wehub-resource-sync
46 lines
1.7 KiB
Python
46 lines
1.7 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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# limitations under the License.
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from AlgorithmImports import *
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from time import sleep
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### <summary>
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### Example algorithm showing how to use QCAlgorithm.train method
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### </summary>
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### <meta name="tag" content="using quantconnect" />
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### <meta name="tag" content="training" />
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class TrainingExampleAlgorithm(QCAlgorithm):
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'''Example algorithm showing how to use QCAlgorithm.train method'''
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def initialize(self):
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self.set_start_date(2013, 10, 7)
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self.set_end_date(2013, 10, 14)
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self.add_equity("SPY", Resolution.DAILY)
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# Set TrainingMethod to be executed immediately
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self.train(self.training_method)
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# Set TrainingMethod to be executed at 8:00 am every Sunday
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self.train(self.date_rules.every(DayOfWeek.SUNDAY), self.time_rules.at(8 , 0), self.training_method)
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def training_method(self):
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self.log(f'Start training at {self.time}')
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# Use the historical data to train the machine learning model
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history = self.history(["SPY"], 200, Resolution.DAILY)
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# ML code:
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pass
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