Overall Statistics |
Total Trades 312 Average Win 4.02% Average Loss -1.08% Compounding Annual Return 31.713% Drawdown 18.000% Expectancy 2.256 Net Profit 3501.560% Sharpe Ratio 1.654 Probabilistic Sharpe Ratio 96.890% Loss Rate 31% Win Rate 69% Profit-Loss Ratio 3.73 Alpha 0.259 Beta 0.129 Annual Standard Deviation 0.164 Annual Variance 0.027 Information Ratio 0.754 Tracking Error 0.23 Treynor Ratio 2.096 Total Fees $14146.38 |
""" v2.5 Dual Momentum with Out Days by Vladimir inspired by Peter Guenther, Tentor Testivis, Dan Whitnable, Thomas Chang and T Smith. converted to QC Algorithm Framework (Alpha Model + Portfolio Construction Model) by Joao Antunes based on Intersection of ROC comparison using OUT_DAY approach by Vladimir modified parameters BASE_RET = 83; https://www.quantconnect.com/forum/discussion/10039/dual-momentum-with-out-days/p1/comment-29928 """ from itertools import groupby from dual_momentum_with_out_days_alpha import DualMomentumWithOutDaysAlphaModel VOLA = 126; BASE_RET = 83; RET = 252; EXCL = 21; LEV = 1.00; class HorizontalQuantumCoil(QCAlgorithm): def Initialize(self): self.SetStartDate(2008, 1, 1) self.SetEndDate(2020, 12, 31) self.SetCash(100_000) self.SetWarmUp(timedelta(350)) # self.Portfolio.MarginCallModel = MarginCallModel.Null self.SetAlpha(DualMomentumWithOutDaysAlphaModel(self, VOLA, BASE_RET, Resolution.Daily)) symbols = [ Symbol.Create('QQQ', SecurityType.Equity, Market.USA), Symbol.Create('FDN', SecurityType.Equity, Market.USA), Symbol.Create('TLT', SecurityType.Equity, Market.USA), Symbol.Create('TLH', SecurityType.Equity, Market.USA), ] self.SetUniverseSelection(ManualUniverseSelectionModel(symbols)) self.UniverseSettings.Resolution = Resolution.Hour self.SetPortfolioConstruction(OutDays(self, RET, EXCL, LEV, Resolution.Hour)) self.createPlots("SPY") def createPlots(self, benchmark): self.__benchmark = benchmark self.__plot_every_n_days = 5 self.__plot_every_n_days_i = 0 plot = Chart('Performance') plot.AddSeries(Series(self.__benchmark, SeriesType.Line, 0, '%')) plot.AddSeries(Series("Algorithm", SeriesType.Line, 0, '%')) self.AddChart(plot) self.ResetPlot() def ResetPlot(self): self.year = self.Time.year self.__cost_portfolio = None self.__cost_benchmark = None def CalculateBenchmarkPerformance(self): price = self.Securities[self.__benchmark].Price if self.__cost_benchmark == None: self.__cost_benchmark = price return 100.0 * ((price / self.__cost_benchmark) - 1.0) def CalculatePortfolioPerformance(self): if self.__cost_portfolio == None: self.__cost_portfolio = self.Portfolio.TotalPortfolioValue return 100.0 * ((self.Portfolio.TotalPortfolioValue / self.__cost_portfolio) - 1.0) def OnEndOfDay(self): if self.IsWarmingUp or not self.Securities[self.__benchmark].HasData: return if self.Time.year != self.year: self.ResetPlot() self.__plot_every_n_days_i == -1 self.__plot_every_n_days_i += 1 if self.__plot_every_n_days_i % self.__plot_every_n_days != 0: return self.Plot('Performance', self.__benchmark, self.CalculateBenchmarkPerformance()) self.Plot('Performance', "Algorithm", self.CalculatePortfolioPerformance()) class OutDays(PortfolioConstructionModel): def __init__(self, algorithm, RET=252, EXCL=21, LEV=1.00, resolution = Resolution.Hour): self.algorithm = algorithm self.RET = RET self.EXCL = EXCL self.LEV = LEV self.STK1 = self.algorithm.AddEquity('QQQ', resolution).Symbol self.STK2 = self.algorithm.AddEquity('FDN', resolution).Symbol self.BND1 = self.algorithm.AddEquity('TLT', resolution).Symbol self.BND2 = self.algorithm.AddEquity('TLH', resolution).Symbol self.ASSETS = [self.STK1, self.STK2, self.BND1, self.BND2] def returns(self, symbol): prices = self.algorithm.History(symbol, TimeSpan.FromDays(self.RET + self.EXCL), Resolution.Daily).close return prices[-self.EXCL] / prices[0] def CreateTargets(self, algorithm, insights): if algorithm.IsWarmingUp: return [] targets = [] # We expect at most only one active insight since we only # generate insights for one equity. assert len(insights) <= 1 if len(insights) == 1: insight = insights[0] self.bull = insight.Direction == InsightDirection.Up if self.bull: if self.returns(self.STK1) < self.returns(self.STK2): selected = self.STK2 else: selected = self.STK1 else: if self.returns(self.BND1) < self.returns(self.BND2): selected = self.BND2 else: selected = self.BND1 for asset in self.ASSETS: if asset != selected: targets.append(PortfolioTarget.Percent(algorithm, asset, 0.0)) targets.append(PortfolioTarget.Percent(algorithm, selected, 1.0)) return targets
""" v2.5 Dual Momentum with Out Days by Vladimir inspired by Peter Guenther, Tentor Testivis, Dan Whitnable, Thomas Chang and T Smith. converted to QC Algorithm Framework (Alpha Model + Portfolio Construction Model) by Joao Antunes based on Intersection of ROC comparison using OUT_DAY approach by Vladimir modified parameters BASE_RET = 83; https://www.quantconnect.com/forum/discussion/10039/dual-momentum-with-out-days/p1/comment-29928 """ import numpy as np class DualMomentumWithOutDaysAlphaModel(AlphaModel): def __init__(self, algorithm, VOLA = 126, BASE_RET = 83, resolution = Resolution.Daily): self.VOLA = VOLA self.BASE_RET = BASE_RET self.resolution = resolution self.MKT = algorithm.AddEquity('SPY', resolution).Symbol self.SLV = algorithm.AddEquity('SLV', resolution).Symbol self.GLD = algorithm.AddEquity('GLD', resolution).Symbol self.XLI = algorithm.AddEquity('XLI', resolution).Symbol self.XLU = algorithm.AddEquity('XLU', resolution).Symbol self.DBB = algorithm.AddEquity('DBB', resolution).Symbol self.UUP = algorithm.AddEquity('UUP', resolution).Symbol self.count = 0 pairs = [self.MKT, self.SLV, self.GLD, self.XLI, self.XLU, self.DBB, self.UUP] for symbol in pairs: self.consolidator = TradeBarConsolidator(timedelta(days=1)) self.consolidator.DataConsolidated += self.consolidation_handler algorithm.SubscriptionManager.AddConsolidator(symbol, self.consolidator) self.history = algorithm.History(pairs, self.VOLA + 1, resolution) self.history = self.history['close'].unstack(level=0).dropna() self.predictionInterval = Time.Multiply(Extensions.ToTimeSpan(self.resolution), 30) resolutionString = Extensions.GetEnumString(resolution, Resolution) self.Name = f"{self.__class__.__name__}({resolutionString})" # Force alpha to only produce insights daily at 11.10am self.set_flag = False algorithm.Schedule.On(algorithm.DateRules.EveryDay(), algorithm.TimeRules.AfterMarketOpen('SPY', 100), self.SetFlag) def SetFlag(self): self.set_flag = True def consolidation_handler(self, sender, consolidated): self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close self.history = self.history.iloc[-(self.VOLA + 1):] def Update(self, algorithm, _data): if algorithm.IsWarmingUp or not self.set_flag: return [] self.set_flag = False insights = [] vola = self.history[self.MKT].pct_change().std() * np.sqrt(252) wait_days = int(vola * self.BASE_RET) period = int((1.0 - vola) * self.BASE_RET) r = self.history.pct_change(period).iloc[-1] exit_market = r[self.SLV] < r[self.GLD] and r[self.XLI] < r[self.XLU] and r[self.DBB] < r[self.UUP] direction = InsightDirection.Down if exit_market: self.count = 0 else: self.count += 1 if self.count >= wait_days: direction = InsightDirection.Up insights.append(Insight.Price(self.MKT, self.predictionInterval, direction)) return insights