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adding missing validations

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Miha Kralj
2026-02-26 09:59:44 -08:00
parent 467a8c1cef
commit 9ab37c1200
231 changed files with 60015 additions and 302 deletions
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lib/numerics/lognormdist/Lognormdist.Quantower.Tests.cs
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using Xunit;
using TradingPlatform.BusinessLayer;
namespace QuanTAlib.Tests;
public class LognormdistIndicatorTests
{
[Fact]
public void LognormdistIndicator_Constructor_SetsDefaults()
{
var indicator = new LognormdistIndicator();
Assert.Equal(SourceType.Close, indicator.Source);
Assert.Equal(0.0, indicator.Mu);
Assert.Equal(1.0, indicator.Sigma);
Assert.Equal(14, indicator.Period);
Assert.True(indicator.ShowColdValues);
Assert.Equal("LOGNORMDIST - Log-Normal Distribution CDF", indicator.Name);
Assert.True(indicator.SeparateWindow);
Assert.True(indicator.OnBackGround);
}
[Fact]
public void LognormdistIndicator_MinHistoryDepths_EqualsPeriod()
{
var indicator = new LognormdistIndicator { Period = 30 };
Assert.Equal(30, indicator.MinHistoryDepths);
}
[Fact]
public void LognormdistIndicator_ShortName_IsCorrect()
{
var indicator = new LognormdistIndicator { Mu = -1.0, Sigma = 0.5, Period = 20 };
Assert.Equal("LOGNORMDIST(-1.00,0.50,20)", indicator.ShortName);
}
[Fact]
public void LognormdistIndicator_Initialize_CreatesTwoLineSeries()
{
var indicator = new LognormdistIndicator();
indicator.Initialize();
Assert.Equal(2, indicator.LinesSeries.Count);
Assert.Equal("LogNormDist", indicator.LinesSeries[0].Name);
Assert.Equal("Mid", indicator.LinesSeries[1].Name);
}
[Fact]
public void LognormdistIndicator_ProcessUpdate_HistoricalBar_ComputesValue()
{
var indicator = new LognormdistIndicator { 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 5 bars (= period), 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 LognormdistIndicator_ProcessUpdate_NewBar_AddsNewValue()
{
var indicator = new LognormdistIndicator { 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 LognormdistIndicator_ProcessUpdate_NewTick_ProcessesWithoutError()
{
var indicator = new LognormdistIndicator { 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 LognormdistIndicator_MidLine_IsAlwaysHalf()
{
var indicator = new LognormdistIndicator { 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 LognormdistIndicator_DifferentSourceType_Works()
{
var indicator = new LognormdistIndicator { 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 LognormdistIndicator_OutputInRange_AfterManyBars()
{
var indicator = new LognormdistIndicator { Period = 20 };
indicator.Initialize();
var now = DateTime.UtcNow;
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 86001);
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 LognormdistIndicator_CustomMuSigma_ShortNameReflects()
{
var indicator = new LognormdistIndicator { Mu = 0.5, Sigma = 1.5, Period = 14 };
Assert.Equal("LOGNORMDIST(0.50,1.50,14)", indicator.ShortName);
}
[Fact]
public void LognormdistIndicator_DefaultShortName_IsCorrect()
{
var indicator = new LognormdistIndicator();
Assert.Equal("LOGNORMDIST(0.00,1.00,14)", indicator.ShortName);
}
[Fact]
public void LognormdistIndicator_FlatPrices_OutputIsFinite()
{
var indicator = new LognormdistIndicator { Period = 5, Mu = 0.0, Sigma = 1.0 };
indicator.Initialize();
var now = DateTime.UtcNow;
for (int i = 0; i < 10; i++)
{
indicator.HistoricalData.AddBar(now.AddMinutes(i), 0, 101, 99, 100);
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);
}
}
lib/numerics/lognormdist/Lognormdist.Quantower.cs
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using System.Drawing;
using TradingPlatform.BusinessLayer;
using static QuanTAlib.IndicatorExtensions;
namespace QuanTAlib;
/// <summary>
/// LOGNORMDIST (Log-Normal Distribution CDF) Quantower indicator.
