#region imports
from AlgorithmImports import *
#endregion
# 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

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

# 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://www.quandl.com/data/OECD-Organisation-for-Economic-Co-operation-and-Development
        self.symbols = {
                        "CME_AD1" : "OECD/KEI_IR3TIB01_AUS_ST_M",   # Australian Dollar Futures, Continuous Contract #1
                        "CME_BP1" : "OECD/KEI_IR3TIB01_GBR_ST_M",   # British Pound Futures, Continuous Contract #1
                        "CME_CD1" : "OECD/KEI_IR3TIB01_CAN_ST_M",   # Canadian Dollar Futures, Continuous Contract #1
                        "CME_EC1" : "OECD/KEI_IR3TIB01_EA19_ST_M",  # Euro FX Futures, Continuous Contract #1
                        "CME_JY1" : "OECD/KEI_IR3TIB01_JPN_ST_M",   # Japanese Yen Futures, Continuous Contract #1
                        "CME_MP1" : "OECD/KEI_IR3TIB01_MEX_ST_M",   # Mexican Peso Futures, Continuous Contract #1
                        "CME_NE1" : "OECD/KEI_IR3TIB01_NZL_ST_M",   # New Zealand Dollar Futures, Continuous Contract #1
                        "CME_SF1" : "SNB/ZIMOMA"                    # Swiss Franc Futures, Continuous Contract #1
        }
        
        self.max_missing_days:int = 31
        self.traded_count:int = 3
        self.leverage:int = 5

        for symbol, rate_symbol in self.symbols.items():
            self.AddData(Quandl, 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] = {}

        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

                # IR data is still comming in
                if self.Securities[int_rate].GetLastData() and (self.Time.date() - self.Securities[int_rate].GetLastData().Time.date()).days <= self.max_missing_days:
                    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))