chore: import upstream snapshot with attribution

Algorithm.Python/ZeroedBenchmarkRegressionAlgorithm.py

@@ -0,0 +1,48 @@
+# 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>
+### Regression algorithm to test zeroed benchmark through BrokerageModel override
+### </summary>
+### <meta name="tag" content="regression test" />
+class ZeroedBenchmarkRegressionAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        self.set_cash(100000)
+        self.set_start_date(2013,10,7)
+        self.set_end_date(2013,10,8)
+
+        # Add Equity
+        self.add_equity("SPY", Resolution.HOUR)
+
+        # Use our Test Brokerage Model with zerod default benchmark
+        self.set_brokerage_model(TestBrokerageModel())
+
+    def on_data(self, data):
+        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
+
+        Arguments:
+            data: Slice object keyed by symbol containing the stock data
+        '''
+        if not self.portfolio.invested:
+            self.set_holdings("SPY", 1)
+
+class TestBrokerageModel(DefaultBrokerageModel):
+
+    def get_benchmark(self, securities):
+        return FuncBenchmark(self.func)
+
+    def func(self, datetime):
+        return 0