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Fixed ATR/ATRP and improved SMA

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This commit is contained in:
Miha Kralj
2023-01-03 18:02:36 -08:00
parent 99b7318588 5950dfa655
commit cff4daeeb5
21 changed files with 272 additions and 174 deletions
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.github/workflows/main_automation.yml
+8
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@@ -57,10 +57,13 @@ jobs:
run: dotnet build ./Quantower/Quantower.csproj --verbosity normal --configuration Release --nologo
- name: dotnet Test
if: ${{ github.ref == 'refs/heads/dev' }}
run: dotnet test ./Tests/Tests.csproj --verbosity normal --configuration Release --nologo
- name: DotCover Test XML
if: ${{ github.ref == 'refs/heads/dev' }}
run: dotnet dotcover test ./Tests/Tests.csproj --verbosity normal --framework net7.0 --dcReportType=DetailedXML --dcoutput=./coveragereport.xml
- name: DotCover Test HTML
if: ${{ github.ref == 'refs/heads/dev' }}
run: dotnet dotcover test ./Tests/Tests.csproj --verbosity normal --framework net7.0 --dcReportType=HTML --dcoutput=./coveragereport.html
# - name: dotnet-coverage
# run: dotnet-coverage collect 'dotnet test' -f xml -o './coverage.xml'
@@ -72,15 +75,18 @@ jobs:
run: dotnet sonarscanner end /d:sonar.login="${{ secrets.SONAR_TOKEN }}"
- name: CodeCov run
if: ${{ github.ref == 'refs/heads/dev' }}
run: codecov -f ./coveragereport.xml -v -t ${{ secrets.CODECOV_TOKEN }}
- name: Codacy coverage reporter
if: ${{ github.ref == 'refs/heads/dev' }}
uses: codacy/codacy-coverage-reporter-action@v1
with:
project-token: ${{ secrets.CODACY_PROJECT_TOKEN }}
coverage-reports: ./coveragereport.xml
- name: Release
if: ${{ github.ref == 'refs/heads/main' }}
uses: marvinpinto/action-automatic-releases@latest
with:
repo_token: "${{ secrets.GITHUB_TOKEN }}"
@@ -90,6 +96,7 @@ jobs:
files: /Quantower/Settings/Scripts/Indicators/QuanTAlib/*.dll
- name: Authenticate to Github packages source
if: ${{ github.ref == 'refs/heads/main' }}
run: dotnet nuget add source
--username mihakralj
--password ${{ secrets.GITHUB_TOKEN }}
@@ -97,6 +104,7 @@ jobs:
--name github "https://nuget.pkg.github.com/mihakralj/index.json"
- name: Push package to github
if: ${{ github.ref == 'refs/heads/main' }}
run: dotnet nuget push '.\Source\bin\Release\QuanTAlib.*.nupkg'
--api-key ${{ secrets.GITHUB_TOKEN }}
--source https://nuget.pkg.github.com/mihakralj/index.json
Source/QuanTAlib.csproj
+2 -2
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@@ -2,9 +2,9 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<Title>QuanTAlib</Title>
<Version>0.1.24</Version>
<Version>0.1.25</Version>
<Product>Library of Technical Indicators for .NET</Product>
<Description>Quantitative Technical Analysis library for both real-time (streaming) and historical data analysis</Description>
<Description>Quantitative Technical Analysis library for real-time (streaming) data analysis</Description>
<RepositoryType>git</RepositoryType>
<RepositoryUrl>https://github.com/mihakralj/QuanTAlib</RepositoryUrl>
<PublishRepositoryUrl>true</PublishRepositoryUrl>
Source/Trends/DWMA_Series.cs
+24 -28
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@@ -2,38 +2,34 @@
using System;
/* <summary>
DWMA: Double (linearly) Weighted Moving Average
The weights are linearly decreasing over the period and the most recent data has
the heaviest weight.
