mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-07 13:37:44 +00:00
ZSCORE
This commit is contained in:
Miha Kralj
@@ -12,6 +12,7 @@
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.vscode/
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||||
*.deps.json
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.Sandbox/
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||||
.sonarlint/
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.DS_Store
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||||
|
||||
# User-specific files (MonoDevelop/Xamarin Studio)
|
||||
|
||||
@@ -22,24 +22,17 @@ public class Yahoo_Feed : TBars
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||||
System.Net.Http.HttpClient client = new();
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var msg = client.GetStringAsync(requestUrl).Result;
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||||
var jresult = JsonSerializer.Deserialize<JsonDocument>(msg).RootElement;
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JsonElement json = new();
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JsonElement datetime = new();
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JsonElement open = new();
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JsonElement high = new();
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JsonElement low = new();
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JsonElement close = new();
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JsonElement volume = new();
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jresult.TryGetProperty("chart",out json);
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jresult.TryGetProperty("chart",out JsonElement json);
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json.TryGetProperty("result",out json);
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json[0].TryGetProperty("timestamp",out datetime);
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json[0].TryGetProperty("timestamp",out JsonElement datetime);
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json[0].TryGetProperty("indicators",out json);
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json.TryGetProperty("quote",out json);
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json[0].TryGetProperty("open",out open);
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json[0].TryGetProperty("high",out high);
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json[0].TryGetProperty("low",out low);
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json[0].TryGetProperty("close",out close);
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json[0].TryGetProperty("volume",out volume);
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json[0].TryGetProperty("open",out JsonElement open);
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json[0].TryGetProperty("high",out JsonElement high);
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json[0].TryGetProperty("low",out JsonElement low);
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json[0].TryGetProperty("close",out JsonElement close);
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json[0].TryGetProperty("volume",out JsonElement volume);
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for (int i=0; i<datetime.GetArrayLength(); i++) {
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DateTime d = DateTimeOffset.FromUnixTimeSeconds(long.Parse(datetime[i].GetRawText())).DateTime;
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@@ -51,7 +51,11 @@
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<PackageIcon>QuanTAlib2.png</PackageIcon>
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<PackageIconUrl>https://raw.githubusercontent.com/mihakralj/QuanTAlib/main/.github/QuanTAlib2.png</PackageIconUrl>
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<EnforceCodeStyleInBuild>True</EnforceCodeStyleInBuild>
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<CodeAnalysisRuleSet>..\.sonarlint\mihakralj_quantalibcsharp.ruleset</CodeAnalysisRuleSet>
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</PropertyGroup>
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<ItemGroup>
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<AdditionalFiles Include="..\.sonarlint\mihakralj_quantalib\CSharp\SonarLint.xml" Link="SonarLint.xml" />
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</ItemGroup>
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<ItemGroup>
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<None Include="..\Docs\readme.md">
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<Pack>True</Pack>
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@@ -64,10 +68,11 @@
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</None>
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</ItemGroup>
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<ItemGroup>
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<PackageReference Include="System.Text.Json" Version="7.0.0" />
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<PackageReference Include="GitVersion.MsBuild" Version="5.11.1">
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<PrivateAssets>All</PrivateAssets>
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<PrivateAssets>all</PrivateAssets>
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<IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
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</PackageReference>
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<PackageReference Include="System.Text.Json" Version="7.0.0" />
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</ItemGroup>
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</Project>
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@@ -0,0 +1,51 @@
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namespace QuanTAlib;
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using System;
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/* <summary>
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ZSCORE: number of standard deviations from SMA
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Z-score describes a value's relationship to the mean of a series, as measured in
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terms of standard deviations from the mean. If a Z-score is 0, it indicates that
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the data point's score is identical to the mean score. A Z-score of 1.0 would
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indicate a value that is one standard deviation from the mean. Z-scores may be
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positive or negative, with a positive value indicating the score is above the
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mean and a negative score indicating it is below the mean.
