mirror of
https://github.com/mihakralj/QuanTAlib.git
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Refactoring the structure, upgrading to .NET 6.0/7.0/8.0
This commit is contained in:
Miha Kralj
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namespace QuanTAlib;
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using System;
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/* <summary>
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ADL: Chaikin Accumulation/Distribution Line
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ADL is a volume-based indicator that measures the cumulative Money Flow Volume:
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1. Money Flow Multiplier = [(Close - Low) - (High - Close)] /(High - Low)
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2. Money Flow Volume = Money Flow Multiplier x Volume for the Period
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3. ADL = Previous ADL + Current Period's Money Flow Volume
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Sources:
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https://school.stockcharts.com/doku.php?id=technical_indicators:accumulation_distribution_line
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</summary> */
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public class ADL_Series : Single_TBars_Indicator
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{
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private double _lastadl, _lastlastadl;
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public ADL_Series(TBars source, bool useNaN = false) : base(source, 0, useNaN)
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{
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_lastadl = _lastlastadl = 0;
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if (_bars.Count > 0) { base.Add(_bars); }
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}
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public override void Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update)
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{
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if (update) { this._lastadl = this._lastlastadl; }
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double _adl = 0;
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double tmp = TBar.h - TBar.l;
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if (tmp > 0.0 ) { _adl = _lastadl + ((2*TBar.c - TBar.l - TBar.h) / tmp * TBar.v); }
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this._lastlastadl = this._lastadl;
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this._lastadl = _adl;
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base.Add((TBar.t, _adl), update, _NaN);
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}
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}
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@@ -0,0 +1,87 @@
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namespace QuanTAlib;
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using System;
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/* <summary>
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ADO: Chaikin Accumulation/Distribution Oscillator
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ADO measures the momentum of ADL using the difference between slow (10-day) EMA(ADL)
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and fast (3-day) EMA(ADL):
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Chaikin A/D Oscillator = (3-day EMA of ADL) - (10-day EMA of ADL)
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Sources:
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https://school.stockcharts.com/doku.php?id=technical_indicators:chaikin_oscillator
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</summary> */
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public class ADOSC_Series : Single_TBars_Indicator
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{
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private readonly double _k1, _k2;
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private double _lastema1, _lastlastema1, _lastema2, _lastlastema2;
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private double _lastadl, _lastlastadl;
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public ADOSC_Series(TBars source, int shortPeriod = 3, int longPeriod =10, bool useNaN = false) : base(source, period: 0, useNaN)
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{
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_k1 = 2.0 / (shortPeriod + 1);
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_k2 = 2.0 / (longPeriod + 1);
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_lastadl = _lastlastadl = _lastema1 = _lastlastema1 = _lastema2 = _lastlastema2 = 0;
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if (_bars.Count > 0) { base.Add(_bars); }
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}
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public override void Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update)
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{
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if (update) {
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_lastadl = _lastlastadl;
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_lastema1 = _lastlastema1;
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_lastema2 = _lastlastema2;
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}
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double _adl = 0;
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double tmp = TBar.h - TBar.l;
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if (tmp > 0.0) { _adl = _lastadl + ((2 * TBar.c - TBar.l - TBar.h) / tmp * TBar.v); }
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if (this.Count == 0) { _lastema1 = _lastema2 = _adl; }
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double _ema1 = (_adl - _lastema1) * _k1 + _lastema1;
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double _ema2 = (_adl - _lastema2) * _k2 + _lastema2;
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_lastlastadl = _lastadl; _lastadl = _adl;
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_lastlastema1 = _lastema1; _lastema1 = _ema1;
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_lastlastema2 = _lastema2; _lastema2 = _ema2;
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double _adosc = _ema1 - _ema2;
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base.Add((TBar.t, _adosc), update, _NaN);
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}
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}
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/*
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public class ADOSC_Series : Single_TBars_Indicator
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{
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private readonly ADL_Series _TSadl;
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private readonly EMA_Series _TSslow;
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private readonly EMA_Series _TSfast;
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private readonly SUB_Series _TSado;
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public ADOSC_Series(TBars source, bool useNaN = false) : base(source, period: 0, useNaN)
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{
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_TSadl = new(source: source, useNaN: false);
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_TSslow = new(source: _TSadl, period: 10, useNaN: false);
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_TSfast = new(source: _TSadl, period: 3, useNaN: false);
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_TSado = new(_TSfast, _TSslow);
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if (source.Count > 0)
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{ base.Add(_TSado); }
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Console.WriteLine(base.Count);
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}
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public override void Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update)
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{
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if (update)
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{ _TSadl.Add(TBar, true); }
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double _ado = this._TSado[(this.Count < this._TSado.Count) ? this.Count : this._TSado.Count - 1].v;
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var result = (TBar.t, _ado);
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base.Add(result, update);
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}
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}
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*/
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@@ -0,0 +1,48 @@
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namespace QuanTAlib;
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using System;
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/* <summary>
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ATRP: Average True Range Percent
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Average True Range Percent is (ATR/Close Price)*100.
