Список проголосовавших за комментарий "Василий Федорович, даже для подобного графика можно посчитать доминантный цикл, там в общем не особо сложно, <br/> у Джона Эллерса последняя версия, такая<br/> Запускаете для своего графика и считаете, это для WealthLab 6.9<br/> <pre>using System; using System.Collections.Generic; using System.Text; using System.Drawing; using WealthLab; using WealthLab.Indicators; namespace WealthLab.Strategies { public class TASC201609 : WealthScript { private StrategyParameter paramEnhance; public TASC201609() { paramEnhance = CreateParameter("Enhance Resolution", 0, 0, 1, 1); } protected override void Execute() { bool EnhanceResolution = paramEnhance.ValueInt == 0 ? false : true; DataSeries HP = new DataSeries(Bars, "HP"); DataSeries Filt = new DataSeries(Bars, "Filt"); DataSeries DominantCycle = new DataSeries(Bars, "DominantCycle"); double Deg2Rad = Math.PI / 180.0; double cosInDegrees = Math.Cos((.707 * 360 / 48d) * Deg2Rad); double sinInDegrees = Math.Sin((.707 * 360 / 48d) * Deg2Rad); double alpha1 = (cosInDegrees + sinInDegrees - 1) / cosInDegrees; double a1 = Math.Exp(-1.414 * Math.PI / 8.0); double b1 = 2.0 * a1 * Math.Cos((1.414 * 180d / 8.0) * Deg2Rad); double c2 = b1; double c3 = -a1 * a1; double c1 = 1 - c2 - c3; for (int bar = 2; bar < Bars.Count; bar++) { HP[bar] = 0.5*(1 + alpha1)*(Close[bar] - Close[bar-1]) + alpha1*HP[bar-1]; //Smooth with a SuperSmoother Filter Filt[bar] = c1*(HP[bar] + HP[bar-1]) / 2 + c2*Filt[bar-1] + c3*Filt[bar-2]; } DataSeries[] ds = new DataSeries[48]; ChartPane tp = CreatePane(50,false,false); for( int n = 0; n < 48; n++ ) { ds[n] = new DataSeries(Bars,n.ToString()); for( int bar = 0; bar < Bars.Count; bar++ ) ds[n][bar] = n; PlotSeries(tp, ds[n], Color.Black, LineStyle.Solid, 16); } HideVolume(); int dominantCycle = 0; int sumDominantCycle = 0; int countDominantCycle = 0; for( int bar = 0; bar < Bars.Count; bar++) { SetPaneBackgroundColor(PricePane,bar,Color.Black); int AvgLength = 3, M = 0; double X = 0, Y = 0, Sx = 0, Sy = 0, Sxx = 0, Syy = 0, Sxy = 0; double[] Corr = new double[70]; double[] CosinePart = new double[70]; double[] SinePart = new double[70]; double[] SqSum = new double[70]; double[,] R = new double[70,2]; double[] Pwr = new double[70]; //Pearson correlation for each value of lag for(int Lag = 0; Lag <= 48; Lag++) { //Set the averaging length as M M = AvgLength; if( AvgLength == 0 ) M = Lag; Sx = 0; Sy = 0; Sxx = 0; Syy = 0; Sxy = 0; for(int count = 0; count <= M-1; count++) { X = bar-count < 0 ? 0 : Filt[bar-count]; //Y = bar-Lag+count < 0 ? 0 : Filt[bar-Lag+count]; Y = bar-(Lag+count) < 0 ? 0 : Filt[bar-(Lag+count)]; // Rev.A Sx = Sx + X; Sy = Sy + Y; Sxx = Sxx + X*X; Sxy = Sxy + X*Y; Syy = Syy + Y*Y; } if( (M*Sxx - Sx*Sx)*(M*Syy - Sy*Sy) > 0 ) Corr[Lag] = (M*Sxy-Sx*Sy)/Math.Sqrt((M*Sxx - Sx*Sx)*(M*Syy - Sy*Sy)); } //Compute the Fourier Transform for each Correlation for(int Period = 0; Period <= 48; Period++) { CosinePart[Period] = 0; SinePart[Period] = 0; for(int N = 3; N <= 48; N++) { double _cosInDegrees = Math.Cos(((double)N * 360 / (double)Period) * Deg2Rad); double _sinInDegrees = Math.Sin(((double)N * 360 / (double)Period) * Deg2Rad); CosinePart[Period] = CosinePart[Period] + Corr[N]*_cosInDegrees; SinePart[Period] = SinePart[Period] + Corr[N]*_sinInDegrees; } SqSum[Period] = CosinePart[Period]*CosinePart[Period] + SinePart[Period]*SinePart[Period]; } for(int Period = 8; Period <= 48; Period++) { R[Period, 1] = R[Period, 0]; R[Period, 0] = 0.2*SqSum[Period]*SqSum[Period] + 0.8*R[Period,1]; } //Find Maximum Power Level for Normalization double MaxPwr = 0; for(int Period = 8; Period <= 48; Period++) { if( R[Period, 0] > MaxPwr ) MaxPwr = R[Period, 0]; } for(int Period = 8; Period <= 48; Period++) { Pwr[Period] = R[Period, 0] / MaxPwr; } //Optionally increase Display Resolution by raising the NormPwr to //a higher mathematically power (since the maximum amplitude is //unity, cubing all amplitudes further reduces the smaller ones) if( EnhanceResolution ) { for(int Period = 8; Period <= 48; Period++) { Pwr[Period] = Math.Pow(Pwr[Period], 3); } } //Compute the dominant cycle using the CG of the spectrum double PeakPwr = 0, Spx = 0, Sp = 0; for(int Period = 8; Period <= 48; Period++) { if( Pwr[Period] > PeakPwr ) PeakPwr = Pwr[Period]; } double spx = 0, sp = 0; for (int period = 8; period <= 48; period++) if (Pwr[period] >= 0.25) { spx += period * Pwr[period]; sp += Pwr[period]; } if (sp >= 0.25) { dominantCycle = Convert.ToInt32(spx / sp); sumDominantCycle += dominantCycle; countDominantCycle++; } // PrintDebug(bar + " - " + dominantCycle); //Plot as a Heatmap double Color1 = 255, Color2 = 0, Color3 = 0; for(int Period = 8; Period < 48; Period++) { if( Pwr[Period] > 0.5 ) { Color1 = 255; Color2 = 255*(2*Pwr[Period] - 1); } else { Color1 = 2*255*Pwr[Period]; Color2 = 0; } Color1 = Math.Min((int)Color1,255); Color1 = Math.Max((int)Color1,0); Color2 = Math.Min((int)Color2,255); Color2 = Math.Max((int)Color2,0); Color3 = Math.Min((int)Color3,255); Color3 = Math.Max((int)Color3,0); SetSeriesBarColor(bar, ds[Period], Color.FromArgb(100,(int)Color1,(int)Color2,(int)Color3) ); } } if (countDominantCycle > 20) // Если получили статистически значимую выборку доминантных циклов PrintDebug("Dom "+ Bars.Symbol +" = "+(1d * sumDominantCycle / countDominantCycle)); } } } </pre>"