Added UnconstrainedMeanVariancePortfolioOptimizer tests

MaxSharpe tests added rounding to boundaries test

Clean up pythonnet runtime exception build paths (QuantConnect#7687)

- Clean up pythonnet runtime exception build paths

Added license info to files

Common/Extensions.cs

@@ -66,7 +66,7 @@ namespace QuantConnect
     /// </summary>
     public static class Extensions
     {
-        private static readonly Regex LeanPathRegex = new Regex("(?:\\S*?\\\\Lean\\\\)|(?:\\S*?/Lean/)", RegexOptions.Compiled);
+        private static readonly Regex LeanPathRegex = new Regex("(?:\\S*?\\\\pythonnet\\\\)|(?:\\S*?\\\\Lean\\\\)|(?:\\S*?/Lean/)|(?:\\S*?/pythonnet/)", RegexOptions.Compiled);
         private static readonly Dictionary<string, bool> _emptyDirectories = new ();
         private static readonly HashSet<string> InvalidSecurityTypes = new HashSet<string>();
         private static readonly Regex DateCheck = new Regex(@"\d{8}", RegexOptions.Compiled);

Tests/Algorithm/Framework/Portfolio/MaximumSharpeRatioPortfolioOptimizerTests.cs

@@ -1,12 +1,23 @@
-using Accord.Statistics;
+/*
+ * 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 aaplicable 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.
+*/
+
 using NUnit.Framework;
-using QLNet;
 using QuantConnect.Algorithm.Framework.Portfolio;
 using System;
 using System.Collections.Generic;
 using System.Linq;
-using System.Text;
-using System.Threading.Tasks;
 
 namespace QuantConnect.Tests.Algorithm.Framework.Portfolio
 {
@@ -181,8 +192,9 @@ public void BoundariesAreNotViolated()
 
             foreach (double x in result)
             {
-                Assert.GreaterOrEqual(x, lower);
-                Assert.LessOrEqual(x, upper);
+                var rounded = Math.Round(x, 6);
+                Assert.GreaterOrEqual(rounded, lower);
+                Assert.LessOrEqual(rounded, upper);
             };
         }
     }

Tests/Algorithm/Framework/Portfolio/MinimumVariancePortfolioOptimizerTests.cs

@@ -1,11 +1,23 @@
+/*
+ * 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 aaplicable 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.
+*/
+
 using NUnit.Framework;
 using QuantConnect.Algorithm.Framework.Portfolio;
 using System;
 using System.Collections.Generic;
-using System.ComponentModel.DataAnnotations;
 using System.Linq;
-using System.Text;
-using System.Threading.Tasks;
 
 namespace QuantConnect.Tests.Algorithm.Framework.Portfolio
 {
@@ -30,13 +42,11 @@ public void Setup()
             double[,] historicalReturns3 = new double[,] { { -0.02, 0.65, 1.25 }, { -0.29, -0.39, -0.50 }, { 0.29, 0.58, 2.39 }, { 0.00, -0.01, 0.06 } };
             double[,] historicalReturns4 = new double[,] { { 0.76, 0.25, 0.21 }, { 0.02, -0.15, 0.45 }, { -0.50, -0.44, 0.07 }, { 0.81, 0.04, 0.01 }, { -0.02, 0.01, 0.02 } };
 
-
             double[] expectedReturns1 = new double[] { 0.21, 0.08, 0.88, 0.08 };
             double[] expectedReturns2 = new double[] { -0.13, 0.05, 0.14 };
             double[] expectedReturns3 = null;
             double[] expectedReturns4 = null;
 
-
             double[,] covariance1 = new double[,] { { 0.31, 0.05, 0.55, 0.07 }, { 0.05, 0.04, 0.18, 0.01 }, { 0.55, 0.18, 1.28, 0.12 }, { 0.07, 0.01, 0.12, 0.02 } };
             double[,] covariance2 = new double[,] { { 0.05, -0.02, -0.01 }, { -0.02, 0.21, 0.09 }, { -0.01, 0.09, 0.04 } };
             double[,] covariance3 = new double[,] { { 0.06, 0.09, 0.28 }, { 0.09, 0.25, 0.58 }, { 0.28, 0.58, 1.66 } };
@@ -107,6 +117,7 @@ public void TestOptimizeWeightings(int testCaseNumber)
                 _covariances[testCaseNumber]);
 