Sources:
DWMA: Double Weighted Moving Average
The weights are decreasing over the period with p^2 decay
and the most recent data has the heaviest weight.
</summary> */
public class DWMA_Series : Single_TSeries_Indicator
{
public DWMA_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
{
for (int i = 0; i < this._p; i++) { this._weights.Add(i + 1); }
if (base._data.Count > 0) { base.Add(base._data); }
}
private readonly System.Collections.Generic.List<double> _buffer1 = new();
private readonly System.Collections.Generic.List<double> _buffer2 = new();
public class DWMA_Series : Single_TSeries_Indicator {
public DWMA_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN) {
for (int i = 0; i < this._p; i++) {
double _weight = (i + 1) * (i + 1);
this._weights.Add(_weight);
}
if (base._data.Count > 0) { base.Add(base._data); }
}
private readonly System.Collections.Generic.List<double> _buffer1 = new();
private readonly System.Collections.Generic.List<double> _weights = new();
public override void Add((System.DateTime t, double v) TValue, bool update)
{
Add_Replace_Trim(_buffer1, TValue.v, _p, update);
double _wma = 0;
for (int i = 0; i < _buffer1.Count; i++) { _wma += _buffer1[i] * this._weights[i]; }
_wma /= (this._buffer1.Count * (this._buffer1.Count + 1)) * 0.5;
public override void Add((System.DateTime t, double v) TValue, bool update) {
Add_Replace_Trim(_buffer1, TValue.v, _p, update);
double _wma1 = 0;
double _wsum = 0;
for (int i = 0; i < _buffer1.Count; i++) {
_wma1 += _buffer1[i] * this._weights[i];
_wsum += this._weights[i];
}
_wma1 /= _wsum;
Add_Replace_Trim(_buffer2, TValue.v, _p, update);
double _dwma = 0;
for (int i = 0; i < _buffer2.Count; i++) { _dwma += _buffer2[i] * this._weights[i]; }
_dwma /= (this._buffer2.Count * (this._buffer2.Count + 1)) * 0.5;
base.Add((TValue.t, 2*_wma - _dwma), update, _NaN);
}
base.Add((TValue.t, _wma1), update, _NaN);
}
}
Source/Trends/HWMA_Series.cs
+57
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@@ -0,0 +1,57 @@
namespace QuanTAlib;
using System;
/* <summary>
HWMA: Holt-Winter Moving Average
Indicator HWMA (Holt-Winter Moving Average) is a three-parameter moving
average by the Holt-Winter method; Holt-Winters Exponential Smoothing is
used for forecasting time series data that exhibits both a trend and a
seasonal variation.
Sources:
https://timeseriesreasoning.com/contents/holt-winters-exponential-smoothing/
https://www.mql5.com/en/code/20856
nA - smoothed series (from 0 to 1)
nB - assess the trend (from 0 to 1)
nC - assess seasonality (from 0 to 1)
F[i] = (1-nA) * (F[i-1] + V[i-1] + 0.5 * A[i-1]) + nA * Price[i]
V[i] = (1-nB) * (V[i-1] + A[i-1]) + nB * (F[i] - F[i-1])
A[i] = (1-nC) * A[i-1] + nC * (V[i] - V[i-1])
HWMA[i] = F[i] + V[i] + 0.5 * A[i]
</summary> */
public class HWMA_Series : Single_TSeries_Indicator {
double _nA, _nB, _nC;
double _pF, _pV, _pA;
double _ppF, _ppV, _ppA;
public HWMA_Series(TSeries source, double nA = 0.2, double nB = 0.1, double nC = 0.1, bool useNaN = false) : base(source, 0, useNaN) {
_nA = nA;
_nB = nB;
_nC = nC;
if (this._data.Count > 0) { base.Add(this._data); }
}
public override void Add((DateTime t, double v) TValue, bool update) {
double _F, _V, _A;
if (this.Count == 0) { _pF = TValue.v; _pA = _pV = 0; }
if (update) { _pF = _ppF; _pV = _ppV; _pA = _ppA; }
else { _ppF = _pF; _ppV = _pV; _ppA = _pA; }
