QuanTAlib/archive/Calculations/_Updated/SMA_Series.cs

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

/* <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 : TSeries
{
    private readonly System.Collections.Generic.List<double> _buffer = new();

    private double _sum, _oldsum;
    private readonly int _period;
    private readonly TSeries _data;
    protected readonly bool _NaN;

    //core constructor
    public SMA_Series(int period, bool useNaN)
    {
        _period = Math.Max(0, period);
        _NaN = useNaN;
        Name = $"SMA({period})";
        _sum = _oldsum = 0;
    }
    public SMA_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 SMA_Series() : this(0, false) { }
    public SMA_Series(int period) : this(period, false) { }
    public SMA_Series(TBars source) : this(source.Close, 0, false) { }
    public SMA_Series(TBars source, int period) : this(source.Close, period, false) { }
    public SMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { }
    public SMA_Series(TSeries source) : this(source, 0, false) { }
    public SMA_Series(TSeries source, int period) : this(source, period, 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 (TValue.t, double.NaN);
        }
        else
        {
            if (update && _buffer.Count > 0)
            {
                _sum -= _buffer[^1];
                _buffer[^1] = TValue.v;
                _oldsum = _sum;
            }
            else
            {
                _buffer.Add(TValue.v);
                _oldsum = _sum;
            }

            _sum += TValue.v;
            if (_period != 0 && _buffer.Count > _period)
            {
                _sum -= _buffer[0];
                _buffer.RemoveAt(0);
            }
        }

        double _div = _period == 0 ? _buffer.Count : Math.Min(_buffer.Count, _period);
        var _sma = _sum / _div;
        var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _sma);
        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 (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()
    {
        _sum = _oldsum = 0;
        _buffer.Clear();
    }
}