QuanTAlib/Calculations/_Updated/ZLEMA_Series.cs

namespace QuanTAlib;

using System;
using System.Linq;

/* <summary>
ZLEMA: Zero Lag Exponential Moving Average
    The Zero lag exponential moving average (ZLEMA) indicator was created by John
    Ehlers and Ric Way.

The formula for a given N-Day period and for a given Data series is:
    Lag = (Period-1)/2
    Ema Data = {Data+(Data-Data(Lag days ago))
    ZLEMA = EMA (EmaData,Period)

Remark:
    The idea is do a regular exponential moving average (EMA) calculation but on a
    de-lagged data instead of doing it on the regular data. Data is de-lagged by
    removing the data from "lag" days ago thus removing (or attempting to remove)
    the cumulative lag effect of the moving average.

</summary> */

public class ZLEMA_Series : TSeries {
	private readonly System.Collections.Generic.List<double> _buffer = new();
	private int _len;
	protected readonly int _period;
	protected readonly bool _NaN;
	protected readonly TSeries _data;
	private readonly EMA_Series _ema;

	//core constructor
	public ZLEMA_Series(int period, bool useNaN, bool useSMA) : base() {
		_period = period;
		_NaN = useNaN;
		Name = $"ZLEMA({period})";
		_len = 1;
		_ema = new(period);
	}
	//generic constructors (source)

	public ZLEMA_Series() : this(0, false, true) { }
	public ZLEMA_Series(int period) : this(period, false, true) { }
	public ZLEMA_Series(TBars source) : this(source.Close, 0, false) { }
	public ZLEMA_Series(TBars source, int period) : this(source.Close, period, false) { }
	public ZLEMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { }
	public ZLEMA_Series(TSeries source, int period) : this(source, period, false, true) { }
	public ZLEMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { }
	public ZLEMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) {
		_data = source;
		Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})";
		_data.Pub += Sub;
		Add(_data);
	}

	// core Add() algo
	public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update) {
		BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update);
		int _lag;
		if (_period == 0) { 
			_lag = (int)((_len - 1) * 0.5);
			_len++;
		}
		else { _lag = (int)((_period - 1) * 0.5); }
		_lag = Math.Min(_lag, _buffer.Count - 1);
		_lag = Math.Max(_lag, 0) + 1;
		double _zlValue = 2 * TValue.v - _buffer[^_lag];
		double _zlema = _ema.Add((TValue.t, _zlValue), update).v;
		var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _zlema);
		return base.Add(res, update);
	}

	//variation of Add()
	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();
		_ema.Reset();
	}
}