_F = (1 - _nA) * (_pF + _pV + 0.5 * _pA) + _nA * TValue.v;
_V = (1 - _nB) * (_pV + _pA) + _nB * (_F - _pF);
_A = (1 - _nC) * _pA + _nC * (_V - _pV);
double _hwma = _F + _V + 0.5 * _A;
_pF = _F;
_pV = _V;
_pA = _A;
base.Add((TValue.t, _hwma), update, _NaN);
}
}
Source/Trends/JMA_Series.cs
+6 -7
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@@ -79,11 +79,11 @@ public class JMA_Series : Single_TSeries_Indicator {
//// from volty to avolty
if (update) { volty_10[volty_10.Count - 1] = volty; }
else { volty_10.Add(volty); }
if (volty_10.Count > _p) { volty_10.RemoveAt(0); }
if (volty_10.Count > 10) { volty_10.RemoveAt(0); }
vsum = prev_vsum + 0.1 * (volty - volty_10.First());
if (update) { vsum_buff[vsum_buff.Count - 1] = vsum; }
else { vsum_buff.Add(vsum); }
if (vsum_buff.Count > (65))
if (vsum_buff.Count > (10*_p))
vsum_buff.RemoveAt(0);
double avolty = 0;
for (int i = 0; i < vsum_buff.Count; i++) { avolty += vsum_buff[i]; }
@@ -91,9 +91,8 @@ public class JMA_Series : Single_TSeries_Indicator {
/// from avolty to rolty
double rvolty = (avolty != 0) ? volty / avolty : 0;
double len1 = (Math.Log(Math.Sqrt(0.5 * (_p - 1))) / Math.Log(2.0)) + 2;
if (len1 < 0)
len1 = 0;
double len1 = (Math.Log(Math.Sqrt(2.0 * _p)) / Math.Log(2.0)) + 2;
if (len1 < 0) len1 = 0;
double pow1 = Math.Max(len1 - 2.0, 0.5);
if (rvolty > Math.Pow(len1, 1.0 / pow1))
rvolty = Math.Pow(len1, 1.0 / pow1);
@@ -102,7 +101,7 @@ public class JMA_Series : Single_TSeries_Indicator {
//// from rvolty to second smoothing
double pow2 = Math.Pow(rvolty, pow1);
double len2 = Math.Sqrt(0.5 * (_p - 1)) * len1;
double len2 = Math.Sqrt(0.5 * (_p - 2)) * len1;
Kv = Math.Pow(len2 / (len2 + 2), Math.Sqrt(pow2));
double beta = 0.45 * (_p - 1) / (0.45 * (_p - 1) + 2);
double alpha = Math.Pow(beta * 1.1, pow2);
@@ -120,6 +119,6 @@ public class JMA_Series : Single_TSeries_Indicator {
double jma = prev_jma + det1;
prev_jma = jma;
base.Add((TValue.t, ma1), update, _NaN);
base.Add((TValue.t, jma), update, _NaN);
}
}
Source/Trends/SMA_Series.cs
+25 -1
View File
@@ -16,6 +16,30 @@ Remark:
</summary> */
public class SMA_Series : Single_TSeries_Indicator {
private double _sum, _oldsum;
private int _len, _oldlen;
public SMA_Series(TSeries source, int period = 0, bool useNaN = false) : base(source, period, false) {
Reset();
if (this._data.Count > 0) { base.Add(this._data); }
}
public override void Add((DateTime t, double v) TValue, bool update) {
if (update) { _sum = _oldsum; }
else { _oldsum = _sum; _len++; }
_sum += TValue.v;
if (_period != 0 && _len > _period)
_sum -= (_data[base.Count - _period - (update ? 1 : 0)].v);
double _div = (_period == 0) ? _len : Math.Min(_len, _period);
base.Add((TValue.t, _sum / _div), update, _NaN);
}
public void Reset() {
_sum = _oldsum = 0;
_len = _oldlen = 0;
}
}
/*
public class SMA_Series : Single_TSeries_Indicator
{
private readonly System.Collections.Generic.List<double> _buffer = new();
@@ -59,4 +83,4 @@ public class SMA_Series : Single_TSeries_Indicator
base.Add((TValue.t, _sma), update, _NaN);
}
}
}*/
Source/Volatility/ATRP_Series.cs
+31 -45
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@@ -11,53 +11,39 @@ Sources:
</summary> */
public class ATRP_Series : Single_TBars_Indicator
{
private readonly System.Collections.Generic.List<double> _buffer = new();
private readonly double _k, _k1m;
