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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/dynamics/ghla/Ghla.Quantower.Tests.cs
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using TradingPlatform.BusinessLayer;
using QuanTAlib;
namespace QuanTAlib.Tests;
public class GhlaIndicatorTests
{
[Fact]
public void GhlaIndicator_Constructor_SetsDefaults()
{
var indicator = new GhlaIndicator();
Assert.Equal(13, indicator.Period);
Assert.True(indicator.ShowColdValues);
Assert.Equal("GHLA - Gann High-Low Activator", indicator.Name);
Assert.False(indicator.SeparateWindow); // Overlay
Assert.True(indicator.OnBackGround);
}
[Fact]
public void GhlaIndicator_ShortName_IncludesParameters()
{
var indicator = new GhlaIndicator { Period = 5 };
Assert.Equal("GHLA 5", indicator.ShortName);
}
[Fact]
public void GhlaIndicator_MinHistoryDepths_EqualsZero()
{
var indicator = new GhlaIndicator();
Assert.Equal(0, GhlaIndicator.MinHistoryDepths);
Assert.Equal(0, ((IWatchlistIndicator)indicator).MinHistoryDepths);
}
[Fact]
public void GhlaIndicator_Initialize_CreatesInternalGhla()
{
var indicator = new GhlaIndicator();
indicator.Initialize();
Assert.Single(indicator.LinesSeries);
}
[Fact]
public void GhlaIndicator_ProcessUpdate_HistoricalBar_ComputesValue()
{
var indicator = new GhlaIndicator { Period = 5 };
indicator.Initialize();
var now = DateTime.UtcNow;
for (int i = 0; i < 20; i++)
{
double basePrice = 100 + i;
indicator.HistoricalData.AddBar(now.AddMinutes(i), basePrice, basePrice + 5, basePrice - 5, basePrice + 2, 1000);
var args = new UpdateArgs(UpdateReason.HistoricalBar);
indicator.ProcessUpdate(args);
}
double ghlaVal = indicator.LinesSeries[0].GetValue(0);
Assert.True(double.IsFinite(ghlaVal));
}
[Fact]
public void GhlaIndicator_ProcessUpdate_NewBar_ComputesValue()
{
var indicator = new GhlaIndicator { Period = 5 };
indicator.Initialize();
var now = DateTime.UtcNow;
for (int i = 0; i < 20; i++)
{
double basePrice = 100 + i;
indicator.HistoricalData.AddBar(now.AddMinutes(i), basePrice, basePrice + 5, basePrice - 5, basePrice + 2, 1000);
}
indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
indicator.HistoricalData.AddBar(now.AddMinutes(20), 120, 128, 115, 125, 1500);
indicator.ProcessUpdate(new UpdateArgs(UpdateReason.NewBar));
Assert.Equal(2, indicator.LinesSeries[0].Count);
}
[Fact]
public void GhlaIndicator_DifferentPeriods_Work()
{
int[] periods = { 3, 5, 13, 21, 50 };
foreach (var period in periods)
{
var indicator = new GhlaIndicator { Period = period };
indicator.Initialize();
var now = DateTime.UtcNow;
for (int i = 0; i < 60; i++)
{
double basePrice = 100 + i;
indicator.HistoricalData.AddBar(now.AddMinutes(i), basePrice, basePrice + 5, basePrice - 5, basePrice + 2, 1000);
indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
}
double ghlaVal = indicator.LinesSeries[0].GetValue(0);
Assert.True(double.IsFinite(ghlaVal), $"Period {period} should produce finite GHLA value");
}
}
[Fact]
public void GhlaIndicator_Period_CanBeChanged()
{
var indicator = new GhlaIndicator();
Assert.Equal(13, indicator.Period);
indicator.Period = 5;
Assert.Equal(5, indicator.Period);
indicator.Period = 21;
Assert.Equal(21, indicator.Period);
}
[Fact]
public void GhlaIndicator_ShowColdValues_CanBeToggled()
{
var indicator = new GhlaIndicator();
Assert.True(indicator.ShowColdValues);
indicator.ShowColdValues = false;
Assert.False(indicator.ShowColdValues);
indicator.ShowColdValues = true;
Assert.True(indicator.ShowColdValues);
}
[Fact]
public void GhlaIndicator_SourceCodeLink_IsValid()
{
var indicator = new GhlaIndicator();
Assert.Contains("github.com", indicator.SourceCodeLink, StringComparison.Ordinal);
Assert.Contains("Ghla.Quantower.cs", indicator.SourceCodeLink, StringComparison.Ordinal);
}
[Fact]
public void GhlaIndicator_HasOneLineSeries_WithCorrectName()
{
var indicator = new GhlaIndicator();
indicator.Initialize();
Assert.Single(indicator.LinesSeries);
Assert.Equal("GHLA", indicator.LinesSeries[0].Name);
}
[Fact]
public void GhlaIndicator_IsOverlay_NotSeparateWindow()
{
var indicator = new GhlaIndicator();
Assert.False(indicator.SeparateWindow);
}
}
lib/dynamics/ghla/Ghla.Quantower.cs
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using System.Drawing;
using System.Runtime.CompilerServices;
using TradingPlatform.BusinessLayer;
namespace QuanTAlib;
[SkipLocalsInit]
public sealed class GhlaIndicator : Indicator, IWatchlistIndicator
{
[InputParameter("Period", sortIndex: 1, 1, 1000, 1, 0)]
public int Period { get; set; } = 13;
[InputParameter("Show cold values", sortIndex: 21)]
public bool ShowColdValues { get; set; } = true;
private Ghla _ghla = null!;
private readonly LineSeries _ghlaSeries;
public static int MinHistoryDepths => 0;
int IWatchlistIndicator.MinHistoryDepths => MinHistoryDepths;
public override string ShortName => $"GHLA {Period}";
public override string SourceCodeLink => "https://github.com/mihakralj/QuanTAlib/blob/main/lib/dynamics/ghla/Ghla.Quantower.cs";
public GhlaIndicator()
{
OnBackGround = true;
SeparateWindow = false; // Overlay indicator — plots on price chart
Name = "GHLA - Gann High-Low Activator";
Description = "SMA(High)/SMA(Low) alternating trailing stop with hysteresis trend detection";
_ghlaSeries = new LineSeries(name: "GHLA", color: Color.Yellow, width: 2, style: LineStyle.Solid);
AddLineSeries(_ghlaSeries);
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
protected override void OnInit()
{
_ghla = new Ghla(Period);
base.OnInit();
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
protected override void OnUpdate(UpdateArgs args)
{
TBar bar = this.GetInputBar(args);
TValue result = _ghla.Update(bar, args.IsNewBar());
_ghlaSeries.SetValue(result.Value, _ghla.IsHot, ShowColdValues);
}
}
lib/dynamics/ghla/Ghla.Tests.cs
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namespace QuanTAlib.Tests;
public class GhlaTests
{
// ============== A) Constructor & Parameter Validation ==============
[Fact]
public void Constructor_ValidatesInput()
{
Assert.Throws<ArgumentException>(() => new Ghla(0));
Assert.Throws<ArgumentException>(() => new Ghla(-1));
Assert.Throws<ArgumentException>(() => new Ghla(-100));
var ghla = new Ghla(13);
Assert.NotNull(ghla);
}
[Fact]
public void Constructor_DefaultPeriod_Is13()
{
var ghla = new Ghla();
Assert.Contains("13", ghla.Name, StringComparison.Ordinal);
}
[Fact]
public void Constructor_Period1_Works()
{
var ghla = new Ghla(1);
Assert.NotNull(ghla);
Assert.Contains("1", ghla.Name, StringComparison.Ordinal);
}
[Fact]
public void Constructor_ArgumentException_HasParamName()
{
var ex = Assert.Throws<ArgumentException>(() => new Ghla(0));
Assert.Equal("period", ex.ParamName);
}
// ============== B) Basic Calculation ==============
[Fact]
public void BasicCalculation_DoesNotCrash()
{
var ghla = new Ghla(13);
var gbm = new GBM();
var bars = gbm.Fetch(100, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
foreach (var bar in bars)
{
ghla.Update(bar);
}
