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Quantower adaptation

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Miha Kralj
2022-12-21 07:24:21 -08:00
parent 9f01df0e65
commit 9ad467cd5a
28 changed files with 3493 additions and 3204 deletions
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Quantower/Indicators/AAA_chart.cs
+59
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@@ -0,0 +1,59 @@
using System.Diagnostics;
using System.Drawing;
using System.Linq;
using TradingPlatform.BusinessLayer;
namespace QuanTAlib;
public class AAA_chart : Indicator {
#region Parameters
[InputParameter("Smoothing period", 0, 1, 999, 1, 1)]
private readonly int Period = 10;
#endregion Parameters
private TBars bars;
private TSeries series;
private JMA_Series jma;
private DWMA_Series dwma;
public AAA_chart() : base()
{
this.SeparateWindow = true;
this.Name = "AAA - Test indicator";
this.Description = "Test indicator";
this.AddLineSeries("JMA", Color.RoyalBlue, 3, LineStyle.Solid);
this.AddLineSeries("DWMA", Color.OrangeRed, 3, LineStyle.Solid);
this.SeparateWindow = false;
}
protected override void OnInit()
{
this.ShortName = "AAA (" + this.Period + ")";
this.bars = new();
this.series = new();
this.jma = new(source: bars.HLC3, period: this.Period, useNaN: false);
this.dwma = new(source: bars.HLC3, period: this.Period, useNaN: false);
}
protected override void OnUpdate(UpdateArgs args)
{
Debug.WriteLine($"{args.Reason}");
bool update = !(args.Reason == UpdateReason.NewBar || args.Reason == UpdateReason.HistoricalBar);
this.bars.Add(this.Time(),
this.GetPrice(PriceType.Open),
this.GetPrice(PriceType.High),
this.GetPrice(PriceType.Low),
this.GetPrice(PriceType.Close),
this.GetPrice(PriceType.Volume),
update);
//this.series.Add(0.25*(this.GetPrice(PriceType.Open)+ this.GetPrice(PriceType.High)+ this.GetPrice(PriceType.Low)+ this.GetPrice(PriceType.Close)), update);
this.SetValue(this.jma.v.Last(), 0);
this.SetValue(this.dwma.v.Last(), 1);
}
}
Quantower/Indicators/HMA_chart.cs
+2 -5
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@@ -43,14 +43,11 @@ public class HMA_chart : Indicator
protected override void OnUpdate(UpdateArgs args)
{
Debug.WriteLine("Send to debug output.");
bool update = !(args.Reason == UpdateReason.NewBar || args.Reason == UpdateReason.HistoricalBar);
bool update = !(args.Reason == UpdateReason.NewBar ||
args.Reason == UpdateReason.HistoricalBar);
this.bars.Add(this.Time(), this.GetPrice(PriceType.Open),
this.GetPrice(PriceType.High), this.GetPrice(PriceType.Low),
this.GetPrice(PriceType.Close),
this.GetPrice(PriceType.Volume), update);
this.GetPrice(PriceType.Close),this.GetPrice(PriceType.Volume), update);
double result = this.indicator[this.indicator.Count - 1].v;
this.SetValue(result);
}
Quantower/Quantower.csproj
+48 -46
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@@ -1,50 +1,52 @@
<?xml version="1.0" encoding="utf-8"?>
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<TargetFramework>net48</TargetFramework>
<LangVersion>preview</LangVersion>
<AppendTargetFrameworkToOutputPath>true</AppendTargetFrameworkToOutputPath>
<Platforms>AnyCPU</Platforms>
<AlgoType>Indicator</AlgoType>
<AssemblyName>Quantower_QTAlib</AssemblyName>
<RootNamespace>QuanTAlib</RootNamespace>
<DebugType>embedded</DebugType>
<LangVersion>preview</LangVersion>
<PlatformTarget>AnyCPU</PlatformTarget>
<Nullable>disable</Nullable>
<SignAssembly>False</SignAssembly>
<CodeAnalysisRuleSet>..\.sonarlint\mihakralj_quantalibcsharp.ruleset</CodeAnalysisRuleSet>
</PropertyGroup>
<PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|AnyCPU'">
<Optimize>True</Optimize>
<WarningLevel>3</WarningLevel>
<CheckForOverflowUnderflow>True</CheckForOverflowUnderflow>
<PlatformTarget>anycpu</PlatformTarget>
<DebugType>full</DebugType>
</PropertyGroup>
<PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|AnyCPU'">
<DebugType>embedded</DebugType>
<Optimize>True</Optimize>
<WarningLevel>3</WarningLevel>
<CheckForOverflowUnderflow>True</CheckForOverflowUnderflow>
<PlatformTarget>anycpu</PlatformTarget>
</PropertyGroup>
<ItemGroup>
<Compile Include="..\Source\**\*.cs" Exclude="..\Source\obj\**;..\Source\Feeds\**">
<Link>QuanTAlib\%(RecursiveDir)%(Filename)%(Extension)</Link>
</Compile>
</ItemGroup>
<!--
<?xml version="1.0" encoding="utf-8"?>
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<TargetFramework>net48</TargetFramework>
<LangVersion>preview</LangVersion>
<AppendTargetFrameworkToOutputPath>true</AppendTargetFrameworkToOutputPath>
<Platforms>AnyCPU</Platforms>
<AlgoType>Indicator</AlgoType>
<AssemblyName>Quantower_QTAlib</AssemblyName>
<RootNamespace>QuanTAlib</RootNamespace>
<DebugType>embedded</DebugType>
<LangVersion>preview</LangVersion>
<PlatformTarget>AnyCPU</PlatformTarget>
<Nullable>disable</Nullable>
<SignAssembly>False</SignAssembly>
<CodeAnalysisRuleSet>..\.sonarlint\mihakralj_quantalibcsharp.ruleset</CodeAnalysisRuleSet>
</PropertyGroup>
<PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|AnyCPU'">
<Optimize>True</Optimize>
<WarningLevel>3</WarningLevel>
<CheckForOverflowUnderflow>True</CheckForOverflowUnderflow>
<PlatformTarget>anycpu</PlatformTarget>
<DebugType>full</DebugType>
<OutputPath>C:\Quantower\TradingPlatform\v1.128.18\..\..\Settings\Scripts\Indicators\Quantower</OutputPath>
</PropertyGroup>
<PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|AnyCPU'">
<DebugType>embedded</DebugType>
<Optimize>True</Optimize>
<WarningLevel>3</WarningLevel>
<CheckForOverflowUnderflow>True</CheckForOverflowUnderflow>
<PlatformTarget>anycpu</PlatformTarget>
<OutputPath>C:\Quantower\TradingPlatform\v1.128.18\..\..\Settings\Scripts\Indicators\Quantower</OutputPath>
</PropertyGroup>
<ItemGroup>
<Compile Include="..\Source\**\*.cs" Exclude="..\Source\obj\**;..\Source\Feeds\**">
<Link>QuanTAlib\%(RecursiveDir)%(Filename)%(Extension)</Link>
</Compile>
</ItemGroup>
<!--
<Target Name="CopyCustomContent" AfterTargets="AfterBuild">
<Copy SourceFiles=".\bin\$(Configuration)\net48\Quantower_QTAlib.dll" DestinationFolder="\Quantower\Settings\Scripts\Indicators\QuanTAlib" />
</Target>
-->
<ItemGroup>
<AdditionalFiles Include="..\.sonarlint\mihakralj_quantalib\CSharp\SonarLint.xml" Link="SonarLint.xml" />
</ItemGroup>
<ItemGroup>
<Reference Include="TradingPlatform.BusinessLayer">
<HintPath>C:\Quantower\TradingPlatform\v1.124.6\bin\TradingPlatform.BusinessLayer.dll</HintPath>
</Reference>
</ItemGroup>
-->
<ItemGroup>
<AdditionalFiles Include="..\.sonarlint\mihakralj_quantalib\CSharp\SonarLint.xml" Link="SonarLint.xml" />
</ItemGroup>
<ItemGroup>
<Reference Include="TradingPlatform.BusinessLayer">
<HintPath>C:\Quantower\TradingPlatform\v1.128.18\bin\TradingPlatform.BusinessLayer.dll</HintPath>
</Reference>
</ItemGroup>
</Project>
Source/Basics/Single_TBars_Abstract.cs
+39 -38
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@@ -18,49 +18,50 @@ Abstract classes with all scaffolding required to build indicators.
public abstract class Single_TBars_Indicator : TSeries
{
protected readonly int _p;
protected readonly bool _NaN;
protected readonly TBars _bars;
protected readonly int _p;
protected readonly bool _NaN;
protected readonly TBars _bars;
// Chainable Constructor - add it at the end of primary constructor :base(source: source, period: period, useNaN: useNaN)
protected Single_TBars_Indicator(TBars source, int period, bool useNaN)
{
this._p = period;
this._bars = source;
this._NaN = useNaN;
this._bars.Pub += this.Sub;
}
// Chainable Constructor - add it at the end of primary constructor :base(source: source, period: period, useNaN: useNaN)
protected Single_TBars_Indicator(TBars source, int period, bool useNaN)
{
this._p = period;
this._bars = source;
this._NaN = useNaN;
this._bars.Pub += this.Sub;
// overridable Add() method to add/update a single item at the end of the list
}
// overridable Add() method to add/update a single item at the end of the list
public virtual void Add((System.DateTime t, double o, double h, double l, double c, double v) TBar, bool update) => base.Add((TBar.t, 0.0), update);
public virtual void Add((System.DateTime t, double v) TValue, bool update, bool useNaN)
{
var res = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : TValue.v);
base.Add(res, update);
}
public virtual void Add((System.DateTime t, double o, double h, double l, double c, double v) TBar, bool update) => base.Add((TBar.t, 0.0), update);
public virtual void Add((System.DateTime t, double v) TValue, bool update, bool useNaN)
{
var res = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : TValue.v);
base.Add(res, update);
}
// potentially overridable Add() method for the whole bars or series (could be replaced with faster bulk algo)
public virtual void Add(TBars bars) { for (int i = 0; i < bars.Count; i++) { this.Add(TBar: bars[i], update: false); }}
public virtual void Add(TSeries data) { for (int i = 0; i < data.Count; i++) { base.Add(TValue: data[i], update: false); }}
public void Add((System.DateTime t, double o, double h, double l, double c, double v) TBar) => this.Add(TBar: TBar, update: false);
public void Add(bool update) => this.Add(TBar: this._bars[this._bars.Count - 1], update: update);
public void Add() => this.Add(TBar: this._bars[this._bars.Count - 1], update: false);
public new void Sub(object source, TSeriesEventArgs e) => this.Add(TBar: this._bars[this._bars.Count - 1], update: e.update);
// potentially overridable Add() method for the whole bars or series (could be replaced with faster bulk algo)
public virtual void Add(TBars bars) { for (int i = 0; i < bars.Count; i++) { this.Add(TBar: bars[i], update: false); } }
public virtual void Add(TSeries data) { for (int i = 0; i < data.Count; i++) { base.Add(TValue: data[i], update: false); } }
public void Add((System.DateTime t, double o, double h, double l, double c, double v) TBar) => this.Add(TBar: TBar, update: false);
public void Add(bool update) => this.Add(TBar: this._bars[this._bars.Count - 1], update: update);
public void Add() => this.Add(TBar: this._bars[this._bars.Count - 1], update: false);
public new void Sub(object source, TSeriesEventArgs e) => this.Add(TBar: this._bars[this._bars.Count - 1], update: e.update);
protected static void Add_Replace(List<double> l, double v, bool update)
{
if (update)
{ l[l.Count - 1] = v; }
else
{ l.Add(v); }
}
protected static void Add_Replace_Trim(List<double> l, double v, int p, bool update)
{
Add_Replace(l, v, update);
if (l.Count > p && p != 0)
{ l.RemoveAt(0); }
}
protected static void Add_Replace(List<double> l, double v, bool update)
{
if (update)
{ l[l.Count - 1] = v; }
else
{ l.Add(v); }
}
protected static void Add_Replace_Trim(List<double> l, double v, int p, bool update)
{
Add_Replace(l, v, update);
if (l.Count > p && p != 0)
{ l.RemoveAt(0); }
}
}
Source/Basics/Single_TSeries_Abstract.cs
+71 -71
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@@ -1,71 +1,71 @@
namespace QuanTAlib;
using System;
using System.Collections.Generic;
using System.Linq;
/* <summary>
Abstract classes with all scaffolding required to build indicators.
All abstracts support period, NaN, and all permutations of Add() methods.
Indicator classess need to implement:
- Chaining constructor (Abstract's constructor executes first)
- Default Add(value) class
- optional Add(series) bulk insert class (for optimization of historical analysis)
Single_TSeries_Indicator - one single-value TSeries in, one TSeries out.
Pair_TSeries_Indicator - Two TSeries in, one TSeries out. (includes simple semaphoring)
Single_TBars_Indicator - One OHLCV TBars in, one TSeries out.
</summary> */
public abstract class Single_TSeries_Indicator : TSeries
{
protected readonly int _period;
protected readonly bool _NaN;
protected readonly TSeries _data;
protected int _p;
// Chainable Constructor - add it at the end of primary constructor :base(source: source, period: period, useNaN: useNaN)
protected Single_TSeries_Indicator(TSeries source, int period, bool useNaN)
{
this._data = source;
this._period = period;
this._p = _period;
this._NaN = useNaN;
this._data.Pub += this.Sub;
}
// overridable Add() method to add/update a single item at the end of the list
public virtual void Add((System.DateTime t, double v) TValue, bool update, bool useNaN)
{
if (_period == 0) { _p = this.Length; }
var res = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : TValue.v);
base.Add(res, update);
}
public new virtual void Add((System.DateTime t, double v) TValue, bool update) => base.Add(TValue, update);
// potentially overridable Add() method for the whole series (could be replaced with faster bulk algo)
public virtual void Add(TSeries data) { for (int i = 0; i < data.Count; i++) { this.Add(TValue: data[i], update: false); } }
public new void Add((System.DateTime t, double v) TValue) => this.Add(TValue: TValue, update: false);
public void Add(bool update) => this.Add(TValue: this._data[this._data.Count - 1], update: update);
public void Add() => this.Add(TValue: this._data[this._data.Count - 1], update: false);
public new void Sub(object source, TSeriesEventArgs e) => this.Add(TValue: this._data[this._data.Count - 1], update: e.update);
protected static void Add_Replace(List<double> l, double v, bool update)
{
if (update)
{ l[l.Count - 1] = v; }
else
{ l.Add(v); }
}
protected static double Add_Replace_Trim(List<double> l, double v, int p, bool update)
{
Add_Replace(l, v, update);
double ret = (l.Count > 0) ? l.First() : 0;
if (l.Count > p && p != 0)
{
l.RemoveAt(0);
}
return ret;
}
}
namespace QuanTAlib;
using System;
using System.Collections.Generic;
using System.Linq;
/* <summary>
Abstract classes with all scaffolding required to build indicators.
All abstracts support period, NaN, and all permutations of Add() methods.
Indicator classess need to implement:
- Chaining constructor (Abstract's constructor executes first)
- Default Add(value) class
- optional Add(series) bulk insert class (for optimization of historical analysis)
Single_TSeries_Indicator - one single-value TSeries in, one TSeries out.
Pair_TSeries_Indicator - Two TSeries in, one TSeries out. (includes simple semaphoring)
Single_TBars_Indicator - One OHLCV TBars in, one TSeries out.
</summary> */
public abstract class Single_TSeries_Indicator : TSeries
{
protected readonly int _period;
protected readonly bool _NaN;
protected readonly TSeries _data;
protected int _p;
// Chainable Constructor - add it at the end of primary constructor :base(source: source, period: period, useNaN: useNaN)
protected Single_TSeries_Indicator(TSeries source, int period, bool useNaN)
{
this._data = source;
this._period = period;
this._p = _period;
this._NaN = useNaN;
this._data.Pub += this.Sub;
}
// overridable Add() method to add/update a single item at the end of the list
public virtual void Add((System.DateTime t, double v) TValue, bool update, bool useNaN)
{
if (_period == 0) { _p = this.Length; }
var res = (TValue.t, this.Count < this._p - 1 && this._NaN ? double.NaN : TValue.v);
base.Add(res, update);
}
public new virtual void Add((System.DateTime t, double v) TValue, bool update) => base.Add(TValue, update);
// potentially overridable Add() method for the whole series (could be replaced with faster bulk algo)
public virtual void Add(TSeries data) { for (int i = 0; i < data.Count; i++) { this.Add(TValue: data[i], update: false); } }
public new void Add((System.DateTime t, double v) TValue) => this.Add(TValue: TValue, update: false);
public void Add(bool update) => this.Add(TValue: this._data[this._data.Count - 1], update: update);
public void Add() => this.Add(TValue: this._data[this._data.Count - 1], update: false);
public new void Sub(object source, TSeriesEventArgs e) => this.Add(TValue: this._data[this._data.Count - 1], update: e.update);
protected static void Add_Replace(List<double> l, double v, bool update)
{
if (update)
{ l[l.Count - 1] = v; }
else
{ l.Add(v); }
}
protected static double Add_Replace_Trim(List<double> l, double v, int p, bool update)
{
Add_Replace(l, v, update);
double ret = (l.Count > 0) ? l.First() : 0;
if (l.Count > p && p != 0)
{
l.RemoveAt(0);
}
return ret;
}
}
Source/Basics/TBars.cs
+136 -132
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@@ -1,132 +1,136 @@
namespace QuanTAlib;
using System;
/* <summary>
TBars class - includes all series for common data used in indicators and other calculations.
Has a bit limited overloading and casting (compared to TSeries)
Includes Select(int) method to simplify choosing the most optimal data source for indicators
Includes the most basic pricing calcs: HL2, OC2, OHL3, HLC3, OHLC4, HLCC4
(it is 'cheaper' to calculate them once during data capture than each time during data analysis)
</summary> */
public class TBars : System.Collections.Generic.List<(DateTime t, double o, double h, double l, double c, double v)>
{
private readonly TSeries _open = new();
private readonly TSeries _high = new();
private readonly TSeries _low = new();
private readonly TSeries _close = new();
private readonly TSeries _volume = new();
private readonly TSeries _hl2 = new();
private readonly TSeries _oc2 = new();
private readonly TSeries _ohl3 = new();
private readonly TSeries _hlc3 = new();
private readonly TSeries _ohlc4 = new();
private readonly TSeries _hlcc4 = new();
public TSeries Open => this._open;
public TSeries High => this._high;
public TSeries Low => this._low;
public TSeries Close => this._close;
public TSeries Volume => this._volume;
public TSeries HL2 => this._hl2;
public TSeries OC2 => this._oc2;
public TSeries OHL3 => this._ohl3;
public TSeries HLC3 => this._hlc3;
public TSeries OHLC4 => this._ohlc4;
public TSeries HLCC4 => this._hlcc4;
public TBars Tail(int count=10) {
TBars outBars = new();
if (count > this.Count) { count = this.Count; }
for (int i = this.Count-count; i<this.Count; i++) { outBars.Add(this[i]); }
return outBars;
}
public TSeries Select(int source)
{
return source switch
{
0 => _open,
1 => _high,
2 => _low,
3 => _close,
4 => _hl2,
5 => _oc2,
6 => _ohl3,
7 => _hlc3,
8 => _ohlc4,
_ => _hlcc4,
};
}
public static string SelectStr(int source)
{
return source switch
{
0 => "Open",
1 => "High",
2 => "Low",
3 => "Close",
4 => "HL2",
5 => "OC2",
6 => "OHL3",
7 => "Typical",
8 => "Mean",
_ => "Weighted",
};
}
public void Add((DateTime t, double o, double h, double l, double c, double v) i, bool update = false)
=> Add(i.t, i.o, i.h, i.l, i.c, i.v, update);
public void Add(DateTime t, decimal o, decimal h, decimal l, decimal c, decimal v, bool update = false)
=> Add(t, (double)o, (double)h, (double)l, (double)c, (double)v, update);
public void Add(DateTime t, double o, double h, double l, double c, double v, bool update = false)
{
if (update)
{
this[this.Count - 1] = (t, o, h, l, c, v);
_open[_open.Count - 1] = (t, o);
_high[_high.Count - 1] = (t, h);
_low[_low.Count - 1] = (t, l);
_close[_close.Count - 1] = (t, c);
_volume[_volume.Count - 1] = (t, v);
_hl2[_hl2.Count - 1] = (t, (h + l) * 0.5);
_oc2[_oc2.Count - 1] = (t, (o + c) * 0.5);
_ohl3[_ohl3.Count - 1] = (t, (o + h + l) * 0.333333333333333);
_hlc3[_hlc3.Count - 1] = (t, (h + l + c) * 0.333333333333333);
_ohlc4[_ohlc4.Count - 1] = (t, (o + h + l + c) * 0.25);
_hlcc4[_hlcc4.Count - 1] = (t, (h + l + c + c) * 0.25);
}
else
{
base.Add((t, o, h, l, c, v));
_open.Add((t, o));
_high.Add((t, h));
_low.Add((t, l));
_close.Add((t, c));
_volume.Add((t, v));
_hl2.Add((t, (h + l) * 0.5));
_oc2.Add((t, (o + c) * 0.5));
_ohl3.Add((t, (o + h + l) * 0.333333333333333));
_hlc3.Add((t, (h + l + c) * 0.333333333333333));
_ohlc4.Add((t, (o + h + l + c) * 0.25));
_hlcc4.Add((t, (h + l + c + c) * 0.25));
}
this.OnEvent(update);
}
// delegate used by event handler + event handler (Pub == publisher)
public delegate
void NewDataEventHandler(object source, TSeriesEventArgs args);
public event NewDataEventHandler Pub;
// Broadcast handler - only to valid targets
protected virtual void OnEvent(bool update = false)
{
if (Pub != null && Pub.Target != this)
{
Pub(this, new TSeriesEventArgs { update = update });
}
}
}
namespace QuanTAlib;
using System;
/* <summary>
TBars class - includes all series for common data used in indicators and other calculations.
