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>
+### Regression algorithm showing how to define a custom insight scoring function and using the insight manager
+### </summary>
+class InsightScoringRegressionAlgorithm(QCAlgorithm):
+    '''Regression algorithm showing how to define a custom insight evaluator'''
+
+    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(2013,10,7)
+        self.set_end_date(2013,10,11)
+        
+        symbols = [ Symbol.create("SPY", SecurityType.EQUITY, Market.USA) ]
+
+        self.set_universe_selection(ManualUniverseSelectionModel(symbols))
+        self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(minutes = 20), 0.025, None))
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel(Resolution.DAILY))
+        self.set_execution(ImmediateExecutionModel())
+        self.set_risk_management(MaximumDrawdownPercentPerSecurity(0.01))
+        
+        # we specify a custom insight evaluator
+        self.insights.set_insight_score_function(CustomInsightScoreFunction(self.securities))
+
+    def on_end_of_algorithm(self):
+        all_insights = self.insights.get_insights(lambda insight: True)
+
+        if len(all_insights) != 100 or len(self.insights.get_insights()) != 100:
+            raise ValueError(f'Unexpected insight count found {all_insights.count}')
+
+        if sum(1 for insight in all_insights if insight.score.magnitude == 0 or insight.score.direction == 0) < 5:
+            raise ValueError(f'Insights not scored!')
+
+        if sum(1 for insight in all_insights if insight.score.is_final_score) < 99:
+            raise ValueError(f'Insights not finalized!')
+
+class CustomInsightScoreFunction():
+
+    def __init__(self, securities):
+        self._securities = securities
+        self._open_insights = {}
+
+    def score(self, insight_manager, utc_time):
+        open_insights = insight_manager.get_active_insights(utc_time)
+
+        for insight in open_insights:
+            self._open_insights[insight.id] = insight
+
+        to_remove = []
+        for open_insight in self._open_insights.values():
+            security = self._securities[open_insight.symbol]
+            open_insight.reference_value_final = security.price
+
+            score = open_insight.reference_value_final - open_insight.reference_value
+            open_insight.score.set_score(InsightScoreType.DIRECTION, score, utc_time)
+            open_insight.score.set_score(InsightScoreType.MAGNITUDE, score * 2, utc_time)
+            open_insight.estimated_value = score * 100
+
+            if open_insight.is_expired(utc_time):
+                open_insight.score.finalize(utc_time)
+                to_remove.append(open_insight)
+
+        # clean up
+        for insight_to_remove in to_remove:
+            self._open_insights.pop(insight_to_remove.id)