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HWMA

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Miha Kralj
2022-12-27 12:06:54 -08:00
parent 509bf244d9
commit 83e5d89cbb
12 changed files with 110 additions and 14 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/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
+4 -5
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@@ -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);
}
}
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
+12 -1
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@@ -165,7 +165,18 @@ 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);
docs/HWMA.md
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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
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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)
docs/img/HWMA_chart.svg
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docs/img/MAMA_chart.svg
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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|