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This normalizes so it can be compared to other stocks.
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Sources:
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https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/atrp
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</summary> */
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public class ATRP_Series : Single_TBars_Indicator {
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private readonly System.Collections.Generic.List<double> _buffer = new();
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private readonly double _k;
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private double _lastatr, _lastlastatr, _cm1, _lastcm1, _sum, _oldsum;
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private readonly int _period;
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public ATRP_Series(TBars source, int period, bool useNaN = false) : base(source, period, useNaN) {
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_period = period;
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_k = 1.0 / (double)(_p);
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_lastatr = _lastlastatr = _cm1 = _lastcm1 = _sum = _oldsum = 0;
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if (this._bars.Count > 0) { base.Add(this._bars); }
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}
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public override void Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update) {
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if (update) { _lastatr = _lastlastatr; _cm1 = _lastcm1; _sum = _oldsum; }
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else { _lastlastatr = _lastatr; _lastcm1 = _cm1; _oldsum = _sum; }
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if (this.Count == 0) { _cm1 = TBar.c; }
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double d1 = Math.Abs(TBar.h - TBar.l);
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double d2 = Math.Abs(_cm1 - TBar.h);
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double d3 = Math.Abs(_cm1 - TBar.l);
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(DateTime t, double v) d = (TBar.t, Math.Max(d1, Math.Max(d2, d3)));
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_cm1 = TBar.c;
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double _atr = 0;
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if (this.Count == 0) { _atr = d.v; }
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else if (this.Count < _p + 1) { _sum += d.v; _atr = _sum / (this.Count); }
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else { _atr = _k * (d.v - _lastatr) + _lastatr; }
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_lastatr = _atr;
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double _atrp = 100 * (_atr / TBar.c);
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var ret = (d.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _atrp);
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base.Add(ret, update);
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}
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}
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@@ -0,0 +1,49 @@
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namespace QuanTAlib;
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using System;
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/* <summary>
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ATR: wildeR Moving Average
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The average true range (ATR) is a price volatility indicator
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showing the average price variation of assets within a given time period.
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Sources:
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https://en.wikipedia.org/wiki/Average_true_range
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https://www.tradingview.com/wiki/Average_True_Range_(ATR)
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https://www.investopedia.com/terms/a/atr.asp
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</summary> */
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public class ATR_Series : Single_TBars_Indicator {
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private readonly System.Collections.Generic.List<double> _buffer = new();
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private readonly double _k;
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private double _lastatr, _lastlastatr, _cm1, _lastcm1, _sum, _oldsum;
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private readonly int _period;
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public ATR_Series(TBars source, int period, bool useNaN = false) : base(source, period, useNaN) {
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_period = period;
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_k = 1.0 / (double)(_p);
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_lastatr = _lastlastatr = _cm1 = _lastcm1 = _sum = _oldsum = 0;
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if (this._bars.Count > 0) { base.Add(this._bars); }
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}
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public override void Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update) {
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if (update) { _lastatr = _lastlastatr; _cm1 = _lastcm1; _sum = _oldsum; }
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else { _lastlastatr = _lastatr; _lastcm1 = _cm1; _oldsum = _sum; }
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if (this.Count == 0) { _cm1 = TBar.c; }
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double d1 = Math.Abs(TBar.h - TBar.l);
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double d2 = Math.Abs(_cm1 - TBar.h);
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double d3 = Math.Abs(_cm1 - TBar.l);
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(DateTime t, double v) d = (TBar.t, Math.Max(d1, Math.Max(d2, d3)));
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_cm1 = TBar.c;
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double _atr = 0;
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if (this.Count == 0) { _atr = d.v; }
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else if (this.Count < _p + 1) { _sum += d.v; _atr = _sum / (this.Count); }
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else { _atr = _k * (d.v - _lastatr) + _lastatr; }
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_lastatr = _atr;
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var ret = (d.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _atr);
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base.Add(ret, update);
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}
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}
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@@ -0,0 +1,73 @@
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namespace QuanTAlib;
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using System;
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/* <summary>
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BBANDS: Bollinger Bands®
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Price channels created by John Bollinger, depict volatility as standard deviation boundary
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line range from a moving average of price. The bands automatically widen when volatility
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increases and contract when volatility decreases. Their dynamic nature allows them to be
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used on different securities with the standard settings.
