Pine Scipt conversion – Polynomial Regression Extrapolation

This topic has 22 replies, 6 voices, and was last updated 1 year ago by LucasBest.

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10/05/2022 at 10:30 AM #201933

Khaled

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)

//-----------------------------------------------------------------------------}

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// 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)

//-----------------------------------------------------------------------------}

Khaled

Participant

Topics: 59

Replies: 314

Been thanked: 65 times

08/27/2023 at 10:09 AM #219832

LucasBest

If you want to come over multidimentional Arrays in PRT, the solution is not to split it into multiple Arrays, because this would mean changing the code of the script each time one or both size of the multidimentional Arrays will change… which is not possible!

In reality, coming over 2 dimensions arrays (in PRT) with only 1 dimension array is not a big deal. It has ben done for years since early stage of computer science. What you see on your screen can be compared to a 2 dimentional array (rows and columns of the screen) and it is simply stored in the memory of the computer in a one dimention array… It is possible as far as you know how much rows and columns you need to store in your 1 dimension array.

I did the conversion of the begining of Polynomial Regression Extrapolation by LuxAlgo. You can find it below.

The biggest deal here is to come over those built-in function for multidimentional Arrays that come with pine script.

For example “matrix.inv” calculate the inverse of a square matrix, which is not easy to code and will require several lines of code and take long time to calculate if it is not done in a built-in function…

defparam drawonlastbaronly = true
Maxlength = 500
long = Min(barindex,Min(Maxlength,length))
// degree = 0-8 Polynomial Degree
src = customclose
// lock = Lock Forecast
once init = 1

n = barindex

If init = 1 then
For k = 0 to degree*long-1 do
$design[k]=0
Next
For i = 0 to degree do
For j = 0 to long-1 do
P = pow(j,i)
$designT[i*long+j]= P
$design[j*degree+i]= P
Next
Next
//Matrix.mult
For i = 0 to degree do
For j = 0 to degree do
sum = 0
For k = 0 to long-1 do
sum=sum+$designT[i*long+k]*$design[k*degree+j]
Next
$produit[i*degree+j]=sum
Next
Next
init = 0
Endif

...

defparam drawonlastbaronly = true

Maxlength = 500

long = Min(barindex,Min(Maxlength,length))

// degree = 0-8 Polynomial Degree

src = customclose

// lock = Lock Forecast

once init = 1

n = barindex

If init = 1 then

For k = 0 to degree*long-1 do

$design[k]=0

For i = 0 to degree do

For j = 0 to long-1 do

P = pow(j,i)

$designT[i*long+j]= P

$design[j*degree+i]= P

//Matrix.mult

For i = 0 to degree do

For j = 0 to degree do

sum = 0

For k = 0 to long-1 do

sum=sum+$designT[i*long+k]*$design[k*degree+j]

$produit[i*degree+j]=sum

init = 0

Endif

...

1 user thanked author for this post.

Khaled

LucasBest

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Replies: 208

Been thanked: 72 times

08/27/2023 at 10:30 AM #219834

Khaled

@LucasBest Bravo. It’s clear, you’re talented. How can I finish translating the remaining part so that I can get the full code working? Thanks

Khaled

Participant

Topics: 59

Replies: 314

Been thanked: 65 times

08/27/2023 at 10:46 AM #219835

LucasBest

I am trying to found a way to code simply the calculation of the inverse of a square matrix.

If someone can help me to do so, it would be appreciate, because all i learned in school about matrix is very far now… 🙂

The rest of LuxAlgo pine script will be quit easy to translate then.

For example, in the link below the script in c++ for inverse matrix calculation…

https://www.tutorialspoint.com/cplusplus-program-to-find-inverse-of-a-graph-matrix

1 user thanked author for this post.

Khaled

LucasBest

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Replies: 208

Been thanked: 72 times

08/27/2023 at 10:53 AM #219836

Khaled

Lucas, can i suggest that you replace this inverse matrix by whatever pleases you to get over this step (for now until we find a solution) and continue the rest with an alternative option to this inverse matrix? Thanks

Khaled

Participant

Topics: 59

Replies: 314

Been thanked: 65 times

08/27/2023 at 11:08 AM #219839

LucasBest

Impossible! It would be like cooking a sauerkraut without cabbage or a couscous without semolina…
We need this matrix inversion to calculate Matrix a, then Matrix b, and everything that come after!

LucasBest

Participant

Topics: 16

Replies: 208

Been thanked: 72 times

08/27/2023 at 11:17 AM #219841

Khaled

Fair enough! Bravo anyway! France has talents.

Khaled

Participant

Topics: 59

Replies: 314

Been thanked: 65 times

08/27/2023 at 11:33 AM #219846

PeterSt

LucasBest wrote:

will require several lines of code and take long time to calculate

LucasBest wrote:

like cooking a sauerkraut without cabbage

The latter can be overwon by a. growing the gabbage and b. ferment it. Yes, take many months for that indeed. The semolina needs further investigation.