Assert.True(double.IsFinite(ghla.Last.Value));
}
[Fact]
public void Calc_ReturnsValue()
{
var ghla = new Ghla(13);
var bar = new TBar(DateTime.UtcNow, 100, 105, 95, 102, 1000);
Assert.Equal(0, ghla.Last.Value);
TValue result = ghla.Update(bar);
Assert.True(double.IsFinite(result.Value));
Assert.Equal(result.Value, ghla.Last.Value);
}
[Fact]
public void FirstBar_OutputIsSmaValue()
{
var ghla = new Ghla(3);
var bar = new TBar(DateTime.UtcNow, 100, 110, 90, 105, 1000);
TValue result = ghla.Update(bar);
// First bar: SMA(high,1)=110, SMA(low,1)=90
// close=105 < smaHigh=110, close=105 > smaLow=90 → neutral zone
// Seed: close >= smaHigh? No. close <= smaLow? No. default = 1 (bullish)
// Bullish → output = smaLow = 90
Assert.Equal(90.0, result.Value, 1e-10);
}
[Fact]
public void Properties_Accessible()
{
var ghla = new Ghla(13);
Assert.Equal(0, ghla.Last.Value);
Assert.False(ghla.IsHot);
Assert.Contains("Ghla", ghla.Name, StringComparison.Ordinal);
Assert.True(ghla.WarmupPeriod > 0);
var bar = new TBar(DateTime.UtcNow, 100, 105, 95, 102, 1000);
ghla.Update(bar);
Assert.True(ghla.Trend != 0 || ghla.Last.Value >= 0);
}
[Fact]
public void Trend_Property_ReturnsDirection()
{
var ghla = new Ghla(3);
// Feed rising bars to establish bullish trend
var baseTime = DateTime.UtcNow;
for (int i = 0; i < 10; i++)
{
double price = 100 + (i * 5);
var bar = new TBar(baseTime.AddMinutes(i), price, price + 2, price - 2, price + 1, 1000);
ghla.Update(bar);
}
// With strongly rising prices, trend should be bullish
Assert.Equal(1, ghla.Trend);
}
// ============== C) State Management & Bar Correction ==============
[Fact]
public void Calc_IsNew_AcceptsParameter()
{
var ghla = new Ghla(5);
var bar1 = new TBar(DateTime.UtcNow, 100, 105, 95, 102, 1000);
ghla.Update(bar1, isNew: true);
var bar2 = new TBar(DateTime.UtcNow.AddMinutes(1), 102, 110, 100, 108, 1000);
ghla.Update(bar2, isNew: true);
Assert.True(double.IsFinite(ghla.Last.Value));
}
[Fact]
public void Calc_IsNew_False_UpdatesValue()
{
var ghla = new Ghla(5);
var bar1 = new TBar(DateTime.UtcNow, 100, 110, 90, 105, 1000);
ghla.Update(bar1, isNew: true);
var bar2 = new TBar(DateTime.UtcNow.AddMinutes(1), 105, 115, 85, 108, 1000);
ghla.Update(bar2, isNew: true);
double beforeUpdate = ghla.Last.Value;
// Modify bar2 with very different range
var bar2Modified = new TBar(DateTime.UtcNow.AddMinutes(1), 105, 200, 50, 108, 1000);
ghla.Update(bar2Modified, isNew: false);
double afterUpdate = ghla.Last.Value;
Assert.NotEqual(beforeUpdate, afterUpdate);
}
[Fact]
public void IsNew_Consistency()
{
var ghla = new Ghla(5);
var gbm = new GBM();
var bars = gbm.Fetch(100, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
// Feed first 99
for (int i = 0; i < 99; i++)
{
ghla.Update(bars[i]);
}
// Update with 100th bar (isNew=true)
ghla.Update(bars[99], true);
// Update with modified 100th bar (isNew=false)
var modifiedBar = new TBar(bars[99].Time, bars[99].Open, bars[99].High + 10.0, bars[99].Low - 10.0, bars[99].Close, bars[99].Volume);
double val2 = ghla.Update(modifiedBar, false).Value;
// Create new instance and feed up to modified
var ghla2 = new Ghla(5);
for (int i = 0; i < 99; i++)
{
ghla2.Update(bars[i]);
}
double val3 = ghla2.Update(modifiedBar, true).Value;
Assert.Equal(val3, val2, 1e-9);
}
[Fact]
public void IterativeCorrections_RestoreToOriginalState()
{
var ghla = new Ghla(5);
var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1);
var bars = gbm.Fetch(20, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
// Feed 10 new values
TBar tenthBar = default;
for (int i = 0; i < 10; i++)
{
tenthBar = bars[i];
ghla.Update(tenthBar, isNew: true);
}
double stateAfterTen = ghla.Last.Value;
// Generate 9 corrections with isNew=false
for (int i = 10; i < 19; i++)
{
ghla.Update(bars[i], isNew: false);
}
// Feed the remembered 10th bar again with isNew=false
TValue finalResult = ghla.Update(tenthBar, isNew: false);
Assert.Equal(stateAfterTen, finalResult.Value, 1e-10);
}
[Fact]
public void Reset_Works()
{
var ghla = new Ghla(5);
var gbm = new GBM();
var bars = gbm.Fetch(50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
foreach (var bar in bars)
{
ghla.Update(bar);
}
Assert.True(ghla.IsHot);
ghla.Reset();
Assert.Equal(0, ghla.Last.Value);
Assert.False(ghla.IsHot);
Assert.Equal(0, ghla.Trend);
// After reset, should accept new values
ghla.Update(bars[0]);
Assert.True(double.IsFinite(ghla.Last.Value));
}
// ============== D) Warmup & Convergence ==============
[Fact]
public void IsHot_BecomesTrueAfterWarmup()
{
var ghla = new Ghla(5);
Assert.False(ghla.IsHot);
var baseTime = DateTime.UtcNow;
for (int i = 0; i < 5; i++)
{
var bar = new TBar(baseTime.AddMinutes(i), 100 + i, 110 + i, 90 + i, 100 + i, 1000);
ghla.Update(bar);
}
Assert.True(ghla.IsHot);
}
[Fact]
public void WarmupPeriod_IsPositive()
{
var ghla = new Ghla(13);
Assert.True(ghla.WarmupPeriod > 0);
Assert.Equal(13, ghla.WarmupPeriod);
var ghla2 = new Ghla(50);
Assert.Equal(50, ghla2.WarmupPeriod);
}
// ============== E) NaN/Infinity Handling ==============
[Fact]
public void NaN_High_UsesLastValidValue()
{
var ghla = new Ghla(5);
var bar1 = new TBar(DateTime.UtcNow, 100, 105, 95, 102, 1000);
ghla.Update(bar1);
var bar2 = new TBar(DateTime.UtcNow.AddMinutes(1), 102, 110, 98, 108, 1000);
ghla.Update(bar2);
// Feed bar with NaN high
var barWithNaN = new TBar(DateTime.UtcNow.AddMinutes(2), 108, double.NaN, 100, 112, 1000);
var resultAfterNaN = ghla.Update(barWithNaN);
Assert.True(double.IsFinite(resultAfterNaN.Value));
}
[Fact]
public void NaN_Low_UsesLastValidValue()
{
var ghla = new Ghla(5);
var bar1 = new TBar(DateTime.UtcNow, 100, 105, 95, 102, 1000);
ghla.Update(bar1);
var bar2 = new TBar(DateTime.UtcNow.AddMinutes(1), 102, 110, 98, 108, 1000);
ghla.Update(bar2);
var barWithNaN = new TBar(DateTime.UtcNow.AddMinutes(2), 108, 115, double.NaN, 112, 1000);
var resultAfterNaN = ghla.Update(barWithNaN);
Assert.True(double.IsFinite(resultAfterNaN.Value));
}
[Fact]
public void NaN_Close_UsesLastValidValue()
{
var ghla = new Ghla(5);
var bar1 = new TBar(DateTime.UtcNow, 100, 105, 95, 102, 1000);
ghla.Update(bar1);
var bar2 = new TBar(DateTime.UtcNow.AddMinutes(1), 102, 110, 98, 108, 1000);
ghla.Update(bar2);
var barWithNaN = new TBar(DateTime.UtcNow.AddMinutes(2), 108, 115, 100, double.NaN, 1000);
var resultAfterNaN = ghla.Update(barWithNaN);
Assert.True(double.IsFinite(resultAfterNaN.Value));
}
[Fact]
public void Infinity_Input_UsesLastValidValue()
{
var ghla = new Ghla(5);
var bar1 = new TBar(DateTime.UtcNow, 100, 105, 95, 102, 1000);
ghla.Update(bar1);
var bar2 = new TBar(DateTime.UtcNow.AddMinutes(1), 102, 110, 98, 108, 1000);
ghla.Update(bar2);
var barWithInf = new TBar(DateTime.UtcNow.AddMinutes(2), 108, double.PositiveInfinity, double.NegativeInfinity, double.PositiveInfinity, 1000);
var resultAfterInf = ghla.Update(barWithInf);
Assert.True(double.IsFinite(resultAfterInf.Value));
}
[Fact]
public void BatchNaN_Safe()
{
var ghla = new Ghla(5);
var gbm = new GBM();
