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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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namespace QuanTAlib.Tests;
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public class SlopeTests
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{
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[Fact]
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public void Properties_Accessible()
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{
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var slope = new Slope();
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Assert.Equal(0, slope.Last.Value);
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Assert.False(slope.IsHot);
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Assert.Contains("Slope", slope.Name, StringComparison.Ordinal);
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Assert.Equal(2, slope.WarmupPeriod);
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}
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[Fact]
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public void Calc_IsNew_False_UpdatesValue()
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{
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var slope = new Slope();
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slope.Update(new TValue(DateTime.UtcNow, 10));
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slope.Update(new TValue(DateTime.UtcNow, 20));
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double valueBefore = slope.Last.Value;
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// Update with isNew=false should change the result
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slope.Update(new TValue(DateTime.UtcNow, 100), isNew: false);
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double valueAfter = slope.Last.Value;
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Assert.NotEqual(valueBefore, valueAfter);
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}
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[Fact]
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public void NaN_Input_UsesLastValidValue()
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{
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var slope = new Slope();
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slope.Update(new TValue(DateTime.UtcNow, 10));
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slope.Update(new TValue(DateTime.UtcNow, 20));
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var result = slope.Update(new TValue(DateTime.UtcNow, double.NaN));
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Assert.True(double.IsFinite(result.Value));
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}
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[Fact]
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public void Infinity_Input_UsesLastValidValue()
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{
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var slope = new Slope();
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slope.Update(new TValue(DateTime.UtcNow, 10));
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slope.Update(new TValue(DateTime.UtcNow, 20));
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var resultPosInf = slope.Update(new TValue(DateTime.UtcNow, double.PositiveInfinity));
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Assert.True(double.IsFinite(resultPosInf.Value));
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var resultNegInf = slope.Update(new TValue(DateTime.UtcNow, double.NegativeInfinity));
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Assert.True(double.IsFinite(resultNegInf.Value));
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}
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[Fact]
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public void IterativeCorrections_RestoreToOriginalState()
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{
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var slope = new Slope();
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var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1, seed: 42);
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// Feed 10 new values
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TValue tenthInput = default;
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for (int i = 0; i < 10; i++)
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{
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var bar = gbm.Next(isNew: true);
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tenthInput = new TValue(bar.Time, bar.Close);
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slope.Update(tenthInput, isNew: true);
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}
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// Remember state after 10 values
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double stateAfterTen = slope.Last.Value;
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// Generate 9 corrections with isNew=false (different values)
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for (int i = 0; i < 9; i++)
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{
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var bar = gbm.Next(isNew: false);
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slope.Update(new TValue(bar.Time, bar.Close), isNew: false);
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}
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// Feed the remembered 10th input again with isNew=false
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TValue finalResult = slope.Update(tenthInput, isNew: false);
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// State should match the original state after 10 values
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Assert.Equal(stateAfterTen, finalResult.Value, 1e-9);
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}
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[Fact]
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public void SpanBatch_ValidatesInput()
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{
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double[] source = [1, 2, 3, 4, 5];
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double[] wrongSizeOutput = new double[3];
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// Output must be same length as source
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Assert.Throws<ArgumentException>(() =>
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Slope.Batch(source.AsSpan(), wrongSizeOutput.AsSpan()));
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}
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[Fact]
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public void AllModes_ProduceSameResult()
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{
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var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
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var bars = gbm.Fetch(1000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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var series = bars.Close;
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// 1. Batch Mode (static span)
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var tValues = series.Values.ToArray();
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var batchOutput = new double[tValues.Length];
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Slope.Batch(tValues, batchOutput);
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double expected = batchOutput[^1];
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// 2. Streaming Mode
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var streamingInd = new Slope();
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for (int i = 0; i < series.Count; i++)
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{
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streamingInd.Update(series[i]);
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}
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double streamingResult = streamingInd.Last.Value;
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// 3. TSeries Batch Mode
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var batchSeriesResult = Slope.Batch(series);
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double tseriesResult = batchSeriesResult.Last.Value;
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Assert.Equal(expected, streamingResult, precision: 9);
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Assert.Equal(expected, tseriesResult, precision: 9);
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}
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[Fact]
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public void Calculation_KnownValues()
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{
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// slope[i] = source[i] - source[i-1]
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// Data: 10, 20, 25, 30, 28
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// Slopes: 0, 10, 5, 5, -2
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double[] data = [10, 20, 25, 30, 28];
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double[] expected = [0, 10, 5, 5, -2];
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var slope = new Slope();
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for (int i = 0; i < data.Length; i++)
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{