Has a bit limited overloading and casting (compared to TSeries)
Includes Select(int) method to simplify choosing the most optimal data source for indicators
Includes the most basic pricing calcs: HL2, OC2, OHL3, HLC3, OHLC4, HLCC4
(it is 'cheaper' to calculate them once during data capture than each time during data analysis)
</summary> */
public class TBars : System.Collections.Generic.List<(DateTime t, double o, double h, double l, double c, double v)>
{
private readonly TSeries _open = new();
private readonly TSeries _high = new();
private readonly TSeries _low = new();
private readonly TSeries _close = new();
private readonly TSeries _volume = new();
private readonly TSeries _hl2 = new();
private readonly TSeries _oc2 = new();
private readonly TSeries _ohl3 = new();
private readonly TSeries _hlc3 = new();
private readonly TSeries _ohlc4 = new();
private readonly TSeries _hlcc4 = new();
public TSeries Open => this._open;
public TSeries High => this._high;
public TSeries Low => this._low;
public TSeries Close => this._close;
public TSeries Volume => this._volume;
public TSeries HL2 => this._hl2;
public TSeries OC2 => this._oc2;
public TSeries OHL3 => this._ohl3;
public TSeries HLC3 => this._hlc3;
public TSeries OHLC4 => this._ohlc4;
public TSeries HLCC4 => this._hlcc4;
public TBars Tail(int count = 10)
{
TBars outBars = new();
if (count > this.Count) { count = this.Count; }
for (int i = this.Count - count; i < this.Count; i++) { outBars.Add(this[i]); }
return outBars;
}
public TSeries Select(int source)
{
return source switch
{
0 => _open,
1 => _high,
2 => _low,
3 => _close,
4 => _hl2,
5 => _oc2,
6 => _ohl3,
7 => _hlc3,
8 => _ohlc4,
_ => _hlcc4,
};
}
public static string SelectStr(int source)
{
return source switch
{
0 => "Open",
1 => "High",
2 => "Low",
3 => "Close",
4 => "HL2",
5 => "OC2",
6 => "OHL3",
7 => "Typical",
8 => "Mean",
_ => "Weighted",
};
}
public void Add((DateTime t, double o, double h, double l, double c, double v) i, bool update = false)
=> Add(i.t, i.o, i.h, i.l, i.c, i.v, update);
public void Add(DateTime t, decimal o, decimal h, decimal l, decimal c, decimal v, bool update = false)
=> Add(t, (double)o, (double)h, (double)l, (double)c, (double)v, update);
public void Add(DateTime t, double o, double h, double l, double c, double v, bool update = false)
{
if (update) {
this[this.Count - 1] = (t, o, h, l, c, v);
}
else {
base.Add((t, o, h, l, c, v));
}
_open.Add((t, o),update);
_high.Add((t, h), update);
_low.Add((t, l), update);
_close.Add((t, c), update);
_volume.Add((t, v), update);
_hl2.Add((t, (h + l) * 0.5), update);
_oc2.Add((t, (o + c) * 0.5), update);
_ohl3.Add((t, (o + h + l) * 0.333333333333333), update);
_hlc3.Add((t, (h + l + c) * 0.333333333333333), update);
_ohlc4.Add((t, (o + h + l + c) * 0.25), update);
_hlcc4.Add((t, (h + l + c + c) * 0.25), update);
this.OnEvent(update);
}
// delegate used by event handler + event handler (Pub == publisher)
public delegate void NewDataEventHandler(object source, TSeriesEventArgs args);
public event NewDataEventHandler Pub;
// Broadcast handler - only to valid targets
protected virtual void OnEvent(bool update = false)
{
if (Pub != null && Pub.Target != this)
{
Pub(this, new TSeriesEventArgs { update = update });
}
}
public void Sub(object source, TSeriesEventArgs e)
{
TBars ss = (TBars)source;
if (ss.Count > 1)
{
for (int i = 0; i < ss.Count; i++)
{
this.Add(ss[i]);
}
}
else
{
this.Add(ss[ss.Count - 1], e.update);
}
}
}
Source/Trends/DEMA_Series.cs
+7 -5
View File
@@ -22,13 +22,15 @@ public class DEMA_Series : Single_TSeries_Indicator
private readonly System.Collections.Generic.List<double> _buffer1 = new();
private readonly System.Collections.Generic.List<double> _buffer2 = new();
private readonly double _k;
private double _lastema1, _lastlastema1;
private readonly bool _useSMA;
private double _lastema1, _lastlastema1;
private double _lastema2, _lastlastema2;
public DEMA_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
public DEMA_Series(TSeries source, int period, bool useNaN = false, bool useSMA = true) : base(source, period, useNaN)
{
_k = 2.0 / (_p + 1);
if (_data.Count > 0) { base.Add(_data); }
_useSMA = useSMA;
if (_data.Count > 0) { base.Add(_data); }
}
public override void Add((DateTime t, double v) TValue, bool update)
@@ -40,7 +42,7 @@ public class DEMA_Series : Single_TSeries_Indicator
}
double _ema1, _ema2, _dema;
if (this.Count < _p)
if (this.Count < _p && _useSMA)
{
Add_Replace_Trim(_buffer1, TValue.v, _p, update);
_ema1 = 0;
@@ -52,7 +54,7 @@ public class DEMA_Series : Single_TSeries_Indicator
for (int i = 0; i < _buffer2.Count; i++) { _ema2 += _buffer2[i]; }
_ema2 /= _buffer2.Count;
}
else if(this.Count < (2*_p - 1)) // second _p
else if(this.Count < (2*_p - 1) && _useSMA) // second _p
{
_ema1 = (TValue.v - _lastema1) * _k + _lastema1;
Source/Trends/DWMA_Series.cs
+38 -38
View File
@@ -1,39 +1,39 @@
namespace QuanTAlib;
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:
</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();
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;
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, _dwma), update, _NaN);
}
namespace QuanTAlib;
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:
</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();
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;
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);
}
}
Source/Trends/EMA_Series.cs
+1 -1
View File
@@ -25,7 +25,7 @@ public class EMA_Series : Single_TSeries_Indicator
private readonly System.Collections.Generic.List<double> _buffer = new();
private readonly double _k, _k1m;
private double _lastema, _lastlastema;
private bool _useSMA;
private readonly bool _useSMA;
public EMA_Series(TSeries source, int period, bool useNaN = false, bool useSMA = true) : base(source, period, useNaN)
{
Source/Trends/JMA_Series.cs
+53 -27
View File
@@ -23,9 +23,11 @@ Issues:
public class JMA_Series : Single_TSeries_Indicator {
private readonly System.Collections.Generic.List<double> volty_10 = new();
private readonly System.Collections.Generic.List<double> vsum_buff = new();
private readonly double pr, beta;
private readonly double pr;
public TSeries mma1 { get; }
public TSeries mma2 { get; }
private double upperBand, lowerBand, _phase, vsum, Kv, del1, del2, prev_del1, prev_del2;
private double upperBand, lowerBand, vsum, Kv, del1, del2;
private double prev_ma1, prev_det0, prev_det1, prev_vsum, prev_jma;
private double p_upperBand, p_lowerBand, p_Kv, p_prev_ma1, p_prev_det0, p_prev_det1, p_prev_vsum, p_prev_jma;
@@ -35,18 +37,34 @@ public class JMA_Series : Single_TSeries_Indicator {
pr = (phase * 0.01) + 1.5;
if (phase < -100) pr = 0.5;
if (phase > 100) pr = 2.5;
beta = 0.45 * (_p - 1) / (0.45 * (_p - 1) + 2);
mma1 = new();
mma2 = new();
if (base._data.Count > 0) { base.Add(base._data); }
}
public override void Add((System.DateTime t, double v) TValue, bool update) {
if (this.Count == 0) { prev_ma1 = TValue.v; }
if (update) {
upperBand = p_upperBand; lowerBand = p_lowerBand; Kv = p_Kv; prev_vsum = p_prev_vsum;
prev_ma1 = p_prev_ma1; prev_det0 = p_prev_det0; prev_det1 = p_prev_det1; prev_jma = p_prev_jma;
} else {
p_upperBand = upperBand; p_lowerBand = lowerBand; p_Kv = Kv; p_prev_vsum = prev_vsum;
p_prev_ma1 = prev_ma1; p_prev_det0 = prev_det0; p_prev_det1 = prev_det1; p_prev_jma = prev_jma;
upperBand = p_upperBand;
lowerBand = p_lowerBand;
Kv = p_Kv;
prev_vsum = p_prev_vsum;
prev_ma1 = p_prev_ma1;
prev_det0 = p_prev_det0;
prev_det1 = p_prev_det1;
prev_jma = p_prev_jma;
}
else {
p_upperBand = upperBand;
p_lowerBand = lowerBand;
p_Kv = Kv;
p_prev_vsum = prev_vsum;
p_prev_ma1 = prev_ma1;
p_prev_det0 = prev_det0;
p_prev_det1 = prev_det1;
p_prev_jma = prev_jma;
}
// from Tvalue to volty
@@ -59,41 +77,49 @@ public class JMA_Series : Single_TSeries_Indicator {
if (Math.Abs(del1) < Math.Abs(del2)) { volty = Math.Abs(del2); }
//// from volty to avolty
if (update) { volty_10[volty_10.Count - 1] = volty; } else { volty_10.Add(volty); }
if (volty_10.Count > 10) { volty_10.RemoveAt(0); }
if (update) { volty_10[volty_10.Count - 1] = volty; }
else { volty_10.Add(volty); }
if (volty_10.Count > _p) { 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) vsum_buff.RemoveAt(0);
if (update) { vsum_buff[vsum_buff.Count - 1] = vsum; }
else { vsum_buff.Add(vsum); }
if (vsum_buff.Count > (65))
vsum_buff.RemoveAt(0);
double avolty = 0;
for (int i = 0; i < vsum_buff.Count; i++) { avolty += vsum_buff[i]; }
avolty /= vsum_buff.Count;
/// from avolty to rolty
double rvolty = (avolty > 0) ? volty / avolty : 0;
double len1 = (Math.Log(Math.Sqrt(_p)) / Math.Log(2.0)) + 2;
if (len1 < 0) len1 = 0;
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 pow1 = Math.Max(len1 - 2.0, 0.5);
if (rvolty > Math.Pow(len1, 1.0 / pow1)) rvolty = Math.Pow(len1, 1.0 / pow1);
if (rvolty < 1) rvolty = 1;
if (rvolty > Math.Pow(len1, 1.0 / pow1))
rvolty = Math.Pow(len1, 1.0 / pow1);
if (rvolty < 1)
rvolty = 1;
//// from rvolty to second smoothing
double pow2 = Math.Pow(rvolty, pow1);
double len2 = Math.Sqrt(0.5 * (_p - 1)) * len1;
Kv = Math.Pow(len2 / (len2 + 1), Math.Sqrt(pow2));
double alpha = Math.Pow(beta, pow2);
double ma1 = (1 - alpha) * TValue.v + alpha * prev_ma1;
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);
double ma1 = (1 - alpha) * TValue.v + alpha * prev_ma1;
prev_ma1 = ma1;
double det0 = (1 - beta) * (TValue.v - ma1) + beta * prev_det0;
prev_det0 = det0;
mma1.Add(ma1);
/// from second smoothing to jma
double det0 = (1 - beta) * (TValue.v - ma1) + beta * prev_det0;
prev_det0 = det0;
double ma2 = ma1 + pr * det0;
double det1 = (1 - alpha) * (1 - alpha) * (ma2 - prev_jma) + alpha * alpha * prev_det1;
mma2.Add(ma2);
double det1 = ((1 - alpha) * (1 - alpha) * (ma2 - prev_jma)) + (alpha * alpha * prev_det1);
prev_det1 = det1;
double jma = prev_jma + det1;
prev_jma = jma;
base.Add((TValue.t, jma), update, _NaN);
base.Add((TValue.t, ma1), update, _NaN);
}
}
}
Source/Trends/MAMA_Series.cs
+118 -118
View File
@@ -1,118 +1,118 @@
namespace QuanTAlib;
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
to reduce noise.
KAMAi = KAMAi - 1 + SC * ( price - KAMAi-1 )
Sources:
https://mesasoftware.com/papers/MAMA.pdf
https://www.tradingview.com/script/foQxLbU3-Ehlers-MESA-Adaptive-Moving-Average-LazyBear/
</summary> */
public class MAMA_Series : Single_TSeries_Indicator
{
public MAMA_Series(TSeries source, double fastlimit = 0.5, double slowlimit = 0.05, bool useNaN = false) : base(source, period: 5, useNaN)
{
fastl = fastlimit;
slowl = slowlimit;
Fama = new();
if (base._data.Count > 0) { base.Add(base._data); }
}
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) {
// 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;
}
int i = base.Count;
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));
}
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);
}
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);
}
}
namespace QuanTAlib;
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
to reduce noise.
KAMAi = KAMAi - 1 + SC * ( price - KAMAi-1 )
Sources:
https://mesasoftware.com/papers/MAMA.pdf
https://www.tradingview.com/script/foQxLbU3-Ehlers-MESA-Adaptive-Moving-Average-LazyBear/
</summary> */
public class MAMA_Series : Single_TSeries_Indicator
{
public MAMA_Series(TSeries source, double fastlimit = 0.5, double slowlimit = 0.05, bool useNaN = false) : base(source, period: 5, useNaN)
{
fastl = fastlimit;
slowl = slowlimit;
Fama = new();
if (base._data.Count > 0) { base.Add(base._data); }
}
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) {
// 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;
}
int i = base.Count;
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));
}
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);
}
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
+109 -126
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@@ -1,127 +1,110 @@
namespace QuanTAlib;
using System;
using System.Linq;
using System.Numerics;
/* <summary>
T3: Tillson T3 Moving Average
Tim Tillson described it in "Technical Analysis of Stocks and Commodities", January 1998 in the
article "Better Moving Averages". Tillson’s moving average becomes a popular indicator of
technical analysis as it gets less lag with the price chart and its curve is considerably smoother.
Sources:
https://technicalindicators.net/indicators-technical-analysis/150-t3-moving-average
http://www.binarytribune.com/forex-trading-indicators/t3-moving-average-indicator/
Calculation:
a = 0.7 (but also 0.618);
Ema1 = Ema (Close);
Ema2 = Ema (Ema1);
Ema3 = Ema (Ema2);
Ema4 = Ema (Ema3);
Ema5 = Ema (Ema4);
Ema6 = Ema (Ema5);
T3 = –(a*a*a) * Ema6 + (3*a*a + 3*a*a*a) * Ema5 + (–6*a*a – 3*a – 3*a*a*a) * Ema4 + (1 + 3*a + a*a*a + 3*a*a) * Ema3
</summary> */
public class T3_Series : Single_TSeries_Indicator
{
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 = 0.7, bool useNaN = false) : base(source, period, useNaN)
{
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;
int i = base.Count;
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 / Math.Max(_p, base.Count);
}
}
}
else {
sum5 += e4;
if (i == 5 * (_p - 1)) {
sum6 = e5 = sum5 / Math.Max(_p, base.Count);
}
}
}
else {
sum4 += e3;
if (i == 4 * (_p - 1)) {
sum5 = e4 = sum4 / Math.Max(_p, base.Count);
}
}
}
else {
sum3 += e2;
if (i == 3 * (_p - 1)) {
sum4 = e3 = sum3 / Math.Max(_p, base.Count);
}
}
}
else {
sum2 += e1;
if (i == 2 * (_p - 1)) {
sum3 = e2 = sum2 / Math.Max(_p, base.Count);
}
}
}
else {
sum1 += v;
if (i == _p - 1) {
sum2 = e1 = sum1 / Math.Max(_p, base.Count);
}
}
double t3 = (c1 * e6) + (c2 * e5) + (c3 * e4) + (c4 * e3);
base.Add(TValue: (TValue.t, t3), update: update, useNaN: _NaN);
}
namespace QuanTAlib;
using System;
using System.Linq;
using System.Numerics;
/* <summary>
T3: Tillson T3 Moving Average
Tim Tillson described it in "Technical Analysis of Stocks and Commodities", January 1998 in the
article "Better Moving Averages". Tillson’s moving average becomes a popular indicator of
technical analysis as it gets less lag with the price chart and its curve is considerably smoother.
Sources:
https://technicalindicators.net/indicators-technical-analysis/150-t3-moving-average
http://www.binarytribune.com/forex-trading-indicators/t3-moving-average-indicator/
Calculation:
Volume Factor is typically 0.7 (but also 0.618);
Ema1 = Ema (Close);
Ema2 = Ema (Ema1);
Ema3 = Ema (Ema2);
Ema4 = Ema (Ema3);
Ema5 = Ema (Ema4);
Ema6 = Ema (Ema5);
T3 = –(a*a*a) * Ema6 + (3*a*a + 3*a*a*a) * Ema5 + (–6*a*a – 3*a – 3*a*a*a) * Ema4 + (1 + 3*a + a*a*a + 3*a*a) * Ema3
</summary> */
public class T3_Series : Single_TSeries_Indicator {
private readonly double _k, _k1m, _c1, _c2, _c3, _c4;
private readonly System.Collections.Generic.List<double> _buffer1 = new();
private readonly System.Collections.Generic.List<double> _buffer2 = new();
private readonly System.Collections.Generic.List<double> _buffer3 = new();
private readonly System.Collections.Generic.List<double> _buffer4 = new();
private readonly System.Collections.Generic.List<double> _buffer5 = new();
private readonly System.Collections.Generic.List<double> _buffer6 = new();
private double _lastema1, _lastema2, _lastema3, _lastema4, _lastema5, _lastema6;
private double _llastema1, _llastema2, _llastema3, _llastema4, _llastema5, _llastema6;
private bool _useSMA;
public T3_Series(TSeries source, int period, double vfactor = 0.7, bool useNaN = false, bool useSMA = true) : base(source, period, useNaN) {
double _a = vfactor; //0.7; //0.618
_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 + _a * _a * _a + 3 * _a * _a;
_k = 2.0 / (_p + 1);
_k1m = 1.0 - _k;
_lastema1 = _llastema1 = _lastema2 = _llastema2 = _lastema3 = _llastema3 = _lastema4 = _llastema4 = _lastema5 = _llastema5 = _lastema5 = _llastema5 = 0;
_useSMA = useSMA;
if (this._data.Count > 0) { base.Add(this._data); }
}
public override void Add((DateTime t, double v) TValue, bool update) {
double _ema1, _ema2, _ema3, _ema4, _ema5, _ema6;
if (update) { _lastema1 = _llastema1; _lastema2 = _llastema2; _lastema3 = _llastema3; _lastema4 = _llastema4; _lastema5 = _llastema5; _lastema6 = _llastema6; }
else { _llastema1 = _lastema1; _llastema2 = _lastema2; _llastema3 = _lastema3; _llastema4 = _lastema4; _llastema5 = _lastema5; _llastema6 = _lastema6; }
if (this.Count == 0) { _lastema1 = _lastema2 = _lastema3 = _lastema4 = _lastema5 = _lastema6 = TValue.v; }
if ((this.Count < _p) && _useSMA) {
Add_Replace(_buffer1, TValue.v, update);
_ema1 = 0;
for (int i = 0; i < _buffer1.Count; i++) { _ema1 += _buffer1[i]; }
_ema1 /= _buffer1.Count;
Add_Replace(_buffer2, _ema1, update);
_ema2 = 0;
for (int i = 0; i < _buffer2.Count; i++) { _ema2 += _buffer2[i]; }
_ema2 /= _buffer2.Count;
Add_Replace(_buffer3, _ema2, update);
_ema3 = 0;
for (int i = 0; i < _buffer3.Count; i++) { _ema3 += _buffer3[i]; }
_ema3 /= _buffer3.Count;
Add_Replace(_buffer4, _ema3, update);
_ema4 = 0;
for (int i = 0; i < _buffer4.Count; i++) { _ema4 += _buffer4[i]; }
_ema4 /= _buffer4.Count;
Add_Replace(_buffer5, _ema4, update);
_ema5 = 0;
for (int i = 0; i < _buffer5.Count; i++) { _ema5 += _buffer5[i]; }
_ema5 /= _buffer5.Count;
Add_Replace(_buffer6, _ema5, update);
_ema6 = 0;
for (int i = 0; i < _buffer6.Count; i++) { _ema6 += _buffer6[i]; }
_ema6 /= _buffer6.Count;
}
else {
_ema1 = (TValue.v * this._k) + (this._lastema1 * this._k1m);
_ema2 = (_ema1 * this._k) + (this._lastema2 * this._k1m);
_ema3 = (_ema2 * this._k) + (this._lastema3 * this._k1m);
_ema4 = (_ema3 * this._k) + (this._lastema4 * this._k1m);
_ema5 = (_ema4 * this._k) + (this._lastema5 * this._k1m);
_ema6 = (_ema5 * this._k) + (this._lastema6 * this._k1m);
}
_lastema1 = _ema1;
_lastema2 = _ema2;
_lastema3 = _ema3;
_lastema4 = _ema4;
_lastema5 = _ema5;
_lastema6 = _ema6;
double _T3 = _c1 * _ema6 + _c2 * _ema5 + _c3 * _ema4 + _c4 * _ema3;
base.Add((TValue.t, _T3), update, _NaN);
}
}
Source/Trends/TRIX_Series.cs
+82
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@@ -0,0 +1,82 @@
namespace QuanTAlib;
using System;
using System.Linq;
using System.Numerics;
/* <summary>
TRIX: Triple Exponential Average
Developed by Jack Hutson in the early 1980s, the triple exponential average (TRIX)
has become a popular technical analysis tool to aid chartists in spotting diversions
and directional cues in stock trading patterns.
Calculation:
Ema1 = Ema (Close);
Ema2 = Ema (Ema1);
Ema3 = Ema (Ema2);
TRIX = (Ema3-Ema3[1]) / Ema3[1]
Sources:
https://www.investopedia.com/terms/t/trix.asp
</summary> */
public class TRIX_Series : Single_TSeries_Indicator
{
private readonly double _k, _k1m;
private readonly System.Collections.Generic.List<double> _buffer1 = new();
private readonly System.Collections.Generic.List<double> _buffer2 = new();
private readonly System.Collections.Generic.List<double> _buffer3 = new();
private double _lastema1, _lastema2, _lastema3;
private double _llastema1, _llastema2, _llastema3;
private bool _useSMA;
public TRIX_Series(TSeries source, int period, bool useNaN = false, bool useSMA = true) : base(source, period, useNaN)
{
_k = 2.0 / (_p + 1);
_k1m = 1.0 - _k;
_lastema1 = _llastema1 = _lastema2 = _llastema2 = _lastema3 = _llastema3 = 0;
_useSMA = useSMA;
if (this._data.Count > 0) { base.Add(this._data); }
}
public override void Add((DateTime t, double v) TValue, bool update)
{
double _ema1, _ema2, _ema3;
if (this.Count == 0) { _lastema1 = _lastema2 = _lastema3 = TValue.v; }
if (update) { _lastema1 = _llastema1; _lastema2 = _llastema2; _lastema3 = _llastema3; }
else { _llastema1 = _lastema1; _llastema2 = _lastema2; _llastema3 = _lastema3; }
if ((this.Count < _p) && _useSMA)
{
Add_Replace(_buffer1, TValue.v, update);
_ema1 = 0;
for (int i = 0; i < _buffer1.Count; i++) { _ema1 += _buffer1[i]; }
_ema1 /= _buffer1.Count;
Add_Replace(_buffer2, _ema1, update);
_ema2 = 0;
for (int i = 0; i < _buffer2.Count; i++) { _ema2 += _buffer2[i]; }
_ema2 /= _buffer2.Count;
Add_Replace(_buffer3, _ema2, update);
_ema3 = 0;
for (int i = 0; i < _buffer3.Count; i++) { _ema3 += _buffer3[i]; }
_ema3 /= _buffer3.Count;
}
else
{
_ema1 = (TValue.v * this._k) + (this._lastema1 * this._k1m);
_ema2 = (_ema1 * this._k) + (this._lastema2 * this._k1m);
_ema3 = (_ema2 * this._k) + (this._lastema3 * this._k1m);
}
double _trix = 100 * (_ema3 - _lastema3) / _lastema3;
_lastema1 = _ema1;
_lastema2 = _ema2;
_lastema3 = _ema3;
base.Add((TValue.t, _trix), update, _NaN);
}
}
Source/Volatility/CMO_Series.cs
+47
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@@ -0,0 +1,47 @@
namespace QuanTAlib;
using System;
/* <summary>
CMO: Chande Momentum Oscillator
Chande Momentum Oscillator (also known as CMO indicator) was developed by Tushar S. Chande
CMO is similar to other momentum oscillators (e.g. RSI or Stochastics). Alike RSI oscillator,
the CMO values move in the range from -100 to +100 points and its aim is to detect the
overbought and oversold market conditions. CMO calculates the price momentum on both the up
days as well as the down days. The CMO calculation is based on non-smoothed price values
meaning that it can reach its extremes more frequently and the short-time swings are more visible.