var bars = gbm.Fetch(50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
for (int i = 0; i < 10; i++)
{
ghla.Update(bars[i]);
}
for (int i = 0; i < 5; i++)
{
var nanBar = new TBar(DateTime.UtcNow.AddMinutes(100 + i), double.NaN, double.NaN, double.NaN, double.NaN, 0);
var result = ghla.Update(nanBar);
Assert.True(double.IsFinite(result.Value));
}
for (int i = 10; i < 20; i++)
{
var result = ghla.Update(bars[i]);
Assert.True(double.IsFinite(result.Value));
}
}
// ============== F) Consistency Tests ==============
[Fact]
public void BatchCalc_MatchesIterativeCalc()
{
var ghlaIterative = new Ghla(5);
var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1);
var bars = gbm.Fetch(100, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
var iterativeResults = new TSeries();
foreach (var bar in bars)
{
iterativeResults.Add(ghlaIterative.Update(bar));
}
var batchResults = Ghla.Batch(bars, 5);
Assert.Equal(iterativeResults.Count, batchResults.Count);
for (int i = 0; i < iterativeResults.Count; i++)
{
Assert.Equal(iterativeResults[i].Value, batchResults[i].Value, 1e-10);
}
}
[Fact]
public void TBarSeries_Update_MatchesStreaming()
{
var ghla1 = new Ghla(5);
var ghla2 = new Ghla(5);
var gbm = new GBM();
var bars = gbm.Fetch(100, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
foreach (var bar in bars)
{
ghla1.Update(bar);
}
ghla2.Update(bars);
Assert.Equal(ghla1.Last.Value, ghla2.Last.Value, 1e-10);
}
[Fact]
public void SpanBatch_MatchesStreaming()
{
var ghla = new Ghla(5);
var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1);
var bars = gbm.Fetch(100, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
var streamResults = new double[100];
for (int i = 0; i < 100; i++)
{
streamResults[i] = ghla.Update(bars[i]).Value;
}
double[] highs = new double[100];
double[] lows = new double[100];
double[] closes = new double[100];
for (int i = 0; i < 100; i++)
{
highs[i] = bars[i].High;
lows[i] = bars[i].Low;
closes[i] = bars[i].Close;
}
double[] spanResults = new double[100];
Ghla.Batch(highs, lows, closes, spanResults, 5);
for (int i = 0; i < 100; i++)
{
Assert.Equal(streamResults[i], spanResults[i], 1e-10);
}
}
[Fact]
public void EventBased_MatchesStreaming()
{
var ghla1 = new Ghla(5);
var gbm = new GBM();
var bars = gbm.Fetch(50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
var eventResults = new List<double>();
ghla1.Pub += (object? _, in TValueEventArgs e) => eventResults.Add(e.Value.Value);
foreach (var bar in bars)
{
ghla1.Update(bar);
}
var ghla2 = new Ghla(5);
var streamResults = new List<double>();
foreach (var bar in bars)
{
streamResults.Add(ghla2.Update(bar).Value);
}
Assert.Equal(streamResults.Count, eventResults.Count);
for (int i = 0; i < streamResults.Count; i++)
{
Assert.Equal(streamResults[i], eventResults[i], 1e-10);
}
}
// ============== G) Span API Tests ==============
[Fact]
public void SpanBatch_ValidatesHighLowLength()
{
double[] high = new double[10];
double[] low = new double[5]; // mismatched
double[] close = new double[10];
double[] output = new double[10];
var ex = Assert.Throws<ArgumentException>(() => Ghla.Batch(high, low, close, output));
Assert.Equal("low", ex.ParamName);
}
[Fact]
public void SpanBatch_ValidatesCloseLength()
{
double[] high = new double[10];
double[] low = new double[10];
double[] close = new double[5]; // mismatched
double[] output = new double[10];
var ex = Assert.Throws<ArgumentException>(() => Ghla.Batch(high, low, close, output));
Assert.Equal("close", ex.ParamName);
}
[Fact]
public void SpanBatch_ValidatesOutputLength()
{
double[] high = new double[10];
double[] low = new double[10];
double[] close = new double[10];
double[] output = new double[5]; // too small
var ex = Assert.Throws<ArgumentException>(() => Ghla.Batch(high, low, close, output));
Assert.Equal("output", ex.ParamName);
}
[Fact]
public void SpanBatch_ValidatesPeriod()
{
double[] high = new double[10];
double[] low = new double[10];
double[] close = new double[10];
double[] output = new double[10];
Assert.Throws<ArgumentException>(() => Ghla.Batch(high, low, close, output, period: 0));
Assert.Throws<ArgumentException>(() => Ghla.Batch(high, low, close, output, period: -1));
}
[Fact]
public void SpanBatch_EmptyInput_NoOp()
{
double[] high = Array.Empty<double>();
double[] low = Array.Empty<double>();
double[] close = Array.Empty<double>();
double[] output = Array.Empty<double>();
var ex = Record.Exception(() => Ghla.Batch(high, low, close, output));
Assert.Null(ex);
}
[Fact]
public void SpanBatch_NaN_HandledGracefully()
{
double[] high = { 110, 115, double.NaN, 120, 125 };
double[] low = { 90, 85, double.NaN, 88, 92 };
double[] close = { 100, 105, double.NaN, 110, 115 };
double[] output = new double[5];
Ghla.Batch(high, low, close, output);
for (int i = 0; i < output.Length; i++)
{
Assert.True(double.IsFinite(output[i]), $"Output[{i}] should be finite but was {output[i]}");
}
}
// ============== H) Chainability ==============
[Fact]
public void Chainability_Works()
{
var ghla = new Ghla(5);
var gbm = new GBM();
var bars = gbm.Fetch(50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
var result = ghla.Update(bars);
Assert.Equal(50, result.Count);
Assert.Equal(ghla.Last.Value, result.Last.Value);
}
[Fact]
public void PubEvent_Fires()
{
var ghla = new Ghla(5);
int eventCount = 0;
ghla.Pub += (object? _, in TValueEventArgs _) => eventCount++;
var gbm = new GBM();
var bars = gbm.Fetch(10, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
foreach (var bar in bars)
{
ghla.Update(bar);
}
Assert.Equal(10, eventCount);
}
[Fact]
public void Chaining_ViaConstructor_Works()
{
var tr = new Tr();
var ghla = new Ghla(tr, 5);
var gbm = new GBM();
var bars = gbm.Fetch(30, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
foreach (var bar in bars)
{
tr.Update(bar);
}
Assert.True(double.IsFinite(ghla.Last.Value));
}
// ============== GHLA-Specific Tests ==============
[Fact]
public void Hysteresis_RetainsTrend_InNeutralZone()
{
var ghla = new Ghla(3);
// Establish bullish trend with strongly rising bars
var baseTime = DateTime.UtcNow;
for (int i = 0; i < 5; i++)
{
double price = 100 + (i * 10);
var bar = new TBar(baseTime.AddMinutes(i), price, price + 5, price - 5, price + 3, 1000);
ghla.Update(bar);
}
Assert.Equal(1, ghla.Trend);
// Feed a bar inside the neutral zone (between smaLow and smaHigh)
// With period=3 and rising prices, smaHigh and smaLow are high
// Feed a bar whose close is between the two SMAs → trend should stay +1
var neutralBar = new TBar(baseTime.AddMinutes(5), 140, 142, 138, 140, 1000);
ghla.Update(neutralBar);
// Trend should remain bullish (hysteresis)
Assert.Equal(1, ghla.Trend);
}
[Fact]
public void TrendFlip_OnStrongMove()
{
var ghla = new Ghla(3);
// Feed rising bars → bullish
var baseTime = DateTime.UtcNow;
for (int i = 0; i < 5; i++)
{
double price = 100 + (i * 5);
var bar = new TBar(baseTime.AddMinutes(i), price, price + 2, price - 2, price + 1, 1000);
ghla.Update(bar);
}
Assert.Equal(1, ghla.Trend);
// Feed strongly falling bars → eventually bearish
