adding missing validations

lib/numerics/fdist/Fdist.Quantower.Tests.cs

@@ -0,0 +1,194 @@
+using Xunit;
+using TradingPlatform.BusinessLayer;
+
+namespace QuanTAlib.Tests;
+
+public class FdistIndicatorTests
+{
+    [Fact]
+    public void FdistIndicator_Constructor_SetsDefaults()
+    {
+        var indicator = new FdistIndicator();
+
+        Assert.Equal(SourceType.Close, indicator.Source);
+        Assert.Equal(1, indicator.D1);
+        Assert.Equal(1, indicator.D2);
+        Assert.Equal(14, indicator.Period);
+        Assert.True(indicator.ShowColdValues);
+        Assert.Equal("FDIST - F-Distribution CDF", indicator.Name);
+        Assert.True(indicator.SeparateWindow);
+        Assert.True(indicator.OnBackGround);
+    }
+
+    [Fact]
+    public void FdistIndicator_MinHistoryDepths_EqualsPeriod()
+    {
+        var indicator = new FdistIndicator { Period = 30 };
+        Assert.Equal(30, indicator.MinHistoryDepths);
+    }
+
+    [Fact]
+    public void FdistIndicator_ShortName_IsCorrect()
+    {
+        var indicator = new FdistIndicator { D1 = 5, D2 = 10, Period = 20 };
+        Assert.Equal("FDIST(5,10,20)", indicator.ShortName);
+    }
+
+    [Fact]
+    public void FdistIndicator_Initialize_CreatesTwoLineSeries()
+    {
+        var indicator = new FdistIndicator();
+        indicator.Initialize();
+
+        Assert.Equal(2, indicator.LinesSeries.Count);
+        Assert.Equal("FDist", indicator.LinesSeries[0].Name);
+        Assert.Equal("Mid", indicator.LinesSeries[1].Name);
+    }
+
+    [Fact]
+    public void FdistIndicator_ProcessUpdate_HistoricalBar_ComputesValue()
+    {
+        var indicator = new FdistIndicator { D1 = 5, D2 = 5, Period = 5 };
+        indicator.Initialize();
+
+        var now = DateTime.UtcNow;
+        for (int i = 0; i < 5; i++)
+        {
+            indicator.HistoricalData.AddBar(now.AddMinutes(i), 0, 105 + i, 95 - i, 100 + i);
+            var args = new UpdateArgs(UpdateReason.HistoricalBar);
+            indicator.ProcessUpdate(args);
+        }
+
+        // After period bars, should have valid output
+        double val = indicator.LinesSeries[0].GetValue(0);
+        Assert.True(double.IsFinite(val), "Output must be finite after warmup");
+        Assert.True(val >= 0.0 && val <= 1.0, $"Output {val} must be in [0,1]");
+    }
+
+    [Fact]
+    public void FdistIndicator_ProcessUpdate_NewBar_AddsNewValue()
+    {
+        var indicator = new FdistIndicator { D1 = 5, D2 = 5, Period = 3 };
+        indicator.Initialize();
+
+        var now = DateTime.UtcNow;
+        // Feed 3 historical bars
+        for (int i = 0; i < 3; i++)
+        {
+            indicator.HistoricalData.AddBar(now.AddMinutes(i), 0, 105, 95, 100 + i);
+            indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
+        }
+
+        // Feed a new bar
+        indicator.HistoricalData.AddBar(now.AddMinutes(3), 0, 106, 96, 103);
+        indicator.ProcessUpdate(new UpdateArgs(UpdateReason.NewBar));
+
+        Assert.Equal(4, indicator.LinesSeries[0].Count);
+    }
+
+    [Fact]
+    public void FdistIndicator_ProcessUpdate_NewTick_ProcessesWithoutError()
+    {
+        var indicator = new FdistIndicator { Period = 3 };
+        indicator.Initialize();
+
+        var now = DateTime.UtcNow;
+        indicator.HistoricalData.AddBar(now, 0, 105, 95, 100);
+        indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
+        indicator.ProcessUpdate(new UpdateArgs(UpdateReason.NewTick));
+
+        // 2 values: one historical, one intra-bar update
+        Assert.Equal(2, indicator.LinesSeries[0].Count);
+    }
+
+    [Fact]
+    public void FdistIndicator_MidLine_IsAlwaysHalf()
+    {
+        var indicator = new FdistIndicator { Period = 3 };
+        indicator.Initialize();
+
+        var now = DateTime.UtcNow;
+        for (int i = 0; i < 5; i++)
+        {
+            indicator.HistoricalData.AddBar(now.AddMinutes(i), 0, 105, 95, 100 + i);
+            indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
+        }
+
+        // Mid line should always be 0.5
+        for (int i = 0; i < indicator.LinesSeries[1].Count; i++)
+        {
+            double mid = indicator.LinesSeries[1].GetValue(i);
+            Assert.Equal(0.5, mid, 1e-10);
+        }
+    }
+
+    [Fact]
+    public void FdistIndicator_DifferentSourceType_Works()
+    {
+        var indicator = new FdistIndicator { Period = 3, Source = SourceType.High };
+        indicator.Initialize();
+
+        var now = DateTime.UtcNow;
+        for (int i = 0; i < 3; i++)
+        {
+            // High = 110+i, Low = 90, Close = 100
+            indicator.HistoricalData.AddBar(now.AddMinutes(i), 0, 110 + i, 90, 100);
+            indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
+        }
+
+        double val = indicator.LinesSeries[0].GetValue(0);
+        Assert.True(double.IsFinite(val));
+    }
+
+    [Fact]
+    public void FdistIndicator_OutputInRange_AfterManyBars()
+    {
+        var indicator = new FdistIndicator { D1 = 5, D2 = 5, Period = 20 };
+        indicator.Initialize();
+
+        var now = DateTime.UtcNow;
+        var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 67001);
+        var bars = gbm.Fetch(50, now.Ticks, TimeSpan.FromMinutes(1));
+
+        for (int i = 0; i < bars.Close.Count; i++)
+        {
+            double price = bars.Close[i].Value;
+            indicator.HistoricalData.AddBar(
+                new DateTime(bars.Close[i].Time, DateTimeKind.Utc),
+                0, price * 1.01, price * 0.99, price);
+            indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
+        }
+
+        // Check all computed values are in [0, 1]
+        for (int i = 0; i < indicator.LinesSeries[0].Count; i++)
+        {
+            double val = indicator.LinesSeries[0].GetValue(i);
+            Assert.True(val >= 0.0 && val <= 1.0, $"Value {val} at index {i} out of range");
+        }
+    }
+
+    [Fact]
+    public void FdistIndicator_HighDoF_ValidOutput()
+    {
+        var indicator = new FdistIndicator { D1 = 10, D2 = 10, Period = 5 };
+        indicator.Initialize();
+
+        var now = DateTime.UtcNow;
+        for (int i = 0; i < 5; i++)
+        {
+            indicator.HistoricalData.AddBar(now.AddMinutes(i), 0, 101 + i, 99 + i, 100 + i);
+            indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
+        }
+
+        double val = indicator.LinesSeries[0].GetValue(0);
+        Assert.True(double.IsFinite(val));
+        Assert.True(val >= 0.0 && val <= 1.0);
+    }
+
+    [Fact]
+    public void FdistIndicator_CustomDoF_ShortNameReflects()
+    {
+        var indicator = new FdistIndicator { D1 = 3, D2 = 7, Period = 14 };
+        Assert.Equal("FDIST(3,7,14)", indicator.ShortName);
+    }
+}

lib/numerics/fdist/Fdist.Quantower.cs

@@ -0,0 +1,72 @@
+using System.Drawing;
+using TradingPlatform.BusinessLayer;
+using static QuanTAlib.IndicatorExtensions;
+
+namespace QuanTAlib;
+
+/// <summary>
+/// FDIST (F-Distribution CDF) Quantower indicator.
