Overall Statistics
Total Trades
13
Average Win
0.03%
Average Loss
-0.02%
Compounding Annual Return
20.601%
Drawdown
44.300%
Expectancy
1.118
Net Profit
75.920%
Sharpe Ratio
0.811
Loss Rate
14%
Win Rate
86%
Profit-Loss Ratio
1.47
Alpha
0.086
Beta
0.869
Annual Standard Deviation
0.279
Annual Variance
0.078
Information Ratio
0.268
Tracking Error
0.241
Treynor Ratio
0.261
Total Fees
$33.81
 
import numpy as np

### <summary>
### Basic template algorithm simply initializes the date range and cash. This is a skeleton
### framework you can use for designing an algorithm.
### </summary>
class BasicTemplateAlgorithm(QCAlgorithm):
    

    def Initialize(self):
      

        self.SetStartDate(2010,10, 7)  #Set Start Date
        self.SetEndDate(2013,10,11)    #Set End Date
        self.SetCash(100000)           #Set Strategy Cash
      
        self.AddEquity("SPY", Resolution.Minute)
        self.AddEquity("AAPL", Resolution.Minute)
        self._EMA = self.EMA("SPY", 30, Resolution.Minute)
        
        self.SetBenchmark("SPY")
        self.position = None
 
    
    
    

    def OnData(self, data):
        if not self._EMA.IsReady:
            return
        
        if self._EMA.Current.Value >= self.Portfolio["SPY"].Price and not self.Portfolio.Invested:
            self.SetHoldings("SPY", 1)
            self.position == "SPY"
            
        
        else:
            self.Liquidate("SPY")
            self.SetHoldings("AAPL", 1)
            self.position == "AAPL"