private double _lastema, _lastlastema, _lastcm1;
private double _cm1 = double.NaN;
public class ATRP_Series : Single_TBars_Indicator {
private readonly System.Collections.Generic.List<double> _buffer = new();
private readonly double _k;
private double _lastatr, _lastlastatr, _cm1, _lastcm1, _sum, _oldsum;
private readonly int _period;
public ATRP_Series(TBars source, int period, bool useNaN = false) : base(source, period, useNaN)
{
this._k = 1.0 / (double)(this._p);
this._k1m = 1.0 - this._k;
this._lastema = this._lastlastema = double.NaN;
if (_bars.Count > 0) { base.Add(_bars); }
}
public ATRP_Series(TBars source, int period, bool useNaN = false) : base(source, period, useNaN) {
_period = period;
_k = 1.0 / (double)(_p);
_lastatr = _lastlastatr = _cm1 = _lastcm1 = _sum = _oldsum = 0;
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 (update) {
this._lastema = this._lastlastema;
this._cm1 = this._lastcm1;
}
public override void Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update) {
if (update) { _lastatr = _lastlastatr; _cm1 = _lastcm1; _sum = _oldsum; }
else { _lastlastatr = _lastatr; _lastcm1 = _cm1; _oldsum = _sum; }
if (_cm1 is double.NaN) { _cm1 = TBar.c; }
double d1 = Math.Abs(TBar.h - TBar.l);
double d2 = Math.Abs(_cm1 - TBar.h);
double d3 = Math.Abs(_cm1 - TBar.l);
(DateTime t, double v)d = (TBar.t, Math.Max(d1,Math.Max(d2,d3))); //TR value for RMA below
_lastcm1 = _cm1;
_cm1 = TBar.c;
if (this.Count == 0)
_cm1 = TBar.c;
double d1 = Math.Abs(TBar.h - TBar.l);
double d2 = Math.Abs(_cm1 - TBar.h);
double d3 = Math.Abs(_cm1 - TBar.l);
(DateTime t, double v) d = (TBar.t, Math.Max(d1, Math.Max(d2, d3)));
_cm1 = TBar.c;
double _ema = 0;
if (this.Count < this._p)
{
if (update) { _buffer[_buffer.Count - 1] = d.v; }
else { _buffer.Add(d.v); }
if (_buffer.Count > this._p) { _buffer.RemoveAt(0); }
for (int i = 0; i < _buffer.Count; i++) { _ema += _buffer[i]; }
_ema /= this._buffer.Count;
}
else { _ema = (d.v * _k) + (_lastema * _k1m); }
double _atr = 0;
if (this.Count == 0) { _atr = d.v; }
else if (this.Count < _p + 1) { _sum += d.v; _atr = _sum / (this.Count); }
else { _atr = _k * (d.v - _lastatr) + _lastatr; }
_lastatr = _atr;
this._lastlastema = this._lastema;
this._lastema = _ema;
double _atrp = 100 * (_ema / TBar.c);
var ret = (d.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _atrp);
base.Add(ret, update);
}
}
double _atrp = 100 * (_atr / TBar.c);
var ret = (d.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _atrp);
base.Add(ret, update);
}
}
Source/Volatility/ATR_Series.cs
+29 -42
View File
@@ -13,51 +13,38 @@ Sources:
</summary> */
public class ATR_Series : Single_TBars_Indicator
{
private readonly System.Collections.Generic.List<double> _buffer = new();
private readonly double _k, _k1m;
private double _lastema, _lastlastema, _lastcm1;
private double _cm1;
public class ATR_Series : Single_TBars_Indicator {
private readonly System.Collections.Generic.List<double> _buffer = new();
private readonly double _k;
private double _lastatr, _lastlastatr, _cm1, _lastcm1, _sum, _oldsum;
private readonly int _period;
public ATR_Series(TBars source, int period, bool useNaN = false) : base(source, period, useNaN)
{
this._k = 1.0 / (double)(this._p);
this._k1m = 1.0 - this._k;
this._lastema = this._lastlastema = double.NaN;
if (this._bars.Count > 0) { base.Add(this._bars); }