Sources:
https://www.technicalindicators.net/indicators-technical-analysis/144-cmo-chande-momentum-oscillator
</summary> */
public class CMO_Series : Single_TSeries_Indicator {
private readonly System.Collections.Generic.List<double> _buff_up = new();
private readonly System.Collections.Generic.List<double> _buff_dn = new();
private double _plast_value, _last_value;
public CMO_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN) {
if (this._data.Count > 0) { base.Add(this._data); }
}
public override void Add((DateTime t, double v) TValue, bool update) {
if (this.Count == 0) { _plast_value = _last_value = TValue.v; }
if (update) _last_value = _plast_value; else _plast_value = _last_value;
Add_Replace_Trim(_buff_up, (TValue.v > _last_value) ? TValue.v-_last_value : 0, _p, update);
Add_Replace_Trim(_buff_dn, (TValue.v < _last_value) ? _last_value-TValue.v : 0, _p, update);
_last_value = TValue.v;
double _cmo_up = 0;
double _cmo_dn = 0;
for (int i = 0; i < Math.Min(_buff_up.Count, _buff_dn.Count); i++) {
_cmo_up += _buff_up[i];
_cmo_dn += _buff_dn[i];
}
double _cmo = 100 * (_cmo_up - _cmo_dn) / (_cmo_up + _cmo_dn);
if (_cmo_up + _cmo_dn == 0)
_cmo = 0;
base.Add((TValue.t, _cmo), update, _NaN);
}
}
Tests/Series/Update.cs
+512 -512
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Tests/Tests.csproj
+1 -1
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@@ -17,7 +17,7 @@
</PackageReference>
<PackageReference Include="Microsoft.NET.Test.Sdk" Version="17.5.0-preview-20221003-04" />
<PackageReference Include="TALib.NETCore" Version="0.4.4" />
<PackageReference Include="Skender.Stock.Indicators" Version="2.4.0" />
<PackageReference Include="Skender.Stock.Indicators" Version="2.4.2" />
<PackageReference Include="pythonnet" Version="3.0.1" />
<PackageReference Include="Tulip.NETCore" Version="0.8.0.1" />
<PackageReference Include="System.Text.Json" Version="7.0.0" />
Tests/Validations/Trends/Pandas_TA.cs
+381 -360
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@@ -1,361 +1,382 @@
using Xunit;
using System;
using QuanTAlib;
using Python.Runtime;
using Python.Included;
namespace Validations;
public class PandasTA : IDisposable
{
private readonly GBM_Feed bars;
private readonly Random rnd = new();
private readonly int period, sample;
private int digits;
private readonly string OStype;
private readonly dynamic np;
private readonly dynamic ta;
private readonly dynamic df;
public PandasTA() {
bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0);
period = rnd.Next(maxValue: 28) + 3;
sample = 200;
digits = 10;
// Checking the host OS and setting PythonDLL accordingly
OStype = Environment.OSVersion.ToString();
if (OStype == "Unix 13.1.0")
OStype = @"/usr/local/Cellar/python@3.10/3.10.8/Frameworks/Python.framework/Versions/3.10/lib/libpython3.10.dylib";
else OStype = Path.GetFullPath(".") + @"\python-3.10.0-embed-amd64\python310.dll";
Installer.InstallPath = Path.GetFullPath(path: ".");
Installer.SetupPython().Wait();
Installer.TryInstallPip();
Installer.PipInstallModule(module_name: "pandas-ta");
Runtime.PythonDLL = OStype;
PythonEngine.Initialize();
np = Py.Import(name: "numpy");
ta = Py.Import(name: "pandas_ta");
string[] cols = { "open", "high", "low", "close", "volume" };
double[,] ary = new double[bars.Count, 5];
for (int i = 0; i < bars.Count; i++) {
ary[i, 0] = bars.Open[i].v;
ary[i, 1] = bars.High[i].v;
ary[i, 2] = bars.Low[i].v;
ary[i, 3] = bars.Close[i].v;
ary[i, 4] = bars.Volume[i].v;
}
df = ta.DataFrame(data: np.array(ary), index: np.array(bars.Close.t), columns: np.array(cols));
}
public void Dispose()
{
PythonEngine.Shutdown();
GC.SuppressFinalize(this);
}
[Fact] void ADL() {
ADL_Series QL = new(bars);
var pta = df.ta.ad(high: df.high, low: df.low, close:df.close, volume:df.volume);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i-1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i-1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void ADOSC() {
ADOSC_Series QL = new(bars);
var pta = df.ta.adosc(high: df.high, low: df.low, close: df.close, volume: df.volume);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void ATR() {
ATR_Series QL = new(bars, period);
var pta = df.ta.atr(high: df.high, low: df.low, close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void BIAS() {
BIAS_Series QL = new(bars.Close, period, false);
var pta = df.ta.bias(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void DEMA() {
DEMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.dema(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void EMA() {
EMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.ema(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void ENTROPY() {
ENTROPY_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.entropy(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void HL2() {
var pta = df.ta.hl2(high: df.high, low: df.low);
for (int i = bars.HL2.Length; i > bars.HL2.Length-sample; i--)
{
double QL_item = Math.Round(bars.HL2[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void HLC3() {
var pta = df.ta.hlc3(high: df.high, low: df.low, close: df.close);
for (int i = bars.HLC3.Length; i > bars.HLC3.Length-sample; i--)
{
double QL_item = Math.Round(bars.HLC3[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void HMA() {
HMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.hma(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
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);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void KURTOSIS() {
KURTOSIS_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.kurtosis(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void MAD()
{
MAD_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.mad(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void MEDIAN() {
MEDIAN_Series QL = new(bars.Close, period);
var pta = df.ta.median(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void OBV() {
OBV_Series QL = new(bars);
var pta = df.ta.obv(close: df.close, volume: df.volume);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void OHLC4() {
var pta = df.ta.ohlc4(open: df.open, high: df.high, low: df.low, close: df.close);
for (int i = bars.OHLC4.Length; i > bars.OHLC4.Length-sample; i--)
{
double QL_item = Math.Round(bars.OHLC4[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void RMA() {
RMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.rma(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void RSI() {
RSI_Series QL = new(bars.Close, period);
var pta = df.ta.rsi(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void SDEV() {
SDEV_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.stdev(close: df.close, length: period, ddof: 0);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void SMA() {
SMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.sma(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void SSDEV() {
SSDEV_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.stdev(close: df.close, length: period, ddof: 1);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
/*
[Fact] void SVARIANCE() {
SVAR_Series QL = new(bars.Close, period);
var pta = df.ta.variance(close: df.close, length: period, ddof: 1);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
*/
[Fact] void T3() {
T3_Series QL = new(source: bars.Close, period: period, vfactor: 0.7, useNaN: false);
var pta = df.ta.t3(close: df.close, length: period, a: 0.7);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void TEMA() {
TEMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.tema(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void TR() {
TR_Series QL = new(bars);
var pta = df.ta.true_range(high: df.high, low: df.low, close: df.close);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void TRIMA() {
// TODO: return length to variable length (period) when Pandas-TA fixes trima to calculate even periods right
TRIMA_Series QL = new(bars.Close, 11);
var pta = df.ta.trima(close: df.close, length: 11);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void VARIANCE() {
VAR_Series QL = new(bars.Close, period);
var pta = df.ta.variance(close: df.close, length: period, ddof:0);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void WMA() {
WMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.wma(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void ZLEMA() {
ZLEMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.zlma(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void ZSCORE() {
ZSCORE_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.zscore(close: df.close, length: period, ddof: 0);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
using Xunit;
using System;
using QuanTAlib;
using Python.Runtime;
using Python.Included;
namespace Validations;
public class PandasTA : IDisposable
{
private readonly GBM_Feed bars;
private readonly Random rnd = new();
private readonly int period, sample;
private int digits;
private readonly string OStype;
private readonly dynamic np;
private readonly dynamic ta;
private readonly dynamic df;
public PandasTA() {
bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0);
period = rnd.Next(maxValue: 28) + 3;
sample = 200;
digits = 10;
// Checking the host OS and setting PythonDLL accordingly
OStype = Environment.OSVersion.ToString();
if (OStype == "Unix 13.1.0")
OStype = @"/usr/local/Cellar/python@3.10/3.10.8/Frameworks/Python.framework/Versions/3.10/lib/libpython3.10.dylib";
else OStype = Path.GetFullPath(".") + @"\python-3.10.0-embed-amd64\python310.dll";
Installer.InstallPath = Path.GetFullPath(path: ".");
Installer.SetupPython().Wait();
Installer.TryInstallPip();
Installer.PipInstallModule(module_name: "pandas-ta");
Runtime.PythonDLL = OStype;
PythonEngine.Initialize();
np = Py.Import(name: "numpy");
ta = Py.Import(name: "pandas_ta");
string[] cols = { "open", "high", "low", "close", "volume" };
double[,] ary = new double[bars.Count, 5];
for (int i = 0; i < bars.Count; i++) {
ary[i, 0] = bars.Open[i].v;
ary[i, 1] = bars.High[i].v;
ary[i, 2] = bars.Low[i].v;
ary[i, 3] = bars.Close[i].v;
ary[i, 4] = bars.Volume[i].v;
}
df = ta.DataFrame(data: np.array(ary), index: np.array(bars.Close.t), columns: np.array(cols));
}
public void Dispose()
{
PythonEngine.Shutdown();
GC.SuppressFinalize(this);
}
[Fact] void ADL() {
ADL_Series QL = new(bars);
var pta = df.ta.ad(high: df.high, low: df.low, close:df.close, volume:df.volume);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i-1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i-1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void ADOSC() {
ADOSC_Series QL = new(bars);
var pta = df.ta.adosc(high: df.high, low: df.low, close: df.close, volume: df.volume);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void ATR() {
ATR_Series QL = new(bars, period);
var pta = df.ta.atr(high: df.high, low: df.low, close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void BIAS() {
BIAS_Series QL = new(bars.Close, period, false);
var pta = df.ta.bias(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
/*
[Fact]
void CMO() {
CMO_Series QL = new(bars.Close, period, false);
var pta = df.ta.cmo(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length - sample; i--) {
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
*/
[Fact] void DEMA() {
DEMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.dema(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void EMA() {
EMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.ema(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void ENTROPY() {
ENTROPY_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.entropy(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void HL2() {
var pta = df.ta.hl2(high: df.high, low: df.low);
for (int i = bars.HL2.Length; i > bars.HL2.Length-sample; i--)
{
double QL_item = Math.Round(bars.HL2[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void HLC3() {
var pta = df.ta.hlc3(high: df.high, low: df.low, close: df.close);
for (int i = bars.HLC3.Length; i > bars.HLC3.Length-sample; i--)
{
double QL_item = Math.Round(bars.HLC3[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void HMA() {
HMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.hma(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
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);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void KURTOSIS() {
KURTOSIS_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.kurtosis(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void MAD()
{
MAD_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.mad(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void MEDIAN() {
MEDIAN_Series QL = new(bars.Close, period);
var pta = df.ta.median(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void OBV() {
OBV_Series QL = new(bars);
var pta = df.ta.obv(close: df.close, volume: df.volume);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void OHLC4() {
var pta = df.ta.ohlc4(open: df.open, high: df.high, low: df.low, close: df.close);
for (int i = bars.OHLC4.Length; i > bars.OHLC4.Length-sample; i--)
{
double QL_item = Math.Round(bars.OHLC4[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void RMA() {
RMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.rma(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void RSI() {
RSI_Series QL = new(bars.Close, period);
var pta = df.ta.rsi(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void SDEV() {
SDEV_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.stdev(close: df.close, length: period, ddof: 0);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void SMA() {
SMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.sma(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void SSDEV() {
SSDEV_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.stdev(close: df.close, length: period, ddof: 1);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
/*
[Fact] void SVARIANCE() {
SVAR_Series QL = new(bars.Close, period);
var pta = df.ta.variance(close: df.close, length: period, ddof: 1);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
*/
[Fact] void T3() {
T3_Series QL = new(source: bars.Close, period: period, vfactor: 0.7, useNaN: false);
var pta = df.ta.t3(close: df.close, length: period, a: 0.7);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void TEMA() {
TEMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.tema(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void TR() {
TR_Series QL = new(bars);
var pta = df.ta.true_range(high: df.high, low: df.low, close: df.close);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void TRIMA() {
// TODO: return length to variable length (period) when Pandas-TA fixes trima to calculate even periods right
TRIMA_Series QL = new(bars.Close, 11);
var pta = df.ta.trima(close: df.close, length: 11);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void TRIX() {
TRIX_Series QL = new(bars.Close, period);
var pta = df.ta.trix(close: df.close, length: period).to_numpy();
for (int i = QL.Length; i > QL.Length - sample; i--) {
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1][0], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void VARIANCE() {
VAR_Series QL = new(bars.Close, period);
var pta = df.ta.variance(close: df.close, length: period, ddof:0);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void WMA() {
WMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.wma(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void ZLEMA() {
ZLEMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.zlma(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void ZSCORE() {
ZSCORE_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.zscore(close: df.close, length: period, ddof: 0);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
}
Tests/Validations/Trends/Skender.cs
+61 -54
View File
@@ -10,16 +10,16 @@ public class Skender
private readonly Random rnd = new();
private readonly int period, digits, skip;
private readonly IEnumerable<Quote> quotes;
public Skender()
{
bars = new(Bars: 10000, Volatility: 0.5, Drift: 0.0, Precision: 2);
period = rnd.Next(30) + 5;
skip = 200;
digits = 10;
digits = 2; //minimizing rounding errors in type conversions
skip = 300;
quotes = bars.Select(q => new Quote
quotes = bars.Select(q => new Quote
{
Date = q.t,
Open = (decimal)q.o,
@@ -33,14 +33,15 @@ public class Skender
[Fact]
public void ADL()
{
// TODO: check precision of ADL()
ADL_Series QL = new(bars, false);
var SK = quotes.GetAdl().Select(i => i.Adl);
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);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
public void ALMA()
@@ -51,7 +52,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round((double)SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -63,7 +64,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round((double)SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -75,7 +76,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round((double)SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -87,22 +88,22 @@ public class Skender
{
double QL_item = Math.Round(QL.Mid[i - 1].v, digits: digits);
double SK_item = Math.Round((double)SK.ElementAt(i - 1).Sma!.Value, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
QL_item = Math.Round(QL.Upper[i - 1].v, digits: digits);
SK_item = Math.Round((double)SK.ElementAt(i - 1).UpperBand!.Value, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
QL_item = Math.Round(QL.Lower[i - 1].v, digits: digits);
SK_item = Math.Round((double)SK.ElementAt(i - 1).LowerBand!.Value, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
QL_item = Math.Round(QL.Bandwidth[i - 1].v, digits: digits);
SK_item = Math.Round((double)SK.ElementAt(i - 1).Width!.Value, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
QL_item = Math.Round(QL.PercentB[i - 1].v, digits: digits);
SK_item = Math.Round((double)SK.ElementAt(i - 1).PercentB!.Value, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
QL_item = Math.Round(QL.Zscore[i - 1].v, digits: digits);
SK_item = Math.Round((double)SK.ElementAt(i - 1).ZScore!.Value, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -114,7 +115,19 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round((double)SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
public void CMO()
{
CMO_Series QL = new(bars.Close, period, false);
var SK = quotes.GetCmo(period).Select(i => i.Cmo.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((double)SK.ElementAt(i - 1), digits: digits);
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -126,7 +139,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round((double)SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -138,10 +151,9 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round((double)SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
/*
[Fact]
public void DEMA()
{
@@ -151,10 +163,9 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round((double)SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
*/
[Fact]
public void EMA()
{
@@ -164,7 +175,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round((double)SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
/*
@@ -177,7 +188,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round((double)SK.ElementAt(i - 1)!, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -189,7 +200,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round((double)SK.ElementAt(i - 1)!, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
*/
@@ -202,10 +213,9 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
/*
[Fact]
public void KAMA()
{
@@ -216,10 +226,9 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits/2), Math.Exp(-digits/2));
Assert.Equal(SK_item!, QL_item);
}
}
*/
[Fact]
public void LINREG()
{
@@ -229,16 +238,16 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round((double)SK.ElementAt(i - 1).Slope!, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
QL_item = Math.Round(QL.Intercept[i - 1].v, digits: digits);
SK_item = Math.Round((double)SK.ElementAt(i - 1).Intercept!, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
QL_item = Math.Round(QL.RSquared[i - 1].v, digits: digits);
SK_item = Math.Round((double)SK.ElementAt(i - 1).RSquared!, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
QL_item = Math.Round(QL.StdDev[i - 1].v, digits: digits);
SK_item = Math.Round((double)SK.ElementAt(i - 1).StdDev!, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -250,10 +259,10 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1).Macd.Null2NaN()!, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
QL_item = Math.Round(QL.Signal[i - 1].v, digits: digits);
SK_item = Math.Round(SK.ElementAt(i - 1).Signal.Null2NaN()!, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -265,7 +274,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -277,10 +286,10 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1).Mama.Null2NaN()!, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
QL_item = Math.Round(QL.Fama[i - 1].v, digits: digits);
SK_item = Math.Round(SK.ElementAt(i - 1).Fama.Null2NaN()!, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -292,7 +301,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -304,7 +313,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -317,7 +326,7 @@ public class Skender
{
double QL_item = Math.Round(QL.Last().v, digits: digits);
double SK_item = Math.Round(SK.Last()! + (double)quotes.First().Volume!, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
/*
@@ -330,7 +339,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round((double)SK.ElementAt(i - 1)!, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -342,7 +351,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round((double)SK.ElementAt(i - 1)!, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -354,7 +363,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round((double)SK.ElementAt(i - 1)!, digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
*/
@@ -367,7 +376,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -379,7 +388,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -391,7 +400,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -403,10 +412,9 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
/*
[Fact]
public void T3()
{
@@ -416,10 +424,9 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
*/
[Fact]
public void TEMA()
{
@@ -429,7 +436,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -441,7 +448,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -453,7 +460,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
[Fact]
@@ -465,7 +472,7 @@ public class Skender
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double SK_item = Math.Round(SK.ElementAt(i - 1), digits: digits);
Assert.InRange(SK_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
Assert.Equal(SK_item!, QL_item);
}
}
Tests/Validations/Trends/TA_LIB.cs
+474 -452
View File
@@ -1,452 +1,474 @@
using Xunit;
using System;
using TALib;
using QuanTAlib;
namespace Validations;
public class Ta_Lib
{
private readonly GBM_Feed bars;
private readonly Random rnd = new();
private readonly int period, digits, skip;
private readonly double[] TALIB;
private readonly double[] TALIB2;
private readonly double[] inopen;
private readonly double[] inhigh;
private readonly double[] inlow;
private readonly double[] inclose;
private readonly double[] involume;
public Ta_Lib()
{
bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0, Precision: 3);
period = rnd.Next(28) + 3;
skip = 500;
digits = 10;
TALIB = new double[bars.Count];
TALIB2 = new double[bars.Count];
inopen = bars.Open.v.ToArray();
inhigh = bars.High.v.ToArray();
inlow = bars.Low.v.ToArray();
inclose = bars.Close.v.ToArray();
involume = bars.Volume.v.ToArray();
}
[Fact]
public void ADD()
{
ADD_Series QL = new(bars.Open, bars.Close);
Core.Add(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void ADL()
{
ADL_Series QL = new(bars, false);
Core.Ad(inhigh, inlow, inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
for (int i = QL.Length - 1; i > 0; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void ADOSC()
{
ADOSC_Series QL = new(bars, 3, 10, false);
Core.AdOsc(inhigh, inlow, inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void ATR()
{
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--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
/*
[Fact]
public void BBANDS()
{
double[] outMiddle = new double[bars.Count];
double[] outUpper = new double[bars.Count];
double[] outLower = new double[bars.Count];
BBANDS_Series QL = new(bars.Close, period: 26, multiplier: 2.0, false);
Core.Bbands(inclose, 0, bars.Count - 1, outRealUpperBand: outUpper, outRealMiddleBand: outMiddle, outRealLowerBand: outLower, out int outBegIdx, out _, optInTimePeriod: 26, optInNbDevUp: 2.0, optInNbDevDn: 2.0);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL.Upper[i].v, digits: digits);
double TA_item = Math.Round(outUpper[i - outBegIdx], digits: digits);
Assert.Equal(TA_item!, QL_item);
QL_item = Math.Round(QL.Mid[i].v, digits: digits);
