Overall Statistics |
Total Trades 107 Average Win 10.71% Average Loss -3.13% Compounding Annual Return 21.492% Drawdown 24.200% Expectancy 2.087 Net Profit 1864.122% Sharpe Ratio 1.157 Probabilistic Sharpe Ratio 64.030% Loss Rate 30% Win Rate 70% Profit-Loss Ratio 3.42 Alpha 0.139 Beta 0.195 Annual Standard Deviation 0.133 Annual Variance 0.018 Information Ratio 0.408 Tracking Error 0.188 Treynor Ratio 0.79 Total Fees $10238.10 Estimated Strategy Capacity $2400000.00 Lowest Capacity Asset TLT SGNKIKYGE9NP Portfolio Turnover 1.90% |
#region imports from AlgorithmImports import * #endregion """ Based on 'In & Out' strategy by Peter Guenther 4 Oct 2020 expanded/inspired by Tentor Testivis, Dan Whitnable (Quantopian), Vladimir, Thomas Chang, Derek Melchin (QuantConnect), Nathan Swenson, and Goldie Yalamanchi. https://www.quantopian.com/posts/new-strategy-in-and-out https://www.quantconnect.com/forum/discussion/9597/the-in-amp-out-strategy-continued-from-quantopian/p1 """ # Import packages import numpy as np import pandas as pd from collections import deque import pickle class InOut(QCAlgorithm): def Initialize(self): self.SetStartDate(2008, 1, 1) # Set Start Date #self.SetStartDate(2021, 6, 1) self.cap = 100000 self.SetCash(self.cap) # Set Strategy Cash res = Resolution.Minute #self.AddRiskManagement(MaximumDrawdownPercentPortfolio(maximumDrawdownPercent=0.02, isTrailing=False)) # Holdings ### 'Out' holdings and weights self.HLD_OUT = {self.AddEquity('TLT', res).Symbol: 1} #TLT; TMF for 3xlev ### 'In' holdings and weights (static stock selection strategy) self.HLD_IN = {self.AddEquity('SPY', res).Symbol: 1} # self.HLD_IN = {self.AddEquity('TQQQ', res).Symbol: 1, self.AddEquity('QLD', res).Symbol: 0} #SPY or QQQ; TQQQ for 3xlev # Market and list of signals based on ETFs self.BONDS = self.AddEquity('TLT', res).Symbol self.MRKT = self.AddEquity('QQQ', res).Symbol # market; QQQ self.PRDC = self.AddEquity('XLI', res).Symbol # production (industrials) self.METL = self.AddEquity('DBB', res).Symbol # input prices (metals) self.NRES = self.AddEquity('IGE', res).Symbol # input prices (natural res) self.DEBT = self.AddEquity('SHY', res).Symbol # cost of debt (bond yield) self.USDX = self.AddEquity('UUP', res).Symbol # safe haven (USD) self.GOLD = self.AddEquity('GLD', res).Symbol # gold self.SLVA = self.AddEquity('SLV', res).Symbol # vs silver self.UTIL = self.AddEquity('XLU', res).Symbol # utilities self.INDU = self.PRDC # vs industrials # Define the rolling windows for TLT and UUP self.window_length = 90 self.tlt_window = RollingWindow[float](self.window_length) self.uup_window = RollingWindow[float](self.window_length) # Add momentum indicators for TLT and UUP self.tlt_momentum = self.ROC("TLT", 90, Resolution.Daily) self.uup_momentum = self.ROC("UUP", 90, Resolution.Daily) self.SIGNALS = [self.PRDC, self.METL, self.NRES, self.USDX, self.DEBT, self.MRKT] self.FORPAIRS = [self.GOLD, self.SLVA, self.UTIL, self.INDU] self.pairlist = ['G_S', 'U_I'] # Initialize parameters and tracking variables self.lookback, self.shift_vars, self.stat_alpha, self.ema_f = [252*5, [11, 60, 45], 5, 2/(1+50)] self.be_in, self.portf_val, self.signal_dens, self.reg_slope = [[1], [self.cap], deque([0, 0, 0, 0, 0], maxlen = 100), deque([0, 0, 0, 0, 0], maxlen = 100)] self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('QQQ', 120), self.inout_check) # Symbols for charts self.SPY = self.AddEquity('SPY', res).Symbol self.QQQ = self.MRKT # Setup daily consolidation symbols = list(set(self.SIGNALS + [self.MRKT] + self.FORPAIRS + list(self.HLD_OUT.keys()) + list(self.HLD_IN.keys()) + [self.SPY] + [self.QQQ])) for symbol in symbols: self.consolidator = TradeBarConsolidator(timedelta(days=1)) self.consolidator.DataConsolidated += self.consolidation_handler self.SubscriptionManager.AddConsolidator(symbol, self.consolidator) # Warm up history self.history = self.History(symbols, self.lookback, Resolution.Daily) if self.history.empty or 'close' not in self.history.columns: return self.history = self.history['close'].unstack(level=0).dropna() self.update_history_shift() # Benchmarks for charts self.benchmarks = [self.history[self.SPY].iloc[-2], self.history[self.QQQ].iloc[-2]] def consolidation_handler(self, sender, consolidated): self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close self.history = self.history.iloc[-self.lookback:] self.update_history_shift() def update_history_shift(self): self.history_shift = self.history.rolling(self.shift_vars[0], center=True).mean().shift(self.shift_vars[1]) def replace_tqqq(self): if self.Time.date() <= datetime.strptime('2010-02-09', '%Y-%m-%d').date(): self.HLD_IN[list(self.HLD_IN.keys())[0]] = 0; self.HLD_IN[list(self.HLD_IN.keys())[1]] = 1 else: self.HLD_IN[list(self.HLD_IN.keys())[0]] = 1; self.HLD_IN[list(self.HLD_IN.keys())[1]] = 0 def inout_check(self): if self.history.empty: return if Symbol.Create('TQQQ', SecurityType.Equity, Market.USA) in self.HLD_IN.keys(): self.replace_tqqq() # Load saved signal density (for live interruptions): if self.LiveMode and sum(list(self.signal_dens))==0 and self.ObjectStore.ContainsKey('OS_signal_dens'): OS = self.ObjectStore.ReadBytes('OS_signal_dens') OS = pickle.loads(bytearray(OS)) self.signal_dens = deque(OS, maxlen = 100) # Returns sample to detect extreme observations returns_sample = (self.history / self.history_shift - 1) # Determine the 'Out' asset based on momentum conditions tlt_momentum_positive = self.tlt_momentum.Current.Value > 0 uup_momentum_positive = self.uup_momentum.Current.Value > 0 if tlt_momentum_positive and uup_momentum_positive: if self.tlt_momentum.Current.Value > self.uup_momentum.Current.Value: self.HLD_OUT = {self.BONDS: 1} else: self.HLD_OUT = {self.USDX: 1} elif not tlt_momentum_positive and uup_momentum_positive: self.HLD_OUT = {self.USDX: 1} else: self.HLD_OUT = {self.BONDS: 1} # No out asset if both conditions are false # Reverse code USDX: sort largest changes to bottom returns_sample[self.USDX] = returns_sample[self.USDX] * (-1) # For pairs, take returns differential, reverse coded returns_sample['G_S'] = -(returns_sample[self.GOLD] - returns_sample[self.SLVA]) returns_sample['U_I'] = -(returns_sample[self.UTIL] - returns_sample[self.INDU]) # Extreme observations; statistical significance = X% (stat_alpha) extreme_b = returns_sample.iloc[-1] < np.nanpercentile(returns_sample, self.stat_alpha, axis=0) # Re-assess/disambiguate double-edged signals abovemedian = returns_sample.iloc[-1] > np.nanmedian(returns_sample, axis=0) ### Interest rate expectations (cost of debt) may increase because the economic outlook improves (showing in rising input prices) = actually not a negative signal extreme_b.loc[self.DEBT] = np.where((extreme_b.loc[self.DEBT].any()) & (abovemedian[[self.METL, self.NRES]].any()), False, extreme_b.loc[self.DEBT]) cur_signal_dens = extreme_b[self.SIGNALS + self.pairlist].sum() / len(self.SIGNALS + self.pairlist) add_dens = np.array((1-self.ema_f) * self.signal_dens[-1] + self.ema_f * cur_signal_dens) self.signal_dens.append(add_dens) # Determine