Updated financial calcs and unit tests to deal with NaN return

src/Numerics/Financial/AbsoluteReturnMeasures.cs

@@ -0,0 +1,103 @@
+// <copyright file="AbsoluteReturnMeasures.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+//
+// Copyright (c) 2009-2010 Math.NET
+//
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+//
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+//
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace MathNet.Numerics.Financial
+{
+    using System;
+    using System.Collections.Generic;
+    using System.Linq;
+    using System.Text;
+    using MathNet.Numerics.Statistics;
+
+    public static class AbsoluteReturnMeasures
+    {
+        /// <summary>
+        /// Compound Monthly Return or Geometric Return or Annualized Return
+        /// </summary>
+        /// <param name="data"></param>
+        /// <returns></returns>
+        public static double CompoundMonthlyReturn(this IEnumerable<double> data)
+        {
+            if (data == null)
+            {
+                throw new ArgumentNullException("data");
+            }
+
+            var samples = data.Count();
+            if (samples == 0)
+                return double.NaN;
+
+            double compoundReturn = 1.0;
+            foreach (var item in data)
+            {
+                compoundReturn *= (1 + item);
+            }
+            return Math.Pow(compoundReturn, 1.0 / (double)samples) - 1.0;
+        }
+
+        /// <summary>
+        /// Average Gain or Gain Mean
+        /// This is a simple average (arithmetic mean) of the periods with a gain. It is calculated by summing the returns for gain periods (return 0) 
+        /// and then dividing the total by the number of gain periods.
+        /// </summary>
+        /// <param name="data"></param>
+        /// <returns></returns>
+        /// <remarks>http://www.offshore-library.com/kb/statistics.php</remarks>
+        public static double GainMean(this IEnumerable<double> data)
+        {
+            if (data == null)
+            {
+                throw new ArgumentNullException("data");
+            }
+
+            var gains = data.Where(x => x >= 0);
+            return gains.Mean();
+        }
+
+        /// <summary>
+        /// Average Loss or LossMean
+        /// This is a simple average (arithmetic mean) of the periods with a loss. It is calculated by summing the returns for loss periods (return < 0)
+        /// and then dividing the total by the number of loss periods.
+        /// </summary>
+        /// <param name="data"></param>
+        /// <returns></returns>
+        /// <remarks>http://www.offshore-library.com/kb/statistics.php</remarks>
+        public static double LossMean(this IEnumerable<double> data)
+        {
+            if (data == null)
+            {
+                throw new ArgumentNullException("data");
+            }
+
+            var losses = data.Where(x => x < 0);
+            return losses.Mean();
+        }
+    }
+}

src/Numerics/Financial/AbsoluteRiskStatistics.cs

@@ -36,7 +36,7 @@ namespace MathNet.Numerics.Financial
     using System.Text;
     using MathNet.Numerics.Statistics;
 
-    public static class AbsoluteRiskStatistics
+    public static class AbsoluteRiskMeasures
     {
         //Note: The following statistics would be condidered an absolute risk statistic in the finance realm as well.
         // Standard Deviation 
@@ -135,44 +135,6 @@ namespace MathNet.Numerics.Financial
             return belowMeanData.StandardDeviation();
         }
 
-        /// <summary>
-        /// Average Gain or Gain Mean
-        /// This is a simple average (arithmetic mean) of the periods with a gain. It is calculated by summing the returns for gain periods (return 0) 
-        /// and then dividing the total by the number of gain periods.
-        /// </summary>
-        /// <param name="data"></param>
-        /// <returns></returns>
-        /// <remarks>http://www.offshore-library.com/kb/statistics.php</remarks>
-        public static double GainMean(this IEnumerable<double> data)
-        {
-            if (data == null)
-            {
-                throw new ArgumentNullException("data");
-            }
-
-            var gains = data.Where(x => x >= 0);
-            return gains.Mean();
-        }
-
-        /// <summary>
-        /// Average Loss or LossMean
-        /// This is a simple average (arithmetic mean) of the periods with a loss. It is calculated by summing the returns for loss periods (return < 0)
-        /// and then dividing the total by the number of loss periods.
-        /// </summary>
-        /// <param name="data"></param>
-        /// <returns></returns>
-        /// <remarks>http://www.offshore-library.com/kb/statistics.php</remarks>
-        public static double LossMean(this IEnumerable<double> data)
-        {
-            if (data == null)
-            {
-                throw new ArgumentNullException("data");
-            }
-
-            var losses = data.Where(x => x < 0);
-            return losses.Mean();
-        }
-
         /// <summary>
         /// Measures a fund’s average gain in a gain period divided by the fund’s average loss in a losing
         /// period. Periods can be monthly or quarterly depending on the data frequency.
@@ -190,9 +152,9 @@ namespace MathNet.Numerics.Financial
             var losses = data.Where(x => x < 0);
 
             var lossMean = losses.Mean();
-            if(lossMean != 0.0)
+
-                return Math.Abs(gains.Mean() / losses.Mean());
+            return Math.Abs(gains.Mean() / losses.Mean());
-            return 0.0;
+
         }
     }
 }

