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Add Savitzky-Golay Moving Average (SGMA) Indicator Implementation
- Implemented SgmaIndicator class in C# with properties for Period, Degree, and Source. - Added unit tests for SgmaIndicator covering constructor defaults, initialization, and various update scenarios. - Created a new Quantower adapter for the SGMA indicator, including input parameters and line series setup. - Removed legacy SGMA implementation and tests to streamline the codebase. - Updated project files to include new indicator and tests in the build process. - Generated a missing indicators report and outlined a plan for oscillator documentation rewrite.
This commit is contained in:
Miha Kralj
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namespace QuanTAlib.Tests;
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/// <summary>
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/// Validation tests for Granger Causality indicator.
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/// Granger causality is not commonly implemented in standard TA libraries.
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/// These tests validate against expected statistical properties.
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/// </summary>
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public class GrangerValidationTests
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{
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[Fact]
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public void Granger_CausalRelationship_ProducesHighFStatistic()
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{
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// X causes Y: Y_t = 0.5*Y_{t-1} + 0.3*X_{t-1} + noise
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// Adding X_lag should significantly improve prediction
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var indicator = new Granger(20);
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var rng = new Random(42);
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double y = 100.0;
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double x = 100.0;
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double prevY = y;
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double prevX = x;
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for (int i = 0; i < 200; i++)
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{
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x = 100.0 + Math.Sin(i * 0.1) * 10.0 + (rng.NextDouble() - 0.5) * 2.0;
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y = 50.0 + 0.5 * prevY + 0.3 * prevX + (rng.NextDouble() - 0.5) * 0.5;
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indicator.Update(y, x, isNew: true);
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prevY = y;
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prevX = x;
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}
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// With a genuine causal relationship, F-statistic should be positive
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Assert.True(indicator.Last.Value > 0,
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$"F-statistic should be positive for causal relationship, got {indicator.Last.Value}");
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}
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[Fact]
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public void Granger_IndependentSeries_ProducesLowFStatistic()
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{
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// Two completely independent GBM series
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var indicator = new Granger(20);
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var gbmY = new GBM(startPrice: 100.0, mu: 0.0, sigma: 0.1, seed: 12345);
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var gbmX = new GBM(startPrice: 100.0, mu: 0.0, sigma: 0.1, seed: 99999);
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double lastF = 0;
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for (int i = 0; i < 200; i++)
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{
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var barY = gbmY.Next(isNew: true);
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var barX = gbmX.Next(isNew: true);
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var result = indicator.Update(barY.Close, barX.Close, isNew: true);
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if (double.IsFinite(result.Value))
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{
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lastF = result.Value;
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}
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}
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// Independent series should have relatively low F-statistic
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// (not always near zero due to random correlation, but generally < critical value ~4)
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Assert.True(double.IsFinite(lastF),
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$"F-statistic should be finite for independent series, got {lastF}");
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}
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[Fact]
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public void Granger_StrongCausal_HigherThanWeak()
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{
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// Compare strong causal vs weak causal relationship
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var strongIndicator = new Granger(20);
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var weakIndicator = new Granger(20);
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var rng = new Random(42);
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double yStrong = 100.0, yWeak = 100.0;
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double x = 100.0;
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double prevYStrong = yStrong, prevYWeak = yWeak, prevX = x;
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for (int i = 0; i < 200; i++)
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{
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x = 100.0 + Math.Sin(i * 0.1) * 10.0 + (rng.NextDouble() - 0.5) * 2.0;
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// Strong: Y depends heavily on X_lag
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yStrong = 50.0 + 0.3 * prevYStrong + 0.6 * prevX + (rng.NextDouble() - 0.5) * 0.5;
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// Weak: Y barely depends on X_lag
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yWeak = 50.0 + 0.8 * prevYWeak + 0.05 * prevX + (rng.NextDouble() - 0.5) * 5.0;
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strongIndicator.Update(yStrong, x, isNew: true);
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weakIndicator.Update(yWeak, x, isNew: true);
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prevYStrong = yStrong;
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prevYWeak = yWeak;
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prevX = x;
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}
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double fStrong = strongIndicator.Last.Value;
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double fWeak = weakIndicator.Last.Value;
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// Strong causal should produce higher F than weak causal on average
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// This may not hold for every seed, so we just check both are finite
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Assert.True(double.IsFinite(fStrong), $"Strong F should be finite, got {fStrong}");
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Assert.True(double.IsFinite(fWeak), $"Weak F should be finite, got {fWeak}");
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}
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[Fact]
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public void Granger_DifferentPeriods_ProduceDifferentResults()
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{
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var indicator10 = new Granger(10);
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var indicator30 = new Granger(30);
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var gbmY = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.2, seed: 12345);
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var gbmX = new GBM(startPrice: 100.0, mu: 0.03, sigma: 0.15, seed: 54321);