/// Computes F(x; μ, σ) = Φ((ln(x) - μ) / σ) applied to a min-max normalized
/// price series over a rolling lookback window.
/// </summary>
public class LognormdistIndicator : Indicator, IWatchlistIndicator
{
[DataSourceInput]
public SourceType Source { get; set; } = SourceType.Close;
[InputParameter("Log-Mean (μ)", sortIndex: 0, minimum: -100.0, maximum: 100.0, increment: 0.1, decimalPlaces: 3)]
public double Mu { get; set; } = 0.0;
[InputParameter("Log-Std (σ)", sortIndex: 1, minimum: 0.001, maximum: 100.0, increment: 0.1, decimalPlaces: 3)]
public double Sigma { get; set; } = 1.0;
[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 Lognormdist? _lognormdist;
private Func<IHistoryItem, double>? _selector;
public int MinHistoryDepths => Period;
public override string ShortName => $"LOGNORMDIST({Mu:F2},{Sigma:F2},{Period})";
public LognormdistIndicator()
{
Name = "LOGNORMDIST - Log-Normal Distribution CDF";
Description = "Applies the log-normal CDF to a min-max normalized price series";
SeparateWindow = true;
OnBackGround = true;
}
protected override void OnInit()
{
_lognormdist = new Lognormdist(Mu, Sigma, Period);
_selector = Source.GetPriceSelector();
AddLineSeries(new LineSeries("LogNormDist", Color.Yellow, 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 (_lognormdist == null || _selector == null)
{
return;
}
var item = HistoricalData[0, SeekOriginHistory.End];
double value = _selector(item);
bool isNew = args.IsNewBar();
TValue input = new(item.TimeLeft, value);
_lognormdist.Update(input, isNew);
bool isHot = _lognormdist.IsHot;
LinesSeries[0].SetValue(_lognormdist.Last.Value, isHot, ShowColdValues);
LinesSeries[1].SetValue(0.5, isHot, ShowColdValues);
}
}
lib/numerics/lognormdist/Lognormdist.Tests.cs
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using Xunit;
namespace QuanTAlib.Tests;
public class LognormdistTests
{
private const double Tolerance = 1e-10;
// ─── A) Constructor validation ────────────────────────────────────────────
[Fact]
public void Constructor_DefaultParameters_SetsProperties()
{
var indicator = new Lognormdist();
Assert.Equal("Lognormdist(0.00,1.00,14)", indicator.Name);
Assert.Equal(14, indicator.WarmupPeriod);
Assert.False(indicator.IsHot);
}
[Fact]
public void Constructor_CustomParameters_SetsName()
{
var indicator = new Lognormdist(mu: -1.0, sigma: 0.5, period: 20);
Assert.Equal("Lognormdist(-1.00,0.50,20)", indicator.Name);
Assert.Equal(20, indicator.WarmupPeriod);
}
[Fact]
public void Constructor_ZeroSigma_ThrowsArgumentException()
{
var ex = Assert.Throws<ArgumentException>(() => new Lognormdist(sigma: 0.0));
Assert.Equal("sigma", ex.ParamName);
}
[Fact]
public void Constructor_NegativeSigma_ThrowsArgumentException()
{
var ex = Assert.Throws<ArgumentException>(() => new Lognormdist(sigma: -1.0));
Assert.Equal("sigma", ex.ParamName);
}
[Fact]
public void Constructor_PeriodOne_ThrowsArgumentException()
{
var ex = Assert.Throws<ArgumentException>(() => new Lognormdist(period: 1));
Assert.Equal("period", ex.ParamName);
}
[Fact]
public void Constructor_ZeroPeriod_ThrowsArgumentException()
{
var ex = Assert.Throws<ArgumentException>(() => new Lognormdist(period: 0));
Assert.Equal("period", ex.ParamName);
}
[Fact]
public void Constructor_NegativePeriod_ThrowsArgumentException()
{
var ex = Assert.Throws<ArgumentException>(() => new Lognormdist(period: -1));
Assert.Equal("period", ex.ParamName);
}
// ─── B) Basic calculation ─────────────────────────────────────────────────
[Fact]
