# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Data import SubscriptionDataSource
from QuantConnect.Python import PythonData
from datetime import date, timedelta, datetime
import numpy as np
import math
import json

### <summary>
### This demonstration imports indian NSE index "NIFTY" as a tradable security in addition to the USDINR currency pair. We move into the
### NSE market when the economy is performing well.
### </summary>
### <meta name="tag" content="strategy example" />
### <meta name="tag" content="using data" />
### <meta name="tag" content="custom data" />
class CustomDataNIFTYAlgorithm(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2008, 1, 1)
        self.SetEndDate(2014, 12, 31)
        self.SetCash(100000)

        # Define the symbol and "type" of our generic data:
        rupee = self.AddData(DollarRupee, "USDINR", Resolution.Daily).Symbol
        nifty = self.AddData(Nifty, "NIFTY", Resolution.Daily).Symbol

        self.EnableAutomaticIndicatorWarmUp = True
        rupeeSma = self.SMA(rupee, 20)
        niftySma = self.SMA(rupee, 20)
        self.Log(f"SMA - Is ready? USDINR: {rupeeSma.IsReady} NIFTY: {niftySma.IsReady}")

        self.minimumCorrelationHistory = 50
        self.today = CorrelationPair()
        self.prices = []


    def OnData(self, data):
        if data.ContainsKey("USDINR"):
            self.today = CorrelationPair(self.Time)
            self.today.CurrencyPrice = data["USDINR"].Close

        if not data.ContainsKey("NIFTY"): return

        self.today.NiftyPrice = data["NIFTY"].Close

        if self.today.date() == data["NIFTY"].Time.date():
            self.prices.append(self.today)
            if len(self.prices) > self.minimumCorrelationHistory:
                self.prices.pop(0)

        # Strategy
        if self.Time.weekday() != 2: return

        cur_qnty = self.Portfolio["NIFTY"].Quantity
        quantity = math.floor(self.Portfolio.MarginRemaining * 0.9) / data["NIFTY"].Close
        hi_nifty = max(price.NiftyPrice for price in self.prices)
        lo_nifty = min(price.NiftyPrice for price in self.prices)

        if data["NIFTY"].Open >= hi_nifty:
            code = self.Order("NIFTY",  quantity - cur_qnty)
            self.Debug("LONG  {0} Time: {1} Quantity: {2} Portfolio: {3} Nifty: {4} Buying Power: {5}".format(code, self.Time, quantity, self.Portfolio["NIFTY"].Quantity, data["NIFTY"].Close, self.Portfolio.TotalPortfolioValue))
        elif data["NIFTY"].Open <= lo_nifty:
            code = self.Order("NIFTY", -quantity - cur_qnty)
            self.Debug("SHORT {0} Time: {1} Quantity: {2} Portfolio: {3} Nifty: {4} Buying Power: {5}".format(code, self.Time, quantity, self.Portfolio["NIFTY"].Quantity, data["NIFTY"].Close, self.Portfolio.TotalPortfolioValue))


class Nifty(PythonData):
    '''NIFTY Custom Data Class'''
    def GetSource(self, config, date, isLiveMode):
        return SubscriptionDataSource("https://www.dropbox.com/s/rsmg44jr6wexn2h/CNXNIFTY.csv?dl=1", SubscriptionTransportMedium.RemoteFile)


    def Reader(self, config, line, date, isLiveMode):
        if not (line.strip() and line[0].isdigit()): return None

        # New Nifty object
        index = Nifty()
        index.Symbol = config.Symbol

        try:
            # Example File Format:
            # Date,       Open       High        Low       Close     Volume      Turnover
            # 2011-09-13  7792.9    7799.9     7722.65    7748.7    116534670    6107.78
            data = line.split(',')
            index.Time = datetime.strptime(data[0], "%Y-%m-%d")
            index.Value = data[4]
            index["Open"] = float(data[1])
            index["High"] = float(data[2])
            index["Low"] = float(data[3])
            index["Close"] = float(data[4])


        except ValueError:
                # Do nothing
                return None

        return index


class DollarRupee(PythonData):
    '''Dollar Rupe is a custom data type we create for this algorithm'''
    def GetSource(self, config, date, isLiveMode):
        return SubscriptionDataSource("https://www.dropbox.com/s/m6ecmkg9aijwzy2/USDINR.csv?dl=1", SubscriptionTransportMedium.RemoteFile)

    def Reader(self, config, line, date, isLiveMode):
        if not (line.strip() and line[0].isdigit()): return None

        # New USDINR object
        currency = DollarRupee()
        currency.Symbol = config.Symbol

        try:
            data = line.split(',')
            currency.Time = datetime.strptime(data[0], "%Y-%m-%d")
            currency.Value = data[1]
            currency["Close"] = float(data[1])

        except ValueError:
            # Do nothing
            return None

        return currency


class CorrelationPair:
    '''Correlation Pair is a helper class to combine two data points which we'll use to perform the correlation.'''
    def __init__(self, *args):
        self.NiftyPrice = 0        # Nifty price for this correlation pair
        self.CurrencyPrice = 0     # Currency price for this correlation pair
        self._date = datetime.min    # Date of the correlation pair
        if len(args) > 0: self._date = args[0]

    def date(self):
        return self._date.date()