Overall Statistics Total Trades21Average Win1.51%Average Loss-1.42%Compounding Annual Return-9.114%Drawdown9.100%Expectancy-0.172Net Profit-3.398%Sharpe Ratio-0.636Probabilistic Sharpe Ratio15.583%Loss Rate60%Win Rate40%Profit-Loss Ratio1.07Alpha-0.164Beta0.202Annual Standard Deviation0.129Annual Variance0.017Information Ratio-2.588Tracking Error0.189Treynor Ratio-0.406Total Fees$62.64 """ Based on 'In & Out' strategy by Peter Guenther 4 Oct 2020 expanded/inspired by Tentor Testivis, Dan Whitnable (Quantopian), Vladimir, and Thomas Chang. 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(2020, 8, 1) # Set Start Date self.SetCash(100000) # Set Strategy Cash self.UniverseSettings.Resolution = Resolution.Daily res = Resolution.Hour # 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 self.BND2 = self.AddEquity('IEF', res).Symbol self.BNDselect = self.BND1 self.HLD_OUT = {self.BNDselect: 1} ### 'In' holdings and weights (static stock selection strategy) self.STK1 = self.AddEquity('QQQ', res).Symbol self.STK2 = self.AddEquity('IWF', res).Symbol self.STKselect = self.STK1 self.HLD_IN = {self.STKselect: 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] # 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.returnWindowLength = 100 # set a warm-up period to initialize the indicator self.SetWarmUp(timedelta(350)) self.Schedule.On( self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 1), self.calculate_signal ) 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', 121), self.rebalance_when_in_the_market ) def Returns(self, symbol, period): closingBars = self.History(symbol, TimeSpan.FromDays(period),Resolution.Daily).close return (closingBars[-1] - closingBars[0])/closingBars[-1] def calculate_signal(self): # Returns sample to detect extreme observations hist = self.History( self.SIGNALS + [self.MRKT] + self.FORPAIRS, 252, Resolution.Daily)['close'].unstack(level=0).dropna() hist_shift = hist.apply(lambda x: (x.shift(65) + x.shift(64) + x.shift(63) + x.shift(62) + x.shift( 61) + x.shift(60) + x.shift(59) + x.shift(58) + x.shift(57) + x.shift(56) + x.shift(55)) / 11) returns_sample = (hist / hist_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]) self.pairlist = ['G_S', 'U_I', 'C_A'] # Extreme observations; statist. significance = 1% pctl_b = np.nanpercentile(returns_sample, 1, axis=0) extreme_b = returns_sample.iloc[-1] < pctl_b # 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.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) # select momentumin asset if self.Returns(self.BND1,self.returnWindowLength) < self.Returns(self.BND2,self.returnWindowLength): self.BNDselect = self.BND2 elif self.Returns(self.BND1,self.returnWindowLength) > self.Returns(self.BND2,self.returnWindowLength): self.BNDselect = self.BND1 # select momentumin asset if self.Returns(self.STK1,self.returnWindowLength) < self.Returns(self.STK2,self.returnWindowLength): self.STKselect = self.STK2 elif self.Returns(self.STK1,self.returnWindowLength) > self.Returns(self.STK2,self.returnWindowLength): self.STKselect = self.STK1 self.HLD_IN = {self.STKselect: 1} self.HLD_OUT = {self.BNDselect: 1} def rebalance_when_out_of_the_market(self): # 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}) 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)}) self.Log(f"TotalPortfolioValue: {self.Portfolio.TotalPortfolioValue}, TotalMarginUsed: {self.Portfolio.TotalMarginUsed}, MarginRemaining: {self.Portfolio.MarginRemaining}, Cash: {self.Portfolio.Cash}") for key in sorted(self.Portfolio.keys()): if self.Portfolio[key].Quantity > 0.0: self.Log(f"Symbol/Qty: {key} / {self.Portfolio[key].Quantity}, Avg: {self.Portfolio[key].AveragePrice}, Curr: { self.Portfolio[key].Price}, Profit($): {self.Portfolio[key].UnrealizedProfit}")

if self.Portfolio.Invested:
for symbol in self.Portfolio.Keys:
if symbol not in weight_by_sec:
self.Liquidate(symbol)

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:
self.Order(sec, quantity)