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Sources:
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https://en.wikipedia.org/wiki/Z-score
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https://www.investopedia.com/terms/z/zscore.asp
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Calculation:
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std = std * STDEV(close, length)
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mean = SMA(close, length)
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ZSCORE = (close - mean) / std
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</summary> */
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public class ZSCORE_Series : Single_TSeries_Indicator
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{
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public ZSCORE_Series(TSeries source, int period, bool useNaN = false) : base(source, period, useNaN)
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{
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if (base._data.Count > 0) { base.Add(base._data); }
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}
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private readonly System.Collections.Generic.List<double> _buffer = new();
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public override void Add((System.DateTime t, double v) TValue, bool update)
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{
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if (update) { _buffer[_buffer.Count - 1] = TValue.v; }
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else { _buffer.Add(TValue.v); }
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if (_buffer.Count > this._p && this._p != 0) { _buffer.RemoveAt(0); }
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double _sma = 0;
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for (int i = 0; i < _buffer.Count; i++) { _sma += _buffer[i]; }
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_sma /= this._buffer.Count;
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double _pvar = 0;
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for (int i = 0; i < _buffer.Count; i++) { _pvar += (_buffer[i] - _sma) * (_buffer[i] - _sma); }
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_pvar /= this._buffer.Count;
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double _psdev = Math.Sqrt(_pvar);
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double _zscore = (_psdev == 0) ? double.NaN : (TValue.v - _sma) / _psdev;
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var result = (TValue.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _zscore);
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base.Add(result, update);
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}
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}
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@@ -57,8 +57,9 @@ public class PandasTA : IDisposable
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public void Dispose()
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{
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PythonEngine.Shutdown();
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}
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PythonEngine.Shutdown();
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GC.SuppressFinalize(this);
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}
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[Fact]
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void HL2()
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@@ -192,9 +193,33 @@ public class PandasTA : IDisposable
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TEMA_Series QL = new(bars.Close, period, false);
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var pta = df.ta.tema(close: df.close, length: period);
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Assert.Equal(Math.Round((double)pta.tail(1), 7), Math.Round(QL.Last().v, 7));
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}
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[Fact]
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}
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[Fact]
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void SDEV()
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{
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SDEV_Series QL = new(bars.Close, period, useNaN: false);
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var pta = df.ta.stdev(close: df.close, length: period, ddof: 0);
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Assert.Equal(Math.Round((double)pta.tail(1), 4), Math.Round(QL.Last().v, 4));
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}
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[Fact]
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void SSDEV()
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{
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SSDEV_Series QL = new(bars.Close, period, useNaN: false);
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var pta = df.ta.stdev(close: df.close, length: period, ddof: 1);
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Assert.Equal(Math.Round((double)pta.tail(1), 4), Math.Round(QL.Last().v, 4));
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}
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[Fact]
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void ZSCORE()
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{
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ZSCORE_Series QL = new(bars.Close, period, useNaN: false);
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var pta = df.ta.zscore(close: df.close, length: period, ddof: 0);
