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CustomDataNIFTYAlgorithm.py
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156 lines (122 loc) · 6.05 KB
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# 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
C2DB
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, 8)
self.SetEndDate(2014, 7, 25)
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 = int(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.EndTime = 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 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.EndTime = 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.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()