chore: import upstream snapshot with attribution

Algorithm.Framework/Selection/FundamentalUniverseSelectionModel.py

@@ -0,0 +1,115 @@
+# 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 FundamentalUniverseSelectionModel:
+    '''Provides a base class for defining equity coarse/fine fundamental selection models'''
+    
+    def __init__(self,
+                 filter_fine_data = None,
+                 universe_settings = None):
+        '''Initializes a new instance of the FundamentalUniverseSelectionModel class
+        Args:
+            filter_fine_data: [Obsolete] Fine and Coarse selection are merged
+            universeSettings: The settings used when adding symbols to the algorithm, specify null to use algorithm.universe_settings'''
+        self.filter_fine_data = filter_fine_data
+        if self.filter_fine_data == None:
+            self.fundamental_data = True
+        else:
+            self.fundamental_data = False
+        self.market = Market.USA
+        self.universe_settings = universe_settings
+
+
+    def create_universes(self, algorithm: QCAlgorithm) -> list[Universe]:
+        '''Creates a new fundamental universe using this class's selection functions
+        Args:
+            algorithm: The algorithm instance to create universes for
+        Returns:
+            The universe defined by this model'''
+        if self.fundamental_data:
+            universe_settings = algorithm.universe_settings if self.universe_settings is None else self.universe_settings
+            # handle both 'Select' and 'select' for backwards compatibility
+            selection = lambda fundamental: self.select(algorithm, fundamental)
+            if hasattr(self, "Select") and callable(self.Select):
+                selection = lambda fundamental: self.Select(algorithm, fundamental)
+            universe = FundamentalUniverseFactory(self.market, universe_settings, selection)
+            return [universe]
+        else:
+            universe = self.create_coarse_fundamental_universe(algorithm)
+            if self.filter_fine_data:
+                if universe.universe_settings.asynchronous:
+                    raise ValueError("Asynchronous universe setting is not supported for coarse & fine selections, please use the new Fundamental single pass selection")
+                selection = lambda fine: self.select_fine(algorithm, fine)
+                if hasattr(self, "SelectFine") and callable(self.SelectFine):
+                    selection = lambda fine: self.SelectFine(algorithm, fine)
+                universe = FineFundamentalFilteredUniverse(universe, selection)
+            return [universe]
+
+
+    def create_coarse_fundamental_universe(self, algorithm: QCAlgorithm) -> Universe:
+        '''Creates the coarse fundamental universe object.
+        This is provided to allow more flexibility when creating coarse universe.
+        Args:
+            algorithm: The algorithm instance
+        Returns:
+            The coarse fundamental universe'''
+        universe_settings = algorithm.universe_settings if self.universe_settings is None else self.universe_settings
+        return CoarseFundamentalUniverse(universe_settings, lambda coarse: self.filtered_select_coarse(algorithm, coarse))
+
+
+    def filtered_select_coarse(self, algorithm: QCAlgorithm, fundamental: list[Fundamental]) -> list[Symbol]:
+        '''Defines the coarse fundamental selection function.
+        If we're using fine fundamental selection than exclude symbols without fine data
+        Args:
+            algorithm: The algorithm instance
+            coarse: The coarse fundamental data used to perform filtering
+        Returns:
+            An enumerable of symbols passing the filter'''
+        if self.filter_fine_data:
+            fundamental = filter(lambda c: c.has_fundamental_data, fundamental)
+        if hasattr(self, "SelectCoarse") and callable(self.SelectCoarse):
+            # handle both 'select_coarse' and 'SelectCoarse' for backwards compatibility
+            return self.SelectCoarse(algorithm, fundamental)
+        return self.select_coarse(algorithm, fundamental)
+
+
+    def select(self, algorithm: QCAlgorithm, fundamental: list[Fundamental]) -> list[Symbol]:
+        '''Defines the fundamental selection function.
+        Args:
+            algorithm: The algorithm instance
+            fundamental: The fundamental data used to perform filtering
+        Returns:
+            An enumerable of symbols passing the filter'''
+        raise NotImplementedError("Please overrride the 'select' fundamental function")
+
+
+    def select_coarse(self, algorithm: QCAlgorithm, fundamental: list[Fundamental]) -> list[Symbol]:
+        '''Defines the coarse fundamental selection function.
+        Args:
+            algorithm: The algorithm instance
+            coarse: The coarse fundamental data used to perform filtering
+        Returns:
+            An enumerable of symbols passing the filter'''
+        raise NotImplementedError("Please overrride the 'select' fundamental function")
+
+
+    def select_fine(self, algorithm: QCAlgorithm, fundamental: list[Fundamental]) -> list[Symbol]:
+        '''Defines the fine fundamental selection function.
+        Args:
+            algorithm: The algorithm instance
+            fine: The fine fundamental data used to perform filtering
+        Returns:
+            An enumerable of symbols passing the filter'''
+        return [f.symbol for f in fundamental]