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T3

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This commit is contained in:
Miha Kralj
2022-11-18 21:48:41 -08:00
parent cfa10c79e8
commit f393ec336d
13 changed files with 1264 additions and 985 deletions
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Source/Basics/TR_Series.cs
+4 -2
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@@ -19,14 +19,16 @@ Sources:
public class TR_Series : Single_TBars_Indicator
{
private double _cm1 = double.NaN;
private double _cm1, _cm1_o;
public TR_Series(TBars source, bool useNaN = false) : base(source, period:0, useNaN:useNaN) {
_cm1 =_cm1_o = double.NaN;
if (this._bars.Count > 0) { base.Add(this._bars); }
}
public override void Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update)
{
if (_cm1 is double.NaN) { _cm1 = TBar.c; }
if (update) {_cm1 = _cm1_o; } else { _cm1_o = _cm1; }
if (_cm1 is double.NaN) { _cm1 = TBar.c; } //first bar
double d1 = Math.Abs(TBar.h - TBar.l);
double d2 = Math.Abs(_cm1 - TBar.h);
Source/Feeds/RND_Feed.cs
+7 -7
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@@ -11,18 +11,18 @@ Random Bars generator - used for testing, validation and fun
public class RND_Feed : TBars
{
public RND_Feed(int bars, double volatility = 0.05, double startvalue = 100.0)
public RND_Feed(int Bars, double Volatility = 0.05, double Startvalue = 100.0)
{
Random rnd = new();
double c = startvalue;
for (int i = 0; i < bars; i++)
double c = Startvalue;
for (int i = 0; i < Bars; i++)
{
double o = Math.Round(c + (c * (((volatility * 0.1) * rnd.NextDouble()) - 0.005)), 2);
double h = Math.Round(o + (c * volatility * rnd.NextDouble()), 2);
double l = Math.Round(o - (c * volatility * rnd.NextDouble()), 2);
double o = Math.Round(c + (c * (((Volatility * 0.1) * rnd.NextDouble()) - 0.005)), 2);
double h = Math.Round(o + (c * Volatility * rnd.NextDouble()), 2);
double l = Math.Round(o - (c * Volatility * rnd.NextDouble()), 2);
c = Math.Round(l + ((h - l) * rnd.NextDouble()), 2);
double v = Math.Round(1000 * rnd.NextDouble(), 2);
this.Add(DateTime.Today.AddDays(i - bars), o, h, l, c, v);
this.Add(DateTime.Today.AddDays(i - Bars), o, h, l, c, v);
}
}
}
Source/QuanTAlib.csproj
+3 -2
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@@ -2,7 +2,7 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<Title>QuanTAlib</Title>
<Version>0.1.21</Version>
<Version>0.1.22</Version>
<Product>Library of Technical Indicators for .NET</Product>
<Description>Quantitative Technical Analysis library for both real-time (streaming) and historical data analysis</Description>
<RepositoryType>git</RepositoryType>
@@ -11,7 +11,7 @@
<Authors>Miha Kralj</Authors>
<Copyright>Miha Kralj</Copyright>
<PackageReadmeFile>readme.md</PackageReadmeFile>
<TargetFrameworks>net7.0;</TargetFrameworks>
<TargetFrameworks>net7.0;net6.0;netstandard2.1</TargetFrameworks>
<ImplicitUsings>disable</ImplicitUsings>
<LangVersion>preview</LangVersion>
<Nullable>disable</Nullable>
@@ -66,6 +66,7 @@
<Visible>False</Visible>
<PackagePath></PackagePath>
</None>
<PackageReference Include="System.Collections" Version="4.3.0" />
<PackageReference Include="System.Text.Json" Version="7.0.0" />
</ItemGroup>
</Project>
Source/Trends/MAMA_Series.cs
+41 -51
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@@ -3,8 +3,8 @@ using System;
/* <summary>
MAMA: MESA Adaptive Moving Average
Created by John Ehlers, the MAMA indicator is a 5-period adaptive moving average of
high/low price that uses classic electrical radio-frequency signal processing algorithms
Created by John Ehlers, the MAMA indicator is a 5-period adaptive moving average of
high/low price that uses classic electrical radio-frequency signal processing algorithms
to reduce noise.
