Overall Statistics |
Total Trades 1650 Average Win 1.28% Average Loss -0.73% Compounding Annual Return 28.408% Drawdown 24.400% Expectancy 0.543 Net Profit 2488.046% Sharpe Ratio 1.24 Probabilistic Sharpe Ratio 66.165% Loss Rate 44% Win Rate 56% Profit-Loss Ratio 1.76 Alpha 0.243 Beta 0.121 Annual Standard Deviation 0.205 Annual Variance 0.042 Information Ratio 0.6 Tracking Error 0.262 Treynor Ratio 2.11 Total Fees $11050.04 |
""" SEL(stock selection part) Based on the 'Momentum Strategy with Market Cap and EV/EBITDA' strategy introduced by Jing Wu, 6 Feb 2018 adapted and recoded by Jack Simonson, Goldie Yalamanchi, Vladimir, and Peter Guenther https://www.quantconnect.com/forum/discussion/3377/momentum-strategy-with-market-cap-and-ev-ebitda/p1 https://www.quantconnect.com/forum/discussion/9678/quality-companies-in-an-uptrend/p1 https://www.quantconnect.com/forum/discussion/9632/amazing-returns-superior-stock-selection-strategy-superior-in-amp-out-strategy/p1 I/O(in & out part) Based on the 'In & Out' strategy introduced by Peter Guenther, 4 Oct 2020 expanded/inspired by Tentor Testivis, Dan Whitnable (Quantopian), Vladimir, Thomas Chang, Mateusz Pulka, Derek Melchin (QuantConnect), Nathan Swenson, Goldie Yalamanchi, and Sudip Sil 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 code version: In_out_flex_v5_disambiguate_v2 """ from QuantConnect.Data.UniverseSelection import * import math import numpy as np import pandas as pd import scipy as sp class EarningsFactor_InOut(QCAlgorithm): def Initialize(self): self.SetStartDate(2008, 1, 1) #Set Start Date #self.SetEndDate(2009, 12, 31) #Set End Date self.cap = 100000 self.SetCash(self.cap) res = Resolution.Minute # Holdings ### 'Out' holdings and weights self.BND1 = self.AddEquity('TLT', res).Symbol #TLT; TMF for 3xlev self.HLD_OUT = {self.BND1: 1} ### 'In' holdings and weights (static stock selection strategy) ##### These are determined flexibly via sorting on fundamentals ##### In & Out parameters ##### # Feed-in constants self.INI_WAIT_DAYS = 15 # out for 3 trading weeks # 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.INFL = self.AddEquity('RINF', res).Symbol # disambiguate GPLD/SLVA pair via inflaction expectations 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, self.INFL] 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) self.be_in_prior = 999 ## 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 # set a warm-up period to initialize the indicator self.SetWarmUp(timedelta(350)) ##### Momentum & fundamentals strategy parameters ##### #self.UniverseSettings.Resolution = Resolution.Daily self.UniverseSettings.Resolution = Resolution.Minute self.AddUniverse(self.UniverseCoarseFilter, self.UniverseFundamentalsFilter) self.num_screener = 100 self.num_stocks = 10 self.formation_days = 70 self.lowmom = False self.data = {} # rebalance the universe selection once a month self.rebalance_flag = 0 # make sure to run the universe selection at the start of the algorithm even if it's not the month start self.flip_flag = 0 self.first_month_trade_flag = 1 self.trade_flag = 0 self.symbols = None self.month = -1 self.reb_count = 0 self.Schedule.On( self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 120), self.rebalance_when_out_of_the_market ) self.Schedule.On( self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose('SPY', 0), self.record_vars ) # 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() # Benchmark = record SPY self.spy = [] def UniverseCoarseFilter(self, coarse): #self.Debug(str(self.Time) + "UniverseCoarseFilter: be_in:" + str(self.be_in) + " flip_flag:" + str(self.flip_flag)) #if (self.rebalance_flag or self.first_month_trade_flag) and (self.be_in or self.flip_flag): if self.month == self.Time.month: return Universe.Unchanged self.month = self.Time.month # drop stocks which have no fundamental data or have too low prices selected = [x for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)] # rank the stocks by dollar volume filtered = sorted(selected, key=lambda x: x.DollarVolume, reverse=True) return [x.Symbol for x in filtered[:200]] #else: # return self.symbols def UniverseFundamentalsFilter(self, fundamental): #self.Debug(str(self.Time) + "UniverseFundamentalsFilter: be_in:" + str(self.be_in) + " flip_flag:" + str(self.flip_flag)) #if (self.rebalance_flag or self.first_month_trade_flag) and (self.be_in or self.flip_flag): #hist = self.History([i.Symbol for i in fundamental], 1, Resolution.Daily) try: filtered_fundamental = [x for x in fundamental if (x.ValuationRatios.EVToEBITDA > 0) and (x.EarningReports.BasicAverageShares.ThreeMonths > 0) and float(x.EarningReports.BasicAverageShares.ThreeMonths) * x.Price > 2e9] #and float(x.EarningReports.BasicAverageShares.ThreeMonths) * hist.loc[str(x.Symbol)]['close'][0] > 