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var result = slope.Update(new TValue(DateTime.UtcNow, data[i]));
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Assert.Equal(expected[i], result.Value, precision: 9);
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}
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}
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[Fact]
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public void IsHot_BecomesTrueAfterWarmup()
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{
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var slope = new Slope();
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Assert.False(slope.IsHot);
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slope.Update(new TValue(DateTime.UtcNow, 10));
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Assert.False(slope.IsHot);
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slope.Update(new TValue(DateTime.UtcNow, 20));
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Assert.True(slope.IsHot);
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}
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[Fact]
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public void Reset_ClearsState()
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{
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var slope = new Slope();
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for (int i = 0; i < 10; i++)
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{
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slope.Update(new TValue(DateTime.UtcNow, i));
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}
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Assert.True(slope.IsHot);
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slope.Reset();
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Assert.False(slope.IsHot);
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Assert.Equal(0, slope.Last.Value);
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}
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[Fact]
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public void Batch_Matches_Iterative()
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{
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int count = 1000;
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var data = new double[count];
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var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
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for (int i = 0; i < count; i++)
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{
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data[i] = gbm.Next().Close;
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}
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// Iterative
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var slope = new Slope();
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var iterativeResults = new double[count];
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for (int i = 0; i < count; i++)
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{
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slope.Update(new TValue(DateTime.UtcNow, data[i]));
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iterativeResults[i] = slope.Last.Value;
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}
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// Batch
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var batchResults = new double[count];
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Slope.Batch(data, batchResults);
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// Compare
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for (int i = 0; i < count; i++)
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{
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Assert.Equal(iterativeResults[i], batchResults[i], precision: 9);
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}
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}
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[Fact]
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public void Update_TSeries_Matches_Iterative()
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{
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int count = 1000;
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var data = new TSeries();
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var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
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for (int i = 0; i < count; i++)
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{
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var bar = gbm.Next();
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data.Add(new TValue(bar.Time, bar.Close));
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}
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// Iterative
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var slope = new Slope();
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var iterativeResults = new double[count];
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for (int i = 0; i < count; i++)
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{
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slope.Update(data[i]);
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iterativeResults[i] = slope.Last.Value;
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}
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// TSeries Batch
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var slopeBatch = new Slope();
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var batchSeries = slopeBatch.Update(data);
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// Compare
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for (int i = 0; i < count; i++)
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{
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Assert.Equal(iterativeResults[i], batchSeries[i].Value, precision: 9);
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}
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}
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[Fact]
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public void EventSubscription_Works()
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{
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var source = new TSeries();
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var slope = new Slope(source);
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source.Add(new TValue(DateTime.UtcNow, 10));
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source.Add(new TValue(DateTime.UtcNow, 20));
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Assert.True(slope.IsHot);
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Assert.Equal(10, slope.Last.Value);
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}
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}
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@@ -0,0 +1,173 @@
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using Skender.Stock.Indicators;
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namespace QuanTAlib.Tests;
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/// <summary>
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/// Validation tests for Slope using synthetic data with known mathematical results.
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/// </summary>
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public class SlopeValidationTests
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{
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[Fact]
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public void LinearSequence_ProducesConstantSlope()
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{
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// Linear sequence: 0, 2, 4, 6, 8, 10 (slope = 2)
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double[] data = [0, 2, 4, 6, 8, 10];
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double[] expected = [0, 2, 2, 2, 2, 2]; // First is 0 (no history), rest are 2
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var slope = new Slope();
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for (int i = 0; i < data.Length; i++)
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{
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var result = slope.Update(new TValue(DateTime.UtcNow, data[i]));
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Assert.Equal(expected[i], result.Value, precision: 9);
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}
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}
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[Fact]
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public void ConstantSequence_ProducesZeroSlope()
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{
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// Constant sequence: 5, 5, 5, 5, 5 (slope = 0)
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double[] data = [5, 5, 5, 5, 5];
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double[] expected = [0, 0, 0, 0, 0];
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var slope = new Slope();
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for (int i = 0; i < data.Length; i++)
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{
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var result = slope.Update(new TValue(DateTime.UtcNow, data[i]));
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Assert.Equal(expected[i], result.Value, precision: 9);
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}
