Overall Statistics Total Trades61Average Win3.57%Average Loss-1.14%Compounding Annual Return74.174%Drawdown15.500%Expectancy1.751Net Profit71.982%Sharpe Ratio3.185Probabilistic Sharpe Ratio89.897%Loss Rate33%Win Rate67%Profit-Loss Ratio3.13Alpha0.769Beta0.105Annual Standard Deviation0.249Annual Variance0.062Information Ratio1.445Tracking Error0.39Treynor Ratio7.573Total Fees$61.00 """ 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.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin) self.SetStartDate(2020, 1, 1) self.SetCash(12000) # Set Strategy Cash res = Resolution.Minute # Feed-in constants self.INI_WAIT_DAYS = 2 # out for 3 trading weeks # Holdings ### 'Out' holdings and weights self.SAFE1 = self.AddEquity('TLT', res).Symbol #TLT; TMF for 3xlev self.SAFE2 = self.AddEquity('IEF', res).Symbol #IEF; TYD for 3xlev self.HLD_OUT = {self.SAFE1: .70, self.SAFE2: .30} ### '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.USDX, self.DEBT] 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.DateRules.EveryDay(), 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 # 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 self.Log(str(extreme_b[self.SIGNALS + self.pairlist])) 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)) 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}")

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)