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 *
+from EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel
+
+class InsightWeightingPortfolioConstructionModel(EqualWeightingPortfolioConstructionModel):
+    '''Provides an implementation of IPortfolioConstructionModel that generates percent targets based on the
+    Insight.WEIGHT. The target percent holdings of each Symbol is given by the Insight.WEIGHT from the last
+    active Insight for that symbol.
+    For insights of direction InsightDirection.UP, long targets are returned and for insights of direction
+    InsightDirection.DOWN, short targets are returned.
+    If the sum of all the last active Insight per symbol is bigger than 1, it will factor down each target
+    percent holdings proportionally so the sum is 1.
+    It will ignore Insight that have no Insight.WEIGHT value.'''
+
+    def __init__(self, rebalance = Resolution.DAILY, portfolio_bias = PortfolioBias.LONG_SHORT):
+        '''Initialize a new instance of InsightWeightingPortfolioConstructionModel
+        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__(rebalance, portfolio_bias)
+
+    def should_create_target_for_insight(self, insight):
+        '''Method that will determine if the portfolio construction model should create a
+        target for this insight
+        Args:
+            insight: The insight to create a target for'''
+        # Ignore insights that don't have Weight value
+        return insight.weight is not None
+
+    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 = {}
+
+        # We will adjust weights proportionally in case the sum is > 1 so it sums to 1.
+        weight_sums = sum(self.get_value(insight) for insight in active_insights if self.respect_portfolio_bias(insight))
+        weight_factor = 1.0
+        if weight_sums > 1:
+            weight_factor = 1 / weight_sums
+        for insight in active_insights:
+            result[insight] = (insight.direction if self.respect_portfolio_bias(insight) else InsightDirection.FLAT) * self.get_value(insight) * weight_factor
+        return result
+
+    def get_value(self, insight):
+        '''Method that will determine which member will be used to compute the weights and gets its value
+        Args:
+            insight: The insight to create a target for
+        Returns:
+            The value of the selected insight member'''
+        return abs(insight.weight)