KAMAi = KAMAi - 1 + SC * ( price - KAMAi-1 )
@@ -22,84 +22,87 @@ public class MAMA_Series : Single_TSeries_Indicator
fastl = fastlimit;
slowl = slowlimit;
i = 0;
Fama = new();
if (base._data.Count > 0) { base.Add(base._data); }
}
private int i;
private double sumPr, jI, jQ, fastl, slowl;
private (double i, double i1, double i2, double i3, double i4, double i5, double i6, double io) pr, i1, q1, sm, dt;
private (double i, double i1, double io) i2, q2, re, im, pd, ph, mama, fama;
public TSeries Fama { get; }
public override void Add((System.DateTime t, double v) TValue, bool update)
{
if (update) {
i--;
pr.i = pr.i1; pr.i1 = pr.i2; pr.i2 = pr.i3; pr.i3 = pr.i4; pr.i4 = pr.i5; pr.i5 = pr.i6; pr.i6 = pr.io;
i1.i = i1.i1; i1.i1 = i1.i2; i1.i2 = i1.i3; i1.i3 = i1.i4; i1.i4 = i1.i5; i1.i5 = i1.i6; i1.i6 = i1.io;
q1.i = q1.i1; q1.i1 = q1.i2; q1.i2 = q1.i3; q1.i3 = q1.i4; q1.i4 = q1.i5; q1.i5 = q1.i6; q1.i6 = q1.io;
dt.i = dt.i1; dt.i1 = dt.i2; dt.i2 = dt.i3; dt.i3 = dt.i4; dt.i4 = dt.i5; dt.i5 = dt.i6; dt.i6 = dt.io;
sm.i = sm.i1; sm.i1 = sm.i2; sm.i2 = sm.i3; sm.i3 = sm.i4; dt.i4 = sm.i5; sm.i5 = sm.i6; sm.i6 = sm.io;
i2.i = i2.i1; i2.i1 = i2.io;
q2.i = q2.i1; q2.i1 = q2.io;
re.i = re.i1; re.i1 = re.io;
im.i = im.i1; im.i1 = im.io;
pd.i = pd.i1; pd.i1 = pd.io;
ph.i = ph.i1; ph.i1 = ph.io;
mama.i = mama.i1; mama.i1 = mama.io;
fama.i = fama.i1; fama.i1 = fama.io;
}
if (!update) {
// roll forward (oldx = x)
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;
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;
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;
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;
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;
i2.io = i2.i1; i2.i1 = i2.i;
q2.io = q2.i1; q2.i1 = q2.i;
re.io = re.i1; re.i1 = re.i;
im.io = im.i1; im.i1 = im.i;
pd.io = pd.i1; pd.i1 = pd.i;
ph.io = ph.i1; ph.i1 = ph.i;
mama.io = mama.i1; mama.i1 = mama.i;
fama.io = fama.i1; fama.i1 = fama.i;
}
pr.i = TValue.v;
if (i > 5) {
double adj = (0.075 * pd.i1) + 0.54;
// smooth and detrender
sm.i = ((4 * pr.i) + (3 * pr.i1) + (2 * pr.i2) + pr.i3) / 10;
dt.i = ((0.0962 * sm.i) + (0.5769 * sm.i2) - (0.5769 * sm.i4) - (0.0962 * sm.i6)) * adj;
// in-phase and quadrature
q1.i = ((0.0962 * dt.i) + (0.5769 * dt.i2) - (0.5769 * dt.i4) - (0.0962 * dt.i6)) * adj;
i1.i = dt.i3;
// advance the phases by 90 degrees
jI = ((0.0962 * i1.i) + (0.5769 * i1.i2) - (0.5769 * i1.i4) - (0.0962 * i1.i6)) * adj;
jQ = ((0.0962 * q1.i) + (0.5769 * q1.i2) - (0.5769 * q1.i4) - (0.0962 * q1.i6)) * adj;
// phasor addition for 3-bar averaging
i2.i = i1.i - jQ;
q2.i = q1.i + jI;
i2.i = (0.2 * i2.i) + (0.8 * i2.i1); // smoothing it
q2.i = (0.2 * q2.i) + (0.8 * q2.i1);
// homodyne discriminator
re.i = (i2.i * i2.i1) + (q2.i * q2.i1);
im.i = (i2.i * q2.i1) - (q2.i * i2.i1);
re.i = (0.2 * re.i) + (0.8 * re.i1); // smoothing it
im.i = (0.2 * im.i) + (0.8 * im.i1);
// calculate period
pd.i = (im.i != 0 && re.i != 0) ? (6.283185307179586 / Math.Atan(im.i / re.i)) : 0d;
// adjust period to thresholds
pd.i = (pd.i > 1.5 * pd.i1) ? 1.5 * pd.i1 : pd.i;
pd.i = (pd.i < 0.67 * pd.i1) ? 0.67 * pd.i1 : pd.i;
pd.i = (pd.i < 6d) ? 6d : pd.i;
pd.i = (pd.i > 50d) ? 50d : pd.i;
// smooth the period
pd.i = (0.2 * pd.i) + (0.8 * pd.i1);
// determine phase position
ph.i = (i1.i != 0) ? Math.Atan(q1.i / i1.i) * 57.29577951308232 : 0;
// change in phase
double delta = Math.Max(ph.i1 - ph.i, 1d);
// adaptive alpha value
double alpha = Math.Max(fastl / delta, slowl);
// final indicators
mama.i = ((alpha * pr.i) + ((1d - alpha) * mama.i1));