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Mid Band = simple moving average (SMA)
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Upper Band = SMA + (standard deviation of price x multiplier)
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Lower Band = SMA - (standard deviation of price x multiplier)
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Bandwidth = Width of the channel: (Upper-Lower)/SMA
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%B = The location of the data point within the channel: (Price-Lower)/(Upper/Lower)
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Z-Score = number of standard deviations of the data point from SMA
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Sources:
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https://www.investopedia.com/terms/b/bollingerbands.asp
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https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_bands
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Note:
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Bollinger Bands® is a registered trademark of John A. Bollinger.
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</summary> */
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public class BBANDS_Series : Single_TSeries_Indicator
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{
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public SMA_Series Mid { get; }
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public ADD_Series Upper { get; }
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public SUB_Series Lower { get; }
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public DIV_Series PercentB { get; }
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public DIV_Series Bandwidth { get; }
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public DIV_Series Zscore { get; }
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private readonly SDEV_Series _sdev;
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private readonly MUL_Series _mulsdev;
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private readonly SUB_Series _pbdnd;
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private readonly SUB_Series _pbdvr;
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private readonly SUB_Series _zdnd;
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public BBANDS_Series(TSeries source, int period = 26, double multiplier = 2.0, bool useNaN = false)
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: base(source, period: 0, useNaN)
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{
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this.Mid = new(source: source, period: period, useNaN: useNaN);
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_sdev = new(source, period, useNaN: useNaN);
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_mulsdev = new(_sdev, multiplier);
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this.Upper = new(Mid, _mulsdev);
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this.Lower = new(Mid, _mulsdev);
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_pbdnd = new(source, Lower);
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_pbdvr = new(Upper, Lower);
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this.PercentB = new(_pbdnd, _pbdvr);
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this.Bandwidth = new(_pbdvr, Mid);
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_zdnd = new(source, Mid);
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this.Zscore = new(_zdnd, _sdev);
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if (source.Count > 0)
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{ base.Add(this.Bandwidth); }
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}
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public override void Add((System.DateTime t, double v) TValue, bool update)
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{
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double _bbandwidth;
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if (update)
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{ _sdev.Add(TValue, true); }
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_bbandwidth = this.Bandwidth[(this.Count < this.Bandwidth.Count) ? this.Count : this.Bandwidth.Count - 1].v;
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var result = (TValue.t, _bbandwidth);
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base.Add(result, update);
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}
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}
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@@ -0,0 +1,47 @@
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namespace QuanTAlib;
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using System;
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/* <summary>
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CMO: Chande Momentum Oscillator
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Chande Momentum Oscillator (also known as CMO indicator) was developed by Tushar S. Chande
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CMO is similar to other momentum oscillators (e.g. RSI or Stochastics). Alike RSI oscillator,
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the CMO values move in the range from -100 to +100 points and its aim is to detect the
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overbought and oversold market conditions. CMO calculates the price momentum on both the up
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days as well as the down days. The CMO calculation is based on non-smoothed price values
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meaning that it can reach its extremes more frequently and the short-time swings are more visible.
