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 illustrating how to request history data for different data normalization modes.
+### </summary>
+class HistoryWithDifferentDataNormalizationModeRegressionAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2013, 10, 7)
+        self.set_end_date(2014, 1, 1)
+        self.aapl_equity_symbol = self.add_equity("AAPL", Resolution.DAILY).symbol
+        self.es_future_symbol = self.add_future(Futures.Indices.SP_500_E_MINI, Resolution.DAILY).symbol
+
+    def on_end_of_algorithm(self):
+        equity_data_normalization_modes = [
+            DataNormalizationMode.RAW,
+            DataNormalizationMode.ADJUSTED,
+            DataNormalizationMode.SPLIT_ADJUSTED
+        ]
+        self.check_history_results_for_data_normalization_modes(self.aapl_equity_symbol, self.start_date, self.end_date, Resolution.DAILY,
+            equity_data_normalization_modes)
+
+        future_data_normalization_modes = [
+            DataNormalizationMode.RAW,
+            DataNormalizationMode.BACKWARDS_RATIO,
+            DataNormalizationMode.BACKWARDS_PANAMA_CANAL,
+            DataNormalizationMode.FORWARD_PANAMA_CANAL
+        ]
+        self.check_history_results_for_data_normalization_modes(self.es_future_symbol, self.start_date, self.end_date, Resolution.DAILY,
+            future_data_normalization_modes)
+
+    def check_history_results_for_data_normalization_modes(self, symbol, start, end, resolution, data_normalization_modes):
+        history_results = [self.history([symbol], start, end, resolution, data_normalization_mode=x) for x in data_normalization_modes]
+        history_results = [x.droplevel(0, axis=0) for x in history_results] if len(history_results[0].index.levels) == 3 else history_results
+        history_results = [x.loc[symbol].close for x in history_results]
+
+        if any(x.size == 0 or x.size != history_results[0].size for x in history_results):
+            raise AssertionError(f"History results for {symbol} have different number of bars")
+
+        # Check that, for each history result, close prices at each time are different for these securities (AAPL and ES)
+        for j in range(history_results[0].size):
+            close_prices = set(history_results[i][j] for i in range(len(history_results)))
+            if len(close_prices) != len(data_normalization_modes):
+                raise AssertionError(f"History results for {symbol} have different close prices at the same time")