Add TRAMA implementation and comprehensive tests

- Implemented the TRAMA (Trend Regularity Adaptive Moving Average) class with adaptive EMA logic.
- Added unit tests for TRAMA functionality, including constructor validation, basic calculations, state management, and robustness checks.
- Created validation tests to ensure consistency across different modes of operation (streaming, batch, and static calculations).
- Enhanced documentation for TRAMA, including performance profiles and quality metrics.
- Updated workspace configuration by removing unnecessary folder references.

lib/trends_FIR/hend/Hend.Validation.Tests.cs

@@ -0,0 +1,160 @@
+using Xunit;
+using Xunit.Abstractions;
+
+namespace QuanTAlib.Tests;
+
+public class HendValidationTests(ITestOutputHelper output)
+{
+    private readonly ValidationTestData _testData = new();
+    private readonly ITestOutputHelper _output = output;
+    private const int DefaultPeriod = 7;
+
+    // ── Batch vs Streaming consistency ──────────────────────────────────
+
+    [Fact]
+    public void BatchVsStreaming_Match()
+    {
+        var source = new TSeries();
+        var gbm = new GBM(startPrice: 100, seed: 42);
+        const int count = 100;
+
+        for (int i = 0; i < count; i++)
+        {
+            var bar = gbm.Next();
+            source.Add(bar.C);
+        }
+
+        // Streaming
+        var hend = new Hend(DefaultPeriod);
+        var streaming = new double[count];
+        for (int i = 0; i < count; i++)
+        {
+            streaming[i] = hend.Update(source[i]).Value;
+        }
+
+        // Batch
+        var batchResult = Hend.Batch(source, DefaultPeriod);
+
+        for (int i = 0; i < count; i++)
+        {
+            Assert.Equal(streaming[i], batchResult[i].Value, 1e-10);
+        }
+    }
+
+    // ── Span vs Streaming consistency ──────────────────────────────────
+
+    [Fact]
+    public void SpanVsStreaming_Match()
+    {
+        var source = new TSeries();
+        var gbm = new GBM(startPrice: 100, seed: 42);
+        const int count = 100;
+
+        for (int i = 0; i < count; i++)
+        {
+            var bar = gbm.Next();
+            source.Add(bar.C);
+        }
+
+        // Streaming
+        var hend = new Hend(DefaultPeriod);
+        var streaming = new double[count];
+        for (int i = 0; i < count; i++)
+        {
+            streaming[i] = hend.Update(source[i]).Value;
+        }
+
+        // Span
+        double[] spanOutput = new double[count];
+        Hend.Batch(source.Values, spanOutput, DefaultPeriod);
+
+        for (int i = 0; i < count; i++)
+        {
+            Assert.Equal(streaming[i], spanOutput[i], 1e-10);
+        }
+    }
+
+    // ── Polynomial exact-fit validation ────────────────────────────────
+
+    [Fact]
+    public void LinearPolynomial_ExactFit()
+    {
+        // Henderson preserves linear trends at the CENTER of the window.
+        // For period=7, half=3, output at bar N = polynomial at bar N-3.
+        int half = (DefaultPeriod - 1) / 2;
+        var hend = new Hend(DefaultPeriod);
+        const int total = 50;
+        const double a = 5.0, b = 3.0;
+
+        for (int i = 0; i < total; i++)
+        {
+            double val = a + b * i;
+            hend.Update(new TValue(DateTime.UtcNow.AddSeconds(i), val));
+        }
+
+        int centerIdx = total - 1 - half;
+        double expected = a + b * centerIdx;
+        _output.WriteLine($"Linear: expected={expected}, actual={hend.Last.Value}");
+        Assert.Equal(expected, hend.Last.Value, 1e-6);
+    }
+
+    [Fact]
+    public void QuadraticPolynomial_ExactFit()
+    {
+        int half = (DefaultPeriod - 1) / 2;
+        var hend = new Hend(DefaultPeriod);
+        const int total = 50;
+        const double a = 2.0, b = 1.5, c = 0.3;
+
+        for (int i = 0; i < total; i++)
+        {
+            double val = a + b * i + c * i * i;
+            hend.Update(new TValue(DateTime.UtcNow.AddSeconds(i), val));
+        }
+
+        int centerIdx = total - 1 - half;
+        double expected = a + b * centerIdx + c * centerIdx * centerIdx;
+        _output.WriteLine($"Quadratic: expected={expected}, actual={hend.Last.Value}");
+        Assert.Equal(expected, hend.Last.Value, 0.1);
+    }
+
+    [Fact]
+    public void CubicPolynomial_ExactFit()
+    {
+        int half = (DefaultPeriod - 1) / 2;
+        var hend = new Hend(DefaultPeriod);
+        const int total = 50;
+        const double a = 1.0, b = 0.5, c = 0.1, d = 0.005;
+
+        for (int i = 0; i < total; i++)
+        {
+            double val = a + b * i + c * i * i + d * i * i * i;
+            hend.Update(new TValue(DateTime.UtcNow.AddSeconds(i), val));
+        }
+
+        int centerIdx = total - 1 - half;
+        double expected = a + b * centerIdx + c * centerIdx * centerIdx + d * centerIdx * centerIdx * centerIdx;
+        _output.WriteLine($"Cubic: expected={expected}, actual={hend.Last.Value}");
+        Assert.Equal(expected, hend.Last.Value, 1.0);
+    }
+
+    // ── Calculate returns hot indicator ─────────────────────────────────
+
+    [Fact]
+    public void Calculate_ReturnsHotIndicator()
+    {
+        var source = new TSeries();
+        var gbm = new GBM(startPrice: 100, seed: 42);
+
+        for (int i = 0; i < 50; i++)
+        {
+            var bar = gbm.Next();
+            source.Add(bar.C);
+        }
+
+        var (results, indicator) = Hend.Calculate(source, DefaultPeriod);
+
+        Assert.True(indicator.IsHot);
+        Assert.Equal(50, results.Count);
+    }
+}