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MaximumSharpeRatio and MinVariance optimizer tests
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added boundary constraints test

Added MinimumVariancePortfolioOptimizer test class

Added more test cases for max sharpe ratio optimizer

Changed MaxSharpeRatio optimizer test cases inputs to 2dp

Added min variance test cases
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@wtindall1
wtindall1 committed Jan 11, 2024
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191 changes: 191 additions & 0 deletions Tests/Algorithm/Framework/Portfolio/MaximumSharpeRatioPortfolioOptimizerTests.cs
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using Accord.Statistics;
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
{
[TestFixture]
public class MaximumSharpeRatioPortfolioOptimizerTests
{
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[,] { { 0.02, -0.02, 0.28 }, { -0.50, -0.29, -0.13 }, { 0.81, 0.29, 0.31 }, { -0.03, -0.00, 0.01 } };
double[,] historicalReturns2 = new double[,] { { 0.10, 0.20, 0.4 }, { 0.12, 0.25, 0.4 }, { 0.11, 0.22, 0.4 } };
double[,] historicalReturns3 = new double[,] { { -0.19, 0.50, 0.45 }, { -0.62, -0.65, 0.07 }, { -0.14, 1.02, 0.01 }, { 0.00, -0.03, 0.01 } };
double[,] historicalReturns4 = new double[,] { { 0.46, 0.28, 0.58, 0.26, 0.14 }, { 0.52, 0.31, 0.43, 7.43, -0.00 }, { 0.13, 0.65, 0.52, 0.50, -0.08 }, { -0.41, -0.39, -0.28, -0.65, -0.20 }, { 0.77, 0.58, 0.58, 1.02, 0.03 }, { -0.03, -0.01, -0.01, -0.03, 0.07 } };
double[,] historicalReturns5 = new double[,] { { -0.50, -0.13 }, { 0.81, 0.31 }, { -0.02, 0.01 } };
double[,] historicalReturns6 = new double[,] { { 0.31, 0.25, 0.43 }, { 0.65, 0.60, 0.52 }, { -0.39, -0.22, -0.28 }, { 0.58, 0.13, 0.58 }, { -0.01, -0.00, -0.01 } };
double[,] historicalReturns7 = new double[,] { { 0.13, 0.65, 1.25 }, { -0.41, -0.39, -0.50 }, { 0.77, 0.58, 2.39 }, { -0.03, -0.01, 0.04 } };
double[,] historicalReturns8 = new double[,] { { 0.31, 0.43, 1.22, 0.03 }, { 0.65, 0.52, 1.25, 0.67 }, { -0.39, -0.28, -0.50, -0.10 }, { 0.58, 0.58, 2.39, -0.41 }, { -0.01, -0.01, 0.04, 0.03 } };


double[] expectedReturns1 = new double[] { 0.08, -0.01, 0.12 };
double[] expectedReturns2 = new double[] { 0.11, 0.23, 0.4 };
double[] expectedReturns3 = new double[] { -0.24, 0.21, 0.14 };
double[] expectedReturns4 = null;
double[] expectedReturns5 = new double[] { 0.10, 0.06 };
double[] expectedReturns6 = new double[] { 0.23, 0.15, 0.25 };
double[] expectedReturns7 = null;
double[] expectedReturns8 = new double[] { 0.23, 0.25, 0.88, 0.04 };

double[,] covariance1 = new double[,] { { 0.29, 0.13, 0.10 }, { 0.13, 0.06, 0.04 }, { 0.10, 0.04, 0.05 } };
double[,] covariance2 = null;
double[,] covariance3 = new double[,] { { 0.07, 0.12, -0.00 }, { 0.12, 0.51, 0.03 }, { -0.00, 0.03, 0.04 } };
double[,] covariance4 = null;
double[,] covariance5 = new double[,] { { 0.44, 0.15 }, { 0.15, 0.05 } };
double[,] covariance6 = new double[,] { { 0.19, 0.11, 0.16 }, { 0.11, 0.09, 0.09 }, { 0.16, 0.09, 0.14 } };
double[,] covariance7 = new double[,] { { 0.24, 0.20, 0.61 }, { 0.20, 0.25, 0.58 }, { 0.61, 0.58, 1.67 } };
double[,] covariance8 = new double[,] { { 0.19, 0.16, 0.44, 0.05 }, { 0.16, 0.14, 0.40, 0.02 }, { 0.44, 0.40, 1.29, -0.06 }, { 0.05, 0.02, -0.06, 0.15 } };


double[] expectedResult1 = new double[] { -0.562396, 0.608942, 0.953453};
double[] expectedResult2 = new double[] { 0.686025, -0.269589, 0.583023};
double[] expectedResult3 = new double[] { 0.26394, -0.043374, 0.779434 };
double[] expectedResult4 = new double[] { -0.223905, 0.401036, 1, 0.065329, -0.24246 };
double[] expectedResult5 = new double[] { 0.5, 0.5 };
double[] expectedResult6 = new double[] { -0.5, 0.5, 1 };
double[] expectedResult7 = new double[] { -0.242647, 1, 0.242647 };
double[] expectedResult8 = new double[] { -1, 0.922902, 0.364512, 0.712585 };

