momentum-effect-in-commodities.py

#region imports
from AlgorithmImports import *
#endregion
# 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_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.SetWarmUp(self.period, Resolution.Daily)
        self.data = {}
        
        for symbol in self.symbols:
            data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
            data.SetFeeModel(CustomFeeModel())
            data.SetLeverage(5)
            self.data[symbol] = self.ROC(symbol, self.period, Resolution.Daily)
        
        self.recent_month = -1

    def OnData(self, data):
        if self.IsWarmingUp:
            return

        # rebalance once a month
        if self.recent_month == self.Time.month:
            return
        self.recent_month = self.Time.month

        perf = { x[0] : x[1].Current.Value for x in self.data.items() if self.data[x[0]].IsReady and x[0] in data and data[x[0]] }

        long = []
        short = []
        if len(perf) >= 5:
            sorted_by_performance = sorted(perf.items(), key = lambda x:x[1], reverse=True)
            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():
    def GetOrderFee(self, parameters):
        fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
        return OrderFee(CashAmount(fee, "USD"))