+/// Computes F(x; d1, d2) = I(d1·x/(d1·x+d2), d1/2, d2/2) applied to a
+/// min-max normalized price series over a rolling lookback window.
+/// </summary>
+public class FdistIndicator : Indicator, IWatchlistIndicator
+{
+    [DataSourceInput]
+    public SourceType Source { get; set; } = SourceType.Close;
+
+    [InputParameter("Numerator DoF (d1)", sortIndex: 0, minimum: 1, maximum: 999, increment: 1)]
+    public int D1 { get; set; } = 1;
+
+    [InputParameter("Denominator DoF (d2)", sortIndex: 1, minimum: 1, maximum: 999, increment: 1)]
+    public int D2 { get; set; } = 1;
+
+    [InputParameter("Period", sortIndex: 2, minimum: 2, maximum: 2000, increment: 1)]
+    public int Period { get; set; } = 14;
+
+    [InputParameter("Show Cold Values", sortIndex: 100)]
+    public bool ShowColdValues { get; set; } = true;
+
+    private Fdist? _fdist;
+    private Func<IHistoryItem, double>? _selector;
+
+    public int MinHistoryDepths => Period;
+    public override string ShortName => $"FDIST({D1},{D2},{Period})";
+
+    public FdistIndicator()
+    {
+        Name = "FDIST - F-Distribution CDF";
+        Description = "Applies the F-Distribution (Fisher-Snedecor) CDF to a min-max normalized price series";
+        SeparateWindow = true;
+        OnBackGround = true;
+    }
+
+    protected override void OnInit()
+    {
+        _fdist = new Fdist(D1, D2, Period);
+        _selector = Source.GetPriceSelector();
+
+        AddLineSeries(new LineSeries("FDist", Color.Cyan, 2, LineStyle.Solid));
+        // Reference level at 0.5 (midpoint)
+        AddLineSeries(new LineSeries("Mid", Color.Gray, 1, LineStyle.Dash));
+    }
+
+    protected override void OnUpdate(UpdateArgs args)
+    {
+        if (_fdist == null || _selector == null)
+        {
+            return;
+        }
+
+        var item = HistoricalData[0, SeekOriginHistory.End];
+        double value = _selector(item);
+        bool isNew = args.IsNewBar();
+
+        TValue input = new(item.TimeLeft, value);
+        _fdist.Update(input, isNew);
+
+        bool isHot = _fdist.IsHot;
+
+        LinesSeries[0].SetValue(_fdist.Last.Value, isHot, ShowColdValues);
+        LinesSeries[1].SetValue(0.5, isHot, ShowColdValues);
+    }
+}

lib/numerics/fdist/Fdist.Tests.cs

@@ -0,0 +1,671 @@
+using Xunit;
+
+namespace QuanTAlib.Tests;
+
+public class FdistTests
+{
+    private const double Tolerance = 1e-10;
+
+    // ─── A) Constructor validation ────────────────────────────────────────────
+
+    [Fact]
+    public void Constructor_DefaultParameters_SetsProperties()
+    {
+        var indicator = new Fdist();
+        Assert.Equal("Fdist(1,1,14)", indicator.Name);
+        Assert.Equal(14, indicator.WarmupPeriod);
+        Assert.False(indicator.IsHot);
+    }
+
+    [Fact]
+    public void Constructor_CustomParameters_SetsName()
+    {
+        var indicator = new Fdist(d1: 5, d2: 10, period: 20);
+        Assert.Equal("Fdist(5,10,20)", indicator.Name);
+        Assert.Equal(20, indicator.WarmupPeriod);
+    }
+
+    [Fact]
+    public void Constructor_D1Zero_ThrowsArgumentException()
+    {
+        var ex = Assert.Throws<ArgumentException>(() => new Fdist(d1: 0));
+        Assert.Equal("d1", ex.ParamName);
+    }
+
+    [Fact]
+    public void Constructor_D1Negative_ThrowsArgumentException()
+    {
+        var ex = Assert.Throws<ArgumentException>(() => new Fdist(d1: -1));
+        Assert.Equal("d1", ex.ParamName);
+    }
+
+    [Fact]
+    public void Constructor_D2Zero_ThrowsArgumentException()
+    {
+        var ex = Assert.Throws<ArgumentException>(() => new Fdist(d2: 0));
+        Assert.Equal("d2", ex.ParamName);
+    }
+
+    [Fact]
+    public void Constructor_D2Negative_ThrowsArgumentException()
+    {
+        var ex = Assert.Throws<ArgumentException>(() => new Fdist(d2: -1));
+        Assert.Equal("d2", ex.ParamName);
+    }
+
+    [Fact]
+    public void Constructor_PeriodOne_ThrowsArgumentException()
+    {
+        var ex = Assert.Throws<ArgumentException>(() => new Fdist(period: 1));
+        Assert.Equal("period", ex.ParamName);
+    }
+
+    [Fact]
+    public void Constructor_PeriodZero_ThrowsArgumentException()
+    {
+        var ex = Assert.Throws<ArgumentException>(() => new Fdist(period: 0));
+        Assert.Equal("period", ex.ParamName);
+    }
+
+    [Fact]
+    public void Constructor_PeriodNegative_ThrowsArgumentException()
+    {
+        var ex = Assert.Throws<ArgumentException>(() => new Fdist(period: -5));
+        Assert.Equal("period", ex.ParamName);
+    }
+
+    // ─── B) Basic calculation ─────────────────────────────────────────────────
+
+    [Fact]
+    public void Update_ReturnsValidTValue()
+    {
+        var indicator = new Fdist(period: 5);
+        var time = DateTime.UtcNow;
+        var input = new TValue(time, 100.0);
+        var result = indicator.Update(input);
+        Assert.Equal(input.Time, result.Time);
+        Assert.True(double.IsFinite(result.Value));
+    }
+
+    [Fact]
+    public void Update_OutputInRange()
+    {
+        var indicator = new Fdist(d1: 5, d2: 5, period: 5);
+        var time = DateTime.UtcNow;
+        double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
+
+        foreach (var p in prices)
+        {
+            indicator.Update(new TValue(time, p));
+            time = time.AddMinutes(1);
+        }
+
+        Assert.True(indicator.Last.Value >= 0.0, "Output must be >= 0");
+        Assert.True(indicator.Last.Value <= 1.0, "Output must be <= 1");
+    }
+
+    [Fact]
+    public void Last_IsAccessible_AfterUpdate()
+    {
+        var indicator = new Fdist(period: 3);
+        var time = DateTime.UtcNow;
+        indicator.Update(new TValue(time, 50.0));
+        Assert.NotEqual(default, indicator.Last);
+    }
+
+    [Fact]
+    public void IsHot_Property_ReflectsWarmup()
+    {
+        var indicator = new Fdist(period: 5);
+        var time = DateTime.UtcNow;
+
+        for (int i = 0; i < 4; i++)
+        {
