This is toolkit/library bridges advanced regression approaches not natively supported in Pinescript, to Pinescript. Advanced regression frameworks that can be critical to ticker data, such as Ridge, Lasso, ElasticNET, and Logistic (normalized) regression, colinarity measuring and quantile regression. As well as approaches to linear based feature selection and importance assessments.
I hope you find it helpful!
Library "Regression_Toolkit"
multipleRegression(y, x1, x2, length)
Parameters:
y (float)
x1 (float)
x2 (float)
length (simple int)
ridgeRegression(y, x1, x2, x3, x4, nVars, length, lambda)
Parameters:
y (float)
x1 (float)
x2 (float)
x3 (float)
x4 (float)
nVars (simple int)
length (simple int)
lambda (simple float)
lassoRegression(y, x1, x2, x3, x4, nVars, length, lambda, iterations)
Parameters:
y (float)
x1 (float)
x2 (float)
x3 (float)
x4 (float)
nVars (simple int)
length (simple int)
lambda (simple float)
iterations (simple int)
logisticRegression(y, x1, x2, x3, x4, nVars, length, learningRate, iterations)
Parameters:
y (float)
x1 (float)
x2 (float)
x3 (float)
x4 (float)
nVars (simple int)
length (simple int)
learningRate (simple float)
iterations (simple int)
featureSelection(y, x1, x2, x3, x4, nVars, length)
Parameters:
y (float)
x1 (float)
x2 (float)
x3 (float)
x4 (float)
nVars (simple int)
length (simple int)
regressionStats(y, x1, x2, x3, x4, nVars, length, b0, b1, b2, b3, b4)
Parameters:
y (float)
x1 (float)
x2 (float)
x3 (float)
x4 (float)
nVars (simple int)
length (simple int)
b0 (float)
b1 (float)
b2 (float)
b3 (float)
b4 (float)
elasticNetRegression(y, x1, x2, x3, x4, nVars, length, lambda, alpha, iterations)
Parameters:
y (float)
x1 (float)
x2 (float)
x3 (float)
x4 (float)
nVars (simple int)
length (simple int)
lambda (simple float)
alpha (simple float)
iterations (simple int)
huberRegression(y, x1, x2, x3, x4, nVars, length, huberK, iterations)
Parameters:
y (float)
x1 (float)
x2 (float)
x3 (float)
x4 (float)
nVars (simple int)
length (simple int)
huberK (simple float)
iterations (simple int)
quantileRegression(y, x1, x2, x3, x4, nVars, length, tau, learningRate, iterations)
Parameters:
y (float)
x1 (float)
x2 (float)
x3 (float)
x4 (float)
nVars (simple int)
length (simple int)
tau (simple float)
learningRate (simple float)
iterations (simple int)
I hope you find it helpful!
Library "Regression_Toolkit"
multipleRegression(y, x1, x2, length)
Parameters:
y (float)
x1 (float)
x2 (float)
length (simple int)
ridgeRegression(y, x1, x2, x3, x4, nVars, length, lambda)
Parameters:
y (float)
x1 (float)
x2 (float)
x3 (float)
x4 (float)
nVars (simple int)
length (simple int)
lambda (simple float)
lassoRegression(y, x1, x2, x3, x4, nVars, length, lambda, iterations)
Parameters:
y (float)
x1 (float)
x2 (float)
x3 (float)
x4 (float)
nVars (simple int)
length (simple int)
lambda (simple float)
iterations (simple int)
logisticRegression(y, x1, x2, x3, x4, nVars, length, learningRate, iterations)
Parameters:
y (float)
x1 (float)
x2 (float)
x3 (float)
x4 (float)
nVars (simple int)
length (simple int)
learningRate (simple float)
iterations (simple int)
featureSelection(y, x1, x2, x3, x4, nVars, length)
Parameters:
y (float)
x1 (float)
x2 (float)
x3 (float)
x4 (float)
nVars (simple int)
length (simple int)
regressionStats(y, x1, x2, x3, x4, nVars, length, b0, b1, b2, b3, b4)
Parameters:
y (float)
x1 (float)
x2 (float)
x3 (float)
x4 (float)
nVars (simple int)
length (simple int)
b0 (float)
b1 (float)
b2 (float)
b3 (float)
b4 (float)
elasticNetRegression(y, x1, x2, x3, x4, nVars, length, lambda, alpha, iterations)
Parameters:
y (float)
x1 (float)
x2 (float)
x3 (float)
x4 (float)
nVars (simple int)
length (simple int)
lambda (simple float)
alpha (simple float)
iterations (simple int)
huberRegression(y, x1, x2, x3, x4, nVars, length, huberK, iterations)
Parameters:
y (float)
x1 (float)
x2 (float)
x3 (float)
x4 (float)
nVars (simple int)
length (simple int)
huberK (simple float)
iterations (simple int)
quantileRegression(y, x1, x2, x3, x4, nVars, length, tau, learningRate, iterations)
Parameters:
y (float)
x1 (float)
x2 (float)
x3 (float)
x4 (float)
nVars (simple int)
length (simple int)
tau (simple float)
learningRate (simple float)
iterations (simple int)
Libreria Pine
Nello spirito di TradingView, l'autore ha pubblicato questo codice Pine come libreria open source affinché altri programmatori della nostra community possano riutilizzarlo. Complimenti all'autore! È possibile utilizzare questa libreria privatamente o in altre pubblicazioni open source, ma il riutilizzo di questo codice nelle pubblicazioni è soggetto al Regolamento.
Check out the new Website!
merlintrades.net
Get access to:
- Indicators
- Merlin Trading Software
- Discord
- Documentation
merlintrades.net
Get access to:
- Indicators
- Merlin Trading Software
- Discord
- Documentation
Declinazione di responsabilità
Le informazioni e le pubblicazioni non sono intese come, e non costituiscono, consulenza o raccomandazioni finanziarie, di investimento, di trading o di altro tipo fornite o approvate da TradingView. Per ulteriori informazioni, consultare i Termini di utilizzo.
Libreria Pine
Nello spirito di TradingView, l'autore ha pubblicato questo codice Pine come libreria open source affinché altri programmatori della nostra community possano riutilizzarlo. Complimenti all'autore! È possibile utilizzare questa libreria privatamente o in altre pubblicazioni open source, ma il riutilizzo di questo codice nelle pubblicazioni è soggetto al Regolamento.
Check out the new Website!
merlintrades.net
Get access to:
- Indicators
- Merlin Trading Software
- Discord
- Documentation
merlintrades.net
Get access to:
- Indicators
- Merlin Trading Software
- Discord
- Documentation
Declinazione di responsabilità
Le informazioni e le pubblicazioni non sono intese come, e non costituiscono, consulenza o raccomandazioni finanziarie, di investimento, di trading o di altro tipo fornite o approvate da TradingView. Per ulteriori informazioni, consultare i Termini di utilizzo.