# https://quantpedia.com/strategies/currency-momentum-factor/
#
# Create an investment universe consisting of several currencies (10-20). Go long three currencies with the highest 12-month momentum against USD
# and go short three currencies with the lowest 12-month momentum against USD. Cash not used as margin invest on overnight rates. Rebalance monthly.

import data_tools

class CurrencyMomentumFactor(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2000, 1, 1)
        self.SetCash(100000)

        self.data = {}
        self.period = 12 * 21
        self.SetWarmUp(self.period)

        self.symbols = [
                        "CME_AD1", # Australian Dollar Futures, Continuous Contract #1
                        "CME_BP1", # British Pound Futures, Continuous Contract #1
                        "CME_CD1", # Canadian Dollar Futures, Continuous Contract #1
                        "CME_EC1", # Euro FX Futures, Continuous Contract #1
                        "CME_JY1", # Japanese Yen Futures, Continuous Contract #1
                        "CME_MP1", # Mexican Peso Futures, Continuous Contract #1
                        "CME_NE1", # New Zealand Dollar Futures, Continuous Contract #1
                        "CME_SF1"  # Swiss Franc Futures, Continuous Contract #1
                        ]
                        
        for symbol in self.symbols:
            data = self.AddData(data_tools.QuantpediaFutures, symbol, Resolution.Daily)
            data.SetFeeModel(data_tools.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):
        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 = [x[0] for x in sorted_by_performance[:3]]
        short = [x[0] for x in sorted_by_performance[-3:]]

        # 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))

# Custom fee model
class CustomFeeModel(FeeModel):
    def GetOrderFee(self, parameters):
        fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
        return OrderFee(CashAmount(fee, "USD"))

# Quandl "value" data
class QuandlValue(PythonQuandl):
    def __init__(self):
        self.ValueColumnName = 'Value'

# 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