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
Probabilistic 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
-2.048
Tracking Error
0.162
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
 
import numpy as np

class FatBrownChimpanzee(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2020, 6, 1)  # Set Start Date
        self.SetCash(100000)  # Set Strategy Cash
        self.AddEquity("SPY", Resolution.Daily)
        self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol

        
        self.logr = self.LOGR("SPY", 1, Resolution.Daily)   #LogReturns for a single day?
        # self.logrWindow = [] # I believe we should somehow store a rolling window of the above (last 100days)?
        # Then calculate the STD of the LogReturns over the last 100days
        # self.stdDev = self.STD(self.logrWindow, 100)

        


    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.logrWindow.append(self.logr)
        self.Plot('logr', 'logr', self.logr.Current.Value )


        sigma = np.log1p(self.History([self.spy], 100, Resolution.Daily).close.pct_change()).std()
        self.Plot('sigma', 'sigma', sigma)