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 OptionAssignmentRegressionAlgorithm import OptionAssignmentRegressionAlgorithm
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### <summary>
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### Regression algorithm asserting we can specify a custom option exercise model
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### </summary>
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class CustomOptionExerciseModelRegressionAlgorithm(OptionAssignmentRegressionAlgorithm):
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def initialize(self):
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self.set_security_initializer(self.custom_security_initializer)
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super().initialize()
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def custom_security_initializer(self, security):
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if Extensions.is_option(security.symbol.security_type):
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security.set_option_exercise_model(CustomExerciseModel())
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def on_data(self, data):
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super().on_data(data)
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class CustomExerciseModel(DefaultExerciseModel):
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def option_exercise(self, option: Option, order: OptionExerciseOrder):
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order_event = OrderEvent(
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order.id,
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option.symbol,
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Extensions.convert_to_utc(option.local_time, option.exchange.time_zone),
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OrderStatus.FILLED,
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Extensions.get_order_direction(order.quantity),
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0.0,
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order.quantity,
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OrderFee.ZERO,
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"Tag"
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)
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order_event.is_assignment = False
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return [ order_event ]
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