Overall Statistics |
Total Trades 179 Average Win 5.46% Average Loss -1.61% Compounding Annual Return 26.647% Drawdown 16.500% Expectancy 2.113 Net Profit 2042.077% Sharpe Ratio 1.874 Probabilistic Sharpe Ratio 99.028% Loss Rate 29% Win Rate 71% Profit-Loss Ratio 3.40 Alpha 0.265 Beta 0.137 Annual Standard Deviation 0.15 Annual Variance 0.023 Information Ratio 0.696 Tracking Error 0.231 Treynor Ratio 2.056 Total Fees $3772.05 |
""" 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 import scipy as sc class InOut(QCAlgorithm): def Initialize(self): self.SetStartDate(2008, 1, 1) # Set Start Date self.SetCash(100000) # Set Strategy Cash res = Resolution.Minute # Feed-in constants self.INI_WAIT_DAYS = 15 # out for 3 trading weeks # Holdings ### 'Out' holdings and weights self.BND1 = self.AddEquity('TLT', res).Symbol #TLT; TMF for 3xlev self.BND2 = self.AddEquity('IEF', res).Symbol #IEF; TYD for 3xlev self.HLD_OUT = {self.BND1: .5, self.BND2: .5} ### 'In' holdings and weights (static stock selection strategy) self.STKS = self.AddEquity('QQQ', res).Symbol #SPY or QQQ; TQQQ for 3xlev self.HLD_IN = {self.STKS: 1} # Market and list of signals based on ETFs self.MRKT = self.AddEquity('SPY', res).Symbol # market 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 self.SHCU = self.AddEquity('FXF', res).Symbol # safe haven currency (CHF) self.RICU = self.AddEquity('FXA', res).Symbol # vs risk currency (AUD) self.FORPAIRS = [self.GOLD, self.SLVA, self.UTIL, self.SHCU, self.RICU] self.SIGNALS = [self.PRDC, self.METL, self.NRES, self.DEBT, self.USDX] self.pairlist = ['G_S', 'U_I', 'C_A'] # Initialize variables ## 'In'/'out' indicator self.be_in = 999 #initially, set to an arbitrary value different from 1 (in) and 0 (out) ## Day count variables self.dcount = 0 # count of total days since start self.outday = 0 # dcount when self.be_in=0 ## Flexi wait days self.WDadjvar = self.INI_WAIT_DAYS self.Schedule.On( self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 120), self.rebalance_when_out_of_the_market ) self.Schedule.On( self.DateRules.WeekEnd(), self.TimeRules.AfterMarketOpen('SPY', 120), self.rebalance_when_in_the_market ) # Setup daily consolidation symbols = self.SIGNALS + [self.MRKT] + self.FORPAIRS 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.lookback = 252 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() 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(11, center=True).mean().shift(60) def rebalance_when_out_of_the_market(self): # Returns sample to detect extreme observations returns_sample = (self.history / self.history_shift - 1) # 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]) returns_sample['C_A'] = -(returns_sample[self.SHCU] - returns_sample[self.RICU]) # Extreme observations; statist. significance = 1% pctl_b = np.nanpercentile(returns_sample, 1, axis=0) extreme_b = returns_sample.iloc[-1] < pctl_b # Re-assess double-edged signals median = np.nanmedian(returns_sample, axis=0) abovemedian = returns_sample.iloc[-1] > median ### Interest rate expectations (cost of debt) may increase because the economic outlook improves (showing in input prices) which actually is 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]]) # Determine waitdays empirically via safe haven excess returns, 50% decay self.WDadjvar = int( max(0.50 * self.WDadjvar, self.INI_WAIT_DAYS * max(1, np.where((returns_sample[self.GOLD].iloc[-1]>0) & (returns_sample[self.SLVA].iloc[-1]<0) & (returns_sample[self.SLVA].iloc[-2]>0), self.INI_WAIT_DAYS, 1), np.where((returns_sample[self.UTIL].iloc[-1]>0) & (returns_sample[self.INDU].iloc[-1]<0) & (returns_sample[self.INDU].iloc[-2]>0), self.INI_WAIT_DAYS, 1), np.where((returns_sample[self.SHCU].iloc[-1]>0) & (returns_sample[self.RICU].iloc[-1]<0) & (returns_sample[self.RICU].iloc[-2]>0), self.INI_WAIT_DAYS, 1) )) ) adjwaitdays = min(60, self.WDadjvar) # self.Debug('{}'.format(self.WDadjvar)) # Determine whether 'in' or 'out' of the market if (extreme_b[self.SIGNALS + self.pairlist]).any(): self.be_in = False self.outday = self.dcount if self.dcount >= self.outday + adjwaitdays: self.be_in = True self.dcount += 1 #self.be_in = True # for testing; sets the algo to being always in # Swap to 'out' assets if applicable if not self.be_in: # Only trade when changing from in to out self.trade({**dict.fromkeys(self.HLD_IN, 0), **self.HLD_OUT}) self.Plot("In Out", "in_market", int(self.be_in)) # self.Plot("In Out", "num_out_signals", extreme_b[self.SIGNALS + self.pairlist].sum()) # self.Plot("Wait Days", "waitdays", adjwaitdays) def rebalance_when_in_the_market(self): # Swap to 'in' assets if applicable if self.be_in: # Only trade when changing from out to in self.trade({**self.HLD_IN, **dict.fromkeys(self.HLD_OUT, 0)}) def trade(self, weight_by_sec): buys = [] 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 cond1 = weight == 0 and self.Portfolio[sec].IsLong cond2 = weight > 0 and not self.Portfolio[sec].Invested if cond1 or cond2: quantity = self.CalculateOrderQuantity(sec, weight) if quantity > 0: buys.append((sec, quantity)) elif quantity < 0: self.Order(sec, quantity) for sec, quantity in buys: self.Order(sec, quantity)