namespace QuanTAlib; using System; using System.Collections.Generic; using System.Linq; /* CORR: Pearson's Correlation Coefficient PCC is a measure of linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; it is essentially a normalized measurement of the covariance, such that the result always has a value between −1 and 1. Sources: https://en.wikipedia.org/wiki/Pearson_correlation_coefficient */ public class CORR_Series : Pair_TSeries_Indicator { public CORR_Series(TSeries d1, TSeries d2, int period, bool useNaN = false) : base(d1, d2, period, useNaN) { if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } } private readonly System.Collections.Generic.List _x = new(); private readonly System.Collections.Generic.List _xx = new(); private readonly System.Collections.Generic.List _y = new(); private readonly System.Collections.Generic.List _yy = new(); private readonly System.Collections.Generic.List _xy = new(); public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) { Add_Replace_Trim(_x, TValue1.v, _p, update); Add_Replace_Trim(_xx, TValue1.v * TValue1.v, _p, update); Add_Replace_Trim(_y, TValue2.v, _p, update); Add_Replace_Trim(_yy, TValue2.v * TValue2.v, _p, update); Add_Replace_Trim(_xy, TValue1.v * TValue2.v, _p, update); double _sumx = _x.Sum(); double _sumxx = _xx.Sum(); double _sumy = _y.Sum(); double _sumyy = _yy.Sum(); double _sumxy = _xy.Sum(); double _covar = (_sumxx - _sumx * _sumx / _p) * (_sumyy - _sumy * _sumy / _p); double _cor = (_covar != 0) ? (_sumxy - _sumx * _sumy / _p) / Math.Sqrt(_covar) : 0.0; var result = (TValue1.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _cor); if (update) { base[base.Count - 1] = result; } else { base.Add(result); } } }