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https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-18 02:28:05 +00:00
Refactor documentation links in numerics, oscillators, reversals, and statistics modules to use relative paths; update Bias class to handle division by zero more robustly; remove obsolete CUMMEAN Pine script; enhance trend indicators documentation; add Visual Studio Code workspace configuration.
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Miha Kralj
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using Xunit;
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namespace QuanTAlib.Tests;
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/// <summary>
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/// Validation tests for DSP (Detrended Synthetic Price).
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/// DSP is Ehlers' indicator not commonly implemented in trading libraries
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/// (TA-Lib, Skender, Tulip), so validation is done against mathematical properties
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/// and known theoretical results based on the original PineScript implementation.
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/// </summary>
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public class DspValidationTests
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{
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private const double Tolerance = 1e-9;
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#region Mathematical Property Validation
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[Fact]
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public void Validation_ConstantSeries_DspConvergesToZero()
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{
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// For constant input, both EMAs converge to the same value
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// DSP = fast_ema - slow_ema = constant - constant = 0
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var dsp = new Dsp(40);
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for (int i = 0; i < 500; i++)
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{
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dsp.Update(new TValue(DateTime.UtcNow.AddSeconds(i), 100.0));
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}
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Assert.Equal(0.0, dsp.Last.Value, Tolerance);
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}
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[Fact]
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public void Validation_OscillatesAroundZero()
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{
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// DSP should oscillate around zero over time
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var dsp = new Dsp(40);
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var values = new List<double>();
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var gbm = new GBM(seed: 42);
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var bars = gbm.Fetch(500, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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foreach (var bar in bars)
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{
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dsp.Update(new TValue(bar.Time, bar.Close));
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if (dsp.IsHot)
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{
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values.Add(dsp.Last.Value);
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}
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}
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// Should have both positive and negative values
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int positiveCount = values.Count(v => v > 0);
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int negativeCount = values.Count(v => v < 0);
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Assert.True(positiveCount > 0, "Should have positive DSP values");
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Assert.True(negativeCount > 0, "Should have negative DSP values");
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}
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[Fact]
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public void Validation_ZeroCrossings_IndicateMomentumShifts()
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{
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// DSP should cross zero when momentum shifts
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var dsp = new Dsp(20);
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var values = new List<double>();
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// Generate sine wave to simulate price oscillation
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for (int i = 0; i < 200; i++)
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{
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double price = 100.0 + 10.0 * Math.Sin(i * 0.1);
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dsp.Update(new TValue(DateTime.UtcNow.AddSeconds(i), price));
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if (dsp.IsHot)
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{
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values.Add(dsp.Last.Value);
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}
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}
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// Count zero crossings
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int crossings = 0;
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for (int i = 1; i < values.Count; i++)
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{
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if (values[i - 1] * values[i] < 0)
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{
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crossings++;
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}
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}
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// Should have multiple zero crossings for oscillating price
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Assert.True(crossings >= 3, $"Should have multiple zero crossings, got {crossings}");
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}
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#endregion
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#region PineScript Formula Verification
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[Fact]
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public void Validation_PeriodCalculation_QuarterAndHalfCycle()
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{
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// Verify period calculations match PineScript
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// For period = 40:
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// fast_period = max(2, round(40/4)) = max(2, 10) = 10
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// slow_period = max(3, round(40/2)) = max(3, 20) = 20
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const int period = 40;
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int expectedFast = Math.Max(2, (int)Math.Round(period / 4.0));
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int expectedSlow = Math.Max(3, (int)Math.Round(period / 2.0));
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Assert.Equal(10, expectedFast);
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Assert.Equal(20, expectedSlow);
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// The indicator should use these periods internally
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var dsp = new Dsp(period);
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Assert.True(dsp.Name.Contains("40", StringComparison.Ordinal));
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}
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[Fact]
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public void Validation_SmallPeriod_MinimumPeriodClamping()
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{
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// For period = 4:
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// fast_period = max(2, round(4/4)) = max(2, 1) = 2