             Assert.AreEqual(_expectedResults[testCaseNumber], result.Select(x => Math.Round(x, 6)));
+            Assert.AreEqual(1d, result.Select(x => Math.Round(Math.Abs(x), 6)).Sum());
         }
 
         [TestCase(5)]
@@ -123,23 +134,61 @@ public void TestOptimizeWeightingsSpecifyingTargetReturns(int testCaseNumber)
                 _covariances[testCaseNumber]);
 
             Assert.AreEqual(_expectedResults[testCaseNumber], result.Select(x => Math.Round(x, 6)));
+            Assert.AreEqual(1d, result.Select(x => Math.Round(Math.Abs(x), 6)).Sum());
         }
 
         [TestCase(1)]
         public void EqualWeightingsWhenNoSolutionFound(int testCaseNumber)
         {
-            var historicalReturns = new double[,] { { } }
-            var testOptimizer = new MinimumVariancePortfolioOptimizer(lower: 0);
-            var expectedResult = new double[] { 0.5d, 0.5d };
+            var testOptimizer = new MinimumVariancePortfolioOptimizer(upper: -1);
+            var expectedResult = new double[] { 0.25, 0.25, 0.25, 0.25 };
 
             var result = testOptimizer.Optimize(_historicalReturns[testCaseNumber]);
 
             Assert.AreEqual(expectedResult, result);
         }
 
+        [TestCase(1)]
+        [TestCase(2)]
+        [TestCase(3)]
+        [TestCase(4)]
         public void BoundariesAreNotViolated(int testCaseNumber)
         {
+            double lower = 0;
+            double upper = 0.5;
+            var testOptimizer = new MinimumVariancePortfolioOptimizer(lower, upper);
+
+            var result = testOptimizer.Optimize(
+                _historicalReturns[testCaseNumber],
+                _expectedReturns[testCaseNumber],
+                _covariances[testCaseNumber]);
+
+            foreach (double x in result)
+            {
+                var rounded = Math.Round(x, 6);
+                Assert.GreaterOrEqual(rounded, lower);
+                Assert.LessOrEqual(rounded, upper);
+            };
+        }
+
+        public void EmptyPortfolioReturnsEmptyArrayOfDouble()
+        {
+            var testOptimizer = new MinimumVariancePortfolioOptimizer();
+            var historicalReturns = new double[,] { { } };
+
+            var result = testOptimizer.Optimize(historicalReturns);
+
+            Assert.AreEqual(Array.Empty<double>(), result);
+        }
+
+        public void SingleSecurityPortfolioReturnsOne()
+        {
+            var testOptimizer = new MinimumVariancePortfolioOptimizer();
+            double[,] historicalReturns = new double[,] { { 0.76 }, { 0.02 }, { -0.50 } };
+
+            var result = testOptimizer.Optimize(historicalReturns);
 
+            Assert.AreEqual(1d, result);
         }
     }
 }

Tests/Algorithm/Framework/Portfolio/UnconstrainedMeanVariancePortfolioOptimizerTests.cs

@@ -1,44 +1,101 @@
-using Accord.Statistics;
+/*
+ * 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 aaplicable 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.
+*/
+
 using NUnit.Framework;
 using QuantConnect.Algorithm.Framework.Portfolio;
 using System;
 using System.Collections.Generic;
 using System.Linq;
-using System.Text;
-using System.Threading.Tasks;
 
 namespace QuantConnect.Tests.Algorithm.Framework.Portfolio
 {
     public class UnconstrainedMeanVariancePortfolioOptimizerTests
     {
         private Dictionary<int, double[,]> _historicalReturns = new();
+        private Dictionary<int, double[]> _expectedReturns = new();
+        private Dictionary<int, double[,]> _covariances = new();
         private Dictionary<int, double[]> _expectedResults = new();
 
