feature-<#5631>-<Create SpreadExecutionModel> (#5633)

* Create SpreadExecutionModel.py * Update SpreadExecutionModel.py * Update SpreadExecutionModel.py * Update SpreadExecutionModel.py * Update SpreadExecutionModel.py * Update SpreadExecutionModel.py * Update SpreadExecutionModel.py * Update SpreadExecutionModel.py * Create SpreadExecutionModel.cs * Create SpreadExecutionModelTests.cs * Update SpreadExecutionModelTests.cs * Update SpreadExecutionModel.cs * Update SpreadExecutionModelTests.cs * Update SpreadExecutionModel.cs * Update SpreadExecutionModel.cs * Update SpreadExecutionModelTests.cs * Update SpreadExecutionModel.cs * Update QuantConnect.Algorithm.Framework.csproj * Create SpreadExecutionModelRegressionAlgorithm.py * Update SpreadExecutionModelTests.cs * Update SpreadExecutionModelTests.cs * Update SpreadExecutionModelTests.cs * Update SpreadExecutionModelTests.cs * Update SpreadExecutionModel.py * Update SpreadExecutionModel.cs * Address review - Adding CSharp regression algorithm implementation - Updating unit tests, minor format changes * Address review. Add comment and Abs spread percent Co-authored-by: Martin-Molinero <martin@quantconnect.com>
2021-06-10 01:55:00 +08:00
parent d23ac25b67
commit 9f15ca4c9c
6 changed files with 599 additions and 1 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 clr import AddReference
+AddReference("System")
+AddReference("QuantConnect.Algorithm")
+AddReference("QuantConnect.Algorithm.Framework")
+AddReference("QuantConnect.Common")
+
+from System import *
+from QuantConnect import *
+from QuantConnect.Orders import *
+from QuantConnect.Algorithm import *
+from QuantConnect.Algorithm.Framework import *
+from QuantConnect.Algorithm.Framework.Selection import *
+from Alphas.RsiAlphaModel import RsiAlphaModel
+from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel
+from Execution.SpreadExecutionModel import SpreadExecutionModel
+
+### <summary>
+### Regression algorithm for the SpreadExecutionModel.
+### This algorithm shows how the execution model works to 
+### submit orders only when the price is on desirably tight spread.
+### </summary>
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="using quantconnect" />
+### <meta name="tag" content="trading and orders" />
+class SpreadExecutionModelRegressionAlgorithm(QCAlgorithm):
+    '''Regression algorithm for the SpreadExecutionModel.
+    This algorithm shows how the execution model works to 
+    submit orders only when the price is on desirably tight spread.'''
+
+    def Initialize(self):
+        self.SetStartDate(2013,10,7)
+        self.SetEndDate(2013,10,11)
+
+        self.SetUniverseSelection(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.SetAlpha(RsiAlphaModel(14, Resolution.Hour))
+        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
+        self.SetExecution(SpreadExecutionModel())
+
+        self.InsightsGenerated += self.OnInsightsGenerated
+
+    def OnInsightsGenerated(self, algorithm, data):
+        self.Log(f"{self.Time}: {', '.join(str(x) for x in data.Insights)}")
+
+    def OnOrderEvent(self, orderEvent):
+        self.Log(f"{self.Time}: {orderEvent}")