Overall Statistics |
Total Orders
2926
Average Win
1.38%
Average Loss
-1.71%
Compounding Annual Return
0.401%
Drawdown
79.600%
Expectancy
0.023
Start Equity
100000
End Equity
110465.63
Net Profit
10.466%
Sharpe Ratio
0.01
Sortino Ratio
0.011
Probabilistic Sharpe Ratio
0.000%
Loss Rate
44%
Win Rate
56%
Profit-Loss Ratio
0.81
Alpha
0.007
Beta
-0.11
Annual Standard Deviation
0.205
Annual Variance
0.042
Information Ratio
-0.151
Tracking Error
0.27
Treynor Ratio
-0.018
Total Fees
$2030.61
Estimated Strategy Capacity
$0
Lowest Capacity Asset
CME_PA1.QuantpediaFutures 2S
Portfolio Turnover
3.54%
|
# 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. #region imports from AlgorithmImports import * #endregion class MomentumEffectCommodities(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) tickers: List[str] = [ "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_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: int = 12 * 21 self.quantile: int = 5 self.SetWarmUp(self.period, Resolution.Daily) self.data: Dict[Symbol, RateOfChange] = {} for ticker in tickers: data: Security = self.AddData(QuantpediaFutures, ticker, Resolution.Daily) data.SetFeeModel(CustomFeeModel()) data.SetLeverage(5) self.data[data.Symbol] = self.ROC(ticker, self.period, Resolution.Daily) self.recent_month: int = -1 def OnData(self, slice: Slice) -> None: if self.IsWarmingUp: return if any([self.securities[symbol].get_last_data() and self.time.date() > QuantpediaFutures.get_last_update_date()[symbol] for symbol in list(self.data.keys())]): self.liquidate() return # rebalance once a month if self.recent_month == self.Time.month: return self.recent_month = self.Time.month perf: Dict[Symbol, float] = { x[0] : x[1].Current.Value for x in self.data.items() if self.data[x[0]].IsReady and x[0] in slice and slice[x[0]] } long: List[Symbol] = [] short: List[Symbol] = [] if len(perf) >= self.quantile: sorted_by_performance: List[Symbol] = sorted(perf, key = perf.get, reverse=True) quintile: int = int(len(sorted_by_performance) / self.quantile) long = sorted_by_performance[:quintile] short = sorted_by_performance[-quintile:] # trade execution invested: List[Symbol] = [x.Key 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): _last_update_date:Dict[str, datetime.date] = {} @staticmethod def get_last_update_date() -> Dict[str, datetime.date]: return QuantpediaFutures._last_update_date def GetSource(self, config:SubscriptionDataConfig, date:datetime, isLiveMode:bool) -> SubscriptionDataSource: return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv) def Reader(self, config:SubscriptionDataConfig, line:str, date:datetime, isLiveMode:bool) -> BaseData: 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]) # store last update date if config.Symbol not in QuantpediaFutures._last_update_date: QuantpediaFutures._last_update_date[config.Symbol] = datetime(1,1,1).date() if data.Time.date() > QuantpediaFutures._last_update_date[config.Symbol]: QuantpediaFutures._last_update_date[config.Symbol] = data.Time.date() return data # Custom fee model. class CustomFeeModel(): def GetOrderFee(self, parameters): fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD"))