for (int i = 5; i < 15; i++)
{
double price = 120 - ((i - 5) * 10);
var bar = new TBar(baseTime.AddMinutes(i), price, price + 2, price - 2, price - 1, 1000);
ghla.Update(bar);
}
Assert.Equal(-1, ghla.Trend);
}
[Fact]
public void Bearish_OutputIsSmaHigh()
{
var ghla = new Ghla(3);
// Create strongly bearish scenario: close far below smaLow
var baseTime = DateTime.UtcNow;
// First fill buffers with high prices
for (int i = 0; i < 3; i++)
{
var bar = new TBar(baseTime.AddMinutes(i), 100, 105, 95, 100, 1000);
ghla.Update(bar);
}
// Then crash the close far below → bearish
var crashBar = new TBar(baseTime.AddMinutes(3), 50, 55, 45, 50, 1000);
ghla.Update(crashBar);
if (ghla.Trend == -1)
{
// In bearish mode, output should be SMA of highs (resistance)
// The value should be positive and finite
Assert.True(ghla.Last.Value > 0);
}
}
[Fact]
public void StaticBatch_Works()
{
var gbm = new GBM();
var bars = gbm.Fetch(50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
var results = Ghla.Batch(bars, 5);
Assert.Equal(50, results.Count);
Assert.True(double.IsFinite(results.Last.Value));
}
[Fact]
public void Calculate_ReturnsResultsAndIndicator()
{
var gbm = new GBM();
var bars = gbm.Fetch(50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
var (results, indicator) = Ghla.Calculate(bars, 5);
Assert.Equal(50, results.Count);
Assert.NotNull(indicator);
Assert.True(double.IsFinite(indicator.Last.Value));
Assert.True(indicator.Trend != 0);
}
[Fact]
public void FlatBars_OutputEqualsPrice()
{
var ghla = new Ghla(3);
// Flat bars: H=L=C=100 → SMA(H)=100, SMA(L)=100, close is NOT > smaH and NOT < smaL
// Seed: close >= smaHigh (100 >= 100)? Yes → trend=1 → output = smaLow = 100
for (int i = 0; i < 10; i++)
{
var bar = new TBar(DateTime.UtcNow.AddMinutes(i), 100, 100, 100, 100, 1000);
ghla.Update(bar);
}
Assert.Equal(100.0, ghla.Last.Value, 1e-10);
}
[Fact]
public void OverlayValue_TracksPrice()
{
var ghla = new Ghla(5);
var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1);
var bars = gbm.Fetch(100, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
foreach (var bar in bars)
{
ghla.Update(bar);
}
// GHLA is an overlay — value should be in same ballpark as price
double lastClose = bars[^1].Close;
Assert.True(ghla.Last.Value > 0, "GHLA overlay should be positive for positive prices");
Assert.True(Math.Abs(ghla.Last.Value - lastClose) < lastClose, "GHLA should be within 100% of close price");
}
}
lib/dynamics/ghla/Ghla.Validation.Tests.cs
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namespace QuanTAlib.Tests;
/// <summary>
/// GHLA Validation Tests — Self-consistency and cross-library validation.
/// Skender.Stock.Indicators has HiLoActivator for potential validation.
/// </summary>
public sealed class GhlaValidationTests : IDisposable
{
private readonly ValidationTestData _testData;
private bool _disposed;
public GhlaValidationTests()
{
_testData = new ValidationTestData();
}
public void Dispose()
{
Dispose(true);
}
private void Dispose(bool disposing)
{
if (_disposed)
{
return;
}
_disposed = true;
if (disposing)
{
_testData?.Dispose();
}
}
// ============== Self-Consistency ==============
[Fact]
public void Validation_BatchMatchesStreaming()
{
int[] periods = { 3, 5, 13, 21 };
foreach (var period in periods)
{
var ghlaStream = new Ghla(period);
var streamResults = new List<double>();
foreach (var bar in _testData.Bars)
{
streamResults.Add(ghlaStream.Update(bar).Value);
}
var batchResults = Ghla.Batch(_testData.Bars, period);
Assert.Equal(streamResults.Count, batchResults.Count);
for (int i = 0; i < streamResults.Count; i++)
{
Assert.Equal(streamResults[i], batchResults[i].Value, 1e-10);
}
}
}
[Fact]
public void Validation_SpanMatchesStreaming()
{
int[] periods = { 3, 5, 13 };
int len = _testData.Bars.Count;
double[] highs = new double[len];
double[] lows = new double[len];
double[] closes = new double[len];
for (int i = 0; i < len; i++)
{
highs[i] = _testData.Bars[i].High;
lows[i] = _testData.Bars[i].Low;
closes[i] = _testData.Bars[i].Close;
}
foreach (var period in periods)
{
var ghlaStream = new Ghla(period);
var streamResults = new double[len];
for (int i = 0; i < len; i++)
{
streamResults[i] = ghlaStream.Update(_testData.Bars[i]).Value;
}
double[] spanResults = new double[len];
Ghla.Batch(highs, lows, closes, spanResults, period);
for (int i = 0; i < len; i++)
{
Assert.Equal(streamResults[i], spanResults[i], 1e-10);
}
}
}
// ============== Known-Value Tests ==============
[Fact]
public void Validation_FlatMarket_OutputEqualsPrice()
{
var ghla = new Ghla(5);
var baseTime = DateTime.UtcNow;
for (int i = 0; i < 20; i++)
{
var bar = new TBar(baseTime.AddMinutes(i), 100, 100, 100, 100, 1000);
ghla.Update(bar);
}
// Flat market: SMA(H)=SMA(L)=100, close=100
// Trend seeded as bullish (close >= smaHigh), output = smaLow = 100
Assert.Equal(100.0, ghla.Last.Value, 1e-10);
}
[Fact]
public void Validation_StrongUptrend_OutputIsSmaLow()
{
var ghla = new Ghla(3);
var baseTime = DateTime.UtcNow;
// Strongly rising bars
for (int i = 0; i < 10; i++)
{
double price = 100 + (i * 10);
var bar = new TBar(baseTime.AddMinutes(i), price, price + 5, price - 5, price + 3, 1000);
ghla.Update(bar);
}
Assert.Equal(1, ghla.Trend);
// Output should be SMA of lows (trailing support)
// Last 3 lows: 185-5=180, 175-5=170, 165-5=160 → not exact due to feed, but should be < close
double lastClose = 100 + (9 * 10) + 3; // 193
Assert.True(ghla.Last.Value < lastClose, "Bullish activator (SMA(Low)) should be below close");
}
[Fact]
public void Validation_StrongDowntrend_OutputIsSmaHigh()
{
var ghla = new Ghla(3);
var baseTime = DateTime.UtcNow;
// Strongly falling bars
for (int i = 0; i < 10; i++)
{
double price = 200 - (i * 10);
var bar = new TBar(baseTime.AddMinutes(i), price, price + 5, price - 5, price - 3, 1000);
ghla.Update(bar);
}
Assert.Equal(-1, ghla.Trend);
// Output should be SMA of highs (overhead resistance)
double lastClose = 200 - (9 * 10) - 3; // 107
Assert.True(ghla.Last.Value > lastClose, "Bearish activator (SMA(High)) should be above close");
}
// ============== Different Periods ==============
[Fact]
public void Validation_DifferentPeriods_ProduceDifferentOutputs()
{
var ghla3 = new Ghla(3);
var ghla13 = new Ghla(13);
var ghla50 = new Ghla(50);
var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.5);
var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
foreach (var bar in bars)
{
ghla3.Update(bar);
ghla13.Update(bar);
ghla50.Update(bar);
}
// Different periods should generally produce different outputs
Assert.True(double.IsFinite(ghla3.Last.Value));
Assert.True(double.IsFinite(ghla13.Last.Value));
Assert.True(double.IsFinite(ghla50.Last.Value));
// With volatile GBM data, at least two should differ
bool allSame = Math.Abs(ghla3.Last.Value - ghla13.Last.Value) < 1e-10
&& Math.Abs(ghla13.Last.Value - ghla50.Last.Value) < 1e-10;
Assert.False(allSame, "Different periods should generally produce different GHLA values");
}
[Fact]