public void Update_ReturnsValidTValue()
{
var indicator = new Lognormdist(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 Lognormdist(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 Lognormdist(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 Lognormdist(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 Name_IsAccessible()
{
var indicator = new Lognormdist(mu: 0.0, sigma: 1.0, period: 14);
Assert.Equal("Lognormdist(0.00,1.00,14)", indicator.Name);
}
// ─── C) State + bar correction ────────────────────────────────────────────
[Fact]
public void Update_IsNewTrue_AdvancesState()
{
var indicator = new Lognormdist(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), true);
double second = indicator.Last.Value;
Assert.NotEqual(first, second, Tolerance);
}
[Fact]
public void Update_IsNewFalse_RewritesLastBar()
{
var indicator = new Lognormdist(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 very different value B
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: 82001);
var bars = gbm.Fetch(20, time.Ticks, TimeSpan.FromMinutes(1));
// Streaming without corrections
var straight = new Lognormdist(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 Lognormdist(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 Lognormdist(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 Lognormdist(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");
}
[Fact]
public void WarmupPeriod_EqualsConstructorPeriod()
{
var indicator = new Lognormdist(period: 25);
Assert.Equal(25, indicator.WarmupPeriod);
}
// ─── E) Robustness ────────────────────────────────────────────────────────
[Fact]
public void Update_NaN_UsesLastValidValue()
{
var indicator = new Lognormdist(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 Lognormdist(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 Lognormdist(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 Lognormdist(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_FlatValues_OutputIsFinite()
{
// When all values identical, range=0 → x=0.5 → finite CDF output
var indicator = new Lognormdist(mu: 0.0, sigma: 1.0, period: 5);
var time = DateTime.UtcNow;
for (int i = 0; i < 10; i++)
{
var result = indicator.Update(new TValue(time.AddMinutes(i), 100.0));
Assert.True(double.IsFinite(result.Value));
}
}
// ─── 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: 82002);
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
var source = bars.Close;
// Streaming
var streaming = new Lognormdist(period: period);
for (int i = 0; i < source.Count; i++)
{
streaming.Update(source[i]);
}
// Batch (TSeries)
var batch = Lognormdist.Batch(source, 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];
Lognormdist.Batch(rawValues, spanOutput, period: period);
// Eventing
var eventResults = new List<double>();
var eventSource = new TSeries();
var eventIndicator = new Lognormdist(eventSource, 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: 82003);
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
var source = bars.Close;
var streaming = new Lognormdist(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 = Lognormdist.Batch(source, 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>(() =>
Lognormdist.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>(() =>
Lognormdist.Batch(src, dst));
Assert.Equal("output", ex.ParamName);
}
[Fact]
public void Batch_Span_InvalidSigma_ThrowsArgumentException()
{
double[] src = { 1.0, 2.0, 3.0 };
double[] dst = new double[3];
var ex = Assert.Throws<ArgumentException>(() =>
