Overall Statistics
Total Trades
85
Average Win
1.55%
Average Loss
-1.03%
Compounding Annual Return
9.315%
Drawdown
11.200%
Expectancy
0.192
Net Profit
8.756%
Sharpe Ratio
0.677
Probabilistic Sharpe Ratio
37.691%
Loss Rate
52%
Win Rate
48%
Profit-Loss Ratio
1.50
Alpha
-0.095
Beta
0.744
Annual Standard Deviation
0.147
Annual Variance
0.021
Information Ratio
-1.386
Tracking Error
0.117
Treynor Ratio
0.133
Total Fees
$91.35
 
import operator

class MOMPAlgorithm(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2019, 1, 1)
        self.SetEndDate(2019, 12, 10)
        self.SetCash(10000)
        self.mom = {}
        
        for ticker in ["XLK", "XLF", "XLI", "XLY", "XLB", "XLP", "XLV", "XLU", "XLE"]:
            symbol = self.AddEquity(ticker, Resolution.Daily).Symbol
            self.mom[symbol] = self.MOMP(symbol, 5, Resolution.Daily)
        
        self.SetWarmUp(26, Resolution.Daily)
        
        self.SetBenchmark("SPY")
        
        self.Schedule.On(self.DateRules.Every(DayOfWeek.Wednesday), self.TimeRules.At(12, 0), self.EveryWedAtNoon)
        
    
    def EveryWedAtNoon(self):
        
        #check if indicators are ready to go
        if not all([MOMP.IsReady for symbol, MOMP in self.mom.items()]): 
            return
        
        #find key with highest MOMP value
        highestKey = sorted(self.mom, key = lambda k: self.mom[k].Current.Value, reverse = True)[0]
        
        #restructure portfolio according to changes in MOMP
        if self.Portfolio.Invested and not self.Portfolio[highestKey].Invested:
            self.Liquidate()
        self.SetHoldings(highestKey, 1)