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Miha Kralj
2022-04-24 20:27:42 -07:00
parent ea41a73ed0
commit d74e28b6bd
4 changed files with 193 additions and 196 deletions
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Quantower/Indicators/ZLMA_chart.cs
+4 -5
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@@ -30,13 +30,12 @@ public class ZLMA_chart : Indicator
private int matype = 2; private int matype = 2;
#endregion Parameters #endregion Parameters
private TBars bars; private TBars bars;
/////// ///////
private ZL_Series zerolag;
private TSeries indicator; private TSeries indicator;
/////// ///////
public ZLMA_chart() public ZLMA_chart()
{ {
this.SeparateWindow = false; this.SeparateWindow = false;
Source/Indicators/JMA_Series.cs
-2
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@@ -41,8 +41,6 @@ public class JMA_Series : Single_TSeries_Indicator
this.rvolty = Math.Exp((1 / this.pow1) * Math.Log(len1)); this.rvolty = Math.Exp((1 / this.pow1) * Math.Log(len1));
this.len2 = Math.Sqrt(0.5 * (_p - 1)) * len1; this.len2 = Math.Sqrt(0.5 * (_p - 1)) * len1;
this.beta = 0.45 * (_p - 1) / (0.45 * (_p - 1) + 2); this.beta = 0.45 * (_p - 1) / (0.45 * (_p - 1) + 2);
this._l = (int)Math.Round(this._p - 1 * 0.5);
if (base._data.Count > 0) { base.Add(base._data); } if (base._data.Count > 0) { base.Add(base._data); }
} }
Source/Indicators/ZLEMA_Series.cs
+70 -70
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@@ -1,71 +1,71 @@
namespace QuanTAlib; namespace QuanTAlib;
using System; using System;
/* <summary> /* <summary>
ZLEMA: Zero Lag Exponential Moving Average ZLEMA: Zero Lag Exponential Moving Average
The Zero lag exponential moving average (ZLEMA) indicator was created by John The Zero lag exponential moving average (ZLEMA) indicator was created by John
Ehlers and Ric Way. Ehlers and Ric Way.
The formula for a given N-Day period and for a given Data series is: The formula for a given N-Day period and for a given Data series is:
Lag = (Period-1)/2 Lag = (Period-1)/2
Ema Data = {Data+(Data-Data(Lag days ago)) Ema Data = {Data+(Data-Data(Lag days ago))
ZLEMA = EMA (EmaData,Period) ZLEMA = EMA (EmaData,Period)
Remark: Remark:
The idea is do a regular exponential moving average (EMA) calculation but on a The idea is do a regular exponential moving average (EMA) calculation but on a
de-lagged data instead of doing it on the regular data. Data is de-lagged by de-lagged data instead of doing it on the regular data. Data is de-lagged by
removing the data from "lag" days ago thus removing (or attempting to remove) removing the data from "lag" days ago thus removing (or attempting to remove)
the cumulative lag effect of the moving average. the cumulative lag effect of the moving average.
</summary> */ </summary> */
public class ZLEMA_Series : Single_TSeries_Indicator public class ZLEMA_Series : Single_TSeries_Indicator
{ {
private readonly System.Collections.Generic.List<double> _buffer = new(); private readonly System.Collections.Generic.List<double> _buffer = new();
private readonly double _k, _k1m; private readonly double _k, _k1m;
private double _lastema, _lastlastema; private double _lastema, _lastlastema;
public ZLEMA_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN) public ZLEMA_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
{ {
this._k = 2.0 / (this._p + 1); this._k = 2.0 / (this._p + 1);
this._k1m = 1.0 - this._k; this._k1m = 1.0 - this._k;
this._lastema = this._lastlastema = double.NaN; this._lastema = this._lastlastema = double.NaN;
if (base._data.Count > 0) if (base._data.Count > 0)
{ base.Add(base._data); } { base.Add(base._data); }
} }
public override void Add((System.DateTime t, double v) TValue, bool update) public override void Add((System.DateTime t, double v) TValue, bool update)
{ {
int _lag = (int)((_p - 1) * 0.5); int _lag = (int)((_p - 1) * 0.5);
_lag = (this.Count - _lag < 0) ? 0 : this.Count - _lag; _lag = (this.Count - _lag < 0) ? 0 : this.Count - _lag;
double _zl = TValue.v + (TValue.v - _data[_lag].v); double _zl = TValue.v + (TValue.v - _data[_lag].v);
double _ema = 0; double _ema = 0;
if (update) if (update)
{ this._lastema = this._lastlastema; } { this._lastema = this._lastlastema; }
if (this.Count < this._p) if (this.Count < this._p)
{ {
if (update) if (update)
{ this._buffer[this._buffer.Count - 1] = _zl; } { this._buffer[this._buffer.Count - 1] = _zl; }
else else
{ {
this._buffer.Add(_zl); this._buffer.Add(_zl);
} }
if (this._buffer.Count > this._p) if (this._buffer.Count > this._p)
{ this._buffer.RemoveAt(0); } { this._buffer.RemoveAt(0); }
for (int i = 0; i < this._buffer.Count; i++) for (int i = 0; i < this._buffer.Count; i++)
{ _ema += this._buffer[i]; } { _ema += this._buffer[i]; }
_ema /= this._buffer.Count; _ema /= this._buffer.Count;
} }
else else
{ {
_ema = TValue.v * this._k + this._lastema * this._k1m; _ema = TValue.v * this._k + this._lastema * this._k1m;
} }
this._lastlastema = this._lastema; this._lastlastema = this._lastema;
this._lastema = _ema; this._lastema = _ema;
var ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _ema); var ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _ema);
base.Add(ret, update); base.Add(ret, update);
} }
} }
Tests/Validations/Pandas_TA.cstemp
+119 -119
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@@ -1,120 +1,120 @@
using Xunit; using Xunit;
using System; using System;
