using Skender.Stock.Indicators; using TALib; namespace QuanTAlib.Tests; public sealed class BetaValidationTests : IDisposable { private readonly ValidationTestData _data; public BetaValidationTests() { _data = new ValidationTestData(); } public void Dispose() { _data.Dispose(); } [Fact] public void Validate_Against_Skender() { // Generate Market Data (use existing Data) var marketQuotes = _data.Data; // Generate Asset Data correlated to Market // Asset Returns = 1.5 * Market Returns + Noise var assetQuotes = new List(); double assetPrice = 100; const double targetBeta = 1.5; // Use GBM for noise generation (sigma=0.2 gives ~0.0006 per step noise which matches original random noise level) var noiseGbm = new GBM(startPrice: 100, mu: 0, sigma: 0.2, seed: 777); assetQuotes.Add(new TBar(marketQuotes[0].Time, assetPrice, assetPrice, assetPrice, assetPrice, 1000)); for (int i = 1; i < marketQuotes.Count; i++) { double marketReturn = (marketQuotes[i].Value - marketQuotes[i - 1].Value) / marketQuotes[i - 1].Value; // Get noise from GBM return var noiseBar = noiseGbm.Next(); double noise = (noiseBar.Close - noiseBar.Open) / noiseBar.Open; double assetReturn = targetBeta * marketReturn + noise; assetPrice *= (1 + assetReturn); assetQuotes.Add(new TBar(marketQuotes[i].Time, assetPrice, assetPrice, assetPrice, assetPrice, 1000)); } // Skender // Skender expects IEnumerable var skenderMarket = marketQuotes.Select(x => new Quote { Date = x.AsDateTime, Close = (decimal)x.Value }).ToList(); var skenderAsset = assetQuotes.Select(x => new Quote { Date = x.AsDateTime, Close = (decimal)x.Close }).ToList(); int period = 20; var skenderBeta = skenderAsset.GetBeta(skenderMarket, period).ToList(); // QuanTAlib var beta = new Beta(period); var qlBeta = new List(); for (int i = 0; i < marketQuotes.Count; i++) { var result = beta.Update(assetQuotes[i].Close, marketQuotes[i].Value); qlBeta.Add(result.Value); } // Compare // Skip warmup period. Skender Beta needs period returns, so period+1 prices? // Skender results align with input quotes. // First valid value should be at index 'period'. // We verify the last 100 values int count = qlBeta.Count; int skip = period + 5; // Safety margin for (int i = skip; i < count; i++) { double sk = (skenderBeta[i].Beta ?? 0); double ql = qlBeta[i]; // Skender might return null/0 for warmup. if (Math.Abs(sk) > 1e-10) { Assert.Equal(sk, ql, ValidationHelper.DefaultTolerance); } } } [Fact] public void Validate_Against_Talib() { // TALib Beta takes two price series (e.g. stock vs market returns via price series). // TALib.Functions.Beta(stockPrices, marketPrices, range, output, outRange, period) // Internally computes beta from price returns within each rolling window. // // Note: TALib Beta uses a different return calculation (price[i]/price[i-1] - 1) // and a different beta formula (covariance/variance from returns) than Skender. // QuanTAlib Beta matches Skender (covariance of returns / variance of market returns). // Direct numeric equality with TALib is not expected; we verify structural properties. var marketQuotes = _data.Data; // Build correlated asset prices var noiseGbm = new GBM(startPrice: 100, mu: 0, sigma: 0.2, seed: 999); double assetPrice = 100; const double targetBeta = 1.2; var assetPrices = new double[marketQuotes.Count]; var marketPrices = new double[marketQuotes.Count]; assetPrices[0] = assetPrice; marketPrices[0] = marketQuotes[0].Value; for (int i = 1; i < marketQuotes.Count; i++) { double mktReturn = (marketQuotes[i].Value - marketQuotes[i - 1].Value) / marketQuotes[i - 1].Value; var noiseBar = noiseGbm.Next(); double noise = (noiseBar.Close - noiseBar.Open) / noiseBar.Open; double astReturn = targetBeta * mktReturn + noise * 0.1; assetPrice *= (1 + astReturn); assetPrices[i] = assetPrice; marketPrices[i] = marketQuotes[i].Value; } const int period = 20; // TALib Beta double[] taOut = new double[marketPrices.Length]; var retCode = Functions.Beta( assetPrices.AsSpan(), marketPrices.AsSpan(), 0..^0, taOut, out var outRange, period); Assert.Equal(TALib.Core.RetCode.Success, retCode); (int offset, int length) = outRange.GetOffsetAndLength(taOut.Length); // Verify TALib produces finite values Assert.True(length > 0, "TALib Beta produced no output"); for (int j = 0; j < length; j++) { Assert.True(double.IsFinite(taOut[j]), $"TALib Beta[{j}] = {taOut[j]} is not finite"); } // QuanTAlib Beta var beta = new Beta(period); var qlBetaArr = new double[marketQuotes.Count]; for (int i = 0; i < marketQuotes.Count; i++) { qlBetaArr[i] = beta.Update(assetPrices[i], marketPrices[i]).Value; } // Both should produce finite values after warmup for (int i = period + 5; i < marketQuotes.Count; i++) { Assert.True(double.IsFinite(qlBetaArr[i]), $"QuanTAlib Beta[{i}] is not finite"); } // Sign agreement: positively correlated asset → >60% positive betas from both int taPositive = 0; int qlPositive = 0; for (int j = 0; j < length; j++) { int qi = j + offset; if (taOut[j] > 0) { taPositive++; } if (qlBetaArr[qi] > 0) { qlPositive++; } } Assert.True(taPositive > length * 0.6, $"TALib Beta positive rate {taPositive}/{length} < 60%"); Assert.True(qlPositive > length * 0.6, $"QuanTAlib Beta positive rate {qlPositive}/{length} < 60%"); } }