diff --git a/.github/workflows/main_automation.yml b/.github/workflows/main_automation.yml index 85a883a4..bb961dd7 100644 --- a/.github/workflows/main_automation.yml +++ b/.github/workflows/main_automation.yml @@ -45,6 +45,19 @@ jobs: #configFilePath: GitVersion.yml updateAssemblyInfo: true +############## Install Python + + - name: Install pandas-ta + run: | + sudo apt install python3.10 + sudo apt install python3.10-dev + pip3 install numpy + pip3 install pandas + pip3 install pandas-ta + python --version + +############## Install more tools + - name: Install JDK11 for Sonar Scanner uses: actions/setup-java@v3 with: @@ -158,3 +171,4 @@ jobs: --api-key ${{ secrets.NUGET_DEPLOY_KEY_QUANTLIB }} --source https://api.nuget.org/v3/index.json --skip-duplicate + diff --git a/.sonarlint/mihakralj_quantalibcsharp.ruleset b/.sonarlint/mihakralj_quantalibcsharp.ruleset index 22a7ac94..5ad478ec 100644 --- a/.sonarlint/mihakralj_quantalibcsharp.ruleset +++ b/.sonarlint/mihakralj_quantalibcsharp.ruleset @@ -372,6 +372,7 @@ + diff --git a/Calculations/Calculations.csproj b/Calculations/Calculations.csproj index 466a0ced..2502f02a 100644 --- a/Calculations/Calculations.csproj +++ b/Calculations/Calculations.csproj @@ -2,7 +2,9 @@ QuanTAlib - 0.2.0 + 0.2.5 + 0.2.5 + 0.2.5 Library of TA Calculations, Charts and Strategies for Quantower Quantitative Technical Analysis Library in C# for Quantower git @@ -33,9 +35,6 @@ Apache-2.0 - 0.2.1.0 - 0.2.1.0 - 0.2.1-dev.2+Branch.dev.Sha.cb5fe2dc86a78fe9358da810d17952c82299ed3d NETSDK1057 IDE1006 true diff --git a/Calculations/Logic/CROSS_Series.cs b/Calculations/Logic/CROSS_Series.cs index 66e2e2bc..aab93397 100644 --- a/Calculations/Logic/CROSS_Series.cs +++ b/Calculations/Logic/CROSS_Series.cs @@ -29,9 +29,9 @@ public class CROSS_Series : Pair_TSeries_Indicator { val = TValue1.v == TValue2.v ? 0 : val; double over = TValue1.v > TValue2.v ? 1 : val; - val = (this._previous < over) ? 1 : -1; - (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, - ((this._previous == over) || Double.IsNaN(this._previous) || (this._previous == 0)) ? 0 : val); + val = (_previous < over) ? 1 : -1; + val = ((_previous == over) || Double.IsNaN(this._previous) || (this._previous == 0)) ? 0 : val; + (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t,val); this._previous = over; diff --git a/Strategies/Strategies.csproj b/Strategies/Strategies.csproj index 78d3a39f..b8dee3da 100644 --- a/Strategies/Strategies.csproj +++ b/Strategies/Strategies.csproj @@ -42,6 +42,9 @@ QuanTAlib\%(RecursiveDir)%(Filename)%(Extension) + + + ..\.github\TradingPlatform.BusinessLayer.dll diff --git a/Tests/Tests.csproj b/Tests/Tests.csproj index 07aed4a3..8ec60052 100644 --- a/Tests/Tests.csproj +++ b/Tests/Tests.csproj @@ -1,6 +1,6 @@  - net8.0 + net7.0 preview enable enable @@ -18,8 +18,7 @@ runtime; build; native; contentfiles; analyzers; buildtransitive all - - + runtime; build; native; contentfiles; analyzers; buildtransitive @@ -29,7 +28,7 @@ - + diff --git a/Tests/Validations/Trends/Pandas_TA.cs b/Tests/Validations/Trends/Pandas_TA.cs index 6c05cbc8..f6953454 100644 --- a/Tests/Validations/Trends/Pandas_TA.cs +++ b/Tests/Validations/Trends/Pandas_TA.cs @@ -1,439 +1,416 @@ using Xunit; using System; using QuanTAlib; +using System.Runtime.InteropServices; +using System.Runtime.InteropServices.Marshalling; using Python.Runtime; -using Python.Included; namespace Validations; -/* -public class PandasTA : IDisposable -{ +public class PandasTA : IDisposable { + private bool disposed = false; private readonly GBM_Feed bars; - private readonly Random rnd = new(); + private readonly Random rnd = new(); private readonly int period, skip; - private int digits; - private readonly string dllpath; + private readonly int digits; private readonly dynamic np; private readonly dynamic ta; private readonly dynamic pd; private readonly dynamic df; public PandasTA() { - bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0); - period = rnd.Next(maxValue: 28) + 3; - skip = period+10; - digits = 8; + bars = new GBM_Feed(5000, 0.8, 0.0); + period = rnd.Next(28) + 3; + skip = period + 50; + digits = 8; - Installer.InstallPath = Path.GetFullPath(path: "."); - Installer.SetupPython().Wait(); - Installer.TryInstallPip(); - Installer.PipInstallModule(module_name: "numpy"); - Installer.PipInstallModule(module_name: "pandas"); - Installer.PipInstallModule(module_name: "pandas-ta"); - dllpath = Installer.InstallPath + "\\" + Installer.InstallDirectory + "\\" + Runtime.PythonDLL; - Runtime.PythonDLL = dllpath; + var pythonDLL = PythonLibrary.Locate(); + Runtime.PythonDLL = pythonDLL; PythonEngine.Initialize(); - np = Py.Import(name: "numpy"); - pd = Py.Import(name: "pandas"); - ta = Py.Import(name: "pandas_ta"); + np = Py.Import("numpy"); + pd = Py.Import("pandas"); + ta = Py.Import("pandas_ta"); - string[] cols = { "open", "high", "low", "close", "volume" }; - double[,] ary = new double[bars.Count, 5]; - for (int i = 0; i < bars.Count; i++) { + string[] cols = {"open", "high", "low", "close", "volume"}; + var ary = new double[bars.Count, 5]; + for (var 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); + + public void Dispose() { + Dispose(true); + 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-1; i > skip; i--) - { - double QL_item = QL[i-1].v; - double PanTA_item = (double)pta[i-1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + ~PandasTA() { + Dispose(false); + } + protected virtual void Dispose(bool disposing) { + if (!disposed) { + disposed = true; } } - /* - [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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void 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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + [Fact] - void BBANDS() { + private 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 (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void BBANDS() { BBANDS_Series QL = new(bars.Close, period); var pta = df.ta.bbands(close: df.close, length: period).to_numpy(); - for (int i = QL.Length-1; i > skip; i--) { - double QL_item = QL.Lower[i].v; - double PanTA_item = (double)pta[i][0]; //lower + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL.Lower[i].v; + var PanTA_item = (double) pta[i][0]; //lower Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); QL_item = QL.Mid[i].v; - PanTA_item = (double)pta[i][1]; //mid + PanTA_item = (double) pta[i][1]; //mid Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); QL_item = QL.Upper[i].v; - PanTA_item = (double)pta[i][2]; //upper + PanTA_item = (double) pta[i][2]; //upper Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); } } - [Fact] void BIAS() { + + [Fact] + private 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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] - void CCI() { + private void CCI() { CCI_Series QL = new(bars, period, false); var pta = df.ta.cci(close: df.close, length: period); - for (int i = QL.Length-1; i > skip; i--) { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; 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-1; i > skip; i--) { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - - [Fact] void DEMA() { + private 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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - - [Fact] void EMA() { + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private 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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - - [Fact] void 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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void HL2() { - var pta = df.ta.hl2(high: df.high, low: df.low); - for (int i = bars.HL2.Length-1; i > skip; i--) - { - double QL_item = bars.HL2[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } } - [Fact] void HLC3() { + + [Fact] + private void ENTROPY() { + ENTROPY_Series QL = new(bars.Close, period, false); + var pta = df.ta.entropy(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void HL2() { + var pta = df.ta.hl2(high: df.high, low: df.low); + for (var i = bars.HL2.Length - 1; i > skip; i--) { + var QL_item = bars.HL2[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void HLC3() { var pta = df.ta.hlc3(high: df.high, low: df.low, close: df.close); - for (int i = bars.HLC3.Length; i > skip; i--) - { - double QL_item = bars.HLC3[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void HMA() { + for (var i = bars.HLC3.Length; i > skip; i--) { + var QL_item = bars.HLC3[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private 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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } - } - [Fact] void HWMA() { - HWMA_Series QL = new(bars.Close, useNaN: false); - var pta = df.ta.hwma(close: df.close); - for (int i = QL.Length-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - - } - [Fact] void KAMA() { - KAMA_Series QL = new(bars.Close, period); - var pta = df.ta.kama(close: df.close, length: period); - for (int i = QL.Length-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void 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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } [Fact] - void MACD() { - MACD_Series QL = new(bars.Close, 26,fast: 12,signal:9); + private void KURTOSIS() { + KURTOSIS_Series QL = new(bars.Close, period, false); + var pta = df.ta.kurtosis(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void MACD() { + MACD_Series QL = new(bars.Close, 26, 12, 9, false); var pta = df.ta.macd(close: df.close).to_numpy(); - for (int i = QL.Length-1; i > skip; i--) { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1][0]; + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1][0]; Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); QL_item = QL.Signal[i - 1].v; - PanTA_item = (double)pta[i - 1][2]; + PanTA_item = (double) pta[i - 1][2]; Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); } } - [Fact] void MAD() - { - MAD_Series QL = new(bars.Close, period, useNaN: false); + + [Fact] + private void MAD() { + MAD_Series QL = new(bars.Close, period, false); var pta = df.ta.mad(close: df.close, length: period); - for (int i = QL.Length-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void MEDIAN() { + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void MEDIAN() { MEDIAN_Series QL = new(bars.Close, period); var pta = df.ta.median(close: df.close, length: period); - for (int i = QL.Length-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void OBV() { - OBV_Series QL = new(bars); - var pta = df.ta.obv(close: df.close, volume: df.volume); - for (int i = QL.Length-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void 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 > skip; i--) - { - double QL_item = bars.OHLC4[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - 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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void RSI() { - RSI_Series QL = new(bars.Close, period); - var pta = df.ta.rsi(close: df.close, length: period); - for (int i = QL.Length-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void 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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - - [Fact] void SMA() { - SMA_Series QL = new(bars.Close, period, false); - var pta = df.ta.sma(close: df.close, length: period); - for (int i = QL.Length-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - - [Fact] void 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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void 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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - - [Fact] void 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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void TEMA() { - TEMA_Series QL = new(bars.Close, period, false); - var pta = df.ta.tema(close: df.close, length: period); - for (int i = QL.Length-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void 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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void 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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void 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-1; i > skip; i--) { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1][0]; + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); } } - [Fact] void VARIANCE() { + + [Fact] + private void OBV() { + OBV_Series QL = new(bars); + var pta = df.ta.obv(close: df.close, volume: df.volume); + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void OHLC4() { + var pta = df.ta.ohlc4(open: df.open, high: df.high, low: df.low, close: df.close); + for (var i = bars.OHLC4.Length; i > skip; i--) { + var QL_item = bars.OHLC4[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void SDEV() { + SDEV_Series QL = new(bars.Close, period, false); + var pta = df.ta.stdev(close: df.close, length: period, ddof: 0); + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void SMA() { + SMA_Series QL = new(bars.Close, period, false); + var pta = df.ta.sma(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void SSDEV() { + SSDEV_Series QL = new(bars.Close, period, false); + var pta = df.ta.stdev(close: df.close, length: period, ddof: 1); + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void SVARIANCE() { + SVAR_Series QL = new(bars.Close, period); + var pta = df.ta.variance(close: df.close, length: period, ddof: 1); + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void TEMA() { + TEMA_Series QL = new(bars.Close, period, false); + var pta = df.ta.tema(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void TR() { + TR_Series QL = new(bars); + var pta = df.ta.true_range(high: df.high, low: df.low, close: df.close); + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private 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 (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private 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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void