Overall Statistics |
Total Trades 442 Average Win 0.99% Average Loss -0.66% Compounding Annual Return 40.756% Drawdown 37.000% Expectancy 0.564 Net Profit 106.845% Sharpe Ratio 1.114 Probabilistic Sharpe Ratio 47.707% Loss Rate 37% Win Rate 63% Profit-Loss Ratio 1.50 Alpha 0 Beta 0 Annual Standard Deviation 0.287 Annual Variance 0.082 Information Ratio 1.114 Tracking Error 0.287 Treynor Ratio 0 Total Fees $557.52 Estimated Strategy Capacity $10000000.00 Lowest Capacity Asset SHY SGNKIKYGE9NP |
''' Intersection of ROC comparison using OUT_DAY approach by Vladimir v1.3 (with dynamic selector for fundamental factors and momentum) inspired by Peter Guenther, Tentor Testivis, Dan Whitnable, Thomas Chang, Miko M, Leandro Maia Leandro Maia setup modified by Vladimir https://www.quantconnect.com/forum/discussion/9632/amazing-returns-superior-stock-selection-strategy-superior-in-amp-out-strategy/p2/comment-29437 https://www.quantconnect.com/forum/discussion/10246/intersection-of-roc-comparison-using-out-day-approach/p1 BONDS = symbols('TMF') if data.can_trade(symbol('TMF')) else symbols('TLT') This can be modified to use for futures ''' from QuantConnect.Data.UniverseSelection import * import numpy as np import pandas as pd # -------------------------------------------------------------------------------------------------------- BONDS = ['TLT','GLD','SHY']; VOLA = 126; BASE_RET = 85; STK_MOM = 126; N_COARSE = 100; N_FACTOR = 20; N_MOM = 5; LEV = .98; # -------------------------------------------------------------------------------------------------------- class Fundamental_Factors_Momentum_ROC_Comparison_OUT_DAY(QCAlgorithm): def Initialize(self): # LIVE TRADING if self.LiveMode: self.Debug("Trading Live!") self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin) # Group Trading # Use a default FA Account Group with an Allocation Method self.DefaultOrderProperties = InteractiveBrokersOrderProperties() # account group created manually in IB/TWS self.DefaultOrderProperties.FaGroup = "TE1x" # supported allocation methods are: EqualQuantity, NetLiq, AvailableEquity, PctChange self.DefaultOrderProperties.FaMethod = "AvailableEquity" # set a default FA Allocation Profile # Alex: I commented the following line out, since it would "reset" the previous settings #self.DefaultOrderProperties = InteractiveBrokersOrderProperties() # allocation profile created manually in IB/TWS # self.DefaultOrderProperties.FaProfile = "TestProfileP" #Algo Start self.SetStartDate(2020, 1, 1) #self.SetEndDate(2010, 12, 31) self.InitCash = 100000 self.SetCash(self.InitCash) self.MKT = self.AddEquity("SPY", Resolution.Hour).Symbol self.mkt = [] self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin) res = Resolution.Hour self.BONDS = [self.AddEquity(ticker, res).Symbol for ticker in BONDS] self.INI_WAIT_DAYS = 5 self.wait_days = self.INI_WAIT_DAYS self.GLD = self.AddEquity('GLD', res).Symbol self.SLV = self.AddEquity('SLV', res).Symbol self.XLU = self.AddEquity('XLU', res).Symbol self.XLI = self.AddEquity('XLI', res).Symbol self.UUP = self.AddEquity('UUP', res).Symbol self.DBB = self.AddEquity('DBB', res).Symbol self.pairs = [self.GLD, self.SLV, self.XLU, self.XLI, self.UUP, self.DBB] self.bull = 1 self.bull_prior = 0 self.count = 0 self.outday = (-self.INI_WAIT_DAYS+1) self.SetWarmUp(timedelta(350)) self.UniverseSettings.Resolution = res self.AddUniverse(self.CoarseFilter, self.FineFilter) self.data = {} self.RebalanceFreq = 60 self.UpdateFineFilter = 0 self.symbols = None self.RebalanceCount = 0 self.wt = {} self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 120), self.daily_check) symbols = [self.MKT] + self.pairs for symbol in symbols: self.consolidator = TradeBarConsolidator(timedelta(days=1)) self.consolidator.DataConsolidated += self.consolidation_handler