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 asserting that universe symbols selection can be done by returning the symbol IDs in the selection function
+### </summary>
+class SelectUniverseSymbolsFromIDRegressionAlgorithm(QCAlgorithm):
+    '''
+    Regression algorithm asserting that universe symbols selection can be done by returning the symbol IDs in the selection function
+    '''
+
+    def initialize(self):
+        self.set_start_date(2014, 3, 24)
+        self.set_end_date(2014, 3, 26)
+        self.set_cash(100000)
+
+        self._securities = []
+        self.universe_settings.resolution = Resolution.DAILY
+        self.add_universe(self.select_symbol)
+
+    def select_symbol(self, fundamental):
+        symbols = [x.symbol for x in fundamental]
+
+        if not symbols:
+            return []
+
+        self.log(f"Symbols: {', '.join([str(s) for s in symbols])}")
+        # Just for testing, but more filtering could be done here as shown below:
+        #symbols = [x.symbol for x in fundamental if x.asset_classification.morningstar_sector_code == MorningstarSectorCode.TECHNOLOGY]
+
+        history = self.history(symbols, datetime(1998, 1, 1), self.time, Resolution.DAILY)
+
+        all_time_highs = history['high'].unstack(0).max()
+        last_closes = history['close'].unstack(0).iloc[-1]
+        security_ids = (last_closes / all_time_highs).sort_values().index[-5:]
+
+        return security_ids
+
+    def on_securities_changed(self, changes):
+        self._securities.extend(changes.added_securities)
+
+    def on_end_of_algorithm(self):
+        if not self._securities:
+            raise AssertionError("No securities were selected")