_historicalReturns.Add(1, historicalReturns1);
_historicalReturns.Add(2, historicalReturns2);
_historicalReturns.Add(3, historicalReturns3);
_historicalReturns.Add(4, historicalReturns4);
_historicalReturns.Add(5, historicalReturns5);
_historicalReturns.Add(6, historicalReturns6);
_historicalReturns.Add(7, historicalReturns7);
_historicalReturns.Add(8, historicalReturns8);

_expectedReturns.Add(1, expectedReturns1);
_expectedReturns.Add(2, expectedReturns2);
_expectedReturns.Add(3, expectedReturns3);
_expectedReturns.Add(4, expectedReturns4);
_expectedReturns.Add(5, expectedReturns5);
_expectedReturns.Add(6, expectedReturns6);
_expectedReturns.Add(7, expectedReturns7);
_expectedReturns.Add(8, expectedReturns8);

_covariances.Add(1, covariance1);
_covariances.Add(2, covariance2);
_covariances.Add(3, covariance3);
_covariances.Add(4, covariance4);
_covariances.Add(5, covariance5);
_covariances.Add(6, covariance6);
_covariances.Add(7, covariance7);
_covariances.Add(8, covariance8);

_expectedResults.Add(1, expectedResult1);
_expectedResults.Add(2, expectedResult2);
_expectedResults.Add(3, expectedResult3);
_expectedResults.Add(4, expectedResult4);
_expectedResults.Add(5, expectedResult5);
_expectedResults.Add(6, expectedResult6);
_expectedResults.Add(7, expectedResult7);
_expectedResults.Add(8, expectedResult8);
}

[TestCase(1)]
[TestCase(2)]
[TestCase(3)]
[TestCase(4)]
[TestCase(5)]
[TestCase(6)]
[TestCase(7)]
[TestCase(8)]
public void TestOptimizeWeightings(int testCaseNumber)
{
var testOptimizer = new MaximumSharpeRatioPortfolioOptimizer();

var result = testOptimizer.Optimize(
_historicalReturns[testCaseNumber],
_expectedReturns[testCaseNumber],
_covariances[testCaseNumber]);

Assert.AreEqual(_expectedResults[testCaseNumber], result.Select(x => Math.Round(x, 6)));
}

[TestCase(1)]
public void TestOptimizeWeightingsSpecifyingLowerBoundAndRiskFreeRate(int testCaseNumber)
{
var testOptimizer = new MaximumSharpeRatioPortfolioOptimizer(lower: 0, riskFreeRate: 0.04);
double[] expectedResult = new double[] { 0, 0.44898, 0.55102 };

var result = testOptimizer.Optimize(_historicalReturns[testCaseNumber]);

Assert.AreEqual(expectedResult, result.Select(x => Math.Round(x, 6)));
}

[Test]
public void SingleSecurityPortfolioReturnsNaN()
{
var testOptimizer = new MaximumSharpeRatioPortfolioOptimizer();
var historicalReturns = new double[,] { { -0.1 } };
var expectedReturns = new double[] { -0.1 };

var expectedResult = new double[] { double.NaN };

var result = testOptimizer.Optimize(historicalReturns, expectedReturns);

Assert.AreEqual(result, expectedResult);
}

[Test]
public void EmptyPortfolioReturnsEmptyArrayOfDouble()
{
var testOptimizer = new MaximumSharpeRatioPortfolioOptimizer();
var historicalReturns = new double[,] { { } };

var expectedResult = Array.Empty<double>();

var result = testOptimizer.Optimize(historicalReturns);

Assert.AreEqual(result, expectedResult);
}

[Test]
public void EqualWeightingsWhenNoSolutionFound()
{
var testOptimizer = new MaximumSharpeRatioPortfolioOptimizer();
var historicalReturns = new double[,] { { -0.10, -0.20 }, { -0.12, -0.25 } };
var expectedReturns = new double[] { -0.10, -0.25 };
var covariance = new double[,] { { 0.25, 0.12 }, { 0.45, 0.2 } }; // non positive definite

var expectedResult = new double[] { 0.5, 0.5 };

var result = testOptimizer.Optimize(historicalReturns, expectedReturns, covariance);

Assert.AreEqual(result, expectedResult);
}

[Test]
public void BoundariesAreNotViolated()
{
var testCaseNumber = 1;
double lower = 0d;
double upper = 0.5d;
var testOptimizer = new MaximumSharpeRatioPortfolioOptimizer(lower, upper);

var result = testOptimizer.Optimize(_historicalReturns[testCaseNumber], null, _covariances[testCaseNumber]);

foreach (double x in result)
{
Assert.GreaterOrEqual(x, lower);
Assert.LessOrEqual(x, upper);
};
}
}
}


146 changes: 146 additions & 0 deletions Tests/Algorithm/Framework/Portfolio/MinimumVariancePortfolioOptimizerTests.cs
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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
{
public class MinimumVariancePortfolioOptimizerTests
{
private Dictionary<int, double> _targetReturns = new();
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 targetReturn1 = 0.15;
double targetReturn2 = 0.25;
double targetReturn3 = 0.5;
double targetReturn4 = 0.125;

