Short-Term Commodity Momentum Long-Short Strategy

The strategy invests in 48 commodities, longing top-performing quintiles and shorting bottom-performing ones, with equal weighting and monthly rebalancing, leveraging momentum to exploit recent performance trends.

ASSET CLASS: CFDs, futures | REGION: Global | FREQUENCY:
Monthly | MARKET: commodities | KEYWORD: Momentum

I. STRATEGY IN A NUTSHELL

Trades 48 commodities across agriculture, industrial, energy, and precious metals, going long on the top quintile and short on the bottom quintile based on past month returns. Portfolios are equally weighted and rebalanced monthly.

II. ECONOMIC RATIONALE

Last month’s commodity returns predict short-term performance, independent of traditional factors. Weak but significant correlations across asset classes suggest a common driver, making short-term momentum robust and exploitable.

III. SOURCE PAPER

Short-Term Momentum (Almost) Everywhere [Click to Open PDF]

Adam Zaremba, Andreas Karathanasopoulos Huaigang Long and Huaigang Long. Montpellier Business School; Poznan University of Economics and Business; University of Cape Town (UCT).University of Dubai.Zhejiang University; Zhejiang University of Finance and Economics (ZUFE)

<Abstract>

Is there a short-term reversal effect outside the universe of individual stocks? To answer this, we investigate a comprehensive dataset of more than two centuries of returns on five major asset classes: equity indices, government bonds, treasury bills, commodities, and currencies. Contrary to stock-level evidence, we find a striking short-term momentum pattern: the most recent month’s return positively predicts future performance. The effect is not explained by established return predictors — including the standard momentum — and is robust to many considerations. The short-term momentum is strongest among assets of high idiosyncratic volatility and in periods of elevated return dispersion. Also, the strategy payoffs display partial commonality across different asset classes.

IV. BACKTEST PERFORMANCE

Annualised Return	21.12%
Volatility	20.86%
Beta	-0.079
Sharpe Ratio	1.01
Sortino Ratio	-0.035
Maximum Drawdown	N/A
Win Rate	52%

V. FULL PYTHON CODE

from AlgorithmImports import *
class OneMonthMomentuminCommodities(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(1991, 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_PA1",  # Palladium Futures, Continuous Contract 
            "CME_RR1",  # Rough Rice Futures, Continuous Contract
            "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 = 21
        self.quantile = 5
        self.SetWarmUp(self.period)
        
        self.data = {}
        for symbol in self.symbols:
            data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
            data.SetFeeModel(CustomFeeModel())
            data.SetLeverage(5)
            
            self.data[symbol] = SymbolData(self, symbol, self.period)
        
        self.rebalance_flag: bool = False
        self.Schedule.On(self.DateRules.MonthStart(self.symbols[0]), self.TimeRules.At(0, 0), self.Rebalance)
        self.settings.minimum_order_margin_portfolio_percentage = 0.
    
    def on_data(self, slice: Slice) -> None:
        if self.IsWarmingUp: return
        
        if not self.rebalance_flag:
            return
        self.rebalance_flag = False
        # Return sorting
        performance = { x : self.data[x].roc.Current.Value for x in self.data if self.data[x].is_ready() and self.Securities[x].GetLastData() and self.Time.date() < QuantpediaFutures.get_last_update_date()[x] }
        
        long = []
        short = []
        if len(performance) >= self.quantile:
            sorted_by_return = sorted(performance.items(), key = lambda x: x[1], reverse = True)
            quintile = int(len(sorted_by_return) / self.quantile)
            long = [x[0] for x in sorted_by_return[:quintile]]
            short = [x[0] for x in sorted_by_return[-quintile:]]
        targets: List[PortfolioTarget] = []
        for i, portfolio in enumerate([long, short]):
            for symbol in portfolio:
                if symbol in slice and slice[symbol]:
                    targets.append(PortfolioTarget(symbol, ((-1) ** i) / len(portfolio)))
        
        self.SetHoldings(targets, True)
    def Rebalance(self):
        self.rebalance_flag = True
class SymbolData():
    def __init__(self, algorithm, symbol, period:int) -> None:
        self.roc = algorithm.ROC(symbol, period, Resolution.Daily)
        self.algorithm = algorithm
    
    def is_ready(self) -> bool:
        return self.roc.IsReady
# Quantpedia data.
# NOTE: IMPORTANT: Data order must be ascending (datewise)
class QuantpediaFutures(PythonData):
    _last_update_date:Dict[Symbol, datetime.date] = {}
    @staticmethod
    def get_last_update_date() -> Dict[Symbol, datetime.date]:
       return QuantpediaFutures._last_update_date
    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])
        if config.Symbol.Value not in QuantpediaFutures._last_update_date:
            QuantpediaFutures._last_update_date[config.Symbol.Value] = datetime(1,1,1).date()
        if data.Time.date() > QuantpediaFutures._last_update_date[config.Symbol.Value]:
            QuantpediaFutures._last_update_date[config.Symbol.Value] = data.Time.date()
        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"))

VI. Backtest Performance