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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### Custom data universe selection regression algorithm asserting it's behavior. See GH issue #6396
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### </summary>
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class NoUniverseSelectorRegressionAlgorithm(QCAlgorithm):
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def initialize(self):
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'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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self.set_start_date(2014, 3, 24)
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self.set_end_date(2014, 3, 31)
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self.universe_settings.resolution = Resolution.DAILY
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self.add_universe(CoarseFundamental)
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self.changes = None
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def on_data(self, data):
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# if we have no changes, do nothing
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if not self.changes: 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.liquidate(security.symbol)
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active_and_with_data_securities = sum(x.value.has_data for x in self.active_securities)
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# we want 1/N allocation in each security in our universe
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for security in self.changes.added_securities:
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if security.has_data:
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self.set_holdings(security.symbol, 1 / active_and_with_data_securities)
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self.changes = None
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def on_securities_changed(self, changes):
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self.changes = changes
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