using QuanTAlib; using QuanTAlib;
using Python.Runtime; using Python.Runtime;
using Python.Included; using Python.Included;
namespace Validation; namespace Validation;
public class PandasTA public class PandasTA
{ {
private readonly RND_Feed bars; private readonly RND_Feed bars;
private readonly Random rnd = new(); private readonly Random rnd = new();
private readonly int period; private readonly int period;
private readonly dynamic ta; private readonly dynamic ta;
private readonly dynamic df; private readonly dynamic df;
public PandasTA() public PandasTA()
{ {
this.bars = new(1000); this.bars = new(1000);
this.period = this.rnd.Next(28) + 3; this.period = this.rnd.Next(28) + 3;
Installer.SetupPython().Wait(); Installer.SetupPython().Wait();
Installer.TryInstallPip(); Installer.TryInstallPip();
Installer.PipInstallModule("numpy"); Installer.PipInstallModule("numpy");
Installer.PipInstallModule("pandas"); Installer.PipInstallModule("pandas");
Installer.PipInstallModule("pandas-ta"); Installer.PipInstallModule("pandas-ta");
PythonEngine.Initialize(); PythonEngine.Initialize();
this.ta = Py.Import("pandas_ta"); this.ta = Py.Import("pandas_ta");
this.df = this.ta.DataFrame(this.bars.Close.v); this.df = this.ta.DataFrame(this.bars.Close.v);
} }
~PandasTA() ~PandasTA()
{ {
PythonEngine.Shutdown(); PythonEngine.Shutdown();
} }
[Fact] [Fact]
void SMA() void SMA()
{ {
SMA_Series QL = new(this.bars.Close, this.period, false); SMA_Series QL = new(this.bars.Close, this.period, false);
var pta = this.ta.sma(close: this.df[0], length: this.period); var pta = this.ta.sma(close: this.df[0], length: this.period);
Assert.Equal(System.Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7)); Assert.Equal(System.Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
} }
/* /*
[Fact] [Fact]
void EMA() void EMA()
{ {
EMA_Series QL = new(this.bars.Close, this.period, false); EMA_Series QL = new(this.bars.Close, this.period, false);
var pta = this.ta.ema(close: this.df[0], length: this.period); var pta = this.ta.ema(close: this.df[0], length: this.period);
Assert.Equal(System.Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7)); Assert.Equal(System.Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
} }
[Fact] [Fact]
void TEMA() void TEMA()
{ {
TEMA_Series QL = new(this.bars.Close, this.period, false); TEMA_Series QL = new(this.bars.Close, this.period, false);
var pta = this.ta.tema(close: this.df[0], length: this.period); var pta = this.ta.tema(close: this.df[0], length: this.period);
Assert.Equal(System.Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7)); Assert.Equal(System.Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
} }
[Fact] [Fact]
void ENTP() void ENTP()
{ {
ENTP_Series QL = new(this.bars.Close, this.period, useNaN:false); ENTP_Series QL = new(this.bars.Close, this.period, useNaN:false);
var pta = this.ta.entropy(close: this.df[0], length: this.period); var pta = this.ta.entropy(close: this.df[0], length: this.period);
Assert.Equal(System.Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7)); Assert.Equal(System.Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
} }
[Fact] [Fact]
void WMA() void WMA()
{ {
WMA_Series QL = new(this.bars.Close, this.period, false); WMA_Series QL = new(this.bars.Close, this.period, false);
var pta = this.ta.wma(close: this.df[0], length: this.period); var pta = this.ta.wma(close: this.df[0], length: this.period);
Assert.Equal(System.Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7)); Assert.Equal(System.Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
} }
[Fact] [Fact]
void DEMA() void DEMA()
{ {
DEMA_Series QL = new(this.bars.Close, this.period, false); DEMA_Series QL = new(this.bars.Close, this.period, false);
var pta = this.ta.dema(close: this.df[0], length: this.period); var pta = this.ta.dema(close: this.df[0], length: this.period);
Assert.Equal(System.Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7)); Assert.Equal(System.Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
} }
[Fact] [Fact]
void BIAS() void BIAS()
{ {
BIAS_Series QL = new(this.bars.Close, this.period, false); BIAS_Series QL = new(this.bars.Close, this.period, false);
var pta = this.ta.bias(close: this.df[0], length: this.period); var pta = this.ta.bias(close: this.df[0], length: this.period);
Assert.Equal(System.Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7)); Assert.Equal(System.Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
} }
[Fact] [Fact]
void KURT() void KURT()
{ {
KURT_Series QL = new(this.bars.Close, this.period, useNaN: false); KURT_Series QL = new(this.bars.Close, this.period, useNaN: false);
var pta = this.ta.kurtosis(close: this.df[0], length: this.period); var pta = this.ta.kurtosis(close: this.df[0], length: this.period);
Assert.Equal(System.Math.Round((double)pta.tail(1), 4), Math.Round(QL.Last().v, 4)); Assert.Equal(System.Math.Round((double)pta.tail(1), 4), Math.Round(QL.Last().v, 4));
} }
[Fact] [Fact]
void MAD() void MAD()
{ {
MAD_Series QL = new(this.bars.Close, this.period, useNaN: false); MAD_Series QL = new(this.bars.Close, this.period, useNaN: false);
var pta = this.ta.mad(close: this.df[0], length: this.period); var pta = this.ta.mad(close: this.df[0], length: this.period);
Assert.Equal(System.Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7)); Assert.Equal(System.Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
} }
*/ */
} }