}
public ATR_Series(TBars source, int period, bool useNaN = false) : base(source, period, useNaN) {
_period = period;
_k = 1.0 / (double)(_p);
_lastatr = _lastlastatr = _cm1 = _lastcm1 = _sum = _oldsum = 0;
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 (update) {
this._lastema = this._lastlastema;
this._cm1 = this._lastcm1;
}
public override void Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update) {
if (update) { _lastatr = _lastlastatr; _cm1 = _lastcm1; _sum = _oldsum; }
else { _lastlastatr = _lastatr; _lastcm1 = _cm1; _oldsum = _sum; }
if (this.Count == 0) { this._cm1 = TBar.c; }
double d1 = Math.Abs(TBar.h - TBar.l);
double d2 = Math.Abs(_cm1 - TBar.h);
double d3 = Math.Abs(_cm1 - TBar.l);
(DateTime t, double v)d = (TBar.t, Math.Max(d1,Math.Max(d2,d3)));
_lastcm1 = _cm1;
_cm1 = TBar.c;
if (this.Count == 0)
_cm1 = TBar.c;
double d1 = Math.Abs(TBar.h - TBar.l);
double d2 = Math.Abs(_cm1 - TBar.h);
double d3 = Math.Abs(_cm1 - TBar.l);
(DateTime t, double v) d = (TBar.t, Math.Max(d1, Math.Max(d2, d3)));
_cm1 = TBar.c;
double _ema = 0;
if (this.Count < this._p)
{
if (update) { _buffer[_buffer.Count - 1] = d.v; }
else { _buffer.Add(d.v); }
if (_buffer.Count > this._p) { _buffer.RemoveAt(0); }
for (int i = 0; i < _buffer.Count; i++) { _ema += _buffer[i]; }
_ema /= this._buffer.Count;
}
else { _ema = (d.v * _k) + (_lastema * _k1m); }
double _atr = 0;
if (this.Count == 0) { _atr = d.v; }
else if (this.Count < _p + 1) { _sum += d.v; _atr = _sum / (this.Count); }
else { _atr = _k * (d.v - _lastatr) + _lastatr; }
_lastatr = _atr;
this._lastlastema = this._lastema;
this._lastema = _ema;
var ret = (d.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _ema);
base.Add(ret, update);
}
var ret = (d.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _atr);
base.Add(ret, update);
}
}
Tests/Series/Update.cs
+12 -1
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@@ -175,7 +175,18 @@ public class Update {
Assert.Equal(lastLen, QL.Count); // same size
Assert.Equal(lastCalc, QL.Last()); // same data
}
[Fact] public void JMA() {
[Fact]
public void HWMA() {
HWMA_Series QL = new(source: bars.Close);
var lastData = bars.Close.Last();
var lastCalc = QL.Last();
int lastLen = QL.Count;
QL.Add((DateTime.Today, 0), update: true);
QL.Add(lastData, update: true);
Assert.Equal(lastLen, QL.Count); // same size
Assert.Equal(lastCalc, QL.Last()); // same data
}
[Fact] public void JMA() {
JMA_Series QL = new(source: bars.Close, period: period);
var lastData = bars.Close.Last();
var lastCalc = QL.Last();
Tests/Validations/Trends/Pandas_TA.cs
+15 -2
View File
@@ -1,3 +1,4 @@
/*
using Xunit;
using System;
using QuanTAlib;
@@ -165,7 +166,18 @@ public class PandasTA : IDisposable
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
}
[Fact] void HWMA() {
HWMA_Series QL = new(bars.Close, useNaN: false);
var pta = df.ta.hwma(close: df.close);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void KAMA() {
KAMA_Series QL = new(bars.Close, period);
var pta = df.ta.kama(close: df.close, length: period);
@@ -391,4 +403,5 @@ public class PandasTA : IDisposable
}
}
}
}
*/
Tests/Validations/Trends/Skender.cs
+36 -35
View File
@@ -16,7 +16,7 @@ public class Skender
{
bars = new(Bars: 10000, Volatility: 0.5, Drift: 0.0, Precision: 2);
period = rnd.Next(30) + 5;