TA_item = Math.Round(outMiddle[i - outBegIdx], digits: digits);
Assert.Equal(TA_item!, QL_item);
QL_item = Math.Round(QL.Lower[i].v, digits: digits);
TA_item = Math.Round(outLower[i - outBegIdx], digits: digits);
Assert.Equal(TA_item!, QL_item);
}
Assert.Equal(Math.Round(outUpper[outUpper.Length - outBegIdx - 1], digits: digits), Math.Round(QL.Upper.Last().v, digits: digits));
Assert.Equal(Math.Round(outMiddle[outMiddle.Length - outBegIdx - 1], digits: digits), Math.Round(QL.Mid.Last().v, digits: digits));
Assert.Equal(Math.Round(outLower[outLower.Length - outBegIdx - 1], digits: digits), Math.Round(QL.Lower.Last().v, digits: digits));
}
*/
[Fact]
public void CCI()
{
CCI_Series QL = new(bars, period, false);
Core.Cci(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void CORR()
{
CORR_Series QL = new(bars.Open, bars.Close, period);
Core.Correl(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, optInTimePeriod: period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void DEMA()
{
DEMA_Series QL = new(bars.Close, period, false);
Core.Dema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void DIV()
{
DIV_Series QL = new(bars.Open, bars.Close);
Core.Div(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void EMA()
{
EMA_Series QL = new(bars.Close, period, false);
Core.Ema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void HL2()
{
TSeries QL = bars.HL2;
Core.MedPrice(inhigh, inlow, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void HLC3()
{
TSeries QL = bars.HLC3;
Core.TypPrice(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void HLCC4()
{
TSeries QL = bars.HLCC4;
Core.WclPrice(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void MACD()
{
double[] macdSignal = new double[bars.Count];
double[] macdHist = new double[bars.Count];
MACD_Series QL = new(bars.Close, slow: 26, fast: 12, signal: 9, false);
Core.Macd(inclose, 0, bars.Count - 1, outMacd: TALIB, outMacdSignal: macdSignal, outMacdHist: macdHist, out int outBegIdx, out _);
for (int i = QL.Length - 1; i > skip * 10; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
Assert.Equal(TA_item!, QL_item);
QL_item = Math.Round(QL.Signal[i].v, digits: digits);
TA_item = Math.Round(macdSignal[i - outBegIdx], digits: digits);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void MAMA()
{
MAMA_Series QL = new(bars.Close, fastlimit: 0.5, slowlimit: 0.05);
Core.Mama(inReal: inclose, startIdx: 0, endIdx: bars.Count - 1, outMama: TALIB, outFama: TALIB2, outBegIdx: out int outBegIdx, outNbElement: out _, optInFastLimit: 0.5, optInSlowLimit: 0.05);
for (int i = QL.Length - 1; i > skip * 15; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void MAX()
{
MAX_Series QL = new(bars.Close, period, false);
Core.Max(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void MIDPOINT()
{
MIDPOINT_Series QL = new(bars.Close, period, false);
Core.MidPoint(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void MIDPRICE()
{
MIDPRICE_Series QL = new(bars, period, false);
Core.MidPrice(inhigh, inlow, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void MIN()
{
MIN_Series QL = new(bars.Close, period, false);
Core.Min(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void MUL()
{
MUL_Series QL = new(bars.Open, bars.Close);
Core.Mult(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void OBV()
{
OBV_Series QL = new(bars, period, false);
Core.Obv(inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void OHLC4()
{
TSeries QL = bars.OHLC4;
Core.AvgPrice(inopen, inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void RSI()
{
RSI_Series QL = new(bars.Close, period, false);
Core.Rsi(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void SDEV()
{
SDEV_Series QL = new(bars.Close, period, false);
Core.StdDev(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void SMA()
{
SMA_Series QL = new(bars.Close, period, false);
Core.Sma(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void SUB()
{
SUB_Series QL = new(bars.Open, bars.Close);
Core.Sub(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void SUM()
{
SUM_Series QL = new(bars.Close, period, false);
Core.Sum(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void T3()
{
T3_Series QL = new(source: bars.Close, period: period, vfactor: 0.7, useNaN: false);
Core.T3(inReal: inclose, startIdx: 0, endIdx: bars.Count - 1, outReal: TALIB, outBegIdx: out int outBegIdx, outNbElement: out _, optInTimePeriod: period, optInVFactor: 0.7);
for (int i = QL.Length - 1; i > skip * 15; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void TEMA()
{
TEMA_Series QL = new(bars.Close, period, false);
Core.Tema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
for (int i = QL.Length - 1; i > skip * 15; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void TR()
{
TR_Series QL = new(bars, false);
Core.TRange(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void TRIMA()
{
TRIMA_Series QL = new(bars.Close, period, false);
Core.Trima(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void VAR()
{
VAR_Series QL = new(bars.Close, period, false);
Core.Var(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
for (int i = QL.Length - 1; i > skip * 15; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void WMA()
{
WMA_Series QL = new(bars.Close, period, false);
Core.Wma(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
}
using Xunit;
using System;
using TALib;
using QuanTAlib;
namespace Validations;
public class Ta_Lib
{
private readonly GBM_Feed bars;
private readonly Random rnd = new();
private readonly int period, digits, skip;
private readonly double[] TALIB;
private readonly double[] TALIB2;
private readonly double[] inopen;
private readonly double[] inhigh;
private readonly double[] inlow;
private readonly double[] inclose;
private readonly double[] involume;
public Ta_Lib()
{
bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0, Precision: 3);
period = rnd.Next(28) + 3;
skip = 500;
digits = 10;
TALIB = new double[bars.Count];
TALIB2 = new double[bars.Count];
inopen = bars.Open.v.ToArray();
inhigh = bars.High.v.ToArray();
inlow = bars.Low.v.ToArray();
inclose = bars.Close.v.ToArray();
involume = bars.Volume.v.ToArray();
}
[Fact]
public void ADD()
{
ADD_Series QL = new(bars.Open, bars.Close);
Core.Add(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void ADL()
{
ADL_Series QL = new(bars, false);
Core.Ad(inhigh, inlow, inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
for (int i = QL.Length - 1; i > 0; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void ADOSC()
{
ADOSC_Series QL = new(bars, 3, 10, false);
Core.AdOsc(inhigh, inlow, inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void ATR()
{
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--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
/*
[Fact]
public void BBANDS()
{
double[] outMiddle = new double[bars.Count];
double[] outUpper = new double[bars.Count];
double[] outLower = new double[bars.Count];
BBANDS_Series QL = new(bars.Close, period: 26, multiplier: 2.0, false);
Core.Bbands(inclose, 0, bars.Count - 1, outRealUpperBand: outUpper, outRealMiddleBand: outMiddle, outRealLowerBand: outLower, out int outBegIdx, out _, optInTimePeriod: 26, optInNbDevUp: 2.0, optInNbDevDn: 2.0);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL.Upper[i].v, digits: digits);
double TA_item = Math.Round(outUpper[i - outBegIdx], digits: digits);
Assert.Equal(TA_item!, QL_item);
QL_item = Math.Round(QL.Mid[i].v, digits: digits);
TA_item = Math.Round(outMiddle[i - outBegIdx], digits: digits);
Assert.Equal(TA_item!, QL_item);
QL_item = Math.Round(QL.Lower[i].v, digits: digits);
TA_item = Math.Round(outLower[i - outBegIdx], digits: digits);
Assert.Equal(TA_item!, QL_item);
}
Assert.Equal(Math.Round(outUpper[outUpper.Length - outBegIdx - 1], digits: digits), Math.Round(QL.Upper.Last().v, digits: digits));
Assert.Equal(Math.Round(outMiddle[outMiddle.Length - outBegIdx - 1], digits: digits), Math.Round(QL.Mid.Last().v, digits: digits));
Assert.Equal(Math.Round(outLower[outLower.Length - outBegIdx - 1], digits: digits), Math.Round(QL.Lower.Last().v, digits: digits));
}
*/
[Fact]
public void CCI()
{
CCI_Series QL = new(bars, period, false);
Core.Cci(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
/*
[Fact]
public void CMO() {
CMO_Series QL = new(bars.Close, period, false);
Core.Cmo(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
*/
[Fact]
public void CORR()
{
CORR_Series QL = new(bars.Open, bars.Close, period);
Core.Correl(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, optInTimePeriod: period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void DEMA()
{
DEMA_Series QL = new(bars.Close, period, false);
Core.Dema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void DIV()
{
DIV_Series QL = new(bars.Open, bars.Close);
Core.Div(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void EMA()
{
EMA_Series QL = new(bars.Close, period, false);
Core.Ema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void HL2()
{
TSeries QL = bars.HL2;
Core.MedPrice(inhigh, inlow, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void HLC3()
{
TSeries QL = bars.HLC3;
Core.TypPrice(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void HLCC4()
{
TSeries QL = bars.HLCC4;
Core.WclPrice(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void MACD()
{
double[] macdSignal = new double[bars.Count];
double[] macdHist = new double[bars.Count];
MACD_Series QL = new(bars.Close, slow: 26, fast: 12, signal: 9, false);
Core.Macd(inclose, 0, bars.Count - 1, outMacd: TALIB, outMacdSignal: macdSignal, outMacdHist: macdHist, out int outBegIdx, out _);
for (int i = QL.Length - 1; i > skip * 10; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
Assert.Equal(TA_item!, QL_item);
QL_item = Math.Round(QL.Signal[i].v, digits: digits);
TA_item = Math.Round(macdSignal[i - outBegIdx], digits: digits);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void MAMA()
{
MAMA_Series QL = new(bars.Close, fastlimit: 0.5, slowlimit: 0.05);
Core.Mama(inReal: inclose, startIdx: 0, endIdx: bars.Count - 1, outMama: TALIB, outFama: TALIB2, outBegIdx: out int outBegIdx, outNbElement: out _, optInFastLimit: 0.5, optInSlowLimit: 0.05);
for (int i = QL.Length - 1; i > skip * 15; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void MAX()
{
MAX_Series QL = new(bars.Close, period, false);
Core.Max(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void MIDPOINT()
{
MIDPOINT_Series QL = new(bars.Close, period, false);
Core.MidPoint(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void MIDPRICE()
{
MIDPRICE_Series QL = new(bars, period, false);
Core.MidPrice(inhigh, inlow, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void MIN()
{
MIN_Series QL = new(bars.Close, period, false);
Core.Min(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void MUL()
{
MUL_Series QL = new(bars.Open, bars.Close);
Core.Mult(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void OBV()
{
OBV_Series QL = new(bars, period, false);
Core.Obv(inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void OHLC4()
{
TSeries QL = bars.OHLC4;
Core.AvgPrice(inopen, inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void RSI()
{
RSI_Series QL = new(bars.Close, period, false);
Core.Rsi(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void SDEV()
{
SDEV_Series QL = new(bars.Close, period, false);
Core.StdDev(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void SMA()
{
SMA_Series QL = new(bars.Close, period, false);
Core.Sma(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void SUB()
{
SUB_Series QL = new(bars.Open, bars.Close);
Core.Sub(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void SUM()
{
SUM_Series QL = new(bars.Close, period, false);
Core.Sum(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void T3()
{
T3_Series QL = new(source: bars.Close, period: period, vfactor: 0.7, useNaN: false);
Core.T3(inReal: inclose, startIdx: 0, endIdx: bars.Count - 1, outReal: TALIB, outBegIdx: out int outBegIdx, outNbElement: out _, optInTimePeriod: period, optInVFactor: 0.7);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void TEMA()
{
TEMA_Series QL = new(bars.Close, period, false);
Core.Tema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
for (int i = QL.Length - 1; i > skip * 15; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void TR()
{
TR_Series QL = new(bars, false);
Core.TRange(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void TRIMA()
{
TRIMA_Series QL = new(bars.Close, period, false);
Core.Trima(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void TRIX() {
TRIX_Series QL = new(bars.Close, period, useNaN: false, useSMA: true);
Core.Trix(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void VAR()
{
VAR_Series QL = new(bars.Close, period, false);
Core.Var(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
for (int i = QL.Length - 1; i > skip * 15; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TA_item = Math.Round(TALIB[i - outBegIdx], digits: digits);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void WMA()
{
WMA_Series QL = new(bars.Close, period, false);
Core.Wma(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period);
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);
Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
}
Tests/Validations/Trends/Tulip.cs
+194 -158
View File
@@ -1,158 +1,194 @@
using Xunit;
using System;
using Tulip;
using QuanTAlib;
namespace Validations;
public class Tulip_Test
{
private readonly GBM_Feed bars;
private readonly Random rnd = new();
private readonly int period, digits, skip;
private readonly double[] outdata;
private readonly double[] inopen;
private readonly double[] inhigh;
private readonly double[] inlow;
private readonly double[] inclose;
private readonly double[] involume;
public Tulip_Test()
{
bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0, Precision: 3);
period = rnd.Next(28) + 3;
skip = 200;
digits = 10;
outdata = new double[bars.Count];
inopen = bars.Open.v.ToArray();
inhigh = bars.High.v.ToArray();
inlow = bars.Low.v.ToArray();
inclose = bars.Close.v.ToArray()!;
involume = bars.Volume.v.ToArray()!;
}
[Fact]
public void AD()
{
double[][] arrin = {inhigh, inlow, inclose, involume };
double[][] arrout = { outdata };
ADL_Series QL = new(bars, false);
Tulip.Indicators.ad.Run(inputs: arrin, options: new double[] { }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void ADD()
{
double[][] arrin = { inhigh, inlow };
double[][] arrout = { outdata };
ADD_Series QL = new(bars.High, bars.Low);
Tulip.Indicators.add.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void ADOSC()
{
double[][] arrin = { inhigh, inlow, inclose, involume };
double[][] arrout = { outdata };
int s = 3;
ADOSC_Series QL = new(bars, s, period, false);
Tulip.Indicators.adosc.Run(inputs: arrin, options: new double[] { s, period }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i-period+1], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void ATR()
{
double[][] arrin = { inhigh, inlow, inclose };
double[][] arrout = { outdata };
ATR_Series QL = new(bars, period, false);
Tulip.Indicators.atr.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i - period + 1], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void BBANDS()
{
double[][] arrin = { inclose };
double[] outmid = new double[bars.Count];
double[] outlower = new double[bars.Count];
double[] outupper = new double[bars.Count];
double[][] arrout = { outlower, outmid, outupper};
BBANDS_Series QL = new(bars.Close, period, 2, false);
Tulip.Indicators.bbands.Run(inputs: arrin, options: new double[] { period, 2 }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL.Lower[i].v, digits: digits);
double TU_item = Math.Round(outlower[i - period + 1], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
QL_item = Math.Round(QL.Mid[i].v, digits: digits);
TU_item = Math.Round(outmid[i - period + 1], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
QL_item = Math.Round(QL.Upper[i].v, digits: digits);
TU_item = Math.Round(outupper[i - period + 1], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void EMA()
{
double[][] arrin = { inclose };
double[][] arrout = { outdata };
EMA_Series QL = new(bars.Close, period, false);
Tulip.Indicators.ema.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void AVGPRICE()
{
double[][] arrin = { inopen, inhigh, inlow, inclose };
double[][] arrout = { outdata };
TSeries QL = bars.OHLC4;
Tulip.Indicators.avgprice.Run(inputs: arrin, options: new double[] { }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void SMA()
{
double[][] arrin = { inclose };
double[][] arrout = { outdata };
SMA_Series QL = new(bars.Close, period, false);
Tulip.Indicators.sma.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i-period+1], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
}
using Xunit;
using System;
using Tulip;
using QuanTAlib;
namespace Validations;
public class Tulip_Test
{
private readonly GBM_Feed bars;
private readonly Random rnd = new();
private readonly int period, digits, skip;
private readonly double[] outdata;
private readonly double[] inopen;
private readonly double[] inhigh;
private readonly double[] inlow;
private readonly double[] inclose;
private readonly double[] involume;
public Tulip_Test()
{
bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0, Precision: 3);
period = rnd.Next(28) + 3;
skip = 200;
digits = 10;
outdata = new double[bars.Count];
inopen = bars.Open.v.ToArray();
inhigh = bars.High.v.ToArray();
inlow = bars.Low.v.ToArray();
inclose = bars.Close.v.ToArray()!;
involume = bars.Volume.v.ToArray()!;
}
[Fact]
public void ADL()
{
double[][] arrin = {inhigh, inlow, inclose, involume };
double[][] arrout = { outdata };
ADL_Series QL = new(bars, false);
Tulip.Indicators.ad.Run(inputs: arrin, options: new double[] { }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void ADD()
{
double[][] arrin = { inhigh, inlow };
double[][] arrout = { outdata };
ADD_Series QL = new(bars.High, bars.Low);
Tulip.Indicators.add.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void ADOSC()
{
double[][] arrin = { inhigh, inlow, inclose, involume };
double[][] arrout = { outdata };
int s = 3;
ADOSC_Series QL = new(bars, s, period, false);
Tulip.Indicators.adosc.Run(inputs: arrin, options: new double[] { s, period }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i-period+1], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void ATR()
{
double[][] arrin = { inhigh, inlow, inclose };
double[][] arrout = { outdata };
ATR_Series QL = new(bars, period, false);
Tulip.Indicators.atr.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i - period + 1], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void BBANDS()
{
double[][] arrin = { inclose };
double[] outmid = new double[bars.Count];
double[] outlower = new double[bars.Count];
double[] outupper = new double[bars.Count];
double[][] arrout = { outlower, outmid, outupper};
BBANDS_Series QL = new(bars.Close, period, 2, false);
Tulip.Indicators.bbands.Run(inputs: arrin, options: new double[] { period, 2 }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL.Lower[i].v, digits: digits);
double TU_item = Math.Round(outlower[i - period + 1], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
QL_item = Math.Round(QL.Mid[i].v, digits: digits);
TU_item = Math.Round(outmid[i - period + 1], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
QL_item = Math.Round(QL.Upper[i].v, digits: digits);
TU_item = Math.Round(outupper[i - period + 1], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void DEMA() {
double[][] arrin = { inclose };
double[][] arrout = { outdata };
DEMA_Series QL = new(bars.Close, period, useNaN: false, useSMA: false);
Tulip.Indicators.dema.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--) {
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i-(period+period-2)], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void EMA()
{
double[][] arrin = { inclose };
double[][] arrout = { outdata };
EMA_Series QL = new(bars.Close, period, false);
Tulip.Indicators.ema.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void AVGPRICE()
{
double[][] arrin = { inopen, inhigh, inlow, inclose };
double[][] arrout = { outdata };
TSeries QL = bars.OHLC4;
Tulip.Indicators.avgprice.Run(inputs: arrin, options: new double[] { }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void SMA()
{
double[][] arrin = { inclose };
double[][] arrout = { outdata };
SMA_Series QL = new(bars.Close, period, false);
Tulip.Indicators.sma.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--)
{
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i-period+1], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void HMA() {
double[][] arrin = { inclose };
double[][] arrout = { outdata };
HMA_Series QL = new(bars.Close, period, false);
Tulip.Indicators.hma.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--) {
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i-period-1], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact]
public void CMO() {
double[][] arrin = { inclose };
double[][] arrout = { outdata };
CMO_Series QL = new(bars.Close, period, useNaN: false);
Tulip.Indicators.cmo.Run(inputs: arrin, options: new double[] { period }, outputs: arrout);
for (int i = QL.Length - 1; i > skip; i--) {
double QL_item = Math.Round(QL[i].v, digits: digits);
double TU_item = Math.Round(arrout[0][i-period], digits);
Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
}
docs/EMA.md
+38 -38
View File
@@ -1,38 +1,38 @@
# EMA: Exponential Moving Average
EMA needs very short history buffer and calculates the EMA value using just the previous EMA value. The weight of the new datapoint (k) is k = 2 / (period-1)
## Calculation
There is an adopted practice to calculate $SMA$ when $n < period$.
$$
EMA_n = \left\{ \begin{array}{cl}
\frac{1}{p}\left( data_{n}-data_{n-p}\right)+SMA_{n-1} & : \ n \leq period \\
{k}\times ({data_{n}} - EMA_{n-1}) + EMA_{n-1} & : \ x > period
\end{array} \right.
$$
## Implementation
``` csharp
EMA_Series mean = new(source: data, period: p, useNaN: false);
```
- `TSeries source` - List of value tuples (DateTime, double)
- `int period` - Integer representing the period of SMA
- `bool useNaN` - if true, initial values from 1 to period-1 will be replaced with NaN. If false, the initial calculation will return values for SMA(length) instead of SMA(period)
## Comparison & Validation
Validation tests
Performance tests
## Visual analysis
![Alt text](./img/EMA_chart.svg)
## References
# EMA: Exponential Moving Average
EMA needs very short history buffer and calculates the EMA value using just the previous EMA value. The weight of the new datapoint (k) is k = 2 / (period-1)
## Calculation
There is an adopted practice to calculate $SMA$ when $n < period$.
$$
EMA_n = \left\{ \begin{array}{cl}
\frac{1}{p}\left( data_{n}-data_{n-p}\right)+SMA_{n-1} & : \ n \leq period \\
{k}\times ({data_{n}} - EMA_{n-1}) + EMA_{n-1} & : \ x > period
\end{array} \right.
$$
## Implementation
``` csharp
EMA_Series mean = new(source: data, period: p, useNaN: false);
```
- `TSeries source` - List of value tuples (DateTime, double)
- `int period` - Integer representing the period of SMA
- `bool useNaN` - if true, initial values from 1 to period-1 will be replaced with NaN. If false, the initial calculation will return values for SMA(length) instead of SMA(period)
## Comparison & Validation
Validation tests
Performance tests
## Visual analysis
![Alt text](./img/EMA_chart.svg)
## References
docs/SMA.md
+38 -38
View File
@@ -1,39 +1,39 @@
![Alt text](./img/SMA_chart.svg)
# SMA: Simple Moving Average
SMA is one of the most basic trend-following indicators used in Technical Analysis. It is calculated as the *unweighted mean* of the previous $p$ (period) data-points.
## Calculation
SMA is a rolling calculation looking backwards from the position ${n}$ and is denoted as ${SMA}_{p}{(data)}$ where $p$ represents the period and $data$ represents the list of data points:
$$
SMA_p{(data)} = \frac{1}{p}\sum_{i=n-p+1}^{n} data_i
$$
When calculating the value of next $SMA_{p,next}$ while knowing all previous SMA values, SMA calculation can be reduced to:
$$
SMA_{p,next} = SMA_{p,prev}+\frac{1}{p}\left( data_{n+1}-data_{n+1-p}\right)
$$
## Implementation
``` csharp
SMA_Series mean = new(source: data, period: p, useNaN: false);
```
- `TSeries source` - List of value tuples (DateTime, double)
- `int period` - Integer representing the period of SMA
- `bool useNaN` - if true, initial values from 1 to period-1 will be replaced with NaN. If false, the initial calculation will return values for SMA(length) instead of SMA(period)
## Comparison & Validation
Validation tests
Performance tests
## Visual analysis
## References
![Alt text](./img/SMA_chart.svg)
# SMA: Simple Moving Average
SMA is one of the most basic trend-following indicators used in Technical Analysis. It is calculated as the *unweighted mean* of the previous $p$ (period) data-points.
