mirror of
https://github.com/mihakralj/QuanTAlib.git
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docs: remove C# Implementation Considerations sections, clean up temp scripts, reorganize test files
- Remove 'C# Implementation Considerations' sections from 34 indicator .md files - Delete 29 temp PowerShell scripts (_fix_mojibake.ps1, _hex_scan.ps1, etc.) - Move test files into tests/ subdirectories for consistent project structure - Add trader-focused bullet points to indicator documentation
This commit is contained in:
Miha Kralj
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using TradingPlatform.BusinessLayer;
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namespace QuanTAlib.Tests;
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public class FramaIndicatorTests
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{
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[Fact]
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public void FramaIndicator_Constructor_SetsDefaults()
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{
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var indicator = new FramaIndicator();
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Assert.Equal(16, indicator.Period);
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Assert.True(indicator.ShowColdValues);
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Assert.Equal("FRAMA - Ehlers Fractal Adaptive Moving Average", indicator.Name);
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Assert.False(indicator.SeparateWindow);
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Assert.True(indicator.OnBackGround);
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}
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[Fact]
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public void FramaIndicator_MinHistoryDepths_ReturnsZero()
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{
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var indicator = new FramaIndicator { Period = 20 };
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Assert.Equal(0, FramaIndicator.MinHistoryDepths);
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Assert.Equal(0, ((IWatchlistIndicator)indicator).MinHistoryDepths);
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}
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[Fact]
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public void FramaIndicator_ShortName_IncludesPeriod()
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{
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var indicator = new FramaIndicator { Period = 21 };
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Assert.Contains("FRAMA", indicator.ShortName, StringComparison.Ordinal);
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Assert.Contains("21", indicator.ShortName, StringComparison.Ordinal);
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}
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[Fact]
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public void FramaIndicator_SourceCodeLink_IsValid()
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{
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var indicator = new FramaIndicator();
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Assert.Contains("github.com", indicator.SourceCodeLink, StringComparison.Ordinal);
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Assert.Contains("Frama.Quantower.cs", indicator.SourceCodeLink, StringComparison.Ordinal);
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}
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[Fact]
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public void FramaIndicator_Initialize_CreatesLineSeries()
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{
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var indicator = new FramaIndicator { Period = 10 };
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indicator.Initialize();
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Assert.Single(indicator.LinesSeries);
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}
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[Fact]
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public void FramaIndicator_ProcessUpdate_HistoricalBar_ComputesValue()
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{
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var indicator = new FramaIndicator { Period = 4 };
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indicator.Initialize();
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var now = DateTime.UtcNow;
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int warmup = indicator.Period % 2 == 0 ? indicator.Period : indicator.Period + 1;
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for (int i = 0; i < warmup; i++)
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{
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indicator.HistoricalData.AddBar(now.AddMinutes(i), 100, 105, 95, 102);
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indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
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}
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Assert.Equal(warmup, indicator.LinesSeries[0].Count);
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Assert.True(double.IsFinite(indicator.LinesSeries[0].GetValue(0)));
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}
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[Fact]
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public void FramaIndicator_ProcessUpdate_NewBar_ComputesValue()
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{
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var indicator = new FramaIndicator { Period = 4 };
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indicator.Initialize();
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var now = DateTime.UtcNow;
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indicator.HistoricalData.AddBar(now, 100, 105, 95, 102);
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indicator.HistoricalData.AddBar(now.AddMinutes(1), 102, 108, 100, 106);
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indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
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indicator.ProcessUpdate(new UpdateArgs(UpdateReason.NewBar));
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Assert.Equal(2, indicator.LinesSeries[0].Count);
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}
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[Fact]
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public void FramaIndicator_ProcessUpdate_NewTick_ProcessesWithoutError()
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{
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var indicator = new FramaIndicator { Period = 4 };
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indicator.Initialize();
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var now = DateTime.UtcNow;
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int warmup = indicator.Period % 2 == 0 ? indicator.Period : indicator.Period + 1;
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for (int i = 0; i < warmup; i++)
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{
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indicator.HistoricalData.AddBar(now.AddMinutes(i), 100, 105, 95, 102);
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indicator.ProcessUpdate(new UpdateArgs(UpdateReason.HistoricalBar));
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}
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double firstValue = indicator.LinesSeries[0].GetValue(0);
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indicator.ProcessUpdate(new UpdateArgs(UpdateReason.NewTick));
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double secondValue = indicator.LinesSeries[0].GetValue(0);
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Assert.True(double.IsFinite(firstValue));
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Assert.True(double.IsFinite(secondValue));
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}
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}
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@@ -0,0 +1,160 @@
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using System;
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using System.Collections.Generic;
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namespace QuanTAlib.Tests;
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public class FramaTests
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{
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[Fact]
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public void Frama_Constructor_ValidatesInput()
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{
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Assert.Throws<ArgumentOutOfRangeException>(() => new Frama(1));
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Assert.Throws<ArgumentOutOfRangeException>(() => new Frama(0));
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Assert.Throws<ArgumentOutOfRangeException>(() => new Frama(-5));
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}
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[Fact]
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public void Frama_BasicCalculation_ReturnsFinite()
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{
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var frama = new Frama(16);
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var series = BuildSeries(40, seed: 42);
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TValue result = default;
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for (int i = 0; i < series.Count; i++)
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{
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result = frama.Update(series[i], isNew: true);
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}
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Assert.True(double.IsFinite(result.Value));
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Assert.True(frama.IsHot);
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}
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[Fact]
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public void Frama_IsNewFalse_RestoresState()
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{
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var frama = new Frama(16);
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var series = BuildSeries(20, seed: 7);