src/Numerics/Numerics.csproj

@@ -105,6 +105,7 @@
     <Compile Include="Constants.cs" />
     <Compile Include="Control.cs" />
     <Compile Include="Complex32.cs" />
+    <Compile Include="Financial\AbsoluteReturnMeasures.cs" />
     <Compile Include="Financial\AbsoluteRiskStatistics.cs" />
     <Compile Include="LinearAlgebra\Generic\Matrix.BCL.cs" />
     <Compile Include="LinearAlgebra\Generic\Vector.BCL.cs" />

src/UnitTests/FinancialTests/CompoundMonthlyReturnTests.cs

@@ -0,0 +1,75 @@
+// <copyright file="CompoundMonthlyReturnTests.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace MathNet.Numerics.UnitTests.FinancialTests
+{
+    using System;
+    using System.Collections.Generic;
+    using System.Linq;
+    using System.Text;
+    using MathNet.Numerics.Financial;
+    using NUnit.Framework;
+
+    [TestFixture]
+    [Category("FinancialTests")]
+    public class CompoundMonthlyReturnTests
+    {
+        [Test]
+        [ExpectedException(typeof(ArgumentNullException))] //assert
+        public void throws_when_input_data_is_null()
+        {
+            //arrange
+            List<double> inputData = null;
+            //act
+            inputData.CompoundMonthlyReturn();
+        }
+
+        [Test]
+        public void returns_undefined_with_empty_input_data()
+        {
+            //arrange
+            List<double> inputData = new List<double>();
+            //act
+            var cmpdReturn = inputData.CompoundMonthlyReturn();
+            //assert
+            Assert.AreEqual(double.NaN, cmpdReturn);
+        }
+
+        [Test]
+        public void calculates_the_compound_monthly_return()
+        {
+            //arrange
+            var inputData = new[] { 0.2, 0.06, 0.01 };
+            //act
+            var cmpdReturn = inputData.CompoundMonthlyReturn();
+            //assert
+            AssertHelpers.AlmostEqual(0.0870999982199265, cmpdReturn, 15);
+        }
+
+        //Definitly need more tests here.  Would love to find test data for these stats similar to the .dat files used for other tests.
+    }
+
+}

src/UnitTests/FinancialTests/DownsideDeviationTests.cs

@@ -34,6 +34,7 @@ namespace MathNet.Numerics.UnitTests.FinancialTests
     using NUnit.Framework;
 
     [TestFixture]
+    [Category("FinancialTests")]
     public class DownsideDeviationTests
     {
         [Test]

src/UnitTests/FinancialTests/GainLossRatioTests.cs

@@ -34,6 +34,7 @@ namespace MathNet.Numerics.UnitTests.FinancialTests
     using NUnit.Framework;
 
     [TestFixture]
+    [Category("FinancialTests")]
     public class GainLossRatioTests
     {
         [Test]
@@ -48,61 +49,61 @@ namespace MathNet.Numerics.UnitTests.FinancialTests
 
         [Test]
         //Not sure this is correct.  Undefined may be more correct.
-        public void returns_zero_for_a_single_positive_input()
+        public void returns_NaN_for_a_single_positive_input()
         {
             //arrange
             var inputData = new[] { 1.0 };
             //act
             var gainLossRatio = inputData.GainLossRatio();
             //assert
-            Assert.AreEqual(0.0, gainLossRatio);
+            Assert.AreEqual(double.NaN, gainLossRatio);
         }
 
         [Test]
         //Not sure this is correct.  Undefined may be more correct.
-        public void returns_zero_for_a_single_negative_input()
+        public void returns_NaN_for_a_single_negative_input()
         {
             //arrange
             var inputData = new[] { -1.0 };
             //act
             var gainLossRatio = inputData.GainLossRatio();
             //assert
-            Assert.AreEqual(0.0, gainLossRatio);
+            Assert.AreEqual(double.NaN, gainLossRatio);
         }
 
         [Test]
         //Not sure this is correct.  Undefined may be more correct.
-        public void returns_zero_for_a_set_of_all_positive_numbers()
+        public void returns_NaN_for_a_set_of_all_positive_numbers()
         {
             //arrange
             var inputData = new[] { 1.0, 2.0, 3.0 };
             //act
             var gainLossRatio = inputData.GainLossRatio();
             //assert
-            Assert.AreEqual(0.0, gainLossRatio);
+            Assert.AreEqual(double.NaN, gainLossRatio);
         }
 
         [Test]
         //Not sure this is correct.  Undefined may be more correct.
-        public void returns_zero_for_a_set_of_all_negative_numbers()
+        public void returns_NaN_for_a_set_of_all_negative_numbers()
         {
             //arrange
             var inputData = new[] { -1.0, -2.0, -3.0 };
             //act
             var gainLossRatio = inputData.GainLossRatio();
             //assert
-            Assert.AreEqual(0.0, gainLossRatio);
+            Assert.AreEqual(double.NaN, gainLossRatio);
         }
 