## Calculation
SMA is a rolling calculation looking backwards from the position ${n}$ and is denoted as ${SMA}_{p}{(data)}$ where $p$ represents the period and $data$ represents the list of data points:
$$
SMA_p{(data)} = \frac{1}{p}\sum_{i=n-p+1}^{n} data_i
$$
When calculating the value of next $SMA_{p,next}$ while knowing all previous SMA values, SMA calculation can be reduced to:
$$
SMA_{p,next} = SMA_{p,prev}+\frac{1}{p}\left( data_{n+1}-data_{n+1-p}\right)
$$
## Implementation
``` csharp
SMA_Series mean = new(source: data, period: p, useNaN: false);
```
- `TSeries source` - List of value tuples (DateTime, double)
- `int period` - Integer representing the period of SMA
- `bool useNaN` - if true, initial values from 1 to period-1 will be replaced with NaN. If false, the initial calculation will return values for SMA(length) instead of SMA(period)
## Comparison & Validation
Validation tests
Performance tests
## Visual analysis
## References
- https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/
docs/_sidebar.md
+12 -12
View File
@@ -1,13 +1,13 @@
* [Home](/)
* [Indicators](indicators.md "Indocators coverage")
* [SMA - Simple Moving Average](SMA.md "SMA - Simple Moving Average")
* [WMA - Weighted Moving Average](WMA.md "WMA - Weighted Moving Average")
* [EMA - Exponential Moving Average](EMA.md "EMA - Exponential Moving Average")
* [DEMA - Double Exponential Moving Average](DEMA.md "DEMA - Double Exponential Moving Average")
* [TEMA - Triple Exponential Moving Average](TEMA.md "TEMA - Triple Exponential Moving Average")
* [HMA - Hull Moving Average](HMA.md "HMA - Hull Moving Average")
* [ZLEMA - Zero-Lag Exponential Moving Average](ZLEMA.md "ZLEMA - Zero-Lag Exponential Moving Average")
* [KAMA - Kaufman Adaptive Moving Average](KAMA.md "KAMA - Kaufman Adaptive Moving Average")
* [Home](/)
* [Indicators](indicators.md "Indocators coverage")
* [SMA - Simple Moving Average](SMA.md "SMA - Simple Moving Average")
* [WMA - Weighted Moving Average](WMA.md "WMA - Weighted Moving Average")
* [EMA - Exponential Moving Average](EMA.md "EMA - Exponential Moving Average")
* [DEMA - Double Exponential Moving Average](DEMA.md "DEMA - Double Exponential Moving Average")
* [TEMA - Triple Exponential Moving Average](TEMA.md "TEMA - Triple Exponential Moving Average")
* [HMA - Hull Moving Average](HMA.md "HMA - Hull Moving Average")
* [ZLEMA - Zero-Lag Exponential Moving Average](ZLEMA.md "ZLEMA - Zero-Lag Exponential Moving Average")
* [KAMA - Kaufman Adaptive Moving Average](KAMA.md "KAMA - Kaufman Adaptive Moving Average")
* [MAMA - Mesa Adaptive Moving Average](MAMA.md "MAMA - Mesa Adaptive Moving Average")
docs/getting_started.ipynb
+272 -272
View File
@@ -1,272 +1,272 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"source": [
"# Quick Start\n",
"\n",
"In order to use this .NET Interactive Notebook and play along with QuanTAlib (outside of making your own app or plugging QuanTAlib into Quantower platform), you will need:\n",
"\n",
"- Installed <a href=\"https://code.visualstudio.com/\" target=\"_blank\">Visual Studio Code</a>\n",
"- Installed <a href=\"https://dotnet.microsoft.com/download/dotnet/6.0\" target=\"_blank\">.NET 6 SDK</a>\n",
"- Installed <a href=\"https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.dotnet-interactive-vscode\" target=\"_blank\">.NET Interactive Notebooks</a> extension\n",
"\n",
"**For impatient**, here is a simple example of calculating three moving averages - SMA(data), EMA(SMA(data)) and WMA(EMA(SMA(data))) from 10 days of AAPL stock data using QuanTAlib:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"vscode": {
"languageId": "polyglot-notebook"
}
},
"outputs": [],
"source": [
"#r \"nuget:QuanTAlib;\"\n",
"using QuanTAlib;\n",
"\n",
"Yahoo_Feed aapl = new(\"AAPL\", 10);\n",
"TSeries data = aapl.Close;\n",
"SMA_Series sma = new(source: data, period: 5, useNaN: false);\n",
"EMA_Series ema = new(sma, period: 5); // by default, indicators expose all data, no NaN values\n",
"WMA_Series wma = new(ema, 5, useNaN: true); // for the final calculation we can hide early data with NaNs\n",
"\n",
"Console.Write($\"index\\t data\\t\\t sma(data)\\t ema(sma(data))\\t wma(ema(sma(data)))\\n\");\n",
"for (int i=0; i<aapl.Count; i++)\n",
" Console.Write($\"{i}\\t {data[i].t:yyyy-MM-dd}\\t {sma[i].v:f2}\\t\\t {ema[i].v:f2}\\t\\t {wma[i].v:f2}\\n\");"
]
},
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"cell_type": "markdown",
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"source": [
"## Understanding QuanTAlib data model\n",
"\n",
"QuanTAlib expects that every data item is a tuple (TimeDate t, double v) and TSeries is a list of (t,v) tuples. There are several helpers built into the TSeries class to simplify adding elements:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"vscode": {
"languageId": "polyglot-notebook"
}
},
"outputs": [],
"source": [
"var item1 = (DateTime.Today, 105.3); // (DateTime, Value) tuple\n",
"double item2 = 293.1; // a simple double\n",
"\n",
"TSeries data = new();\n",
"data.Add(item1); // adding tuple variable\n",
"data.Add(item2); // QuanTAlib stamps the (double) with current time\n",
"data.Add(0); // directly adding a number (stamped with current time)\n",
"data.Add((DateTime.Now.AddDays(-3), 10)); // adding a tuple with timestamp 3 days ago\n",
"\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"source": [
"TSeries list can display only values (without timestamps) or only timestamps (without values) by using `.v` or `.t` properties"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"vscode": {
"languageId": "polyglot-notebook"
}
},
"outputs": [],
"source": [
"data.v"
]
},
{
"cell_type": "markdown",
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"source": [
"The last element on the list can be accessed by .Last() or by [^1] - and using `.t` (time) and `.v` (value) properties. Also, casting a TSeries into (double) will return the value of the last element"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"vscode": {
"languageId": "polyglot-notebook"
}
},
"outputs": [],
"source": [
"bool IsTheSame = data.Last().v == data[^1].v;\n",
"double lastvalue = data;\n",
"\n",
"lastvalue"
]
},
{
"cell_type": "markdown",
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"source": [
"All indicators are just modified TSeries classes; they get all required input during class construction (source of the datafeed, period...) and they automatically subscribe to events of the datafeed. Whenever datafeed gets a new value, indicator will calculate its own value. Indicators are also event publishers, so other indicators can subscribe to their results, chaining indicators together:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"vscode": {
"languageId": "polyglot-notebook"
}
},
"outputs": [],
"source": [
"TSeries t1 = new() {0,1,2,3,4,5,6,7,8,9}; // t1 is loaded with data and activated as a publisher\n",
"EMA_Series t2 = new(t1, 3); // t2 will auto-load all history of t1 and wait for events from t1\n",
"ADD_Series t3 = new(t1, t2); // t3 is an ADDition of t1 and t2 - will also load history and wait for t2 events\n",
"DIV_Series t4 = new(1, t3); // t4 is calculating 1/t3 - and waiting for t3 events\n",
"\n",
"TSeries t5 = new(); // a wild indicator appeared! And it is empty!\n",
"t4.Pub += t5.Sub; // let us add a manual subscription to events coming from t4 - t5 is now listening to t4\n",
"t1.Add(0); // we add one new value to t1 - and trigger the full cascade of calculation! t5 is now full!\n",
"\n",
"t5.v"
]
},
{
"cell_type": "markdown",
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"source": [
"# MACD compounded indicator\n",
"\n",
"With QuanTAlib we can chain indicators together, creating complex compounded indicators. For example, we can create Moving Average Convergence/Divergence (MACD) indicators by chaining all required operations in a sequence:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"vscode": {
"languageId": "polyglot-notebook"
}
},
"outputs": [],
"source": [
"Yahoo_Feed aapl = new(\"AAPL\", 100);\n",
"TSeries close = aapl.Close; // close will get data from history\n",
"EMA_Series slow = new(close,26); // slow gets data from slow through pub-sub eventing\n",
"EMA_Series fast = new(close,12); // fast gets data from slow (via eventing)\n",
"SUB_Series macd = new(fast,slow); // macd is a SUBtraction: fast-slow\n",
"EMA_Series signal = new(macd,9); // signal is EMA of macd\n",
"SUB_Series histogram = new(macd, signal); // histogram is SUBtraction macd-signal\n",
"\n",
"histogram.v\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".NET (C#)",
"language": "C#",
"name": ".net-csharp"
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{
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{
"cell_type": "markdown",
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"source": [
"# Quick Start\n",
"\n",
"In order to use this .NET Interactive Notebook and play along with QuanTAlib (outside of making your own app or plugging QuanTAlib into Quantower platform), you will need:\n",
"\n",
"- Installed <a href=\"https://code.visualstudio.com/\" target=\"_blank\">Visual Studio Code</a>\n",
"- Installed <a href=\"https://dotnet.microsoft.com/download/dotnet/6.0\" target=\"_blank\">.NET 6 SDK</a>\n",
"- Installed <a href=\"https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.dotnet-interactive-vscode\" target=\"_blank\">.NET Interactive Notebooks</a> extension\n",
"\n",
"**For impatient**, here is a simple example of calculating three moving averages - SMA(data), EMA(SMA(data)) and WMA(EMA(SMA(data))) from 10 days of AAPL stock data using QuanTAlib:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"vscode": {
"languageId": "polyglot-notebook"
}
},
"outputs": [],
"source": [
"#r \"nuget:QuanTAlib;\"\n",
"using QuanTAlib;\n",
"\n",
"Yahoo_Feed aapl = new(\"AAPL\", 10);\n",
"TSeries data = aapl.Close;\n",
"SMA_Series sma = new(source: data, period: 5, useNaN: false);\n",
"EMA_Series ema = new(sma, period: 5); // by default, indicators expose all data, no NaN values\n",
"WMA_Series wma = new(ema, 5, useNaN: true); // for the final calculation we can hide early data with NaNs\n",
"\n",
"Console.Write($\"index\\t data\\t\\t sma(data)\\t ema(sma(data))\\t wma(ema(sma(data)))\\n\");\n",
"for (int i=0; i<aapl.Count; i++)\n",
" Console.Write($\"{i}\\t {data[i].t:yyyy-MM-dd}\\t {sma[i].v:f2}\\t\\t {ema[i].v:f2}\\t\\t {wma[i].v:f2}\\n\");"
]
},
{
"cell_type": "markdown",
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"source": [
"## Understanding QuanTAlib data model\n",
"\n",
"QuanTAlib expects that every data item is a tuple (TimeDate t, double v) and TSeries is a list of (t,v) tuples. There are several helpers built into the TSeries class to simplify adding elements:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"vscode": {
"languageId": "polyglot-notebook"
}
},
"outputs": [],
"source": [
"var item1 = (DateTime.Today, 105.3); // (DateTime, Value) tuple\n",
"double item2 = 293.1; // a simple double\n",
"\n",
"TSeries data = new();\n",
"data.Add(item1); // adding tuple variable\n",
"data.Add(item2); // QuanTAlib stamps the (double) with current time\n",
"data.Add(0); // directly adding a number (stamped with current time)\n",
"data.Add((DateTime.Now.AddDays(-3), 10)); // adding a tuple with timestamp 3 days ago\n",
"\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"source": [
"TSeries list can display only values (without timestamps) or only timestamps (without values) by using `.v` or `.t` properties"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"vscode": {
"languageId": "polyglot-notebook"
}
},
"outputs": [],
"source": [
"data.v"
]
},
{
"cell_type": "markdown",
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"source": [
"The last element on the list can be accessed by .Last() or by [^1] - and using `.t` (time) and `.v` (value) properties. Also, casting a TSeries into (double) will return the value of the last element"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"vscode": {
"languageId": "polyglot-notebook"
}
},
"outputs": [],
"source": [
"bool IsTheSame = data.Last().v == data[^1].v;\n",
"double lastvalue = data;\n",
"\n",
"lastvalue"
]
},
{
"cell_type": "markdown",
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"source": [
"All indicators are just modified TSeries classes; they get all required input during class construction (source of the datafeed, period...) and they automatically subscribe to events of the datafeed. Whenever datafeed gets a new value, indicator will calculate its own value. Indicators are also event publishers, so other indicators can subscribe to their results, chaining indicators together:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"vscode": {
"languageId": "polyglot-notebook"
}
},
"outputs": [],
"source": [
"TSeries t1 = new() {0,1,2,3,4,5,6,7,8,9}; // t1 is loaded with data and activated as a publisher\n",
"EMA_Series t2 = new(t1, 3); // t2 will auto-load all history of t1 and wait for events from t1\n",
"ADD_Series t3 = new(t1, t2); // t3 is an ADDition of t1 and t2 - will also load history and wait for t2 events\n",
"DIV_Series t4 = new(1, t3); // t4 is calculating 1/t3 - and waiting for t3 events\n",
"\n",
"TSeries t5 = new(); // a wild indicator appeared! And it is empty!\n",
"t4.Pub += t5.Sub; // let us add a manual subscription to events coming from t4 - t5 is now listening to t4\n",
"t1.Add(0); // we add one new value to t1 - and trigger the full cascade of calculation! t5 is now full!\n",
"\n",
"t5.v"
]
},
{
"cell_type": "markdown",
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"source": [
"# MACD compounded indicator\n",
"\n",
"With QuanTAlib we can chain indicators together, creating complex compounded indicators. For example, we can create Moving Average Convergence/Divergence (MACD) indicators by chaining all required operations in a sequence:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"vscode": {
"languageId": "polyglot-notebook"
}
},
"outputs": [],
"source": [
"Yahoo_Feed aapl = new(\"AAPL\", 100);\n",
"TSeries close = aapl.Close; // close will get data from history\n",
"EMA_Series slow = new(close,26); // slow gets data from slow through pub-sub eventing\n",
"EMA_Series fast = new(close,12); // fast gets data from slow (via eventing)\n",
"SUB_Series macd = new(fast,slow); // macd is a SUBtraction: fast-slow\n",
"EMA_Series signal = new(macd,9); // signal is EMA of macd\n",
"SUB_Series histogram = new(macd, signal); // histogram is SUBtraction macd-signal\n",
"\n",
"histogram.v\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".NET (C#)",
"language": "C#",
"name": ".net-csharp"
},
"polyglot_notebook": {
"kernelInfo": {
"defaultKernelName": "csharp",
"items": [
{
"aliases": [
"c#",
"C#"
],
"languageName": "C#",
"name": "csharp"
},
{
"aliases": [
"frontend"
],
"languageName": null,
"name": "vscode"
},
{
"aliases": [],
"languageName": "KQL",
"name": "kql"
}
]
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
docs/img/EMA_chart.ipynb
+251 -251
View File
@@ -1,251 +1,251 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div><div></div><div></div><div></div></div>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Loading extensions from `C:\\Users\\miha\\.nuget\\packages\\plotly.net.interactive\\3.0.2\\interactive-extensions\\dotnet\\Plotly.NET.Interactive.dll`"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"//#r \"nuget: QuanTAlib;\"\n",
"\n",
"#r \"nuget: Plotly.NET;\"\n",
"#r \"nuget: Plotly.NET.Interactive;\"\n",
"#r \"nuget: Plotly.NET.ImageExport;\"\n",
"#r \"..\\..\\Source\\bin\\Debug\\net6.0\\QuanTAlib.dll\"\n",
"\n",
"using QuanTAlib;\n",
"using Plotly.NET;\n",
"using Plotly.NET.LayoutObjects;\n",
"using Plotly.NET.ImageExport;"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"outputs": [],
"source": [
"TSeries d1a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};\n",
"TSeries d2a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1};\n",
"TSeries d3a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};\n",
"TSeries d4a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,33,32,31,30,29,28,27,26,25,24,23,22,21,20,19,18,17,16,15,14,13,12,11,10,9,8,7,6,5,4,3,2};\n",
"TSeries d5a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0.32,0.56,0.72,0.84,0.93,0.99,1,0.97,0.91,0.81,0.68,0.52,0.33,0.14,-0.06,-0.26,-0.44,-0.61,-0.76,-0.87,-0.95,-0.99,-1,-0.96,-0.88,-0.77,-0.63,-0.46,-0.28,-0.08,0.12,0.31,0.49,0.66,0.79,0.9,0.97,1,0.99,0.94,0.85,0.73,0.58,0.41,0.22,0.02,-0.17,-0.37,-0.54,-0.7,-0.83,-0.92,-0.98,-1,-0.98,-0.92,-0.82,-0.69,-0.54,-0.36,-0.17,0.03,0.23,0.42,0.59,0.74};\n",
"TSeries d6a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1};\n",
"TSeries d7a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0.93,0.27,-0.59,-1,-0.71,0.05,0.75,1,0.67,0,-0.67,-0.99,-0.85,-0.34,0.31,0.81,1,0.82,0.35,-0.22,-0.71,-0.98,-0.95,-0.66,-0.2,0.31,0.72,0.96,0.98,0.78,0.43,-0.01,-0.43,-0.77,-0.96,-0.99,-0.85,-0.58,-0.23,0.16,0.51,0.79,0.95,1,0.92,0.73,0.47,0.15,-0.17,-0.47,-0.72,-0.9,-0.99,-0.99,-0.9,-0.74,-0.52,-0.26,0.01,0.28,0.53,0.73,0.88,0.97,1,0.97};\n",
"TSeries d8a = new() {-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,0.03,-0.4,-0.47,0.19,-0.4,-0.23,0.31,0.41,0.19,0.16,-0.5,-0.31,-0.21,0.25,0.18,-0.48,-0.1,0.38,0.29,-0.38,-0.08,-0.21,0.34,0.01,-0.46,0.28,-0.48,0.11,0.02,-0.37,0.19,-0.2,0.1,0.24,0.08,-0.22,-0.12,0.15,0.36,-0.43,-0.03,-0.32,0.45,-0.5,-0.04,-0.04,-0.08,-0.18,0.13,-0.33,-0.19,0.36,-0.39,0.2,-0.31,0.28,-0.13,-0.07,-0.29,0.37,0.03,-0.25,-0.06,-0.3,-0.08,-0.09};\n",
"TSeries d9a = new() {-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,0,0.03,0.11,-0.1,-0.43,-0.08,0.36,-0.04,-0.04,-0.21,-0.3,0.26,0.2,0.28,0.2,0.27,-0.01,-0.1,-0.23,-0.13,-0.41,-0.23,-0.07,-0.21,0.32,-0.18,-0.48,0.3,0.46,-0.2,0.52,-0.81,-0.25,-0.21,-0.12,-0.18,0.18,0.52,0.29,0.44,0.18,-1.2,0.38,0.24,0.06,0.28,0.34,0.3,-0.13,0.19,-0.5,0.59,-0.36,0.22,-0.23,0.24,0.39,0.13,-0.33,-0.57,-0.23,0.49,-0.13,0.76,0.59,0.61};\n",
"TSeries d10a = new() {-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0,-0.28,0.41,-0.54,0.65,-0.75,0.84,-0.91,0.96,-0.99,1,-0.99,0.96,-0.92,0.85,-0.77,0.67,-0.56,0.44,-0.3,0.17,-0.03,-0.11,0.25,-0.39,0.51,-0.63,0.73,-0.82,0.89,-0.95,0.98,-1,0.99,-0.97,0.93,-0.86,0.78,-0.69,0.58,-0.46,0.33,-0.19,0.05,0.09,-0.23,0.36,-0.49,0.61,-0.71,0.81,-0.88,0.94,-0.98,1,-1,0.98,-0.94,0.88,-0.8,0.71,-0.6,0.48,-0.35,0.22,-0.08,-0.06};\n",
"TSeries d11a = new() {-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,0,0.14,-0.76,-0.96,-0.28,0.66,0.99,0.41,-0.54,-1,-0.54,0.42,0.99,0.65,-0.29,-0.96,-0.75,0.15,0.91,0.84,-0.01,-0.85,-0.91,-0.13,0.76,0.96,0.27,-0.66,-0.99,-0.4,0.55,1,0.53,-0.43,-0.99,-0.64,0.3,0.96,0.75,-0.16,-0.92,-0.83,0.02,0.85,0.9,0.12,-0.77,-0.95,-0.26,0.67,0.99,0.4,-0.56,-1,-0.52,0.44,0.99,0.64,-0.3,-0.97,-0.74,0.17,0.92,0.83,-0.03,-0.86};\n",
"TSeries d12a = new() {-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0,0,0.05,-0.25,-0.32,-0.09,0.22,0.33,0.14,-0.18,-0.33,-0.18,0.14,0.33,0.22,-0.1,-0.32,-0.25,0.05,0.3,0.28,0,-0.28,-0.3,-0.04,0.25,0.32,0.09,-0.22,-0.33,-0.13,0.18,0.33,0.18,0.86,0.67,0.79,1.1,1.32,1.25,0.95,0.69,0.72,1.01,1.28,1.3,1.04,0.74,0.68,0.91,1.22,1.33,1.13,0.81,0.67,0.83,1.15,1.33,1.21,0.9,0.68,0.75,1.06,1.31,1.28,0.99,0.71};\n",
"TSeries d13a = new() {-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0,0,2.7,-0.8,-0.8,3.6,9.3,11.95,10.05,6.3,5,8.3,14.1,17.95,17.25,13.55,11.2,13.25,18.75,23.55,24.2,20.95,17.75,18.45,23.35,28.8,30.8,28.35,24.7,24.05,28,33.75,37,35.65,31.85,28.05,-3.2,1.5,4.8,3.75,-0.8,-4.6,-4.15,0.1,4.25,4.5,0.6,-3.85,-4.75,-1.3,3.35,4.95,2,-2.8,-5,-2.6,2.2,4.95,3.2,-1.5,-4.85,-3.7,0.85,4.6,4.15,-0.15,-4.3};\n",
"TSeries d14a = new() {-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0,0,0.59,0.83,0.74,0.5,0.91,1.36,0.93,0.87,0.6,0.38,0.78,0.53,0.42,0.14,0.01,-0.45,-0.71,-0.99,-1,-1.36,-1.22,-1.07,-1.17,-0.56,-0.95,-1.11,-0.16,0.18,-0.28,0.64,-0.5,0.24,0.45,0.67,0.72,1.15,1.52,1.28,1.38,1.03,-0.47,0.96,0.65,0.28,0.3,0.17,-0.07,-0.67,-0.51,-1.33,-0.33,-1.34,-0.78,-1.21,-0.68,-0.43,-0.56,-0.87,-0.93,-0.4,0.52,0.1,1.18,1.18,1.35};\n",