+            indicator.Update(new TValue(time.AddMinutes(i), 100.0 + i));
+            Assert.False(indicator.IsHot);
+        }
+
+        indicator.Update(new TValue(time.AddMinutes(4), 104.0));
+        Assert.True(indicator.IsHot);
+    }
+
+    [Fact]
+    public void Update_AtMaxOfWindow_ReturnsHighValue()
+    {
+        // When current value equals window max, xNorm=1, xF=10 → F-CDF near 1
+        var indicator = new Fdist(d1: 5, d2: 5, period: 5);
+        var time = DateTime.UtcNow;
+        double[] prices = { 100.0, 102.0, 98.0, 101.0, 110.0 };
+
+        foreach (var p in prices)
+        {
+            indicator.Update(new TValue(time, p));
+            time = time.AddMinutes(1);
+        }
+
+        Assert.True(indicator.Last.Value > 0.9, $"Expected near 1 but got {indicator.Last.Value}");
+    }
+
+    [Fact]
+    public void Update_AtMinOfWindow_ReturnsZero()
+    {
+        // When current value equals window min, xNorm=0, xF=0 → F-CDF(0) = 0
+        var indicator = new Fdist(d1: 5, d2: 5, period: 5);
+        var time = DateTime.UtcNow;
+        double[] prices = { 110.0, 102.0, 98.0, 101.0, 90.0 };
+
+        foreach (var p in prices)
+        {
+            indicator.Update(new TValue(time, p));
+            time = time.AddMinutes(1);
+        }
+
+        Assert.Equal(0.0, indicator.Last.Value, Tolerance);
+    }
+
+    // ─── C) State + bar correction ────────────────────────────────────────────
+
+    [Fact]
+    public void Update_IsNewTrue_AdvancesState()
+    {
+        var indicator = new Fdist(period: 5);
+        var time = DateTime.UtcNow;
+        double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
+
+        foreach (var p in prices)
+        {
+            indicator.Update(new TValue(time, p));
+            time = time.AddMinutes(1);
+        }
+
+        double first = indicator.Last.Value;
+
+        indicator.Update(new TValue(time, 110.0));
+        double second = indicator.Last.Value;
+
+        Assert.NotEqual(first, second, Tolerance);
+    }
+
+    [Fact]
+    public void Update_IsNewFalse_RewritesLastBar()
+    {
+        var indicator = new Fdist(period: 5);
+        var time = DateTime.UtcNow;
+
+        double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
+        foreach (var p in prices)
+        {
+            indicator.Update(new TValue(time, p));
+            time = time.AddMinutes(1);
+        }
+
+        // New bar with value A
+        indicator.Update(new TValue(time, 110.0), true);
+        double valueA = indicator.Last.Value;
+
+        // Correct same bar with value B (min of window → 0)
+        indicator.Update(new TValue(time, 90.0), false);
+        double valueB = indicator.Last.Value;
+
+        Assert.NotEqual(valueA, valueB, Tolerance);
+    }
+
+    [Fact]
+    public void Update_IterativeCorrection_RestoresState()
+    {
+        var time = DateTime.UtcNow;
+        var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 65001);
+        var bars = gbm.Fetch(20, time.Ticks, TimeSpan.FromMinutes(1));
+
+        // Streaming without corrections
+        var straight = new Fdist(d1: 5, d2: 5, period: 5);
+        for (int i = 0; i < bars.Close.Count; i++)
+        {
+            straight.Update(bars.Close[i]);
+        }
+
+        double finalStraight = straight.Last.Value;
+
+        // With corrections (wrong → corrected)
+        var corrected = new Fdist(d1: 5, d2: 5, period: 5);
+        for (int i = 0; i < bars.Close.Count; i++)
+        {
+            corrected.Update(new TValue(bars.Close[i].Time, 999.0), true);
+            corrected.Update(bars.Close[i], false);
+        }
+
+        Assert.Equal(finalStraight, corrected.Last.Value, Tolerance);
+    }
+
+    [Fact]
+    public void Reset_ClearsState()
+    {
+        var indicator = new Fdist(period: 5);
+        var time = DateTime.UtcNow;
+        double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
+
+        foreach (var p in prices)
+        {
+            indicator.Update(new TValue(time, p));
+            time = time.AddMinutes(1);
+        }
+
+        Assert.True(indicator.IsHot);
+
+        indicator.Reset();
+
+        Assert.False(indicator.IsHot);
+        Assert.Equal(default, indicator.Last);
+    }
+
+    // ─── D) Warmup / convergence ──────────────────────────────────────────────
+
+    [Fact]
+    public void IsHot_FlipsAtPeriod()
+    {
+        int period = 10;
+        var indicator = new Fdist(period: period);
+        var time = DateTime.UtcNow;
+
+        for (int i = 0; i < period - 1; i++)
+        {
+            indicator.Update(new TValue(time.AddMinutes(i), 100.0 + i));
+            Assert.False(indicator.IsHot, $"Should not be hot at bar {i + 1}");
+        }
+
+        indicator.Update(new TValue(time.AddMinutes(period - 1), 100.0 + period));
+        Assert.True(indicator.IsHot, "Should be hot after period bars");
+    }
+
+    // ─── E) Robustness ────────────────────────────────────────────────────────
+
+    [Fact]
+    public void Update_NaN_UsesLastValidValue()
+    {
+        var indicator = new Fdist(period: 5);
+        var time = DateTime.UtcNow;
+        double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
+
+        foreach (var p in prices)
+        {
+            indicator.Update(new TValue(time, p));
+            time = time.AddMinutes(1);
+        }
+
+        double before = indicator.Last.Value;
+
+        indicator.Update(new TValue(time, double.NaN));
+        Assert.Equal(before, indicator.Last.Value, Tolerance);
+    }
+
+    [Fact]
+    public void Update_PositiveInfinity_UsesLastValidValue()
+    {
+        var indicator = new Fdist(period: 5);
+        var time = DateTime.UtcNow;
+        double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
+
+        foreach (var p in prices)
+        {
+            indicator.Update(new TValue(time, p));
+            time = time.AddMinutes(1);
+        }
+
+        double before = indicator.Last.Value;
