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PINE LIBRARY

Regression_Toolkit

1 031
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.

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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)

Penafian

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