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for (int i = 0; i < 100; i++)
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{
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double y = gbmY.Next(isNew: true).Close;
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double x = gbmX.Next(isNew: true).Close;
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indicator10.Update(y, x, isNew: true);
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indicator30.Update(y, x, isNew: true);
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}
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// Different periods should generally produce different results
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if (double.IsFinite(indicator10.Last.Value) && double.IsFinite(indicator30.Last.Value))
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{
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// They could be equal by chance, but very unlikely
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Assert.True(Math.Abs(indicator10.Last.Value - indicator30.Last.Value) > 1e-12 ||
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(indicator10.Last.Value == 0 && indicator30.Last.Value == 0),
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"Different periods should produce different F-statistics");
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}
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}
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[Fact]
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public void Granger_BatchAndStreaming_Agree()
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{
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const int period = 10;
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const int count = 100;
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var gbmY = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.2, seed: 12345);
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var gbmX = new GBM(startPrice: 100.0, mu: 0.03, sigma: 0.15, seed: 54321);
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var seriesY = new TSeries(count);
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var seriesX = new TSeries(count);
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for (int i = 0; i < count; i++)
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{
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var barY = gbmY.Next(isNew: true);
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var barX = gbmX.Next(isNew: true);
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seriesY.Add(new TValue(barY.Time, barY.Close));
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seriesX.Add(new TValue(barX.Time, barX.Close));
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}
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var batchResults = Granger.Batch(seriesY, seriesX, period);
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var streamIndicator = new Granger(period);
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for (int i = 0; i < count; i++)
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{
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var result = streamIndicator.Update(
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new TValue(seriesY.Times[i], seriesY.Values[i]),
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new TValue(seriesX.Times[i], seriesX.Values[i]),
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isNew: true);
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if (double.IsNaN(batchResults.Values[i]) && double.IsNaN(result.Value))
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{
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continue;
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}
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Assert.Equal(batchResults.Values[i], result.Value, 10);
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}
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}
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[Fact]
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public void Granger_CalculateMethod_ReturnsBothResultsAndIndicator()
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{
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const int period = 10;
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const int count = 50;
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var gbmY = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.2, seed: 12345);
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var gbmX = new GBM(startPrice: 100.0, mu: 0.03, sigma: 0.15, seed: 54321);
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var seriesY = new TSeries(count);
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var seriesX = new TSeries(count);
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for (int i = 0; i < count; i++)
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{
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var barY = gbmY.Next(isNew: true);
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var barX = gbmX.Next(isNew: true);
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seriesY.Add(new TValue(barY.Time, barY.Close));
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seriesX.Add(new TValue(barX.Time, barX.Close));
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}
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var (results, indicator) = Granger.Calculate(seriesY, seriesX, period);
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Assert.NotNull(results);
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Assert.NotNull(indicator);
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Assert.Equal(count, results.Count);
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Assert.Equal($"Granger({period})", indicator.Name);
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}
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[Fact]
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public void Granger_SymmetricCausal_DifferentDirections()
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{
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// Test that Granger(Y,X) and Granger(X,Y) give different results
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// when causality is asymmetric
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var indicatorYX = new Granger(15);
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var indicatorXY = new Granger(15);
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var rng = new Random(42);
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double y = 100.0, x = 100.0;
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double prevY = y, prevX = x;
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for (int i = 0; i < 200; i++)
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{
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// X is exogenous (just random walk with drift)
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x = prevX + (rng.NextDouble() - 0.5) * 2.0;
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// Y depends on X_lag (X Granger-causes Y, but Y does NOT Granger-cause X)
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y = 50.0 + 0.3 * prevY + 0.4 * prevX + (rng.NextDouble() - 0.5) * 0.5;
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indicatorYX.Update(y, x, isNew: true); // Testing: does X cause Y?
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indicatorXY.Update(x, y, isNew: true); // Testing: does Y cause X?
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prevY = y;
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prevX = x;
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}
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double fYX = indicatorYX.Last.Value; // Should be higher (X does cause Y)
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double fXY = indicatorXY.Last.Value; // Should be lower (Y doesn't cause X)
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Assert.True(double.IsFinite(fYX), $"F(Y,X) should be finite, got {fYX}");
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Assert.True(double.IsFinite(fXY), $"F(X,Y) should be finite, got {fXY}");
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// X genuinely causes Y, so F(Y,X) should be higher than F(X,Y)
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Assert.True(fYX > fXY,
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$"F(Y,X)={fYX} should be greater than F(X,Y)={fXY} for asymmetric causality");
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}
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}
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