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# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from AlgorithmImports import *
### <summary>
### Abstract regression framework algorithm for multiple framework regression tests
### </summary>
class BaseFrameworkRegressionAlgorithm(QCAlgorithm):
def initialize(self):
self.set_start_date(2014, 6, 1)
self.set_end_date(2014, 6, 30)
self.universe_settings.resolution = Resolution.HOUR
self.universe_settings.data_normalization_mode = DataNormalizationMode.RAW
symbols = [Symbol.create(ticker, SecurityType.EQUITY, Market.USA)
for ticker in ["AAPL", "AIG", "BAC", "SPY"]]
# Manually add AAPL and AIG when the algorithm starts
self.set_universe_selection(ManualUniverseSelectionModel(symbols[:2]))
# At midnight, add all securities every day except on the last data
# With this procedure, the Alpha Model will experience multiple universe changes
self.add_universe_selection(ScheduledUniverseSelectionModel(
self.date_rules.every_day(), self.time_rules.midnight,
lambda dt: symbols if dt < self.end_date - timedelta(1) else []))
self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(31), 0.025, None))
self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
self.set_execution(ImmediateExecutionModel())
self.set_risk_management(NullRiskManagementModel())
def on_end_of_algorithm(self):
# The base implementation checks for active insights
insights_count = len(self.insights.get_insights(lambda insight: insight.is_active(self.utc_time)))
if insights_count != 0:
raise AssertionError(f"The number of active insights should be 0. Actual: {insights_count}")