# 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"))