Overall Statistics |
Total Trades 537 Average Win 0.16% Average Loss -0.14% Compounding Annual Return 5.118% Drawdown 26.400% Expectancy 0.949 Net Profit 64.586% Sharpe Ratio 0.451 Probabilistic Sharpe Ratio 2.813% Loss Rate 10% Win Rate 90% Profit-Loss Ratio 1.16 Alpha 0.055 Beta -0.069 Annual Standard Deviation 0.106 Annual Variance 0.011 Information Ratio -0.304 Tracking Error 0.198 Treynor Ratio -0.695 Total Fees $547.16 |
'''An implementation of Meb Faber's basic Global Tactical Asset Allocation model (GTAA)(5) B&H portfolio with monthly rebalancing, as found in the following paper: "A Quantitative Approach to Tactical Asset Allocation" published May 2006. ''' class GlobalTacticalAssetAllocationBase(QCAlgorithm): def Initialize(self): self.SetStartDate(datetime.now() - timedelta(365*10)) self.SetEndDate(datetime.now() - timedelta(5)) self.SetCash(100000) self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin) self.UniverseSettings.Resolution = Resolution.Daily symbols = [ Symbol.Create("SPY", SecurityType.Equity, Market.USA), #US Large Cap Symbol.Create("VEA", SecurityType.Equity, Market.USA), #Developed Foreign Stocks (2007/8) Symbol.Create("IEF", SecurityType.Equity, Market.USA), #US 10Y Gov.Bonds Symbol.Create("DBC", SecurityType.Equity, Market.USA), #GSCI Commodities (2006/3) Symbol.Create("VNQ", SecurityType.Equity, Market.USA) #US RealEstate ] self.AddUniverseSelection(ManualUniverseSelectionModel(symbols)) self.AddAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(days = 20*365), None, None)) self.Settings.RebalancePortfolioOnInsightChanges = False self.Settings.RebalancePortfolioOnSecurityChanges = False self.SetPortfolioConstruction( EqualWeightingPortfolioConstructionModel(self.DateRules.MonthStart()) ) self.SetExecution( ImmediateExecutionModel() ) self.AddRiskManagement( NullRiskManagementModel() ) # 1) Setting a Benchmark to plot with equity self.benchmarkTicker = 'SPY' self.SetBenchmark(self.benchmarkTicker) self.initBenchmarkPrice = None def UpdateBenchmarkValue(self): ''' Simulate buy and hold the Benchmark ''' if self.initBenchmarkPrice is None: self.initBenchmarkCash = self.Portfolio.Cash self.initBenchmarkPrice = self.Benchmark.Evaluate(self.Time) self.benchmarkValue = self.initBenchmarkCash else: currentBenchmarkPrice = self.Benchmark.Evaluate(self.Time) self.benchmarkValue = (currentBenchmarkPrice / self.initBenchmarkPrice) * self.initBenchmarkCash def OnData(self, data): # 2) simulate buy and hold the benchmark and plot its daily value as we are using daily data. # Otherwise schedule when to call this function! self.UpdateBenchmarkValue() self.Plot('Strategy Equity', self.benchmarkTicker, self.benchmarkValue)