2e9] #and x.EarningReports.BasicAverageShares.ThreeMonths * (x.EarningReports.BasicEPS.TwelveMonths*x.ValuationRatios.PERatio) > 2e9] except: filtered_fundamental = [x for x in fundamental if (x.ValuationRatios.EVToEBITDA > 0) and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)] top = sorted(filtered_fundamental, key = lambda x: x.ValuationRatios.EVToEBITDA, reverse=True)[:self.num_screener] self.symbols = [x.Symbol for x in top] self.rebalance_flag = 0 self.first_month_trade_flag = 0 self.trade_flag = 1 return self.symbols #else: # return self.symbols def OnSecuritiesChanged(self, changes): for security in changes.RemovedSecurities: if security.Symbol in self.data: del self.data[security.Symbol] addedSymbols = [] for security in changes.AddedSecurities: addedSymbols.append(security.Symbol) if security.Symbol not in self.data: self.data[security.Symbol] = SymbolData(security.Symbol, self.formation_days) if len(addedSymbols) > 0: history = self.History(addedSymbols, 1 + self.formation_days, Resolution.Daily).loc[addedSymbols] for symbol in addedSymbols: try: self.data[symbol].Warmup(history.loc[symbol]) except: self.Debug(str(symbol)) continue self.RegisterIndicator(symbol, self.data[symbol].Roc, Resolution.Daily, Field.Close) 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): if self.be_in == 999: self.flip_flag = 1 self.rebalance() self.flip_flag = 0 # 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 # Re-assess/disambiguate double-edged signals median = np.nanmedian(returns_sample, axis=0) abovemedian = returns_sample.iloc[-1] > median ### 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]]) ### GOLD/SLVA differential may increase due to inflation expectations which actually suggest an economic improvement = actually not a negative signal try: extreme_b.loc['G_S'] = np.where((extreme_b.loc[['G_S']].any()) & (abovemedian.loc[[self.INFL]].any()), False, extreme_b.loc['G_S']) except: pass # 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) # 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.trade({**dict.fromkeys(self.Portfolio.Keys, 0), **self.HLD_OUT}) if self.dcount >= self.outday + adjwaitdays: self.be_in = True self.dcount += 1 # Only re-shuffle stock allocation when switching from out to in, not in-between if not self.be_in_prior and self.be_in: self.flip_flag = 1 self.rebalance() self.reb_count = self.dcount self.flip_flag = 0 if self.be_in and self.reb_count > 0 and (self.dcount - self.reb_count) == 20: self.rebalance() self.reb_count = self.dcount 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) self.be_in_prior = self.be_in def rebalance(self): self.rebalance_flag = 1 #self.Debug(str(self.Time) + "rebalance: be_in:" + str(self.be_in) + " flip_flag:" + str(self.flip_flag)) if self.symbols is None: return chosen_df = self.calc_return(self.symbols) chosen_df = chosen_df.iloc[:self.num_stocks] #for symbol in chosen_df.index: # self.AddEquity(symbol) weight = 0.99/len(chosen_df) self.trade({**dict.fromkeys(chosen_df.index.tolist(), weight), **dict.fromkeys(list(dict.fromkeys(set(self.Portfolio.Keys) - set(chosen_df.index))), 0), **dict.fromkeys(self.HLD_OUT, 0)}) def calc_return(self, stocks): #hist = self.History(stocks, self.formation_days, Resolution.Daily) #current = self.History(stocks, 1, Resolution.Minute) #self.price = {} ret = {} #for symbol in stocks: # if str(symbol) in hist.index.levels[0] and str(symbol) in current.index.levels[0]: # self.price[symbol.Value] = list(hist.loc[str(symbol)]['close']) # self.price[symbol.Value].append(current.loc[str(symbol)]['close'][0]) #for symbol in self.price.keys(): for symbol in stocks: try: #ret[symbol] = (self.price[symbol][-1] - self.price[symbol][0]) / self.price[symbol][0] ret[symbol] = self.data[symbol].Roc.Current.Value except: self.Debug(str(symbol)) continue df_ret = pd.DataFrame.from_dict(ret, orient='index') df_ret.columns = ['return'] sort_return = df_ret.sort_values(by = ['return'], ascending = self.lowmom) return sort_return def trade(self, weight_by_sec): buys = [] 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: buys.append((sec, quantity)) elif quantity < 0: self.Order(sec, quantity) for sec, quantity in buys: self.Order(sec, quantity) def record_vars(self): self.spy.append(self.history[self.MRKT].iloc[-1]) spy_perf = self.spy[-1] / self.spy[0] * self.cap self.Plot('Strategy Equity', 'SPY', spy_perf) account_leverage = self.Portfolio.TotalHoldingsValue / self.Portfolio.TotalPortfolioValue self.Plot('Holdings', 'leverage', round(account_leverage, 2)) class SymbolData(object): def __init__(self, symbol, roc): self.Symbol = symbol self.Roc = RateOfChange(roc) def Warmup(self, history): for index, row in history.iterrows(): self.Roc.Update(index, row['close'])