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 *
+from Portfolio.MeanVarianceOptimizationPortfolioConstructionModel import *
+
+### <summary>
+### Mean Variance Optimization algorithm
+### Uses the HistoricalReturnsAlphaModel and the MeanVarianceOptimizationPortfolioConstructionModel
+### to create an algorithm that rebalances the portfolio according to modern portfolio theory
+### </summary>
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="using quantconnect" />
+### <meta name="tag" content="trading and orders" />
+class MeanVarianceOptimizationFrameworkAlgorithm(QCAlgorithm):
+    '''Mean Variance Optimization algorithm.'''
+
+    def initialize(self):
+
+        # Set requested data resolution
+        self.universe_settings.resolution = Resolution.MINUTE
+
+        self.settings.rebalance_portfolio_on_insight_changes = False
+
+        self.set_start_date(2013,10,7)   #Set Start Date
+        self.set_end_date(2013,10,11)    #Set End Date
+        self.set_cash(100000)           #Set Strategy Cash
+
+        self._symbols = [ Symbol.create(x, SecurityType.EQUITY, Market.USA) for x in [ 'AIG', 'BAC', 'IBM', 'SPY' ] ]
+
+        # set algorithm framework models
+        self.set_universe_selection(CoarseFundamentalUniverseSelectionModel(self.coarse_selector))
+        self.set_alpha(HistoricalReturnsAlphaModel(resolution = Resolution.DAILY))
+        self.set_portfolio_construction(MeanVarianceOptimizationPortfolioConstructionModel())
+        self.set_execution(ImmediateExecutionModel())
+        self.set_risk_management(NullRiskManagementModel())
+
+    def coarse_selector(self, coarse):
+        # Drops SPY after the 8th
+        last = 3 if self.time.day > 8 else len(self._symbols)
+
+        return self._symbols[0:last]
+
+    def on_order_event(self,  order_event):
+        if order_event.status == OrderStatus.FILLED:
+            self.log(str(order_event))