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
@@ -0,0 +1,58 @@
+# 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 Alphas.RsiAlphaModel import RsiAlphaModel
+from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel
+from Execution.VolumeWeightedAveragePriceExecutionModel import VolumeWeightedAveragePriceExecutionModel
+
+### <summary>
+### Regression algorithm for the VolumeWeightedAveragePriceExecutionModel.
+### This algorithm shows how the execution model works to split up orders and
+### submit them only when the price is on the favorable side of the intraday VWAP.
+### </summary>
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="using quantconnect" />
+### <meta name="tag" content="trading and orders" />
+class VolumeWeightedAveragePriceExecutionModelRegressionAlgorithm(QCAlgorithm):
+    '''Regression algorithm for the VolumeWeightedAveragePriceExecutionModel.
+    This algorithm shows how the execution model works to split up orders and
+    submit them only when the price is on the favorable side of the intraday VWAP.'''
+
+    def initialize(self):
+
+        self.universe_settings.resolution = Resolution.MINUTE
+
+        self.set_start_date(2013,10,7)
+        self.set_end_date(2013,10,11)
+        self.set_cash(1000000)
+
+        self.set_universe_selection(ManualUniverseSelectionModel([
+            Symbol.create('AIG', SecurityType.EQUITY, Market.USA),
+            Symbol.create('BAC', SecurityType.EQUITY, Market.USA),
+            Symbol.create('IBM', SecurityType.EQUITY, Market.USA),
+            Symbol.create('SPY', SecurityType.EQUITY, Market.USA)
+        ]))
+
+        # using hourly rsi to generate more insights
+        self.set_alpha(RsiAlphaModel(14, Resolution.HOUR))
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+        self.set_execution(VolumeWeightedAveragePriceExecutionModel())
+
+        self.insights_generated += self.on_insights_generated
+
+    def on_insights_generated(self, algorithm, data):
+        self.log(f"{self.time}: {', '.join(str(x) for x in data.insights)}")
+
+    def on_order_event(self, orderEvent):
+        self.log(f"{self.time}: {orderEvent}")