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Assert.Equal(Math.Round((double)pta.tail(1), 4), Math.Round(QL.Last().v, 4));
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}
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[Fact]
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void ENTP()
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{
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ENTP_Series QL = new(bars.Close, period, useNaN: false);
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@@ -80,7 +80,16 @@ public class Skender_Stock
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Assert.Equal(Math.Round((double)SK.Last().Mad!, 6), Math.Round(QL.Last().v, 6));
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}
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[Fact]
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[Fact]
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public void MSE()
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{
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MSE_Series QL = new(bars.Close, period, false);
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var SK = quotes.GetSmaAnalysis(period);
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Assert.Equal(Math.Round((double)SK.Last().Mse!, 6), Math.Round(QL.Last().v, 6));
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}
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[Fact]
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public void MAPE()
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{
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MAPE_Series QL = new(bars.Close, period, false);
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@@ -115,7 +124,7 @@ public class Skender_Stock
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ADL_Series QL = new(bars, false);
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var SK = quotes.GetAdl();
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Assert.Equal(Math.Round((double)SK.Last().Adl!, 5), Math.Round(QL.Last().v, 5));
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Assert.Equal(Math.Round(SK.Last().Adl!, 5), Math.Round(QL.Last().v, 5));
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}
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[Fact]
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@@ -214,7 +223,16 @@ public class Skender_Stock
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Assert.Equal(Math.Round((double)SK.Last().StdDev!, 6), Math.Round(QL.Last().v, 6));
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}
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[Fact]
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[Fact]
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public void ZSCORE()
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{
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ZSCORE_Series QL = new(bars.Close, period, useNaN: false);
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var SK = quotes.GetStdDev(period);
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Assert.Equal(Math.Round((double)SK.Last().ZScore!, 6), Math.Round(QL.Last().v, 6));
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}
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[Fact]
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public void LINREG()
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{
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LINREG_Series QL = new(bars.Close, period, useNaN: false);
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@@ -241,7 +259,7 @@ public class Skender_Stock
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TSeries QL = bars.HL2;
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var SK = quotes.GetBaseQuote(CandlePart.HL2);
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Assert.Equal(Math.Round((double)SK.Last().Value!, 6), Math.Round(QL.Last().v, 6));
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Assert.Equal(Math.Round(SK.Last().Value!, 6), Math.Round(QL.Last().v, 6));
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}
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[Fact]
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@@ -250,7 +268,7 @@ public class Skender_Stock
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TSeries QL = bars.OC2;
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var SK = quotes.GetBaseQuote(CandlePart.OC2);
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Assert.Equal(Math.Round((double)SK.Last().Value!, 6), Math.Round(QL.Last().v, 6));
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Assert.Equal(Math.Round(SK.Last().Value!, 6), Math.Round(QL.Last().v, 6));
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}
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[Fact]
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@@ -259,7 +277,7 @@ public class Skender_Stock
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TSeries QL = bars.HLC3;
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var SK = quotes.GetBaseQuote(CandlePart.HLC3);
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Assert.Equal(Math.Round((double)SK.Last().Value!, 6), Math.Round(QL.Last().v, 6));
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Assert.Equal(Math.Round(SK.Last().Value!, 6), Math.Round(QL.Last().v, 6));
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}
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[Fact]
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@@ -268,7 +286,7 @@ public class Skender_Stock
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TSeries QL = bars.OHL3;
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var SK = quotes.GetBaseQuote(CandlePart.OHL3);
