chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
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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 *
+
+class EqualWeightingPortfolioConstructionModel(PortfolioConstructionModel):
+    '''Provides an implementation of IPortfolioConstructionModel that gives equal weighting to all securities.
+    The target percent holdings of each security is 1/N where N is the number of securities.
+    For insights of direction InsightDirection.UP, long targets are returned and
+    for insights of direction InsightDirection.DOWN, short targets are returned.'''
+
+    def __init__(self, rebalance = Resolution.DAILY, portfolio_bias = PortfolioBias.LONG_SHORT):
+        '''Initialize a new instance of EqualWeightingPortfolioConstructionModel
+        Args:
+            rebalance: Rebalancing parameter. If it is a timedelta, date rules or Resolution, it will be converted into a function.
+                              If None will be ignored.
+                              The function returns the next expected rebalance time for a given algorithm UTC DateTime.
+                              The function returns null if unknown, in which case the function will be called again in the
+                              next loop. Returning current time will trigger rebalance.
+            portfolio_bias: Specifies the bias of the portfolio (Short, Long/Short, Long)'''
+        super().__init__()
+        self.portfolio_bias = portfolio_bias
+
+        # If the argument is an instance of Resolution or Timedelta
+        # Redefine rebalancing_func
+        rebalancing_func = rebalance
+        if isinstance(rebalance, Resolution):
+            rebalance = Extensions.to_time_span(rebalance)
+        if isinstance(rebalance, timedelta):
+            rebalancing_func = lambda dt: dt + rebalance
+        if rebalancing_func:
+            self.set_rebalancing_func(rebalancing_func)
+
+    def determine_target_percent(self, active_insights):
+        '''Will determine the target percent for each insight
+        Args:
+            active_insights: The active insights to generate a target for'''
+        result = {}
+
+        # give equal weighting to each security
+        count = sum(x.direction != InsightDirection.FLAT and self.respect_portfolio_bias(x) for x in active_insights)
+        percent = 0 if count == 0 else 1.0 / count
+        for insight in active_insights:
+            result[insight] = (insight.direction if self.respect_portfolio_bias(insight) else InsightDirection.FLAT) * percent
+        return result
+
+    def respect_portfolio_bias(self, insight):
+        '''Method that will determine if a given insight respects the portfolio bias
+        Args:
+            insight: The insight to create a target for
+        '''
+        return self.portfolio_bias == PortfolioBias.LONG_SHORT or insight.direction == self.portfolio_bias