchmark (will plot Buy&Hold of this benchmark)
        ticker = 'IBM'
        self.benchmarkTicker = 'SPY'
        
        self.symbol = self.AddEquity(ticker, Resolution.Daily).Symbol
        self.SetBenchmark(self.benchmarkTicker)
        self.initBenchmarkPrice = None
                
    def OnData(self, slice):

        # simulate buy and hold the benchmark and plot its daily value
        self.UpdateBenchmarkValue()
        self.Plot('Strategy Equity', self.benchmarkTicker, self.benchmarkValue)
            
        if not self.Portfolio.Invested:
            self.SetHoldings(self.symbol, 1)

    def UpdateBenchmarkValue(self):
            
        ''' Simulate buy and hold the Benchmark '''
        
        if self.initBenchmarkPrice is None:
            self.initBenchmarkCash = self.Portfolio.Cash
            self.initBenchmarkPrice = self.Benchmark.Evaluate(self.Time)
            self.benchmarkValue = self.initBenchmarkCash
        else:
            currentBenchmarkPrice = self.Benchmark.Evaluate(self.Time)
            self.benchmarkValue = (currentBenchmarkPrice / self.initBenchmarkPrice) * self.initBenchmarkCash