mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-05 20:47:43 +00:00
Miha Kralj
119 lines
4.8 KiB
C#
119 lines
4.8 KiB
C#
namespace QuanTAlib;
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using System;
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/* <summary>
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MAMA: MESA Adaptive Moving Average
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Created by John Ehlers, the MAMA indicator is a 5-period adaptive moving average of
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high/low price that uses classic electrical radio-frequency signal processing algorithms
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to reduce noise.
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KAMAi = KAMAi - 1 + SC * ( price - KAMAi-1 )
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Sources:
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https://mesasoftware.com/papers/MAMA.pdf
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https://www.tradingview.com/script/foQxLbU3-Ehlers-MESA-Adaptive-Moving-Average-LazyBear/
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</summary> */
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public class MAMA_Series : Single_TSeries_Indicator
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{
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public MAMA_Series(TSeries source, double fastlimit = 0.5, double slowlimit = 0.05, bool useNaN = false) : base(source, period: 5, useNaN)
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{
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fastl = fastlimit;
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slowl = slowlimit;
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Fama = new();
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if (base._data.Count > 0) { base.Add(base._data); }
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}
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private double sumPr, jI, jQ, fastl, slowl;
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private (double i, double i1, double i2, double i3, double i4, double i5, double i6, double io) pr, i1, q1, sm, dt;
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private (double i, double i1, double io) i2, q2, re, im, pd, ph, mama, fama;
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public TSeries Fama { get; }
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public override void Add((System.DateTime t, double v) TValue, bool update)
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{
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if (!update) {
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// roll forward (oldx = x)
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pr.io = pr.i6; pr.i6 = pr.i5; pr.i5 = pr.i4; pr.i4 = pr.i3; pr.i3 = pr.i2; pr.i2 = pr.i1; pr.i1 = pr.i;
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i1.io = i1.i6; i1.i6 = i1.i5; i1.i5 = i1.i4; i1.i4 = i1.i3; i1.i3 = i1.i2; i1.i2 = i1.i1; i1.i1 = i1.i;
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q1.io = q1.i6; q1.i6 = q1.i5; q1.i5 = q1.i4; q1.i4 = q1.i3; q1.i3 = q1.i2; q1.i2 = q1.i1; q1.i1 = q1.i;
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dt.io = dt.i6; dt.i6 = dt.i5; dt.i5 = dt.i4; dt.i4 = dt.i3; dt.i3 = dt.i2; dt.i2 = dt.i1; dt.i1 = dt.i;
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sm.io = sm.i6; sm.i6 = sm.i5; sm.i5 = sm.i4; sm.i4 = sm.i3; sm.i3 = sm.i2; sm.i2 = sm.i1; sm.i1 = sm.i;
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i2.io = i2.i1; i2.i1 = i2.i;
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q2.io = q2.i1; q2.i1 = q2.i;
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re.io = re.i1; re.i1 = re.i;
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im.io = im.i1; im.i1 = im.i;
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pd.io = pd.i1; pd.i1 = pd.i;
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ph.io = ph.i1; ph.i1 = ph.i;
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mama.io = mama.i1; mama.i1 = mama.i;
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fama.io = fama.i1; fama.i1 = fama.i;
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}
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int i = base.Count;
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pr.i = TValue.v;
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if (i > 5) {
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double adj = (0.075 * pd.i1) + 0.54;
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// smooth and detrender
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sm.i = ((4 * pr.i) + (3 * pr.i1) + (2 * pr.i2) + pr.i3) / 10;
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dt.i = ((0.0962 * sm.i) + (0.5769 * sm.i2) - (0.5769 * sm.i4) - (0.0962 * sm.i6)) * adj;
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// in-phase and quadrature
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q1.i = ((0.0962 * dt.i) + (0.5769 * dt.i2) - (0.5769 * dt.i4) - (0.0962 * dt.i6)) * adj;
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i1.i = dt.i3;
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// advance the phases by 90 degrees
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jI = ((0.0962 * i1.i) + (0.5769 * i1.i2) - (0.5769 * i1.i4) - (0.0962 * i1.i6)) * adj;
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jQ = ((0.0962 * q1.i) + (0.5769 * q1.i2) - (0.5769 * q1.i4) - (0.0962 * q1.i6)) * adj;
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// phasor addition for 3-bar averaging
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i2.i = i1.i - jQ;
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q2.i = q1.i + jI;
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i2.i = (0.2 * i2.i) + (0.8 * i2.i1); // smoothing it
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q2.i = (0.2 * q2.i) + (0.8 * q2.i1);
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// homodyne discriminator
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re.i = (i2.i * i2.i1) + (q2.i * q2.i1);
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im.i = (i2.i * q2.i1) - (q2.i * i2.i1);
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re.i = (0.2 * re.i) + (0.8 * re.i1); // smoothing it
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im.i = (0.2 * im.i) + (0.8 * im.i1);
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// calculate period
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pd.i = (im.i != 0 && re.i != 0) ? (6.283185307179586 / Math.Atan(im.i / re.i)) : 0d;
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// adjust period to thresholds
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pd.i = (pd.i > 1.5 * pd.i1) ? 1.5 * pd.i1 : pd.i;
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pd.i = (pd.i < 0.67 * pd.i1) ? 0.67 * pd.i1 : pd.i;
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pd.i = (pd.i < 6d) ? 6d : pd.i;
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pd.i = (pd.i > 50d) ? 50d : pd.i;
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// smooth the period
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pd.i = (0.2 * pd.i) + (0.8 * pd.i1);
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// determine phase position
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ph.i = (i1.i != 0) ? Math.Atan(q1.i / i1.i) * 57.29577951308232 : 0;
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// change in phase
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double delta = Math.Max(ph.i1 - ph.i, 1d);
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// adaptive alpha value
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double alpha = Math.Max(fastl / delta, slowl);
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// final indicators
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mama.i = ((alpha * pr.i) + ((1d - alpha) * mama.i1));
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fama.i = ((0.5d * alpha * mama.i) + ((1d - (0.5d * alpha)) * fama.i1));
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}
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else {
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sumPr += pr.i;
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pd.i = sm.i = dt.i = i1.i = q1.i = i2.i = q2.i = re.i = im.i = ph.i = 0;
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mama.i = fama.i = sumPr / (i+1);
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}
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base.Add((TValue.t, mama.i), update, _NaN);
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var result = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : fama.i);
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Fama.Add(result, update);
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}
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}
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