Overall Statistics |
Total Trades 2814 Average Win 1.19% Average Loss -1.62% Compounding Annual Return 1.843% Drawdown 70.700% Expectancy 0.032 Net Profit 48.109% Sharpe Ratio 0.181 Probabilistic Sharpe Ratio 0.001% Loss Rate 40% Win Rate 60% Profit-Loss Ratio 0.73 Alpha 0.036 Beta 0.044 Annual Standard Deviation 0.216 Annual Variance 0.047 Information Ratio -0.128 Tracking Error 0.274 Treynor Ratio 0.892 Total Fees $3405.21 Estimated Strategy Capacity $0 Lowest Capacity Asset CME_GC1.QuantpediaFutures 2S |
# https://quantpedia.com/strategies/1-month-momentum-in-commodities/ # # Create a universe of tradable commodity futures. Rank futures performance for each commodity for the last 12 months and divide them into quintiles. # Go long on the quintile with the highest momentum and go short on the quintile with the lowest momentum. Rebalance each month. class MomentumEffectCommodities(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) self.symbols = [ "CME_S1", # Soybean Futures, Continuous Contract "CME_W1", # Wheat Futures, Continuous Contract "CME_SM1", # Soybean Meal Futures, Continuous Contract "CME_BO1", # Soybean Oil Futures, Continuous Contract "CME_C1", # Corn Futures, Continuous Contract "CME_O1", # Oats Futures, Continuous Contract "CME_LC1", # Live Cattle Futures, Continuous Contract "CME_FC1", # Feeder Cattle Futures, Continuous Contract "CME_LN1", # Lean Hog Futures, Continuous Contract "CME_GC1", # Gold Futures, Continuous Contract "CME_SI1", # Silver Futures, Continuous Contract "CME_PL1", # Platinum Futures, Continuous Contract "CME_CL1", # Crude Oil Futures, Continuous Contract "CME_HG1", # Copper Futures, Continuous Contract "CME_LB1", # Random Length Lumber Futures, Continuous Contract "CME_NG1", # Natural Gas (Henry Hub) Physical Futures, Continuous Contract "CME_PA1", # Palladium Futures, Continuous Contract "CME_RR1", # Rough Rice Futures, Continuous Contract "CME_CU1", # Chicago Ethanol (Platts) Futures "CME_DA1", # Class III Milk Futures "ICE_RS1", # Canola Futures, Continuous Contract "ICE_GO1", # Gas Oil Futures, Continuous Contract "CME_RB2", # Gasoline Futures, Continuous Contract "CME_KW2", # Wheat Kansas, Continuous Contract "ICE_WT1", # WTI Crude Futures, Continuous Contract "ICE_CC1", # Cocoa Futures, Continuous Contract "ICE_CT1", # Cotton No. 2 Futures, Continuous Contract "ICE_KC1", # Coffee C Futures, Continuous Contract "ICE_O1", # Heating Oil Futures, Continuous Contract "ICE_OJ1", # Orange Juice Futures, Continuous Contract "ICE_SB1", # Sugar No. 11 Futures, Continuous Contract ] self.period = 12 * 21 self.data = {} for symbol in self.symbols: data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily) data.SetFeeModel(CustomFeeModel(self)) data.SetLeverage(5) self.data[symbol] = self.ROC(symbol, self.period, Resolution.Daily) self.Schedule.On(self.DateRules.MonthStart(self.symbols[0]), self.TimeRules.AfterMarketOpen(self.symbols[0]), self.Rebalance) def Rebalance(self): # Performance sorting. sorted_by_performance = sorted([x for x in self.data.items() if x[1].IsReady], key = lambda x: x[1].Current.Value, reverse = True) long = [] short = [] if len(sorted_by_performance) != 0: quintile = int(len(sorted_by_performance) / 5) long = [x[0] for x in sorted_by_performance[:quintile]] short = [x[0] for x in sorted_by_performance[-quintile:]] # Trade execution. invested = [x.Key.Value for x in self.Portfolio if x.Value.Invested] for symbol in invested: if symbol not in long + short: self.Liquidate(symbol) for symbol in long: self.SetHoldings(symbol, 1 / len(long)) for symbol in short: self.SetHoldings(symbol, -1 / len(short)) # Quantpedia data. # NOTE: IMPORTANT: Data order must be ascending (datewise) class QuantpediaFutures(PythonData): def GetSource(self, config, date, isLiveMode): return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv) def Reader(self, config, line, date, isLiveMode): data = QuantpediaFutures() data.Symbol = config.Symbol if not line[0].isdigit(): return None split = line.split(';') data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1) data['back_adjusted'] = float(split[1]) data['spliced'] = float(split[2]) data.Value = float(split[1]) return data # Custom fee model. class CustomFeeModel(FeeModel): def GetOrderFee(self, parameters): fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD"))