"TSeries d15a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,1.3,0.3,-0.48,-1.1,-1.14,-0.03,1.11,0.96,0.63,-0.21,-0.97,-0.73,-0.65,-0.06,0.51,1.08,0.99,0.72,0.12,-0.35,-1.12,-1.21,-1.02,-0.87,0.12,0.13,0.24,1.26,1.44,0.58,0.95,-0.82,-0.68,-0.98,-1.08,-1.17,-0.67,-0.06,0.06,0.6,0.69,-0.41,1.33,1.24,0.98,1.01,0.81,0.45,-0.3,-0.28,-1.22,-0.31,-1.35,-0.77,-1.13,-0.5,-0.13,-0.13,-0.32,-0.29,0.3,1.22,0.75,1.73,1.59,1.58};\n",
"TSeries d16a = new() {175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.44,176.27,176.04,176.99,175.49,175.68,174.34,176.4,174.05,174.4,174.2,176.16,175,177.72,174.33,176.96,174.62,174.76,170.9,171.12,171.05,170.01,169.24,172.64,171.96,175.72,174.16,175.81,177.3,178.38,176.75,177.19,175.55,178.49,176.52,178.45,178.04,178.25,177.8,176.97,172.94,174.92,173.98,172.29,171.19,172.54,172.11,175.32,175.63,176.65,173.8,176.04,172.74,175.24,171.84,171.54,172.17,171.85,172.38,170.78,173.49,173.69,171.71,174.38,173.99,174.83};"
]
},
{
"cell_type": "code",
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"metadata": {
"dotnet_interactive": {
"language": "csharp"
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"polyglot_notebook": {
"kernelName": "csharp"
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"outputs": [],
"source": [
"int period = 10;\n",
"int cut = 26;\n",
"\n",
"EMA_Series d1b = new(d1a, period);\n",
"EMA_Series d2b = new(d2a, period);\n",
"EMA_Series d3b = new(d3a, period);\n",
"EMA_Series d4b = new(d4a, period);\n",
"EMA_Series d5b = new(d5a, period);\n",
"EMA_Series d6b = new(d6a, period);\n",
"EMA_Series d7b = new(d7a, period);\n",
"EMA_Series d8b = new(d8a, period);\n",
"EMA_Series d9b = new(d9a, period);\n",
"EMA_Series d10b = new(d10a, period);\n",
"EMA_Series d11b = new(d11a, period);\n",
"EMA_Series d12b = new(d12a, period);\n",
"EMA_Series d13b = new(d13a, period);\n",
"EMA_Series d14b = new(d14a, period);\n",
"EMA_Series d15b = new(d15a, period);\n",
"EMA_Series d16b = new(d16a, period);\n",
"\n",
"List<int> x = Enumerable.Range(-cut,96).ToList<int>();\n",
"GenericChart.GenericChart ch1a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d1a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch1b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d1b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch2a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d2a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch2b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d2b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch3a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d3a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch3b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d3b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch4a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d4a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch4b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d4b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch5a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d5a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch5b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d5b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch6a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d6a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch6b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d6b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch7a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d7a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch7b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d7b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch8a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d8a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch8b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d8b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch9a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d9a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch9b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d9b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch10a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d10a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch10b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d10b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch11a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d11a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch11b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d11b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch12a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d12a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch12b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d12b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch13a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d13a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch13b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d13b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch14a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d14a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch14b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d14b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch15a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d15a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch15b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d15b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch16a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d16a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch16b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d16b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"\n",
"var ch1 = Chart.Combine(new []{ch1a,ch1b});\n",
"var ch2 = Chart.Combine(new []{ch2a,ch2b});\n",
"var ch3 = Chart.Combine(new []{ch3a,ch3b});\n",
"var ch4 = Chart.Combine(new []{ch4a,ch4b});\n",
"var ch5 = Chart.Combine(new []{ch5a,ch5b});\n",
"var ch6 = Chart.Combine(new []{ch6a,ch6b});\n",
"var ch7 = Chart.Combine(new []{ch7a,ch7b});\n",
"var ch8 = Chart.Combine(new []{ch8a,ch8b});\n",
"var ch9 = Chart.Combine(new []{ch9a,ch9b});\n",
"var ch10 = Chart.Combine(new []{ch10a,ch10b});\n",
"var ch11 = Chart.Combine(new []{ch11a,ch11b});\n",
"var ch12 = Chart.Combine(new []{ch12a,ch12b});\n",
"var ch13 = Chart.Combine(new []{ch13a,ch13b});\n",
"var ch14 = Chart.Combine(new []{ch14a,ch14b});\n",
"var ch15 = Chart.Combine(new []{ch15a,ch15b});\n",
"var ch16 = Chart.Combine(new []{ch16a,ch16b});\n",
"\n",
"Layout layout = new Layout(); layout.SetValue(\"showlegend\",false);\n",
"var chart1 = new []{ch1,ch2,ch3,ch4,ch5,ch6,ch7,ch8,ch9,ch10,ch11,ch12,ch13,ch14,ch15,ch16};\n",
"var full = Chart.Grid<IEnumerable<GenericChart.GenericChart>>(8,2).Invoke(chart1).WithSize(1000,2200).WithMargin(Margin.init<int, int, int, int, int, bool>(30,20,20,30,7,false)).WithLayout(layout);\n",
"full.SaveSVG(\"EMA_chart\", Width: 1000, Height: 2200);"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".NET (C#)",
"language": "C#",
"name": ".net-csharp"
},
"polyglot_notebook": {
"kernelInfo": {
"defaultKernelName": "csharp",
"items": [
{
"aliases": [
"c#",
"C#"
],
"languageName": "C#",
"name": "csharp"
},
{
"aliases": [],
"name": ".NET"
},
{
"aliases": [
"f#",
"F#"
],
"languageName": "F#",
"name": "fsharp"
},
{
"aliases": [],
"languageName": "HTML",
"name": "html"
},
{
"aliases": [],
"languageName": "KQL",
"name": "kql"
},
{
"aliases": [],
"languageName": "Mermaid",
"name": "mermaid"
},
{
"aliases": [
"powershell"
],
"languageName": "PowerShell",
"name": "pwsh"
},
{
"aliases": [],
"languageName": "SQL",
"name": "sql"
},
{
"aliases": [],
"name": "value"
},
{
"aliases": [
"frontend"
],
"name": "vscode"
},
{
"aliases": [
"js"
],
"languageName": "JavaScript",
"name": "javascript"
},
{
"aliases": [],
"name": "webview"
}
]
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div><div></div><div></div><div></div></div>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Loading extensions from `C:\\Users\\miha\\.nuget\\packages\\plotly.net.interactive\\3.0.2\\interactive-extensions\\dotnet\\Plotly.NET.Interactive.dll`"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"//#r \"nuget: QuanTAlib;\"\n",
"\n",
"#r \"nuget: Plotly.NET;\"\n",
"#r \"nuget: Plotly.NET.Interactive;\"\n",
"#r \"nuget: Plotly.NET.ImageExport;\"\n",
"#r \"..\\..\\Source\\bin\\Debug\\net6.0\\QuanTAlib.dll\"\n",
"\n",
"using QuanTAlib;\n",
"using Plotly.NET;\n",
"using Plotly.NET.LayoutObjects;\n",
"using Plotly.NET.ImageExport;"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"outputs": [],
"source": [
"TSeries d1a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};\n",
"TSeries d2a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1};\n",
"TSeries d3a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};\n",
"TSeries d4a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,33,32,31,30,29,28,27,26,25,24,23,22,21,20,19,18,17,16,15,14,13,12,11,10,9,8,7,6,5,4,3,2};\n",
"TSeries d5a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0.32,0.56,0.72,0.84,0.93,0.99,1,0.97,0.91,0.81,0.68,0.52,0.33,0.14,-0.06,-0.26,-0.44,-0.61,-0.76,-0.87,-0.95,-0.99,-1,-0.96,-0.88,-0.77,-0.63,-0.46,-0.28,-0.08,0.12,0.31,0.49,0.66,0.79,0.9,0.97,1,0.99,0.94,0.85,0.73,0.58,0.41,0.22,0.02,-0.17,-0.37,-0.54,-0.7,-0.83,-0.92,-0.98,-1,-0.98,-0.92,-0.82,-0.69,-0.54,-0.36,-0.17,0.03,0.23,0.42,0.59,0.74};\n",
"TSeries d6a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1};\n",
"TSeries d7a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0.93,0.27,-0.59,-1,-0.71,0.05,0.75,1,0.67,0,-0.67,-0.99,-0.85,-0.34,0.31,0.81,1,0.82,0.35,-0.22,-0.71,-0.98,-0.95,-0.66,-0.2,0.31,0.72,0.96,0.98,0.78,0.43,-0.01,-0.43,-0.77,-0.96,-0.99,-0.85,-0.58,-0.23,0.16,0.51,0.79,0.95,1,0.92,0.73,0.47,0.15,-0.17,-0.47,-0.72,-0.9,-0.99,-0.99,-0.9,-0.74,-0.52,-0.26,0.01,0.28,0.53,0.73,0.88,0.97,1,0.97};\n",
"TSeries d8a = new() {-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,0.03,-0.4,-0.47,0.19,-0.4,-0.23,0.31,0.41,0.19,0.16,-0.5,-0.31,-0.21,0.25,0.18,-0.48,-0.1,0.38,0.29,-0.38,-0.08,-0.21,0.34,0.01,-0.46,0.28,-0.48,0.11,0.02,-0.37,0.19,-0.2,0.1,0.24,0.08,-0.22,-0.12,0.15,0.36,-0.43,-0.03,-0.32,0.45,-0.5,-0.04,-0.04,-0.08,-0.18,0.13,-0.33,-0.19,0.36,-0.39,0.2,-0.31,0.28,-0.13,-0.07,-0.29,0.37,0.03,-0.25,-0.06,-0.3,-0.08,-0.09};\n",
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"TSeries d11a = new() {-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,0,0.14,-0.76,-0.96,-0.28,0.66,0.99,0.41,-0.54,-1,-0.54,0.42,0.99,0.65,-0.29,-0.96,-0.75,0.15,0.91,0.84,-0.01,-0.85,-0.91,-0.13,0.76,0.96,0.27,-0.66,-0.99,-0.4,0.55,1,0.53,-0.43,-0.99,-0.64,0.3,0.96,0.75,-0.16,-0.92,-0.83,0.02,0.85,0.9,0.12,-0.77,-0.95,-0.26,0.67,0.99,0.4,-0.56,-1,-0.52,0.44,0.99,0.64,-0.3,-0.97,-0.74,0.17,0.92,0.83,-0.03,-0.86};\n",
"TSeries d12a = new() {-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0,0,0.05,-0.25,-0.32,-0.09,0.22,0.33,0.14,-0.18,-0.33,-0.18,0.14,0.33,0.22,-0.1,-0.32,-0.25,0.05,0.3,0.28,0,-0.28,-0.3,-0.04,0.25,0.32,0.09,-0.22,-0.33,-0.13,0.18,0.33,0.18,0.86,0.67,0.79,1.1,1.32,1.25,0.95,0.69,0.72,1.01,1.28,1.3,1.04,0.74,0.68,0.91,1.22,1.33,1.13,0.81,0.67,0.83,1.15,1.33,1.21,0.9,0.68,0.75,1.06,1.31,1.28,0.99,0.71};\n",
"TSeries d13a = new() {-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0,0,2.7,-0.8,-0.8,3.6,9.3,11.95,10.05,6.3,5,8.3,14.1,17.95,17.25,13.55,11.2,13.25,18.75,23.55,24.2,20.95,17.75,18.45,23.35,28.8,30.8,28.35,24.7,24.05,28,33.75,37,35.65,31.85,28.05,-3.2,1.5,4.8,3.75,-0.8,-4.6,-4.15,0.1,4.25,4.5,0.6,-3.85,-4.75,-1.3,3.35,4.95,2,-2.8,-5,-2.6,2.2,4.95,3.2,-1.5,-4.85,-3.7,0.85,4.6,4.15,-0.15,-4.3};\n",
"TSeries d14a = new() {-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0,0,0.59,0.83,0.74,0.5,0.91,1.36,0.93,0.87,0.6,0.38,0.78,0.53,0.42,0.14,0.01,-0.45,-0.71,-0.99,-1,-1.36,-1.22,-1.07,-1.17,-0.56,-0.95,-1.11,-0.16,0.18,-0.28,0.64,-0.5,0.24,0.45,0.67,0.72,1.15,1.52,1.28,1.38,1.03,-0.47,0.96,0.65,0.28,0.3,0.17,-0.07,-0.67,-0.51,-1.33,-0.33,-1.34,-0.78,-1.21,-0.68,-0.43,-0.56,-0.87,-0.93,-0.4,0.52,0.1,1.18,1.18,1.35};\n",
"TSeries d15a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,1.3,0.3,-0.48,-1.1,-1.14,-0.03,1.11,0.96,0.63,-0.21,-0.97,-0.73,-0.65,-0.06,0.51,1.08,0.99,0.72,0.12,-0.35,-1.12,-1.21,-1.02,-0.87,0.12,0.13,0.24,1.26,1.44,0.58,0.95,-0.82,-0.68,-0.98,-1.08,-1.17,-0.67,-0.06,0.06,0.6,0.69,-0.41,1.33,1.24,0.98,1.01,0.81,0.45,-0.3,-0.28,-1.22,-0.31,-1.35,-0.77,-1.13,-0.5,-0.13,-0.13,-0.32,-0.29,0.3,1.22,0.75,1.73,1.59,1.58};\n",
"TSeries d16a = new() {175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.44,176.27,176.04,176.99,175.49,175.68,174.34,176.4,174.05,174.4,174.2,176.16,175,177.72,174.33,176.96,174.62,174.76,170.9,171.12,171.05,170.01,169.24,172.64,171.96,175.72,174.16,175.81,177.3,178.38,176.75,177.19,175.55,178.49,176.52,178.45,178.04,178.25,177.8,176.97,172.94,174.92,173.98,172.29,171.19,172.54,172.11,175.32,175.63,176.65,173.8,176.04,172.74,175.24,171.84,171.54,172.17,171.85,172.38,170.78,173.49,173.69,171.71,174.38,173.99,174.83};"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"outputs": [],
"source": [
"int period = 10;\n",
"int cut = 26;\n",
"\n",
"EMA_Series d1b = new(d1a, period);\n",
"EMA_Series d2b = new(d2a, period);\n",
"EMA_Series d3b = new(d3a, period);\n",
"EMA_Series d4b = new(d4a, period);\n",
"EMA_Series d5b = new(d5a, period);\n",
"EMA_Series d6b = new(d6a, period);\n",
"EMA_Series d7b = new(d7a, period);\n",
"EMA_Series d8b = new(d8a, period);\n",
"EMA_Series d9b = new(d9a, period);\n",
"EMA_Series d10b = new(d10a, period);\n",
"EMA_Series d11b = new(d11a, period);\n",
"EMA_Series d12b = new(d12a, period);\n",
"EMA_Series d13b = new(d13a, period);\n",
"EMA_Series d14b = new(d14a, period);\n",
"EMA_Series d15b = new(d15a, period);\n",
"EMA_Series d16b = new(d16a, period);\n",
"\n",
"List<int> x = Enumerable.Range(-cut,96).ToList<int>();\n",
"GenericChart.GenericChart ch1a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d1a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch1b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d1b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch2a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d2a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch2b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d2b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch3a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d3a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch3b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d3b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch4a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d4a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch4b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d4b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch5a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d5a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch5b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d5b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch6a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d6a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch6b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d6b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch7a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d7a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch7b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d7b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch8a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d8a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch8b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d8b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch9a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d9a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch9b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d9b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch10a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d10a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch10b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d10b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch11a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d11a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch11b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d11b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch12a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d12a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch12b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d12b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch13a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d13a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch13b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d13b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch14a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d14a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch14b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d14b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch15a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d15a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch15b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d15b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch16a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d16a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch16b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d16b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"\n",
"var ch1 = Chart.Combine(new []{ch1a,ch1b});\n",
"var ch2 = Chart.Combine(new []{ch2a,ch2b});\n",
"var ch3 = Chart.Combine(new []{ch3a,ch3b});\n",
"var ch4 = Chart.Combine(new []{ch4a,ch4b});\n",
"var ch5 = Chart.Combine(new []{ch5a,ch5b});\n",
"var ch6 = Chart.Combine(new []{ch6a,ch6b});\n",
"var ch7 = Chart.Combine(new []{ch7a,ch7b});\n",
"var ch8 = Chart.Combine(new []{ch8a,ch8b});\n",
"var ch9 = Chart.Combine(new []{ch9a,ch9b});\n",
"var ch10 = Chart.Combine(new []{ch10a,ch10b});\n",
"var ch11 = Chart.Combine(new []{ch11a,ch11b});\n",
"var ch12 = Chart.Combine(new []{ch12a,ch12b});\n",
"var ch13 = Chart.Combine(new []{ch13a,ch13b});\n",
"var ch14 = Chart.Combine(new []{ch14a,ch14b});\n",
"var ch15 = Chart.Combine(new []{ch15a,ch15b});\n",
"var ch16 = Chart.Combine(new []{ch16a,ch16b});\n",
"\n",
"Layout layout = new Layout(); layout.SetValue(\"showlegend\",false);\n",
"var chart1 = new []{ch1,ch2,ch3,ch4,ch5,ch6,ch7,ch8,ch9,ch10,ch11,ch12,ch13,ch14,ch15,ch16};\n",
"var full = Chart.Grid<IEnumerable<GenericChart.GenericChart>>(8,2).Invoke(chart1).WithSize(1000,2200).WithMargin(Margin.init<int, int, int, int, int, bool>(30,20,20,30,7,false)).WithLayout(layout);\n",
"full.SaveSVG(\"EMA_chart\", Width: 1000, Height: 2200);"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".NET (C#)",
"language": "C#",
"name": ".net-csharp"
},
"polyglot_notebook": {
"kernelInfo": {
"defaultKernelName": "csharp",
"items": [
{
"aliases": [
"c#",
"C#"
],
"languageName": "C#",
"name": "csharp"
},
{
"aliases": [],
"name": ".NET"
},
{
"aliases": [
"f#",
"F#"
],
"languageName": "F#",
"name": "fsharp"
},
{
"aliases": [],
"languageName": "HTML",
"name": "html"
},
{
"aliases": [],
"languageName": "KQL",
"name": "kql"
},
{
"aliases": [],
"languageName": "Mermaid",
"name": "mermaid"
},
{
"aliases": [
"powershell"
],
"languageName": "PowerShell",
"name": "pwsh"
},
{
"aliases": [],
"languageName": "SQL",
"name": "sql"
},
{
"aliases": [],
"name": "value"
},
{
"aliases": [
"frontend"
],
"name": "vscode"
},
{
"aliases": [
"js"
],
"languageName": "JavaScript",
"name": "javascript"
},
{
"aliases": [],
"name": "webview"
}
]
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
docs/img/SMA_chart.ipynb
+242 -242
View File
@@ -1,242 +1,242 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div><div></div><div></div><div></div></div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"//#r \"nuget: QuanTAlib;\"\n",
"\n",
"#r \"nuget: Plotly.NET;\"\n",
"#r \"nuget: Plotly.NET.Interactive;\"\n",
"#r \"nuget: Plotly.NET.ImageExport;\"\n",
"#r \"..\\..\\Source\\bin\\Debug\\net6.0\\QuanTAlib.dll\"\n",
"\n",
"using QuanTAlib;\n",
"using Plotly.NET;\n",
"using Plotly.NET.LayoutObjects;\n",
"using Plotly.NET.ImageExport;"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"outputs": [],
