# 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.ETFConstituentsUniverseSelectionModel import *### <summary>### Demonstration of using the ETFConstituentsUniverseSelectionModel### </summary>class ETFConstituentsFrameworkAlgorithm(QCAlgorithm):def initialize(self):self.set_start_date(2020, 12, 1)self.set_end_date(2020, 12, 7)self.set_cash(100000)self.universe_settings.resolution = Resolution.DAILYsymbol = Symbol.create("SPY", SecurityType.EQUITY, Market.USA)self.add_universe_selection(ETFConstituentsUniverseSelectionModel(symbol, self.universe_settings, self.etf_constituents_filter))self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(days=1)))self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())def etf_constituents_filter(self, constituents: List[ETFConstituentData]) -> List[Symbol]:# Get the 10 securities with the largest weight in the indexselected = sorted([c for c in constituents if c.weight],key=lambda c: c.weight, reverse=True)[:8]return [c.symbol for c in selected]
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