self.SubscriptionManager.AddConsolidator(symbol, self.consolidator) self.history = self.History(symbols, VOLA, Resolution.Daily) if self.history.empty or 'close' not in self.history.columns: return self.history = self.history['close'].unstack(level=0).dropna() def consolidation_handler(self, sender, consolidated): self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close self.history = self.history.iloc[-VOLA:] def derive_vola_waitdays(self): sigma = 0.6 * np.log1p(self.history[[self.MKT]].pct_change()).std() * np.sqrt(252) wait_days = int(sigma * BASE_RET) period = int((1.0 - sigma) * BASE_RET) return wait_days, period def CoarseFilter(self, coarse): if not (((self.count-self.RebalanceCount) == self.RebalanceFreq) or (self.count == self.outday + self.wait_days - 1)): self.UpdateFineFilter = 0 return Universe.Unchanged self.UpdateFineFilter = 1 selected = [x for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)] filtered = sorted(selected, key=lambda x: x.DollarVolume, reverse=True) return [x.Symbol for x in filtered[:N_COARSE]] def FineFilter(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 > 10e9 and x.SecurityReference.IsPrimaryShare and x.SecurityReference.SecurityType == "ST00000001" and x.SecurityReference.IsDepositaryReceipt == 0 and x.CompanyReference.IsLimitedPartnership == 0] top = sorted(filtered_fundamental, key = lambda x: x.ValuationRatios.EVToEBITDA, reverse=True)[:N_FACTOR] self.symbols = [x.Symbol for x in top] self.UpdateFineFilter = 0 self.RebalanceCount = self.count return self.symbols def OnSecuritiesChanged(self, changes): addedSymbols = [] for security in changes.AddedSecurities: addedSymbols.append(security.Symbol) if security.Symbol not in self.data: self.data[security.Symbol] = SymbolData(security.Symbol, STK_MOM, self) if len(addedSymbols) > 0: history = self.History(addedSymbols, 1 + STK_MOM, Resolution.Daily).loc[addedSymbols] for symbol in addedSymbols: try: self.data[symbol].Warmup(history.loc[symbol]) except: self.Debug(str(symbol)) continue def daily_check(self): self.wait_days, period = self.derive_vola_waitdays() r = self.history.pct_change(period).iloc[-1] bear = ((r[self.SLV] < r[self.GLD]) and (r[self.XLI] < r[self.XLU]) and (r[self.DBB] < r[self.UUP])) if bear: self.bull = False self.outday = self.count if (self.count >= self.outday + self.wait_days): self.bull = True self.wt_stk = LEV if self.bull else 0 self.wt_bnd = 0 if self.bull else LEV if bear: self.trade_out() if (self.bull and not self.bull_prior) or (self.bull and (self.count==self.RebalanceCount)): self.trade_in() self.bull_prior = self.bull self.count += 1 def trade_out(self): for sec in self.BONDS: self.wt[sec] = self.wt_bnd/len(self.BONDS) for sec in self.Portfolio.Keys: if sec not in self.BONDS: self.wt[sec] = 0 for sec, weight in self.wt.items(): if weight == 0 and self.Portfolio[sec].IsLong: self.Liquidate(sec) for sec, weight in self.wt.items(): if weight != 0: self.SetHoldings(sec, weight) def trade_in(self): if self.symbols is None: return output = self.calc_return(self.symbols) stocks = output.iloc[:N_MOM].index for sec in self.Portfolio.Keys: if sec not in stocks: self.wt[sec] = 0 for sec in stocks: self.wt[sec] = self.wt_stk/N_MOM for sec, weight in self.wt.items(): self.SetHoldings(sec, weight) def calc_return(self, 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 = False) return sort_return def OnEndOfDay(self): mkt_price = self.Securities[self.MKT].Close self.mkt.append(mkt_price) mkt_perf = self.InitCash * self.mkt[-1] / self.mkt[0] self.Plot('Strategy Equity', self.MKT, mkt_perf) account_leverage = self.Portfolio.TotalHoldingsValue / self.Portfolio.TotalPortfolioValue self.Plot('Holdings', 'leverage', round(account_leverage, 2)) self.Plot('Holdings', 'Target Leverage', LEV) 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'])