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Assert.Equal(Math.Round((double)SK.Last().Value!, 6), Math.Round(QL.Last().v, 6));
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Assert.Equal(Math.Round(SK.Last().Value!, 6), Math.Round(QL.Last().v, 6));
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}
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[Fact]
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@@ -1,87 +0,0 @@
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## 1. Prepare the Peloton tablet
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Stop Peloton overlay app:
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- tap on Settings in the top right corner and select Device Settings
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- tap Apps and scroll down to find and tap Peloton app (not Peloton Launcher, just Peloton)
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- tap FORCE STOP to stop the app overlay
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- confrm by tapping OK
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|
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Turn on developer mode
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- return to Settings page
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- tap About tablet in System section
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- tap Build number repeatedly until you activate developer mode
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||||
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||||
Enable USB debugging
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- return to Settings page
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- tap (now visible) Developer options in System section
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- scroll down to find USB Debugging option
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||||
- Enable USB debugging
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||||
- Confirm by tapping OK
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||||
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## 3. Prepare the PC with Zwift/Rouvy
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||||
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||||
- Create your Splashtop account https://my.splashtop.com/login
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||||
- Download and install Splashtop Streamer https://www.splashtop.com/downloads#pers
|
||||
- Download Android Platform Tools https://developer.android.com/studio/releases/platform-tools
|
||||
- Download Nova launcher APK (or any other launcher that works on Android 7) https://apkpure.com/nova-launcher/com.teslacoilsw.launcher/download/62019-APK
|
||||
- Download Splashtop APK https://apkpure.com/splashtop-personal-access/com.splashtop.remote.pad.v2
|
||||
- Unzip Android tools into a new folder
|
||||
- move both APKs to the same folder
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||||
- Run Command Prompt (CMD) and move to the same folder
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||||
- Launch Android Debuging Bridge: adb start-server
|
||||
- Connect PC and Peloton tablet with USB cable
|
||||
- Peloton tablet will check for confirmation; Accept debugging over USB
|
||||
- Verify connectivity on PC in the Command window: adb devices
|
||||
|
||||
## 4. Side-load APKs
|
||||
|
||||
- Execute the following three commands on PC:
|
||||
adb shell settings put secure install_non_market_apps 1
|
||||
adb install <name_of_nova_launcher.apk>
|
||||
adb install <name_of_splashtop.apk>
|
||||
- Disconnect USB cable
|
||||
- On Peloton tablet tap Peloton 'P' logo at the bottom
|
||||
- Select Nova as a default launcher
|
||||
- Accept all defaults for Nova launcher - you can customize it later
|
||||
|
||||
|
||||
- (optional) Bring Peloton and Splashtop icons to the main page of Nova launcher
|
||||
- Choosing Peloton launches Peloton app; Choosing Splashtop launches Splashtop app
|
||||
- Swiping down from the top of the screen reveals the hidden 'P' launcher button
|
||||
|
||||
## 5. Connect Peloton tablet and PC
|
||||
|
||||
- Launch Splashtop app on Peloton tablet
|
||||
- Login with Splashtop credentials
|
||||
- Connect to PC that runs Splashtop streamer (and Zwift/Rouvy)
|
||||
- Launch Zwift/Rouvy
|
||||
|
||||
## 5. Enable sensors
|
||||
|
||||
- (optional): buy ANT+ USB dongle https://www.amazon.com/s?k=ant%2B+USB+stick
|
||||
|
||||
Peloton Tread:
|
||||
Speed: Runn https://npe-inc.com/runn-smart-treadmill-sensor-2/
|
||||
(or Stryd https://www.stryd.com/us/en)
|
||||
Cadence: Garmin foodpod (or Stryd)
|
||||
Heartrate: any HR monitor (BT or ANT+) https://www.amazon.com/s?k=bluetooth+HR+monitor
|
||||
Power: Stryd
|
||||
|
||||
Peloton Bike (gen1):
|
||||
Power & Cadence: DFC (Data Fitness Connector) https://www.crowdsupply.com/intelligenate/data-fitness-connector
|
||||
Heartrate:vany HR monitor (BT or ANT+)
|
||||
|
||||
## 6. Navigation
|
||||
|
||||
Nova is now a default launcher on Android tablet, but on Tread we need to run Peloton app in the background to prevent locking of treadmill:
|
||||
|
||||
- Launch Peloton app
|
||||
- Swipe down from the top and return to Nova launcher
|
||||
- Launch Splashtop app
|
||||
- Connect to PC
|
||||
- Launch Zwift or Rouvy
|
||||
- Connect all sensors
|
||||
- Run/Ride!
|
||||
|
||||
|
||||
+28
-28
@@ -38,13 +38,13 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
|
||||
| **BASIC TRANSFORMS** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
|
||||
|--|:--:|:--:|:--:|:--:|
|
||||
| ⭐ OC2 - (Open+Close)/2 |️ `.OC2` || CandlePart.OC2 ||
|
||||
| ⭐ HL2 - Median Price | `.HL2` | MEDPRICE | CandlePart.HL2 ||
|
||||
