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 *
+from QuantConnect.Data.Auxiliary import *
+from QuantConnect.Lean.Engine.DataFeeds import DefaultDataProvider
+
+_ticker = "GOOGL"
+_expected_raw_prices = [ 1158.72,
+1131.97, 1114.28, 1120.15, 1114.51, 1134.89, 1135.1, 571.50, 545.25, 540.63 ]
+
+# <summary>
+# In this algorithm we demonstrate how to use the raw data for our securities
+# and verify that the behavior is correct.
+# </summary>
+# <meta name="tag" content="using data" />
+# <meta name="tag" content="regression test" />
+class RawDataRegressionAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2014, 3, 25)
+        self.set_end_date(2014, 4, 7)
+        self.set_cash(100000)
+
+        # Set our DataNormalizationMode to raw
+        self.universe_settings.data_normalization_mode = DataNormalizationMode.RAW
+        self._googl = self.add_equity(_ticker, Resolution.DAILY).symbol
+
+        # Get our factor file for this regression
+        data_provider = DefaultDataProvider()
+        map_file_provider = LocalDiskMapFileProvider()
+        map_file_provider.initialize(data_provider)
+        factor_file_provider = LocalDiskFactorFileProvider()
+        factor_file_provider.initialize(map_file_provider, data_provider)
+
+        # Get our factor file for this regression
+        self._factor_file = factor_file_provider.get(self._googl)
+
+
+    def on_data(self, data):
+        if not self.portfolio.invested:
+            self.set_holdings(self._googl, 1)
+
+        if data.bars.contains_key(self._googl):
+            googl_data = data.bars[self._googl]
+
+            # Assert our volume matches what we expected
+            expected_raw_price = _expected_raw_prices.pop(0)
+            if expected_raw_price != googl_data.close:
+                # Our values don't match lets try and give a reason why
+                day_factor = self._factor_file.get_price_factor(googl_data.time, DataNormalizationMode.ADJUSTED)
+                probable_raw_price = googl_data.close / day_factor  # Undo adjustment
+
+                raise AssertionError("Close price was incorrect; it appears to be the adjusted value"
+                    if expected_raw_price == probable_raw_price else
+                   "Close price was incorrect; Data may have changed.")