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}
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[Fact]
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public void DecreasingSequence_ProducesNegativeSlope()
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{
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// Decreasing sequence: 10, 7, 4, 1, -2 (slope = -3)
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double[] data = [10, 7, 4, 1, -2];
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double[] expected = [0, -3, -3, -3, -3];
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var slope = new Slope();
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for (int i = 0; i < data.Length; i++)
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{
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var result = slope.Update(new TValue(DateTime.UtcNow, data[i]));
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Assert.Equal(expected[i], result.Value, precision: 9);
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}
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}
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[Fact]
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public void QuadraticSequence_ProducesLinearSlope()
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{
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// Quadratic sequence: 0, 1, 4, 9, 16, 25 (x^2)
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// Slope: n^2 - (n-1)^2 = 2n - 1 → 1, 3, 5, 7, 9
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double[] data = [0, 1, 4, 9, 16, 25];
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double[] expected = [0, 1, 3, 5, 7, 9];
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var slope = new Slope();
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for (int i = 0; i < data.Length; i++)
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{
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var result = slope.Update(new TValue(DateTime.UtcNow, data[i]));
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Assert.Equal(expected[i], result.Value, precision: 9);
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}
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}
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[Fact]
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public void AlternatingSequence_ProducesAlternatingSlope()
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{
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// Alternating: 0, 10, 0, 10, 0
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double[] data = [0, 10, 0, 10, 0];
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double[] expected = [0, 10, -10, 10, -10];
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var slope = new Slope();
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for (int i = 0; i < data.Length; i++)
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{
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var result = slope.Update(new TValue(DateTime.UtcNow, data[i]));
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Assert.Equal(expected[i], result.Value, precision: 9);
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}
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}
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[Fact]
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public void FibonacciSequence_ProducesCorrectSlope()
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{
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// Fibonacci: 1, 1, 2, 3, 5, 8, 13
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// Slope: 0, 1, 1, 2, 3, 5
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double[] data = [1, 1, 2, 3, 5, 8, 13];
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double[] expected = [0, 0, 1, 1, 2, 3, 5];
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var slope = new Slope();
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for (int i = 0; i < data.Length; i++)
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{
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var result = slope.Update(new TValue(DateTime.UtcNow, data[i]));
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Assert.Equal(expected[i], result.Value, precision: 9);
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}
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}
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[Fact]
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public void BatchCalculation_MatchesSyntheticData()
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{
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double[] data = [0, 2, 4, 6, 8, 10];
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double[] expected = [0, 2, 2, 2, 2, 2];
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double[] output = new double[data.Length];
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Slope.Batch(data, output);
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for (int i = 0; i < data.Length; i++)
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{
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Assert.Equal(expected[i], output[i], precision: 9);
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}
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}
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// === Skender Cross-Validation ===
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/// <summary>
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/// Structural validation against Skender <c>GetSlope</c>.
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/// Skender Slope computes linear regression slope over a lookback window,
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/// while QuanTAlib Slope computes simple first difference (current - previous).
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/// Different formulas mean numeric equality is not expected.
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/// Both must produce finite output and agree on trend direction for simple linear data.
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/// </summary>
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[Fact]
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public void Validate_Skender_Slope_Structural()
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{
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using var data = new ValidationTestData();
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const int period = 14;
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// QuanTAlib Slope (streaming, simple difference)
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var slope = new Slope();
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var qResults = new List<double>();
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foreach (var tv in data.Data)
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{
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qResults.Add(slope.Update(tv).Value);
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}
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// Skender Slope (linear regression slope)
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var sResult = data.SkenderQuotes.GetSlope(period).ToList();
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// Structural: both produce finite output after warmup
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Assert.True(double.IsFinite(slope.Last.Value), "QuanTAlib Slope last must be finite");
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int finiteCount = sResult.Count(r => r.Slope is not null && double.IsFinite(r.Slope.Value));
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Assert.True(finiteCount > 100, $"Skender Slope should produce >100 finite values, got {finiteCount}");
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}
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[Fact]
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public void LargeLinearSequence_ProducesConstantSlope()
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{
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// Generate 1000 points with slope = 0.5
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int count = 1000;
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double[] data = new double[count];
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for (int i = 0; i < count; i++)
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{
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data[i] = 100.0 + i * 0.5;
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}
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var slope = new Slope();
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// First element - no previous value, slope = 0
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slope.Update(new TValue(DateTime.UtcNow, data[0]));
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Assert.Equal(0.0, slope.Last.Value, precision: 9);
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// Rest should have constant slope of 0.5
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for (int i = 1; i < count; i++)
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{
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slope.Update(new TValue(DateTime.UtcNow, data[i]));
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Assert.Equal(0.5, slope.Last.Value, precision: 9);
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}
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}
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}
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