Lognormdist.Batch(src, dst, sigma: 0.0));
Assert.Equal("sigma", ex.ParamName);
}
[Fact]
public void Batch_Span_NegativeSigma_ThrowsArgumentException()
{
double[] src = { 1.0, 2.0, 3.0 };
double[] dst = new double[3];
var ex = Assert.Throws<ArgumentException>(() =>
Lognormdist.Batch(src, dst, sigma: -1.0));
Assert.Equal("sigma", 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>(() =>
Lognormdist.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: 82004);
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];
Lognormdist.Batch(src, dst, 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];
Lognormdist.Batch(src, dst, period: 4);
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];
Lognormdist.Batch(src, dst, 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: 82005);
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];
Lognormdist.Batch(src, spanOut, period: 14);
var streaming = new Lognormdist(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 Lognormdist(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 Lognormdist(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 Lognormdist(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: Parameter effects and Calculate ──────────────────────────
[Fact]
public void DifferentSigma_ProduceDifferentResults()
{
int count = 60;
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 82006);
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
var ind1 = new Lognormdist(mu: 0.0, sigma: 0.5, period: 20);
var ind2 = new Lognormdist(mu: 0.0, sigma: 1.0, period: 20);
var ind3 = new Lognormdist(mu: 0.0, sigma: 3.0, period: 20);
for (int i = 0; i < count; i++)
{
ind1.Update(bars.Close[i]);
ind2.Update(bars.Close[i]);
ind3.Update(bars.Close[i]);
}
Assert.InRange(ind1.Last.Value, 0.0, 1.0);
Assert.InRange(ind2.Last.Value, 0.0, 1.0);
Assert.InRange(ind3.Last.Value, 0.0, 1.0);
Assert.NotEqual(ind1.Last.Value, ind3.Last.Value, 1e-4);
}
[Fact]
public void Calculate_StaticMethod_ReturnsTuple()
{
int count = 50;
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 82007);
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
var (results, instance) = Lognormdist.Calculate(bars.Close, period: 20);
Assert.Equal(count, results.Count);
Assert.True(instance.IsHot);
Assert.Equal(results[^1].Value, instance.Last.Value, Tolerance);
}
}
lib/numerics/lognormdist/Lognormdist.Validation.Tests.cs
+363
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using Xunit;
using MathNet.Numerics.Distributions;
namespace QuanTAlib.Tests;
/// <summary>
/// LognormdistValidationTests — validates against known mathematical properties
/// of the Log-Normal Distribution CDF and against MathNet.Numerics LogNormal.
/// Known-value tests call Lognormdist.StaticCdf / LogNormalCdf directly (bypassing windowing).
/// Tolerance 1e-6 for the 5-term A&amp;S 7.1.26 approximation (max error ~1.5e-7;
/// using 1e-6 to give headroom). MathNet cross-validation uses 1e-6.
/// </summary>
public class LognormdistValidationTests
{
private const double ApproxTolerance = 1e-6; // A&S 7.1.26 five-term max error ~1.5e-7
private const double LooseTolerance = 1e-4;
// ─── Boundary: x <= 0 → CDF = 0 ──────────────────────────────────────────
[Theory]
[InlineData(0.0, 0.0, 1.0)]
[InlineData(-1.0, 0.0, 1.0)]
[InlineData(-5.0, 0.0, 1.0)]
[InlineData(0.0, -1.0, 0.5)]
public void StaticCdf_NonPositiveX_IsZero(double x, double mu, double sigma)
{
double cdf = Lognormdist.StaticCdf(x, mu, sigma);
Assert.Equal(0.0, cdf, 1e-10);
}