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// slow_period = max(3, round(4/2)) = max(3, 2) = 3
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const int period = 4;
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int expectedFast = Math.Max(2, (int)Math.Round(period / 4.0));
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int expectedSlow = Math.Max(3, (int)Math.Round(period / 2.0));
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Assert.Equal(2, expectedFast);
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Assert.Equal(3, expectedSlow);
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// Indicator should still work with minimum period
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var dsp = new Dsp(period);
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dsp.Update(new TValue(DateTime.UtcNow, 100.0));
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Assert.True(double.IsFinite(dsp.Last.Value));
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}
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[Fact]
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public void Validation_EmaFormula_CorrectAlpha()
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{
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// alpha = 2 / (period + 1)
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// For fast_period = 10: alpha_fast = 2/11 ≈ 0.1818
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// For slow_period = 20: alpha_slow = 2/21 ≈ 0.0952
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const int period = 40;
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int fastPeriod = Math.Max(2, (int)Math.Round(period / 4.0));
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int slowPeriod = Math.Max(3, (int)Math.Round(period / 2.0));
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double alphaFast = 2.0 / (fastPeriod + 1);
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double alphaSlow = 2.0 / (slowPeriod + 1);
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Assert.Equal(2.0 / 11.0, alphaFast, 1e-10);
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Assert.Equal(2.0 / 21.0, alphaSlow, 1e-10);
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}
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[Fact]
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public void Validation_DspSign_MatchesPriceDirection()
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{
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// Rising prices -> fast EMA > slow EMA -> DSP > 0
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// Falling prices -> fast EMA < slow EMA -> DSP < 0
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var dspUp = new Dsp(20);
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var dspDown = new Dsp(20);
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// Uptrend
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for (int i = 0; i < 100; i++)
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{
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dspUp.Update(new TValue(DateTime.UtcNow.AddSeconds(i), 100.0 + i));
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}
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// Downtrend
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for (int i = 0; i < 100; i++)
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{
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dspDown.Update(new TValue(DateTime.UtcNow.AddSeconds(i), 200.0 - i));
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}
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Assert.True(dspUp.Last.Value > 0, $"Uptrend DSP should be positive, got {dspUp.Last.Value}");
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Assert.True(dspDown.Last.Value < 0, $"Downtrend DSP should be negative, got {dspDown.Last.Value}");
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}
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#endregion
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#region Streaming vs Batch Consistency
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[Theory]
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[InlineData(42)]
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[InlineData(123)]
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[InlineData(999)]
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public void Validation_StreamingMatchesBatch(int seed)
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{
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const int period = 40;
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const int dataLen = 100;
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var gbm = new GBM(seed: seed);
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var bars = gbm.Fetch(dataLen, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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// Streaming
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var streaming = new Dsp(period);
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foreach (var bar in bars)
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{
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streaming.Update(new TValue(bar.Time, bar.Close));
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}
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// Batch via TSeries
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var tSeries = new TSeries();
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foreach (var bar in bars)
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{
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tSeries.Add(new TValue(bar.Time, bar.Close));
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}
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var batch = Dsp.Calculate(tSeries, period);
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// Compare last values
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Assert.Equal(batch[^1].Value, streaming.Last.Value, Tolerance);
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}
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[Fact]
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public void Validation_SpanMatchesTSeries()
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{
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const int period = 20;
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const int dataLen = 200;
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var gbm = new GBM(seed: 77);
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var bars = gbm.Fetch(dataLen, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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// TSeries approach
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var tSeries = new TSeries();
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foreach (var bar in bars)
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{
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tSeries.Add(new TValue(bar.Time, bar.Close));
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}
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var tSeriesResult = Dsp.Calculate(tSeries, period);
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// Span approach
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double[] source = new double[dataLen];
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double[] spanResult = new double[dataLen];
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for (int i = 0; i < dataLen; i++)
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{
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source[i] = bars[i].Close;
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}
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Dsp.Batch(source, spanResult, period);
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// Compare all values
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for (int i = 0; i < dataLen; i++)
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{
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Assert.Equal(tSeriesResult[i].Value, spanResult[i], Tolerance);
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}
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}
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#endregion
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#region Different Period Sizes
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[Theory]
