Overall Statistics |
Total Trades
685
Average Win
0.84%
Average Loss
-1.38%
Compounding Annual Return
10.676%
Drawdown
29.200%
Expectancy
0.207
Net Profit
175.984%
Sharpe Ratio
0.566
Loss Rate
25%
Win Rate
75%
Profit-Loss Ratio
0.61
Alpha
0.06
Beta
2.937
Annual Standard Deviation
0.202
Annual Variance
0.041
Information Ratio
0.475
Tracking Error
0.202
Treynor Ratio
0.039
Total Fees
$964.09
|
# 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.mom = {} # 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''' fine = [f for f in fine if f.ValuationRatios.PERatio > 0 and f.EarningReports.BasicEPS.TwelveMonths > 0 and f.EarningReports.BasicAverageShares.ThreeMonths > 0] selected = sorted(fine, key=lambda f: f.ValuationRatios.PERatio * f.EarningReports.BasicEPS.TwelveMonths * f.EarningReports.BasicAverageShares.ThreeMonths, reverse=True) return [x.Symbol for x in selected[:self.num_fine]] def OnData(self, data): # Update the indicator for symbol, mom in self.mom.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.mom.items() if v.IsReady], key=lambda x: self.mom[x].Current.Value, reverse=True) selected = sorted_mom[:self.num_long] # Liquidate securities that are not in the list for symbol, mom in self.mom.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.mom.pop(symbol, None) is not None: self.Liquidate(symbol, 'Removed from universe') for security in changes.AddedSecurities: if security.Symbol not in self.mom: self.mom[security.Symbol] = Momentum(self.lookback) # Warm up the indicator with history price if it is not ready addedSymbols = [k for k,v in self.mom.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 = str(symbol) if ticker in history: for time, value in history[ticker].items(): item = IndicatorDataPoint(symbol, time, value) self.mom[symbol].Update(item)