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TBar lastBar = default;
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for (int i = 0; i < 10; i++)
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{
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lastBar = series[i];
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frama.Update(lastBar, isNew: true);
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}
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double original = frama.Last.Value;
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var corrected = new TBar(lastBar.Time, lastBar.Open, lastBar.High * 1.05, lastBar.Low * 0.95, lastBar.Close, lastBar.Volume);
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frama.Update(corrected, isNew: false);
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frama.Update(lastBar, isNew: false);
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Assert.Equal(original, frama.Last.Value, precision: 10);
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}
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[Fact]
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public void Frama_NaNFirstBar_RecoversOnValidInput()
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{
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var frama = new Frama(10);
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int warmup = frama.WarmupPeriod;
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var nanBar = new TBar(DateTime.UtcNow.Ticks, 1, double.NaN, 1, 1, 0);
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TValue first = frama.Update(nanBar, isNew: true);
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Assert.True(double.IsNaN(first.Value));
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DateTime start = DateTime.UtcNow.AddMinutes(1);
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TValue next = default;
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for (int i = 0; i < warmup; i++)
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{
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var valid = new TBar(start.AddMinutes(i).Ticks, 100, 110, 90, 105, 1000);
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next = frama.Update(valid, isNew: true);
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}
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Assert.True(double.IsFinite(next.Value));
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Assert.True(frama.IsHot);
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}
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[Fact]
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public void Frama_BatchMatchesStreaming()
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{
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int period = 20;
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var series = BuildSeries(80, seed: 11);
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TSeries batch = FramaBatch(series, period);
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var frama = new Frama(period);
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var streamValues = new List<double>(series.Count);
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for (int i = 0; i < series.Count; i++)
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{
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streamValues.Add(frama.Update(series[i]).Value);
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}
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for (int i = 0; i < series.Count; i++)
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{
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Assert.Equal(batch[i].Value, streamValues[i], precision: 10);
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}
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}
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[Fact]
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public void Frama_SpanMatchesBatch()
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{
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int period = 18;
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var series = BuildSeries(60, seed: 21);
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double[] output = new double[series.Count];
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Frama.Batch(series.High.Values, series.Low.Values, period, output);
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TSeries batch = FramaBatch(series, period);
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for (int i = 0; i < series.Count; i++)
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{
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Assert.Equal(batch[i].Value, output[i], precision: 10);
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}
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}
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[Fact]
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public void Frama_Eventing_WorksWithTSeries()
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{
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int period = 12;
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var source = new TSeries();
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var frama = new Frama(source, period);
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int count = 0;
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frama.Pub += (object? sender, in TValueEventArgs args) => count++;
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var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1, seed: 31);
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for (int i = 0; i < 25; i++)
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{
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var bar = gbm.Next(isNew: true);
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source.Add(bar.Time, bar.Close);
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}
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Assert.Equal(25, count);
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}
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[Fact]
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public void Frama_WarmupPeriod_TransitionsIsHot()
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{
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var frama = new Frama(15);
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int warmup = frama.WarmupPeriod;
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var series = BuildSeries(warmup, seed: 100);
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for (int i = 0; i < warmup - 1; i++)
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{
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frama.Update(series[i], isNew: true);
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Assert.False(frama.IsHot);
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}
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frama.Update(series[warmup - 1], isNew: true);
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Assert.True(frama.IsHot);
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}
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private static TBarSeries BuildSeries(int count, int seed)
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{
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var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1, seed: seed);
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return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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}
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private static TSeries FramaBatch(TBarSeries series, int period)
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{
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return Frama.Batch(series, period);
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}
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}
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@@ -0,0 +1,203 @@
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using System;
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using OoplesFinance.StockIndicators;
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using OoplesFinance.StockIndicators.Models;
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namespace QuanTAlib.Tests;
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public class FramaValidationTests
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{
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[Fact]
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public void Frama_Streaming_MatchesReference()
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{
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int period = 16;
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TBarSeries series = BuildSeries(200, seed: 5);
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double[] reference = new double[series.Count];
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ReferenceFrama(series.High.Values, series.Low.Values, period, reference);
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var frama = new Frama(period);
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for (int i = 0; i < series.Count; i++)
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{
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double actual = frama.Update(series[i], isNew: true).Value;
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Assert.Equal(reference[i], actual, precision: 10);
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}
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}
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[Fact]
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public void Frama_Batch_MatchesReference()
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{
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int period = 20;
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TBarSeries series = BuildSeries(180, seed: 7);
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double[] reference = new double[series.Count];
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ReferenceFrama(series.High.Values, series.Low.Values, period, reference);
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TSeries batch = Frama.Batch(series, period);
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for (int i = 0; i < series.Count; i++)
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{