whether 'in' or 'out' of the market if self.signal_dens[-1] > self.signal_dens[-2]: self.be_in.append(0) if self.signal_dens[-1] < min(list(self.signal_dens)[-(self.shift_vars[2]):-2]): self.be_in.append(1) # Swap to 'out' assets if applicable if not self.be_in[-1]: self.trade({**dict.fromkeys(self.HLD_IN, 0), **self.HLD_OUT}) if self.be_in[-1]: self.trade({**self.HLD_IN, **dict.fromkeys(self.HLD_OUT, 0)}) self.charts(extreme_b) # Save data: signal density from live trading for interruptions (note: backtest saves data at the end so that it's available for live trading). if self.LiveMode: self.SaveData() def trade(self, weight_by_sec): # sort: execute largest sells first, largest buys last hold_wt = {k: (self.Portfolio[k].Quantity*self.Portfolio[k].Price)/self.Portfolio.TotalPortfolioValue for k in self.Portfolio.Keys} order_wt = {k: weight_by_sec[k] - hold_wt.get(k, 0) for k in weight_by_sec} weight_by_sec = {k: weight_by_sec[k] for k in dict(sorted(order_wt.items(), key=lambda item: item[1]))} for sec, weight in weight_by_sec.items(): # Check that we have data in the algorithm to process a trade if not self.CurrentSlice.ContainsKey(sec) or self.CurrentSlice[sec] is None: continue # Only trade if holdings fundamentally change cond1 = (weight==0) and self.Portfolio[sec].IsLong cond2 = (weight>0) and not self.Portfolio[sec].Invested if cond1 or cond2: # Set up a trailing stop loss of 5% for long positions if self.Portfolio[sec].Invested and self.Portfolio[sec].IsLong: current_price = self.CurrentSlice[sec].Close trailing_stop_price = (1 - 0.02) * current_price if self.Portfolio[sec].AveragePrice >= trailing_stop_price: self.Debug(f'Selling {self.Portfolio[sec].Quantity} shares of {sec.Value} due to trailing stop loss') self.Liquidate(sec) continue self.SetHoldings(sec, weight) def charts(self, extreme_b): # Market comparisons spy_perf = self.history[self.SPY].iloc[-1] / self.benchmarks[0] * self.cap qqq_perf = self.history[self.QQQ].iloc[-1] / self.benchmarks[1] * self.cap self.Plot('Strategy Equity', 'SPY', spy_perf) self.Plot('Strategy Equity', 'QQQ', qqq_perf) # Signals self.Plot("In Out", "in_market", self.be_in[-1]) self.Plot("In Out", "signal_dens", self.signal_dens[-1]) self.Plot("In Out", "reg_slope", self.reg_slope[-1]) # self.Plot("Signals", "PRDC", int(extreme_b[self.SIGNALS + self.pairlist][0])) # self.Plot("Signals", "METL", int(extreme_b[self.SIGNALS + self.pairlist][1])) # self.Plot("Signals", "NRES", int(extreme_b[self.SIGNALS + self.pairlist][2])) # self.Plot("Signals", "USDX", int(extreme_b[self.SIGNALS + self.pairlist][3])) # self.Plot("Signals", "DEBT", int(extreme_b[self.SIGNALS + self.pairlist][4])) # self.Plot("Signals", "MRKT", int(extreme_b[self.SIGNALS + self.pairlist][5])) # self.Plot("Signals", "G_S", int(extreme_b[self.SIGNALS + self.pairlist][6])) # self.Plot("Signals", "U_I", int(extreme_b[self.SIGNALS + self.pairlist][7])) # Comparison of out returns self.portf_val.append(self.Portfolio.TotalPortfolioValue) if not self.be_in[-1] and len(self.be_in)>=2: period = np.where(np.array(self.be_in)[:-1] != np.array(self.be_in)[1:])[0][-1] - len(self.be_in) mrkt_ret = self.history[self.MRKT].iloc[-1] / self.history[self.MRKT].iloc[period] - 1 strat_ret = self.portf_val[-1] / self.portf_val[period] - 1 strat_vs_mrkt = round(float(strat_ret - mrkt_ret), 4) else: strat_vs_mrkt = 0 self.Plot('Out return', 'PF vs MRKT', strat_vs_mrkt) def SaveData(self): self.ObjectStore.SaveBytes('OS_signal_dens', pickle.dumps(self.signal_dens))