Overall Statistics |
Total Trades 570 Average Win 0.57% Average Loss -0.57% Compounding Annual Return -24.781% Drawdown 38.800% Expectancy -0.162 Net Profit -24.781% Sharpe Ratio -0.704 Probabilistic Sharpe Ratio 1.580% Loss Rate 58% Win Rate 42% Profit-Loss Ratio 1.01 Alpha -0.25 Beta 0.465 Annual Standard Deviation 0.224 Annual Variance 0.05 Information Ratio -1.575 Tracking Error 0.226 Treynor Ratio -0.34 Total Fees $1608.76 Estimated Strategy Capacity $1000.00 Lowest Capacity Asset MJIN XSFV65Y0WJ1H |
# region imports from AlgorithmImports import * # endregion # region imports from AlgorithmImports import * # endregion # Import packages import numpy as np import pandas as pd import scipy as sc class CalculatingRedTapir(QCAlgorithm): def Initialize(self): self.SetStartDate(2021, 1, 1) # Set Start Date self.SetEndDate(2022, 1, 1) self.SetCash(100000) # Set Strategy Cash self.UniverseSettings.Resolution = Resolution.Daily res = Resolution.Minute #self.SetBenchmark("SPY") # stock selection #self.STKSEL = self.AddEquity('SOXX', res).Symbol self.rebalanceTime = datetime.min self.activeStocks = set() self.AddUniverse(self.CoarseFilter, self.FineFilter) self.UniverseSettings.Resolution = Resolution.Hour self.portfolioTargets = [] # Feed-in constants self.INI_WAIT_DAYS = 9 # out for 3 trading weeks #outmarket choices self.TLT = self.AddEquity('MJIN', res).Symbol self.IEF = self.AddEquity('TBF', res).Symbol # Market and list of signals based on ETFs res = Resolution.Minute self.MRKT = self.AddEquity('SPY', res).Symbol 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.portfolioTargets 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 ) # functions for universe def CoarseFilter(self, coarse): # Rebalancing monthly if self.Time <= self.rebalanceTime: return self.Universe.Unchanged self.rebalanceTime = self.Time + timedelta(30) sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True) return [x.Symbol for x in sortedByDollarVolume if x.Price > 10 and x.HasFundamentalData][:200] def FineFilter(self, fine): sortedByPE = sorted(fine, key=lambda x: x.MarketCap) return [x.Symbol for x in sortedByPE if x.MarketCap > 0][:5] def OnSecuritiesChanged(self, changes): # close positions in removed securities for x in changes.RemovedSecurities: self.Debug(f"{self.Time}: Removed {x.Symbol}") self.Liquidate(x.Symbol) self.activeStocks.remove(x.Symbol) # can't open positions here since data might not be added correctly yet for x in changes.AddedSecurities: self.Debug(f"{self.Time}: Added {x.Symbol}") self.activeStocks.add(x.Symbol) # adjust targets if universe has changed self.portfolioTargets = [PortfolioTarget(symbol, 1/len(self.activeStocks)) for symbol in self.activeStocks] def OnData(self, data): if self.portfolioTargets == []: return for symbol in self.activeStocks: if symbol not in data: return 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 #self.be_in = True # for testing, sets to always in #self.TLT = self.AddEquity('MJIN', res).Symbol #self.IEF = self.AddEquity('TBF', res).Symbol #self.HLD_OUT = {self.TLT: .5, self.IEF: .5} # Swap to 'out' assets if applicable if not self.be_in: for x in self.portfolioTargets: self.SetHoldings(x.Symbol, 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): #res = Resolution.Minute #self.TLT = self.AddEquity('MJIN', res).Symbol #self.IEF = self.AddEquity('TBF', res).Symbol #self.HLD_OUT = {self.TLT: .5, self.IEF: .5} # 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) self.SetHoldings(self.portfolioTargets) #self.Debug(self.portfolioTargets) self.portfolioTargets = []