Overall Statistics |
Total Trades 220 Average Win 0.58% Average Loss -0.07% Compounding Annual Return 63.195% Drawdown 16.500% Expectancy 6.900 Net Profit 62.904% Sharpe Ratio 2.034 Probabilistic Sharpe Ratio 79.312% Loss Rate 11% Win Rate 89% Profit-Loss Ratio 7.90 Alpha 0.221 Beta 1.08 Annual Standard Deviation 0.214 Annual Variance 0.046 Information Ratio 1.32 Tracking Error 0.18 Treynor Ratio 0.404 Total Fees $252.42 Estimated Strategy Capacity $3900000.00 Lowest Capacity Asset TLT SGNKIKYGE9NP |
""" 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 from collections import deque import pickle from dateutil.relativedelta import relativedelta class InOut(QCAlgorithm): def Initialize(self): if not self.LiveMode: yr_delta = int(self.GetParameter("m")) # following will it for 1 year, you can run it in paralllel in optimizer by selectong value of m from 0 to 13 yr, m, d, n = 2008, 1, 1, 365*1 # following willrun it for 14 years # yr, m, d, n = 2008, 1, 1, 365 * 14 # yr, m, d, n = 2008, 1, 1, 10 std = date(yr + yr_delta, m, d) edt = std + relativedelta(days=+n) self.SetStartDate(std.year, std.month, std.day) self.SetEndDate(edt.year, edt.month, edt.day) self.cap = 100000 self.SetCash(self.cap) # Set Strategy Cash res = Resolution.Minute self.frequent_rebalance = True # self.frequent_rebalance = False self.stat_alpha = 5 signal_history_period = 20 self.lookback = 5 * 252 self.exp_f = 2 self.smoothing_factor = self.exp_f / (signal_history_period + 1) # Initialize parameters and tracking variables self.price_smoothing_period, self.momentum_period = 11, 60 # Holdings ### 'Out' holdings and weights self.HLD_OUT = {self.AddEquity('TLT', res).Symbol: 1.5} # TLT; TMF for 3xlev ### 'In' holdings and weights (static stock selection strategy) self.HLD_IN = {self.AddEquity('QQQ', res).Symbol: 1.5} # self.HLD_IN = {self.AddEquity('TQQQ', res).Symbol: 1, self.AddEquity('QLD', res).Symbol: 0} #SPY or QQQ; TQQQ for 3xlev # Market and list of signals based on ETFs self.MRKT = self.AddEquity('QQQ', res).Symbol # market; QQQ 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.SIGNALS = [self.PRDC, self.METL, self.NRES, self.USDX, self.DEBT, self.MRKT] self.FORPAIRS = [self.GOLD, self.SLVA, self.UTIL, self.INDU] self.pairlist = ['G_S', 'U_I'] self.signal_dens = deque([0] * signal_history_period,maxlen=signal_history_period) # Symbols for charts self.SPY = self.AddEquity('SPY', res).Symbol self.QQQ = self.MRKT # Setup daily consolidation self.symbols = list(set( self.SIGNALS + [self.MRKT] + self.FORPAIRS + list(self.HLD_OUT.keys()) + list(self.HLD_IN.keys()) + [ self.SPY] + [self.QQQ])) # for symbol in symbols: # self.consolidator = TradeBarConsolidator(timedelta(days=1)) # self.consolidator.DataConsolidated += self.consolidation_handler # self.SubscriptionManager.AddConsolidator(symbol, self.consolidator) for symbol in self.symbols: self.Securities[symbol].MarginModel = PatternDayTradingMarginModel() self.getFreshHistory() # Benchmarks for charts self.benchmarks = [self.history[self.SPY].iloc[-2], self.history[self.QQQ].iloc[-2]] self.SetWarmUp(50, Resolution.Daily) self.bull_signal_up = 1 self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('QQQ', 120), self.trade) # def consolidation_handler(self, sender, consolidated): # return # self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close # self.history = self.history.iloc[-self.lookback:] # # self.update_history_shift() def OnData(self, slice): self.slice = slice if self.IsWarmingUp: self.inout_check() def getFreshHistory(self): self.history = self.History(self.symbols, self.lookback, Resolution.Daily) if self.history is None or self.history.empty or 'close' not in self.history.columns: return # self.history = self.history['close'].unstack(level=0).dropna() self.history = self.history['open'].unstack(level=0).dropna() self.history = self.addLatest(self.history) self.history_shift = self.history.rolling(self.price_smoothing_period, center=True).mean().shift(self.momentum_period) def addLatest(self, hist): hist2 = hist col = 0 for s in hist.columns: # latest = self.Securities[s].Price latest = self.Securities[s].Open hist2.loc[self.Time, s] = latest col += 1 hist2 = hist2.iloc[-self.lookback:] return hist2 def replace_tqqq(self): if self.Time.date() <= datetime.strptime('2010-02-09', '%Y-%m-%d').date(): self.HLD_IN[list(self.HLD_IN.keys())[0]] = 0; self.HLD_IN[list(self.HLD_IN.keys())[1]] = 1 else: self.HLD_IN[list(self.HLD_IN.keys())[0]] = 