Jhonathan Abreu
0472ce33d0
Modified algorithms to use futures contract objects directly instead of accessing their Symbol property. Removed unnecessary import statements and redundant lines in various files.
45 lines
1.9 KiB
Python
45 lines
1.9 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.FuturesChain(Symbol, bool)"/>
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### method to get a future chain.
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### </summary>
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class FuturesChainFullDataRegressionAlgorithm(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2013, 10, 7)
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self.set_end_date(2013, 10, 7)
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future = self.add_future(Futures.Indices.SP_500_E_MINI, Resolution.MINUTE).symbol
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chain = self.futures_chain(future, flatten=True)
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# Demonstration using data frame:
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df = chain.data_frame
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# Get contracts expiring within 6 months, with the latest expiration date, and lowest price
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contracts = df.loc[(df.expiry <= self.time + timedelta(days=180))]
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contracts = contracts.sort_values(['expiry', 'lastprice'], ascending=[False, True])
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self._future_contract = contracts.index[0]
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self.add_future_contract(self._future_contract)
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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._future_contract, 0.5)
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else:
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self.liquidate()
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