Implement ZTEST: One-Sample t-Test Statistic with validation tests

- Added Ztest class to compute the one-sample t-statistic using sample standard deviation with Bessel correction. - Implemented validation tests for Ztest to ensure accuracy against manual calculations and PineScript. - Updated documentation for Ztest, detailing its mathematical foundation, performance profile, and common pitfalls. - Adjusted NDepend badges to reflect changes in code metrics after implementation. - Updated missing indicators report to reflect the completion of statistical indicators, including ZTEST.
2026-08-19 11:08:05 +00:00 · 2026-02-16 16:54:36 -08:00
parent 09ffd31a40
commit b3a64f18fa
73 changed files with 13041 additions and 88 deletions
@@ -0,0 +1,76 @@
+namespace QuanTAlib.Validation;
+
+/// <summary>
+/// Mode validation tests — self-consistency only.
+/// No external library provides rolling mode calculations.
+/// </summary>
+public sealed class ModeValidationTests
+{
+    [Fact]
+    public void Mode_SelfConsistency_KnownValues()
+    {
+        // Test with known mode values
+        // {1, 2, 2, 3, 3, 3, 4, 4, 4, 4} → mode = 4 (appears 4 times)
+        var mode = new Mode(10);
+        mode.Update(new TValue(DateTime.UtcNow, 1));
+        mode.Update(new TValue(DateTime.UtcNow, 2));
+        mode.Update(new TValue(DateTime.UtcNow, 2));
+        mode.Update(new TValue(DateTime.UtcNow, 3));
+        mode.Update(new TValue(DateTime.UtcNow, 3));
+        mode.Update(new TValue(DateTime.UtcNow, 3));
+        mode.Update(new TValue(DateTime.UtcNow, 4));
+        mode.Update(new TValue(DateTime.UtcNow, 4));
+        mode.Update(new TValue(DateTime.UtcNow, 4));
+        var result = mode.Update(new TValue(DateTime.UtcNow, 4));
+
+        Assert.Equal(4, result.Value);
+    }
+
+    [Fact]
+    public void Mode_BatchAndStreaming_Match()
+    {
+        // Use data with known repeated values
+        double[] data = [10, 20, 20, 30, 30, 30, 40, 20, 20, 20, 10, 10, 30, 30, 30];
+        int period = 5;
+
+        // Streaming
+        var mode = new Mode(period);
+        var streamingResults = new double[data.Length];
+        for (int i = 0; i < data.Length; i++)
+        {
+            streamingResults[i] = mode.Update(new TValue(DateTime.UtcNow, data[i])).Value;
+        }
+
+        // Batch via spans
+        var spanOutput = new double[data.Length];
+        Mode.Batch(data.AsSpan(), spanOutput.AsSpan(), period);
+
+        for (int i = 0; i < data.Length; i++)
+        {
+            if (double.IsNaN(streamingResults[i]))
+            {
+                Assert.True(double.IsNaN(spanOutput[i]), $"Index {i}: streaming=NaN but span={spanOutput[i]}");
+            }
+            else
+            {
+                Assert.Equal(streamingResults[i], spanOutput[i], precision: 10);
+            }
+        }
+    }
+
+    [Fact]
+    public void Mode_MatchesWolframAlpha()
+    {
+        // Wolfram Alpha: mode of {1, 2, 2, 3, 3, 3, 4} = {3}
+        var mode = new Mode(7);
+        mode.Update(new TValue(DateTime.UtcNow, 1));
+        mode.Update(new TValue(DateTime.UtcNow, 2));
+        mode.Update(new TValue(DateTime.UtcNow, 2));
+        mode.Update(new TValue(DateTime.UtcNow, 3));
+        mode.Update(new TValue(DateTime.UtcNow, 3));
+        mode.Update(new TValue(DateTime.UtcNow, 3));
+        var result = mode.Update(new TValue(DateTime.UtcNow, 4));
+
+        Assert.Equal(3, result.Value);
+    }
+}