| ⭐ HLC3 - Typical Price | `.HLC3` | TYPPRICE | CandlePart.HLC3 ||
|
||||
| ⭐ 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 - Average Price | `.OHLC4` | AVGPRICE |️ CandlePart.OHLC4 | ohlc4 |
|
||||
| ⭐ HLCC4 - Weighted Price | `.HLCC4` | WCLPRICE | CandlePart.HLCC4 ||
|
||||
| ⭐ MIDPOINT - Midpoint value | `MIDPOINT_Series` | MIDPOINT |||
|
||||
| ⭐ MIDPRICE - Midpoint price | `MIDPRICE_Series` | MIDPRICE |||
|
||||
| ⭐ MIDPOINT - Midpoint value | `MIDPOINT_Series` | MIDPOINT || midpoint |
|
||||
| ⭐ MIDPRICE - Midpoint price | `MIDPRICE_Series` | MIDPRICE || midprice |
|
||||
| ⭐ MAX - Max value | `MAX_Series` | MAX |||
|
||||
| ⭐ MIN - Min value | `MIN_Series` | MIN |||
|
||||
| ⭐ SUM - Summation | `SUM_Series` | SUM |||
|
||||
@@ -63,54 +63,54 @@ See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/gett
|
||||
| ⭐ 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 |||||
|
||||
| ⭐ SDEV - Standard Deviation (Volatility) | `SDEV_Series` | STDDEV |||
|
||||
| ✔️ SSDEV - Sample Standard Deviation | `SSDEV_Series` ||||
|
||||
| ⭐ 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 |
|
||||
| ⛔ QUANT - Quantile |||||
|
||||
| ⛔ QUANTILE - Quantile |||| quantile |
|
||||
| ✔️ WMAPE - Weighted Mean Absolute Percent Error | `WMAPE_Series` ||||
|
||||
| ⛔ ZSCORE - Number of standard deviations from mean |||||
|
||||
| ⭐ ZSCORE - Number of standard deviations from mean | ZSCORE_Series || GetStdDev | zscore |
|
||||
||||||
|
||||
| **TREND INDICATORS & AVERAGES** | **QuanTAlib** | **TA-LIB** | **Skender** | **Pandas TA** |
|
||||
| ⛔ AFIRMA - Autoregressive Finite Impulse Response Moving Average |||||
|
||||
| ⭐ ALMA - Arnaud Legoux Moving Average | `ALMA_Series` || GetAlma ||
|
||||
| ⭐ ALMA - Arnaud Legoux Moving Average | `ALMA_Series` || GetAlma | alma |
|
||||
| ⛔ ARIMA - Autoregressive Integrated Moving Average |||||
|
||||
| ⭐ DEMA - Double EMA Average | `DEMA_Series` | DEMA | GetDema | dema |
|
||||
| ⭐ EMA - Exponential Moving Average | `EMA_Series` || GetEma | ema |
|
||||
| ⛔ EPMA - Endpoint Moving Average ||| GetEpma ||
|
||||
| ⛔ FRAMA - Fractal Adaptive Moving Average |||||
|
||||
| ⛔ FWMA - Fibonacci's Weighted Moving Average |||||
|
||||
| ⛔ HILO - Gann High-Low Activator |||||
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| ⛔ FWMA - Fibonacci's Weighted Moving Average |||| fwma |
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| ⛔ HILO - Gann High-Low Activator |||| hilo |
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| ✔️ HEMA - Hull/EMA Average | `HEMA_Series` ||||
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| ⛔ Hilbert Transform Instantaneous Trendline || HT_TRENDLINE | GetHtTrendline ||
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| ⭐ HMA - Hull Moving Average | `HMA_Series` || GetHma | hma |
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| ⛔ HWMA - Holt-Winter Moving Average |||||
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| ✔️ JMA - Jurik Moving Average | `JMA_Series` ||||
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| ⛔ HWMA - Holt-Winter Moving Average |||| hwma |
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| ✔️ JMA - Jurik Moving Average | `JMA_Series` ||| jma |
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| ⭐ KAMA - Kaufman's Adaptive Moving Average | `KAMA_Series` | KAMA | GetKama | kama |
|
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| ⛔ KDJ - KDJ Indicator (trend reversal) |||||
|
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| ⛔ KDJ - KDJ Indicator (trend reversal) |||| kdj |
|
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| ⛔ LSMA - Least Squares Moving Average |||||
|
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| ⭐ MACD - Moving Average Convergence/Divergence | `MACD_Series` | MACD | GetMacd ||
|
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| ⭐ MACD - Moving Average Convergence/Divergence | `MACD_Series` | MACD | GetMacd | macd |
|
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| ⛔ MAMA - MESA Adaptive Moving Average || MAMA | GetMama ||
|
||||
| ⛔ MCGD - McGinley Dynamic |||||
|
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| ⛔ MCGD - McGinley Dynamic |||| mcgd |
|
||||
| ⛔ MMA - Modified Moving Average |||||
|
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| ⛔ PPMA - Pivot Point Moving Average |||||
|
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| ⛔ PWMA - Pascal's Weighted Moving Average |||||
|
||||
| ⛔ PWMA - Pascal's Weighted Moving Average |||| pwma |
|
||||
| ⭐ RMA - WildeR's Moving Average | `RMA_Series` ||| rma |
|
||||
| ⛔ SINWMA - Sine Weighted Moving Average |||||
|
||||
| ⛔ SINWMA - Sine Weighted Moving Average |||| sinwma |
|
||||
| ⭐ SMA - Simple Moving Average | `SMA_Series` | SMA | GetSma | sma |
|
||||
| ⭐ SMMA - Smoothed Moving Average | `SMMA_Series` || GetSmma ||
|
||||
| ⛔ SSF - Ehler's Super Smoother Filter |||||
|
||||
| ⛔ SUP - Supertrend |||||
|
||||
| ⛔ SWMA - Symmetric Weighted Moving Average |||||
|
||||
| ⛔ T3 - Tillson T3 Moving Average || T3 | GetT3 ||
|
||||
| ⛔ SSF - Ehler's Super Smoother Filter |||| ssf |
|
||||
| ⛔ SUPERTREND - Supertrend |||| supertrend |
|
||||
| ⛔ SWMA - Symmetric Weighted Moving Average |||| swma |
|
||||
| ⛔ T3 - Tillson T3 Moving Average || T3 | GetT3 | t3 |
|
||||
| ⭐ TEMA - Triple EMA Average | `TEMA_Series` | TEMA | GetTema | tema |
|
||||
| ⭐ TRIMA - Triangular Moving Average | `TRIMA_Series` | TRIMA |||
|
||||
| ⭐ TRIMA - Triangular Moving Average | `TRIMA_Series` | TRIMA || trima |
|
||||
| ⛔ TSF - Time Series Forecast || TSF |||
|
||||
| ⛔ VIDYA - Variable Index Dynamic Average |||||
|
||||
| ⛔ VOR - Vortex Indicator |||||
|
||||
| ⛔ 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 |
|
||||
||||||
|
||||
|
||||
Reference in New Issue
Block a user