wehub-resource-sync
28 lines
1.6 KiB
Python
28 lines
1.6 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 OptionPriceModelForSupportedAmericanOptionRegressionAlgorithm import OptionPriceModelForSupportedAmericanOptionRegressionAlgorithm
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### <summary>
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### Regression algorithm exercising an equity covered American style option, using an option price model
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### that supports American style options and asserting that the option price model is used.
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### </summary>
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class OptionPriceModelForSupportedAmericanOptionTimeSpanWarmupRegressionAlgorithm(OptionPriceModelForSupportedAmericanOptionRegressionAlgorithm):
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def initialize(self):
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OptionPriceModelForSupportedAmericanOptionRegressionAlgorithm.initialize(self)
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# We want to match the start time of the base algorithm: Base algorithm warmup is 2 bar of daily resolution.
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# So to match the same start time we go back 4 days, we need to account for a single weekend. This is calculated by 'Time.GET_START_TIME_FOR_TRADE_BARS'
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self.set_warmup(TimeSpan.from_days(4))
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