+        indicator.Update(new TValue(time, double.PositiveInfinity));
+        Assert.Equal(before, indicator.Last.Value, Tolerance);
+    }
+
+    [Fact]
+    public void Update_NegativeInfinity_UsesLastValidValue()
+    {
+        var indicator = new Fdist(period: 5);
+        var time = DateTime.UtcNow;
+        double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
+
+        foreach (var p in prices)
+        {
+            indicator.Update(new TValue(time, p));
+            time = time.AddMinutes(1);
+        }
+
+        double before = indicator.Last.Value;
+        indicator.Update(new TValue(time, double.NegativeInfinity));
+        Assert.Equal(before, indicator.Last.Value, Tolerance);
+    }
+
+    [Fact]
+    public void Update_BatchNaN_Stable()
+    {
+        var indicator = new Fdist(period: 5);
+        var time = DateTime.UtcNow;
+
+        double[] prices = { 100.0, double.NaN, 102.0, double.NaN, 98.0, 105.0, 103.0 };
+        foreach (var p in prices)
+        {
+            var result = indicator.Update(new TValue(time, p));
+            Assert.True(double.IsFinite(result.Value), "Output must always be finite");
+            time = time.AddMinutes(1);
+        }
+    }
+
+    [Fact]
+    public void Update_FlatRange_ReturnsStableValue()
+    {
+        // All identical values → range=0 → xNorm=0.5 → xF=5 → F-CDF(5; d1, d2)
+        var indicator = new Fdist(d1: 5, d2: 5, period: 5);
+        var time = DateTime.UtcNow;
+
+        for (int i = 0; i < 10; i++)
+        {
+            indicator.Update(new TValue(time.AddMinutes(i), 100.0));
+        }
+
+        double expected = Fdist.FCdf(5.0, 5, 5);
+        Assert.Equal(expected, indicator.Last.Value, 1e-6);
+    }
+
+    // ─── F) Consistency: batch == streaming == span == eventing ──────────────
+
+    [Fact]
+    public void AllModes_ConsistencyCheck()
+    {
+        int count = 100;
+        int period = 20;
+        var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 65002);
+        var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
+        var source = bars.Close;
+
+        // Streaming
+        var streaming = new Fdist(d1: 5, d2: 5, period: period);
+        for (int i = 0; i < source.Count; i++)
+        {
+            streaming.Update(source[i]);
+        }
+
+        // Batch (TSeries)
+        var batch = Fdist.Batch(source, d1: 5, d2: 5, period: period);
+
+        // Span
+        var rawValues = new double[source.Count];
+        for (int i = 0; i < source.Count; i++)
+        {
+            rawValues[i] = source[i].Value;
+        }
+
+        var spanOutput = new double[source.Count];
+        Fdist.Batch(rawValues, spanOutput, d1: 5, d2: 5, period: period);
+
+        // Eventing
+        var eventResults = new List<double>();
+        var eventSource = new TSeries();
+        var eventIndicator = new Fdist(eventSource, d1: 5, d2: 5, period: period);
+        eventIndicator.Pub += (object? s, in TValueEventArgs e) => eventResults.Add(e.Value.Value);
+
+        for (int i = 0; i < source.Count; i++)
+        {
+            eventSource.Add(source[i], true);
+        }
+
+        // Verify last value matches across all modes
+        double streamingLast = streaming.Last.Value;
+        double batchLast = batch[source.Count - 1].Value;
+        double spanLast = spanOutput[source.Count - 1];
+        double eventLast = eventResults[^1];
+
+        Assert.Equal(streamingLast, batchLast, Tolerance);
+        Assert.Equal(streamingLast, spanLast, Tolerance);
+        Assert.Equal(streamingLast, eventLast, Tolerance);
+    }
+
+    [Fact]
+    public void Streaming_VsBatch_AllValues_Match()
+    {
+        int count = 80;
+        int period = 15;
+        var gbm = new GBM(startPrice: 50, mu: 0.0, sigma: 0.3, seed: 65003);
+        var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
+        var source = bars.Close;
+
+        var streaming = new Fdist(d1: 3, d2: 7, period: period);
+        var streamingVals = new double[count];
+        for (int i = 0; i < count; i++)
+        {
+            streaming.Update(source[i]);
+            streamingVals[i] = streaming.Last.Value;
+        }
+
+        var batch = Fdist.Batch(source, d1: 3, d2: 7, period: period);
+
+        for (int i = 0; i < count; i++)
+        {
+            Assert.Equal(streamingVals[i], batch[i].Value, Tolerance);
+        }
+    }
+
+    // ─── G) Span API tests ────────────────────────────────────────────────────
+
+    [Fact]
+    public void Batch_Span_EmptySource_ThrowsArgumentException()
+    {
+        var ex = Assert.Throws<ArgumentException>(() =>
+            Fdist.Batch([], Array.Empty<double>()));
+        Assert.Equal("source", ex.ParamName);
+    }
+
+    [Fact]
+    public void Batch_Span_OutputTooShort_ThrowsArgumentException()
+    {
+        double[] src = { 1.0, 2.0, 3.0 };
+        double[] dst = new double[2];
+        var ex = Assert.Throws<ArgumentException>(() =>
+            Fdist.Batch(src, dst));
+        Assert.Equal("output", ex.ParamName);
+    }
+
+    [Fact]
+    public void Batch_Span_InvalidD1_ThrowsArgumentException()
+    {
+        double[] src = { 1.0, 2.0, 3.0 };
+        double[] dst = new double[3];
+        var ex = Assert.Throws<ArgumentException>(() =>
+            Fdist.Batch(src, dst, d1: 0));
+        Assert.Equal("d1", ex.ParamName);
+    }
+
+    [Fact]
+    public void Batch_Span_InvalidD2_ThrowsArgumentException()
+    {
+        double[] src = { 1.0, 2.0, 3.0 };
+        double[] dst = new double[3];
+        var ex = Assert.Throws<ArgumentException>(() =>
+            Fdist.Batch(src, dst, d2: 0));
+        Assert.Equal("d2", ex.ParamName);
+    }
+
+    [Fact]
+    public void Batch_Span_InvalidPeriod_ThrowsArgumentException()
+    {
+        double[] src = { 1.0, 2.0, 3.0 };
+        double[] dst = new double[3];
+        var ex = Assert.Throws<ArgumentException>(() =>
+            Fdist.Batch(src, dst, period: 1));