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[InlineData(4)]
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[InlineData(20)]
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[InlineData(40)]
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[InlineData(80)]
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public void Validation_DifferentPeriods_ConsistentResults(int period)
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{
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var gbm = new GBM(seed: 42);
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var bars = gbm.Fetch(500, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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var dsp = new Dsp(period);
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foreach (var bar in bars)
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{
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dsp.Update(new TValue(bar.Time, bar.Close));
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}
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Assert.True(dsp.IsHot);
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Assert.True(double.IsFinite(dsp.Last.Value));
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}
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[Theory]
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[InlineData(8)]
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[InlineData(20)]
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[InlineData(40)]
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public void Validation_LongerPeriod_SmallerMagnitude(int period)
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{
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// Longer period EMAs are closer together, resulting in smaller DSP magnitude
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var dsp = new Dsp(period);
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var magnitudes = new List<double>();
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var gbm = new GBM(seed: 42);
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var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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foreach (var bar in bars)
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{
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dsp.Update(new TValue(bar.Time, bar.Close));
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if (dsp.IsHot)
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{
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magnitudes.Add(Math.Abs(dsp.Last.Value));
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}
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}
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double avgMagnitude = magnitudes.Average();
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Assert.True(avgMagnitude > 0, "Should have non-zero average magnitude");
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}
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#endregion
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#region Edge Cases
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[Fact]
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public void Validation_VerySmallPrices_HandledCorrectly()
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{
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var dsp = new Dsp(20);
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for (int i = 0; i < 100; i++)
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{
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double price = 0.0001 + i * 0.00001;
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dsp.Update(new TValue(DateTime.UtcNow.AddSeconds(i), price));
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}
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Assert.True(dsp.IsHot);
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Assert.True(double.IsFinite(dsp.Last.Value));
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}
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[Fact]
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public void Validation_VeryLargePrices_HandledCorrectly()
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{
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var dsp = new Dsp(20);
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for (int i = 0; i < 100; i++)
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{
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double price = 1e10 + i * 1e8;
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dsp.Update(new TValue(DateTime.UtcNow.AddSeconds(i), price));
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}
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Assert.True(dsp.IsHot);
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Assert.True(double.IsFinite(dsp.Last.Value));
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}
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[Fact]
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public void Validation_HighVolatility_StableResults()
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{
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var dsp = new Dsp(20);
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var gbm = new GBM(seed: 42, sigma: 0.5); // High volatility
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var bars = gbm.Fetch(500, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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foreach (var bar in bars)
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{
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dsp.Update(new TValue(bar.Time, bar.Close));
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Assert.True(double.IsFinite(dsp.Last.Value), "DSP should remain finite under high volatility");
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}
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}
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#endregion
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#region Detrending Property
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[Fact]
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public void Validation_Detrending_RemovesTrend()
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{
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// DSP should remove the trend component
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// For a strong trend, DSP should still oscillate around zero
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var dsp = new Dsp(20);
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var values = new List<double>();
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// Strong uptrend with some noise
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for (int i = 0; i < 300; i++)
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{
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double trend = 100.0 + i * 0.5;
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double noise = Math.Sin(i * 0.3) * 2.0;
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double price = trend + noise;
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dsp.Update(new TValue(DateTime.UtcNow.AddSeconds(i), price));
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if (dsp.IsHot)
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{
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values.Add(dsp.Last.Value);
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}
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}
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// Mean should be close to some value (biased positive due to trend)
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double mean = values.Average();
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// But should still have oscillations (standard deviation > 0)
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double variance = values.Sum(v => Math.Pow(v - mean, 2)) / values.Count;
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double stdDev = Math.Sqrt(variance);
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Assert.True(stdDev > 0, "DSP should have variance indicating oscillation");
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}
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#endregion
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}
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