chore: import upstream snapshot with attribution

Algorithm.Python/CustomBenchmarkRegressionAlgorithm.py

@@ -0,0 +1,45 @@
+# 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 CustomBrokerageModelRegressionAlgorithm import CustomBrokerageModel
+
+### <summary>
+### Regression algorithm to test we can specify a custom benchmark model, and override some of its methods
+### </summary>
+class CustomBenchmarkRegressionAlgorithm(QCAlgorithm):
+    def initialize(self):
+        self.set_start_date(2013,10,7)
+        self.set_end_date(2013,10,11)
+        self.set_brokerage_model(CustomBrokerageModelWithCustomBenchmark())
+        self.add_equity("SPY", Resolution.DAILY)
+        self.update_request_submitted = False
+
+    def on_data(self, slice):
+        benchmark = self.benchmark.evaluate(self.time)
+        if (self.time.day % 2 == 0) and (benchmark != 1):
+            raise AssertionError(f"Benchmark should be 1, but was {benchmark}")
+
+        if (self.time.day % 2 == 1) and (benchmark != 2):
+            raise AssertionError(f"Benchmark should be 2, but was {benchmark}")
+
+class CustomBenchmark:
+    def evaluate(self, time):
+        if time.day % 2 == 0:
+            return 1
+        else:
+            return 2
+
+class CustomBrokerageModelWithCustomBenchmark(CustomBrokerageModel):
+    def get_benchmark(self, securities):
+        return CustomBenchmark()