#region imports
from AlgorithmImports import *
from dateutil.relativedelta import relativedelta
#endregion

# 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"))

class InterestRate3M(PythonData):
    _last_update_date:Dict[str, datetime.date] = {}

    @staticmethod
    def get_last_update_date() -> Dict[str, datetime.date]:
       return InterestRate3M._last_update_date

    def GetSource(self, config:SubscriptionDataConfig, date:datetime, isLiveMode:bool) -> SubscriptionDataSource:
        return SubscriptionDataSource("data.quantpedia.com/backtesting_data/interbank_rate/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)

    def Reader(self, config:SubscriptionDataConfig, line:str, date:datetime, isLiveMode:bool) -> BaseData:
        data = InterestRate3M()
        data.Symbol = config.Symbol
        
        if not line[0].isdigit(): return None
        split = line.split(';')
        
        data.Time = datetime.strptime(split[0], "%Y-%m-%d") + relativedelta(months=2)
        data['value'] = float(split[1])
        data.Value = float(split[1])

        # store last update date
        if config.Symbol.Value not in InterestRate3M._last_update_date:
            InterestRate3M._last_update_date[config.Symbol.Value] = datetime(1,1,1).date()

        if data.Time.date() > InterestRate3M._last_update_date[config.Symbol.Value]:
            InterestRate3M._last_update_date[config.Symbol.Value] = data.Time.date()

        return data

# https://quantpedia.com/strategies/fx-carry-trade/
#
# Create an investment universe consisting of several currencies (10-20). Go long three currencies with the highest central bank prime rates and
# go short three currencies with the lowest central bank prime rates. The cash not used as the margin is invested in overnight rates. The strategy 
# is rebalanced monthly.

#region imports
from AlgorithmImports import *
import data_tools
#endregion

class ForexCarryTrade(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2000, 1, 1) 
        self.SetCash(100000)
        
        # Source: https://fred.stlouisfed.org/series/IR3TIB01AUM156N
        self.symbols = {
                        "CME_AD1" : "IR3TIB01AUM156N",  # Australian Dollar Futures, Continuous Contract #1
                        "CME_BP1" : "LIOR3MUKM",        # British Pound Futures, Continuous Contract #1
                        "CME_CD1" : "IR3TIB01CAM156N",  # Canadian Dollar Futures, Continuous Contract #1
                        "CME_EC1" : "IR3TIB01EZM156N",  # Euro FX Futures, Continuous Contract #1
                        "CME_JY1" : "IR3TIB01JPM156N",  # Japanese Yen Futures, Continuous Contract #1
                        "CME_MP1" : "IR3TIB01MXM156N",  # Mexican Peso Futures, Continuous Contract #1
                        "CME_NE1" : "IR3TIB01NZM156N",  # New Zealand Dollar Futures, Continuous Contract #1
                        "CME_SF1" : "IR3TIB01CHM156N"   # Swiss Franc Futures, Continuous Contract #1
        }
        
        self.traded_count:int = 3
        self.leverage:int = 5

        for symbol, rate_symbol in self.symbols.items():
            self.AddData(data_tools.InterestRate3M, rate_symbol, Resolution.Daily)
            
            data = self.AddData(data_tools.QuantpediaFutures, symbol, Resolution.Daily)
            data.SetFeeModel(data_tools.CustomFeeModel())
            data.SetLeverage(self.leverage)
            
        self.recent_month = -1

    def OnData(self, data):
        rebalance_flag:bool = False
        rate:dict[str, float] = {}

        ir_last_update_date:Dict[str, datetime.date] = data_tools.InterestRate3M.get_last_update_date()

        for symbol, int_rate in self.symbols.items():
            # futures data is present in the algorithm
            if symbol in data and data[symbol]:
                if self.recent_month != self.Time.month:
                    rebalance_flag = True
                    self.recent_month = self.Time.month

                if rebalance_flag:
                    # IR data is still comming in
                    if self.Securities[int_rate].GetLastData() and ir_last_update_date[int_rate] > self.Time.date():
                        rate[symbol] = self.Securities[int_rate].Price

        if rebalance_flag:
            long = []
            short = []

            if len(rate) >= self.traded_count:
                # interbank rate sorting
                sorted_by_rate = sorted(rate.items(), key = lambda x: x[1], reverse = True)
                long = [x[0] for x in sorted_by_rate[:self.traded_count]]
                short = [x[0] for x in sorted_by_rate[-self.traded_count:]]
                
            # 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))