Overall Statistics
Total Trades
4
Average Win
5.73%
Average Loss
-17.31%
Compounding Annual Return
-6.939%
Drawdown
28.400%
Expectancy
-0.113
Net Profit
-6.896%
Sharpe Ratio
-0.243
Loss Rate
33%
Win Rate
67%
Profit-Loss Ratio
0.33
Alpha
0.121
Beta
-8.617
Annual Standard Deviation
0.207
Annual Variance
0.043
Information Ratio
-0.339
Tracking Error
0.207
Treynor Ratio
0.006
Total Fees
$134.90
 
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):
    '''Basic template algorithm simply initializes the date range and cash'''

    def Initialize(self):
        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

        self.SetStartDate(2017,1, 1)   #Set Start Date
        self.SetEndDate(2017,12,31)    #Set End Date
        self.SetCash(100000)           #Set Strategy Cash
        
        # Set Benchmark SPY
        self.SetBenchmark("SPY")
        # Find more symbols here: http://quantconnect.com/data
        self.AddEquity("BAC")
        self.Schedule.On(self.DateRules.On(2017, 2, 1), self.TimeRules.At(10, 0), Action(self.buy))
        self.Schedule.On(self.DateRules.On(2017, 3, 31), self.TimeRules.At(10, 0), Action(self.sell))
        self.Schedule.On(self.DateRules.On(2017, 11, 1), self.TimeRules.At(10, 0), Action(self.buy))
        self.Schedule.On(self.DateRules.On(2017, 12, 29), self.TimeRules.At(10, 0), Action(self.sell))

    def OnData(self, data):
        
        pass
         
    def buy(self):
        # place buy order
        self.SetHoldings("BAC", 1)
    def sell(self):
        # place sell order
        self.SetHoldings("BAC", -1)