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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
+119
-119
@@ -1,120 +1,120 @@
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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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EMA: Exponential Moving Average
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EMA needs very short history buffer and calculates the EMA value using just the
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previous EMA value. The weight of the new datapoint (k) is k = 2 / (period-1)
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Sources:
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https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages
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https://www.investopedia.com/ask/answers/122314/what-exponential-moving-average-ema-formula-and-how-ema-calculated.asp
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https://blog.fugue88.ws/archives/2017-01/The-correct-way-to-start-an-Exponential-Moving-Average-EMA
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Issues:
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There is no consensus what the first EMA value should be - a zero, a first
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datapoint, or an average of the initial Period bars. All three starting methods
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converge within 20+ bars to the same moving average. Most implementations (including this one)
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use SMA() for the first Period bars as a seeding value for EMA.
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</summary> */
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public class EMA_Series : TSeries {
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private double _k;
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private double _lastema, _oldema;
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private double _sum, _oldsum;
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private int _len;
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private readonly bool _useSMA;
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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 constructors
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public EMA_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 = $"EMA({period})";
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_k = 2.0 / (_period + 1);
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_len = 0;
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_sum = _oldsum = _lastema = _oldema = 0;
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}
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public EMA_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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public EMA_Series() : this(0, false, true) {}
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public EMA_Series(int period) : this(period, false, true) {}
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public EMA_Series(TBars source) : this(source.Close, 0, false) {}
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public EMA_Series(TBars source, int period) : this(source.Close, period, false) {}
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public EMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) {}
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public EMA_Series(TSeries source, int period) : this(source, period, false, true) {}
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public EMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) {}
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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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_lastema = _oldema;
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_sum = _oldsum;
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}
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else {
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_oldema = _lastema;
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_oldsum = _sum;
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_len++;
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}
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double _ema = 0;
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if (_period == 0) {
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_k = 2.0 / (_len + 1);
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}
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if (Count == 0) {
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_ema = _sum = TValue.v;
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}
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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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_ema = _sum / Math.Min(_len, _period);
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}
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else {
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_ema = _k * (TValue.v - _lastema) + _lastema;
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}
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_lastema = double.IsNaN(_ema) ? _lastema : _ema;
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var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _ema);
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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 = _lastema = _oldema = 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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EMA: Exponential Moving Average
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EMA needs very short history buffer and calculates the EMA value using just the
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previous EMA value. The weight of the new datapoint (k) is k = 2 / (period-1)
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Sources:
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https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages
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https://www.investopedia.com/ask/answers/122314/what-exponential-moving-average-ema-formula-and-how-ema-calculated.asp
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https://blog.fugue88.ws/archives/2017-01/The-correct-way-to-start-an-Exponential-Moving-Average-EMA
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Issues:
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There is no consensus what the first EMA value should be - a zero, a first
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datapoint, or an average of the initial Period bars. All three starting methods
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converge within 20+ bars to the same moving average. Most implementations (including this one)
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use SMA() for the first Period bars as a seeding value for EMA.
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</summary> */
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public class EMA_Series : TSeries {
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private double _k;
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private double _lastema, _oldema;
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private double _sum, _oldsum;
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private int _len;
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private readonly bool _useSMA;
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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 constructors
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public EMA_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 = $"EMA({period})";
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_k = 2.0 / (_period + 1);
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_len = 0;
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_sum = _oldsum = _lastema = _oldema = 0;
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}
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public EMA_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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public EMA_Series() : this(0, false, true) {}
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public EMA_Series(int period) : this(period, false, true) {}
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public EMA_Series(TBars source) : this(source.Close, 0, false) {}
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public EMA_Series(TBars source, int period) : this(source.Close, period, false) {}
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public EMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) {}
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public EMA_Series(TSeries source, int period) : this(source, period, false, true) {}
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public EMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) {}
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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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_lastema = _oldema;
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_sum = _oldsum;
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}
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else {
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_oldema = _lastema;
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_oldsum = _sum;
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_len++;
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}
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double _ema = 0;
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if (_period == 0) {
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_k = 2.0 / (_len + 1);
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}
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if (Count == 0) {
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_ema = _sum = TValue.v;
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}
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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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_ema = _sum / Math.Min(_len, _period);
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}
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else {
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_ema = _k * (TValue.v - _lastema) + _lastema;
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}
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_lastema = double.IsNaN(_ema) ? _lastema : _ema;
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var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _ema);
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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 = _lastema = _oldema = 0;
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_len = 0;
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}
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}
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