Overall Statistics
Total Trades
1
Average Win
0%
Average Loss
0%
Compounding Annual Return
12.340%
Drawdown
12.900%
Expectancy
0
Net Profit
79.267%
Sharpe Ratio
1.005
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0.138
Beta
-0.73
Annual Standard Deviation
0.123
Annual Variance
0.015
Information Ratio
0.843
Tracking Error
0.123
Treynor Ratio
-0.17
Total Fees
$3.29
 
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(2013,10, 7)  #Set Start Date
        self.SetEndDate(2018,10,11)    #Set End Date
        self.SetCash(100000)           #Set Strategy Cash
        # Find more symbols here: http://quantconnect.com/data
        self.AddEquity("SPY", Resolution.Daily)
        # define a 10-period RSI indicator with indicator constructor
        self.rsi = RelativeStrengthIndex(10, MovingAverageType.Simple)
        # register the daily data of "SPY" to automatically update the indicator
        self.RegisterIndicator("SPY", self.rsi, Resolution.Daily)
        

    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.Plot('RSI', 'SPY', self.rsi.Current.Value)
        if not self.Portfolio.Invested:
            self.SetHoldings("SPY", 1)