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Assert.Equal(reference[i], batch[i].Value, precision: 10);
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}
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}
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[Fact]
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public void Frama_Span_MatchesReference()
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{
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int period = 24;
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TBarSeries series = BuildSeries(160, seed: 11);
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double[] output = new double[series.Count];
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double[] reference = new double[series.Count];
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ReferenceFrama(series.High.Values, series.Low.Values, period, reference);
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Frama.Batch(series.High.Values, series.Low.Values, period, output);
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for (int i = 0; i < series.Count; i++)
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{
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Assert.Equal(reference[i], output[i], precision: 10);
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}
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}
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private static void ReferenceFrama(ReadOnlySpan<double> high, ReadOnlySpan<double> low, int period, Span<double> output)
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{
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int pe = (period % 2 == 0) ? period : period + 1;
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int h = pe / 2;
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double lastHigh = double.NaN;
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double lastLow = double.NaN;
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double fr = double.NaN;
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bool hasValue = false;
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for (int i = 0; i < high.Length; i++)
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{
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double highVal = high[i];
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double lowVal = low[i];
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if (!double.IsFinite(highVal) || !double.IsFinite(lowVal))
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{
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if (!double.IsFinite(lastHigh) || !double.IsFinite(lastLow))
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{
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output[i] = double.NaN;
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continue;
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}
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highVal = lastHigh;
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lowVal = lastLow;
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}
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lastHigh = highVal;
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lastLow = lowVal;
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if (i < pe - 1)
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{
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output[i] = double.NaN;
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continue;
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}
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double maxRecent = double.MinValue;
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double minRecent = double.MaxValue;
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double maxPrev = double.MinValue;
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double minPrev = double.MaxValue;
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double maxFull = double.MinValue;
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double minFull = double.MaxValue;
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int startFull = i - pe + 1;
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int startRecent = i - h + 1;
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for (int j = startFull; j <= i; j++)
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{
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double hv = high[j];
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double lv = low[j];
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if (!double.IsFinite(hv) || !double.IsFinite(lv))
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{
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hv = lastHigh;
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lv = lastLow;
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}
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if (hv > maxFull)
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{
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maxFull = hv;
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}
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if (lv < minFull)
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{
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minFull = lv;
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}
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if (j >= startRecent)
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{
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if (hv > maxRecent)
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{
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maxRecent = hv;
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}
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if (lv < minRecent)
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{
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minRecent = lv;
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}
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}
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else
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{
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if (hv > maxPrev)
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{
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maxPrev = hv;
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}
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if (lv < minPrev)
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{
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minPrev = lv;
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}
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}
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}
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double n1 = (maxRecent - minRecent) / h;
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double n2 = (maxPrev - minPrev) / h;
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double n3 = (maxFull - minFull) / pe;
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double alpha = 1.0;
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if (n1 > 0.0 && n2 > 0.0 && n3 > 0.0)
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{
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double dimen = (Math.Log(n1 + n2) - Math.Log(n3)) / 0.693147180559945309417232121458176568;
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alpha = Math.Exp(-4.6 * (dimen - 1.0));
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if (alpha < 0.01)
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{
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alpha = 0.01;
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}
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if (alpha > 1.0)
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{
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alpha = 1.0;
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}
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}
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double price = (highVal + lowVal) * 0.5;
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double prev = hasValue && double.IsFinite(fr) ? fr : price;
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fr = Math.FusedMultiplyAdd(prev, 1.0 - alpha, alpha * price);
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hasValue = true;
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output[i] = fr;
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}
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}
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private static TBarSeries BuildSeries(int count, int seed)
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{
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var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1, seed: seed);
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return gbm.Fetch(count, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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}
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[Fact]
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public void Frama_MatchesOoples_Structural()
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{
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var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.15, seed: 42);
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var bars = gbm.Fetch(500, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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var ooplesData = bars.Select(b => new TickerData
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{
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Date = new DateTime(b.Time, DateTimeKind.Utc),
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Open = b.Open, High = b.High, Low = b.Low,
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Close = b.Close, Volume = b.Volume
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}).ToList();
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var result = new StockData(ooplesData).CalculateEhlersFractalAdaptiveMovingAverage();
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var values = result.CustomValuesList;
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int finiteCount = values.Count(v => double.IsFinite(v));
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Assert.True(finiteCount > 100, $"Expected >100 finite values, got {finiteCount}");
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}
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}
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