# 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 * from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel ### ### Regression algorithm showing how to implement a custom universe selection model and asserting it's behavior ### class CustomUniverseSelectionModelRegressionAlgorithm(QCAlgorithm): def initialize(self): self.set_start_date(2014,3,24) self.set_end_date(2014,4,7) self.universe_settings.resolution = Resolution.DAILY self.set_universe_selection(CustomUniverseSelectionModel()) def on_data(self, data): '''on_data event is the primary entry point for your algorithm. Each new data point will be pumped in here. Arguments: data: Slice object keyed by symbol containing the stock data ''' if not self.portfolio.invested: for kvp in self.active_securities: self.set_holdings(kvp.key, 0.1) class CustomUniverseSelectionModel(FundamentalUniverseSelectionModel): def __init__(self, universe_settings = None): super().__init__(universe_settings) self._selected = False def select(self, algorithm, fundamental): if not self._selected: self._selected = True return [ Symbol.create('AAPL', SecurityType.EQUITY, Market.USA) ] return Universe.UNCHANGED