chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
5008 changed files with 910633 additions and 0 deletions
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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>
+### Regression algorithm testing history requests for <see cref="FutureUniverse"/> type work as expected
+### and return the same data as the futures chain provider.
+### </summary>
+class OptionUniverseHistoryRegressionAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2013, 10, 11)
+        self.set_end_date(2013, 10, 11)
+
+        future = self.add_future(Futures.Indices.SP_500_E_MINI).symbol
+
+        historical_futures_data_df = self.history(FutureUniverse, future, 3, flatten=True)
+
+        # Level 0 of the multi-index is the date, we expect 3 dates, 3 future chains
+        if historical_futures_data_df.index.levshape[0] != 3:
+            raise RegressionTestException(f"Expected 3 futures chains from history request, "
+                                          f"but got {historical_futures_data_df.index.levshape[1]}")
+
+        for date in historical_futures_data_df.index.levels[0]:
+            expected_chain = list(self.future_chain_provider.get_future_contract_list(future, date))
+            expected_chain_count = len(expected_chain)
+
+            actual_chain = historical_futures_data_df.loc[date]
+            actual_chain_count = len(actual_chain)
+
+            if expected_chain_count != actual_chain_count:
+                raise RegressionTestException(f"Expected {expected_chain_count} futures in chain on {date}, "
+                                              f"but got {actual_chain_count}")
+
+            for i, symbol in enumerate(actual_chain.index):
+                expected_symbol = expected_chain[i]
+                if symbol != expected_symbol:
+                    raise RegressionTestException(f"Expected symbol {expected_symbol} at index "
+                                                  f" {i} on {date}, but got {symbol}")