Overall Statistics
Total Trades
1
Average Win
0%
Average Loss
0%
Compounding Annual Return
13.901%
Drawdown
23.300%
Expectancy
0
Net Profit
92.525%
Sharpe Ratio
0.858
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0.034
Beta
1.161
Annual Standard Deviation
0.168
Annual Variance
0.028
Information Ratio
0.731
Tracking Error
0.068
Treynor Ratio
0.124
Total Fees
$5.97
 
import numpy as np

class betaExample(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(2014,1, 1)  #Set Start Date
        self.SetEndDate(2019,1,11)    #Set End Date
        self.SetCash(100000)           #Set Strategy Cash
        # Find more symbols here: http://quantconnect.com/data
        self.tickers = ["QQQ","SPY"]
        for symbol in self.tickers:
            self.AddEquity(symbol, Resolution.Daily)

        self.SetBenchmark("SPY")
        #self.Debug("numpy test >>> print numpy.pi: " + str(np.pi))

    def OnData(self, data):
        #if self.Time.Hour==15 and self.Time.Minute ==59:
        if not self.Portfolio.Invested:
            self.SetHoldings("QQQ", 1)