mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-05 12:37:43 +00:00
Miha Kralj
91 lines
3.3 KiB
C#
91 lines
3.3 KiB
C#
using System.Linq;
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namespace QuanTAlib;
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using System;
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using System.Collections.Generic;
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/* <summary>
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ZSCORE: number of standard deviations from SMA
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Z-score describes a value's relationship to the mean of a series, as measured in
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terms of standard deviations from the mean. If a Z-score is 0, it indicates that
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the data point's score is identical to the mean score. A Z-score of 1.0 would
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indicate a value that is one standard deviation from the mean. Z-scores may be
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positive or negative, with a positive value indicating the score is above the
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mean and a negative score indicating it is below the mean.
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Sources:
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https://en.wikipedia.org/wiki/Z-score
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https://www.investopedia.com/terms/z/zscore.asp
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Calculation:
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std = std * STDEV(close, length)
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mean = SMA(close, length)
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ZSCORE = (close - mean) / std
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</summary> */
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public class ZSCORE_Series : TSeries {
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private readonly System.Collections.Generic.List<double> _buffer = new();
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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 ZSCORE_Series(int period, bool useNaN) : base() {
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_period = period;
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_NaN = useNaN;
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Name = $"ZSCORE({period})";
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}
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public ZSCORE_Series(TSeries source, int period, bool useNaN) : this(period, 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 ZSCORE_Series() : this(period: 0, useNaN: false) { }
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public ZSCORE_Series(int period) : this(period: period, useNaN: false) { }
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public ZSCORE_Series(TBars source) : this(source.Close, 0, false) { }
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public ZSCORE_Series(TBars source, int period) : this(source.Close, period, false) { }
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public ZSCORE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { }
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public ZSCORE_Series(TSeries source) : this(source, 0, false) { }
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public ZSCORE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { }
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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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BufferTrim(buffer:_buffer, value:TValue.v, period:_period, update: update);
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double _sma = _buffer.Average();
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double _pvar = 0;
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for (int i = 0; i < _buffer.Count; i++) { _pvar += (_buffer[i] - _sma) * (_buffer[i] - _sma); }
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_pvar /= this._buffer.Count;
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double _psdev = Math.Sqrt(_pvar);
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double _zscore = (_psdev == 0) ? 1 : (TValue.v - _sma) / _psdev;
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var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _zscore);
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return base.Add(res, update);
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}
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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 new (DateTime t, double v) Add((DateTime t, double v) TValue) {
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return Add(TValue, false);
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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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}
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} |