+        Assert.Equal("period", ex.ParamName);
+    }
+
+    [Fact]
+    public void Batch_Span_OutputInRange()
+    {
+        int count = 100;
+        var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 65004);
+        var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
+
+        double[] src = new double[count];
+        for (int i = 0; i < count; i++)
+        {
+            src[i] = bars.Close[i].Value;
+        }
+
+        double[] dst = new double[count];
+        Fdist.Batch(src, dst, d1: 5, d2: 5, period: 20);
+
+        foreach (double v in dst)
+        {
+            Assert.True(v >= 0.0 && v <= 1.0, $"Output {v} out of [0,1] range");
+        }
+    }
+
+    [Fact]
+    public void Batch_Span_HandlesNaN()
+    {
+        double[] src = { 100.0, double.NaN, 102.0, 98.0, 105.0, 103.0 };
+        double[] dst = new double[src.Length];
+        Fdist.Batch(src, dst, period: 5);
+
+        foreach (double v in dst)
+        {
+            Assert.True(double.IsFinite(v), "Span output should always be finite");
+        }
+    }
+
+    [Fact]
+    public void Batch_Span_NoStackOverflow_LargeData()
+    {
+        int count = 5000;
+        double[] src = new double[count];
+        for (int i = 0; i < count; i++)
+        {
+            src[i] = 100.0 + Math.Sin(i * 0.1) * 10.0;
+        }
+
+        double[] dst = new double[count];
+        Fdist.Batch(src, dst, d1: 5, d2: 5, period: 300);
+
+        foreach (double v in dst)
+        {
+            Assert.True(double.IsFinite(v));
+        }
+    }
+
+    [Fact]
+    public void Batch_Span_MatchesStreaming()
+    {
+        int count = 60;
+        var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.25, seed: 65005);
+        var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
+
+        double[] src = new double[count];
+        for (int i = 0; i < count; i++)
+        {
+            src[i] = bars.Close[i].Value;
+        }
+
+        double[] spanOut = new double[count];
+        Fdist.Batch(src, spanOut, d1: 5, d2: 5, period: 14);
+
+        var streaming = new Fdist(d1: 5, d2: 5, period: 14);
+        for (int i = 0; i < count; i++)
+        {
+            streaming.Update(bars.Close[i]);
+            Assert.Equal(streaming.Last.Value, spanOut[i], Tolerance);
+        }
+    }
+
+    // ─── H) Chainability ──────────────────────────────────────────────────────
+
+    [Fact]
+    public void Pub_EventFires()
+    {
+        var indicator = new Fdist(period: 3);
+        int count = 0;
+        indicator.Pub += (object? sender, in TValueEventArgs args) => count++;
+
+        var time = DateTime.UtcNow;
+        indicator.Update(new TValue(time, 100.0));
+        indicator.Update(new TValue(time.AddMinutes(1), 102.0));
+        indicator.Update(new TValue(time.AddMinutes(2), 98.0));
+
+        Assert.Equal(3, count);
+    }
+
+    [Fact]
+    public void Chaining_Constructor_Works()
+    {
+        int period = 5;
+        var source = new TSeries();
+        var indicator = new Fdist(source, period: period);
+
+        var time = DateTime.UtcNow;
+        double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
+
+        foreach (var p in prices)
+        {
+            source.Add(new TValue(time, p), true);
+            time = time.AddMinutes(1);
+        }
+
+        Assert.True(indicator.IsHot);
+        Assert.True(indicator.Last.Value >= 0.0 && indicator.Last.Value <= 1.0);
+    }
+
+    [Fact]
+    public void Pub_EventValue_MatchesLast()
+    {
+        var indicator = new Fdist(period: 5);
+        TValue? lastEvent = null;
+        indicator.Pub += (object? s, in TValueEventArgs e) => lastEvent = e.Value;
+
+        var time = DateTime.UtcNow;
+        double[] prices = { 100.0, 102.0, 98.0, 105.0, 103.0 };
+
+        foreach (var p in prices)
+        {
+            indicator.Update(new TValue(time, p));
+            time = time.AddMinutes(1);
+        }
+
+        Assert.NotNull(lastEvent);
+        Assert.Equal(indicator.Last.Value, lastEvent.Value.Value, Tolerance);
+    }
+
+    // ─── Additional: DoF parameter effects ──────────────────────────────────
+
+    [Fact]
+    public void DifferentDoF_ProduceDifferentResults()
+    {
+        int count = 60;
+        var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 65006);
+        var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
+
+        var ind1 = new Fdist(d1: 1, d2: 1, period: 20);
+        var ind2 = new Fdist(d1: 5, d2: 5, period: 20);
+        var ind3 = new Fdist(d1: 10, d2: 2, period: 20);
+
+        for (int i = 0; i < count; i++)
+        {
+            ind1.Update(bars.Close[i]);
+            ind2.Update(bars.Close[i]);
+            ind3.Update(bars.Close[i]);
+        }
+
+        // Different DoFs produce different CDFs
+        Assert.False(
+            Math.Abs(ind1.Last.Value - ind2.Last.Value) < 1e-6 &&
+            Math.Abs(ind2.Last.Value - ind3.Last.Value) < 1e-6,
+            "Different DoFs should produce at least one distinct result");
+    }
+
+    [Fact]
+    public void Calculate_StaticMethod_ReturnsTuple()
+    {
+        int count = 50;
+        var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 65007);
+        var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
+
+        var (results, instance) = Fdist.Calculate(bars.Close, d1: 5, d2: 5, period: 20);
+
+        Assert.Equal(count, results.Count);
+        Assert.True(instance.IsHot);
+        Assert.Equal(results[^1].Value, instance.Last.Value, Tolerance);
+    }
+}

lib/numerics/fdist/Fdist.Validation.Tests.cs

@@ -0,0 +1,298 @@
+using Xunit;
+using MathNet.Numerics.Distributions;
+
+namespace QuanTAlib.Tests;
+
+/// <summary>
+/// FdistValidationTests — validates against known mathematical properties
+/// of the F-Distribution CDF and against MathNet.Numerics FisherSnedecor.
+/// Known-value tests call Fdist.FCdf directly (bypassing windowing) so results
+/// are exact closed-form comparisons with tolerance 1e-9.