         [OneTimeSetUp]
         public void Setup()
         {
-            var historicalReturns1 = new double[,] { { 0.02, 0.05, -0.10 }, { 0.022, 0.10, 0.20 }, { 0.02, -0.02, 0.15 } };
-            var historicalReturns2 = new double[,] { { 0.25, 0.01 }, { 0.21, -0.02 } }; ;
+            double[,] historicalReturns1 = new double[,] { { 0.76, -0.06, 1.22, 0.17 }, { 0.02, 0.28, 1.25, -0.00 }, { -0.50, -0.13, -0.50, -0.03 }, { 0.81, 0.31, 2.39, 0.26 }, { -0.02, 0.02, 0.06, 0.01 } };
+            double[,] historicalReturns2 = new double[,] { { -0.15, 0.67, 0.45 }, { -0.44, -0.10, 0.07 }, { 0.04, -0.41, 0.01 }, { 0.01, 0.03, 0.02 } };
+            double[,] historicalReturns3 = new double[,] { { -0.02, 0.65, 1.25 }, { -0.29, -0.39, -0.50 }, { 0.29, 0.58, 2.39 }, { 0.00, -0.01, 0.06 } };
+            double[,] historicalReturns4 = new double[,] { { 0.76, 0.25, 0.21 }, { 0.02, -0.15, 0.45 }, { -0.50, -0.44, 0.07 }, { 0.81, 0.04, 0.01 }, { -0.02, 0.01, 0.02 } };
+
+            double[] expectedReturns1 = new double[] { 0.21, 0.08, 0.88, 0.08 };
+            double[] expectedReturns2 = new double[] { -0.13, 0.05, 0.14 };
+            double[] expectedReturns3 = null;
+            double[] expectedReturns4 = null;
+
+            double[,] covariance1 = new double[,] { { 0.31, 0.05, 0.55, 0.07 }, { 0.05, 0.04, 0.18, 0.01 }, { 0.55, 0.18, 1.28, 0.12 }, { 0.07, 0.01, 0.12, 0.02 } };
+            double[,] covariance2 = new double[,] { { 0.05, -0.02, -0.01 }, { -0.02, 0.21, 0.09 }, { -0.01, 0.09, 0.04 } };
+            double[,] covariance3 = new double[,] { { 0.06, 0.09, 0.28 }, { 0.09, 0.25, 0.58 }, { 0.28, 0.58, 1.66 } };
+            double[,] covariance4 = null;
 
-            var expectedResult1 = new double[] { -1.4234053790251934E+20, 2.124485640336107E+18, 5.948559792941101E+17 };
-            var expectedResult2 = new double[] { 1.0058640608512344E+18, -1.341152081134979E+18 };
+            double[] expectedResult1 = new double[] { -13.288136, -23.322034, 8.79661, 9.389831 };
+            double[] expectedResult2 = new double[] { -0.142857, -35.285714, 82.857143 };
+            double[] expectedResult3 = new double[] { -13.232262, -3.709534, 4.009978 };
+            double[] expectedResult4 = new double[] { 4.621852, -9.651736, 5.098332 };
 
             _historicalReturns.Add(1, historicalReturns1);
             _historicalReturns.Add(2, historicalReturns2);
+            _historicalReturns.Add(3, historicalReturns3);
+            _historicalReturns.Add(4, historicalReturns4);
+
+            _expectedReturns.Add(1, expectedReturns1);
+            _expectedReturns.Add(2, expectedReturns2);
+            _expectedReturns.Add(3, expectedReturns3);
+            _expectedReturns.Add(4, expectedReturns4);
+
+            _covariances.Add(1, covariance1);
+            _covariances.Add(2, covariance2);
+            _covariances.Add(3, covariance3);
+            _covariances.Add(4, covariance4);
 
             _expectedResults.Add(1, expectedResult1);
             _expectedResults.Add(2, expectedResult2);
+            _expectedResults.Add(3, expectedResult3);
+            _expectedResults.Add(4, expectedResult4);
         }
 
         [TestCase(1)]
         [TestCase(2)]
+        [TestCase(3)]
+        [TestCase(4)]
         public void TestOptimizeWeightings(int testCaseNumber)
         {
             var testOptimizer = new UnconstrainedMeanVariancePortfolioOptimizer();
 
