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Refactored tests with base test class. Added Python wrapper tests
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Base test class and python wrapper tests
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@wtindall1
wtindall1 committed Jan 19, 2024
1 parent c7d773b commit fd46596
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Showing 5 changed files with 186 additions and 105 deletions.
50 changes: 15 additions & 35 deletions Tests/Algorithm/Framework/Portfolio/MaximumSharpeRatioPortfolioOptimizerTests.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -22,17 +22,12 @@
namespace QuantConnect.Tests.Algorithm.Framework.Portfolio
{
[TestFixture]
public class MaximumSharpeRatioPortfolioOptimizerTests
public class MaximumSharpeRatioPortfolioOptimizerTests : PortfolioOptimizerTestsBase
{
private List<double[,]> _historicalReturns;
private List<double[]> _expectedReturns;
private List<double[,]> _covariances;
private List<double[]> _expectedResults;

[OneTimeSetUp]
public void SetUp()
{
_historicalReturns = new List<double[,]>
HistoricalReturns = new List<double[,]>
{
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 } },
new double[,] { { 0.10, 0.20, 0.4 }, { 0.12, 0.25, 0.4 }, { 0.11, 0.22, 0.4 } },
Expand All @@ -44,7 +39,7 @@ public void SetUp()
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 } }
};

_expectedReturns = new List<double[]>
ExpectedReturns = new List<double[]>
{
new double[] { 0.08, -0.01, 0.12 },
new double[] { 0.11, 0.23, 0.4 },
Expand All @@ -56,7 +51,7 @@ public void SetUp()
new double[] { 0.23, 0.25, 0.88, 0.04 }
};

_covariances = new List<double[,]>
Covariances = new List<double[,]>
{
new double[,] { { 0.29, 0.13, 0.10 }, { 0.13, 0.06, 0.04 }, { 0.10, 0.04, 0.05 } },
null,
Expand All @@ -68,7 +63,7 @@ public void SetUp()
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 } }
};

_expectedResults = new List<double[]>
ExpectedResults = new List<double[]>
{
new double[] { -0.562396, 0.608942, 0.953453 },
new double[] { 0.686025, -0.269589, 0.583023 },
Expand All @@ -81,6 +76,11 @@ public void SetUp()
};
}

protected override IPortfolioOptimizer CreateOptimizer()
{
return new MaximumSharpeRatioPortfolioOptimizer();
}

[TestCase(0)]
[TestCase(1)]
[TestCase(2)]
Expand All @@ -89,25 +89,18 @@ public void SetUp()
[TestCase(5)]
[TestCase(6)]
[TestCase(7)]
public void TestOptimizeWeightings(int testCaseNumber)
public override void OptimizeWeightings(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)));
base.OptimizeWeightings(testCaseNumber);
}

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

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

Assert.AreEqual(expectedResult, result.Select(x => Math.Round(x, 6)));
}
Expand All @@ -126,19 +119,6 @@ public void SingleSecurityPortfolioReturnsNaN()
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()
{
Expand All @@ -162,7 +142,7 @@ public void BoundariesAreNotViolated()
var upper = 0.5d;
var testOptimizer = new MaximumSharpeRatioPortfolioOptimizer(lower, upper);

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

foreach (var x in result)
{
Expand Down
64 changes: 24 additions & 40 deletions Tests/Algorithm/Framework/Portfolio/MinimumVariancePortfolioOptimizerTests.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,6 @@
* limitations under the License.
*/

using Accord;
using NUnit.Framework;
using QuantConnect.Algorithm.Framework.Portfolio;
using System;
Expand All @@ -23,12 +22,8 @@
namespace QuantConnect.Tests.Algorithm.Framework.Portfolio
{
[TestFixture]
public class MinimumVariancePortfolioOptimizerTests
public class MinimumVariancePortfolioOptimizerTests : PortfolioOptimizerTestsBase
{
private List<double[,]> _historicalReturns;
private List<double[]> _expectedReturns;
private List<double[,]> _covariances;
private List<double[]> _expectedResults;
private Dictionary<int, double> _targetReturns;

[OneTimeSetUp]
Expand All @@ -49,7 +44,7 @@ public void Setup()
var covariance3 = new double[,] { { 0.06, 0.09, 0.28 }, { 0.09, 0.25, 0.58 }, { 0.28, 0.58, 1.66 } };
var covariance4 = (double[,])null;

_historicalReturns = new List<double[,]>
HistoricalReturns = new List<double[,]>
{
historicalReturns1,
historicalReturns2,
Expand All @@ -61,7 +56,7 @@ public void Setup()
historicalReturns4
};

_expectedReturns = new List<double[]>
ExpectedReturns = new List<double[]>
{
expectedReturns1,
expectedReturns2,
Expand All @@ -73,7 +68,7 @@ public void Setup()
expectedReturns4
};

_covariances = new List<double[,]>
Covariances = new List<double[,]>
{
covariance1,
covariance2,
Expand All @@ -85,7 +80,7 @@ public void Setup()
covariance4
};

