QuanTAlib/Calculations/_Updated/RSI_Series.cs

using System.Linq;

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

/* <summary>
RSI: Relative Strength Index
    Created by J. Welles Wilder, the Relative Strength Index measures strength
    of the winning/losing streak over N lookback periods on a scale of 0 to 100,
    to depict overbought and oversold conditions.

Sources:
    https://www.investopedia.com/terms/r/rsi.asp

</summary> */

public class RSI_Series : TSeries {
	private readonly System.Collections.Generic.List<double> _gain = new();
	private readonly System.Collections.Generic.List<double> _loss = new();
	protected readonly int _period;
	protected readonly bool _NaN;
	protected readonly TSeries _data;
	private double _avgGain, _avgLoss, _lastValue;
	private double _avgGain_o, _avgLoss_o, _lastValue_o;
	private int i;

	//core constructors
	public RSI_Series(int period, bool useNaN) : base() {
		_period = period;
		_NaN = useNaN;
		Name = $"RSI({period})";
		i = 0;
	}
	public RSI_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 RSI_Series() : this(period: 0, useNaN: false) { }
	public RSI_Series(int period) : this(period: period, useNaN: false) { }
	public RSI_Series(TBars source) : this(source.Close, 0, false) { }
	public RSI_Series(TBars source, int period) : this(source.Close, period, false) { }
	public RSI_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { }
	public RSI_Series(TSeries source) : this(source, 0, false) { }
	public RSI_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) {

		double _rsi = 0;
		if (update) {
			_lastValue = _lastValue_o;
			_avgGain = _avgGain_o;
			_avgLoss = _avgLoss_o;
		}
		else {
			_lastValue_o = _lastValue;
			_avgGain_o = _avgGain;
			_avgLoss_o = _avgLoss;
		}

		if (i == 0) { _lastValue = TValue.v; }

		double _gainval = (TValue.v > _lastValue) ? TValue.v - _lastValue : 0;
		BufferTrim(_gain, _gainval, _period, update);
		double _lossval = (TValue.v < _lastValue) ? _lastValue - TValue.v : 0;
		BufferTrim(_loss, _lossval, _period, update);
		_lastValue = TValue.v;

		// calculate RSI
		if (i > _period && _period != 0) {
			_avgGain = ((_avgGain * (_period - 1)) + _gain[^1]) / _period;
			_avgLoss = ((_avgLoss * (_period - 1)) + _loss[^1]) / _period;
			if (_avgLoss > 0) {
				double rs = _avgGain / _avgLoss;
				_rsi = 100 - (100 / (1 + rs));
			}
			else { _rsi = 100; }
		}
		// initialize average gain
		else {
			double _sumGain = 0;
			for (int p = 0; p < _gain.Count; p++) { _sumGain += _gain[p]; }
			double _sumLoss = 0;
			for (int p = 0; p < _loss.Count; p++) { _sumLoss += _loss[p]; }

			_avgGain = _sumGain / _gain.Count;
			_avgLoss = _sumLoss / _loss.Count;

			_rsi = (_avgLoss > 0) ? 100 - (100 / (1 + (_avgGain / _avgLoss))) : 100;
		}
		if (!update) { i++; }

		var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _rsi);
		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() {
		i = 0;
	}
}