# 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 * ### ### Regression algorithm which tests a fine fundamental filtered universe, ### related to GH issue 4127 ### class FineFundamentalFilteredUniverseRegressionAlgorithm(QCAlgorithm): def initialize(self): '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.''' self.set_start_date(2014, 10, 8) self.set_end_date(2014, 10, 13) self.universe_settings.resolution = Resolution.DAILY symbol = Symbol(SecurityIdentifier.generate_constituent_identifier("constituents-universe-qctest", SecurityType.EQUITY, Market.USA), "constituents-universe-qctest") self.add_universe(ConstituentsUniverse(symbol, self.universe_settings), self.fine_selection_function) def fine_selection_function(self, fine): return [ x.symbol for x in fine if x.company_profile != None and x.company_profile.headquarter_city != None and x.company_profile.headquarter_city.lower() == "cupertino" ] def on_data(self, data): '''OnData 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: if data.keys()[0].value != "AAPL": raise ValueError(f"Unexpected symbol was added to the universe: {data.keys()[0]}") self.set_holdings(data.keys()[0], 1)