Overall Statistics
Total Trades
315
Average Win
0.14%
Average Loss
-0.15%
Compounding Annual Return
4.264%
Drawdown
3.000%
Expectancy
0.018
Net Profit
0.977%
Sharpe Ratio
0.441
Probabilistic Sharpe Ratio
38.423%
Loss Rate
47%
Win Rate
53%
Profit-Loss Ratio
0.94
Alpha
0.019
Beta
0.037
Annual Standard Deviation
0.089
Annual Variance
0.008
Information Ratio
-3.906
Tracking Error
0.13
Treynor Ratio
1.07
Total Fees
$342.06
class EMAMomentumUniverse(QCAlgorithm):
    
    def Initialize(self):
        self.SetStartDate(2019, 1, 7)
        self.SetEndDate(2019, 4, 1)
        self.SetCash(100000)
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.CoarseSelectionFunction) 
        self.averages = { }
    
    def CoarseSelectionFunction(self, universe):  
        selected = []
        universe = sorted(universe, key=lambda c: c.DollarVolume, reverse=True)  
        universe = [c for c in universe if c.Price > 10][:100]

        for coarse in universe:  
            symbol = coarse.Symbol
            
            if symbol not in self.averages:
                # 1. Call history to get an array of 200 days of history data
                history = self.History(symbol, 200, Resolution.Daily)
                
                #2. Adjust SelectionData to pass in the history result
                self.averages[symbol] = SelectionData(history) 

            self.averages[symbol].update(self.Time, coarse.AdjustedPrice)
            
            if  self.averages[symbol].is_ready() and self.averages[symbol].fast > self.averages[symbol].slow:
                selected.append(symbol)
        
        return selected[:10]
        
    def OnSecuritiesChanged(self, changes):
        for security in changes.RemovedSecurities:
            self.Liquidate(security.Symbol)
       
        for security in changes.AddedSecurities:
            self.SetHoldings(security.Symbol, 0.10)
            
class SelectionData():
    #3. Update the constructor to accept a history array
    def __init__(self, history):
        self.slow = ExponentialMovingAverage(200)
        self.fast = ExponentialMovingAverage(50)
        self.keltner = KeltnerChannels(10, 2, MovingAverageType.Simple)
        #4. Loop over the history data and update the indicatorsc
        for bar in history.itertuples():
            tradeBar = TradeBar(bar.Index[1], bar.Index[0], bar.open, bar.high, bar.low, bar.close, bar.volume, timedelta(1))
            self.fast.Update(bar.Index[1], bar.close)
            self.slow.Update(bar.Index[1], bar.close)
            self.keltner.Update(tradeBar)
    
    def is_ready(self):
        return self.slow.IsReady and self.fast.IsReady and self.keltner.IsReady
    
    def update(self, time, price):
        self.fast.Update(time, price)
        self.slow.Update(time, price)