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
-1.724
Tracking Error
0.148
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
 
# MOMP test.
# ---------------------------------
STOCK = 'QQQ'; RETURN_PERIOD = 126;
# ---------------------------------

class EnergeticOrangeBat(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2021, 1, 1) 
        self.SetEndDate(2021, 3, 1)
        self.SetCash(100000) 
        
        self.stock = self.AddEquity(STOCK, Resolution.Daily).Symbol 
        self.EnableAutomaticIndicatorWarmUp = True

        self.mom = self.MOMP(self.stock, RETURN_PERIOD, Resolution.Daily)
        
        # Warm up
        history = self.History(self.stock, RETURN_PERIOD + 1, Resolution.Daily)
        for time, row in history.loc[self.stock].iterrows():
            self.mom .Update(time, row.close)
            
        self.Log(f"Starting values: {self.mom.Current.Value}")
        
    def returns(self, symbol, period):
        prices = self.History(symbol, period + 1, Resolution.Daily).close
        return float(100*(prices.iloc[-1] / prices.iloc[0] - 1))     
            

    def OnData(self, data):
        if not (data.ContainsKey(self.stock) and data[self.stock] is not None):
            return
        
        self.mom_2 = self.returns(self.stock, RETURN_PERIOD)

        self.Plot("Momentum Percent", "MOMP", self.mom.Current.Value)
        self.Plot("Momentum Percent", "RET", self.mom_2)
        
        
        self.Log(f"Ending values: {self.mom.Current.Value} {self.mom_2}")