public void Validation_Calculate_ReturnsHotIndicator()
{
var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.5);
var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
var (results, indicator) = Ghla.Calculate(bars, 13);
Assert.Equal(bars.Count, results.Count);
Assert.True(indicator.IsHot);
Assert.True(indicator.Trend != 0);
}
[Fact]
public void Validation_BarCorrection_Consistent()
{
var ghla1 = new Ghla(5);
var ghla2 = new Ghla(5);
var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.3);
var bars = gbm.Fetch(50, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
foreach (var bar in bars)
{
ghla1.Update(bar, isNew: true);
}
for (int i = 0; i < bars.Count - 1; i++)
{
ghla2.Update(bars[i], isNew: true);
}
var wrongBar = new TBar(bars[^1].Time, 0, 999, 1, 500, 1000);
ghla2.Update(wrongBar, isNew: true);
ghla2.Update(bars[^1], isNew: false);
Assert.Equal(ghla1.Last.Value, ghla2.Last.Value, 1e-10);
Assert.Equal(ghla1.Trend, ghla2.Trend);
}
[Fact]
public void Validation_Output_AlwaysFinite()
{
var ghla = new Ghla(13);
var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 1.0);
var bars = gbm.Fetch(500, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
foreach (var bar in bars)
{
var result = ghla.Update(bar);
Assert.True(double.IsFinite(result.Value), $"GHLA output must be finite, got {result.Value}");
}
}
[Fact]
public void Validation_Output_AlwaysPositive_ForPositivePrices()
{
var ghla = new Ghla(13);
var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.5);
var bars = gbm.Fetch(500, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
foreach (var bar in bars)
{
var result = ghla.Update(bar);
Assert.True(result.Value > 0, $"GHLA output must be positive for positive prices, got {result.Value}");
}
}
[Fact]
public void Validation_TrendValues_OnlyValidStates()
{
var ghla = new Ghla(5);
var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 1.0);
var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
// Before any data, trend should be 0
Assert.Equal(0, ghla.Trend);
foreach (var bar in bars)
{
ghla.Update(bar);
// After first bar, trend must be +1 or -1 (never 0 or any other value)
Assert.True(ghla.Trend == 1 || ghla.Trend == -1, $"Trend must be +1 or -1, got {ghla.Trend}");
}
}
}
lib/dynamics/ghla/Ghla.cs
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using System;
using System.Buffers;
using System.Runtime.CompilerServices;
using System.Runtime.InteropServices;
namespace QuanTAlib;
/// <summary>
/// GHLA: Gann High-Low Activator
/// SMA-based trailing stop with three-state hysteresis trend detection.
/// Output follows SMA(Low) during uptrends and SMA(High) during downtrends.
/// </summary>
/// <remarks>
/// <b>Calculation steps:</b>
/// <list type="number">
/// <item>SMA_high = running sum of last N highs / N</item>
/// <item>SMA_low = running sum of last N lows / N</item>
/// <item>Close &gt; SMA_high → trend = +1 (bullish), output = SMA_low</item>
/// <item>Close &lt; SMA_low → trend = -1 (bearish), output = SMA_high</item>
/// <item>Between both SMAs → retain previous trend (hysteresis)</item>
/// </list>
///
/// <b>Sources:</b>
/// Robert Krausz (1998). "The New Gann Swing Chartist" — Stocks &amp; Commodities V.16:1
/// </remarks>
/// <seealso href="Ghla.md">Detailed documentation</seealso>
[SkipLocalsInit]
public sealed class Ghla : AbstractBase
{
private readonly RingBuffer _highBuffer;
private readonly RingBuffer _lowBuffer;
[StructLayout(LayoutKind.Auto)]
private record struct State(
double HighSum,
double LowSum,
int Trend,
double LastValidHigh,
double LastValidLow,
double LastValidClose,
int TickCount
);
private State _s;
private State _ps;
private const int ResyncInterval = 1000;
/// <summary>
/// Creates GHLA with specified SMA period.
/// </summary>
/// <param name="period">SMA lookback period (must be &gt; 0, default 13)</param>
public Ghla(int period = 13)
{
if (period <= 0)
{
throw new ArgumentException("Period must be greater than 0", nameof(period));
}
_highBuffer = new RingBuffer(period);
_lowBuffer = new RingBuffer(period);
Name = $"Ghla({period})";
WarmupPeriod = period;
_s = new State(0, 0, 0, 0, 0, 0, 0);
_ps = _s;
}
/// <summary>
/// Creates GHLA with specified source and period.
/// </summary>
public Ghla(ITValuePublisher source, int period = 13) : this(period)
{
source.Pub += Handle;
}
private void Handle(object? sender, in TValueEventArgs e) => Update(e.Value, e.IsNew);
/// <summary>
/// True when both SMA buffers are full.
/// </summary>
public override bool IsHot => _highBuffer.IsFull;
/// <summary>
/// The current trend direction: +1 bullish, -1 bearish, 0 undetermined.
/// </summary>
public int Trend => _s.Trend;
/// <summary>
/// Updates the indicator with a TBar input (preferred method).
/// </summary>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public TValue Update(TBar bar, bool isNew = true)
{
return UpdateCore(bar.Time, bar.High, bar.Low, bar.Close, isNew);
}
/// <summary>
/// Updates the indicator with a TValue input.
/// Treats the value as H=L=C (degenerate case, always neutral zone).
/// Prefer Update(TBar) for standard OHLC data.
/// </summary>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public override TValue Update(TValue input, bool isNew = true)
{
return UpdateCore(input.Time, input.Value, input.Value, input.Value, isNew);
}
/// <summary>
/// Updates the indicator with a bar series.
/// </summary>
public TSeries Update(TBarSeries source)
{
if (source.Count == 0)
{
return [];
}
int len = source.Count;
var t = new List<long>(len);
var v = new List<double>(len);
CollectionsMarshal.SetCount(t, len);
CollectionsMarshal.SetCount(v, len);
var tSpan = CollectionsMarshal.AsSpan(t);
var vSpan = CollectionsMarshal.AsSpan(v);
for (int i = 0; i < len; i++)
{
tSpan[i] = source[i].Time;
}
for (int i = 0; i < len; i++)
{
var result = Update(source[i], isNew: true);
vSpan[i] = result.Value;
}
return new TSeries(t, v);
}
/// <inheritdoc/>
public override TSeries Update(TSeries source)
{
// TSeries has no OHLC — treat values as H=L=C (degenerate case)
int len = source.Count;
var t = new List<long>(len);
var v = new List<double>(len);
CollectionsMarshal.SetCount(t, len);
CollectionsMarshal.SetCount(v, len);
var tSpan = CollectionsMarshal.AsSpan(t);
var vSpan = CollectionsMarshal.AsSpan(v);
var values = source.Values;
var times = source.Times;
for (int i = 0; i < len; i++)
{
tSpan[i] = times[i];
var result = Update(new TValue(times[i], values[i]), isNew: true);
vSpan[i] = result.Value;
}
return new TSeries(t, v);
}
/// <inheritdoc/>
public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
{
for (int i = 0; i < source.Length; i++)
{
Update(new TValue(DateTime.UtcNow, source[i]), isNew: true);
}
}
/// <inheritdoc/>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public override void Reset()
{
_highBuffer.Clear();
_lowBuffer.Clear();
_s = new State(0, 0, 0, 0, 0, 0, 0);
_ps = _s;
Last = default;
}
/// <summary>
/// Calculates GHLA for the entire bar series using a new instance.
