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SMA docs

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Miha Kralj
2023-01-07 22:32:33 -08:00
parent 3c9bfb265d
commit 6db1ba7562
3 changed files with 74 additions and 69 deletions
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Tests/Validations/Trends/Pandas_TA.cs
+39 -42
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@@ -9,7 +9,7 @@ public class PandasTA : IDisposable
{
private readonly GBM_Feed bars;
private readonly Random rnd = new();
private readonly int period, sample;
private readonly int period, skip;
private int digits;
private readonly string dllpath;
private readonly dynamic np;
@@ -20,8 +20,8 @@ public class PandasTA : IDisposable
public PandasTA() {
bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0);
period = rnd.Next(maxValue: 28) + 3;
sample = period+1;
digits = 10;
skip = period+10;
digits = 8;
Installer.InstallPath = Path.GetFullPath(path: ".");
Installer.SetupPython().Wait();
@@ -30,10 +30,7 @@ public class PandasTA : IDisposable
Installer.PipInstallModule(module_name: "pandas");
Installer.PipInstallModule(module_name: "pandas-ta");
dllpath = Installer.InstallPath + "\\" + Installer.InstallDirectory + "\\" + Runtime.PythonDLL;
Runtime.PythonDLL = dllpath;
//Runtime.PythonDLL = "python.dll";
PythonEngine.Initialize();
np = Py.Import(name: "numpy");
@@ -60,7 +57,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i-1].v;
double PanTA_item = (double)pta[i-1];
@@ -72,7 +69,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -82,7 +79,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -93,7 +90,7 @@ public class PandasTA : IDisposable
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 > QL.Length - sample; i--) {
for (int i = QL.Length-1; i > skip; i--) {
double QL_item = QL.Lower[i].v;
double PanTA_item = (double)pta[i][0]; //lower
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
@@ -108,7 +105,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -119,7 +116,7 @@ public class PandasTA : IDisposable
void CCI() {
CCI_Series QL = new(bars, period, false);
var pta = df.ta.cci(close: df.close, length: period);
for (int i = QL.Length; i > QL.Length - sample; i--) {
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));
@@ -130,7 +127,7 @@ public class PandasTA : IDisposable
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--) {
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));
@@ -140,7 +137,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -150,7 +147,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -160,7 +157,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -169,7 +166,7 @@ public class PandasTA : IDisposable
}
[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--)
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];
@@ -178,7 +175,7 @@ public class PandasTA : IDisposable
}
[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--)
for (int i = bars.HLC3.Length; i > skip; i--)
{
double QL_item = bars.HLC3[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -188,7 +185,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -199,7 +196,7 @@ public class PandasTA : IDisposable
[Fact] void HWMA() {
HWMA_Series QL = new(bars.Close, useNaN: false);
var pta = df.ta.hwma(close: df.close);
for (int i = QL.Length; i > QL.Length-sample; i--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -210,7 +207,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -220,7 +217,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -231,7 +228,7 @@ public class PandasTA : IDisposable
void MACD() {
MACD_Series QL = new(bars.Close, 26,fast: 12,signal:9);
var pta = df.ta.macd(close: df.close).to_numpy();
for (int i = QL.Length; i > QL.Length - sample; i--) {
for (int i = QL.Length-1; i > skip; i--) {
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1][0];
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
@@ -244,7 +241,7 @@ public class PandasTA : IDisposable
{
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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -254,7 +251,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -264,7 +261,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -273,7 +270,7 @@ public class PandasTA : IDisposable
}
[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--)
for (int i = bars.OHLC4.Length; i > skip; i--)
{
double QL_item = bars.OHLC4[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -283,7 +280,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -293,7 +290,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -303,7 +300,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -313,7 +310,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -323,7 +320,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -333,7 +330,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -344,7 +341,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -354,7 +351,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -364,7 +361,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -375,7 +372,7 @@ public class PandasTA : IDisposable
// 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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -385,7 +382,7 @@ public class PandasTA : IDisposable
[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--) {
for (int i = QL.Length-1; i > skip; i--) {
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1][0];
Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits));
@@ -394,7 +391,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -404,7 +401,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -414,7 +411,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];
@@ -424,7 +421,7 @@ public class PandasTA : IDisposable
[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--)
for (int i = QL.Length-1; i > skip; i--)
{
double QL_item = QL[i - 1].v;
double PanTA_item = (double)pta[i - 1];