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[] { -0.089212, 0.23431, -0.040975, 0.635503};
double[] expectedResult2 = new double[] { 0.366812, -0.139738, 0.49345 };
double[] expectedResult3 = new double[] { 0.562216, 0.36747, -0.070314 };
double[] expectedResult4 = new double[] { -0.119241, 0.443464, 0.437295 };
double[] expectedResult5 = new double[] { -0.215505, 0.130699, 0.084806, 0.56899 };
double[] expectedResult6 = new double[] { -0.275, 0.275, 0.45 };
double[] expectedResult7 = new double[] { -0.129512, 0.551139, 0.319349 };
double[] expectedResult8 = new double[] { 0.052859, 0.144177, 0.802964 };

_targetReturns.Add(5, targetReturn1);
_targetReturns.Add(6, targetReturn2);
_targetReturns.Add(7, targetReturn3);
_targetReturns.Add(8, targetReturn4);

_expectedReturns.Add(1, expectedReturns1);
_expectedReturns.Add(2, expectedReturns2);
_expectedReturns.Add(3, expectedReturns3);
_expectedReturns.Add(4, expectedReturns4);
_expectedReturns.Add(5, expectedReturns1);
_expectedReturns.Add(6, expectedReturns2);
_expectedReturns.Add(7, expectedReturns3);
_expectedReturns.Add(8, expectedReturns4);

_historicalReturns.Add(1, historicalReturns1);
_historicalReturns.Add(2, historicalReturns2);
_historicalReturns.Add(3, historicalReturns3);
_historicalReturns.Add(4, historicalReturns4);
_historicalReturns.Add(5, historicalReturns1);
_historicalReturns.Add(6, historicalReturns2);
_historicalReturns.Add(7, historicalReturns3);
_historicalReturns.Add(8, historicalReturns4);

_covariances.Add(1, covariance1);
_covariances.Add(2, covariance2);
_covariances.Add(3, covariance3);
_covariances.Add(4, covariance4);
_covariances.Add(5, covariance1);
_covariances.Add(6, covariance2);
_covariances.Add(7, covariance3);
_covariances.Add(8, covariance4);

_expectedResults.Add(1, expectedResult1);
_expectedResults.Add(2, expectedResult2);
_expectedResults.Add(3, expectedResult3);
_expectedResults.Add(4, expectedResult4);
_expectedResults.Add(5, expectedResult5);
_expectedResults.Add(6, expectedResult6);
_expectedResults.Add(7, expectedResult7);
_expectedResults.Add(8, expectedResult8);
}

[TestCase(1)]
[TestCase(2)]
[TestCase(3)]
[TestCase(4)]
public void TestOptimizeWeightings(int testCaseNumber)
{
var testOptimizer = new MinimumVariancePortfolioOptimizer();

var result = testOptimizer.Optimize(
_historicalReturns[testCaseNumber],
_expectedReturns[testCaseNumber],
_covariances[testCaseNumber]);

Assert.AreEqual(_expectedResults[testCaseNumber], result.Select(x => Math.Round(x, 6)));
}

[TestCase(5)]
[TestCase(6)]
[TestCase(7)]
[TestCase(8)]
public void TestOptimizeWeightingsSpecifyingTargetReturns(int testCaseNumber)
{
var testOptimizer = new MinimumVariancePortfolioOptimizer(targetReturn: _targetReturns[testCaseNumber]);

var result = testOptimizer.Optimize(
_historicalReturns[testCaseNumber],
_expectedReturns[testCaseNumber],
_covariances[testCaseNumber]);

Assert.AreEqual(_expectedResults[testCaseNumber], result.Select(x => Math.Round(x, 6)));
}

[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 result = testOptimizer.Optimize(_historicalReturns[testCaseNumber]);

Assert.AreEqual(expectedResult, result);
}

public void BoundariesAreNotViolated(int testCaseNumber)
{

}
}
}

44 changes: 44 additions & 0 deletions Tests/Algorithm/Framework/Portfolio/UnconstrainedMeanVariancePortfolioOptimizerTests.cs
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using Accord.Statistics;
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[]> _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 } }; ;

var expectedResult1 = new double[] { -1.4234053790251934E+20, 2.124485640336107E+18, 5.948559792941101E+17 };
var expectedResult2 = new double[] { 1.0058640608512344E+18, -1.341152081134979E+18 };

_historicalReturns.Add(1, historicalReturns1);
_historicalReturns.Add(2, historicalReturns2);

_expectedResults.Add(1, expectedResult1);
_expectedResults.Add(2, expectedResult2);
}

[TestCase(1)]
[TestCase(2)]
public void TestOptimizeWeightings(int testCaseNumber)
{
var testOptimizer = new UnconstrainedMeanVariancePortfolioOptimizer();

var result = testOptimizer.Optimize(_historicalReturns[testCaseNumber]);

Assert.AreEqual(_expectedResults[testCaseNumber], result);
}
}
}

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