// ─── CDF always in [0, 1] ─────────────────────────────────────────────────
[Theory]
[InlineData(0.001, 0.0, 1.0)]
[InlineData(0.5, 0.0, 1.0)]
[InlineData(1.0, 0.0, 1.0)]
[InlineData(10.0, 0.0, 1.0)]
[InlineData(1000.0, 0.0, 1.0)]
[InlineData(0.1, -1.0, 0.5)]
[InlineData(2.0, 1.0, 2.0)]
public void StaticCdf_OutputBounded_ZeroToOne(double x, double mu, double sigma)
{
double cdf = Lognormdist.StaticCdf(x, mu, sigma);
Assert.True(cdf >= 0.0 && cdf <= 1.0,
$"CDF({x},{mu},{sigma})={cdf} out of [0,1]");
}
// ─── Median: F(exp(μ)) = 0.5 ─────────────────────────────────────────────
[Theory]
[InlineData(0.0, 1.0)]
[InlineData(1.0, 1.0)]
[InlineData(-2.0, 0.5)]
[InlineData(0.0, 2.0)]
[InlineData(3.0, 0.25)]
public void StaticCdf_AtMedian_IsHalf(double mu, double sigma)
{
// Median of LogNormal(μ, σ²) = exp(μ)
double median = Math.Exp(mu);
double cdf = Lognormdist.StaticCdf(median, mu, sigma);
Assert.Equal(0.5, cdf, ApproxTolerance);
}
// ─── Standard LogNormal(0,1) known percentiles ────────────────────────────
[Fact]
public void StaticCdf_LogNormal01_At1_IsHalf()
{
// ln(1)=0=μ, so z=0 → Φ(0)=0.5
double cdf = Lognormdist.StaticCdf(1.0, 0.0, 1.0);
Assert.Equal(0.5, cdf, ApproxTolerance);
}
[Fact]
public void StaticCdf_LogNormal01_AtExpPlusSigma_Is0841()
{
// F(exp(μ+σ)) = F(exp(1)) = Φ(1) ≈ 0.8413
double x = Math.Exp(1.0); // exp(μ+σ) with μ=0, σ=1
double cdf = Lognormdist.StaticCdf(x, 0.0, 1.0);
Assert.Equal(0.8413, cdf, 3);
}
[Fact]
public void StaticCdf_LogNormal01_AtExpMinusSigma_Is0159()
{
// F(exp(μ-σ)) = F(exp(-1)) = Φ(-1) ≈ 0.1587
double x = Math.Exp(-1.0); // exp(μ-σ) with μ=0, σ=1
double cdf = Lognormdist.StaticCdf(x, 0.0, 1.0);
Assert.Equal(0.1587, cdf, 3);
}
[Fact]
public void StaticCdf_LogNormal01_AtExpPlus2Sigma_Is0977()
{
// F(exp(μ+2σ)) = Φ(2) ≈ 0.9772
double x = Math.Exp(2.0);
double cdf = Lognormdist.StaticCdf(x, 0.0, 1.0);
Assert.Equal(0.9772, cdf, 3);
}
[Fact]
public void StaticCdf_LogNormal01_AtExpMinus2Sigma_Is0023()
{
// F(exp(-2)) = Φ(-2) ≈ 0.0228
double x = Math.Exp(-2.0);
double cdf = Lognormdist.StaticCdf(x, 0.0, 1.0);
Assert.Equal(0.0228, cdf, 3);
}
// ─── Monotonicity for x > 0 ───────────────────────────────────────────────
[Theory]
[InlineData(0.0, 1.0)]
[InlineData(1.0, 0.5)]
[InlineData(-1.0, 2.0)]
public void StaticCdf_MonotonicIncreasing_ForPositiveX(double mu, double sigma)
{
double prev = -1.0;
for (int i = -20; i <= 20; i++)
{
double x = Math.Exp(i * 0.25); // x in (exp(-5), exp(5)) — always positive
double cdf = Lognormdist.StaticCdf(x, mu, sigma);
Assert.True(cdf >= prev - LooseTolerance,
$"CDF not monotonic at x={x} (μ={mu}, σ={sigma}): got {cdf}, prev={prev}");
prev = cdf;
}
}
// ─── MathNet.Numerics cross-validation ────────────────────────────────────
[Theory]
[InlineData(1.0, 0.0, 1.0)]
[InlineData(2.0, 0.0, 1.0)]
[InlineData(0.5, 0.0, 1.0)]
[InlineData(0.1, 0.0, 1.0)]
[InlineData(10.0, 0.0, 1.0)]
[InlineData(1.0, 1.0, 1.0)]
[InlineData(0.5, 0.0, 2.0)]
[InlineData(3.0, 2.0, 0.5)]
[InlineData(0.1, -1.0, 0.5)]
[InlineData(1.0, 0.0, 0.25)]
public void StaticCdf_VsMathNet_KnownValues(double x, double mu, double sigma)
{
var dist = new LogNormal(mu, sigma);
double expected = dist.CumulativeDistribution(x);
double actual = Lognormdist.StaticCdf(x, mu, sigma);
Assert.Equal(expected, actual, ApproxTolerance);
}
[Theory]
[InlineData(1.0, 0.0, 1.0)]
[InlineData(2.718, 0.0, 1.0)]
[InlineData(0.368, 0.0, 1.0)]
[InlineData(1.0, 1.0, 2.0)]
[InlineData(5.0, 1.0, 0.5)]
public void LogNormalCdf_VsMathNet_KnownValues(double x, double mu, double sigma)
