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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.

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Miha Kralj
2024-07-25 17:42:09 -07:00
parent f7fd3fbf9f
commit 7dd938c368
86 changed files with 9367 additions and 9153 deletions
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Calculations/_Updated/MAD_Series.cs
+78 -78
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@@ -1,79 +1,79 @@
using System.Linq;
namespace QuanTAlib;
using System;
using System.Collections.Generic;
/* <summary>
MAD: Mean Absolute Deviation
Also known as AAD - Average Absolute Deviation, to differentiate it from Median Absolute Deviation
MAD defines the degree of variation across the series.
Calculation:
MAD = Σ(|close-SMA|) / period
Sources:
https://en.wikipedia.org/wiki/Average_absolute_deviation
</summary> */
public class MAD_Series : TSeries {
private readonly System.Collections.Generic.List<double> _buffer = new();
protected readonly int _period;
protected readonly bool _NaN;
protected readonly TSeries _data;
//core constructors
public MAD_Series(int period, bool useNaN) {
_period = period;
_NaN = useNaN;
Name = $"MAD({period})";
}
public MAD_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) {
_data = source;
Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})";
_data.Pub += Sub;
Add(_data);
}
public MAD_Series() : this(period: 0, useNaN: false) { }
public MAD_Series(int period) : this(period: period, useNaN: false) { }
public MAD_Series(TBars source) : this(source.Close, 0, false) { }
public MAD_Series(TBars source, int period) : this(source.Close, period, false) { }
public MAD_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { }
public MAD_Series(TSeries source) : this(source, 0, false) { }
public MAD_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { }
//////////////////
// core Add() algo
public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) {
BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update);
double _sma = _buffer.Average();
double _mad = 0;
for (int i = 0; i < _buffer.Count; i++) { _mad += Math.Abs(_buffer[i] - _sma); }
_mad /= this._buffer.Count;
var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mad);
return base.Add(res, update);
}
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() {
_buffer.Clear();
}
using System.Linq;
namespace QuanTAlib;
using System;
using System.Collections.Generic;
/* <summary>
MAD: Mean Absolute Deviation
Also known as AAD - Average Absolute Deviation, to differentiate it from Median Absolute Deviation
MAD defines the degree of variation across the series.
Calculation:
MAD = Σ(|close-SMA|) / period
Sources:
https://en.wikipedia.org/wiki/Average_absolute_deviation
</summary> */
public class MAD_Series : TSeries {
private readonly System.Collections.Generic.List<double> _buffer = new();
protected readonly int _period;
protected readonly bool _NaN;
protected readonly TSeries _data;
//core constructors
public MAD_Series(int period, bool useNaN) {
_period = period;
_NaN = useNaN;
Name = $"MAD({period})";
}
public MAD_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) {
_data = source;
Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})";
_data.Pub += Sub;
Add(_data);
}
public MAD_Series() : this(period: 0, useNaN: false) { }
public MAD_Series(int period) : this(period: period, useNaN: false) { }
public MAD_Series(TBars source) : this(source.Close, 0, false) { }
public MAD_Series(TBars source, int period) : this(source.Close, period, false) { }
public MAD_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { }
public MAD_Series(TSeries source) : this(source, 0, false) { }
public MAD_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { }
//////////////////
// core Add() algo
public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) {
BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update);
double _sma = _buffer.Average();
double _mad = 0;
for (int i = 0; i < _buffer.Count; i++) { _mad += Math.Abs(_buffer[i] - _sma); }
_mad /= this._buffer.Count;
var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mad);
return base.Add(res, update);
}
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() {
_buffer.Clear();
}
}