-            var result = testOptimizer.Optimize(_historicalReturns[testCaseNumber]);
+            var result = testOptimizer.Optimize(
+                _historicalReturns[testCaseNumber],
+                _expectedReturns[testCaseNumber],
+                _covariances[testCaseNumber]);
+
+            Assert.AreEqual(_expectedResults[testCaseNumber], result.Select(x => Math.Round(x, 6)));
+        }
+
+        public void EmptyPortfolioReturnsEmptyArrayOfDouble()
+        {
+            var testOptimizer = new UnconstrainedMeanVariancePortfolioOptimizer();
+            var historicalReturns = new double[,] { { } };
+
+            var result = testOptimizer.Optimize(historicalReturns);
 
-            Assert.AreEqual(_expectedResults[testCaseNumber], result);
+            Assert.AreEqual(Array.Empty<double>(), result);
         }
     }
 }

Tests/Common/Util/PythonUtilTests.cs

@@ -194,6 +194,22 @@ at CallSite.Target(Closure , CallSite , Object , PythonSlice )
    at QuantConnect.AlgorithmFactory.Python.Wrappers.AlgorithmPythonWrapper.OnData(Slice slice) in /home/jhonathan/QuantConnect/Lean/AlgorithmFactory/Python/Wrappers/AlgorithmPythonWrapper.cs:line 587
    at QuantConnect.Lean.Engine.AlgorithmManager.Run(AlgorithmNodePacket job, IAlgorithm algorithm, ISynchronizer synchronizer, ITransactionHandler transactions, IResultHandler results, IRealTimeHandler realtime, ILeanManager leanManager, IAlphaHandler alphas, CancellationToken token) in /home/jhonathan/QuantConnect/Lean/Engine/AlgorithmManager.cs:line 523",
             0)]
+        [TestCase(@"
+  at OnData
+    raise ValueError(""""ASD"""")
+   at Python.Runtime.PythonException.ThrowLastAsClrException() at src\runtime\PythonException.cs:line 52
+   at Python.Runtime.PyObject.Invoke(PyTuple args in BasicTemplateAlgorithm.py: line 43
+",
+            @"  File ""D:\QuantConnect/MyLean/Lean/Algorithm.Python\BasicTemplateAlgorithm.py"", line 43, in OnData
+    raise ValueError(""""ASD"""")
+   at Python.Runtime.PythonException.ThrowLastAsClrException() in D:\QuantConnect\MyLean\pythonnet\src\runtime\PythonException.cs:line 52
+   at Python.Runtime.PyObject.Invoke(PyTuple args, PyDict kw) in D:\QuantConnect\MyLean\pythonnet\src\runtime\PythonTypes\PyObject.cs:line 837
+   at Python.Runtime.PyObject.TryInvoke(InvokeBinder binder, Object[] args, Object& result) in D:\QuantConnect\MyLean\pythonnet\src\runtime\PythonTypes\PyObject.cs:line 1320
+   at CallSite.Target(Closure , CallSite , Object , PythonSlice )
+   at System.Dynamic.UpdateDelegates.UpdateAndExecuteVoid2[T0,T1](CallSite site, T0 arg0, T1 arg1)
+   at QuantConnect.AlgorithmFactory.Python.Wrappers.AlgorithmPythonWrapper.OnData(Slice slice) in D:\QuantConnect\MyLean\Lean\AlgorithmFactory\Python\Wrappers\AlgorithmPythonWrapper.cs:line 693
+   at QuantConnect.Lean.Engine.AlgorithmManager.Run(AlgorithmNodePacket job, IAlgorithm algorithm, ISynchronizer synchronizer, ITransactionHandler transactions, IResultHandler results, IRealTimeHandler realtime, ILeanManager leanManager, CancellationToken token) in D:\QuantConnect\MyLean\Lean\Engine\AlgorithmManager.cs:line 526",
+            0)]
         public void ParsesPythonExceptionStackTrace(string expected, string original, int shift)
         {
             var originalShiftValue = PythonUtil.ExceptionLineShift;