chore: import upstream snapshot with attribution
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# 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 OptionPriceModelForOptionStylesBaseRegressionAlgorithm import OptionPriceModelForOptionStylesBaseRegressionAlgorithm
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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 OptionPriceModelForSupportedAmericanOptionRegressionAlgorithm(OptionPriceModelForOptionStylesBaseRegressionAlgorithm):
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def initialize(self):
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self.set_start_date(2014, 6, 9)
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self.set_end_date(2014, 6, 9)
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option = self.add_option("AAPL", Resolution.MINUTE)
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option.set_filter(lambda u: u.standards_only().strikes(-1, 1).expiration(0, 35))
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# BaroneAdesiWhaley model supports American style options
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option.price_model = OptionPriceModels.QuantLib.barone_adesi_whaley()
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self.set_warmup(2, Resolution.DAILY)
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self.init(option, option_style_is_supported=True)
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