{
var dist = new LogNormal(mu, sigma);
double expected = dist.CumulativeDistribution(x);
double actual = Lognormdist.LogNormalCdf(x, mu, sigma);
Assert.Equal(expected, actual, ApproxTolerance);
}
// ─── Multiple points all match MathNet ───────────────────────────────────
[Fact]
public void StaticCdf_MultiplePoints_AllMatchMathNet()
{
double mu = 0.0, sigma = 1.0;
var dist = new LogNormal(mu, sigma);
double[] testX = { 0.01, 0.1, 0.25, 0.5, 1.0, 2.0, 5.0, 10.0, 50.0, 100.0 };
foreach (double x in testX)
{
double expected = dist.CumulativeDistribution(x);
double actual = Lognormdist.StaticCdf(x, mu, sigma);
Assert.Equal(expected, actual, ApproxTolerance);
}
}
// ─── Output bounded [0,1] with streaming indicator ───────────────────────
[Fact]
public void LognormdistCdf_OutputBounded_Zero_To_One()
{
int count = 200;
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.3, seed: 85001);
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
var indicator = new Lognormdist(mu: 0.0, sigma: 1.0, 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 → finite output ──────────────────────────────────────────
[Fact]
public void LognormdistCdf_FlatRange_IsFinite()
{
var ind = new Lognormdist(mu: 0.0, sigma: 1.0, period: 10);
var time = DateTime.UtcNow;
for (int i = 0; i < 10; i++)
{
ind.Update(new TValue(time.AddSeconds(i), 100.0));
}
Assert.True(double.IsFinite(ind.Last.Value));
Assert.True(ind.Last.Value >= 0.0 && ind.Last.Value <= 1.0);
}
// ─── NormalCdf internal correctness ──────────────────────────────────────
[Fact]
public void NormalCdf_AtZero_IsHalf()
{
double v = Lognormdist.NormalCdf(0.0);
Assert.Equal(0.5, v, ApproxTolerance);
}
[Fact]
public void NormalCdf_AtLargePositive_ApproachesOne()
{
double v = Lognormdist.NormalCdf(10.0);
Assert.True(v > 0.9999, $"Φ(10) should approach 1, got {v}");
}
[Fact]
public void NormalCdf_AtLargeNegative_ApproachesZero()
{
double v = Lognormdist.NormalCdf(-10.0);
Assert.True(v < 1e-4, $"Φ(-10) should approach 0, got {v}");
}
[Fact]
public void NormalCdf_IsSymmetric()
{
// Φ(z) + Φ(-z) = 1
double[] testZ = { 0.5, 1.0, 1.5, 2.0, 3.0 };
foreach (double z in testZ)
{
double pos = Lognormdist.NormalCdf(z);
double neg = Lognormdist.NormalCdf(-z);
Assert.Equal(1.0, pos + neg, ApproxTolerance);
}
}
// ─── Parameter combos all within [0,1] ────────────────────────────────────
[Theory]
[InlineData(0.0, 1.0, 5)]
[InlineData(0.0, 1.0, 14)]
[InlineData(-1.0, 0.5, 10)]
[InlineData(0.0, 2.0, 20)]
[InlineData(1.0, 1.0, 30)]
public void LognormdistCdf_ParameterCombos_OutputBounded(double mu, double sigma, int period)
{
int count = period + 50;
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 85002 + (int)(sigma * 100));
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
var indicator = new Lognormdist(mu, sigma, 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} (μ={mu}, σ={sigma}, period={period})");
}
}
// ─── Large dataset stable ─────────────────────────────────────────────────
[Fact]
public void LognormdistCdf_LargeDataset_Stable()
{
int count = 2000;
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 85003);
var bars = gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
var indicator = new Lognormdist(mu: 0.0, sigma: 1.0, 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}");
}
}
// ─── Span batch vs TSeries consistency ────────────────────────────────────
[Fact]
public void Batch_Span_MatchesTSeries()
{
int count = 150;
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.25, seed: 85004);
