diff --git a/Quantower/Indicators/ZLMA_chart.cs b/Quantower/Indicators/ZLMA_chart.cs index e8370495..0b556b0a 100644 --- a/Quantower/Indicators/ZLMA_chart.cs +++ b/Quantower/Indicators/ZLMA_chart.cs @@ -30,13 +30,12 @@ public class ZLMA_chart : Indicator private int matype = 2; #endregion Parameters - + private TBars bars; - /////// - private ZL_Series zerolag; + /////// private TSeries indicator; - /////// - + /////// + public ZLMA_chart() { this.SeparateWindow = false; diff --git a/Source/Indicators/JMA_Series.cs b/Source/Indicators/JMA_Series.cs index 4a9068c2..d1639a09 100644 --- a/Source/Indicators/JMA_Series.cs +++ b/Source/Indicators/JMA_Series.cs @@ -41,8 +41,6 @@ public class JMA_Series : Single_TSeries_Indicator this.rvolty = Math.Exp((1 / this.pow1) * Math.Log(len1)); this.len2 = Math.Sqrt(0.5 * (_p - 1)) * len1; 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); } } diff --git a/Source/Indicators/ZLEMA_Series.cs b/Source/Indicators/ZLEMA_Series.cs index 5642cf8f..be2913f7 100644 --- a/Source/Indicators/ZLEMA_Series.cs +++ b/Source/Indicators/ZLEMA_Series.cs @@ -1,71 +1,71 @@ -namespace QuanTAlib; -using System; - -/* -ZLEMA: Zero Lag Exponential Moving Average - The Zero lag exponential moving average (ZLEMA) indicator was created by John - Ehlers and Ric Way. - -The formula for a given N-Day period and for a given Data series is: - Lag = (Period-1)/2 - Ema Data = {Data+(Data-Data(Lag days ago)) - ZLEMA = EMA (EmaData,Period) - -Remark: - 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 - removing the data from "lag" days ago thus removing (or attempting to remove) - the cumulative lag effect of the moving average. - - */ - -public class ZLEMA_Series : Single_TSeries_Indicator -{ - private readonly System.Collections.Generic.List _buffer = new(); - private readonly double _k, _k1m; - private double _lastema, _lastlastema; - - 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); } - } - - 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; - double _zl = TValue.v + (TValue.v - _data[_lag].v); - double _ema = 0; - if (update) - { this._lastema = this._lastlastema; } - if (this.Count < this._p) - { - if (update) - { this._buffer[this._buffer.Count - 1] = _zl; } - else - { - this._buffer.Add(_zl); - } - if (this._buffer.Count > this._p) - { this._buffer.RemoveAt(0); } - - for (int i = 0; i < this._buffer.Count; i++) - { _ema += this._buffer[i]; } - _ema /= this._buffer.Count; - } - else - { - _ema = TValue.v * this._k + this._lastema * this._k1m; - } - - this._lastlastema = this._lastema; - this._lastema = _ema; - - var ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _ema); - base.Add(ret, update); - } +namespace QuanTAlib; +using System; + +/* +ZLEMA: Zero Lag Exponential Moving Average + The Zero lag exponential moving average (ZLEMA) indicator was created by John + Ehlers and Ric Way. + +The formula for a given N-Day period and for a given Data series is: + Lag = (Period-1)/2 + Ema Data = {Data+(Data-Data(Lag days ago)) + ZLEMA = EMA (EmaData,Period) + +Remark: + 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 + removing the data from "lag" days ago thus removing (or attempting to remove) + the cumulative lag effect of the moving average. + + */ + +public class ZLEMA_Series : Single_TSeries_Indicator +{ + private readonly System.Collections.Generic.List _buffer = new(); + private readonly double _k, _k1m; + private double _lastema, _lastlastema; + + 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); } + } + + 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; + double _zl = TValue.v + (TValue.v - _data[_lag].v); + double _ema = 0; + if (update) + { this._lastema = this._lastlastema; } + if (this.Count < this._p) + { + if (update) + { this._buffer[this._buffer.Count - 1] = _zl; } + else + { + this._buffer.Add(_zl); + } + if (this._buffer.Count > this._p) + { this._buffer.RemoveAt(0); } + + for (int i = 0; i < this._buffer.Count; i++) + { _ema += this._buffer[i]; } + _ema /= this._buffer.Count; + } + else + { + _ema = TValue.v * this._k + this._lastema * this._k1m; + } + + this._lastlastema = this._lastema; + this._lastema = _ema; + + var ret = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : _ema); + base.Add(ret, update); + } } \ No newline at end of file diff --git a/Tests/Validations/Pandas_TA.cstemp b/Tests/Validations/Pandas_TA.cstemp index 27c2e9b8..0aa769bd 100644 --- a/Tests/Validations/Pandas_TA.cstemp +++ b/Tests/Validations/Pandas_TA.cstemp @@ -1,120 +1,120 @@ -using Xunit; -using System; -using QuanTAlib; -using Python.Runtime; -using Python.Included; - -namespace Validation; -public class PandasTA -{ - private readonly RND_Feed bars; - private readonly Random rnd = new(); - private readonly int period; - private readonly dynamic ta; - private readonly dynamic df; - - public PandasTA() - { - this.bars = new(1000); - this.period = this.rnd.Next(28) + 3; - - Installer.SetupPython().Wait(); - Installer.TryInstallPip(); - Installer.PipInstallModule("numpy"); - Installer.PipInstallModule("pandas"); - Installer.PipInstallModule("pandas-ta"); - PythonEngine.Initialize(); - this.ta = Py.Import("pandas_ta"); - this.df = this.ta.DataFrame(this.bars.Close.v); - } - - ~PandasTA() - { - PythonEngine.Shutdown(); - } - - [Fact] - void SMA() - { - SMA_Series QL = new(this.bars.Close, this.period, false); - 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)); - } - -/* - [Fact] - void EMA() - { - EMA_Series QL = new(this.bars.Close, this.period, false); - 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)); - } - - [Fact] - void TEMA() - { - TEMA_Series QL = new(this.bars.Close, this.period, false); - 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)); - } - - [Fact] - void ENTP() - { - ENTP_Series QL = new(this.bars.Close, this.period, useNaN:false); - 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)); - } - - - [Fact] - void WMA() - { - WMA_Series QL = new(this.bars.Close, this.period, false); - 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)); - } - - [Fact] - void DEMA() - { - DEMA_Series QL = new(this.bars.Close, this.period, false); - 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)); - } - - [Fact] - void BIAS() - { - BIAS_Series QL = new(this.bars.Close, this.period, false); - 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)); - } - - [Fact] - void KURT() - { - KURT_Series QL = new(this.bars.Close, this.period, useNaN: false); - 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)); - } - - [Fact] - void MAD() - { - MAD_Series QL = new(this.bars.Close, this.period, useNaN: false); - 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)); - } -*/ - +using Xunit; +using System; +using QuanTAlib; +using Python.Runtime; +using Python.Included; + +namespace Validation; +public class PandasTA +{ + private readonly RND_Feed bars; + private readonly Random rnd = new(); + private readonly int period; + private readonly dynamic ta; + private readonly dynamic df; + + public PandasTA() + { + this.bars = new(1000); + this.period = this.rnd.Next(28) + 3; + + Installer.SetupPython().Wait(); + Installer.TryInstallPip(); + Installer.PipInstallModule("numpy"); + Installer.PipInstallModule("pandas"); + Installer.PipInstallModule("pandas-ta"); + PythonEngine.Initialize(); + this.ta = Py.Import("pandas_ta"); + this.df = this.ta.DataFrame(this.bars.Close.v); + } + + ~PandasTA() + { + PythonEngine.Shutdown(); + } + + [Fact] + void SMA() + { + SMA_Series QL = new(this.bars.Close, this.period, false); + 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)); + } + +/* + [Fact] + void EMA() + { + EMA_Series QL = new(this.bars.Close, this.period, false); + 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)); + } + + [Fact] + void TEMA() + { + TEMA_Series QL = new(this.bars.Close, this.period, false); + 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)); + } + + [Fact] + void ENTP() + { + ENTP_Series QL = new(this.bars.Close, this.period, useNaN:false); + 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)); + } + + + [Fact] + void WMA() + { + WMA_Series QL = new(this.bars.Close, this.period, false); + 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)); + } + + [Fact] + void DEMA() + { + DEMA_Series QL = new(this.bars.Close, this.period, false); + 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)); + } + + [Fact] + void BIAS() + { + BIAS_Series QL = new(this.bars.Close, this.period, false); + 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)); + } + + [Fact] + void KURT() + { + KURT_Series QL = new(this.bars.Close, this.period, useNaN: false); + 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)); + } + + [Fact] + void MAD() + { + MAD_Series QL = new(this.bars.Close, this.period, useNaN: false); + 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)); + } +*/ + } \ No newline at end of file