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/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/Validations/Trends/Pandas_TA.cs b/Tests/Validations/Trends/Pandas_TA.cs
index 9ff103d7..f6953454 100644
--- a/Tests/Validations/Trends/Pandas_TA.cs
+++ b/Tests/Validations/Trends/Pandas_TA.cs
@@ -7,9 +7,7 @@ using Python.Runtime;
namespace Validations;
-
-public class PandasTA : IDisposable
-{
+public class PandasTA : IDisposable {
private bool disposed = false;
private readonly GBM_Feed bars;
private readonly Random rnd = new();
@@ -20,429 +18,341 @@ public class PandasTA : IDisposable
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;
- string pythonDLL = PythonLibrary.Locate();
+ 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()
- {
+
+ public void Dispose() {
Dispose(true);
PythonEngine.Shutdown();
- GC.SuppressFinalize(this);
+ GC.SuppressFinalize(this);
}
- ~PandasTA() {
- Dispose(false);
- }
- protected virtual void Dispose(bool disposing) {
- if (!disposed) {
- disposed = true;
- }
+
+ ~PandasTA() {
+ Dispose(false);
+ }
+
+ protected virtual void Dispose(bool disposing) {
+ if (!disposed) {
+ disposed = true;
}
+ }
[Fact]
- void ADL() {
+ 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 (int i = QL.Length-1; i > skip; i--)
- {
- double QL_item = QL[i-1].v;
- double PanTA_item = (double)pta[i-1];
+ 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] 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 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));
- }
- }
+ 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 EMA() {
+ [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);
- var pta = df.ta.zscore(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));
- }
- }
- */
+ 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 (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 {
diff --git a/Tests/Validations/Trends/Skender.cs b/Tests/Validations/Trends/Skender.cs
index fb97799d..d413a391 100644
--- a/Tests/Validations/Trends/Skender.cs
+++ b/Tests/Validations/Trends/Skender.cs
@@ -59,7 +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--)