Overall Statistics |
Total Trades 639 Average Win 1.77% Average Loss -0.41% Compounding Annual Return 28.386% Drawdown 18.000% Expectancy 2.492 Net Profit 2594.202% Sharpe Ratio 1.591 Probabilistic Sharpe Ratio 95.481% Loss Rate 34% Win Rate 66% Profit-Loss Ratio 4.29 Alpha 0.238 Beta 0.049 Annual Standard Deviation 0.153 Annual Variance 0.023 Information Ratio 0.618 Tracking Error 0.234 Treynor Ratio 5.005 Total Fees $794.89 Estimated Strategy Capacity $70000.00 |
""" 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, Peter Guenther, Leandro Maia, Simone Pantaleoni and Strongs (Mirko V.) 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) The Distilled Bear in & out algo based on Dan Whitnable's 22 Oct 2020 algo on Quantopian. Dan's original notes: "This is based on Peter Guenther great “In & Out” algo. Included Tentor Testivis recommendation to use volatility adaptive calculation of WAIT_DAYS and RET. Included Vladimir's ideas to eliminate fixed constants Help from Thomas Chang" 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/ """ from QuantConnect.Data.UniverseSelection import * import math import numpy as np import pandas as pd import scipy as sp class EarningsFactorWithMomentum_InOut(QCAlgorithm): def Initialize(self): self.SetStartDate(2008, 1, 1) #Set Start Date self.SetEndDate(2021, 3, 3) #Set Start Date self.cap = 10000 self.SetCash(self.cap) self.averages = { } res = Resolution.Hour # Holdings ### 'Out' holdings and weights self.BND1 = self.AddEquity('TLT', res).Symbol #TLT; TMF for 3xlev self.quantity = {self.BND1: 0} ##### In & Out parameters ##### # Feed-in constants self.INI_WAIT_DAYS = 15 # out for 3 trading weeks self.wait_days = self.INI_WAIT_DAYS # Market and list of signals based on ETFs self.MRKT = self.AddEquity('SPY', res).Symbol # market 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.AddEquity('XLI', res).Symbol # vs industrials self.METL = self.AddEquity('DBB', res).Symbol # input prices (metals) self.USDX = self.AddEquity('UUP', res).Symbol # safe haven (USD) self.FORPAIRS = [self.GOLD, self.SLVA, self.UTIL, self.INDU, self.METL, self.USDX] # Specific variables self.DISTILLED_BEAR = 1#999 self.BE_IN = 1#999 self.BE_IN_PRIOR = 0 self.VOLA_LOOKBACK = 126 self.WAITD_CONSTANT = 85 self.DCOUNT = 0 # count of total days since start self.OUTDAY = (-self.INI_WAIT_DAYS+1) # dcount when self.be_in=0, initial setting ensures trading right away # set a warm-up period to initialize the indicator self.SetWarmUp(timedelta(350)) ##### Momentum & fundamentals strategy parameters ##### self.UniverseSettings.Resolution = res self.AddUniverse(self.UniverseCoarseFilter, self.UniverseFundamentalsFilter) self.num_coarse = 100#200 self.num_screener = 20#100 # changed from 15 self.num_stocks = 10 # lowered from 10 # 10 stocks self.formation_days = 126 self.lowmom = False self.data = {} self.setrebalancefreq = 250 # Long term hold self.updatefinefilter = 0 self.symbols = None self.reb_count = 0 self.Schedule.On( self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 30), # reduced time self.rebalance_when_out_of_the_market) self.Schedule.On( self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose('SPY', 0), self.record_vars) # Benchmark = record SPY self.spy = [] # Setup daily consolidation symbols = [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 = 126 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() '''def UniverseCoarseFilter(self, coarse): if not (((self.DCOUNT-self.reb_count)==self.setrebalancefreq) or (self.DCOUNT == self.OUTDAY + self.wait_days - 1)): self.updatefinefilter = 0 return Universe.Unchanged self.updatefinefilter = 1 # 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[:self.num_coarse]]''' def UniverseCoarseFilter(self, coarse): # Update at the beginning (by setting self.OUTDAY = -self.INI_WAIT_DAYS), every X days (rebalance frequency), and one day before waitdays are up if not (((self.DCOUNT-self.reb_count)==self.setrebalancefreq) or (self.DCOUNT == self.OUTDAY + self.wait_days - 1)): self.updatefinefilter = 0 return Universe.Unchanged self.updatefinefilter = 1 # 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)] # We are going to use a dictionary to refer the object that will keep the moving averages for cf in selected: symbol = cf.Symbol if cf.Symbol not in self.averages: self.averages[cf.Symbol] = SymbolDataVolume(cf.Symbol, 21, 5, 504) # Updates the SymbolData object with current EOD price avg = self.averages[cf.Symbol] avg.update(cf.EndTime, cf.AdjustedPrice, cf.DollarVolume) # Filter the values of the dict: we only want up-trending securities #values = list(filter(lambda sd: sd.smaw.Current.Value > 0, self.averages.values())) values = list(filter(lambda sd: sd.smaw.Current.Value > 0, self.averages.values())) values.sort(key=lambda x: (x.smaw.Current.Value), reverse=True) #values.sort(key=lambda x: (x.volume_ratiol), reverse=True) # we need to return only the symbol objects return [ x.symbol for x in values[:self.num_coarse] ] def UniverseFundamentalsFilter(self, fundamental): if self.updatefinefilter == 0: return Universe.Unchanged 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 x.SecurityReference.IsPrimaryShare and x.SecurityReference.SecurityType == "ST00000001" and x.SecurityReference.IsDepositaryReceipt == 0 and x.CompanyReference.IsLimitedPartnership == 0 and x.ValuationRatios.EVToEBITDA < 50 # Good quality ratio and x.ValuationRatios.FCFYield > 0] # Good quality ratio top = sorted(filtered_fundamental, key = lambda x: x.ValuationRatios.EVToEBITDA, reverse=True)[:self.num_screener] self.symbols = [x.Symbol for x in top] self.updatefinefilter = 0 self.reb_count = self.DCOUNT return self.symbols def OnSecuritiesChanged(self, changes): #for security in changes.RemovedSecurities: # symbol_data = self.data.pop(security.Symbol, None) # if symbol_data: # symbol_data.dispose() #for security in changes.AddedSecurities: # if security.Symbol not in self.data: # self.data[security.Symbol] = SymbolData(security.Symbol, self.formation_days, self) 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, self) 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 def consolidation_handler(self, sender, consolidated): self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close self.history = self.history.iloc[-self.lookback:] def derive_vola_waitdays(self): volatility = 0.6 * np.log1p(self.history[[self.MRKT]].pct_change()).std() * np.sqrt(252) wait_days = int(volatility * self.WAITD_CONSTANT) returns_lookback = int((1.0 - volatility) * self.WAITD_CONSTANT) return wait_days, returns_lookback def rebalance_when_out_of_the_market(self): self.wait_days, returns_lookback = self.derive_vola_waitdays() ## Check for Bears returns = self.history.pct_change(returns_lookback).iloc[-1] silver_returns = returns[self.SLVA] gold_returns = returns[self.GOLD] industrials_returns = returns[self.INDU] utilities_returns = returns[self.UTIL] metals_returns = returns[self.METL] dollar_returns = returns[self.USDX] self.DISTILLED_BEAR = (((gold_returns > silver_returns) and (utilities_returns > industrials_returns)) and (metals_returns < dollar_returns) ) # Determine whether 'in' or 'out' of the market if self.DISTILLED_BEAR: self.BE_IN = False self.OUTDAY = self.DCOUNT if self.quantity[self.BND1] == 0: for symbol in self.quantity.copy().keys(): if symbol == self.BND1: continue self.Order(symbol, - self.quantity[symbol]) self.Debug([str(self.Time), str(symbol), str(-self.quantity[symbol])]) del self.quantity[symbol] quantity = self.Portfolio.TotalPortfolioValue / self.Securities[self.BND1].Close self.quantity[self.BND1] = math.floor(quantity) self.Order(self.BND1, self.quantity[self.BND1]) self.Debug([str(self.Time), str(self.BND1), str(self.quantity[self.BND1])]) if (self.DCOUNT >= self.OUTDAY + self.wait_days): self.BE_IN = True # Update stock ranking/holdings, when swithing