fama.i = ((0.5d * alpha * mama.i) + ((1d - (0.5d * alpha)) * fama.i1));
@@ -107,25 +110,12 @@ public class MAMA_Series : Single_TSeries_Indicator
else {
sumPr += pr.i;
pd.i = sm.i = dt.i = i1.i = q1.i = i2.i = q2.i = re.i = im.i = ph.i = 0;
mama.i = fama.i = sumPr / (i+1);
mama.i = fama.i = sumPr / (i+1);
}
i++;
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;
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;
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;
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;
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;
i2.io = i2.i1; i2.i1 = i2.i;
q2.io = q2.i1; q2.i1 = q2.i;
re.io = re.i1; re.i1 = re.i;
im.io = im.i1; im.i1 = im.i;
pd.io = pd.i1; pd.i1 = pd.i;
ph.io = ph.i1; ph.i1 = ph.i;
mama.io = mama.i1; mama.i1 = mama.i;
fama.io = fama.i1; fama.i1 = fama.i;
if (!update) { i++; }
base.Add((TValue.t, mama.i), update, _NaN);
var result = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : fama.i);
Fama.Add(result, update);
}
}
Source/Trends/T3_Series.cs
+117
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@@ -0,0 +1,117 @@
namespace QuanTAlib;
using System;
using System.Linq;
using System.Numerics;
/* <summary>
T3: Triple Exponential Moving Average
TEMA uses EMA(EMA(EMA())) to calculate less laggy Exponential moving average.
Sources:
https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triple-exponential-moving-average-tema/
</summary> */
public class T3_Series : Single_TSeries_Indicator
{
private int i;
private double k, a;
private double c1, c2, c3, c4;
private double o_c1, o_c2, o_c3, o_c4;
private double e1, e2, e3, e4, e5, e6;
private double o_e1, o_e2, o_e3, o_e4, o_e5, o_e6;
private double sum1, sum2, sum3, sum4, sum5, sum6;
private double o_sum1, o_sum2, o_sum3, o_sum4, o_sum5, o_sum6;
public T3_Series(TSeries source, int period, double vfactor, bool useNaN = false) : base(source, period, useNaN)
{
i = 0;
k = 2.0 / (_p + 1);
a = vfactor;
c1 = -a * a * a;
c2 = (3 * a * a) + (3 * a * a * a);
c3 = (-6 * a * a) - (3 * a) - (3 * a * a * a);
c4 = 1 + (3 * a) + (3 * a * a) + (a * a * a) ;
e1 = e2 = e3 = e4 = e5 = e6 = 0;
sum1 = sum2 = sum3 = sum4 = sum5 = sum6 = 0;
if (_data.Count > 0) { base.Add(data: _data); }
}
public override void Add((DateTime t, double v) TValue, bool update)
{
if (update) {
// roll back (x = oldx)
c1 = o_c1; c2 = o_c2; c3 = o_c3; c4 = o_c4;
e1 = o_e1; e2 = o_e2; e3 = o_e3; e4 = o_e4; e5 = o_e5; e6 = o_e6;
sum1 = o_sum1; sum2 = o_sum2; sum3 = o_sum3; sum4 = o_sum4; sum5 = o_sum5; sum6 = o_sum6;
} else {
// roll forward (oldx = x)
o_c1 = c1; o_c2 = c2; o_c3 = c3; o_c4 = c4;
o_e1 = e1; o_e2 = e2; o_e3 = e3; o_e4 = e4; o_e5 = e5; o_e6 = e6;
o_sum1 = sum1; o_sum2 = sum2; o_sum3 = sum3; o_sum4 = sum4; o_sum5 = sum5; o_sum6 = sum6;
}
double v = TValue.v;
if (i > _p - 1) {
e1 += k * (v - e1);
if (i > 2 * (_p - 1)) {
e2 += k * (e1 - e2);
if (i > 3 * (_p - 1)) {
e3 += k * (e2 - e3);
if (i > 4 * (_p - 1)) {
e4 += k * (e3 - e4);
if (i > 5 * (_p - 1)) {
e5 += k * (e4 - e5);
if (i > 6 * (_p - 1)) {
e6 += k * (e5 - e6);
}
else {
sum6 += e5;
if (i == 6 * (_p - 1)) {
e6 = sum6 / _p;
}
}
}
else {
sum5 += e4;
if (i == 5 * (_p - 1)) {
sum6 = e5 = sum5 / _p;
}
}
}
else {
sum4 += e3;
if (i == 4 * (_p - 1)) {
sum5 = e4 = sum4 / _p;
}
}
}
else {
sum3 += e2;
if (i == 3 * (_p - 1)) {
sum4 = e3 = sum3 / _p;
}
}
}
else {
sum2 += e1;
if (i == 2 * (_p - 1)) {
sum3 = e2 = sum2 / _p;
}
}
}
else {
sum1 += v;
if (i == _p - 1) {
sum2 = e1 = sum1 / _p;
}
}
if (!update) { i++; }
double t3 = (c1 * e6) + (c2 * e5) + (c3 * e4) + (c4 * e3);
