Overall Statistics
Total Trades
0
Average Win
0%
Average Loss
0%
Compounding Annual Return
0%
Drawdown
0%
Expectancy
0
Net Profit
0%
Sharpe Ratio
0
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0
Beta
0
Annual Standard Deviation
0
Annual Variance
0
Information Ratio
0
Tracking Error
0
Treynor Ratio
0
Total Fees
$0.00
 
import numpy as np
from datetime import timedelta
### <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(2018,8, 1)  #Set Start Date
        self.SetEndDate(2018,8,7)    #Set End Date
        self.SetCash(100000)           #Set Strategy Cash
        # Find more symbols here: http://quantconnect.com/data
        self.AddEquity("IBM", Resolution.Daily)
        self.rsi = self.RSI("IBM", 14, Resolution.Daily)
        self.AverageLossWin = RollingWindow[Decimal](3)
        self.AverageGainWin = RollingWindow[Decimal](3)
        self.rsiWin = RollingWindow[Decimal](3)
        self.SetWarmUp(timedelta(days= 20))
    
    
    def OnData(self, data):
        if self.IsWarmingUp: return
        self.AverageLossWin.Add(self.rsi.AverageLoss.Current.Value) 
        self.AverageGainWin.Add(self.rsi.AverageGain.Current.Value) 
        self.rsiWin.Add(self.rsi.Current.Value) 
        if self.rsiWin.IsReady:
            self.Debug(self.AverageLossWin[0])