# region imports
from AlgorithmImports import *

# endregion
# 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


# https://quantpedia.com/strategies/currency-value-factor-ppp-strategy/
#
# Create an investment universe consisting of several currencies (10-20). Use the latest OECD Purchasing Power Parity figure to assess
# the fair value of each currency versus USD in the month of publishing and then use monthly CPI changes and exchange rate changes to
# create fair PPP value for the month prior to the current month. Go long three currencies that are the most undervalued (lowest PPP
# fair value figure) and go short three currencies that are the most overvalued (highest PPP fair value figure). Invest cash not used
# as margin on overnight rates. Rebalance quarterly or monthly.
#
# QC implementation changes:
#   - Yearly rebalance instead of quarterly is performed.

import data_tools
from AlgorithmImports import *


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

        # currency future symbol and PPP yearly quandl symbol
        # PPP source: https://www.quandl.com/data/ODA-IMF-Cross-Country-Macroeconomic-Statistics?keyword=%20United%20States%20Implied%20PPP%20Conversion%20Rate
        self.symbols = {
            "CME_AD1": "ODA/AUS_PPPEX",  # Australian Dollar Futures, Continuous Contract #1
            "CME_BP1": "ODA/GBR_PPPEX",  # British Pound Futures, Continuous Contract #1
            "CME_CD1": "ODA/CAD_PPPEX",  # Canadian Dollar Futures, Continuous Contract #1
            "CME_EC1": "ODA/DEU_PPPEX",  # Euro FX Futures, Continuous Contract #1
            "CME_JY1": "ODA/JPN_PPPEX",  # Japanese Yen Futures, Continuous Contract #1
            "CME_NE1": "ODA/NZL_PPPEX",  # New Zealand Dollar Futures, Continuous Contract #1
            "CME_SF1": "ODA/CHE_PPPEX",  # 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())
            data.SetLeverage(5)

            # PPP quandl data.
            ppp_symbol = self.symbols[symbol]
            self.AddData(data_tools.QuandlValue, ppp_symbol, Resolution.Daily)

        self.recent_month = -1

    def OnData(self, data):
        if self.recent_month == self.Time.month:
            return
        self.recent_month = self.Time.month

        # January rebalance
        if self.recent_month == 1:
            ppp = {}
            for symbol, ppp_symbol in self.symbols.items():
                # if symbol in data and data[symbol]:
                if (
                    self.Securities[symbol].GetLastData()
                    and (
                        self.Time.date()
                        - self.Securities[symbol].GetLastData().Time.date()
                    ).days
                    < 3
                ):
                    # new ppp data arrived
                    if ppp_symbol in data and data[ppp_symbol]:
                        ppp[symbol] = data[ppp_symbol].Value

            count = 3
            long = []
            short = []
            if len(ppp) >= count * 2:
                # ppp sorting
                sorted_by_ppp = sorted(ppp.items(), key=lambda x: x[1], reverse=True)
                long = [x[0] for x in sorted_by_ppp[-count:]]
                short = [x[0] for x in sorted_by_ppp[: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))