Overall Statistics
Total Trades
256
Average Win
1.79%
Average Loss
-1.58%
Compounding Annual Return
8.457%
Drawdown
21.000%
Expectancy
0.232
Net Profit
55.253%
Sharpe Ratio
0.688
Probabilistic Sharpe Ratio
16.721%
Loss Rate
42%
Win Rate
58%
Profit-Loss Ratio
1.13
Alpha
0.064
Beta
-0.015
Annual Standard Deviation
0.09
Annual Variance
0.008
Information Ratio
-0.243
Tracking Error
0.186
Treynor Ratio
-4.119
Total Fees
$1092.88
Estimated Strategy Capacity
$51000000.00
Lowest Capacity Asset
TSLA UNU3P8Y3WFAD
 
# Fading The Normalized Gap

# https://www.quantconnect.com/project/11953395

# --------------------------------
STOCK = "TSLA"; THRESHOLD = -0.02; 
# --------------------------------

class FadingTheGap(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2017, 1, 1)
        self.SetEndDate(2022, 6, 1)
        self.SetCash(100000) 
        self.stock = self.AddEquity(STOCK, Resolution.Minute).Symbol
        self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose(self.stock, 0), self.ClosingBar) 
        self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen(self.stock, 1), self.OpeningBar)
        self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen(self.stock, 45), self.ClosePositions) 
        self.window = RollingWindow[TradeBar](2)
        
        
    def ClosingBar(self):
        self.window.Add(self.CurrentSlice[self.stock])
        
    
    def OpeningBar(self):
        if self.stock in self.CurrentSlice.Bars:
            self.window.Add(self.CurrentSlice[self.stock])
        if not self.window.IsReady: return

        norm_delta = self.window[0].Open / self.window[1].Close - 1

        if norm_delta < THRESHOLD:
            self.SetHoldings(self.stock, 1)
            
        
    def ClosePositions(self):
        self.Liquidate()