# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals. # Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from AlgorithmImports import * ### ### Regression algorithm illustrating the usage of the method ### to get an option chain, which contains additional data besides the symbols, including prices, implied volatility and greeks. ### It also shows how this data can be used to filter the contracts based on certain criteria. ### class OptionChainFullDataRegressionAlgorithm(QCAlgorithm): def initialize(self): self.set_start_date(2015, 12, 24) self.set_end_date(2015, 12, 24) self.set_cash(100000) goog = self.add_equity("GOOG").symbol # Get contracts expiring within 10 days, with an implied volatility greater than 0.5 and a delta less than 0.5 contracts = [ contract_data for contract_data in self.option_chain(goog) if contract_data.id.date - self.time <= timedelta(days=10) and contract_data.implied_volatility > 0.5 and contract_data.greeks.delta < 0.5 ] # Get the contract with the latest expiration date self._option_contract = sorted(contracts, key=lambda x: x.id.date, reverse=True)[0] self.add_option_contract(self._option_contract) def on_data(self, data): # Do some trading with the selected contract for sample purposes if not self.portfolio.invested: self.market_order(self._option_contract, 1) else: self.liquidate()