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Update calculations, indicators, strategies, and tests for trends

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This commit is contained in:
Miha Kralj
2023-05-10 12:58:32 -07:00
parent 2defde9d94
commit 2b71b307d5
6 changed files with 272 additions and 358 deletions
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.sonarlint/mihakralj_quantalibcsharp.ruleset
+1
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@@ -372,6 +372,7 @@
<Rule Id="S5547" Action="Warning" />
<Rule Id="S5659" Action="Warning" />
<Rule Id="S5773" Action="Warning" />
<Rule Id="S5856" Action="None" />
<Rule Id="S6354" Action="None" />
<Rule Id="S6419" Action="None" />
<Rule Id="S6420" Action="None" />
Strategies/Strategies.csproj
+3
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@@ -42,6 +42,9 @@
<Link>QuanTAlib\%(RecursiveDir)%(Filename)%(Extension)</Link>
</Compile>
</ItemGroup>
<ItemGroup>
<AdditionalFiles Include="..\.sonarlint\mihakralj_quantalib\CSharp\SonarLint.xml" Link="SonarLint.xml" />
</ItemGroup>
<ItemGroup>
<Reference Include="TradingPlatform.BusinessLayer">
<HintPath>..\.github\TradingPlatform.BusinessLayer.dll</HintPath>
Tests/Validations/Trends/Pandas_TA.cs
+265 -355
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@@ -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 {
Tests/Validations/Trends/Skender.cs
+1 -1
View File
@@ -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--)
{
Tests/Validations/Trends/TA_LIB.cs
+1 -1
View File
@@ -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--)
{
Tests/Validations/Trends/Tulip.cs
+1 -1
View File
@@ -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--)