chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
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+# 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 *
+
+### <summary>
+### Demonstration of how to estimate constituents of QC500 index based on the company fundamentals
+### The algorithm creates a default tradable and liquid universe containing 500 US equities
+### which are chosen at the first trading day of each month.
+### </summary>
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="universes" />
+### <meta name="tag" content="coarse universes" />
+### <meta name="tag" content="fine universes" />
+class ConstituentsQC500GeneratorAlgorithm(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.universe_settings.resolution = Resolution.DAILY
+
+        self.set_start_date(2018, 1, 1)   # Set Start Date
+        self.set_end_date(2019, 1, 1)     # Set End Date
+        self.set_cash(100000)            # Set Strategy Cash
+
+        # Add QC500 Universe
+        self.add_universe(self.universe.qc_500)