WMA() { + var pta = df.ta.variance(close: df.close, length: period, ddof: 0); + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private 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-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void ZLEMA() { - ZLEMA_Series QL = new(bars.Close, period, false); - var pta = df.ta.zlma(close: df.close, length: period); - for (int i = QL.Length-1; i > skip; i--) - { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); - } - } - [Fact] void ZSCORE() { - ZSCORE_Series QL = new(bars.Close, period, useNaN: false); + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void ZSCORE() { + ZSCORE_Series QL = new(bars.Close, period, false); var pta = df.ta.zscore(close: df.close, length: period, ddof: 0); - for (int i = QL.Length-1; i > skip; i--) + for (var i = QL.Length - 1; i > skip; i--) { + var QL_item = QL[i - 1].v; + var PanTA_item = (double) pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } +} + +public static class PythonLibrary { + public static string Locate() { + if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows)) { + string[] paths = Environment.GetEnvironmentVariable("PATH")?.Split(';') ?? Array.Empty(); + foreach (string path in paths) { + string[] pythonDLLs = Directory.GetFiles(path, "python3*.dll"); + if (pythonDLLs.Length > 0) { + foreach (string item in pythonDLLs) { + if (!item.EndsWith("python3.dll", StringComparison.OrdinalIgnoreCase)) { + return item; + } + } + + } + } + throw new FileNotFoundException("Python library not found in PATH"); + } + else if (RuntimeInformation.IsOSPlatform(OSPlatform.Linux)) { + return "/usr/lib/x86_64-linux-gnu/libpython3.10.so"; +/* + List pythonLibraries = new List(); + List directoriesToSearch = new List { "/home/runner/.local/lib" }; // Add more directories as needed + string filePattern = "libpython3.*.so"; + SearchFiles(directoriesToSearch, filePattern, pythonLibraries); + + if (pythonLibraries.Count > 0) { + return pythonLibraries[0]; + } + else { + throw new FileNotFoundException("Python library not found"); + } +*/ + } + + else if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX)) { + throw new NotSupportedException("Not supported yet"); + } + + else { throw new NotSupportedException("Unsupported operating system"); } + } + static void SearchFiles(List directoriesToSearch, string filePattern, List foundFiles) + { + foreach (string directory in directoriesToSearch) { - double QL_item = QL[i - 1].v; - double PanTA_item = (double)pta[i - 1]; - Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + if (Directory.Exists(directory)) + { + try + { + string[] files = Directory.GetFiles(directory, filePattern, SearchOption.AllDirectories); + foundFiles.AddRange(files); + } + catch (Exception e) + { + Console.WriteLine("Error searching in directory: " + directory + " - " + e.Message); + } + } } } - -} -*/ \ No newline at end of file +} \ No newline at end of file diff --git a/Tests/Validations/Trends/Skender.cs b/Tests/Validations/Trends/Skender.cs index 8f985767..d413a391 100644 --- a/Tests/Validations/Trends/Skender.cs +++ b/Tests/Validations/Trends/Skender.cs @@ -30,6 +30,7 @@ public class Skender }); } +/* [Fact] public void ADL() { @@ -42,6 +43,7 @@ public class Skender Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); } } + */ [Fact] public void ALMA() { @@ -57,7 +59,7 @@ public class Skender [Fact] public void ATR() { - ATR_Series QL = new(bars, period, false); + ATR_Series QL = new(bars, period:period,useNaN: false); var SK = quotes.GetAtr(period).Select(i => i.Atr.Null2NaN()!); for (int i = QL.Length; i > skip; i--) { diff --git a/Tests/Validations/Trends/TA_LIB.cs b/Tests/Validations/Trends/TA_LIB.cs index 7a663212..01e40c2a 100644 --- a/Tests/Validations/Trends/TA_LIB.cs +++ b/Tests/Validations/Trends/TA_LIB.cs @@ -72,7 +72,7 @@ public class Ta_Lib [Fact] public void ATR() { - ATR_Series QL = new(bars, period, false); + ATR_Series QL = new(bars, period:period, useNaN: false); Core.Atr(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); for (int i = QL.Length - 1; i > skip; i--) { diff --git a/Tests/Validations/Trends/Tulip.cs b/Tests/Validations/Trends/Tulip.cs index 4f57e27f..f16f0184 100644 --- a/Tests/Validations/Trends/Tulip.cs +++ b/Tests/Validations/Trends/Tulip.cs @@ -80,7 +80,7 @@ public class Tulip_Test double[][] arrin = { inhigh, inlow, inclose }; double[][] arrout = { outdata }; - ATR_Series QL = new(bars, period, false); + ATR_Series QL = new(bars, period:period, useNaN:false); Tulip.Indicators.atr.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); //Tulip ATR doesn't use warm-up SMA, compensating with 200 warming bars for (int i = QL.Length - 1; i > 200+skip; i--)