+/// </summary>
+public class FdistValidationTests
+{
+    private const double Tolerance = 1e-9;
+    private const double LooseTolerance = 1e-6;
+
+    // ─── Known-value tests via FCdf static method vs MathNet ─────────────────
+    // F(x; d1, d2) = I(d1*x/(d1*x+d2), d1/2, d2/2)
+
+    [Theory]
+    [InlineData(0.0, 1, 1)]           // F(0; d1, d2) = 0 always
+    [InlineData(0.0, 5, 5)]
+    [InlineData(0.0, 10, 2)]
+    public void FCdf_AtZero_IsAlwaysZero(double x, int d1, int d2)
+    {
+        Assert.Equal(0.0, Fdist.FCdf(x, d1, d2), Tolerance);
+    }
+
+    [Theory]
+    [InlineData(-0.1, 1, 1)]
+    [InlineData(-1.0, 5, 5)]
+    [InlineData(-100.0, 2, 3)]
+    public void FCdf_Negative_IsAlwaysZero(double x, int d1, int d2)
+    {
+        Assert.Equal(0.0, Fdist.FCdf(x, d1, d2), Tolerance);
+    }
+
+    [Theory]
+    [InlineData(100.0, 1, 1, 0.90)]    // F(1,1) is heavy-tailed; F(100) ≈ 0.936
+    [InlineData(100.0, 5, 5, 0.99)]
+    [InlineData(100.0, 10, 2, 0.99)]
+    public void FCdf_AtLargeX_ApproachesOne(double x, int d1, int d2, double minExpected)
+    {
+        double cdf = Fdist.FCdf(x, d1, d2);
+        Assert.True(cdf > minExpected, $"F({x}; {d1},{d2}) = {cdf} should be > {minExpected}");
+    }
+
+    // ─── MathNet.Numerics cross-validation ───────────────────────────────────
+
+    [Theory]
+    [InlineData(1.0, 1, 1)]
+    [InlineData(2.0, 1, 1)]
+    [InlineData(0.5, 2, 3)]
+    [InlineData(1.5, 5, 5)]
+    [InlineData(0.8, 10, 2)]
+    [InlineData(3.0, 3, 7)]
+    [InlineData(0.25, 2, 10)]
+    [InlineData(5.0, 5, 10)]
+    [InlineData(0.1, 1, 5)]
+    [InlineData(2.5, 8, 4)]
+    public void FCdf_VsMathNet_KnownValues(double x, int d1, int d2)
+    {
+        var dist = new FisherSnedecor(d1, d2);
+        double expected = dist.CumulativeDistribution(x);
+        double actual = Fdist.FCdf(x, d1, d2);
+        Assert.Equal(expected, actual, Tolerance);
+    }
+
+    [Theory]
+    [InlineData(1.0, 1, 1)]
+    [InlineData(2.0, 5, 5)]
+    [InlineData(0.5, 2, 3)]
+    [InlineData(1.5, 10, 10)]
+    [InlineData(0.8, 3, 7)]
+    public void StaticCdf_VsMathNet_KnownValues(double x, int d1, int d2)
+    {
+        var dist = new FisherSnedecor(d1, d2);
+        double expected = dist.CumulativeDistribution(x);
+        double actual = Fdist.StaticCdf(x, d1, d2);
+        Assert.Equal(expected, actual, Tolerance);
+    }
+
+    // ─── Monotonicity ─────────────────────────────────────────────────────────
+
+    [Theory]
+    [InlineData(1, 1)]
+    [InlineData(5, 5)]
+    [InlineData(2, 10)]
+    [InlineData(10, 3)]
+    public void FCdf_MonotonicIncreasing(int d1, int d2)
+    {
+        double prev = -1.0;
+
+        for (int i = 0; i <= 30; i++)
+        {
+            double x = i * 0.2;
+            double cdf = Fdist.FCdf(x, d1, d2);
+            Assert.True(cdf >= prev - LooseTolerance,
+                $"CDF not monotonic at x={x} (d1={d1}, d2={d2}): got {cdf}, prev={prev}");
+            prev = cdf;
+        }
+    }
+
+    // ─── Output bounded [0, 1] ────────────────────────────────────────────────
+
+    [Fact]
+    public void FdistCdf_OutputBounded_Zero_To_One()
+    {
+        int count = 200;
+        var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.3, seed: 66001);
+        var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
+
+        var indicator = new Fdist(d1: 5, d2: 5, period: 20);
+
+        for (int i = 0; i < count; i++)
+        {
+            indicator.Update(bars.Close[i]);
+            double v = indicator.Last.Value;
+            Assert.True(v >= 0.0 && v <= 1.0, $"Output {v} at bar {i} out of [0,1]");
+        }
+    }
+
+    // ─── Flat range → F-CDF at 5.0 (xNorm=0.5, xF=5) ────────────────────────
+
+    [Theory]
+    [InlineData(1, 1)]
+    [InlineData(5, 5)]
+    [InlineData(2, 3)]
+    [InlineData(10, 5)]
+    public void FdistCdf_FlatRange_ReturnsCdfAtFive(int d1, int d2)
+    {
+        var ind = new Fdist(d1, d2, period: 20);
+        var time = DateTime.UtcNow;
+
+        for (int i = 0; i < 20; i++)
+        {
+            ind.Update(new TValue(time.AddSeconds(i), 100.0));
+        }
+
+        double expected = Fdist.FCdf(5.0, d1, d2);
+        Assert.Equal(expected, ind.Last.Value, LooseTolerance);
+    }
+
+    // ─── Streaming vs MathNet on raw (unnormalized) values ───────────────────
+
+    [Fact]
+    public void FCdf_MultiplePoints_AllMatchMathNet()
+    {
+        int d1 = 5, d2 = 5;
+        var dist = new FisherSnedecor(d1, d2);
+
+        double[] testX = { 0.0, 0.1, 0.5, 1.0, 2.0, 5.0, 10.0 };
+
+        foreach (double x in testX)
+        {
+            double expected = dist.CumulativeDistribution(x);
+            double actual = Fdist.FCdf(x, d1, d2);
+            Assert.Equal(expected, actual, Tolerance);
+        }
+    }
+
+    // ─── Span batch consistency ───────────────────────────────────────────────
+
+    [Fact]
+    public void Batch_Span_MatchesTSeries()
+    {
+        int count = 150;
+        var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.25, seed: 66002);
+        var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
+
+        double[] rawValues = new double[count];
+        for (int i = 0; i < count; i++)
+        {
+            rawValues[i] = bars.Close[i].Value;
+        }
+
+        var tseriesResult = Fdist.Batch(bars.Close, d1: 5, d2: 5, period: 30);
+        double[] spanResult = new double[count];
+        Fdist.Batch(rawValues, spanResult, d1: 5, d2: 5, period: 30);
+
+        for (int i = 0; i < count; i++)
+        {
+            Assert.Equal(tseriesResult[i].Value, spanResult[i], Tolerance);
+        }
+    }
+
+    // ─── Streaming convergence ────────────────────────────────────────────────
+
+    [Fact]
+    public void FdistCdf_HighPeriod_StillConverges()
+    {
+        int period = 200;
+        var indicator = new Fdist(d1: 5, d2: 5, period: period);
+        var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.3, seed: 66003);
+        var bars = gbm.Fetch(period + 50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
+
+        for (int i = 0; i < bars.Close.Count; i++)
+        {
+            indicator.Update(bars.Close[i]);
