Overall Statistics |
Total Trades 11 Average Win 2.46% Average Loss -3.45% Compounding Annual Return 17.274% Drawdown 11.100% Expectancy 0.371 Net Profit 6.347% Sharpe Ratio 0.831 Probabilistic Sharpe Ratio 43.840% Loss Rate 20% Win Rate 80% Profit-Loss Ratio 0.71 Alpha -0.189 Beta 1.301 Annual Standard Deviation 0.197 Annual Variance 0.039 Information Ratio -1.057 Tracking Error 0.102 Treynor Ratio 0.126 Total Fees $16.15 Estimated Strategy Capacity $110000000.00 Lowest Capacity Asset QQQ RIWIV7K5Z9LX |
""" v2.5 Dual Momentum with Out Days by Vladimir inspired by Peter Guenther, Tentor Testivis, Dan Whitnable, Thomas Chang and T Smith. 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 # ---------------------------------------------------------- STOCKS = ['QQQ', 'FDN']; BONDS = ['TLT', 'TLH']; VOLA = 126; BASE_RET = 83; RET = 252; EXCL = 21; LEV = 1.00; # ---------------------------------------------------------- class DualMomentumInOut(QCAlgorithm): def Initialize(self): self.SetStartDate(2021, 1, 1) #self.SetEndDate(2021, 2, 1) self.cap = 100000 self.STK1 = self.AddEquity('SPY', Resolution.Hour).Symbol self.STK2 = self.AddEquity('QQQ', Resolution.Hour).Symbol self.BND1 = self.AddEquity('TLT', Resolution.Hour).Symbol self.BND2 = self.AddEquity('TLH', Resolution.Hour).Symbol self.ASSETS = [self.STK1, self.STK2, self.BND1, self.BND2] self.SLV = self.AddEquity('SLV', Resolution.Daily).Symbol self.GLD = self.AddEquity('GLD', Resolution.Daily).Symbol self.XLI = self.AddEquity('XLI', Resolution.Daily).Symbol self.XLU = self.AddEquity('XLU', Resolution.Daily).Symbol self.DBB = self.AddEquity('DBB', Resolution.Daily).Symbol self.UUP = self.AddEquity('UUP', Resolution.Daily).Symbol self.MKT = self.AddEquity('SPY', Resolution.Daily).Symbol self.pairs = [self.SLV, self.GLD, self.XLI, self.XLU, self.DBB, self.UUP] self.bull = 1 self.count = 0 self.outday = 0 self.wt = {} self.real_wt = {} self.mkt = [] self.SetWarmUp(timedelta(350)) self.selected_bond = self.BND1 self.selected_stock = self.STK1 self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 100), self.calculate_signal) symbols = [self.MKT] + self.pairs for symbol in symbols: self.consolidator = TradeBarConsolidator(timedelta(days=1)) self.consolidator.DataConsolidated += self.consolidation_handler self.SubscriptionManager.AddConsolidator(symbol, self.consolidator) self.history = self.History(symbols, VOLA + 1, Resolution.Daily) if self.history.empty or 'close' not in self.history.columns: return self.history = self.history['close'].unstack(level=0).dropna() def consolidation_handler(self, sender, consolidated): self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close self.history = self.history.iloc[-(VOLA + 1):] def returns(self, symbol, period, excl): prices = self.History(symbol, TimeSpan.FromDays(period + excl), Resolution.Daily).close return prices[-excl] / prices[0] def calculate_signal(self): vola = self.history[[self.MKT]].pct_change().std() * np.sqrt(252) wait_days = int(vola * BASE_RET) period = int((1.0 - vola) * BASE_RET) r = self.history.pct_change(period).iloc[-1] exit = ((r[self.SLV] < r[self.GLD]) and (r[self.XLI] < r[self.XLU]) and (r[self.DBB] < r[self.UUP])) if exit: self.bull = False self.outday = self.count if self.count >= self.outday + wait_days: self.bull = True self.count += 1 if self.returns(self.BND1, RET, EXCL) < self.returns(self.BND2, RET, EXCL): self.selected_bond = self.BND2 elif self.returns(self.BND1, RET, EXCL) > self.returns(self.BND2, RET, EXCL): self.selected_bond = self.BND1 if self.returns(self.STK1, RET, EXCL) < self.returns(self.STK2, RET, EXCL): self.selected_stock = self.STK2 elif self.returns(self.STK1, RET, EXCL) > self.returns(self.STK2, RET, EXCL): self.selected_stock = self.STK1 if not self.bull: for sec in self.ASSETS: self.wt[sec] = LEV if sec is self.selected_bond else 0 if sec is self.selected_bond else 0 self.trade() elif self.bull: for sec in self.ASSETS: self.wt[sec] = LEV if sec is self.selected_stock else 0 self.trade() def trade(self): for sec, weight in self.wt.items(): if weight == 0 and self.Portfolio[sec].IsLong: self.Liquidate(sec) cond1 = weight == 0 and self.Portfolio[sec].IsLong cond2 = weight > 0 and not self.Portfolio[sec].Invested if cond1 or cond2: self.SetHoldings(sec, weight) def OnEndOfDay(self): mkt_price = self.Securities[self.MKT].Close self.mkt.append(mkt_price) mkt_perf = self.mkt[-1] / self.mkt[0] * self.cap self.Plot('Strategy Equity', 'SPY', mkt_perf) account_leverage = self.Portfolio.TotalHoldingsValue / self.Portfolio.TotalPortfolioValue self.Plot('Holdings', 'leverage', round(account_leverage, 1)) for sec, weight in self.wt.items(): self.real_wt[sec] = round(self.ActiveSecurities[sec].Holdings.Quantity * self.Securities[sec].Price / self.Portfolio.TotalPortfolioValue,4) self.Plot('Holdings', self.Securities[sec].Symbol, round(self.real_wt[sec], 3))