         [Test]
         public void handles_a_value_of_zero_as_a_positive()
         {
             //arrange
-            var inputData = new[] { 0.0, 1.0, 2.0 };
+            var inputData = new[] { 0.0, -1.0 };
             //act
-            var gainLossRatio = inputData.GainLossRatio();
+            var gainLossRatio = inputData.GainLossRatio(); 
             //assert
-            Assert.AreEqual(0.0, gainLossRatio);
+            Assert.AreEqual(0.0, gainLossRatio); //0.0 / -1.0 => 0.0
         }
 
         [Test]

src/UnitTests/FinancialTests/GainMeanTests.cs

@@ -33,6 +33,7 @@ namespace MathNet.Numerics.UnitTests.FinancialTests
     using NUnit.Framework;
 
     [TestFixture]
+    [Category("FinancialTests")]
     public class GainMeanTests
     {
         [Test]
@@ -46,14 +47,14 @@ namespace MathNet.Numerics.UnitTests.FinancialTests
             Assert.AreEqual(0.0, gainMean);
         }
         [Test]
-        public void returns_zero_when_all_input_is_negative()
+        public void returns_NaN_when_all_input_is_negative()
         {
             //arrange
             var inputData = new[] { -1.0, -2.0, -3.0 };
             //act
             var gainMean = inputData.GainMean();
             //assert
-            Assert.AreEqual(0.0, gainMean);
+            Assert.AreEqual(double.NaN, gainMean);
         }
 
         [Test]
@@ -91,14 +92,14 @@ namespace MathNet.Numerics.UnitTests.FinancialTests
         }
 
         [Test]
-        public void returns_zero_with_no_input_data()
+        public void returns_NaN_with_no_input_data()
         {
             //arrange
             var inputData = new List<double>();
             //act
             var gainMean = inputData.GainMean();
             //assert
-            Assert.AreEqual(0.0, gainMean);
+            Assert.AreEqual(double.NaN, gainMean);
         }
 
     }

src/UnitTests/FinancialTests/GainStandardDeviationTests.cs

@@ -34,6 +34,7 @@ namespace MathNet.Numerics.UnitTests.FinancialTests
     using NUnit.Framework;
 
     [TestFixture]
+    [Category("FinancialTests")]
     public class GainStandardDeviationTests
     {
         [Test]

src/UnitTests/FinancialTests/LossMeanTests.cs

@@ -33,28 +33,29 @@ namespace MathNet.Numerics.UnitTests.FinancialTests
     using NUnit.Framework;
 
     [TestFixture]
+    [Category("FinancialTests")]
     public class LossMeanTests
     {
         [Test]
-        public void returns_zero_when_zero_is_the_only_input()
+        public void returns_NaN_when_zero_is_the_only_input()
         {
             //arrange
             var inputData = new[] { 0.0 };
             //act
             var lossMean = inputData.LossMean();
             //assert
-            Assert.AreEqual(0.0, lossMean);
+            Assert.AreEqual(double.NaN, lossMean);
         }
 
         [Test]
-        public void returns_zero_when_all_input_is_positive()
+        public void returns_NaN_when_all_input_is_positive()
         {
             //arrange
-            var inputData = new[] { 1.0 };
+            var inputData = new[] { 0.0, 1.0 };
             //act
             var lossMean = inputData.LossMean();
             //assert
-            Assert.AreEqual(0.0, lossMean);
+            Assert.AreEqual(double.NaN, lossMean);
         }
 
         [Test]
@@ -92,14 +93,14 @@ namespace MathNet.Numerics.UnitTests.FinancialTests
         }
 
         [Test]
-        public void returns_zero_with_no_input_data()
+        public void returns_NaN_with_no_input_data()
         {
             //arrange
             var inputData = new List<double>();
             //act
             var lossMean = inputData.LossMean();
             //assert
-            Assert.AreEqual(0.0, lossMean);
+            Assert.AreEqual(double.NaN, lossMean);
         }
     }
 }

src/UnitTests/FinancialTests/LossStandardDeviationTests.cs

@@ -34,6 +34,7 @@ namespace MathNet.Numerics.UnitTests.FinancialTests
     using NUnit.Framework;
 
     [TestFixture]
+    [Category("FinancialTests")]
     public class LossStandardDeviationTests
     {
         [Test]

src/UnitTests/FinancialTests/SemiDeviationTests.cs

@@ -34,6 +34,7 @@ namespace MathNet.Numerics.UnitTests.FinancialTests
     using NUnit.Framework;
 
     [TestFixture]
+    [Category("FinancialTests")]
     public class SemiDeviationTests
     {
         [Test]

src/UnitTests/UnitTests.csproj

@@ -122,6 +122,7 @@
     <Compile Include="DistributionTests\Multivariate\MultinomialTests.cs" />
     <Compile Include="DistributionTests\Multivariate\NormalGammaTests.cs" />
     <Compile Include="DistributionTests\Multivariate\WishartTests.cs" />
+    <Compile Include="FinancialTests\CompoundMonthlyReturnTests.cs" />
     <Compile Include="FinancialTests\DownsideDeviationTests.cs" />
     <Compile Include="FinancialTests\GainLossRatioTests.cs" />
     <Compile Include="FinancialTests\GainStandardDeviationTests.cs" />