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QuanTAlib/Tests/Validations/Trends/Pandas_TA.cs
T
Miha Kralj 9ad467cd5a Quantower adaptation
2022-12-21 07:24:21 -08:00

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using Xunit;
using System;
using QuanTAlib;
using Python.Runtime;
using Python.Included;
namespace Validations;
public class PandasTA : IDisposable
{
private readonly GBM_Feed bars;
private readonly Random rnd = new();
private readonly int period, sample;
private int digits;
private readonly string OStype;
private readonly dynamic np;
private readonly dynamic ta;
private readonly dynamic df;
public PandasTA() {
bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0);
period = rnd.Next(maxValue: 28) + 3;
sample = 200;
digits = 10;
// Checking the host OS and setting PythonDLL accordingly
OStype = Environment.OSVersion.ToString();
if (OStype == "Unix 13.1.0")
OStype = @"/usr/local/Cellar/python@3.10/3.10.8/Frameworks/Python.framework/Versions/3.10/lib/libpython3.10.dylib";
else OStype = Path.GetFullPath(".") + @"\python-3.10.0-embed-amd64\python310.dll";
Installer.InstallPath = Path.GetFullPath(path: ".");
Installer.SetupPython().Wait();
Installer.TryInstallPip();
Installer.PipInstallModule(module_name: "pandas-ta");
Runtime.PythonDLL = OStype;
PythonEngine.Initialize();
np = Py.Import(name: "numpy");
ta = Py.Import(name: "pandas_ta");
string[] cols = { "open", "high", "low", "close", "volume" };
double[,] ary = new double[bars.Count, 5];
for (int i = 0; i < bars.Count; i++) {
ary[i, 0] = bars.Open[i].v;
ary[i, 1] = bars.High[i].v;
ary[i, 2] = bars.Low[i].v;
ary[i, 3] = bars.Close[i].v;
ary[i, 4] = bars.Volume[i].v;
}
df = ta.DataFrame(data: np.array(ary), index: np.array(bars.Close.t), columns: np.array(cols));
}
public void Dispose()
{
PythonEngine.Shutdown();
GC.SuppressFinalize(this);
}
[Fact] void ADL() {
ADL_Series QL = new(bars);
var pta = df.ta.ad(high: df.high, low: df.low, close:df.close, volume:df.volume);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i-1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i-1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void ADOSC() {
ADOSC_Series QL = new(bars);
var pta = df.ta.adosc(high: df.high, low: df.low, close: df.close, volume: df.volume);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void ATR() {
ATR_Series QL = new(bars, period);
var pta = df.ta.atr(high: df.high, low: df.low, close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void BIAS() {
BIAS_Series QL = new(bars.Close, period, false);
var pta = df.ta.bias(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
/*
[Fact]
void CMO() {
CMO_Series QL = new(bars.Close, period, false);
var pta = df.ta.cmo(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length - sample; i--) {
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
*/
[Fact] void DEMA() {
DEMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.dema(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void EMA() {
EMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.ema(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void ENTROPY() {
ENTROPY_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.entropy(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void HL2() {
var pta = df.ta.hl2(high: df.high, low: df.low);
for (int i = bars.HL2.Length; i > bars.HL2.Length-sample; i--)
{
double QL_item = Math.Round(bars.HL2[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void HLC3() {
var pta = df.ta.hlc3(high: df.high, low: df.low, close: df.close);
for (int i = bars.HLC3.Length; i > bars.HLC3.Length-sample; i--)
{
double QL_item = Math.Round(bars.HLC3[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void HMA() {
HMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.hma(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void KAMA() {
KAMA_Series QL = new(bars.Close, period);
var pta = df.ta.kama(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void KURTOSIS() {
KURTOSIS_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.kurtosis(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void MAD()
{
MAD_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.mad(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void MEDIAN() {
MEDIAN_Series QL = new(bars.Close, period);
var pta = df.ta.median(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void OBV() {
OBV_Series QL = new(bars);
var pta = df.ta.obv(close: df.close, volume: df.volume);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void OHLC4() {
var pta = df.ta.ohlc4(open: df.open, high: df.high, low: df.low, close: df.close);
for (int i = bars.OHLC4.Length; i > bars.OHLC4.Length-sample; i--)
{
double QL_item = Math.Round(bars.OHLC4[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void RMA() {
RMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.rma(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void RSI() {
RSI_Series QL = new(bars.Close, period);
var pta = df.ta.rsi(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void SDEV() {
SDEV_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.stdev(close: df.close, length: period, ddof: 0);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void SMA() {
SMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.sma(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void SSDEV() {
SSDEV_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.stdev(close: df.close, length: period, ddof: 1);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
/*
[Fact] void SVARIANCE() {
SVAR_Series QL = new(bars.Close, period);
var pta = df.ta.variance(close: df.close, length: period, ddof: 1);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
*/
[Fact] void T3() {
T3_Series QL = new(source: bars.Close, period: period, vfactor: 0.7, useNaN: false);
var pta = df.ta.t3(close: df.close, length: period, a: 0.7);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void TEMA() {
TEMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.tema(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void TR() {
TR_Series QL = new(bars);
var pta = df.ta.true_range(high: df.high, low: df.low, close: df.close);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void TRIMA() {
// TODO: return length to variable length (period) when Pandas-TA fixes trima to calculate even periods right
TRIMA_Series QL = new(bars.Close, 11);
var pta = df.ta.trima(close: df.close, length: 11);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void TRIX() {
TRIX_Series QL = new(bars.Close, period);
var pta = df.ta.trix(close: df.close, length: period).to_numpy();
for (int i = QL.Length; i > QL.Length - sample; i--) {
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1][0], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void VARIANCE() {
VAR_Series QL = new(bars.Close, period);
var pta = df.ta.variance(close: df.close, length: period, ddof:0);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void WMA() {
WMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.wma(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void ZLEMA() {
ZLEMA_Series QL = new(bars.Close, period, false);
var pta = df.ta.zlma(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
[Fact] void ZSCORE() {
ZSCORE_Series QL = new(bars.Close, period, useNaN: false);
var pta = df.ta.zscore(close: df.close, length: period, ddof: 0);
for (int i = QL.Length; i > QL.Length-sample; i--)
{
double QL_item = Math.Round(QL[i - 1].v, digits: digits);
double PanTA_item = Math.Round((double)pta[i - 1], digits: digits);
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
}
}
}
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