# 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

from AlgorithmImports import *

### <summary>
### Regression algorithm to demonstrate the use of SetBenchmark() with custom data
### </summary>
class CustomDataBenchmarkRegressionAlgorithm(QCAlgorithm):
 def initialize(self):
 self.set_start_date(2017, 8, 18) # Set Start Date
 self.set_end_date(2017, 8, 21) # Set End Date
 self.set_cash(100000) # Set Strategy Cash

 self.add_equity("SPY", Resolution.HOUR)
 # Load benchmark data
 self.custom_symbol = self.add_data(ExampleCustomData, "ExampleCustomData", Resolution.HOUR).symbol
 self.set_benchmark(self.custom_symbol)

 def on_data(self, data):
 if not self.portfolio.invested:
 self.set_holdings("SPY", 1)

 def on_end_of_algorithm(self):
 security_benchmark = self.benchmark
 if security_benchmark.security.price == 0:
 raise AssertionError("Security benchmark price was not expected to be zero")

class ExampleCustomData(PythonData):

 def get_source(self, config, date, is_live):
 source = "https://www.dl.dropboxusercontent.com/s/d83xvd7mm9fzpk0/path_to_my_csv_data.csv?dl=0"
 return SubscriptionDataSource(source, SubscriptionTransportMedium.REMOTE_FILE)

 def reader(self, config, line, date, is_live):
 data = line.split(',')
 obj_data = ExampleCustomData()
 obj_data.symbol = config.symbol
 obj_data.time = datetime.strptime(data[0], '%Y-%m-%d %H:%M:%S') + timedelta(hours=20)
 obj_data.value = float(data[4])
 obj_data["Open"] = float(data[1])
 obj_data["High"] = float(data[2])
 obj_data["Low"] = float(data[3])
 obj_data["Close"] = float(data[4])
 return obj_data