+            Assert.True(double.IsFinite(indicator.Last.Value),
+                $"Non-finite output at bar {i}");
+        }
+    }
+
+    // ─── Parameter combos all within [0,1] ────────────────────────────────────
+
+    [Theory]
+    [InlineData(1, 1, 5)]
+    [InlineData(2, 3, 14)]
+    [InlineData(5, 5, 20)]
+    [InlineData(10, 2, 30)]
+    [InlineData(1, 10, 10)]
+    public void FdistCdf_ParameterCombos_OutputBounded(int d1, int d2, int period)
+    {
+        int count = period + 50;
+        var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 66004 + d1 * 100 + d2);
+        var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
+
+        var indicator = new Fdist(d1, d2, period);
+
+        for (int i = 0; i < count; i++)
+        {
+            indicator.Update(bars.Close[i]);
+            double v = indicator.Last.Value;
+            Assert.True(v >= 0.0 && v <= 1.0,
+                $"Out of [0,1] at bar {i}: {v} (d1={d1}, d2={d2}, period={period})");
+        }
+    }
+
+    // ─── Large dataset stable ─────────────────────────────────────────────────
+
+    [Fact]
+    public void FdistCdf_LargeDataset_Stable()
+    {
+        int count = 2000;
+        var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 66005);
+        var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
+
+        var indicator = new Fdist(d1: 5, d2: 5, period: 50);
+
+        for (int i = 0; i < count; i++)
+        {
+            indicator.Update(bars.Close[i]);
+            double v = indicator.Last.Value;
+            Assert.True(double.IsFinite(v) && v >= 0.0 && v <= 1.0,
+                $"Invalid output {v} at bar {i}");
+        }
+    }
+
+    // ─── Complementary property F(x;d1,d2) = 1 - G(1/x;d2,d1) ──────────────
+
+    [Theory]
+    [InlineData(0.5, 5, 5)]
+    [InlineData(1.0, 3, 7)]
+    [InlineData(2.0, 2, 4)]
+    [InlineData(0.25, 4, 8)]
+    public void FCdf_ComplementaryProperty(double x, int d1, int d2)
+    {
+        // F(x; d1, d2) = 1 - F(1/x; d2, d1) — the reciprocal (swapped-DoF) relation
+        double direct = Fdist.FCdf(x, d1, d2);
+        // Use local variables to avoid S2234 name-order false positive when intentionally swapping d1/d2
+        double xRecip = 1.0 / x;
+        int swappedD1 = d2;
+        int swappedD2 = d1;
+        double reciprocal = Fdist.FCdf(xRecip, swappedD1, swappedD2);
+        Assert.Equal(1.0, direct + reciprocal, LooseTolerance);
+    }
+
+    // ─── Symmetric case (d1=d2=n) median near 1 ──────────────────────────────
+
+    [Theory]
+    [InlineData(1)]
+    [InlineData(5)]
+    [InlineData(10)]
+    public void FCdf_SymmetricDoF_MedianIsOne(int n)
+    {
+        // When d1==d2, the F distribution median is 1.0 (approx) → CDF(1) ≈ 0.5
+        double cdf = Fdist.FCdf(1.0, n, n);
+        Assert.Equal(0.5, cdf, 1e-6);
+    }
+
+    // ─── Extreme prices don't blow up ─────────────────────────────────────────
+
+    [Fact]
+    public void FdistCdf_ExtremePrices_StillInRange()
+    {
+        var indicator = new Fdist(d1: 5, d2: 5, period: 20);
+        var time = DateTime.UtcNow;
+
+        for (int i = 0; i < 20; i++)
+        {
+            double price = (i % 2 == 0) ? 1e10 : 1e-10;
+            indicator.Update(new TValue(time.AddMinutes(i), price));
+            double v = indicator.Last.Value;
+            Assert.True(v >= 0.0 && v <= 1.0, $"Out of range at {i}: {v}");
+        }
+    }
+}

lib/numerics/fdist/Fdist.cs

@@ -0,0 +1,311 @@
+// FDIST: F-Distribution CDF
+// Applies the Fisher-Snedecor CDF F(x; d1, d2) = I(d1*x/(d1*x+d2), d1/2, d2/2)
+// to a min-max normalized price series over a rolling lookback window.
+// Pipeline: MinMax normalization → scaling → regularized incomplete beta function.
+// Reuses Betadist.IncompleteBeta internally — no gamma/CF reimplementation.
+
+using System.Runtime.CompilerServices;
+using System.Runtime.InteropServices;
+
+namespace QuanTAlib;
+
+/// <summary>
+/// FDIST: F-Distribution (Fisher-Snedecor) CDF
+/// Computes F(x; d1, d2) = I(d1·x/(d1·x+d2), d1/2, d2/2) applied to a
+/// min-max normalized price series scaled to a positive real via a 10× factor.
+/// </summary>
+/// <remarks>
+/// Key properties:
+/// - Output always in [0, 1]
+/// - Rolling window tracks min/max for normalization; flat range returns F(0.5·10; d1, d2)
+/// - d1/d2 degrees of freedom control the shape: equal df → symmetric response,
+///   d1 &gt; d2 → right-skewed, d1 &lt; d2 → left-skewed
+/// - Reuses <see cref="Betadist.IncompleteBeta"/> — no special-function duplication
+/// - NaN/Infinity inputs use last-valid-value substitution
+/// </remarks>
+[SkipLocalsInit]
+public sealed class Fdist : AbstractBase
+{
+    private readonly int _period;
+    private readonly int _d1;
+    private readonly int _d2;
+    private readonly RingBuffer _buffer;
+
+    [StructLayout(LayoutKind.Auto)]
+    private record struct State(double LastValid);
+    private State _state, _p_state;
+
+    public override bool IsHot => _buffer.Count >= _period;
+
+    /// <summary>
+    /// Initializes a new Fdist indicator.
+    /// </summary>
+    /// <param name="d1">Numerator degrees of freedom (integer ≥ 1, default 1)</param>
+    /// <param name="d2">Denominator degrees of freedom (integer ≥ 1, default 1)</param>
+    /// <param name="period">Lookback window for min-max normalization (default 14)</param>
+    public Fdist(int d1 = 1, int d2 = 1, int period = 14)
+    {
+        if (d1 < 1)
+        {
+            throw new ArgumentException("d1 must be >= 1", nameof(d1));
+        }
+
+        if (d2 < 1)
+        {
+            throw new ArgumentException("d2 must be >= 1", nameof(d2));
+        }
+
+        if (period < 2)
+        {
+            throw new ArgumentException("Period must be >= 2", nameof(period));
+        }
+
+        _d1 = d1;
+        _d2 = d2;
+        _period = period;
+        _buffer = new RingBuffer(period);
+        Name = $"Fdist({d1},{d2},{period})";
+        WarmupPeriod = period;
+        _state = new State(0.5);
+        _p_state = _state;
+    }
+
+    /// <summary>
+    /// Initializes a new Fdist indicator with source for event-based chaining.