/// </summary>
public static TSeries Batch(TBarSeries source, int period = 13)
{
var ghla = new Ghla(period);
return ghla.Update(source);
}
/// <summary>
/// Span-based batch calculation for high, low, and close arrays.
/// </summary>
/// <param name="high">High prices.</param>
/// <param name="low">Low prices.</param>
/// <param name="close">Close prices.</param>
/// <param name="output">Output activator values.</param>
/// <param name="period">SMA lookback period.</param>
public static void Batch(
ReadOnlySpan<double> high,
ReadOnlySpan<double> low,
ReadOnlySpan<double> close,
Span<double> output,
int period = 13)
{
int len = high.Length;
if (low.Length != len)
{
throw new ArgumentException("High and low spans must have the same length", nameof(low));
}
if (close.Length != len)
{
throw new ArgumentException("High and close spans must have the same length", nameof(close));
}
if (output.Length < len)
{
throw new ArgumentException("Output span must be at least as long as input spans", nameof(output));
}
if (period <= 0)
{
throw new ArgumentException("Period must be greater than 0", nameof(period));
}
if (len == 0)
{
return;
}
CalculateScalarCore(high, low, close, output, period);
}
/// <summary>
/// Calculates GHLA and returns both results and the indicator instance.
/// </summary>
public static (TSeries Results, Ghla Indicator) Calculate(TBarSeries source, int period = 13)
{
var indicator = new Ghla(period);
TSeries results = indicator.Update(source);
return (results, indicator);
}
// ---- Private implementation ----
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private TValue UpdateCore(long timeTicks, double high, double low, double close, bool isNew)
{
// Snapshot/restore for bar correction
if (isNew)
{
_ps = _s;
}
else
{
_s = _ps;
}
var s = _s;
// Handle non-finite values — use last valid per component
if (!double.IsFinite(high))
{
high = s.LastValidHigh;
}
else
{
s.LastValidHigh = high;
}
if (!double.IsFinite(low))
{
low = s.LastValidLow;
}
else
{
s.LastValidLow = low;
}
if (!double.IsFinite(close))
{
close = s.LastValidClose;
}
else
{
s.LastValidClose = close;
}
// Update running SMA sums via ring buffers
if (isNew)
{
// High buffer
double highRemoved = _highBuffer.Count == _highBuffer.Capacity ? _highBuffer.Oldest : 0.0;
s.HighSum = s.HighSum - highRemoved + high;
_highBuffer.Add(high);
// Low buffer
double lowRemoved = _lowBuffer.Count == _lowBuffer.Capacity ? _lowBuffer.Oldest : 0.0;
s.LowSum = s.LowSum - lowRemoved + low;
_lowBuffer.Add(low);
// Periodic resync to limit floating-point drift
s.TickCount++;
if (_highBuffer.IsFull && s.TickCount >= ResyncInterval)
{
s.TickCount = 0;
s.HighSum = _highBuffer.RecalculateSum();
s.LowSum = _lowBuffer.RecalculateSum();
}
}
else
{
// Bar correction: update newest value in both buffers
_highBuffer.UpdateNewest(high);
s.HighSum = _highBuffer.Sum;
_lowBuffer.UpdateNewest(low);
s.LowSum = _lowBuffer.Sum;
}
// Compute SMAs
int count = _highBuffer.Count;
double smaHigh = count > 0 ? s.HighSum / count : 0.0;
double smaLow = count > 0 ? s.LowSum / count : 0.0;
// Three-state hysteresis trend detection
if (s.Trend == 0)
{
// Seed: classify first bar
if (close >= smaHigh)
{
s.Trend = 1;
}
else if (close <= smaLow)
{
s.Trend = -1;
}
else
{
s.Trend = 1; // default bullish per Pine reference
}
}
if (close > smaHigh)
{
s.Trend = 1;
}
else if (close < smaLow)
{
s.Trend = -1;
}
// else: retain previous trend (hysteresis zone)
// Select activator: bullish → SMA(Low), bearish → SMA(High)
double activator = s.Trend == 1 ? smaLow : smaHigh;
_s = s;
Last = new TValue(timeTicks, activator);
PubEvent(Last, isNew);
return Last;
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private static void CalculateScalarCore(
ReadOnlySpan<double> high,
ReadOnlySpan<double> low,
ReadOnlySpan<double> close,
Span<double> output,
int period)
{
int len = high.Length;
const int StackAllocThreshold = 256;
// High circular buffer
double[]? rentedHigh = period > StackAllocThreshold ? ArrayPool<double>.Shared.Rent(period) : null;
Span<double> highBuf = rentedHigh != null
? rentedHigh.AsSpan(0, period)
: stackalloc double[period];
// Low circular buffer
double[]? rentedLow = period > StackAllocThreshold ? ArrayPool<double>.Shared.Rent(period) : null;
Span<double> lowBuf = rentedLow != null
? rentedLow.AsSpan(0, period)
: stackalloc double[period];
try
{
double highSum = 0;
double lowSum = 0;
double lastValidHigh = 0;
double lastValidLow = 0;
double lastValidClose = 0;
int highIdx = 0;
int lowIdx = 0;
int filled = 0;
int trend = 0;
int tickCount = 0;
// Seed lastValid values
for (int k = 0; k < len; k++)
{
if (double.IsFinite(high[k]))
{
lastValidHigh = high[k];
break;
}
}
for (int k = 0; k < len; k++)
{
if (double.IsFinite(low[k]))
{
lastValidLow = low[k];
break;
}
}
for (int k = 0; k < len; k++)
{
if (double.IsFinite(close[k]))
{
lastValidClose = close[k];
break;
}
}
for (int i = 0; i < len; i++)
{
double h = high[i];
double l = low[i];
double c = close[i];
if (double.IsFinite(h))
{
lastValidHigh = h;
}
else
{
h = lastValidHigh;
}
if (double.IsFinite(l))
{
lastValidLow = l;
}
else
{
l = lastValidLow;
}
if (double.IsFinite(c))
{
lastValidClose = c;
}
else
{
c = lastValidClose;
}
// Update high buffer
if (filled >= period)
{
highSum -= highBuf[highIdx];
}
highSum += h;
highBuf[highIdx] = h;
highIdx++;
if (highIdx >= period)
{
highIdx = 0;
}
// Update low buffer
if (filled >= period)
{
lowSum -= lowBuf[lowIdx];
}
lowSum += l;
lowBuf[lowIdx] = l;
lowIdx++;
if (lowIdx >= period)
{
lowIdx = 0;
}
if (filled < period)
{
filled++;
}
// Resync
tickCount++;
if (filled >= period && tickCount >= ResyncInterval)
{
tickCount = 0;
double recalcH = 0;
double recalcL = 0;
for (int k = 0; k < period; k++)
{
recalcH += highBuf[k];
recalcL += lowBuf[k];
}
highSum = recalcH;
lowSum = recalcL;
}
double smaH = highSum / filled;
double smaL = lowSum / filled;
// Hysteresis
if (trend == 0)
{
if (c >= smaH)
{
trend = 1;
}
else if (c <= smaL)
{
trend = -1;
}
else
{
trend = 1; // default bullish per Pine reference
}
}
if (c > smaH)
{
trend = 1;
}
else if (c < smaL)
{
trend = -1;
}
output[i] = trend == 1 ? smaL : smaH;
}
}
finally
{
if (rentedHigh != null)
{
ArrayPool<double>.Shared.Return(rentedHigh);
}
if (rentedLow != null)
{
ArrayPool<double>.Shared.Return(rentedLow);
}
}
}
}
lib/dynamics/ghla/Ghla.md
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# GHLA: Gann High-Low Activator
> "The simplest indicators are the hardest to argue with. Two averages, one rule, and the market tells you which side of the fence to stand on."