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 = Lognormdist.Batch(bars.Close, period: 30);
double[] spanResult = new double[count];
Lognormdist.Batch(rawValues, spanResult, period: 30);
for (int i = 0; i < count; i++)
{
Assert.Equal(tseriesResult[i].Value, spanResult[i], 1e-10);
}
}
// ─── High-period streaming convergence ────────────────────────────────────
[Fact]
public void LognormdistCdf_HighPeriod_StillConverges()
{
int period = 200;
var indicator = new Lognormdist(mu: 0.0, sigma: 1.0, period: period);
var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.3, seed: 85005);
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}");
}
}
// ─── MathNet parameter sweep ──────────────────────────────────────────────
[Theory]
[InlineData(0.0, 0.5)]
[InlineData(0.0, 1.0)]
[InlineData(0.0, 2.0)]
[InlineData(1.0, 1.0)]
[InlineData(-1.0, 0.5)]
public void StaticCdf_SweepX_VsMathNet(double mu, double sigma)
{
var dist = new LogNormal(mu, sigma);
double[] xs = { 0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 2.0, 5.0, 10.0, 20.0 };
foreach (double x in xs)
{
double expected = dist.CumulativeDistribution(x);
double actual = Lognormdist.StaticCdf(x, mu, sigma);
Assert.Equal(expected, actual, ApproxTolerance);
}
}
}
lib/numerics/lognormdist/Lognormdist.cs
+327
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@@ -0,0 +1,327 @@
// LOGNORMDIST: Log-Normal Distribution CDF
// Applies F(x; μ, σ) = Φ((ln(x) - μ) / σ) to a min-max normalized price series
// over a rolling lookback window.
// Pipeline: MinMax normalization → floor at 1e-10 → log-standardization → normal CDF.
using System.Runtime.CompilerServices;
using System.Runtime.InteropServices;
namespace QuanTAlib;
/// <summary>
/// LOGNORMDIST: Log-Normal Distribution CDF
/// Computes F(x; μ, σ) = Φ((ln(x) - μ) / σ) applied to a min-max normalized
/// price series over a rolling lookback window.
/// </summary>
/// <remarks>
/// Key properties:
/// - Output always in [0, 1]
/// - Rolling window tracks min/max for normalization; flat range uses x=0.5
/// - min-max x is floored at 1e-10 before log to prevent ln(0)
/// - μ shifts the inflection point of the S-curve along the logarithmic axis
/// - σ controls steepness: small σ → sharp transition, large σ → gradual
/// - Normal CDF: Abramowitz &amp; Stegun 7.1.26 (5-term), max error ~1.5e-7
/// - NaN/Infinity inputs use last-valid-value substitution
/// </remarks>
[SkipLocalsInit]
public sealed class Lognormdist : AbstractBase
{
private readonly int _period;
private readonly double _mu;
private readonly double _invSigma; // precomputed: 1 / sigma
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 Lognormdist indicator.
/// </summary>
/// <param name="mu">Log-mean μ — mean of ln(X) (default 0.0)</param>
/// <param name="sigma">Log-std σ &gt; 0 — std dev of ln(X) (default 1.0)</param>
/// <param name="period">Lookback window for min-max normalization (default 14)</param>
public Lognormdist(double mu = 0.0, double sigma = 1.0, int period = 14)
{
if (sigma <= 0.0)
{
throw new ArgumentException("Sigma must be > 0", nameof(sigma));
}
if (period < 2)
{
throw new ArgumentException("Period must be >= 2", nameof(period));
}
_mu = mu;
_period = period;
_invSigma = 1.0 / sigma;
_buffer = new RingBuffer(period);
Name = $"Lognormdist({mu:F2},{sigma:F2},{period})";
WarmupPeriod = period;
_state = new State(0.0);
_p_state = _state;
}
/// <summary>
/// Initializes a new Lognormdist indicator with source for event-based chaining.