"source": [
"TSeries d1a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};\n",
"TSeries d2a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1};\n",
"TSeries d3a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};\n",
"TSeries d4a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,33,32,31,30,29,28,27,26,25,24,23,22,21,20,19,18,17,16,15,14,13,12,11,10,9,8,7,6,5,4,3,2};\n",
"TSeries d5a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0.32,0.56,0.72,0.84,0.93,0.99,1,0.97,0.91,0.81,0.68,0.52,0.33,0.14,-0.06,-0.26,-0.44,-0.61,-0.76,-0.87,-0.95,-0.99,-1,-0.96,-0.88,-0.77,-0.63,-0.46,-0.28,-0.08,0.12,0.31,0.49,0.66,0.79,0.9,0.97,1,0.99,0.94,0.85,0.73,0.58,0.41,0.22,0.02,-0.17,-0.37,-0.54,-0.7,-0.83,-0.92,-0.98,-1,-0.98,-0.92,-0.82,-0.69,-0.54,-0.36,-0.17,0.03,0.23,0.42,0.59,0.74};\n",
"TSeries d6a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1};\n",
"TSeries d7a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0.93,0.27,-0.59,-1,-0.71,0.05,0.75,1,0.67,0,-0.67,-0.99,-0.85,-0.34,0.31,0.81,1,0.82,0.35,-0.22,-0.71,-0.98,-0.95,-0.66,-0.2,0.31,0.72,0.96,0.98,0.78,0.43,-0.01,-0.43,-0.77,-0.96,-0.99,-0.85,-0.58,-0.23,0.16,0.51,0.79,0.95,1,0.92,0.73,0.47,0.15,-0.17,-0.47,-0.72,-0.9,-0.99,-0.99,-0.9,-0.74,-0.52,-0.26,0.01,0.28,0.53,0.73,0.88,0.97,1,0.97};\n",
"TSeries d8a = new() {-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,0.03,-0.4,-0.47,0.19,-0.4,-0.23,0.31,0.41,0.19,0.16,-0.5,-0.31,-0.21,0.25,0.18,-0.48,-0.1,0.38,0.29,-0.38,-0.08,-0.21,0.34,0.01,-0.46,0.28,-0.48,0.11,0.02,-0.37,0.19,-0.2,0.1,0.24,0.08,-0.22,-0.12,0.15,0.36,-0.43,-0.03,-0.32,0.45,-0.5,-0.04,-0.04,-0.08,-0.18,0.13,-0.33,-0.19,0.36,-0.39,0.2,-0.31,0.28,-0.13,-0.07,-0.29,0.37,0.03,-0.25,-0.06,-0.3,-0.08,-0.09};\n",
"TSeries d9a = new() {-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,0,0.03,0.11,-0.1,-0.43,-0.08,0.36,-0.04,-0.04,-0.21,-0.3,0.26,0.2,0.28,0.2,0.27,-0.01,-0.1,-0.23,-0.13,-0.41,-0.23,-0.07,-0.21,0.32,-0.18,-0.48,0.3,0.46,-0.2,0.52,-0.81,-0.25,-0.21,-0.12,-0.18,0.18,0.52,0.29,0.44,0.18,-1.2,0.38,0.24,0.06,0.28,0.34,0.3,-0.13,0.19,-0.5,0.59,-0.36,0.22,-0.23,0.24,0.39,0.13,-0.33,-0.57,-0.23,0.49,-0.13,0.76,0.59,0.61};\n",
"TSeries d10a = new() {-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0,-0.28,0.41,-0.54,0.65,-0.75,0.84,-0.91,0.96,-0.99,1,-0.99,0.96,-0.92,0.85,-0.77,0.67,-0.56,0.44,-0.3,0.17,-0.03,-0.11,0.25,-0.39,0.51,-0.63,0.73,-0.82,0.89,-0.95,0.98,-1,0.99,-0.97,0.93,-0.86,0.78,-0.69,0.58,-0.46,0.33,-0.19,0.05,0.09,-0.23,0.36,-0.49,0.61,-0.71,0.81,-0.88,0.94,-0.98,1,-1,0.98,-0.94,0.88,-0.8,0.71,-0.6,0.48,-0.35,0.22,-0.08,-0.06};\n",
"TSeries d11a = new() {-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,0,0.14,-0.76,-0.96,-0.28,0.66,0.99,0.41,-0.54,-1,-0.54,0.42,0.99,0.65,-0.29,-0.96,-0.75,0.15,0.91,0.84,-0.01,-0.85,-0.91,-0.13,0.76,0.96,0.27,-0.66,-0.99,-0.4,0.55,1,0.53,-0.43,-0.99,-0.64,0.3,0.96,0.75,-0.16,-0.92,-0.83,0.02,0.85,0.9,0.12,-0.77,-0.95,-0.26,0.67,0.99,0.4,-0.56,-1,-0.52,0.44,0.99,0.64,-0.3,-0.97,-0.74,0.17,0.92,0.83,-0.03,-0.86};\n",
"TSeries d12a = new() {-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0,0,0.05,-0.25,-0.32,-0.09,0.22,0.33,0.14,-0.18,-0.33,-0.18,0.14,0.33,0.22,-0.1,-0.32,-0.25,0.05,0.3,0.28,0,-0.28,-0.3,-0.04,0.25,0.32,0.09,-0.22,-0.33,-0.13,0.18,0.33,0.18,0.86,0.67,0.79,1.1,1.32,1.25,0.95,0.69,0.72,1.01,1.28,1.3,1.04,0.74,0.68,0.91,1.22,1.33,1.13,0.81,0.67,0.83,1.15,1.33,1.21,0.9,0.68,0.75,1.06,1.31,1.28,0.99,0.71};\n",
"TSeries d13a = new() {-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0,0,2.7,-0.8,-0.8,3.6,9.3,11.95,10.05,6.3,5,8.3,14.1,17.95,17.25,13.55,11.2,13.25,18.75,23.55,24.2,20.95,17.75,18.45,23.35,28.8,30.8,28.35,24.7,24.05,28,33.75,37,35.65,31.85,28.05,-3.2,1.5,4.8,3.75,-0.8,-4.6,-4.15,0.1,4.25,4.5,0.6,-3.85,-4.75,-1.3,3.35,4.95,2,-2.8,-5,-2.6,2.2,4.95,3.2,-1.5,-4.85,-3.7,0.85,4.6,4.15,-0.15,-4.3};\n",
"TSeries d14a = new() {-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0,0,0.59,0.83,0.74,0.5,0.91,1.36,0.93,0.87,0.6,0.38,0.78,0.53,0.42,0.14,0.01,-0.45,-0.71,-0.99,-1,-1.36,-1.22,-1.07,-1.17,-0.56,-0.95,-1.11,-0.16,0.18,-0.28,0.64,-0.5,0.24,0.45,0.67,0.72,1.15,1.52,1.28,1.38,1.03,-0.47,0.96,0.65,0.28,0.3,0.17,-0.07,-0.67,-0.51,-1.33,-0.33,-1.34,-0.78,-1.21,-0.68,-0.43,-0.56,-0.87,-0.93,-0.4,0.52,0.1,1.18,1.18,1.35};\n",
"TSeries d15a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,1.3,0.3,-0.48,-1.1,-1.14,-0.03,1.11,0.96,0.63,-0.21,-0.97,-0.73,-0.65,-0.06,0.51,1.08,0.99,0.72,0.12,-0.35,-1.12,-1.21,-1.02,-0.87,0.12,0.13,0.24,1.26,1.44,0.58,0.95,-0.82,-0.68,-0.98,-1.08,-1.17,-0.67,-0.06,0.06,0.6,0.69,-0.41,1.33,1.24,0.98,1.01,0.81,0.45,-0.3,-0.28,-1.22,-0.31,-1.35,-0.77,-1.13,-0.5,-0.13,-0.13,-0.32,-0.29,0.3,1.22,0.75,1.73,1.59,1.58};\n",
"TSeries d16a = new() {175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.44,176.27,176.04,176.99,175.49,175.68,174.34,176.4,174.05,174.4,174.2,176.16,175,177.72,174.33,176.96,174.62,174.76,170.9,171.12,171.05,170.01,169.24,172.64,171.96,175.72,174.16,175.81,177.3,178.38,176.75,177.19,175.55,178.49,176.52,178.45,178.04,178.25,177.8,176.97,172.94,174.92,173.98,172.29,171.19,172.54,172.11,175.32,175.63,176.65,173.8,176.04,172.74,175.24,171.84,171.54,172.17,171.85,172.38,170.78,173.49,173.69,171.71,174.38,173.99,174.83};"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"outputs": [],
"source": [
"int period = 10;\n",
"int cut = 26;\n",
"\n",
"SMA_Series d1b = new(d1a, period);\n",
"SMA_Series d2b = new(d2a, period);\n",
"SMA_Series d3b = new(d3a, period);\n",
"SMA_Series d4b = new(d4a, period);\n",
"SMA_Series d5b = new(d5a, period);\n",
"SMA_Series d6b = new(d6a, period);\n",
"SMA_Series d7b = new(d7a, period);\n",
"SMA_Series d8b = new(d8a, period);\n",
"SMA_Series d9b = new(d9a, period);\n",
"SMA_Series d10b = new(d10a, period);\n",
"SMA_Series d11b = new(d11a, period);\n",
"SMA_Series d12b = new(d12a, period);\n",
"SMA_Series d13b = new(d13a, period);\n",
"SMA_Series d14b = new(d14a, period);\n",
"SMA_Series d15b = new(d15a, period);\n",
"SMA_Series d16b = new(d16a, period);\n",
"\n",
"List<int> x = Enumerable.Range(-cut,96).ToList<int>();\n",
"GenericChart.GenericChart ch1a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d1a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch1b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d1b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch2a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d2a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch2b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d2b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch3a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d3a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch3b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d3b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch4a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d4a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch4b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d4b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch5a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d5a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch5b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d5b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch6a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d6a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch6b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d6b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch7a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d7a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch7b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d7b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch8a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d8a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch8b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d8b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch9a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d9a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch9b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d9b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch10a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d10a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch10b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d10b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch11a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d11a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch11b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d11b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch12a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d12a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch12b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d12b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch13a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d13a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch13b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d13b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch14a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d14a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch14b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d14b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch15a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d15a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch15b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d15b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch16a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d16a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch16b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d16b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"\n",
"var ch1 = Chart.Combine(new []{ch1a,ch1b});\n",
"var ch2 = Chart.Combine(new []{ch2a,ch2b});\n",
"var ch3 = Chart.Combine(new []{ch3a,ch3b});\n",
"var ch4 = Chart.Combine(new []{ch4a,ch4b});\n",
"var ch5 = Chart.Combine(new []{ch5a,ch5b});\n",
"var ch6 = Chart.Combine(new []{ch6a,ch6b});\n",
"var ch7 = Chart.Combine(new []{ch7a,ch7b});\n",
"var ch8 = Chart.Combine(new []{ch8a,ch8b});\n",
"var ch9 = Chart.Combine(new []{ch9a,ch9b});\n",
"var ch10 = Chart.Combine(new []{ch10a,ch10b});\n",
"var ch11 = Chart.Combine(new []{ch11a,ch11b});\n",
"var ch12 = Chart.Combine(new []{ch12a,ch12b});\n",
"var ch13 = Chart.Combine(new []{ch13a,ch13b});\n",
"var ch14 = Chart.Combine(new []{ch14a,ch14b});\n",
"var ch15 = Chart.Combine(new []{ch15a,ch15b});\n",
"var ch16 = Chart.Combine(new []{ch16a,ch16b});\n",
"\n",
"Layout layout = new Layout(); layout.SetValue(\"showlegend\",false);\n",
"var chart1 = new []{ch1,ch2,ch3,ch4,ch5,ch6,ch7,ch8,ch9,ch10,ch11,ch12,ch13,ch14,ch15,ch16};\n",
"var full = Chart.Grid<IEnumerable<GenericChart.GenericChart>>(8,2).Invoke(chart1).WithSize(1000,2200).WithMargin(Margin.init<int, int, int, int, int, bool>(30,20,20,30,7,false)).WithLayout(layout);\n",
"full.SaveSVG(\"SMA_chart\", Width: 1000, Height: 2200);"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".NET (C#)",
"language": "C#",
"name": ".net-csharp"
},
"polyglot_notebook": {
"kernelInfo": {
"defaultKernelName": "csharp",
"items": [
{
"aliases": [
"c#",
"C#"
],
"languageName": "C#",
"name": "csharp"
},
{
"aliases": [],
"name": ".NET"
},
{
"aliases": [
"f#",
"F#"
],
"languageName": "F#",
"name": "fsharp"
},
{
"aliases": [],
"languageName": "HTML",
"name": "html"
},
{
"aliases": [],
"languageName": "KQL",
"name": "kql"
},
{
"aliases": [],
"languageName": "Mermaid",
"name": "mermaid"
},
{
"aliases": [
"powershell"
],
"languageName": "PowerShell",
"name": "pwsh"
},
{
"aliases": [],
"languageName": "SQL",
"name": "sql"
},
{
"aliases": [],
"name": "value"
},
{
"aliases": [
"frontend"
],
"name": "vscode"
},
{
"aliases": [
"js"
],
"languageName": "JavaScript",
"name": "javascript"
},
{
"aliases": [],
"name": "webview"
}
]
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div><div></div><div></div><div></div></div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"//#r \"nuget: QuanTAlib;\"\n",
"\n",
"#r \"nuget: Plotly.NET;\"\n",
"#r \"nuget: Plotly.NET.Interactive;\"\n",
"#r \"nuget: Plotly.NET.ImageExport;\"\n",
"#r \"..\\..\\Source\\bin\\Debug\\net6.0\\QuanTAlib.dll\"\n",
"\n",
"using QuanTAlib;\n",
"using Plotly.NET;\n",
"using Plotly.NET.LayoutObjects;\n",
"using Plotly.NET.ImageExport;"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"outputs": [],
"source": [
"TSeries d1a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};\n",
"TSeries d2a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1};\n",
"TSeries d3a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};\n",
"TSeries d4a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,33,32,31,30,29,28,27,26,25,24,23,22,21,20,19,18,17,16,15,14,13,12,11,10,9,8,7,6,5,4,3,2};\n",
"TSeries d5a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0.32,0.56,0.72,0.84,0.93,0.99,1,0.97,0.91,0.81,0.68,0.52,0.33,0.14,-0.06,-0.26,-0.44,-0.61,-0.76,-0.87,-0.95,-0.99,-1,-0.96,-0.88,-0.77,-0.63,-0.46,-0.28,-0.08,0.12,0.31,0.49,0.66,0.79,0.9,0.97,1,0.99,0.94,0.85,0.73,0.58,0.41,0.22,0.02,-0.17,-0.37,-0.54,-0.7,-0.83,-0.92,-0.98,-1,-0.98,-0.92,-0.82,-0.69,-0.54,-0.36,-0.17,0.03,0.23,0.42,0.59,0.74};\n",
"TSeries d6a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1};\n",
"TSeries d7a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0.93,0.27,-0.59,-1,-0.71,0.05,0.75,1,0.67,0,-0.67,-0.99,-0.85,-0.34,0.31,0.81,1,0.82,0.35,-0.22,-0.71,-0.98,-0.95,-0.66,-0.2,0.31,0.72,0.96,0.98,0.78,0.43,-0.01,-0.43,-0.77,-0.96,-0.99,-0.85,-0.58,-0.23,0.16,0.51,0.79,0.95,1,0.92,0.73,0.47,0.15,-0.17,-0.47,-0.72,-0.9,-0.99,-0.99,-0.9,-0.74,-0.52,-0.26,0.01,0.28,0.53,0.73,0.88,0.97,1,0.97};\n",
"TSeries d8a = new() {-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,0.03,-0.4,-0.47,0.19,-0.4,-0.23,0.31,0.41,0.19,0.16,-0.5,-0.31,-0.21,0.25,0.18,-0.48,-0.1,0.38,0.29,-0.38,-0.08,-0.21,0.34,0.01,-0.46,0.28,-0.48,0.11,0.02,-0.37,0.19,-0.2,0.1,0.24,0.08,-0.22,-0.12,0.15,0.36,-0.43,-0.03,-0.32,0.45,-0.5,-0.04,-0.04,-0.08,-0.18,0.13,-0.33,-0.19,0.36,-0.39,0.2,-0.31,0.28,-0.13,-0.07,-0.29,0.37,0.03,-0.25,-0.06,-0.3,-0.08,-0.09};\n",
"TSeries d9a = new() {-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,0,0.03,0.11,-0.1,-0.43,-0.08,0.36,-0.04,-0.04,-0.21,-0.3,0.26,0.2,0.28,0.2,0.27,-0.01,-0.1,-0.23,-0.13,-0.41,-0.23,-0.07,-0.21,0.32,-0.18,-0.48,0.3,0.46,-0.2,0.52,-0.81,-0.25,-0.21,-0.12,-0.18,0.18,0.52,0.29,0.44,0.18,-1.2,0.38,0.24,0.06,0.28,0.34,0.3,-0.13,0.19,-0.5,0.59,-0.36,0.22,-0.23,0.24,0.39,0.13,-0.33,-0.57,-0.23,0.49,-0.13,0.76,0.59,0.61};\n",
"TSeries d10a = new() {-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0.4,-0.4,0,-0.28,0.41,-0.54,0.65,-0.75,0.84,-0.91,0.96,-0.99,1,-0.99,0.96,-0.92,0.85,-0.77,0.67,-0.56,0.44,-0.3,0.17,-0.03,-0.11,0.25,-0.39,0.51,-0.63,0.73,-0.82,0.89,-0.95,0.98,-1,0.99,-0.97,0.93,-0.86,0.78,-0.69,0.58,-0.46,0.33,-0.19,0.05,0.09,-0.23,0.36,-0.49,0.61,-0.71,0.81,-0.88,0.94,-0.98,1,-1,0.98,-0.94,0.88,-0.8,0.71,-0.6,0.48,-0.35,0.22,-0.08,-0.06};\n",
"TSeries d11a = new() {-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,-0.6,0.6,0,0.14,-0.76,-0.96,-0.28,0.66,0.99,0.41,-0.54,-1,-0.54,0.42,0.99,0.65,-0.29,-0.96,-0.75,0.15,0.91,0.84,-0.01,-0.85,-0.91,-0.13,0.76,0.96,0.27,-0.66,-0.99,-0.4,0.55,1,0.53,-0.43,-0.99,-0.64,0.3,0.96,0.75,-0.16,-0.92,-0.83,0.02,0.85,0.9,0.12,-0.77,-0.95,-0.26,0.67,0.99,0.4,-0.56,-1,-0.52,0.44,0.99,0.64,-0.3,-0.97,-0.74,0.17,0.92,0.83,-0.03,-0.86};\n",
"TSeries d12a = new() {-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0,0,0.05,-0.25,-0.32,-0.09,0.22,0.33,0.14,-0.18,-0.33,-0.18,0.14,0.33,0.22,-0.1,-0.32,-0.25,0.05,0.3,0.28,0,-0.28,-0.3,-0.04,0.25,0.32,0.09,-0.22,-0.33,-0.13,0.18,0.33,0.18,0.86,0.67,0.79,1.1,1.32,1.25,0.95,0.69,0.72,1.01,1.28,1.3,1.04,0.74,0.68,0.91,1.22,1.33,1.13,0.81,0.67,0.83,1.15,1.33,1.21,0.9,0.68,0.75,1.06,1.31,1.28,0.99,0.71};\n",
"TSeries d13a = new() {-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0,0,2.7,-0.8,-0.8,3.6,9.3,11.95,10.05,6.3,5,8.3,14.1,17.95,17.25,13.55,11.2,13.25,18.75,23.55,24.2,20.95,17.75,18.45,23.35,28.8,30.8,28.35,24.7,24.05,28,33.75,37,35.65,31.85,28.05,-3.2,1.5,4.8,3.75,-0.8,-4.6,-4.15,0.1,4.25,4.5,0.6,-3.85,-4.75,-1.3,3.35,4.95,2,-2.8,-5,-2.6,2.2,4.95,3.2,-1.5,-4.85,-3.7,0.85,4.6,4.15,-0.15,-4.3};\n",
"TSeries d14a = new() {-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0.2,-0.2,0,0,0.59,0.83,0.74,0.5,0.91,1.36,0.93,0.87,0.6,0.38,0.78,0.53,0.42,0.14,0.01,-0.45,-0.71,-0.99,-1,-1.36,-1.22,-1.07,-1.17,-0.56,-0.95,-1.11,-0.16,0.18,-0.28,0.64,-0.5,0.24,0.45,0.67,0.72,1.15,1.52,1.28,1.38,1.03,-0.47,0.96,0.65,0.28,0.3,0.17,-0.07,-0.67,-0.51,-1.33,-0.33,-1.34,-0.78,-1.21,-0.68,-0.43,-0.56,-0.87,-0.93,-0.4,0.52,0.1,1.18,1.18,1.35};\n",
"TSeries d15a = new() {0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,0,0.01,1.3,0.3,-0.48,-1.1,-1.14,-0.03,1.11,0.96,0.63,-0.21,-0.97,-0.73,-0.65,-0.06,0.51,1.08,0.99,0.72,0.12,-0.35,-1.12,-1.21,-1.02,-0.87,0.12,0.13,0.24,1.26,1.44,0.58,0.95,-0.82,-0.68,-0.98,-1.08,-1.17,-0.67,-0.06,0.06,0.6,0.69,-0.41,1.33,1.24,0.98,1.01,0.81,0.45,-0.3,-0.28,-1.22,-0.31,-1.35,-0.77,-1.13,-0.5,-0.13,-0.13,-0.32,-0.29,0.3,1.22,0.75,1.73,1.59,1.58};\n",
"TSeries d16a = new() {175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.1,175.6,175.44,176.27,176.04,176.99,175.49,175.68,174.34,176.4,174.05,174.4,174.2,176.16,175,177.72,174.33,176.96,174.62,174.76,170.9,171.12,171.05,170.01,169.24,172.64,171.96,175.72,174.16,175.81,177.3,178.38,176.75,177.19,175.55,178.49,176.52,178.45,178.04,178.25,177.8,176.97,172.94,174.92,173.98,172.29,171.19,172.54,172.11,175.32,175.63,176.65,173.8,176.04,172.74,175.24,171.84,171.54,172.17,171.85,172.38,170.78,173.49,173.69,171.71,174.38,173.99,174.83};"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"dotnet_interactive": {
"language": "csharp"
},
"polyglot_notebook": {
"kernelName": "csharp"
}
},
"outputs": [],
"source": [
"int period = 10;\n",
"int cut = 26;\n",
"\n",
"SMA_Series d1b = new(d1a, period);\n",
"SMA_Series d2b = new(d2a, period);\n",
"SMA_Series d3b = new(d3a, period);\n",
"SMA_Series d4b = new(d4a, period);\n",
"SMA_Series d5b = new(d5a, period);\n",
"SMA_Series d6b = new(d6a, period);\n",
"SMA_Series d7b = new(d7a, period);\n",
"SMA_Series d8b = new(d8a, period);\n",
"SMA_Series d9b = new(d9a, period);\n",
"SMA_Series d10b = new(d10a, period);\n",
"SMA_Series d11b = new(d11a, period);\n",
"SMA_Series d12b = new(d12a, period);\n",
"SMA_Series d13b = new(d13a, period);\n",
"SMA_Series d14b = new(d14a, period);\n",
"SMA_Series d15b = new(d15a, period);\n",
"SMA_Series d16b = new(d16a, period);\n",
"\n",
"List<int> x = Enumerable.Range(-cut,96).ToList<int>();\n",