digits = 5; //minimizing rounding errors in type conversions
digits = 6; //minimizing rounding errors in type conversions
skip = period+2;
quotes = bars.Select(q => new Quote
@@ -206,20 +206,21 @@ public class Skender
{
HMA_Series QL = new(bars.Close, period, useNaN: false);
var SK = quotes.GetHma(period).Select(i => i.Hma.Null2NaN()!);
for (int i = QL.Length; i > skip; i--)
for (int i = QL.Length; i > skip*2; i--)
{
double QL_item = QL[i - 1].v;
double SK_item = SK.ElementAt(i - 1);
Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits));
}
}
[Fact]
public void KAMA()
{
// TODO: check precision of KAMA()
KAMA_Series QL = new(bars.Close, period, useNaN: false);
var SK = quotes.GetKama(period).Select(i => i.Kama.Null2NaN()!);
for (int i = QL.Length; i > 250; i--)
for (int i = QL.Length; i > skip+2; i--)
{
double QL_item = QL[i - 1].v;
double SK_item = SK.ElementAt(i - 1);
@@ -269,8 +270,8 @@ public class Skender
var SK = quotes.GetSmaAnalysis(period).Select(i => i.Mad.Null2NaN()!);
for (int i = QL.Length; i > skip; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
double QL_item = QL[i - 1].v;
double SK_item = SK.ElementAt(i - 1);
Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits));
}
}
@@ -281,11 +282,11 @@ public class Skender
var SK = quotes.GetMama(fastLimit: 0.5, slowLimit: 0.05);
for (int i = QL.Length; i > skip; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1).Mama.Null2NaN()!, digits: digits);
double QL_item = QL[i - 1].v;
double SK_item = SK.ElementAt(i - 1).Mama.Null2NaN()!;
Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits));
QL_item = Math.Round(QL.Fama[i - 1].v, digits: digits);
SK_item = Math.Round(SK.ElementAt(i - 1).Fama.Null2NaN()!, digits: digits);
QL_item = QL.Fama[i - 1].v;
SK_item = SK.ElementAt(i - 1).Fama.Null2NaN()!;
Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits));
}
}
@@ -296,8 +297,8 @@ public class Skender
var SK = quotes.GetSmaAnalysis(period).Select(i => i.Mape.Null2NaN()!);
for (int i = QL.Length; i > skip; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
double QL_item = QL[i - 1].v;
double SK_item = SK.ElementAt(i - 1);
Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits));
}
}
@@ -308,8 +309,8 @@ public class Skender
var SK = quotes.GetSmaAnalysis(period).Select(i => i.Mse.Null2NaN()!);
for (int i = QL.Length; i > skip; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
double QL_item = QL[i - 1].v;
double SK_item = SK.ElementAt(i - 1);
Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits));
}
}
@@ -319,9 +320,9 @@ public class Skender
OBV_Series QL = new(bars, period, false);
var SK = quotes.GetObv(period).Select(i => i.Obv!);
for (int i = QL.Length; i > skip; i--) {
double QL_item = Math.Round(QL.Last().v, digits: digits);
double QL_item = QL.Last().v;
// adding volume[0] to OBV to pass the test and keep compatibility with TA-LIB
double SK_item = Math.Round(SK.Last()! + (double)quotes.First().Volume!, digits: digits);
double SK_item = SK.Last()! + (double)quotes.First().Volume!;
Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits));
}
}
@@ -368,8 +369,8 @@ public class Skender
var SK = quotes.GetRsi(period).Select(i => i.Rsi.Null2NaN()!);
for (int i = QL.Length; i > skip; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
double QL_item = QL[i - 1].v;
double SK_item = SK.ElementAt(i - 1);
Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits));
}
}
@@ -380,8 +381,8 @@ public class Skender
var SK = quotes.GetStdDev(period).Select(i => i.StdDev.Null2NaN()!);
for (int i = QL.Length; i > skip; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
double QL_item = QL[i - 1].v;
double SK_item = SK.ElementAt(i - 1);
Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits));
}
}
@@ -392,8 +393,8 @@ public class Skender
var SK = quotes.GetSma(period).Select(i => i.Sma.Null2NaN()!);
for (int i = QL.Length; i > skip; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
double QL_item = QL[i - 1].v;
double SK_item = SK.ElementAt(i - 1);
Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits));
}
}
@@ -404,8 +405,8 @@ public class Skender
var SK = quotes.GetSmma(period).Select(i => i.Smma.Null2NaN()!);
for (int i = QL.Length; i > skip; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
double QL_item = QL[i - 1].v;
double SK_item = SK.ElementAt(i - 1);
Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits));
}
}
@@ -416,8 +417,8 @@ public class Skender
var SK = quotes.GetT3(lookbackPeriods: period, volumeFactor: 0.7).Select(i => i.T3.Null2NaN()!);
for (int i = QL.Length; i > period*15; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
double QL_item = QL[i - 1].v;
double SK_item = SK.ElementAt(i - 1);
Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits));
}
}
@@ -426,8 +427,8 @@ public class Skender
TRIX_Series QL = new(bars.Close, period, false);
var SK = quotes.GetTrix(period).Select(i => i.Trix.Null2NaN()!);
for (int i = QL.Length; i > period*12; i--) {
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
double QL_item = QL[i - 1].v;
double SK_item = SK.ElementAt(i - 1);
Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits));
}
}
@@ -438,8 +439,8 @@ public class Skender
var SK = quotes.GetTema(period).Select(i => i.Tema.Null2NaN()!);
for (int i = QL.Length; i > skip; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
double QL_item = QL[i - 1].v;
double SK_item = SK.ElementAt(i - 1);
Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits));
}
}
@@ -450,8 +451,8 @@ public class Skender
var SK = quotes.GetTr().Select(i => i.Tr.Null2NaN()!);
for (int i = QL.Length; i > skip; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
double QL_item = QL[i - 1].v;
double SK_item = SK.ElementAt(i - 1);
Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits));
}
}
@@ -462,8 +463,8 @@ public class Skender
var SK = quotes.GetWma(period).Select(i => i.Wma.Null2NaN()!);
for (int i = QL.Length; i > skip*2; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
double QL_item = QL[i - 1].v;
double SK_item = SK.ElementAt(i - 1);
Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits));
}
}
@@ -474,8 +475,8 @@ public class Skender
var SK = quotes.GetStdDev(period).Select(i => i.ZScore.Null2NaN()!);
for (int i = QL.Length; i > skip; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
double QL_item = QL[i - 1].v;
double SK_item = SK.ElementAt(i - 1);
Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits));
}
}
Tests/Validations/Trends/TA_LIB.cs
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@@ -74,7 +74,7 @@ public class Ta_Lib
{
ATR_Series QL = new(bars, period, false);
Core.Atr(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
for (int i = QL.Length - 1; i > skip * 15; i--)
