Overall Statistics |
Total Trades 831 Average Win 0.76% Average Loss -1.14% Compounding Annual Return 13.985% Drawdown 27.600% Expectancy 0.245 Net Profit 270.626% Sharpe Ratio 0.643 Probabilistic Sharpe Ratio 6.996% Loss Rate 25% Win Rate 75% Profit-Loss Ratio 0.67 Alpha -0.01 Beta 1.133 Annual Standard Deviation 0.173 Annual Variance 0.03 Information Ratio 0.038 Tracking Error 0.102 Treynor Ratio 0.098 Total Fees $2777.92 Estimated Strategy Capacity $88000000.00 Lowest Capacity Asset LLY R735QTJ8XC9X |
#region imports from AlgorithmImports import * #endregion # https://quantpedia.com/Screener/Details/14 class MomentumEffectAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2009, 7, 1) # Set Start Date self.SetEndDate(2019, 7, 1) # Set Start Date self.SetCash(100000) # Set Strategy Cash self.UniverseSettings.Resolution = Resolution.Daily self.momp = {} # Dict of Momentum indicator keyed by Symbol self.lookback = 252 # Momentum indicator lookback period self.num_coarse = 100 # Number of symbols selected at Coarse Selection self.num_fine = 50 # Number of symbols selected at Fine Selection self.num_long = 5 # Number of symbols with open positions self.month = -1 self.rebalance = False self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) def CoarseSelectionFunction(self, coarse): '''Drop securities which have no fundamental data or have too low prices. Select those with highest by dollar volume''' if self.month == self.Time.month: return Universe.Unchanged self.rebalance = True self.month = self.Time.month selected = sorted([x for x in coarse if x.HasFundamentalData and x.Price > 5], key=lambda x: x.DollarVolume, reverse=True) return [x.Symbol for x in selected[:self.num_coarse]] def FineSelectionFunction(self, fine): '''Select security with highest market cap''' selected = sorted(fine, key=lambda f: f.MarketCap, reverse=True) return [x.Symbol for x in selected[:self.num_fine]] def OnData(self, data): # Update the indicator for symbol, mom in self.momp.items(): mom.Update(self.Time, self.Securities[symbol].Close) if not self.rebalance: return # Selects the securities with highest momentum sorted_mom = sorted([k for k,v in self.momp.items() if v.IsReady], key=lambda x: self.momp[x].Current.Value, reverse=True) selected = sorted_mom[:self.num_long] # Liquidate securities that are not in the list for symbol, mom in self.momp.items(): if symbol not in selected: self.Liquidate(symbol, 'Not selected') # Buy selected securities for symbol in selected: self.SetHoldings(symbol, 1/self.num_long) self.rebalance = False def OnSecuritiesChanged(self, changes): # Clean up data for removed securities and Liquidate for security in changes.RemovedSecurities: symbol = security.Symbol if self.momp.pop(symbol, None) is not None: self.Liquidate(symbol, 'Removed from universe') for security in changes.AddedSecurities: if security.Symbol not in self.momp: self.momp[security.Symbol] = MomentumPercent(self.lookback) # Warm up the indicator with history price if it is not ready addedSymbols = [k for k,v in self.momp.items() if not v.IsReady] history = self.History(addedSymbols, 1 + self.lookback, Resolution.Daily) history = history.close.unstack(level=0) for symbol in addedSymbols: ticker = symbol.ID.ToString() if ticker in history: for time, value in history[ticker].dropna().items(): item = IndicatorDataPoint(symbol, time.date(), value) self.momp[symbol].Update(item)