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 *
+from time import sleep
+
+### <summary>
+### Example algorithm showing how to use QCAlgorithm.train method
+### </summary>
+### <meta name="tag" content="using quantconnect" />
+### <meta name="tag" content="training" />
+class TrainingExampleAlgorithm(QCAlgorithm):
+    '''Example algorithm showing how to use QCAlgorithm.train method'''
+
+    def initialize(self):
+
+        self.set_start_date(2013, 10, 7)
+        self.set_end_date(2013, 10, 14)
+
+        self.add_equity("SPY", Resolution.DAILY)
+
+        # Set TrainingMethod to be executed immediately
+        self.train(self.training_method)
+
+        # Set TrainingMethod to be executed at 8:00 am every Sunday
+        self.train(self.date_rules.every(DayOfWeek.SUNDAY), self.time_rules.at(8 , 0), self.training_method)
+
+    def training_method(self):
+
+        self.log(f'Start training at {self.time}')
+        # Use the historical data to train the machine learning model
+        history = self.history(["SPY"], 200, Resolution.DAILY)
+
+        # ML code:
+        pass