+    /// </summary>
+    /// <param name="source">Source indicator for chaining</param>
+    /// <param name="d1">Numerator degrees of freedom (default 1)</param>
+    /// <param name="d2">Denominator degrees of freedom (default 1)</param>
+    /// <param name="period">Lookback window (default 14)</param>
+    public Fdist(ITValuePublisher source, int d1 = 1, int d2 = 1, int period = 14)
+        : this(d1, d2, period)
+    {
+        source.Pub += HandleUpdate;
+    }
+
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    private void HandleUpdate(object? sender, in TValueEventArgs e) => Update(e.Value, e.IsNew);
+
+    /// <summary>
+    /// F-Distribution CDF: F(x; d1, d2) = I(d1·x/(d1·x+d2), d1/2, d2/2).
+    /// Returns 0 for x ≤ 0, uses regularized incomplete beta for x > 0.
+    /// </summary>
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    public static double FCdf(double x, int d1, int d2)
+    {
+        if (x <= 0.0)
+        {
+            return 0.0;
+        }
+
+        double d1d = d1;
+        double d2d = d2;
+        double xBeta = d1d * x / Math.FusedMultiplyAdd(d1d, x, d2d);
+        return Betadist.IncompleteBeta(xBeta, d1d * 0.5, d2d * 0.5);
+    }
+
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    private static (double min, double max) FindMinMax(ReadOnlySpan<double> values)
+    {
+        if (values.Length == 0)
+        {
+            return (double.MaxValue, double.MinValue);
+        }
+
+        double min = values[0];
+        double max = values[0];
+        for (int i = 1; i < values.Length; i++)
+        {
+            double v = values[i];
+            if (v < min)
+            {
+                min = v;
+            }
+
+            if (v > max)
+            {
+                max = v;
+            }
+        }
+
+        return (min, max);
+    }
+
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    public override TValue Update(TValue input, bool isNew = true)
+    {
+        if (isNew)
+        {
+            _p_state = _state;
+        }
+        else
+        {
+            _state = _p_state;
+        }
+
+        double value = input.Value;
+        double result;
+
+        if (double.IsFinite(value))
+        {
+            _buffer.Add(value, isNew);
+
+            var (min, max) = FindMinMax(_buffer.GetSpan());
+            double range = max - min;
+
+            // Flat range → use midpoint 0.5; scale by 10 to spread F-CDF response across (0,∞)
+            double xNorm = range > 0.0 ? (value - min) / range : 0.5;
+
+            // Map [0,1] → [0,10] to place output in a useful part of the F-CDF response curve
+            double xF = xNorm * 10.0;
+
+            result = FCdf(xF, _d1, _d2);
+            _state = new State(result);
+        }
+        else
+        {
+            result = _state.LastValid;
+        }
+
+        Last = new TValue(input.Time, result);
+        PubEvent(Last, isNew);
+        return Last;
+    }
+
+    public override TSeries Update(TSeries source)
+    {
+        var result = new TSeries(source.Count);
+        ReadOnlySpan<double> values = source.Values;
+        ReadOnlySpan<long> times = source.Times;
+
+        for (int i = 0; i < source.Count; i++)
+        {
+            var tv = Update(new TValue(new DateTime(times[i], DateTimeKind.Utc), values[i]), true);
+            result.Add(tv, true);
+        }
+
+        return result;
+    }
+
+    public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
+    {
+        TimeSpan interval = step ?? TimeSpan.FromSeconds(1);
+        DateTime time = DateTime.UtcNow - (interval * source.Length);
+
+        for (int i = 0; i < source.Length; i++)
+        {
+            Update(new TValue(time, source[i]), true);
+            time += interval;
+        }
+    }
+
+    public static TSeries Batch(TSeries source, int d1 = 1, int d2 = 1, int period = 14)
+    {
+        var indicator = new Fdist(d1, d2, period);
+        return indicator.Update(source);
+    }
+
+    /// <summary>
+    /// Calculates F-Distribution CDF over a span of values.
+    /// Uses a sliding window min-max normalization identical to the streaming path.
+    /// </summary>
+    public static void Batch(
+        ReadOnlySpan<double> source, Span<double> output,
+        int d1 = 1, int d2 = 1, int period = 14)
+    {
+        if (source.Length == 0)
+        {
+            throw new ArgumentException("Source cannot be empty", nameof(source));
+        }
+
+        if (output.Length < source.Length)
+        {
+            throw new ArgumentException("Output length must be >= source length", nameof(output));
+        }
+
+        if (d1 < 1)
+        {
+            throw new ArgumentException("d1 must be >= 1", nameof(d1));
+        }
+
+        if (d2 < 1)
+        {
+            throw new ArgumentException("d2 must be >= 1", nameof(d2));
+        }
+
+        if (period < 2)
+        {
+            throw new ArgumentException("Period must be >= 2", nameof(period));
+        }
+
+        double lastValid = 0.5;
+
+        for (int i = 0; i < source.Length; i++)
+        {
+            double val = source[i];
+            if (!double.IsFinite(val))
+            {
+                output[i] = lastValid;
+                continue;
+            }
+
+            int start = Math.Max(0, i - period + 1);
+
+            double min = double.PositiveInfinity;
+            double max = double.NegativeInfinity;
+
+            for (int j = start; j <= i; j++)
+            {
+                double v = source[j];
+                if (double.IsFinite(v))
+                {
+                    if (v < min)
+                    {
+                        min = v;
+                    }
+
+                    if (v > max)
+                    {
+                        max = v;
+                    }
+                }
+            }
+
+            if (!double.IsFinite(min) || !double.IsFinite(max))
+            {
+                output[i] = lastValid;
+                continue;
+            }
+
+            double range = max - min;
+            double xNorm = range > 0.0 ? (val - min) / range : 0.5;
+            double xF = xNorm * 10.0;
+
+            double result = FCdf(xF, d1, d2);
+            lastValid = result;
+            output[i] = result;
+        }
+    }
+
+    /// <summary>
+    /// Pure static F-CDF helper. Identical to <see cref="FCdf"/> but exposed
+    /// with a more explicit name for downstream consumers and validation tests.
+    /// </summary>
+    public static double StaticCdf(double x, int d1, int d2) => FCdf(x, d1, d2);
+
+    public static (TSeries Results, Fdist Indicator) Calculate(
+        TSeries source, int d1 = 1, int d2 = 1, int period = 14)
+    {
+        var indicator = new Fdist(d1, d2, period);
+        TSeries results = indicator.Update(source);
+        return (results, indicator);
+    }
+
+    public override void Reset()
+    {
+        _buffer.Clear();
+        _state = new State(0.5);
+        _p_state = _state;
+        Last = default;
+    }
+}