namespace QuanTAlib; using System; /* RMA: wildeR Moving Average J. Welles Wilder introduced RMA as an alternative to EMA. RMA's weight (k) is set as 1/period, giving less weight to the new data compared to EMA. Sources: https://archive.org/details/newconceptsintec00wild/page/23/mode/2up https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/V-Z/WildersSmoothing https://www.incrediblecharts.com/indicators/wilder_moving_average.php Issues: Pandas-TA library calculates RMA using straight Exponential Weighted Mean: pandas.ewm().mean() and returns incorrect first (period) of bars compared to published formula. This implementation passess the validation test in Wilder's book. */ public class RMA_Series : Single_TSeries_Indicator { private readonly System.Collections.Generic.List _buffer = new(); private readonly double _k, _k1m; private double _lastema, _lastlastema; public RMA_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN) { this._k = 1.0 / (double)(this._p); this._k1m = 1.0 - this._k; this._lastema = this._lastlastema = double.NaN; if (_data.Count > 0) { base.Add(_data); } } public override void Add((DateTime t, double v) TValue, bool update) { double _ema = 0; if (update) { this._lastema = this._lastlastema; } if (this.Count < this._p) { if (update) { _buffer[_buffer.Count - 1] = TValue.v; } else { _buffer.Add(TValue.v); } if (_buffer.Count > this._p) { _buffer.RemoveAt(0); } for (int i = 0; i < _buffer.Count; i++) { _ema += _buffer[i]; } _ema /= this._buffer.Count; } else { _ema = TValue.v * _k + _lastema * _k1m; } this._lastlastema = this._lastema; this._lastema = _ema; var ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _ema); base.Add(ret, update); } }