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
@@ -0,0 +1,63 @@
+# 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 *
+
+class CoarseFineUniverseSelectionBenchmark(QCAlgorithm):
+
+    def initialize(self):
+
+        self.set_start_date(2017, 11, 1)
+        self.set_end_date(2018, 3, 1)
+        self.set_cash(50000)
+
+        self.universe_settings.resolution = Resolution.MINUTE
+
+        self.add_universe(self.coarse_selection_function, self.fine_selection_function)
+
+        self.number_of_symbols = 150
+        self.number_of_symbols_fine = 40
+        self._changes = None
+
+    # sort the data by daily dollar volume and take the top 'NumberOfSymbols'
+    def coarse_selection_function(self, coarse):
+
+        selected = [x for x in coarse if (x.has_fundamental_data)]
+        # sort descending by daily dollar volume
+        sorted_by_dollar_volume = sorted(selected, key=lambda x: x.dollar_volume, reverse=True)
+
+        # return the symbol objects of the top entries from our sorted collection
+        return [ x.symbol for x in sorted_by_dollar_volume[:self.number_of_symbols] ]
+
+    # sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
+    def fine_selection_function(self, fine):
+        # sort descending by P/E ratio
+        sorted_by_pe_ratio = sorted(fine, key=lambda x: x.valuation_ratios.pe_ratio, reverse=True)
+        # take the top entries from our sorted collection
+        return [ x.symbol for x in sorted_by_pe_ratio[:self.number_of_symbols_fine] ]
+
+    def on_data(self, data):
+        # if we have no changes, do nothing
+        if self._changes is None: return
+
+        # liquidate removed securities
+        for security in self._changes.removed_securities:
+            if security.invested:
+                self.liquidate(security.symbol)
+
+        for security in self._changes.added_securities:
+            self.set_holdings(security.symbol, 0.02)
+        self._changes = None
+
+    def on_securities_changed(self, changes):
+        self._changes = changes