Files
QuanTAlib/archive/Calculations/_Updated/RMA_Series.cs
T
2024-09-24 16:41:26 -07:00

133 lines
4.1 KiB
C#

namespace QuanTAlib;
using System;
using System.Linq;
/* <summary>
RMA: wildeR Moving Average
J. Welles Wilder introduced RMA as an alternative to EMA. RMA's weight (k) is
set as 1/period, giving less weight to the new data compared to EMA.
Sources:
https://archive.org/details/newconceptsintec00wild/page/23/mode/2up
https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/V-Z/WildersSmoothing
https://www.incrediblecharts.com/indicators/wilder_moving_average.php
Issues:
Pandas-TA library calculates RMA using straight Exponential Weighted Mean:
pandas.ewm().mean() and returns incorrect first (period) of bars compared to
published formula. This implementation passess the validation test in Wilder's book.
</summary> */
public class RMA_Series : TSeries
{
private double _k;
private double _lastrma, _oldrma;
private double _sum, _oldsum;
private readonly bool _useSMA;
private int _len;
protected readonly int _period;
protected readonly bool _NaN;
protected readonly TSeries _data;
//core constructor
public RMA_Series(int period, bool useNaN, bool useSMA)
{
_period = period;
_NaN = useNaN;
_useSMA = useSMA;
Name = $"RMA({period})";
_k = 1.0 / (double)(this._period);
_len = 0;
_sum = _oldsum = _lastrma = _oldrma = 0;
}
//generic constructors (source)
public RMA_Series() : this(0, false, true) { }
public RMA_Series(int period) : this(period, false, true) { }
public RMA_Series(TBars source) : this(source.Close, 0, false) { }
public RMA_Series(TBars source, int period) : this(source.Close, period, false) { }
public RMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { }
public RMA_Series(TSeries source, int period) : this(source, period, false, true) { }
public RMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { }
public RMA_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 = false)
{
if (update)
{
_lastrma = _oldrma;
_sum = _oldsum;
}
else
{
_oldrma = _lastrma;
_oldsum = _sum;
_len++;
}
double _rma = 0;
if (_period == 0)
{
_k = 1.0 / (double)(this._len);
}
if (Count == 0)
{
_rma = _sum = TValue.v;
}
else if (_len <= _period && _useSMA && _period != 0)
{
_sum += TValue.v;
if (_period != 0 && _len > _period)
{
_sum -= _data[Count - _period - (update ? 1 : 0)].v;
}
_rma = _sum / Math.Min(_len, _period);
}
else
{
_rma = _k * (TValue.v - _lastrma) + _lastrma;
}
_lastrma = double.IsNaN(_rma) ? _lastrma : _rma;
var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _rma);
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 (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 = _lastrma = _oldrma = 0;
_len = 0;
}
}