chore: import upstream snapshot with attribution

Algorithm.Python/ETFConstituentsFrameworkAlgorithm.py

@@ -0,0 +1,41 @@
+# 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.DAILY
+        symbol = 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 index
+        selected = sorted([c for c in constituents if c.weight],
+            key=lambda c: c.weight, reverse=True)[:8]
+        return [c.symbol for c in selected]
+