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https://github.com/mihakralj/QuanTAlib.git
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Add new data structures and event handling classes for trading platform. Include base classes, value and bar structs, event arguments, emitters, listeners. Update ruleset for SonarLint.
This commit is contained in:
Miha Kralj
+115
-115
@@ -1,116 +1,116 @@
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namespace QuanTAlib;
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using System;
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using System.Linq;
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/* <summary>
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RMA: wildeR Moving Average
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J. Welles Wilder introduced RMA as an alternative to EMA. RMA's weight (k) is
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set as 1/period, giving less weight to the new data compared to EMA.
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Sources:
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https://archive.org/details/newconceptsintec00wild/page/23/mode/2up
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https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/V-Z/WildersSmoothing
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https://www.incrediblecharts.com/indicators/wilder_moving_average.php
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Issues:
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Pandas-TA library calculates RMA using straight Exponential Weighted Mean:
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pandas.ewm().mean() and returns incorrect first (period) of bars compared to
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published formula. This implementation passess the validation test in Wilder's book.
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</summary> */
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public class RMA_Series : TSeries {
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private double _k;
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private double _lastrma, _oldrma;
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private double _sum, _oldsum;
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private readonly bool _useSMA;
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private int _len;
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protected readonly int _period;
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protected readonly bool _NaN;
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protected readonly TSeries _data;
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//core constructor
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public RMA_Series(int period, bool useNaN, bool useSMA) {
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_period = period;
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_NaN = useNaN;
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_useSMA = useSMA;
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Name = $"RMA({period})";
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_k = 1.0 / (double)(this._period);
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_len = 0;
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_sum = _oldsum = _lastrma = _oldrma = 0;
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}
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//generic constructors (source)
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public RMA_Series() : this(0, false, true) {}
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public RMA_Series(int period) : this(period, false, true) {}
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public RMA_Series(TBars source) : this(source.Close, 0, false) {}
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public RMA_Series(TBars source, int period) : this(source.Close, period, false) {}
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public RMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) {}
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public RMA_Series(TSeries source, int period) : this(source, period, false, true) {}
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public RMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) {}
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public RMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) {
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_data = source;
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Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})";
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_data.Pub += Sub;
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Add(_data);
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}
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// core Add() algo
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public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) {
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if (update) {
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_lastrma = _oldrma;
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_sum = _oldsum;
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}
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else {
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_oldrma = _lastrma;
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_oldsum = _sum;
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_len++;
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}
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double _rma = 0;
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if (_period == 0) {
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_k = 1.0 / (double)(this._len);
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}
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if (Count == 0) {
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_rma = _sum = TValue.v;
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} else if (_len <= _period && _useSMA && _period != 0) {
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_sum += TValue.v;
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if (_period != 0 && _len > _period) {
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_sum -= _data[Count - _period - (update ? 1 : 0)].v;
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}
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_rma = _sum / Math.Min(_len, _period);
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}
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else {
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_rma = _k * (TValue.v - _lastrma) + _lastrma;
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}
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_lastrma = double.IsNaN(_rma) ? _lastrma : _rma;
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var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _rma);
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return base.Add(res, update);
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}
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//variation of Add()
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public override (DateTime t, double v) Add(TSeries data) {
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if (data == null) { return (DateTime.Today, Double.NaN); }
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foreach (var item in data) { Add(item, false); }
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return _data.Last;
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}
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public (DateTime t, double v) Add(bool update) {
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return this.Add(TValue: _data.Last, update: update);
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}
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public (DateTime t, double v) Add() {
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return Add(TValue: _data.Last, update: false);
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}
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private new void Sub(object source, TSeriesEventArgs e) {
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Add(TValue: _data.Last, update: e.update);
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}
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//reset calculation
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public override void Reset() {
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_sum = _oldsum = _lastrma = _oldrma = 0;
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_len = 0;
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}
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namespace QuanTAlib;
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using System;
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using System.Linq;
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/* <summary>
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RMA: wildeR Moving Average
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J. Welles Wilder introduced RMA as an alternative to EMA. RMA's weight (k) is
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set as 1/period, giving less weight to the new data compared to EMA.
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Sources:
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https://archive.org/details/newconceptsintec00wild/page/23/mode/2up
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https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/V-Z/WildersSmoothing
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https://www.incrediblecharts.com/indicators/wilder_moving_average.php
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Issues:
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Pandas-TA library calculates RMA using straight Exponential Weighted Mean:
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pandas.ewm().mean() and returns incorrect first (period) of bars compared to
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published formula. This implementation passess the validation test in Wilder's book.
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</summary> */
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public class RMA_Series : TSeries {
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private double _k;
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private double _lastrma, _oldrma;
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private double _sum, _oldsum;
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private readonly bool _useSMA;
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private int _len;
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protected readonly int _period;
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protected readonly bool _NaN;
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protected readonly TSeries _data;
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//core constructor
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public RMA_Series(int period, bool useNaN, bool useSMA) {
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_period = period;
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_NaN = useNaN;
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_useSMA = useSMA;
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Name = $"RMA({period})";
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_k = 1.0 / (double)(this._period);
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_len = 0;
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_sum = _oldsum = _lastrma = _oldrma = 0;
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}
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//generic constructors (source)
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public RMA_Series() : this(0, false, true) {}
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public RMA_Series(int period) : this(period, false, true) {}
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public RMA_Series(TBars source) : this(source.Close, 0, false) {}
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public RMA_Series(TBars source, int period) : this(source.Close, period, false) {}
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public RMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) {}
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public RMA_Series(TSeries source, int period) : this(source, period, false, true) {}
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public RMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) {}
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public RMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) {
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_data = source;
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Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})";
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_data.Pub += Sub;
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Add(_data);
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}
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// core Add() algo
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public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) {
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if (update) {
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_lastrma = _oldrma;
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_sum = _oldsum;
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}
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else {
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_oldrma = _lastrma;
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_oldsum = _sum;
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_len++;
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}
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double _rma = 0;
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if (_period == 0) {
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_k = 1.0 / (double)(this._len);
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}
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if (Count == 0) {
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_rma = _sum = TValue.v;
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} else if (_len <= _period && _useSMA && _period != 0) {
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_sum += TValue.v;
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if (_period != 0 && _len > _period) {
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_sum -= _data[Count - _period - (update ? 1 : 0)].v;
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}
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_rma = _sum / Math.Min(_len, _period);
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}
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else {
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_rma = _k * (TValue.v - _lastrma) + _lastrma;
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}
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_lastrma = double.IsNaN(_rma) ? _lastrma : _rma;
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var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _rma);
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return base.Add(res, update);
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}
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//variation of Add()
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public override (DateTime t, double v) Add(TSeries data) {
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if (data == null) { return (DateTime.Today, Double.NaN); }
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foreach (var item in data) { Add(item, false); }
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return _data.Last;
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}
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public (DateTime t, double v) Add(bool update) {
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return this.Add(TValue: _data.Last, update: update);
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}
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public (DateTime t, double v) Add() {
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return Add(TValue: _data.Last, update: false);
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}
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private new void Sub(object source, TSeriesEventArgs e) {
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Add(TValue: _data.Last, update: e.update);
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}
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//reset calculation
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public override void Reset() {
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_sum = _oldsum = _lastrma = _oldrma = 0;
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_len = 0;
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}
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}
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