/// </summary>
/// <param name="source">Source indicator for chaining</param>
/// <param name="mu">Log-mean μ (default 0.0)</param>
/// <param name="sigma">Log-std σ &gt; 0 (default 1.0)</param>
/// <param name="period">Lookback window (default 14)</param>
public Lognormdist(ITValuePublisher source, double mu = 0.0, double sigma = 1.0, int period = 14)
: this(mu, sigma, period)
{
source.Pub += HandleUpdate;
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private void HandleUpdate(object? sender, in TValueEventArgs e) => Update(e.Value, e.IsNew);
/// <summary>
/// Standard normal CDF Φ(z) via Abramowitz &amp; Stegun 7.1.26 (5-term, max error ~1.5e-7).
/// Φ(z) = 1 - φ(|z|) * (b1*t + b2*t² + b3*t³ + b4*t⁴ + b5*t⁵), t = 1/(1 + 0.2316419|z|)
/// </summary>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
internal static double NormalCdf(double z)
{
const double P = 0.2316419;
const double B1 = 0.319381530;
const double B2 = -0.356563782;
const double B3 = 1.781477937;
const double B4 = -1.821255978;
const double B5 = 1.330274429;
double az = Math.Abs(z);
double t = 1.0 / Math.FusedMultiplyAdd(P, az, 1.0);
double phi = Math.Exp(-0.5 * az * az) * (1.0 / Math.Sqrt(2.0 * Math.PI));
double poly = ((((Math.FusedMultiplyAdd(B5, t, B4) * t) + B3) * t + B2) * t + B1) * t;
double cdf = 1.0 - phi * poly;
return z >= 0.0 ? cdf : 1.0 - cdf;
}
/// <summary>
/// Log-Normal CDF: F(x; μ, σ) = Φ((ln(x) - μ) / σ) for x &gt; 0, else 0.
/// </summary>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public static double LogNormalCdf(double x, double mu, double sigma)
{
if (x <= 0.0)
{
return 0.0;
}
double z = (Math.Log(x) - mu) / sigma;
return NormalCdf(z);
}
/// <summary>
/// Pure static CDF helper — identical to <see cref="LogNormalCdf"/> with an explicit name
/// for downstream consumers and validation tests.
/// </summary>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public static double StaticCdf(double x, double mu, double sigma) => LogNormalCdf(x, mu, sigma);
[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 to avoid degenerate output
double x = range > 0.0 ? (value - min) / range : 0.5;
// Floor to prevent ln(0); safeX in (0, 1]
double safeX = x < 1e-10 ? 1e-10 : x;
// Log-standardize: z = (ln(safeX) - mu) / sigma
double z = Math.FusedMultiplyAdd(Math.Log(safeX), _invSigma, -_mu * _invSigma);
result = NormalCdf(z);
_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, double mu = 0.0, double sigma = 1.0, int period = 14)
{
var indicator = new Lognormdist(mu, sigma, period);
return indicator.Update(source);
}
/// <summary>
/// Calculates Log-Normal 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,
double mu = 0.0, double sigma = 1.0, 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 (sigma <= 0.0)
{
throw new ArgumentException("Sigma must be > 0", nameof(sigma));
}
if (period < 2)
{
throw new ArgumentException("Period must be >= 2", nameof(period));
}
double invSigma = 1.0 / sigma;
double lastValid = 0.0;
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 x = range > 0.0 ? (val - min) / range : 0.5;
double safeX = x < 1e-10 ? 1e-10 : x;
double z = Math.FusedMultiplyAdd(Math.Log(safeX), invSigma, -mu * invSigma);
double result = NormalCdf(z);
lastValid = result;
output[i] = result;
}
}
public static (TSeries Results, Lognormdist Indicator) Calculate(
TSeries source, double mu = 0.0, double sigma = 1.0, int period = 14)
{
var indicator = new Lognormdist(mu, sigma, period);
TSeries results = indicator.Update(source);
return (results, indicator);
}
public override void Reset()
{
_buffer.Clear();
_state = new State(0.0);
_p_state = _state;
Last = default;
}
}