wehub-resource-sync
84 lines
3.6 KiB
Python
84 lines
3.6 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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### <summary>
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### We add an option contract using 'QCAlgorithm.add_option_contract' and place a trade, the underlying
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### gets deselected from the universe selection but should still be present since we manually added the option contract.
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### Later we call 'QCAlgorithm.remove_option_contract' and expect both option and underlying to be removed.
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### </summary>
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class AddOptionContractFromUniverseRegressionAlgorithm(QCAlgorithm):
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def initialize(self):
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'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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self.set_start_date(2014, 6, 5)
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self.set_end_date(2014, 6, 9)
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self._expiration = datetime(2014, 6, 21)
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self._security_changes = None
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self._option = None
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self._traded = False
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self._twx = Symbol.create("TWX", SecurityType.EQUITY, Market.USA)
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self._aapl = Symbol.create("AAPL", SecurityType.EQUITY, Market.USA)
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self.universe_settings.resolution = Resolution.MINUTE
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self.universe_settings.data_normalization_mode = DataNormalizationMode.RAW
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self.add_universe(self.selector, self.selector)
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def selector(self, fundamental):
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if self.time <= datetime(2014, 6, 5):
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return [ self._twx ]
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return [ self._aapl ]
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def on_data(self, data):
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'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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Arguments:
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data: Slice object keyed by symbol containing the stock data
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'''
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if self._option != None and self.securities[self._option].price != 0 and not self._traded:
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self._traded = True
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self.buy(self._option, 1)
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if self.time == datetime(2014, 6, 6, 14, 0, 0):
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# liquidate & remove the option
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self.remove_option_contract(self._option)
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def on_securities_changed(self, changes):
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# keep track of all removed and added securities
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if self._security_changes == None:
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self._security_changes = changes
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else:
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self._security_changes += changes
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if any(security.symbol.security_type == SecurityType.OPTION for security in changes.added_securities):
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return
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for addedSecurity in changes.added_securities:
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option_chain = self.option_chain(addedSecurity.symbol)
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options = sorted(option_chain, key=lambda x: x.id.symbol)
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option = next((option
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for option in options
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if option.id.date == self._expiration and
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option.id.option_right == OptionRight.CALL and
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option.id.option_style == OptionStyle.AMERICAN), None)
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self.add_option_contract(option)
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# just keep the first we got
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if self._option == None:
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self._option = option
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