Overall Statistics
Total Trades
1
Average Win
0%
Average Loss
0%
Compounding Annual Return
239.027%
Drawdown
1.100%
Expectancy
0
Net Profit
1.687%
Sharpe Ratio
4.159
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0
Beta
62.235
Annual Standard Deviation
0.172
Annual Variance
0.03
Information Ratio
4.093
Tracking Error
0.172
Treynor Ratio
0.011
Total Fees
$3.29
 
import numpy as np
import pandas as pd

class portfolioLogReturnsExample(QCAlgorithm):

    def Initialize(self):

        self.cash = 100000

        self.SetStartDate(2013,10, 7)  #Set Start Date
        self.SetEndDate(2013,10,11)    #Set End Date
        self.SetCash(self.cash)           #Set Strategy Cash
        
        self.symbol = "SPY"
        self.AddEquity(self.symbol, Resolution.Daily)
        
        # Create empty DataFrame to store portfolio value
        self.df = pd.DataFrame()
        
    def OnData(self, data):

        if not self.Portfolio.Invested:
            self.SetHoldings(self.symbol, 1)
            self.df = self.df.append({"PortfolioValue":self.cash},ignore_index=True)
            return
        
        # Append dictionary to DataFrame
        self.df = self.df.append({"PortfolioValue":self.Portfolio.TotalHoldingsValue},ignore_index=True)
        
        # Calculate log returns
        logPct = np.log(self.df["PortfolioValue"]).diff().dropna()
        
        # Print log returns
        self.Log(str(logPct))