_expectedResults = new List<double[]>
ExpectedResults = new List<double[]>
{
new double[] { -0.089212, 0.23431, -0.040975, 0.635503 },
new double[] { 0.366812, -0.139738, 0.49345 },
Expand All @@ -106,37 +101,34 @@ public void Setup()
};
}

protected override IPortfolioOptimizer CreateOptimizer()
{
return new MinimumVariancePortfolioOptimizer();
}

[TestCase(0)]
[TestCase(1)]
[TestCase(2)]
[TestCase(3)]
public void TestOptimizeWeightings(int testCaseNumber)
public override void OptimizeWeightings(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)));
Assert.AreEqual(1d, result.Select(x => Math.Round(Math.Abs(x), 6)).Sum());
base.OptimizeWeightings(testCaseNumber);
}

[TestCase(4)]
[TestCase(5)]
[TestCase(6)]
[TestCase(7)]
public void TestOptimizeWeightingsSpecifyingTargetReturns(int testCaseNumber)
public void OptimizeWeightingsSpecifyingTargetReturns(int testCaseNumber)
{
var testOptimizer = new MinimumVariancePortfolioOptimizer(targetReturn: _targetReturns[testCaseNumber]);

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

Assert.AreEqual(_expectedResults[testCaseNumber], result.Select(x => Math.Round(x, 6)));
Assert.AreEqual(ExpectedResults[testCaseNumber], result.Select(x => Math.Round(x, 6)));
Assert.AreEqual(1d, result.Select(x => Math.Round(Math.Abs(x), 6)).Sum());
}

Expand All @@ -146,7 +138,7 @@ public void EqualWeightingsWhenNoSolutionFound(int testCaseNumber)
var testOptimizer = new MinimumVariancePortfolioOptimizer(upper: -1);
var expectedResult = new double[] { 0.25, 0.25, 0.25, 0.25 };

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

Assert.AreEqual(expectedResult, result);
}
Expand All @@ -162,9 +154,9 @@ public void BoundariesAreNotViolated(int testCaseNumber)
var testOptimizer = new MinimumVariancePortfolioOptimizer(lower, upper);

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

foreach (var x in result)
{
Expand All @@ -174,24 +166,16 @@ public void BoundariesAreNotViolated(int testCaseNumber)
};
}

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

var result = testOptimizer.Optimize(historicalReturns);

Assert.AreEqual(Array.Empty<double>(), result);
}

[Test]
public void SingleSecurityPortfolioReturnsOne()
{
var testOptimizer = new MinimumVariancePortfolioOptimizer();
var historicalReturns = new double[,] { { 0.76 }, { 0.02 }, { -0.50 } };
var expectedResult = new double[] { 1 };

var result = testOptimizer.Optimize(historicalReturns);

Assert.AreEqual(1d, result);
Assert.AreEqual(expectedResult, result);
}
}
}
76 changes: 76 additions & 0 deletions Tests/Algorithm/Framework/Portfolio/PortfolioOptimizerPythonWrapperTests.cs
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Original file line number Diff line number Diff line change
@@ -0,0 +1,76 @@
/*
* 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 Python.Runtime;
using QuantConnect.Algorithm.Framework.Portfolio;
using System;

namespace QuantConnect.Tests.Algorithm.Framework.Portfolio;

[TestFixture]
public class PortfolioOptimizerPythonWrapperTests
{
[Test]
public void OptimizeIsCalled()
{
using (Py.GIL())
{
var module = PyModule.FromString(Guid.NewGuid().ToString(),
@$"
from AlgorithmImports import *
class CustomPortfolioOptimizer:
def __init__(self):
self.OptimizeWasCalled = False
def Optimize(self, historicalReturns, expectedReturns = None, covariance = None):
self.OptimizeWasCalled= True");

var pyCustomOptimizer = module.GetAttr("CustomPortfolioOptimizer").Invoke();
var wrapper = new PortfolioOptimizerPythonWrapper(pyCustomOptimizer);
var historicalReturns = new double[,] { { -0.50, -0.13 }, { 0.81, 0.31 }, { -0.02, 0.01 } };

wrapper.Optimize(historicalReturns);
pyCustomOptimizer
.GetAttr("OptimizeWasCalled")
.TryConvert(out bool optimizerWasCalled);

Assert.IsTrue(optimizerWasCalled);
}
}

[Test]
public void WrapperThrowsIfOptimizerDoesNotImplementInterface()
{
using (Py.GIL())
{
var module = PyModule.FromString(Guid.NewGuid().ToString(),
@$"
from AlgorithmImports import *
class CustomPortfolioOptimizer:
def __init__(self):
self.OptimizeWasCalled = False
def Calculate(self, historicalReturns, expectedReturns = None, covariance = None):
pass");

var pyCustomOptimizer = module.GetAttr("CustomPortfolioOptimizer").Invoke();

Assert.Throws<NotImplementedException>(() => new PortfolioOptimizerPythonWrapper(pyCustomOptimizer));
}
}
}
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