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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
@@ -1,114 +1,114 @@
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namespace QuanTAlib;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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/* <summary>
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ALMA: Arnaud Legoux Moving Average
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The ALMA moving average uses the curve of the Normal (Gauss) distribution, which
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can be shifted from 0 to 1. This allows regulating the smoothness and high
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sensitivity of the indicator. Sigma is another parameter that is responsible for
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the shape of the curve coefficients. This moving average reduces lag of the data
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in conjunction with smoothing to reduce noise.
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Sources:
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https://phemex.com/academy/what-is-arnaud-legoux-moving-averages
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https://www.prorealcode.com/prorealtime-indicators/alma-arnaud-legoux-moving-average/
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Discrepancy with Pandas-TA (but passes the validation with Skender.GetAlma)
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</summary> */
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public class ALMA_Series : TSeries {
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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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private readonly System.Collections.Generic.List<double> _buffer = new();
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private readonly System.Collections.Generic.List<double> _weight;
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private double _norm;
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private readonly double _offset, _sigma;
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//core constructors
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public ALMA_Series(int period, double offset, double sigma, bool useNaN) {
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_period = period;
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_NaN = useNaN;
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Name = $"ALMA({period})";
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_offset = offset;
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_sigma = sigma;
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_weight = new();
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}
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public ALMA_Series(TSeries source, int period, double offset, double sigma, bool useNaN) : this(period, offset, sigma, useNaN) {
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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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public ALMA_Series() : this(period:0, offset:0.85, sigma:6.0, useNaN: false) { }
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public ALMA_Series(int period) : this(period: period, offset:0.85, sigma:6.0, useNaN:false) { }
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public ALMA_Series(TBars source) : this(source:source.Close, period:0, offset:0.85, sigma:6.0, useNaN:false) { }
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public ALMA_Series(TBars source, int period) : this(source:source.Close, period:period, offset: 0.85, sigma: 6.0, useNaN: false) { }
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public ALMA_Series(TBars source, int period, double offset, double sigma, bool useNaN) : this(source.Close, period:period, offset: offset, sigma: sigma, useNaN: false) { }
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public ALMA_Series(TSeries source) : this(source, period:0, offset:0.85, sigma:6.0, useNaN:false) { }
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public ALMA_Series(TSeries source, int period) : this(source:source, period:period, offset:0.85, sigma:6.0, useNaN:false) { }
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public ALMA_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, offset: 0.85, sigma: 6.0, useNaN: useNaN) { }
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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 (double.IsNaN(TValue.v)) {
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return base.Add((TValue.t, double.NaN), update);
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}
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BufferTrim(_buffer, TValue.v, _period, update);
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if (_weight.Count < _buffer.Count) {
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for (var i = 0; i < _buffer.Count - _weight.Count; i++) {
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_weight.Add(0.0);
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}
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}
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if (_buffer.Count <= _period || _period == 0) {
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var _len = _buffer.Count;
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_norm = 0;
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var _m = _offset * (_len - 1);
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var _s = _len / _sigma;
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for (var i = 0; i < _len; i++) {
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var _wt = Math.Exp(-((i - _m) * (i - _m)) / (2 * _s * _s));
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_weight[i] = _wt;
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_norm += _wt;
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}
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}
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double _weightedSum = 0;
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for (var i = 0; i < _buffer.Count; i++) {
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_weightedSum += _weight[i] * _buffer[i];
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}
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var _alma = _weightedSum / _norm;
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var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _alma);
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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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_buffer.Clear();
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_weight.Clear();
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}
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namespace QuanTAlib;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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/* <summary>
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ALMA: Arnaud Legoux Moving Average
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The ALMA moving average uses the curve of the Normal (Gauss) distribution, which
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can be shifted from 0 to 1. This allows regulating the smoothness and high
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sensitivity of the indicator. Sigma is another parameter that is responsible for
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the shape of the curve coefficients. This moving average reduces lag of the data
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in conjunction with smoothing to reduce noise.
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Sources:
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https://phemex.com/academy/what-is-arnaud-legoux-moving-averages
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https://www.prorealcode.com/prorealtime-indicators/alma-arnaud-legoux-moving-average/
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Discrepancy with Pandas-TA (but passes the validation with Skender.GetAlma)
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</summary> */
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public class ALMA_Series : TSeries {
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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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private readonly System.Collections.Generic.List<double> _buffer = new();
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private readonly System.Collections.Generic.List<double> _weight;
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private double _norm;
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private readonly double _offset, _sigma;
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//core constructors
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public ALMA_Series(int period, double offset, double sigma, bool useNaN) {
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_period = period;
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_NaN = useNaN;
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Name = $"ALMA({period})";
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_offset = offset;
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_sigma = sigma;
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_weight = new();
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}
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public ALMA_Series(TSeries source, int period, double offset, double sigma, bool useNaN) : this(period, offset, sigma, useNaN) {
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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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public ALMA_Series() : this(period:0, offset:0.85, sigma:6.0, useNaN: false) { }
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public ALMA_Series(int period) : this(period: period, offset:0.85, sigma:6.0, useNaN:false) { }
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public ALMA_Series(TBars source) : this(source:source.Close, period:0, offset:0.85, sigma:6.0, useNaN:false) { }
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public ALMA_Series(TBars source, int period) : this(source:source.Close, period:period, offset: 0.85, sigma: 6.0, useNaN: false) { }
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public ALMA_Series(TBars source, int period, double offset, double sigma, bool useNaN) : this(source.Close, period:period, offset: offset, sigma: sigma, useNaN: false) { }
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public ALMA_Series(TSeries source) : this(source, period:0, offset:0.85, sigma:6.0, useNaN:false) { }
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public ALMA_Series(TSeries source, int period) : this(source:source, period:period, offset:0.85, sigma:6.0, useNaN:false) { }
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public ALMA_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, offset: 0.85, sigma: 6.0, useNaN: useNaN) { }
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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 (double.IsNaN(TValue.v)) {
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return base.Add((TValue.t, double.NaN), update);
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}
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BufferTrim(_buffer, TValue.v, _period, update);
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if (_weight.Count < _buffer.Count) {
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for (var i = 0; i < _buffer.Count - _weight.Count; i++) {
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_weight.Add(0.0);
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}
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}
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if (_buffer.Count <= _period || _period == 0) {
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var _len = _buffer.Count;
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_norm = 0;
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var _m = _offset * (_len - 1);
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var _s = _len / _sigma;
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for (var i = 0; i < _len; i++) {
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var _wt = Math.Exp(-((i - _m) * (i - _m)) / (2 * _s * _s));
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_weight[i] = _wt;
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_norm += _wt;
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}
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}
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double _weightedSum = 0;
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for (var i = 0; i < _buffer.Count; i++) {
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_weightedSum += _weight[i] * _buffer[i];
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}
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var _alma = _weightedSum / _norm;
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var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _alma);
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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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_buffer.Clear();
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_weight.Clear();
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}
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}
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