"GenericChart.GenericChart ch1a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d1a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch1b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d1b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch2a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d2a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch2b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d2b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch3a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d3a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch3b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d3b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch4a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d4a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch4b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d4b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch5a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d5a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch5b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d5b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch6a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d6a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch6b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d6b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch7a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d7a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch7b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d7b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch8a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d8a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch8b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d8b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch9a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d9a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch9b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d9b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch10a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d10a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch10b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d10b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch11a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d11a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch11b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d11b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch12a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d12a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch12b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d12b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch13a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d13a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch13b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d13b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch14a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d14a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch14b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d14b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch15a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d15a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch15b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d15b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"GenericChart.GenericChart ch16a = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d16a.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 1.0, Color: Color.fromString(\"blue\"));\n",
"GenericChart.GenericChart ch16b = Chart2D.Chart.Line<int,double,bool>(x.GetRange(cut,96-cut),d16b.v.GetRange(cut,96-cut),false,\"\").WithLineStyle(Width: 2.5, Color: Color.fromString(\"red\"));\n",
"\n",
"var ch1 = Chart.Combine(new []{ch1a,ch1b});\n",
"var ch2 = Chart.Combine(new []{ch2a,ch2b});\n",
"var ch3 = Chart.Combine(new []{ch3a,ch3b});\n",
"var ch4 = Chart.Combine(new []{ch4a,ch4b});\n",
"var ch5 = Chart.Combine(new []{ch5a,ch5b});\n",
"var ch6 = Chart.Combine(new []{ch6a,ch6b});\n",
"var ch7 = Chart.Combine(new []{ch7a,ch7b});\n",
"var ch8 = Chart.Combine(new []{ch8a,ch8b});\n",
"var ch9 = Chart.Combine(new []{ch9a,ch9b});\n",
"var ch10 = Chart.Combine(new []{ch10a,ch10b});\n",
"var ch11 = Chart.Combine(new []{ch11a,ch11b});\n",
"var ch12 = Chart.Combine(new []{ch12a,ch12b});\n",
"var ch13 = Chart.Combine(new []{ch13a,ch13b});\n",
"var ch14 = Chart.Combine(new []{ch14a,ch14b});\n",
"var ch15 = Chart.Combine(new []{ch15a,ch15b});\n",
"var ch16 = Chart.Combine(new []{ch16a,ch16b});\n",
"\n",
"Layout layout = new Layout(); layout.SetValue(\"showlegend\",false);\n",
"var chart1 = new []{ch1,ch2,ch3,ch4,ch5,ch6,ch7,ch8,ch9,ch10,ch11,ch12,ch13,ch14,ch15,ch16};\n",
"var full = Chart.Grid<IEnumerable<GenericChart.GenericChart>>(8,2).Invoke(chart1).WithSize(1000,2200).WithMargin(Margin.init<int, int, int, int, int, bool>(30,20,20,30,7,false)).WithLayout(layout);\n",
"full.SaveSVG(\"SMA_chart\", Width: 1000, Height: 2200);"
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docs/indicators.md
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# Coverage
⭐= Calculation is validated against one or many TA libraries
✔️= Calculation exists but has no cross-validation tests
⛔= Not implemented (yet)
| **BASIC TRANSFORMS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** | **Tulip** |
|--|:--:|:--:|:--:|:--:|:--:|
| OC2 - (Open+Close)/2 |️ `.OC2` || CandlePart.OC2 ||
| HL2 - Median Price | `.HL2` | MEDPRICE | CandlePart.HL2 | hl2 |
| HLC3 - Typical Price | `.HLC3` | TYPPRICE | CandlePart.HLC3 | hlc3 |
| OHL3 - (Open+High+Low)/3 | `.OHL3` || CandlePart.OHL3 ||
| OHLC4 - Average Price | `.OHLC4` | AVGPRICE |️ CandlePart.OHLC4 | ohlc4 | avgprice |
| HLCC4 - Weighted Price | `.HLCC4` | WCLPRICE | CandlePart.HLCC4 ||
| MIDPOINT - Midpoint value | `MIDPOINT_Series` | MIDPOINT || midpoint |
| MIDPRICE - Midpoint price | `MIDPRICE_Series` | MIDPRICE || midprice |
| MAX - Max value | `MAX_Series` | MAX ||| max |
| MIN - Min value | `MIN_Series` | MIN ||| min |
| SUM - Summation | `SUM_Series` | SUM ||| sum |
| ADD - Addition | `ADD_Series` | ADD ||| add |
| SUB - Subtraction | `SUB_Series` | SUB ||| sub |
| MUL - Multiplication | `MUL_Series` | MUL ||| mul |
| DIV - Division | `DIV_Series` | DIV ||| div |
|||||
| **STATISTICS & NUMERICAL ANALYSIS** |
||||||
| BIAS - Bias | `BIAS_Series` ||| bias |
| CORR - Pearson's Correlation Coefficient | `CORR_Series` | CORREL | GetCorrelation ||
| COVAR - Covariance | `COVAR_Series` || GetCorrelation ||
| DECAY - Linear Decay ||||| decay |
| EDECAY - Exponential Decay ||||| edecay |
| ENTROPY - Entropy | `ENTROPY_Series` ||| entropy |
| KURTOSIS - Kurtosis | `KURT_Series` ||| kurtosis |
| LINREG - Linear Regression | `LINREG_Series` || GetSlope ||
| MAD - Mean Absolute Deviation | `MAD_Series` || GetSma | mad |
| MAPE - Mean Absolute Percent Error | `MAPE_Series` || GetSma ||
| MED - Median value | `MED_Series` ||| median |
| MSE - Mean Squared Error | `MSE_Series` || GetSma ||
| SKEW - Skewness |||| skew |
| SDEV - Standard Deviation (Volatility) | `SDEV_Series` | STDDEV | GetStdDev | stdev |
| SSDEV - Sample Standard Deviation | `SSDEV_Series` ||| stdev |
| SMAPE - Symmetric Mean Absolute Percent Error | `SMAPE_Series` ||||
| VAR - Population Variance | `VAR_Series` | VAR || variance |
| SVAR - Sample Variance | `SVAR_Series` ||| variance |
| QUANTILE - Quantile |||| quantile |
| WMAPE - Weighted Mean Absolute Percent Error | `WMAPE_Series` ||||
| ZSCORE - Number of standard deviations from mean | `ZSCORE_Series` || GetStdDev | zscore |
||||||
| **TREND INDICATORS & AVERAGES** |
||||||
| AFIRMA - Autoregressive Finite Impulse Response Moving Average |||||
| ALMA - Arnaud Legoux Moving Average | `ALMA_Series` || GetAlma | alma |
| ARIMA - Autoregressive Integrated Moving Average |||||
| DEMA - Double EMA Average | `DEMA_Series` | DEMA | GetDema | dema | dema |
| EMA - Exponential Moving Average | `EMA_Series` | EMA | GetEma | ema | ema |
| EPMA - Endpoint Moving Average ||| GetEpma ||
| FRAMA - Fractal Adaptive Moving Average |||||
| FWMA - Fibonacci's Weighted Moving Average |||| fwma |
| HILO - Gann High-Low Activator |||| hilo |
| 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 |
| 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 |
| LSMA - Least Squares Moving Average |||||
| MACD - Moving Average Convergence/Divergence | `MACD_Series` | MACD | GetMacd | macd |
| MAMA - MESA Adaptive Moving Average | `MAMA_Series` | MAMA | GetMama ||
| MCGD - McGinley Dynamic |||| mcgd |
| MMA - Modified Moving Average |||||
| PPMA - Pivot Point Moving Average |||||
| PWMA - Pascal's Weighted Moving Average |||| pwma |
| RMA - WildeR's Moving Average | `RMA_Series` ||| rma |
| SINWMA - Sine Weighted Moving Average |||| sinwma |
| ⭐ [SMA - Simple Moving Average](SMA.md) | `SMA_Series` | ⭐ SMA | ⭐ GetSma | ⭐ sma | ⭐ sma |
| SMMA - Smoothed Moving Average | `SMMA_Series` || GetSmma ||
| SSF - Ehler's Super Smoother Filter |||| ssf |
| SUPERTREND - Supertrend |||| supertrend |
| SWMA - Symmetric Weighted Moving Average |||| swma |
| T3 - Tillson T3 Moving Average | `T3_Series` | T3 | GetT3 | t3 |
| TEMA - Triple EMA Average | `TEMA_Series` | TEMA | GetTema | tema |
| TRIMA - Triangular Moving Average | `TRIMA_Series` | TRIMA || trima |
| TSF - Time Series Forecast || TSF |||
| VIDYA - Variable Index Dynamic Average |||| vidya |
| VORTEX - Vortex Indicator |||| vortex |
| WMA - Weighted Moving Average | `WMA_Series` | WMA | GetWma | wma |
| ZLEMA - Zero Lag EMA Average | `ZLEMA_Series` ||| zlma |
||||||
| **VOLATILITY INDICATORS** |
||||||
| ADL - Chaikin Accumulation Distribution Line | `ADL_Series` | AD | GetAdl | ad | ad |
| ADOSC - Chaikin Accumulation Distribution Oscillator | `ADOSC_Series` | ADOSC| GetAdl | adosc | adosc |
| ATR - Average True Range | `ATR_Series` | ATR | GetAtr | atr | atr |
| ATRP - Average True Range Percent | `ATRP_Series` || GetAtr ||
| BETA - Beta coefficient || BETA | GetBeta ||
| BBANDS - Bollinger Bands® | `BBANDS_Series` | BBANDS | GetBollingerBands || bbands |
| CHAND - Chandelier Exit ||| GetChandelier ||
| CRSI - Connor RSI ||| GetConnorsRsi ||
| CVI - Chaikins Volatility ||||| cvi |
| DON - Donchian Channels ||| GetDonchian ||
| FCB - Fractal Chaos Bands ||| GetFcb ||
| FISHER - Fisher Transform ||| GetFcb || fisher |
| HV - Historical Volatility |||||
| ICH - Ichimoku ||| GetIchimoku ||
| KEL - Keltner Channels ||| GetKeltner ||
| NATR - Normalized Average True Range || NATR | GetAtr ||
| CHN - Price Channel Indicator |||||
| RSI - Relative Strength Index | `RSI_Series` | RSI | GetRsi | rsi |
| SAR - Parabolic Stop and Reverse || SAR | GetParabolicSar ||
| SRSI - Stochastic RSI || STOCHRSI | GetStochRsi ||
| STARC - Starc Bands |||||
| TR - True Range | `TR_Series` | TRANGE | GetTr | true_range |
| UI - Ulcer Index |||||
| VSTOP - Volatility Stop |||||
||||||
| **MOMENTUM INDICATORS & OSCILLATORS** |
||||||
| AC - Acceleration Oscillator |||||
| ADX - Average Directional Movement Index || ADX | GetAdx || adx |
| ADXR - Average Directional Movement Index Rating || ADXR | GetAdx || adxr |
| AO - Awesome Oscillator ||| GetAwesome || ao |
| APO - Absolute Price Oscillator || APO ||| apo |
| AROON - Aroon oscillator || AROON | GetAroon || aroon |
| BOP - Balance of Power || BOP | GetBop || bop |
| CCI - Commodity Channel Index | `CCI_Series` | CCI | GetCci || cci |
| CFO - Chande Forcast Oscillator |||||
| CMO - Chande Momentum Oscillator || CMO | GetCmo || cmo |
| COG - Center of Gravity |||||
| COPPOCK - Coppock Curve |||||
| CTI - Ehler's Correlation Trend Indicator |||||
| DPO - Detrended Price Oscillator ||| GetDpo ||
| DMI - Directional Movement Index || DX | GetAdx ||
| EFI - Elder Ray's Force Index ||| GetElderRay ||
| FOSC - Forecast oscillator ||||| fosc |
| GAT - Alligator oscillator ||| GetGator ||
| HURST - Hurst Exponent ||| GetHurst ||
| KRI - Kairi Relative Index |||||
| KVO - Klinger Volume Oscillator |||||
| MFI - Money Flow Index || MFI | GetMfi ||
| MOM - Momentum || MOM |||
| NVI - Negative Volume Index |||||
| PO - Price Oscillator |||||
| PPO - Percentage Price Oscillator || PPO |||
| PMO - Price Momentum Oscillator |||||
| PVI - Positive Volume Index |||||
| ROC - Rate of Change || MOM | GetRoc ||
| RVGI - Relative Vigor Index |||||
| SMI - Stochastic Momentum Index |||||
| STC - Schaff Trend Cycle |||||
| STOCH - Stochastic Oscillator || STOCH | GetStoch ||
| TRIX - 1-day ROC of TEMA || TRIX | GetTrix ||
| TSI - True Strength Index |||||
| UO - Ultimate Oscillator || ULTOSC | GetUltimate ||
| WILLR - Larry Williams' %R || WILLR | GetWilliamsR ||
| WGAT - Williams Alligator |||||
||||||
| **VOLUME INDICATORS** |
||||||
| AOBV - Archer On-Balance Volume |||||
| CMF - Chaikin Money Flow |||||
| EOM - Ease of Movement ||||| emv |
| KVO - Klinger Volume Oscilaltor ||||| kvo |
| OBV - On-Balance Volume | `OBV_Series` | OBV | GetObv ||
| PRS - Price Relative Strength ||||
| PVOL - Price-Volume |||||
| PVO - Percentage Volume Oscillator |||||
| PVR - Price Volume Rank |||||
| PVT - Price Volume Trend |||||
| VP - Volume Profile |||||
| VWAP - Volume Weighted Average Price |||||
| VWMA - Volume Weighted Moving Average |||||
# Coverage
⭐= Calculation is validated against several TA libraries
✔️= Validation tests passed
❌= Wrong implementation
| **BASIC TRANSFORMS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** | **Tulip** |
|--|:--:|:--:|:--:|:--:|:--:|
| OC2 - (Open+Close)/2 |️ `.OC2` || CandlePart.OC2 ||
| HL2 - Median Price | `.HL2` | MEDPRICE | CandlePart.HL2 | hl2 |
| HLC3 - Typical Price | `.HLC3` | TYPPRICE | CandlePart.HLC3 | hlc3 |
| OHL3 - (Open+High+Low)/3 | `.OHL3` || CandlePart.OHL3 ||
| OHLC4 - Average Price | `.OHLC4` | AVGPRICE |️ CandlePart.OHLC4 | ohlc4 | avgprice |
| HLCC4 - Weighted Price | `.HLCC4` | WCLPRICE | CandlePart.HLCC4 ||
| MIDPOINT - Midpoint value | `MIDPOINT_Series` | MIDPOINT || midpoint |
| MIDPRICE - Midpoint price | `MIDPRICE_Series` | MIDPRICE || midprice |
| MAX - Max value | `MAX_Series` | MAX ||| max |
| MIN - Min value | `MIN_Series` | MIN ||| min |
| SUM - Summation | `SUM_Series` | SUM ||| sum |
| ADD - Addition | `ADD_Series` | ADD ||| add |
| SUB - Subtraction | `SUB_Series` | SUB ||| sub |
| MUL - Multiplication | `MUL_Series` | MUL ||| mul |
| DIV - Division | `DIV_Series` | DIV ||| div |
|||||
| **STATISTICS & NUMERICAL ANALYSIS** |
||||||
| ⭐BIAS - Bias | `BIAS_Series` ||| ✔️bias |
| CORR - Pearson's Correlation Coefficient | `CORR_Series` | CORREL | GetCorrelation ||
| COVAR - Covariance | `COVAR_Series` || GetCorrelation ||
| DECAY - Linear Decay ||||| decay |
| EDECAY - Exponential Decay ||||| edecay |
| ENTROPY - Entropy | `ENTROPY_Series` ||| entropy |
| KURTOSIS - Kurtosis | `KURT_Series` ||| kurtosis |
| LINREG - Linear Regression | `LINREG_Series` || GetSlope ||
| MAD - Mean Absolute Deviation | `MAD_Series` || GetSma | mad |
| MAPE - Mean Absolute Percent Error | `MAPE_Series` || GetSma ||
| MED - Median value | `MED_Series` ||| median |
| MSE - Mean Squared Error | `MSE_Series` || GetSma ||
| SKEW - Skewness |||| skew |
| SDEV - Standard Deviation (Volatility) | `SDEV_Series` | STDDEV | GetStdDev | stdev |
| SSDEV - Sample Standard Deviation | `SSDEV_Series` ||| stdev |
| SMAPE - Symmetric Mean Absolute Percent Error | `SMAPE_Series` ||||
| VAR - Population Variance | `VAR_Series` | VAR || variance |
| SVAR - Sample Variance | `SVAR_Series` ||| variance |
| QUANTILE - Quantile |||| quantile |
| WMAPE - Weighted Mean Absolute Percent Error | `WMAPE_Series` ||||
| ZSCORE - Number of standard deviations from mean | `ZSCORE_Series` || GetStdDev | zscore |
||||||
| **TREND INDICATORS & AVERAGES** |
||||||
| AFIRMA - Autoregressive Finite Impulse Response Moving Average |||||
| ALMA - Arnaud Legoux Moving Average | `ALMA_Series` || GetAlma | alma |
| ARIMA - Autoregressive Integrated Moving Average |||||
| ⭐DEMA - Double EMA Average | `DEMA_Series` | ✔️DEMA | ✔️GetDema | ✔️dema | ✔️dema |
| ⭐EMA - Exponential Moving Average | `EMA_Series` | ✔️EMA | ✔️GetEma | ✔️ema | ✔️ema |
| EPMA - Endpoint Moving Average ||| GetEpma ||
| FRAMA - Fractal Adaptive Moving Average |||||
| FWMA - Fibonacci's Weighted Moving Average |||| fwma |
| HILO - Gann High-Low Activator |||| hilo |
| 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 |
| 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 |
| LSMA - Least Squares Moving Average |||||
| MACD - Moving Average Convergence/Divergence | `MACD_Series` | MACD | GetMacd | macd |
| MAMA - MESA Adaptive Moving Average | `MAMA_Series` | MAMA | GetMama ||
| MCGD - McGinley Dynamic |||| mcgd |
| MMA - Modified Moving Average |||||
| PPMA - Pivot Point Moving Average |||||
| PWMA - Pascal's Weighted Moving Average |||| pwma |
| RMA - WildeR's Moving Average | `RMA_Series` ||| rma |
| SINWMA - Sine Weighted Moving Average |||| sinwma |
| ⭐[SMA - Simple Moving Average](SMA.md) | `SMA_Series` | ✔️SMA | ✔️GetSma | ✔️sma | ✔️sma |
| SMMA - Smoothed Moving Average | `SMMA_Series` || GetSmma ||
| SSF - Ehler's Super Smoother Filter |||| ssf |
| SUPERTREND - Supertrend |||| supertrend |
| SWMA - Symmetric Weighted Moving Average |||| swma |
| T3 - Tillson T3 Moving Average | `T3_Series` | T3 | GetT3 | t3 |
| TEMA - Triple EMA Average | `TEMA_Series` | TEMA | GetTema | tema |
| TRIMA - Triangular Moving Average | `TRIMA_Series` | TRIMA || trima |
| TSF - Time Series Forecast || TSF |||
| VIDYA - Variable Index Dynamic Average |||| vidya |
| VORTEX - Vortex Indicator |||| vortex |
| WMA - Weighted Moving Average | `WMA_Series` | WMA | GetWma | wma |
| ZLEMA - Zero Lag EMA Average | `ZLEMA_Series` ||| zlma |
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| **VOLATILITY INDICATORS** |
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| ⭐ADL - Chaikin Accumulation Distribution Line | `ADL_Series` | ✔️AD | ✔️GetAdl | ✔️ad | ✔️ad |
| ⭐ADOSC - Chaikin Accumulation Distribution Oscillator | `ADOSC_Series` | ✔️ADOSC| | ✔️adosc | ✔️adosc |
| ⭐ATR - Average True Range | `ATR_Series` | ✔️ATR | ✔️GetAtr | ✔️atr | ✔️atr |
| ATRP - Average True Range Percent | `ATRP_Series` || GetAtr ||
| BETA - Beta coefficient || BETA | GetBeta ||
| BBANDS - Bollinger Bands® | `BBANDS_Series` | BBANDS | GetBollingerBands || bbands |
| CHAND - Chandelier Exit ||| GetChandelier ||
| CRSI - Connor RSI ||| GetConnorsRsi ||
| CVI - Chaikins Volatility ||||| cvi |
| DON - Donchian Channels ||| GetDonchian ||
| FCB - Fractal Chaos Bands ||| GetFcb ||
| FISHER - Fisher Transform ||| GetFcb || fisher |
| HV - Historical Volatility |||||
| ICH - Ichimoku ||| GetIchimoku ||
| KEL - Keltner Channels ||| GetKeltner ||
| NATR - Normalized Average True Range || NATR | GetAtr ||
| CHN - Price Channel Indicator |||||
| RSI - Relative Strength Index | `RSI_Series` | RSI | GetRsi | rsi |
| SAR - Parabolic Stop and Reverse || SAR | GetParabolicSar ||
| SRSI - Stochastic RSI || STOCHRSI | GetStochRsi ||
| STARC - Starc Bands |||||
| TR - True Range | `TR_Series` | TRANGE | GetTr | true_range |
| UI - Ulcer Index |||||
| VSTOP - Volatility Stop |||||
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| **MOMENTUM INDICATORS & OSCILLATORS** |
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| AC - Acceleration Oscillator |||||
| ADX - Average Directional Movement Index || ADX | GetAdx || adx |
| ADXR - Average Directional Movement Index Rating || ADXR | GetAdx || adxr |
| AO - Awesome Oscillator ||| GetAwesome || ao |
| APO - Absolute Price Oscillator || APO ||| apo |
| AROON - Aroon oscillator || AROON | GetAroon || aroon |
| BOP - Balance of Power || BOP | GetBop || bop |
| CCI - Commodity Channel Index | `CCI_Series` | CCI | GetCci || cci |
| CFO - Chande Forcast Oscillator |||||
| CMO - Chande Momentum Oscillator | `CMO_Series` | ❌CMO | ❌GetCmo | ❌cmo | cmo |
| COG - Center of Gravity |||||
| COPPOCK - Coppock Curve |||||
| CTI - Ehler's Correlation Trend Indicator |||||
| DPO - Detrended Price Oscillator ||| GetDpo ||
| DMI - Directional Movement Index || DX | GetAdx ||
| EFI - Elder Ray's Force Index ||| GetElderRay ||
| FOSC - Forecast oscillator ||||| fosc |
| GAT - Alligator oscillator ||| GetGator ||
| HURST - Hurst Exponent ||| GetHurst ||
| KRI - Kairi Relative Index |||||
| KVO - Klinger Volume Oscillator |||||
| MFI - Money Flow Index || MFI | GetMfi ||
| MOM - Momentum || MOM |||
| NVI - Negative Volume Index |||||
| PO - Price Oscillator |||||
| PPO - Percentage Price Oscillator || PPO |||
| PMO - Price Momentum Oscillator |||||
| PVI - Positive Volume Index |||||
| ROC - Rate of Change || MOM | GetRoc ||
| RVGI - Relative Vigor Index |||||
| SMI - Stochastic Momentum Index |||||
| STC - Schaff Trend Cycle |||||
| STOCH - Stochastic Oscillator || STOCH | GetStoch ||
| TRIX - 1-day ROC of TEMA | TRIX | TRIX | GetTrix | trix |
| TSI - True Strength Index |||||
| UO - Ultimate Oscillator || ULTOSC | GetUltimate ||
| WILLR - Larry Williams' %R || WILLR | GetWilliamsR ||
| WGAT - Williams Alligator |||||
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| **VOLUME INDICATORS** |
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| AOBV - Archer On-Balance Volume |||||
| CMF - Chaikin Money Flow |||||
| EOM - Ease of Movement ||||| emv |
| KVO - Klinger Volume Oscilaltor ||||| kvo |
| OBV - On-Balance Volume | `OBV_Series` | OBV | GetObv ||
| PRS - Price Relative Strength ||||
| PVOL - Price-Volume |||||
| PVO - Percentage Volume Oscillator |||||
| PVR - Price Volume Rank |||||
| PVT - Price Volume Trend |||||
| VP - Volume Profile |||||
| VWAP - Volume Weighted Average Price |||||
| VWMA - Volume Weighted Moving Average |||||