wehub-resource-sync
63 lines
2.9 KiB
Python
63 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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### Regression algorithm illustrating the usage of the <see cref="QCAlgorithm.OptionChains(IEnumerable{Symbol})"/> method
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### to get multiple future option chains.
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### </summary>
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class FutureOptionChainsMultipleFullDataRegressionAlgorithm(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2020, 1, 6)
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self.set_end_date(2020, 1, 6)
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es_future_contract = self.add_future_contract(
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Symbol.create_future(Futures.Indices.SP_500_E_MINI, Market.CME, datetime(2020, 3, 20)),
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Resolution.MINUTE).symbol
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gc_future_contract = self.add_future_contract(
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Symbol.create_future(Futures.Metals.GOLD, Market.COMEX, datetime(2020, 4, 28)),
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Resolution.MINUTE).symbol
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chains = self.option_chains([es_future_contract, gc_future_contract], flatten=True)
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self._es_option_contract = self.get_contract(chains, es_future_contract)
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self._gc_option_contract = self.get_contract(chains, gc_future_contract)
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self.add_future_option_contract(self._es_option_contract)
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self.add_future_option_contract(self._gc_option_contract)
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def get_contract(self, chains: OptionChains, underlying: Symbol) -> Symbol:
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df = chains.data_frame
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# Index by the requested underlying, by getting all data with canonicals which underlying is the requested underlying symbol:
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canonicals = df.index.get_level_values('canonical')
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condition = [canonical for canonical in canonicals if canonical.underlying == underlying]
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contracts = df.loc[condition]
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# Get contracts expiring within 4 months, with the latest expiration date, highest strike and lowest price
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contracts = contracts.loc[(df.expiry <= self.time + timedelta(days=120))]
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contracts = contracts.sort_values(['expiry', 'strike', 'lastprice'], ascending=[False, False, True])
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return contracts.index[0][1]
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def on_data(self, data):
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# Do some trading with the selected contract for sample purposes
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if not self.portfolio.invested:
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self.set_holdings(self._es_option_contract, 0.25)
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self.set_holdings(self._gc_option_contract, 0.25)
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else:
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self.liquidate()
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