base.Add(TValue: (TValue.t, t3), update: update, useNaN: _NaN);
}
}
Source/Trends/ZLEMA_Series.cs
+18 -20
View File
@@ -24,38 +24,36 @@ public class ZLEMA_Series : Single_TSeries_Indicator
{
private readonly System.Collections.Generic.List<double> _buffer = new();
private readonly double _k, _k1m;
private double _lastema, _lastlastema;
private double _lastema, _lastema_o;
private int _llag;
public ZLEMA_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
{
this._k = 2.0 / (this._p + 1);
this._k1m = 1.0 - this._k;
this._lastema = this._lastlastema = double.NaN;
if (base._data.Count > 0)
{ base.Add(base._data); }
this._lastema = this._lastema_o = double.NaN;
_llag = (int)((_p-1) * 0.5);
if (_data.Count > 0) { base.Add(_data); }
}
public override void Add((System.DateTime t, double v) TValue, bool update)
{
int _lag = (int)((_p-1) * 0.5);
_lag = (this.Count-_lag < 0) ? 0 : this.Count-_lag;
int _lag = Math.Max(this.Count-_llag, 0);
if (update) {
_lastema = _lastema_o; _lag--;
} else {
_lastema_o = _lastema;
}
double _zl = TValue.v + (TValue.v - _data[_lag].v);
double _ema = 0;
if (update)
{ this._lastema = this._lastlastema; }
if (this.Count < this._p)
{
Add_Replace_Trim(_buffer, _zl, _p, update);
_ema = _buffer.Average();
}
else
{
_ema = (_zl * this._k) + (this._lastema * this._k1m);
}
this._lastlastema = this._lastema;
this._lastema = _ema;
if (this.Count < this._p) {
Add_Replace_Trim(_buffer, _zl, _p, update);
_ema = _buffer.Average();
} else {
_ema = (_zl * _k) + (_lastema * _k1m);
}
_lastema = _ema;
base.Add((TValue.t, _ema), update, _NaN);
}
Source/Volatility/RSI_Series.cs
+22 -17
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@@ -16,30 +16,34 @@ public class RSI_Series : Single_TSeries_Indicator
{
private readonly System.Collections.Generic.List<double> _gain = new();
private readonly System.Collections.Generic.List<double> _loss = new();
private double _avgGain;
private double _avgLoss;
private double _lastValue;
private double _lastlastValue;
private double _avgGain, _avgLoss, _lastValue;
private double _avgGain_o, _avgLoss_o, _lastValue_o;
private int i;
public RSI_Series(TSeries source, int period = 10, bool useNaN = false) : base(source, period: period, useNaN: useNaN)
{ if (source.Count > 0) { base.Add(source); } }
public RSI_Series(TSeries source, int period = 10, bool useNaN = false) : base(source, period: period, useNaN: useNaN) {
i = 0;
if (source.Count > 0) { base.Add(source); }
}
public override void Add((System.DateTime t, double v) TValue, bool update)
{
int i = this.Count;
public override void Add((System.DateTime t, double v) TValue, bool update) {
double _rsi = 0;
if (update) { _lastValue = _lastlastValue; }
if (update) {
_lastValue = _lastValue_o;
_avgGain = _avgGain_o;
_avgLoss = _avgLoss_o;
}
else {
_lastValue_o = _lastValue;
_avgGain_o = _avgGain;
_avgLoss_o = _avgLoss;
}
if (i == 0) { _lastValue = TValue.v; }
double _gainval = (TValue.v > _lastValue) ? TValue.v - _lastValue : 0;
if (update) { _gain[_gain.Count - 1] = _gainval; } else { _gain.Add(_gainval); }
if (_gain.Count > this._p) { _gain.RemoveAt(0); }
Add_Replace_Trim(_gain, _gainval, _p, update);
double _lossval = (TValue.v < _lastValue) ? _lastValue - TValue.v : 0;
if (update) { _loss[_loss.Count - 1] = _lossval; } else { _loss.Add(_lossval); }
if (_loss.Count > this._p) { _loss.RemoveAt(0); }
_lastlastValue = _lastValue;
Add_Replace_Trim(_loss, _lossval, _p, update);
_lastValue = TValue.v;
// calculate RSI
@@ -67,6 +71,7 @@ public class RSI_Series : Single_TSeries_Indicator
_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);
}