Overall Statistics
Total Trades
1
Average Win
0%
Average Loss
0%
Compounding Annual Return
-1.601%
Drawdown
0.300%
Expectancy
0
Net Profit
0%
Sharpe Ratio
-0.317
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
-0.071
Beta
1.202
Annual Standard Deviation
0.034
Annual Variance
0.001
Information Ratio
-7.916
Tracking Error
0.008
Treynor Ratio
-0.009
Total Fees
$1.97
 
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(2017,10,07)  #Set Start Date
        self.SetEndDate(2017,10,10)    #Set End Date
        self.SetCash(100000)           #Set Strategy Cash
        # Find more symbols here: http://quantconnect.com/data
        self.AddEquity("SPY", Resolution.Daily)
        self.Debug("numpy test >>> print numpy.pi: " + str(np.pi))

    def OnData(self, data):
        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.

        Arguments:
            data: Slice object keyed by symbol containing the stock data
        '''
        self.Debug(str(self.Time)  + " OnData")        
        if not self.Portfolio.Invested:
            self.SetHoldings("SPY", 1)