mirror of
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
@@ -1,132 +1,132 @@
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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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HWMA: Holt-Winter Moving Average
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Indicator HWMA (Holt-Winter Moving Average) is a three-parameter moving
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average by the Holt-Winter method; Holt-Winters Exponential Smoothing is
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used for forecasting time series data that exhibits both a trend and a
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seasonal variation.
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Sources:
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https://timeseriesreasoning.com/contents/holt-winters-exponential-smoothing/
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https://www.mql5.com/en/code/20856
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nA - smoothed series (from 0 to 1)
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nB - assess the trend (from 0 to 1)
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nC - assess seasonality (from 0 to 1)
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Heuristic for determining alpha, beta, and gamma from period:
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alpha = 2 / (1 + period)
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beta = 1 / period
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gamma = 1 / period
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F[i] = (1-nA) * (F[i-1] + V[i-1] + 0.5 * A[i-1]) + nA * Price[i]
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V[i] = (1-nB) * (V[i-1] + A[i-1]) + nB * (F[i] - F[i-1])
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A[i] = (1-nC) * A[i-1] + nC * (V[i] - V[i-1])
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HWMA[i] = F[i] + V[i] + 0.5 * A[i]
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</summary> */
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public class HWMA_Series : TSeries {
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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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double _nA, _nB, _nC;
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double _pF, _pV, _pA;
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double _ppF, _ppV, _ppA;
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//core constructors
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public HWMA_Series(double nA, double nB, double nC, bool useNaN) {
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_period = (int)((2 - nA) / nA);
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_nA = nA;
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_nB = nB;
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_nC = nC;
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_NaN = useNaN;
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Name = $"HWMA({_period})";
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_len = 0;
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}
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public HWMA_Series(TSeries source, double nA, double nB, double nC, bool useNaN = false) : this(nA, nB, nC, 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 HWMA_Series() : this(period: 0, useNaN: false) { }
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public HWMA_Series(int period) : this(period, useNaN: false) { }
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public HWMA_Series(int period, bool useNaN) : this(nA: 2 / (1 + (double)period), nB: 1 / (double)period, nC: 1 / (double)period, useNaN) {
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_period = period;
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}
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public HWMA_Series(TBars source) : this(source.Close, period: 0, useNaN: false) { }
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public HWMA_Series(TBars source, int period) : this(source.Close, period, false) { }
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public HWMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { }
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public HWMA_Series(TSeries source, int period) : this(source, period, false) { }
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public HWMA_Series(TSeries source, int period, bool useNaN) : this(source, nA: 2 / (1 + (double)period), nB: 1 / (double)period, nC: 1 / (double)period, useNaN: useNaN) { }
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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 (double.IsNaN(TValue.v)) {
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return base.Add((TValue.t, Double.NaN), update);
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}
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double _F, _V, _A;
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if (_len == 0) { _pF = TValue.v; _pA = _pV = 0; }
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if (update) { _pF = _ppF; _pV = _ppV; _pA = _ppA; }
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else {
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_ppF = _pF;
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_ppV = _pV;
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_ppA = _pA;
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_len++;
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}
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if (_period == 0) {
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_nA = 2 / (1 + (double)_len);
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_nB = 1 / (double)_len;
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_nC = 1 / (double)_len;
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}
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if (_period == 1) {
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_nA = 1;
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_nB = 0;
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_nC = 0;
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}
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_F = (1 - _nA) * (_pF + _pV + 0.5 * _pA) + _nA * TValue.v;
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_V = (1 - _nB) * (_pV + _pA) + _nB * (_F - _pF);
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_A = (1 - _nC) * _pA + _nC * (_V - _pV);
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double _hwma = _F + _V + 0.5 * _A;
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_pF = _F;
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_pV = _V;
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_pA = _A;
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var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _hwma);
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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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_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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HWMA: Holt-Winter Moving Average
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Indicator HWMA (Holt-Winter Moving Average) is a three-parameter moving
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average by the Holt-Winter method; Holt-Winters Exponential Smoothing is
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used for forecasting time series data that exhibits both a trend and a
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seasonal variation.
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Sources:
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https://timeseriesreasoning.com/contents/holt-winters-exponential-smoothing/
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https://www.mql5.com/en/code/20856
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nA - smoothed series (from 0 to 1)
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nB - assess the trend (from 0 to 1)
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nC - assess seasonality (from 0 to 1)
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Heuristic for determining alpha, beta, and gamma from period:
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alpha = 2 / (1 + period)
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beta = 1 / period
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gamma = 1 / period
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F[i] = (1-nA) * (F[i-1] + V[i-1] + 0.5 * A[i-1]) + nA * Price[i]
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V[i] = (1-nB) * (V[i-1] + A[i-1]) + nB * (F[i] - F[i-1])
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A[i] = (1-nC) * A[i-1] + nC * (V[i] - V[i-1])
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HWMA[i] = F[i] + V[i] + 0.5 * A[i]
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</summary> */
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public class HWMA_Series : TSeries {
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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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double _nA, _nB, _nC;
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double _pF, _pV, _pA;
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double _ppF, _ppV, _ppA;
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//core constructors
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public HWMA_Series(double nA, double nB, double nC, bool useNaN) {
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_period = (int)((2 - nA) / nA);
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_nA = nA;
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_nB = nB;
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_nC = nC;
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_NaN = useNaN;
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Name = $"HWMA({_period})";
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_len = 0;
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}
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public HWMA_Series(TSeries source, double nA, double nB, double nC, bool useNaN = false) : this(nA, nB, nC, 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 HWMA_Series() : this(period: 0, useNaN: false) { }
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public HWMA_Series(int period) : this(period, useNaN: false) { }
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public HWMA_Series(int period, bool useNaN) : this(nA: 2 / (1 + (double)period), nB: 1 / (double)period, nC: 1 / (double)period, useNaN) {
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_period = period;
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}
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public HWMA_Series(TBars source) : this(source.Close, period: 0, useNaN: false) { }
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public HWMA_Series(TBars source, int period) : this(source.Close, period, false) { }
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public HWMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { }
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public HWMA_Series(TSeries source, int period) : this(source, period, false) { }
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public HWMA_Series(TSeries source, int period, bool useNaN) : this(source, nA: 2 / (1 + (double)period), nB: 1 / (double)period, nC: 1 / (double)period, useNaN: useNaN) { }
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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 (double.IsNaN(TValue.v)) {
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return base.Add((TValue.t, Double.NaN), update);
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}
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double _F, _V, _A;
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if (_len == 0) { _pF = TValue.v; _pA = _pV = 0; }
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if (update) { _pF = _ppF; _pV = _ppV; _pA = _ppA; }
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else {
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_ppF = _pF;
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_ppV = _pV;
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_ppA = _pA;
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_len++;
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}
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if (_period == 0) {
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_nA = 2 / (1 + (double)_len);
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_nB = 1 / (double)_len;
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_nC = 1 / (double)_len;
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}
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if (_period == 1) {
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_nA = 1;
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_nB = 0;
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_nC = 0;
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}
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_F = (1 - _nA) * (_pF + _pV + 0.5 * _pA) + _nA * TValue.v;
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_V = (1 - _nB) * (_pV + _pA) + _nB * (_F - _pF);
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_A = (1 - _nC) * _pA + _nC * (_V - _pV);
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double _hwma = _F + _V + 0.5 * _A;
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_pF = _F;
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_pV = _V;
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_pA = _A;
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var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _hwma);
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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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_len = 0;
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}
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}
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