wehub-resource-sync
50 lines
2.4 KiB
Python
50 lines
2.4 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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### Abstract regression framework algorithm for multiple framework regression tests
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### </summary>
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class BaseFrameworkRegressionAlgorithm(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2014, 6, 1)
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self.set_end_date(2014, 6, 30)
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self.universe_settings.resolution = Resolution.HOUR
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self.universe_settings.data_normalization_mode = DataNormalizationMode.RAW
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symbols = [Symbol.create(ticker, SecurityType.EQUITY, Market.USA)
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for ticker in ["AAPL", "AIG", "BAC", "SPY"]]
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# Manually add AAPL and AIG when the algorithm starts
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self.set_universe_selection(ManualUniverseSelectionModel(symbols[:2]))
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# At midnight, add all securities every day except on the last data
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# With this procedure, the Alpha Model will experience multiple universe changes
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self.add_universe_selection(ScheduledUniverseSelectionModel(
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self.date_rules.every_day(), self.time_rules.midnight,
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lambda dt: symbols if dt < self.end_date - timedelta(1) else []))
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self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(31), 0.025, None))
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self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
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self.set_execution(ImmediateExecutionModel())
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self.set_risk_management(NullRiskManagementModel())
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def on_end_of_algorithm(self):
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# The base implementation checks for active insights
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insights_count = len(self.insights.get_insights(lambda insight: insight.is_active(self.utc_time)))
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if insights_count != 0:
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raise AssertionError(f"The number of active insights should be 0. Actual: {insights_count}")
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