QuanTAlib/Source/Trends/SMA_Series.cs

namespace QuanTAlib;
using System;

/* <summary>
SMA: Simple Moving Average
    The weights are equally distributed across the period, resulting in a mean() of
    the data within the period/

Sources:
    https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/
    https://stats.stackexchange.com/a/24739

Remark:
    This calc doesn't use LINQ or SUM() or any of (slow) iterative methods. It is not as fast as TA-LIB
    implementation, but it does allow incremental additions of inputs and real-time calculations of SMA()

</summary> */

public class SMA_Series : Single_TSeries_Indicator
{
	private readonly System.Collections.Generic.List<double> _buffer = new();
	private double _sma, _oldsma;
	private double _topv, _oldtopv;
	public SMA_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
	{
		if (base._data.Count > 0)
		{ base.Add(base._data); }
	}
	public override void Add((System.DateTime t, double v) TValue, bool update)
	{
		_topv = Add_Replace_Trim(_buffer, TValue.v, _p, update);

		// rolling back if update, storing data for potential future update
		if (update)
		{
			_sma = _oldsma;
			_topv = _oldtopv;
		}
		else
		{
			_oldsma = _sma;
			_oldtopv = _topv;
		}

		// main additive calculation of SMA - for data points that are larger than _p period
		// this.Count > _p
		if (this.Count > _p)
		{
			_sma += (TValue.v - _topv) / _p;
		}
		else
		{
			// calculate SMA the traditional way (sum all, divide with _p) for data points within _p period
			_sma = 0;
			for (int i = 0; i < _buffer.Count; i++)
			{ _sma += _buffer[i]; }
			_sma /= _buffer.Count;
		}

		base.Add((TValue.t, _sma), update, _NaN);
	}
}