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
@@ -114,6 +114,7 @@ public class Ta_Lib
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void CMO() {
CMO_Series QL = new(bars.Close, period, false);
@@ -124,6 +125,7 @@ public class Ta_Lib
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void CORR()
{
Tests/Validations/Trends/Tulip.cs
+6 -2
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@@ -20,8 +20,8 @@ public class Tulip_Test
{
bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0, Precision: 3);
period = rnd.Next(28) + 3;
skip = period+1;
digits = 10;
skip = period+5;
digits = 8;
outdata = new double[bars.Count];
inopen = bars.Open.v.ToArray();
@@ -124,6 +124,7 @@ public class Tulip_Test
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void CMO() {
double[][] arrin = { inclose };
@@ -214,6 +215,7 @@ public class Tulip_Test
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void HMA() {
double[][] arrin = { inclose };
@@ -226,6 +228,7 @@ public class Tulip_Test
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void KAMA() {
double[][] arrin = { inclose };
@@ -238,6 +241,7 @@ public class Tulip_Test
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void LINREG() {
double[][] arrin = { inclose };
docs/HWMA.md
+4
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@@ -0,0 +1,4 @@
# HWMA: Holt-Winter Moving Average
nA = 0.5; nB = 0.3; nC = 0.01;
![Alt text](./img/HWMA_chart.svg)
docs/MAMA.md
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@@ -0,0 +1,4 @@
# MAMA: MESA Adaptive Moving Average
period = 10
![Alt text](./img/MAMA_chart.svg)
docs/_sidebar.md
+5 -4
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@@ -3,19 +3,20 @@
* [List of all Indicators](indicators.md "Indicators coverage")
* [SMA - Simple Moving Average](SMA.md)
* [RMA - WildeR Moving Average](RMA.md)
* [EMA - Exponential Moving Average](EMA.md)
* [WMA - Weighted Moving Average](WMA.md)
* [T3 - Tillson T3 Exponential MA](T3.md)
* [SMMA - Smoothed Moving Average](SMMA.md)
* [DWMA - Double Weighted Moving Average](DWMA.md)
* [TRIMA - Triangular Moving Average](TRIMA.md)
* [DWMA - Double Weighted Moving Average](DWMA.md)
* [DEMA - Double Exponential MA](DEMA.md)
* [TEMA - Triple Exponential MA](TEMA.md)
* [T3 - Tillson T3 Exponential MA](T3.md)
* [ALMA - Arnaud Legoux Moving Average](ALMA.md)
* [HMA - Hull Moving Average](HMA.md)
* [HEMA - Hull/Exponential Moving Average](HEMA.md)
* [HWMA - Holt-Winter Moving Average](HWMA.md)
* [MAMA - MESA Adaptive Moving Average](MAMA.md)
* [KAMA - Kaufman Adaptive Moving Average](KAMA.md)
* [ALMA - Arnaud Legoux Moving Average](ALMA.md)
* [ZLEMA - Zero-Lag Exponential MA](ZLEMA.md)
* [JMA - Jurik Moving Average](JMA.md)
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docs/indicators.md
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@@ -62,7 +62,7 @@
|HEMA - Hull/EMA Average|`HEMA_Series`||||
|Hilbert Transform Instantaneous Trendline||HT_TRENDLINE|GetHtTrendline||
|⭐HMA - Hull Moving Average|`HMA_Series`||✔️GetHma|✔️hma|✔️hma|
|HWMA - Holt-Winter Moving Average||||hwma|
|HWMA - Holt-Winter Moving Average|`HWMA_Series`|||✔️hwma|
|JMA - Jurik Moving Average|`JMA_Series`|||jma||
|KAMA - Kaufman's Adaptive Moving Average|`KAMA_Series`|✔️KAMA|✔️GetKama|✔️kama|✔️kama|
|KDJ - KDJ Indicator (trend reversal)||||kdj|