Derek Melchin
ede2991823
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* Add ETF universe selection model and example algos * Address review * address peer review
42 lines
1.8 KiB
Python
42 lines
1.8 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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from Selection.ETFConstituentsUniverseSelectionModel import *
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### <summary>
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### Demonstration of using the ETFConstituentsUniverseSelectionModel
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### </summary>
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class ETFConstituentsFrameworkAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2020, 12, 1)
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self.SetEndDate(2020, 12, 7)
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self.SetCash(100000)
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self.UniverseSettings.Resolution = Resolution.Daily
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symbol = Symbol.Create("SPY", SecurityType.Equity, Market.USA)
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self.AddUniverseSelection(ETFConstituentsUniverseSelectionModel(symbol, self.UniverseSettings, self.ETFConstituentsFilter))
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self.AddAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(days=1)))
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
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def ETFConstituentsFilter(self, constituents: List[ETFConstituentData]) -> List[Symbol]:
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# Get the 10 securities with the largest weight in the index
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selected = sorted([c for c in constituents if c.Weight],
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key=lambda c: c.Weight, reverse=True)[:8]
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return [c.Symbol for c in selected]
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