Overall Statistics |
Total Trades 3067 Average Win 0.36% Average Loss -0.06% Compounding Annual Return 21.413% Drawdown 15.800% Expectancy 2.716 Net Profit 1107.694% Sharpe Ratio 1.701 Probabilistic Sharpe Ratio 97.275% Loss Rate 43% Win Rate 57% Profit-Loss Ratio 5.53 Alpha 0.218 Beta 0.068 Annual Standard Deviation 0.132 Annual Variance 0.017 Information Ratio 0.493 Tracking Error 0.233 Treynor Ratio 3.319 Total Fees $5779.65 |
""" Based on 'In & Out' strategy by Peter Guenther 10-04-2020 expanded/inspired by Tentor Testivis, Dan Whitnable (Quantopian), Vladimir, and Thomas Chang. https://www.quantopian.com/posts/new-strategy-in-and-out """ # 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 self.UniverseSettings.Resolution = Resolution.Daily # Feed-in constants self.INI_WAIT_DAYS = 15 # out for 3 trading weeks res = Resolution.Minute self.MRKT = self.AddEquity('SPY', res).Symbol self.TLT = self.AddEquity('TLT', res).Symbol self.IEF = self.AddEquity('IEF', res).Symbol # Market and list of signals based on ETFs 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.SHCU = self.AddEquity('FXF', res).Symbol # safe haven (CHF) self.RICU = self.AddEquity('FXA', res).Symbol # risk currency (AUD) self.INDU = self.PRDC # vs industrials self.FORPAIRS = [self.GOLD, self.SLVA, self.UTIL, self.SHCU, self.RICU] self.SIGNALS = [self.PRDC, self.METL, self.NRES, self.DEBT, self.USDX] # 'In' and 'out' holdings incl. weights self.HLD_IN = {self.MRKT: 1.0} self.HLD_OUT = {self.TLT: .5, self.IEF: .5} # Initialize variables ## 'In'/'out' indicator self.be_in = 1 ## 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 ) def rebalance_when_out_of_the_market(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.rolling(66).apply(lambda x: x[:11].mean()) 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, #returns_sample[self.GOLD].iloc[-1] / returns_sample[self.SLVA].iloc[-1], #returns_sample[self.UTIL].iloc[-1] / returns_sample[self.INDU].iloc[-1], #returns_sample[self.SHCU].iloc[-1] / returns_sample[self.RICU].iloc[-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 # Swap to 'out' assets if applicable if not self.be_in: # Close 'In' holdings for asset, weight in self.HLD_IN.items(): self.SetHoldings(asset, 0) for asset, weight in self.HLD_OUT.items(): self.SetHoldings(asset, weight) 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: # Close 'Out' holdings for asset, weight in self.HLD_OUT.items(): self.SetHoldings(asset, 0) for asset, weight in self.HLD_IN.items(): self.SetHoldings(asset, weight)