Overall Statistics |
Total Trades 211 Average Win 10.31% Average Loss -2.03% Compounding Annual Return 38.935% Drawdown 27.900% Expectancy 2.876 Net Profit 13599.381% Sharpe Ratio 1.239 Probabilistic Sharpe Ratio 65.940% Loss Rate 36% Win Rate 64% Profit-Loss Ratio 5.07 Alpha 0.249 Beta 0.466 Annual Standard Deviation 0.234 Annual Variance 0.055 Information Ratio 0.849 Tracking Error 0.238 Treynor Ratio 0.622 Total Fees $0.00 Estimated Strategy Capacity $590000.00 Lowest Capacity Asset UUP TQBX2PUC67OL Portfolio Turnover 3.85% |
#region imports from AlgorithmImports import * from QuantConnect.DataSource import * import datetime as dt import pandas as pd import numpy as np import math from pandas.tseries.offsets import BDay #endregion """ Intersection of ROC comparison using OUT_DAY approach by Vladimir Inspired by Peter Guenther, Tentor Testivis, Dan Whitnable, Thomas Chang. Further improved by ideas from Strongs and Tim Nilson """ # ------------------------------------------------------------------ # The original values. Later Optimization by TN (see below) # STK = QQQ or QLD # BND = TLT, UUP # VOLA = 126; BASE_RET = 85; LEV = 1.0; # ------------------------------------------------------------------ # 21.4.2023 Optimal vola = 127; base_ret = 85 class DualMomentumInOut(QCAlgorithm): def Initialize(self): self.SetBrokerageModel(BrokerageName.QuantConnectBrokerage, AccountType.Margin) # First date for which JJC is available is 2018, 2, 2 # If the start date is earlier, we switch from the old pairs to the new pairs on that date # self.SetStartDate(2008, 1, 1) #original: 2008 self.SetStartDate(2008, 7, 1) # colab start #self.SetEndDate(2021, 11, 17) self.cap = 100000 self.SetCash(self.cap) # Parameters #self.VOLA = int(self.GetParameter("volatility")) self.VOLA = 127 #self.BASE_RET = int(self.GetParameter("base")) self.BASE_RET = 85 # Switch from original pairs to new pairs with copper and turn on the switch to use UUP if TLT is trending down # If using start dates prior to 2018, use production_model = 1. Otherwise, use production_model = 4 self.production_model = 4 if self.LiveMode: self.pairs_model = self.production_model # 1 = Original else: self.pairs_model = 1 # self.STK = self.AddEquity('QQQ', Resolution.Minute).Symbol #original: QLD self.STK = self.AddEquity('QLD', Resolution.Minute).Symbol #original: QLD self.BND1 = self.AddEquity('TLT', Resolution.Minute).Symbol #self.BND2 = self.AddEquity('TMF', Resolution.Minute).Symbol self.UUP = self.AddEquity('UUP', Resolution.Minute).Symbol # Dollar self.SLV = self.AddEquity('SLV', Resolution.Daily).Symbol # Silver self.GLD = self.AddEquity('GLD', Resolution.Daily).Symbol # Gold self.XLI = self.AddEquity('XLI', Resolution.Daily).Symbol # Industrial self.XLU = self.AddEquity('XLU', Resolution.Daily).Symbol # Utilities self.DBB = self.AddEquity('DBB', Resolution.Daily).Symbol # Metals self.EEM = self.AddEquity('EEM', Resolution.Daily).Symbol # Emerging Markets #self.FXA = self.AddEquity('FXA', Resolution.Daily).Symbol #self.FXF = self.AddEquity('FXF', Resolution.Daily).Symbol self.MKT = self.AddEquity('SPY', Resolution.Daily).Symbol # S&P500 #self.MKT = self.AddEquity('QQQ', Resolution.Daily).Symbol # S&P500 # BNC is benchmark for equity plot. Use either self.MKT or self.STK self.BNC = self.STK # added UUP as an alternative to BND self.ASSETS = [self.STK, self.BND1, self.UUP] if self.pairs_model == 4: self.CPR = self.AddEquity('JJC', Resolution.Daily).Symbol # Copper self.pairs = [self.SLV, self.GLD, self.XLI, self.XLU, self.EEM, self.MKT, self.DBB, self.BND1, self.UUP, self.CPR] else: self.pairs = [self.SLV, self.GLD, self.XLI, self.XLU, self.EEM, self.MKT, self.DBB, self.BND1, self.UUP] self.bull = 1 self.count = 0 self.outday = 0 self.wait_days = 0 self.wt = {} self.real_wt = {} self.mkt = [] self.pairs_plot = {} # self.SetWarmUp(timedelta(350)) self.history_was_updated = False # need this when we switch over to JJC self.email_lines = [] # store email strings for daily email self.email_sent = False # Add two smas of TLT to test for TLT trends self.bnd_sma_short = self.SMA(self.BND1, 40, Resolution.Daily) self.bnd_sma_long = self.SMA(self.BND1, 80, Resolution.Daily) self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen(self.MKT, 100), self.daily_check) self.symbols = [self.MKT] + self.pairs + [self.BND1, self.UUP] self.build_history(self.symbols) def OnSecuritiesChanged(self, changes:SecurityChanges) -> None: for security in changes.AddedSecurities: security.SetDataNormalizationMode(DataNormalizationMode.Raw) security.SetFeeModel(CustomFeeModel()) def consolidation_handler(self, sender, consolidated): self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close self.history = self.history.iloc[-(self.VOLA + 1):] def build_history(self, symbols): symbols = list(set(symbols)) # to remove duplicates 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, self.