Overall Statistics |
Total Trades 65 Average Win 12.27% Average Loss -1.69% Compounding Annual Return 27.151% Drawdown 24.600% Expectancy 4.928 Net Profit 1260.797% Sharpe Ratio 1.353 Probabilistic Sharpe Ratio 77.847% Loss Rate 28% Win Rate 72% Profit-Loss Ratio 7.25 Alpha 0.231 Beta 0.052 Annual Standard Deviation 0.175 Annual Variance 0.031 Information Ratio 0.471 Tracking Error 0.23 Treynor Ratio 4.6 Total Fees $2988.41 |
# Intersection of ROC comparison using OUT_DAY approach by Vladimirhttps://www.quantconnect.com/terminal/#backtest-floating-panel import numpy as np # ------------------------------------------------------------------------------ STOCKS = ['TQQQ']; BONDS = ['TMF']; VOLA = 126; BASE_RET = 85; LEV = 1; PAIRS = ['SLV', 'GLD', 'XLI', 'XLU', 'DBB', 'UUP'] # ------------------------------------------------------------------------------ class InOut(QCAlgorithm): def Initialize(self): self.SetStartDate(2010, 3, 1) # self.SetEndDate(2020, 12, 17) self.cap = 100000 self.SetCash(self.cap) self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Cash) #self.Portfolio.MarginCallModel = MarginCallModel.Null #self.SetRiskManagement(MaximumUnrealizedProfitPercentPerSecurity(0.5)) # reduces drawdown more than reduces annual return #self.Settings.FreePortfolioValuePercentage = 0.05 self.STOCKS = [self.AddEquity(ticker, Resolution.Minute).Symbol for ticker in STOCKS] self.BONDS = [self.AddEquity(ticker, Resolution.Minute).Symbol for ticker in BONDS] #self.Securities["QQQ"].SetLeverage(2.0) #self.Securities["TLT"].SetLeverage(2.0) #self.Securities["TLH"].SetLeverage(2.0) #self.Securities["QQQ"].MarginModel = PatternDayTradingMarginModel() #self.Securities["TLT"].MarginModel = PatternDayTradingMarginModel() #self.Securities["TLH"].MarginModel = PatternDayTradingMarginModel() #self.Securities["TQQQ"].MarginModel = PatternDayTradingMarginModel() #self.Securities["TMF"].MarginModel = PatternDayTradingMarginModel() self.SLV = self.AddEquity('SLV', Resolution.Daily).Symbol self.GLD = self.AddEquity('GLD', Resolution.Daily).Symbol self.XLI = self.AddEquity('XLI', Resolution.Daily).Symbol self.XLU = self.AddEquity('XLU', Resolution.Daily).Symbol self.DBB = self.AddEquity('DBB', Resolution.Daily).Symbol self.UUP = self.AddEquity('UUP', Resolution.Daily).Symbol self.MKT = self.AddEquity('SPY', Resolution.Daily).Symbol self.pairs = [self.SLV, self.GLD, self.XLI, self.XLU, self.DBB, self.UUP] self.bull = 1 self.count = 0 self.outday = 0 self.wt = {} self.real_wt = {} self.mkt = [] #self.SetWarmUp(timedelta(350)) self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 120), self.rebalance) 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 + 1, 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 + 1):] def rebalance(self): vola = self.history[[self.MKT]].pct_change().std() * np.sqrt(252) wait_days = int(vola * BASE_RET) period = int((1.0 - vola) * BASE_RET) r = self.history.pct_change(period).iloc[-1] exit = ((r[self.SLV] < r[self.GLD]) and (r[self.XLI] < r[self.XLU]) and (r[self.DBB] < r[self.UUP])) if exit: self.bull = False self.outday = self.count if self.count >= self.outday + wait_days: self.bull = True self.count += 1 if not self.bull: for sec in self.STOCKS: self.wt[sec] = 0.0 for sec in self.BONDS: self.wt[sec] = 0.35 #LEV/len(self.BONDS) self.trade() elif self.bull: for sec in self.STOCKS: self.wt[sec] = 0.35 #LEV/len(self.STOCKS) for sec in self.BONDS: self.wt[sec] = 0.0 self.trade() def trade(self): for sec, weight in self.wt.items(): if weight == 0 and self.Portfolio[sec].IsLong: self.Liquidate(sec) cond1 = weight == 0 and self.Portfolio[sec].IsLong cond2 = weight > 0 and not self.Portfolio[sec].Invested if cond1 or cond2: self.SetHoldings(sec, weight)
from QuantConnect.Data.Custom.USEnergy import * class USEnergyAlphaModel: def __init__(self, algorithm): self.energy = algorithm.AddData(USEnergy, USEnergy.Petroleum.UnitedStates.WeeklyGrossInputsIntoRefineries).Symbol def Update(self, algorithm, data): insights = [] if not data.ContainsKey(self.energy): return insights energy_data = data.Get(USEnergy, self.energy) ## The U.S. Energy Information Administration (EIA) is a principal agency of the U.S. Federal Statistical System ## responsible for collecting, analyzing, and disseminating energy information to promote sound policymaking, ## efficient markets, and public understanding of energy and its interaction with the economy and the environment. ## EIA programs cover data on coal, petroleum, natural gas, electric, renewable and nuclear energy. EIA is part of the U.S. Department of Energy. ## Find more categories here: https://github.com/QuantConnect/Lean/blob/master/Common/Data/Custom/USEnergy/USEnergy.Category.cs return insights def OnSecuritiesChanged(self, algorithm, changes): # For instruction on how to use this method, please visit # https://www.quantconnect.com/docs/algorithm-framework/alpha-creation#Alpha-Creation-Good-Design-Patterns pass