UnconstrainedMeanVariancePortfolioOptimizerTests

Tests/Algorithm/Framework/Portfolio/UnconstrainedMeanVariancePortfolioOptimizerTests.cs

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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 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;
+
+namespace QuantConnect.Tests.Algorithm.Framework.Portfolio
+{
+    [TestFixture]
+    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()
+        {
+            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;
+
+            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],
+                _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(Array.Empty<double>(), result);
+        }
+    }
+}