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Sources:
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https://www.technicalindicators.net/indicators-technical-analysis/144-cmo-chande-momentum-oscillator
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</summary> */
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public class CMO_Series : Single_TSeries_Indicator {
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private readonly System.Collections.Generic.List<double> _buff_up = new();
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private readonly System.Collections.Generic.List<double> _buff_dn = new();
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private double _plast_value, _last_value;
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public CMO_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN) {
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if (this._data.Count > 0) { base.Add(this._data); }
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}
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public override void Add((DateTime t, double v) TValue, bool update) {
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if (this.Count == 0) { _plast_value = _last_value = TValue.v; }
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if (update) _last_value = _plast_value; else _plast_value = _last_value;
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Add_Replace_Trim(_buff_up, (TValue.v > _last_value) ? TValue.v-_last_value : 0, _p, update);
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Add_Replace_Trim(_buff_dn, (TValue.v < _last_value) ? _last_value-TValue.v : 0, _p, update);
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_last_value = TValue.v;
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double _cmo_up = 0;
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double _cmo_dn = 0;
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for (int i = 0; i < Math.Min(_buff_up.Count, _buff_dn.Count); i++) {
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_cmo_up += _buff_up[i];
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_cmo_dn += _buff_dn[i];
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}
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double _cmo = 100 * (_cmo_up - _cmo_dn) / (_cmo_up + _cmo_dn);
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if (_cmo_up + _cmo_dn == 0)
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_cmo = 0;
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base.Add((TValue.t, _cmo), update, _NaN);
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}
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}
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@@ -0,0 +1,78 @@
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namespace QuanTAlib;
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using System;
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/* <summary>
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RSI: Relative Strength Index
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Created by J. Welles Wilder, the Relative Strength Index measures strength
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of the winning/losing streak over N lookback periods on a scale of 0 to 100,
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to depict overbought and oversold conditions.
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Sources:
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https://www.investopedia.com/terms/r/rsi.asp
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</summary> */
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public class RSI_Series : Single_TSeries_Indicator
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{
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private readonly System.Collections.Generic.List<double> _gain = new();
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private readonly System.Collections.Generic.List<double> _loss = new();
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private double _avgGain, _avgLoss, _lastValue;
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private double _avgGain_o, _avgLoss_o, _lastValue_o;
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private int i;
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public RSI_Series(TSeries source, int period = 10, bool useNaN = false) : base(source, period: period, useNaN: useNaN) {
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i = 0;
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if (source.Count > 0) { base.Add(source); }
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}
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public override void Add((System.DateTime t, double v) TValue, bool update) {
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double _rsi = 0;
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if (update) {
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_lastValue = _lastValue_o;
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_avgGain = _avgGain_o;
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_avgLoss = _avgLoss_o;
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}
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else {
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_lastValue_o = _lastValue;
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_avgGain_o = _avgGain;
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_avgLoss_o = _avgLoss;
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}
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if (i == 0) { _lastValue = TValue.v; }
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double _gainval = (TValue.v > _lastValue) ? TValue.v - _lastValue : 0;
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Add_Replace_Trim(_gain, _gainval, _p, update);
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double _lossval = (TValue.v < _lastValue) ? _lastValue - TValue.v : 0;
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Add_Replace_Trim(_loss, _lossval, _p, update);
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_lastValue = TValue.v;
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// calculate RSI
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if (i > _p)
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{
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_avgGain = ((_avgGain * (_p - 1)) + _gain[_gain.Count - 1]) / _p;
|
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_avgLoss = ((_avgLoss * (_p - 1)) + _loss[_loss.Count - 1]) / _p;
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if (_avgLoss > 0) {
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double rs = _avgGain / _avgLoss;
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_rsi = 100 - (100 / (1 + rs));
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}
|
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else { _rsi = 100; }
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}
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// initialize average gain
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else
|
||||
{
|
||||
double _sumGain = 0;
|
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for (int p = 0; p < _gain.Count; p++) { _sumGain += _gain[p]; }
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||||
double _sumLoss = 0;
|
||||
for (int p = 0; p < _loss.Count; p++) { _sumLoss += _loss[p]; }
|
||||
|
||||
_avgGain = _sumGain / _gain.Count;
|
||||
_avgLoss = _sumLoss / _loss.Count;
|
||||
|
||||
_rsi = (_avgLoss > 0) ? 100 - (100 / (1 + (_avgGain / _avgLoss))) : 100;
|
||||
}
|
||||
|
||||
if (!update) { i++; }
|
||||
var result = (TValue.t, (this.Count < this._p && this._NaN) ? double.NaN : _rsi);
|
||||
base.Add(result, update);
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user