Overall Statistics |
Total Trades 8 Average Win 4.60% Average Loss -5.36% Compounding Annual Return 221.679% Drawdown 8.900% Expectancy 0.240 Net Profit 7.296% Sharpe Ratio 2.232 Loss Rate 33% Win Rate 67% Profit-Loss Ratio 0.86 Alpha 1.493 Beta 0.494 Annual Standard Deviation 0.439 Annual Variance 0.192 Information Ratio 4.593 Tracking Error 0.44 Treynor Ratio 1.98 Total Fees $63.82 |
# # QuantConnect Basic Template: # Fundamentals to using a QuantConnect algorithm. # # You can view the QCAlgorithm base class on Github: # https://github.com/QuantConnect/Lean/tree/master/Algorithm # import numpy as np class BasicTemplateAlgorithm(QCAlgorithm): def Initialize(self): # Set the cash we'd like to use for our backtest # This is ignored in live trading self.SetCash(100000) # Start and end dates for the backtest. # These are ignored in live trading. self.SetStartDate(2001,3,9) #Friday self.SetEndDate(2001,4,1) # Add assets you'd like to see self.csco = self.AddEquity("CSCO", Resolution.Daily).Symbol self.intc = self.AddEquity("INTC", Resolution.Daily).Symbol self.cien = self.AddEquity("CIEN", Resolution.Daily).Symbol self.sunw = self.AddEquity("SUNW", Resolution.Daily).Symbol self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol self.qcom = self.AddEquity("QCOM", Resolution.Daily).Symbol self.count = 1 def OnData(self, slice): if self.count == 1: self.SetHoldings(self.csco, 0.5) self.SetHoldings(self.intc, -0.5) elif self.count == 6: self.Liquidate(self.csco) self.Liquidate(self.intc) self.SetHoldings(self.cien, 0.5) self.SetHoldings(self.sunw, -0.5) elif self.count == 11: self.Liquidate(self.cien) self.Liquidate(self.sunw) self.SetHoldings(self.spy, 0.5) self.SetHoldings(self.qcom, -0.5) self.count+=1