QuanTAlib/Calculations/_Updated/SMMA_Series.cs

namespace QuanTAlib;
using System;
using System.Collections.Generic;
using System.Linq;

/* <summary>
SMMA: Smoothed Moving Average
    The Smoothed Moving Average (SMMA) is a combination of a SMA and an EMA. It gives the recent prices
    an equal weighting as the historic prices as it takes all available price data into account.
    The main advantage of a smoothed moving average is that it removes short-term fluctuations.

    SMMA(i) = (SMMA-1*(N-1) + CLOSE (i)) / N

Sources:
    https://blog.earn2trade.com/smoothed-moving-average
    https://guide.traderevolution.com/traderevolution/mobile-applications/phone/android/technical-indicators/moving-averages/smma-smoothed-moving-average
    https://www.chartmill.com/documentation/technical-analysis-indicators/217-MOVING-AVERAGES-%7C-The-Smoothed-Moving-Average-%28SMMA%29

</summary> */

public class SMMA_Series : TSeries {
	private readonly System.Collections.Generic.List<double> _buffer = new();

	protected readonly int _period;
	protected readonly bool _NaN;
	protected readonly TSeries _data;
	private double _lastsmma, _lastlastsmma;

	//core constructors
	public SMMA_Series(int period, bool useNaN) : base() {
		_period = period;
		_NaN = useNaN;
		Name = $"SMMA({period})";
	}
	public SMMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) {
		_data = source;
		Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})";
		_data.Pub += Sub;
		Add(_data);
	}
	public SMMA_Series() : this(period: 0, useNaN: false) { }
	public SMMA_Series(int period) : this(period: period, useNaN: false) { }
	public SMMA_Series(TBars source) : this(source.Close, 0, false) { }
	public SMMA_Series(TBars source, int period) : this(source.Close, period, false) { }
	public SMMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { }
	public SMMA_Series(TSeries source) : this(source, 0, false) { }
	public SMMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { }

	//////////////////
	// core Add() algo
	public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) {
		if (double.IsNaN(TValue.v)) {
			return base.Add((TValue.t, double.NaN),update);
		}

		double _smma = 0;
		if (update) { this._lastsmma = this._lastlastsmma; }

		if (this.Count < this._period) {
			BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update);
			_smma = _buffer.Average();
		}
		else {
			_smma = ((_lastsmma * (_period - 1)) + TValue.v) / _period;
		}

		this._lastlastsmma = this._lastsmma;
		this._lastsmma = _smma;
		var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _smma);
		return base.Add(res, update);
	}

	public override (DateTime t, double v) Add(TSeries data) {
		if (data == null) { return (DateTime.Today, Double.NaN); }
		foreach (var item in data) { Add(item, false); }
		return _data.Last;
	}
	public new (DateTime t, double v) Add((DateTime t, double v) TValue) {
		return Add(TValue, false);
	}
	public (DateTime t, double v) Add(bool update) {
		return this.Add(TValue: _data.Last, update: update);
	}
	public (DateTime t, double v) Add() {
		return Add(TValue: _data.Last, update: false);
	}
	private new void Sub(object source, TSeriesEventArgs e) {
		Add(TValue: _data.Last, update: e.update);
	}

	//reset calculation
	public override void Reset() {
		_buffer.Clear();
		this._lastsmma = this._lastlastsmma = 0;
	}
}