The Gann High-Low Activator (GHLA) is a trend-following stop/reversal indicator that alternates between the Simple Moving Average of Highs and the Simple Moving Average of Lows based on a three-state crossover rule. Developed by Robert Krausz and published in *Technical Analysis of Stocks & Commodities* (February 1998), the indicator produces a single trailing line: SMA(Low) during uptrends (acting as dynamic support) and SMA(High) during downtrends (acting as dynamic resistance). The flip between states occurs only when price closes decisively beyond the opposing SMA, creating a hysteresis zone that filters minor whipsaws. With a default period of 3 bars, GHLA responds aggressively to trend changes while requiring just $O(N)$ additions and one comparison per bar.
## Historical Context
W.D. Gann (1878-1955) built a trading methodology around geometric angles, time cycles, and price levels. His original techniques required manual charting and subjective interpretation, limiting their adoption in systematic trading. Robert Krausz, a Hungarian-born technician and member of the British Society of Technical Analysts, spent years distilling Gann's principles into rule-based indicators. The results appeared in his 1993 book *A W.D. Gann Treasure Discovered* and later in a three-part article series in TASC magazine starting February 1998, titled "The New Gann Swing Chartist Plan."
The plan comprised three indicators working together: the Gann HiLo Activator (entry/exit signals and trailing stops), the Gann Swing Indicator (swing point identification), and the Gann Trend Indicator (trend confirmation). The HiLo Activator became the most widely adopted of the three because it functions effectively as a standalone tool. Its simplicity explains its longevity: two SMAs and one conditional switch.
Prior art in the trailing-stop category includes Wilder's Parabolic SAR (1978), which accelerates toward price and resets on reversal, and the Chandelier Exit (Chuck LeBeau, 1990s), which trails a fixed ATR multiple from the highest high. GHLA occupies a middle ground. Unlike PSAR, it does not accelerate or reset; the trailing distance is simply the SMA lookback window. Unlike the Chandelier Exit, it does not require ATR computation or a separate highest-high tracker. The tradeoff is reduced adaptability to volatility regimes in exchange for extreme computational simplicity.
Most platform implementations (MetaTrader, TradeStation, TradingView, NinjaTrader) compute GHLA identically: SMA of High and SMA of Low with a period-3 default. The only meaningful variation across implementations is the choice of moving average: some vendors offer EMA, HMA, or KAMA alternatives, though Krausz's original specification uses SMA exclusively. This implementation follows the original SMA-only design.
## Architecture and Physics
### 1. SMA Computation
Two independent Simple Moving Averages run in parallel each bar:
$$
\text{SMA}_H(t) = \frac{1}{N} \sum_{i=0}^{N-1} H_{t-i}
$$
$$
\text{SMA}_L(t) = \frac{1}{N} \sum_{i=0}^{N-1} L_{t-i}
$$
where $H_t$ and $L_t$ are the High and Low prices at bar $t$, and $N$ is the lookback period.
For the C# streaming implementation, these are computed via a `RingBuffer` of size $N$, maintaining a running sum for $O(1)$ incremental update (subtract oldest, add newest, divide by $N$). The PineScript reference uses `ta.sma()` which handles this internally.
### 2. Trend State Machine
The trend state is a three-valued variable with hysteresis:
$$
\text{trend}_t = \begin{cases}
+1 & \text{if } C_t > \text{SMA}_H(t) \\
-1 & \text{if } C_t < \text{SMA}_L(t) \\
\text{trend}_{t-1} & \text{otherwise (hysteresis zone)}
\end{cases}
$$
The hysteresis zone sits between $\text{SMA}_L$ and $\text{SMA}_H$. When close falls in this band, the indicator retains its previous state. This prevents rapid oscillation during consolidation when price weaves between the two SMAs.
On the first bar (no prior state), the trend seeds to $+1$ if $C_0 \geq \text{SMA}_H(0)$, $-1$ if $C_0 \leq \text{SMA}_L(0)$, and defaults to $+1$ otherwise.
### 3. Activator Selection
The output line flips between the two SMAs based on the current trend:
$$
\text{GHLA}_t = \begin{cases}
\text{SMA}_L(t) & \text{if trend}_t = +1 \text{ (bullish: support line)} \\
\text{SMA}_H(t) & \text{if trend}_t = -1 \text{ (bearish: resistance line)}
\end{cases}
$$
This creates a visually distinctive pattern: during uptrends the line hugs below price (tracking low averages), and during downtrends it hangs above price (tracking high averages). The line jumps discontinuously at trend reversals.
### 4. Complexity
- **Time:** $O(N)$ per bar for SMA (or $O(1)$ with running sum in streaming mode)
- **Space:** $O(N)$ for rolling window buffers (two ring buffers of size $N$) plus one integer for trend state
- **Warmup:** $N$ bars for the SMAs to fill. Before warmup completion, the SMA values are computed over fewer than $N$ bars if using expanding-window semantics, or are NaN if using fixed-window semantics
- **State footprint:** Two `RingBuffer<double>` (size $N$ each), one `int` for trend, two `double` for running sums
## Mathematical Foundation
### SMA Properties
The Simple Moving Average is a Finite Impulse Response (FIR) filter with uniform weights:
$$
w_i = \frac{1}{N}, \quad i = 0, 1, \ldots, N-1
$$
Group delay is $(N-1)/2$ bars. For $N=3$, group delay is 1.0 bar. For $N=5$, group delay is 2.0 bars.
Frequency response:
$$
H(f) = \frac{\sin(\pi f N)}{N \sin(\pi f)}
$$
The SMA passes low frequencies and attenuates high frequencies, with nulls at $f = k/N$ for integer $k$. With $N=3$, the first null is at $f=1/3$ (3-bar cycles are completely removed).
### State Transition Probability
In a random walk, the probability of close being above $\text{SMA}_H$ or below $\text{SMA}_L$ depends on the volatility-to-range ratio. For typical equity data with daily ATR around 1-2% of price:
- Probability of trend flip per bar (empirical, $N=3$): approximately 5-15% during trending markets, 20-35% during ranging markets
- Average trend duration ($N=3$): 5-12 bars in trending conditions, 2-4 bars in choppy conditions
### Parameter Mapping
| Symbol | Parameter | Default | Constraint |
|--------|-----------|---------|------------|
| $N$ | period | 3 | $N \geq 1$ |
Krausz recommended $N = 3$ for short-term swing trading. Increasing $N$ widens the hysteresis band and reduces whipsaws but increases lag:
| Period | Group Delay | Hysteresis Width | Whipsaw Rate | Best For |
|--------|-------------|------------------|-------------|----------|
| 3 | 1.0 bars | Narrow | Higher | Scalping, day trading |
| 5 | 2.0 bars | Medium | Moderate | Swing trading |
| 10 | 4.5 bars | Wide | Lower | Position trading |
| 20 | 9.5 bars | Very wide | Minimal | Trend following |
### Relationship to SuperTrend
SuperTrend uses ATR-based bands with ratcheting logic (bands only tighten, never widen until reversal). GHLA uses SMA-based lines with no ratchet. The structural difference:
$$
\text{SuperTrend: band}_t = \text{HL2}_t \pm k \cdot \text{ATR}_t, \quad \text{ratcheted}
$$
$$
\text{GHLA: line}_t = \text{SMA}(H \text{ or } L, N), \quad \text{no ratchet}
$$
SuperTrend adapts to volatility; GHLA does not. In high-volatility regimes, GHLA's fixed SMA window produces tighter stops (more whipsaws). In low-volatility regimes, GHLA's stops are looser relative to price action.
