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"                        ```