from 'out' to 'in' plus every X days when 'in' (set rebalance frequency) if (self.BE_IN and not self.BE_IN_PRIOR) or (self.BE_IN and (self.DCOUNT==self.reb_count)): self.rebalance() self.BE_IN_PRIOR = self.BE_IN self.DCOUNT += 1 def rebalance(self): if self.symbols is None: return chosen_df = self.calc_return(self.symbols) chosen_df = chosen_df.iloc[:self.num_stocks] if self.quantity[self.BND1] > 0: self.Order(self.BND1, - self.quantity[self.BND1]) self.Debug([str(self.Time), str(self.BND1), str(-self.quantity[self.BND1])]) self.quantity[self.BND1] = 0 weight = 1 / self.num_stocks for symbol in self.quantity.copy().keys(): if symbol == self.BND1: continue if not self.CurrentSlice.ContainsKey(symbol) or self.CurrentSlice[symbol] is None: continue if symbol not in chosen_df.index: self.Order(symbol, - self.quantity[symbol]) self.Debug([str(self.Time), str(symbol), str(-self.quantity[symbol])]) del self.quantity[symbol] else: quantity = self.Portfolio.TotalPortfolioValue * weight / self.Securities[symbol].Close if math.floor(quantity) != self.quantity[symbol]: self.Order(symbol, math.floor(quantity) - self.quantity[symbol]) self.Debug([str(self.Time), str(symbol), str(math.floor(quantity) -self.quantity[symbol])]) self.quantity[symbol] = math.floor(quantity) for symbol in chosen_df.index: if not self.CurrentSlice.ContainsKey(symbol) or self.CurrentSlice[symbol] is None: continue if symbol not in self.quantity.keys(): quantity = self.Portfolio.TotalPortfolioValue * weight / self.Securities[symbol].Close self.quantity[symbol] = math.floor(quantity) self.Order(symbol, self.quantity[symbol]) self.Debug([str(self.Time), str(symbol), str(self.quantity[symbol])]) def calc_return(self, stocks): #ready = [self.data[symbol] for symbol in stocks if self.data[symbol].Roc.IsReady] #sorted_by_roc = sorted(ready, key=lambda x: x.Roc.Current.Value, reverse = not self.lowmom) #return [symbol_data.Symbol for symbol_data in sorted_by_roc[:self.num_stocks] ] ret = {} for symbol in stocks: try: 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 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, 0)) class SymbolData(object): def __init__(self, symbol, roc, algorithm): self.Symbol = symbol self.Roc = RateOfChange(roc) self.algorithm = algorithm self.consolidator = algorithm.ResolveConsolidator(symbol, Resolution.Daily) algorithm.RegisterIndicator(symbol, self.Roc, self.consolidator) def Warmup(self, history): for index, row in history.iterrows(): self.Roc.Update(index, row['close']) class SymbolDataVolume(object): def __init__(self, symbol, period, periodw, periodlt): self.symbol = symbol #self.tolerance = 1.01 self.tolerance = 0.95 self.fast = ExponentialMovingAverage(10) self.slow = ExponentialMovingAverage(21) self.is_uptrend = False self.scale = 0 self.volume = 0 self.volume_ratio = 0 self.volume_ratiow = 0 self.volume_ratiol = 0 self.sma = SimpleMovingAverage(period) self.smaw = SimpleMovingAverage(periodw) self.smalt = SimpleMovingAverage(periodlt) def update(self, time, value, volume): self.volume = volume if self.smaw.Update(time, volume): # get ratio of this volume bar vs previous 10 before it. if self.smaw.Current.Value != 0: self.volume_ratiow = volume / self.smaw.Current.Value if self.sma.Update(time, volume): # get ratio of this volume bar vs previous 10 before it. if self.sma.Current.Value != 0: self.volume_ratio = self.smaw.Current.Value / self.sma.Current.Value if self.smalt.Update(time, volume): if self.smalt.Current.Value != 0 and self.smaw.Current.Value != 0: self.volume_ratiol = self.smaw.Current.Value / self.smalt.Current.Value if self.fast.Update(time, value) and self.slow.Update(time, value): fast = self.fast.Current.Value slow = self.slow.Current.Value #self.is_uptrend = fast > slow * self.tolerance self.is_uptrend = (fast > (slow * self.tolerance)) and (value > (fast * self.tolerance)) if self.is_uptrend: self.scale = (fast - slow) / ((fast + slow) / 2.0)