Overall Statistics |
Total Trades 389 Average Win 2.18% Average Loss -1.26% Compounding Annual Return 19.490% Drawdown 29.700% Expectancy 0.491 Net Profit 249.382% Sharpe Ratio 0.828 Probabilistic Sharpe Ratio 24.124% Loss Rate 45% Win Rate 55% Profit-Loss Ratio 1.73 Alpha 0.168 Beta 0.251 Annual Standard Deviation 0.227 Annual Variance 0.052 Information Ratio 0.407 Tracking Error 0.269 Treynor Ratio 0.75 Total Fees $1276.15 |
""" 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, and Leandro Maia 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 TARGET_LEV = 1.0; class EarningsFactorWithMomentum_InOut(QCAlgorithm): def Initialize(self): self.SetStartDate(2008, 1, 1) self.SetEndDate(2015, 1, 7) self.InitCash = 100000 self.SetCash(self.InitCash) self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin) res = Resolution.Hour # Holdings ### 'Out' holdings and weights self.BND1 = self.AddEquity('TLT', res).Symbol #TLT; TMF for 3xlev self.HLD_OUT = {self.BND1: TARGET_LEV} ### '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 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 = 999 self.BE_IN = 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 = Resolution.Daily self.UniverseSettings.Resolution = res self.AddUniverse(self.UniverseCoarseFilter, self.UniverseFundamentalsFilter) self.num_screener = 15 self.num_stocks = 10 self.formation_days = 126 self.lowmom = False self.data = {} self.setrebalancefreq = 60 # X days, update universe and momentum calculation self.updatefinefilter = 0 self.symbols = None self.reb_count = 0 self.Schedule.On( self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 90), 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.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.mkt = [] self.state = 0 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)] # rank the stocks by dollar volume filtered = sorted(selected, key=lambda x: x.DollarVolume, reverse=True) return [x.Symbol for x in filtered[:200]] 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] 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) 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 derive_vola_waitdays(self): volatility = 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 Bear 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 self.trade({**dict.fromkeys(self.Portfolio.Keys, 0), **self.HLD_OUT}) self.state = -1 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): #self.Debug(str(self.Time) + "rebalance: be_in:" + str(self.be_in) + " flip_flag:" + str(self.flip_flag)) if self.symbols is None: return symbols = self.calc_return(self.symbols) weight = TARGET_LEV/len(symbols) self.trade({**dict.fromkeys(symbols, weight), **dict.fromkeys(list(dict.fromkeys(set([x.Symbol for x in self.Portfolio.Values if x.Invested]) - set(symbols))), 0)}) self.state = 1 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] ] 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): mkt_price = self.History(self.MRKT, 2, Resolution.Daily)['close'].unstack(level= 0).iloc[-1] self.mkt.append(mkt_price) mkt_perf = self.InitCash * self.mkt[-1] / self.mkt[0] self.Plot('Strategy Equity', self.MRKT, mkt_perf) account_leverage = self.Portfolio.TotalHoldingsValue / self.Portfolio.TotalPortfolioValue self.Plot('Holdings', 'leverage', round(account_leverage, 2)) self.Plot('Holdings', 'TARGET_LEV', TARGET_LEV) self.Plot("State", "Current", self.state) class SymbolData(object): def __init__(self, symbol, roc_period, algorithm): self.Symbol = symbol self.Roc = RateOfChange(roc_period) self.algorithm = algorithm self.consolidator = algorithm.ResolveConsolidator(symbol, Resolution.Daily) algorithm.RegisterIndicator(symbol, self.Roc, self.consolidator) # Warm up ROC history = algorithm.History(symbol, roc_period, Resolution.Daily) if history.empty or 'close' not in history.columns: return for index, row in history.loc[symbol].iterrows(): self.Roc.Update(index, row['close']) def dispose(self): self.algorithm.SubscriptionManager.RemoveConsolidator(self.Symbol, self.consolidator)