# https://quantpedia.com/strategies/dollar-carry-trade/
#
# The investment universe consists of currencies from developed countries (the Euro area, Australia, Canada, Denmark, Japan, New Zealand, Norway, Sweden,
# Switzerland, and the United Kingdom). The average forward discount (AFD) is calculated for this basket of currencies (each currency has an equal weight).
# The average 3-month rate could be used instead of the AFD in the calculation. The AFD is then compared to the 3-month US Treasury rate. The investor
# goes long on the US dollar and goes short on the basket of currencies if the 3-month US Treasury rate is higher than the AFD. The investor goes short
# on the US dollar and long on the basket of currencies if the 3-month US Treasury rate is higher than the AFD. The portfolio is rebalanced monthly.

import numpy as np
from AlgorithmImports import *


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

        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
        }

        for symbol in self.symbols:
            data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
            data.SetFeeModel(CustomFeeModel())
            data.SetLeverage(5)

            # Interbank rate data.
            cash_rate_symbol = self.symbols[symbol]
            self.AddData(QuandlValue, cash_rate_symbol, Resolution.Daily)

        self.treasury_rate = self.AddData(
            QuandlValue, "FRED/DGS3MO", Resolution.Daily
        ).Symbol

    def OnData(self, data):
        fd = {}
        for future_symbol, cash_rate_symbol in self.symbols.items():
            if cash_rate_symbol in data and data[cash_rate_symbol]:
                if (
                    self.Securities[future_symbol].GetLastData()
                    and (
                        self.Time.date()
                        - self.Securities[future_symbol].GetLastData().Time.date()
                    ).days
                    < 5
                ):
                    cash_rate = data[cash_rate_symbol].Value
                    # Update cash rate only once a month.
                    fd[future_symbol] = cash_rate

        if len(fd) == 0:
            return

        afd = np.mean([x[1] for x in fd.items()])

        if (
            self.Securities[self.treasury_rate].GetLastData()
            and (
                self.Time.date()
                - self.Securities[self.treasury_rate].GetLastData().Time.date()
            ).days
            < 5
        ):
            treasuries_3m_rate = self.Securities[self.treasury_rate].Price

            count = len(self.symbols)
            if treasuries_3m_rate > afd:
                # Long on the US dollar and goes short on the basket of currencies.
                for symbol in self.symbols:
                    self.SetHoldings(symbol, -1 / count)
            else:
                # Short on the US dollar and long on the basket of currencies.
                for symbol in self.symbols:
                    self.SetHoldings(symbol, 1 / count)


# 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


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


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