1; self.HLD_IN[list(self.HLD_IN.keys())[1]] = 0 def inout_check(self): self.getFreshHistory() if self.history is None or self.history.empty: return if Symbol.Create('TQQQ', SecurityType.Equity, Market.USA) in self.HLD_IN.keys(): self.replace_tqqq() # Load saved signal density (for live interruptions): if self.LiveMode and sum(list(self.signal_dens)) == 0 and self.ObjectStore.ContainsKey('OS_signal_dens'): OS = self.ObjectStore.ReadBytes('OS_signal_dens') OS = pickle.loads(bytearray(OS)) self.signal_dens = deque(OS, maxlen=100) 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 = returns_sample[self.SIGNALS + self.pairlist] # Extreme observations; statistical significance = X% (stat_alpha) extreme_b = returns_sample.iloc[-1] < np.nanpercentile(returns_sample, self.stat_alpha, axis=0) # Re-assess/disambiguate double-edged signals abovemedian = returns_sample.iloc[-1] > np.nanmedian(returns_sample, axis=0) ### Interest rate expectations (cost of debt) may increase because the economic outlook improves (showing in rising input prices) = actually 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]) n = len(self.SIGNALS + self.pairlist) self.cur_signal_dens = extreme_b.sum() / n * 100 add_dens = (1 - self.smoothing_factor) * self.signal_dens[-1] + self.smoothing_factor * self.cur_signal_dens self.signal_dens.append(add_dens) # Determine whether 'in' or 'out' of the market if self.cur_signal_dens >= self.stat_alpha / 2 and (\ self.signal_dens[-1] > (self.signal_dens[-2])): self.bull_signal_up = 0 elif (self.signal_dens[-1] <= min(self.signal_dens)): self.bull_signal_up = 1 def trade(self): self.inout_check() if self.IsWarmingUp: return if not self.bull_signal_up: weight_by_sec = {**dict.fromkeys(self.HLD_IN, 0), **self.HLD_OUT} if self.bull_signal_up: weight_by_sec = {**self.HLD_IN, **dict.fromkeys(self.HLD_OUT, 0)} # sort: execute largest sells first, largest buys last hold_wt = {k: (self.Portfolio[k].Quantity * self.Portfolio[k].Price) / self.Portfolio.TotalPortfolioValue for k in self.Portfolio.Keys} order_wt = {k: weight_by_sec[k] - hold_wt.get(k, 0) for k in weight_by_sec} weight_by_sec = {k: weight_by_sec[k] for k in dict(sorted(order_wt.items(), key=lambda item: item[1]))} 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 # Only trade if holdings fundamentally change cond1 = (weight == 0) and self.Portfolio[sec].Invested # Change introduced by Manoj Agarwala for frequent rebalancing cond2 = (abs(weight) > 0.01) and \ (self.frequent_rebalance \ or (not self.Portfolio[sec].Invested and not self.frequent_rebalance)) if cond1 or cond2: self.SetHoldings(sec, weight) self.charts() # Save data: signal density from live trading for interruptions (note: backtest saves data at the end so that it's available for live trading). if self.LiveMode: self.SaveData() def charts(self): # Market comparisons spy_perf = self.history[self.SPY].iloc[-1] / self.benchmarks[0] * self.cap qqq_perf = self.history[self.QQQ].iloc[-1] / self.benchmarks[1] * self.cap self.Plot('Strategy Equity', 'SPY', spy_perf) self.Plot('Strategy Equity', 'QQQ', qqq_perf) # Signals # self.Plot("In Out", "in_market", self.be_in[-1]) # self.Plot("In Out", "signal_dens", self.signal_dens[-1]) # self.Plot("Signals", "PRDC", int(extreme_b[self.SIGNALS + self.pairlist][0])) # self.Plot("Signals", "METL", int(extreme_b[self.SIGNALS + self.pairlist][1])) # self.Plot("Signals", "NRES", int(extreme_b[self.SIGNALS + self.pairlist][2])) # self.Plot("Signals", "USDX", int(extreme_b[self.SIGNALS + self.pairlist][3])) # self.Plot("Signals", "DEBT", int(extreme_b[self.SIGNALS + self.pairlist][4])) # self.Plot("Signals", "MRKT", int(extreme_b[self.SIGNALS + self.pairlist][5])) # self.Plot("Signals", "G_S", int(extreme_b[self.SIGNALS + self.pairlist][6])) # self.Plot("Signals", "U_I", int(extreme_b[self.SIGNALS + self.pairlist][7])) # Comparison of out returns # self.portf_val.append(self.Portfolio.TotalPortfolioValue) # if not self.be_in[-1] and len(self.be_in) >= 2: # period = np.where(np.array(self.be_in)[:-1] != np.array(self.be_in)[1:])[0][-1] - len(self.be_in) # mrkt_ret = self.history[self.MRKT].iloc[-1] / self.history[self.MRKT].iloc[period] - 1 # strat_ret = self.portf_val[-1] / self.portf_val[period] - 1 # strat_vs_mrkt = round(float(strat_ret - mrkt_ret), 4) # else: # strat_vs_mrkt = 0 # self.Plot('Out return', 'PF vs MRKT', strat_vs_mrkt) def SaveData(self): self.ObjectStore.SaveBytes('OS_signal_dens', pickle.dumps(self.signal_dens))