Hello, can someone please help converting this code?
I’m aware we don’t have multidimentional Arrays in PRT yet. I suspect, we can come over by splitting into multiple Arrays.
Thank you in advance
// This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/
// © LuxAlgo
//@version=5
indicator("Polynomial Regression Extrapolation [LuxAlgo]"
, overlay = true
, max_lines_count = 500)
//------------------------------------------------------------------------------
//Settings
//-----------------------------------------------------------------------------{
length = input.int(100
, minval = 0)
extrapolate = input.int(10
, minval = 0)
degree = input.int(3, 'Polynomial Degree'
, minval = 0
, maxval = 8)
src = input(close)
lock = input(false, 'Lock Forecast')
//Style
up_css = input.color(#0cb51a, 'Upward Color'
, group = 'Style')
dn_css = input.color(#ff1100, 'Downward Color'
, group = 'Style')
ex_css = input.color(#ff5d00, 'Extrapolation Color'
, group = 'Style')
width = input(1, 'Width'
, group = 'Style')
//-----------------------------------------------------------------------------}
//Fill lines array
//-----------------------------------------------------------------------------{
var lines = array.new_line(0)
if barstate.isfirst
for i = -extrapolate to length-1
array.push(lines, line.new(na, na, na, na))
//-----------------------------------------------------------------------------}
//Get design matrix & partially solve system
//-----------------------------------------------------------------------------{
n = bar_index
var design = matrix.new<float>(0, 0)
var response = matrix.new<float>(0, 0)
if barstate.isfirst
for i = 0 to degree
column = array.new_float(0)
for j = 0 to length-1
array.push(column, math.pow(j,i))
matrix.add_col(design, i, column)
var a = matrix.inv(matrix.mult(matrix.transpose(design), design))
var b = matrix.mult(a, matrix.transpose(design))
//-----------------------------------------------------------------------------}
//Get response matrix and compute roling polynomial regression
//-----------------------------------------------------------------------------{
var pass = 1
var matrix<float> coefficients = na
var x = -extrapolate
var float forecast = na
if barstate.islast
if pass
prices = array.new_float(0)
for i = 0 to length-1
array.push(prices, src[i])
matrix.add_col(response, 0, prices)
coefficients := matrix.mult(b, response)
float y1 = na
idx = 0
for i = -extrapolate to length-1
y2 = 0.
for j = 0 to degree
y2 += math.pow(i, j)*matrix.get(coefficients, j, 0)
if idx == 0
forecast := y2
//------------------------------------------------------------------
//Set lines
//------------------------------------------------------------------
css = y2 < y1 ? up_css : dn_css
get_line = array.get(lines, idx)
line.set_xy1(get_line, n - i + 1, y1)
line.set_xy2(get_line, n - i, y2)
line.set_color(get_line, i <= 0 ? ex_css : css)
line.set_width(get_line, width)
y1 := y2
idx += 1
if lock
pass := 0
else
y2 = 0.
x -= 1
for j = 0 to degree
y2 += math.pow(x, j)*matrix.get(coefficients, j, 0)
forecast := y2
plot(pass == 0 ? forecast : na, 'Extrapolation'
, color = ex_css
, offset = extrapolate
, linewidth = width)
//-----------------------------------------------------------------------------}
