# 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.Common") from System import * from QuantConnect import * from QuantConnect.Data import SubscriptionDataSource from QuantConnect.Python import PythonData, PythonQuandl from datetime import datetime import decimal class QuandlFuture(PythonQuandl): '''Custom quandl data type for setting customized value column name. Value column is used for the primary trading calculations and charting.''' def __init__(self): # Define ValueColumnName: cannot be None, Empty or non-existant column name # If ValueColumnName is "Close", do not use PythonQuandl, use Quandl: # self.AddData[QuandlFuture](self.crude, Resolution.Daily) self.ValueColumnName = "Settle" 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 = decimal.Decimal(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