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 daily_check(self): self.email_lines = [] # if time is past 2018, 2, 1, add JJC (Copper) jjc_start_date = dt.datetime(2018, 2, 1, 0, 0, 0) # if self.Time >= jjc_start_date and not self.history_was_updated: if self.Time >= (jjc_start_date + BDay(self.VOLA+1)) and not self.history_was_updated: if self.pairs_model != 4: self.CPR = self.AddEquity('JJC', Resolution.Daily).Symbol # Copper self.pairs_model = self.production_model # update the history self.symbols = self.symbols + [self.CPR] self.build_history(self.symbols) self.history_was_updated = True # pairs switch based on TLT vs MKT #r30 = self.history.pct_change(30).iloc[-1] #switch_on = False #if switch_on and (r30[self.BND1] - r30[self.MKT] > 0): # self.pairs_model = 1 #else: # self.pairs_model = 5 # if self.Time >= jjc_start_date: # self.pairs_model = 4 vola = self.history[[self.MKT]].pct_change().std() * np.sqrt(252) self.wait_days = int(vola * self.BASE_RET) period = int((1.0 - vola) * self.BASE_RET) r = self.history.pct_change(period).iloc[-1] # Below we can test various pairs. Only 1 and 4 are now used. if self.pairs_model == 1: exit = ((r[self.SLV] < r[self.GLD]) and (r[self.XLI] < r[self.XLU]) and (r[self.DBB] < r[self.UUP])) elif self.pairs_model == 4: exit = ((r[self.SLV] < r[self.GLD]) and (r[self.XLI] < r[self.XLU]) and (r[self.EEM] < r[self.MKT]) and (r[self.CPR] < r[self.GLD])) self.email_lines.append(f"C_G {round(r[self.CPR], 3)} {round(r[self.GLD], 3)} {r[self.CPR] < r[self.GLD]}") self.pairs_plot['C_G'] = (r[self.CPR] - r[self.GLD]) * -1 # for emailed info and end of algo reporting self.email_lines.append(f"S_G {round(r[self.SLV], 3)} {round(r[self.GLD], 3)} {r[self.SLV] < r[self.GLD]}") self.email_lines.append(f"I_U {round(r[self.XLI], 3)} {round(r[self.XLU], 3)} {r[self.XLI] < r[self.XLU]}") self.email_lines.append(f"E_M {round(r[self.EEM], 3)} {round(r[self.MKT], 3)} {r[self.EEM] < r[self.MKT]}") # For pairs plot self.pairs_plot['S_G'] = (r[self.SLV] - r[self.GLD]) * -1 self.pairs_plot['I_U'] = (r[self.XLI] - r[self.XLU]) * -1 self.pairs_plot['E_M'] = (r[self.EEM] - r[self.MKT]) * -1 self.pairs_plot['D_U'] = (r[self.DBB] - r[self.UUP]) * -1 #self.pairs_plot['DBC_M'] = (r[self.DBC] - r[self.MKT]) * -1 #self.pairs_plot['Vola'] = vola wait_text = "" if exit: self.bull = False self.outday = self.count if self.count >= self.outday + self.wait_days: self.bull = True wait_text = "We are RISK ON" if not self.bull and (self.count <= self.outday + self.wait_days): wait_text = "Wait: " + str((self.outday + self.wait_days) - self.count) self.email_lines.insert(0, f"Bull: {self.bull}, Exit: {exit}, {wait_text}") self.count += 1 if not self.bull: # choose the out holding with the highest rate of return out_holding = self.BND1 # default out_holdings = { self.BND1: r[self.BND1], self.UUP: r[self.UUP] } out_holding = max(out_holdings, key=out_holdings.get) # Then, switch to UUP if BND1 is trending down (improves the above) if self.bnd_sma_short.Current.Value < self.bnd_sma_long.Current.Value: out_holding = self.UUP # if out_holding is negative, go into cash (only a very small improvement) if r[out_holding] < 0: out_holding = None self.email_lines.append(f"HOLD -> {out_holding}") for sec in self.ASSETS: self.wt[sec] = LEV if sec is out_holding else 0 self.trade() elif self.bull: self.email_lines.append(f"HOLD -> {self.STK}") for sec in self.ASSETS: self.wt[sec] = LEV if sec is self.STK else 0 self.trade() # Send email with insights today = dt.datetime.now().replace(hour=0, minute=0, second=0, microsecond=0) if self.LiveMode and self.Time >= today: pass #self.Notify.Email("you@gmail.com", "IN/OUT Insights", "\n".join(self.email_lines)) #self.Notify.Email("you@gmail.com", "IN/OUT Insights", "\n".join(self.email_lines)) def trade(self): if self.IsWarmingUp: return # Liquidate first, then set new holdings. This avoids 'not enough funds' warnings. for sec, weight in self.wt.items(): if weight == 0 and self.Portfolio[sec].IsLong: self.Liquidate(sec) for sec, weight in self.wt.items(): if weight > 0 and not self.Portfolio[sec].Invested: self.SetHoldings(sec, weight) def OnEndOfDay(self): if self.IsWarmingUp: return if not self.LiveMode: mkt_price = self.Securities[self.BNC].Close # the below fixes the divide by zero error in the MKT plot if mkt_price > 0 and mkt_price is not None: self.mkt.append(mkt_price) if len(self.mkt) >= 2 and not self.IsWarmingUp: mkt_perf = self.mkt[-1] / self.mkt[0] * self.cap self.Plot('Strategy Equity', self.BNC, 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)) # Pairs chart for key, value in self.pairs_plot.items(): if math.isnan(value): continue self.Plot('Pairs', key, value) # Pairs Model self.Plot('Pairs Model', "Pairs", self.pairs_model) # def OnEndOfAlgorithm(self): # for line in self.email_lines: # self.Debug(line) class CustomFeeModel(FeeModel): def GetOrderFee(self, parameters): fee = 0.0 #parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD"))