## Performance Profile
### Operation Count (Streaming Mode, Scalar)
Per-bar operations with $O(1)$ running-sum SMA:
| Operation | Count | Cost (cycles) | Subtotal |
|:----------|:-----:|:-------------:|:--------:|
| ADD/SUB (running sum update) | 4 | 1 | 4 |
| DIV (sum/N for each SMA) | 2 | 15 | 30 |
| CMP (close vs SMA_H, close vs SMA_L) | 2 | 1 | 2 |
| BRANCH (trend selection) | 1 | 1 | 1 |
| STORE (trend state) | 1 | 1 | 1 |
| **Total** | **10** | | **~38 cycles** |
### Batch Mode (SIMD Analysis)
| Operation | Vectorizable? | Notes |
|:----------|:-------------:|:------|
| SMA(High) | Yes | FIR filter, fully parallelizable with sliding window |
| SMA(Low) | Yes | Same as SMA(High) |
| Trend state | No | Sequential dependency (hysteresis requires previous state) |
| Activator select | Yes | Conditional select after trend is known |
The SMA computation vectorizes well via `Vector<double>` for the summation step. The trend state machine is inherently sequential, limiting end-to-end SIMD benefit. For the `Calculate(Span)` path, compute both SMA spans first (vectorized), then run the scalar trend state loop, then vectorize the final selection.
### Quality Metrics
| Metric | Score | Notes |
|:-------|:-----:|:------|
| **Accuracy** | 10/10 | Exact arithmetic, no approximations |
| **Timeliness** | 7/10 | $(N-1)/2$ bar group delay; $N=3$ gives 1 bar lag |
| **Smoothness** | 5/10 | Discontinuous jumps at trend reversals |
| **Noise Rejection** | 6/10 | Hysteresis helps; small $N$ still whipsaws in ranges |
| **Interpretability** | 9/10 | Green line below = bullish, red line above = bearish |
## Validation
| Library | Status | Notes |
|:--------|:------:|:------|
| **TA-Lib** | N/A | Not implemented |
| **Skender** | Pending | `HiLoActivator` available in Skender.Stock.Indicators |
| **Tulip** | N/A | Not implemented |
| **OoplesFinance** | Pending | Available as `GannHighLowActivator` |
| **TradeStation** | Reference | Built-in; Length=3 default; canonical implementation |
| **TradingView** | Reference | Multiple community scripts; starbolt's version matches Krausz original |
| **MetaTrader** | Reference | Available as custom indicator; matches formula |
Key validation points:
- In bullish state, activator must equal SMA(Low, N)
- In bearish state, activator must equal SMA(High, N)
- Trend must flip only when close crosses SMA threshold (not on touch)
- Hysteresis zone must preserve previous trend when close is between the two SMAs
- With $N=1$, SMA(High) = High and SMA(Low) = Low; reduces to raw high/low comparison
- Warmup: first $N-1$ bars have incomplete SMA windows
## Common Pitfalls
1. **Swapping the SMA assignment.** The activator displays SMA(Low) during uptrends and SMA(High) during downtrends. This is counterintuitive at first glance: the *low* average serves as the bullish trailing stop, not the high average. Getting this backwards produces a line that sits on the wrong side of price in both states. Impact: 100% signal inversion.
2. **Missing hysteresis.** Some implementations assign trend based on the most recent comparison without retaining the previous state when close falls between the two SMAs. Without hysteresis, the indicator oscillates every bar during consolidation, producing 3-5x more false signals than the original design.
3. **Using EMA instead of SMA.** Krausz specified SMA explicitly. EMA with $\alpha = 2/(N+1)$ responds faster and produces a different trailing line. For $N=3$, SMA weights are $[1/3, 1/3, 1/3]$ while EMA equivalent weights decay as $[0.5, 0.25, 0.125, \ldots]$. The EMA version tracks more recent bars disproportionately, tightening stops during trends but increasing whipsaw frequency by approximately 15-20%.
4. **Comparing against the wrong SMA for state transition.** The trend flips bullish when close exceeds SMA(High), not SMA(Low). Using SMA(Low) as the bullish threshold makes the flip too easy (the low average is always below the high average), producing premature signals. Similarly, bearish flip requires close below SMA(Low), not SMA(High).
5. **Ignoring the first-bar seed.** Without explicit initialization, the trend state starts undefined. If the first bar's close sits in the hysteresis zone (between the two SMAs), the "retain previous" rule has no previous to retain. The implementation must seed the initial state from the first bar's close relative to SMA(High)/SMA(Low), defaulting to bullish if ambiguous.
6. **Expecting volatility adaptation.** GHLA has no volatility scaling. A 3-period SMA on a stock moving 5% per day and a stock moving 0.3% per day produces the same structural distance between the activator and price in percentage terms, but the absolute distance differs by 16x. For multi-asset systems, consider normalizing or pairing with ATR-based filters.
7. **Using GHLA as a standalone system.** Krausz designed GHLA as one component of a three-indicator system (with Gann Swing Indicator and Gann Trend Indicator). Used alone without trend confirmation, GHLA generates entry signals during ranging markets that produce net losses in backtesting across most asset classes. The original Krausz system required all three indicators to agree before entry.
## References
- Krausz, Robert. "The New Gann Swing Chartist." *Technical Analysis of Stocks & Commodities*, V16:2, February 1998.
- Krausz, Robert. *A W.D. Gann Treasure Discovered: Simple Trading Plans for Stocks & Commodities.* Doray Publishing, 1993.
- Gann, W.D. *Truth of the Stock Tape.* Financial Guardian Publishing, 1923.
- TradeStation. "HiLoActivator Study Reference." TradeStation Help Center.
- financial-hacker.com. "Petra on Programming: The Gann Hi-Lo Activator." 2020.
- PineScript reference: `ghla.pine` in indicator directory.
lib/dynamics/ghla/ghla.pine
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// The MIT License (MIT)
// © mihakralj
//@version=6
indicator("Gann High-Low Activator", "GHLA", overlay=true)
//@function Calculates Gann High-Low Activator using SMA of Highs/Lows with trend-state switching
//@param period Lookback period for SMA calculation (Krausz default: 3)
//@returns Tuple [activator, trend] where trend is 1 (bullish) or -1 (bearish)
//@optimized O(period) SMA via ta.sma built-in; O(1) state transition with hysteresis
ghla(simple int period) =>
if period <= 0
runtime.error("Period must be greater than 0")
// Step 1: Compute SMA of Highs and SMA of Lows over N periods
float smaHigh = ta.sma(high, period)
float smaLow = ta.sma(low, period)
// Step 2: Determine trend state with hysteresis
// Close > SMA(High) => bullish (+1)
// Close < SMA(Low) => bearish (-1)
// Between the two SMAs => retain previous state
var int trend = 0
if trend == 0
// Seed: classify first bar
trend := close >= smaHigh ? 1 : close <= smaLow ? -1 : 1
if close > smaHigh
trend := 1
else if close < smaLow
trend := -1
// else: trend retains previous value (hysteresis zone)
// Step 3: Select activator line based on trend state
// Bullish: activator = SMA(Low) — trailing support below price
// Bearish: activator = SMA(High) — trailing resistance above price
float activator = trend == 1 ? smaLow : smaHigh
[activator, trend]
// ---------- Main loop ----------
// Inputs
i_period = input.int(3, "Period", minval=1, maxval=100, tooltip="SMA lookback period (Krausz default: 3)")
// Calculation
[ghla_line, ghla_trend] = ghla(i_period)
// Colors
color bullish_color = color.new(color.green, 0)
color bearish_color = color.new(color.red, 0)
color line_color = ghla_trend == 1 ? bullish_color : bearish_color
// Plot
plot(ghla_line, "GHLA", color=line_color, linewidth=2, style=plot.style_line)
// Optional: Plot buy/sell signals when trend flips
bool trend_changed = ghla_trend != nz(ghla_trend[1])
plotshape(trend_changed and ghla_trend == 1, "Buy Signal", shape.labelup, location.belowbar, color=bullish_color, text="BUY", textcolor=color.white, size=size.small)
plotshape(trend_changed and ghla_trend == -1, "Sell Signal", shape.labeldown, location.abovebar, color=bearish_color, text="SELL", textcolor=color.white, size=size.small)