wehub-resource-sync
55 lines
2.3 KiB
Python
55 lines
2.3 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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from AlgorithmImports import *
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### <summary>
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### Regression algorithm to demonstrate the use of SetBenchmark() with custom data
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### </summary>
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class CustomDataBenchmarkRegressionAlgorithm(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2017, 8, 18) # Set Start Date
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self.set_end_date(2017, 8, 21) # Set End Date
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self.set_cash(100000) # Set Strategy Cash
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self.add_equity("SPY", Resolution.HOUR)
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# Load benchmark data
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self.custom_symbol = self.add_data(ExampleCustomData, "ExampleCustomData", Resolution.HOUR).symbol
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self.set_benchmark(self.custom_symbol)
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def on_data(self, data):
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if not self.portfolio.invested:
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self.set_holdings("SPY", 1)
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def on_end_of_algorithm(self):
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security_benchmark = self.benchmark
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if security_benchmark.security.price == 0:
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raise AssertionError("Security benchmark price was not expected to be zero")
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class ExampleCustomData(PythonData):
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def get_source(self, config, date, is_live):
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source = "https://www.dl.dropboxusercontent.com/s/d83xvd7mm9fzpk0/path_to_my_csv_data.csv?dl=0"
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return SubscriptionDataSource(source, SubscriptionTransportMedium.REMOTE_FILE)
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def reader(self, config, line, date, is_live):
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data = line.split(',')
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obj_data = ExampleCustomData()
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obj_data.symbol = config.symbol
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obj_data.time = datetime.strptime(data[0], '%Y-%m-%d %H:%M:%S') + timedelta(hours=20)
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obj_data.value = float(data[4])
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obj_data["Open"] = float(data[1])
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obj_data["High"] = float(data[2])
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obj_data["Low"] = float(data[3])
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obj_data["Close"] = float(data[4])
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return obj_data
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