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>
+### Demonstration of using coarse and fine universe selection together to filter down a smaller universe of stocks.
+### </summary>
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="universes" />
+### <meta name="tag" content="coarse universes" />
+### <meta name="tag" content="fine universes" />
+class CoarseFineFundamentalComboAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
+
+        self.set_start_date(2014,1,1)  #Set Start Date
+        self.set_end_date(2015,1,1)    #Set End Date
+        self.set_cash(50000)            #Set Strategy Cash
+
+        # what resolution should the data *added* to the universe be?
+        self.universe_settings.resolution = Resolution.DAILY
+
+        # this add universe method accepts two parameters:
+        # - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
+        # - fine selection function: accepts an IEnumerable<FineFundamental> and returns an IEnumerable<Symbol>
+        self.add_universe(self.coarse_selection_function, self.fine_selection_function)
+
+        self.__number_of_symbols = 5
+        self.__number_of_symbols_fine = 2
+        self._changes = None
+
+
+    # sort the data by daily dollar volume and take the top 'NumberOfSymbols'
+    def coarse_selection_function(self, coarse):
+        # sort descending by daily dollar volume
+        sorted_by_dollar_volume = sorted(coarse, 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)
+
+        # we want 20% allocation in each security in our universe
+        for security in self._changes.added_securities:
+            self.set_holdings(security.symbol, 0.2)
+
+        self._changes = None
+
+
+    # this event fires whenever we have changes to our universe
+    def on_securities_changed(self, changes):
+        self._changes = changes