chore: import upstream snapshot with attribution
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# 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 to test universe additions and removals with open positions
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### </summary>
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### <meta name="tag" content="regression test" />
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class WeeklyUniverseSelectionRegressionAlgorithm(QCAlgorithm):
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def initialize(self) -> None:
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self.set_cash(100000)
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self.set_start_date(2013,10,1)
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self.set_end_date(2013,10,31)
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self.universe_settings.resolution = Resolution.HOUR
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# select IBM once a week, empty universe the other days
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self.add_universe("my-custom-universe", lambda dt: ["IBM"] if dt.day % 7 == 0 else [])
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def on_data(self, slice: Slice) -> None:
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if not self._changes:
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return
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# liquidate removed securities
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for security in self._changes.removed_securities:
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if security.invested:
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self.log("{} Liquidate {}".format(self.time, security.symbol))
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self.liquidate(security.symbol)
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# we'll simply go long each security we added to the universe
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for security in self._changes.added_securities:
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if not security.invested:
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self.log("{} Buy {}".format(self.time, security.symbol))
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self.set_holdings(security.symbol, 1)
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self._changes = None
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def on_securities_changed(self, changes: SecurityChanges) -> None:
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self._changes = changes
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