# 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 AlgorithmImports import *

### <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.set_start_date(2008, 1, 8)
 self.set_end_date(2014, 7, 25)
 self.set_cash(100000)

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

 self.settings.automatic_indicator_warm_up = True
 rupee_sma = self.sma(rupee, 20)
 nifty_sma = self.sma(rupee, 20)
 self.log(f"SMA - Is ready? USDINR: {rupee_sma.is_ready} NIFTY: {nifty_sma.is_ready}")

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


 def on_data(self, data):
 if data.contains_key("USDINR"):
 self.today = CorrelationPair(self.time)
 self.today.currency_price = data["USDINR"].close

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

 self.today.nifty_price = data["NIFTY"].close

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

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

 cur_qnty = self.portfolio["NIFTY"].quantity
 quantity = int(self.portfolio.margin_remaining * 0.9 / data["NIFTY"].close)
 hi_nifty = max(price.nifty_price for price in self.prices)
 lo_nifty = min(price.nifty_price 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.total_portfolio_value))
 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.total_portfolio_value))


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


 def reader(self, config, line, date, is_live_mode):
 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年09月13日 7792.9 7799.9 7722.65 7748.7 116534670 6107.78
 data = line.split(',')
 index.time = datetime.strptime(data[0], "%Y-%m-%d")
 index.end_time = index.time + timedelta(days=1)
 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 get_source(self, config, date, is_live_mode):
 return SubscriptionDataSource("https://www.dropbox.com/s/m6ecmkg9aijwzy2/USDINR.csv?dl=1", SubscriptionTransportMedium.REMOTE_FILE)

 def reader(self, config, line, date, is_live_mode):
 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.end_time = currency.time + timedelta(days=1)
 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.nifty_price = 0 # Nifty price for this correlation pair
 self.currency_price = 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()