Overall Statistics |
Total Trades 9081 Average Win 0.20% Average Loss -0.17% Compounding Annual Return -4.799% Drawdown 52.500% Expectancy -0.071 Net Profit -37.885% Sharpe Ratio -0.264 Probabilistic Sharpe Ratio 0.001% Loss Rate 57% Win Rate 43% Profit-Loss Ratio 1.17 Alpha -0.029 Beta -0.03 Annual Standard Deviation 0.123 Annual Variance 0.015 Information Ratio -0.851 Tracking Error 0.183 Treynor Ratio 1.085 Total Fees $9081.43 |
class EMAMomentumUniverse(QCAlgorithm): def Initialize(self): self.SetStartDate(2010, 7, 1) self.SetEndDate(2020, 7, 1) self.SetCash(10000) 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) #4. Loop over the history data and update the indicators for bar in history.itertuples(): self.fast.Update(bar.Index[1], bar.close) self.slow.Update(bar.Index[1], bar.close) def is_ready(self): return self.slow.IsReady and self.fast.IsReady def update(self, time, price): self.fast.Update(time, price) self.slow.Update(time, price)