wehub-resource-sync
65 lines
2.9 KiB
Python
65 lines
2.9 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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### Example demonstrating how to define an option price model.
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="options" />
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### <meta name="tag" content="filter selection" />
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### <meta name="tag" content="option price model" />
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class BasicTemplateOptionsPriceModel(QCAlgorithm):
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'''Example demonstrating how to define an option price model.'''
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def initialize(self):
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self.set_start_date(2020, 1, 1)
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self.set_end_date(2020, 1, 5)
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self.set_cash(100000)
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# Add the option
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option = self.add_option("AAPL")
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self.option_symbol = option.symbol
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# Add the initial contract filter
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option.set_filter(-3, +3, 0, 31)
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# Define the Option Price Model
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option.price_model = OptionPriceModels.QuantLib.crank_nicolson_fd()
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#option.price_model = OptionPriceModels.QuantLib.black_scholes()
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#option.price_model = OptionPriceModels.QuantLib.additive_equiprobabilities()
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#option.price_model = OptionPriceModels.QuantLib.barone_adesi_whaley()
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#option.price_model = OptionPriceModels.QuantLib.binomial_cox_ross_rubinstein()
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#option.price_model = OptionPriceModels.QuantLib.binomial_jarrow_rudd()
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#option.price_model = OptionPriceModels.QuantLib.binomial_joshi()
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#option.price_model = OptionPriceModels.QuantLib.binomial_leisen_reimer()
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#option.price_model = OptionPriceModels.QuantLib.binomial_tian()
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#option.price_model = OptionPriceModels.QuantLib.binomial_trigeorgis()
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#option.price_model = OptionPriceModels.QuantLib.bjerksund_stensland()
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#option.price_model = OptionPriceModels.QuantLib.integral()
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# Set warm up with 30 trading days to warm up the underlying volatility model
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self.set_warm_up(30, Resolution.DAILY)
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def on_data(self,slice):
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'''OnData will test whether the option contracts has a non-zero Greeks.delta'''
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if self.is_warming_up or not slice.option_chains.contains_key(self.option_symbol):
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return
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chain = slice.option_chains[self.option_symbol]
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if not any([x for x in chain if x.greeks.delta != 0]):
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self.log(f'No contract with Delta != 0')
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