PeterSt

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Topics: 73

Replies: 2506

Been thanked: 808 times

08/27/2023 at 11:46 AM #219847

LucasBest

PeterSt wrote:

The latter can be overwon by a. growing the gabbage and b. ferment it. Yes, take many months for that indeed. The semolina needs further investigation.

Hey Peter,

this is funny, but it does not help me to code the inverse function of a matrix… 😉

LucasBest

Participant

Topics: 16

Replies: 208

Been thanked: 72 times

08/27/2023 at 6:52 PM #219882

LucasBest

defparam drawonlastbaronly = true
Maxlength = 500
long = Min(barindex,Min(Maxlength,length))
// degree = 0-8 Polynomial Degree
src = customclose
// lock = Lock Forecast
once init = 1

n = barindex

If init = 1 then
For k = 0 to degree*long-1 do
$design[k]=0
Next
For i = 0 to degree-1 do
For j = 0 to long-1 do
P = pow(j,i)
$designT[i*long+j]= P
$design[j*degree+i]= P
Next
Next
//Matrix.mult
For i = 0 to degree-1 do
For j = 0 to degree-1 do
sum = 0
For k = 0 to long-1 do
sum=sum+$designT[i*long+k]*$design[k*degree+j]
Next
$produit[i*degree+j]=sum
Next
Next

//Extracting Minors Matrix i,j
Minordegree = degree-1
For i = 0 to Minordegree do
For j = 0 to Minordegree do
m = 0
For k = 0 to degree*degree-1 do
If (j<>k mod degree) and (i<>floor(k/degree)) then
$Minor[m]=$produit[k]
m=m+1
Endif
Next
//Calculation Determinant
If Minordegree = 1 then
determinant = $Minor[1]
Elsif Minordegree = 2 then
determinant = $Minor[1]*$Minor[4]-$Minor[2]*$Minor[3]
Elsif Minordegree = 3 then
determinant = $Minor[1]*($Minor[5]*$Minor[9]-$Minor[6]*$Minor[8])-$Minor[2]*($Minor[4]*$Minor[9]-$Minor[6]*$Minor[7])+$Minor[3]*($Minor[4]*$Minor[8]-$Minor[5]*$Minor[7])
Endif
//Setting cofactor Matrix
$cofactor[i*degre+j]=pow(-1,i+j)*determinant
Next
Next
//Calculating overall determinant
determinant = 0
For k = 0 to degree-1 do
determinant = determinant + $produit[k]*$cofactor[k]
$Inverse[k] = 0
Next
//Setting Inverse Matrix
For i = 0 to degree-1 do
For j = 0 to degree-1 do
$Inverse[i+j*degree] = $cofactor[i*degree+j]*(1/determinant)
Next
Next
init = 0
Endif

defparam drawonlastbaronly = true

Maxlength = 500

long = Min(barindex,Min(Maxlength,length))

// degree = 0-8 Polynomial Degree

src = customclose

// lock = Lock Forecast

once init = 1

n = barindex

If init = 1 then

For k = 0 to degree*long-1 do

$design[k]=0

For i = 0 to degree-1 do

For j = 0 to long-1 do

P = pow(j,i)

$designT[i*long+j]= P

$design[j*degree+i]= P

//Matrix.mult

For i = 0 to degree-1 do

For j = 0 to degree-1 do

sum = 0

For k = 0 to long-1 do

sum=sum+$designT[i*long+k]*$design[k*degree+j]

$produit[i*degree+j]=sum

//Extracting Minors Matrix i,j

Minordegree = degree-1

For i = 0 to Minordegree do

For j = 0 to Minordegree do

m = 0

For k = 0 to degree*degree-1 do

If (j<>k mod degree) and (i<>floor(k/degree)) then

$Minor[m]=$produit[k]

m=m+1

Endif

//Calculation Determinant

If Minordegree = 1 then

determinant = $Minor[1]

Elsif Minordegree = 2 then

determinant = $Minor[1]*$Minor[4]-$Minor[2]*$Minor[3]

Elsif Minordegree = 3 then

determinant = $Minor[1]*($Minor[5]*$Minor[9]-$Minor[6]*$Minor[8])-$Minor[2]*($Minor[4]*$Minor[9]-$Minor[6]*$Minor[7])+$Minor[3]*($Minor[4]*$Minor[8]-$Minor[5]*$Minor[7])

Endif

//Setting cofactor Matrix

$cofactor[i*degre+j]=pow(-1,i+j)*determinant

//Calculating overall determinant

determinant = 0

For k = 0 to degree-1 do

determinant = determinant + $produit[k]*$cofactor[k]

$Inverse[k] = 0

//Setting Inverse Matrix

For i = 0 to degree-1 do

For j = 0 to degree-1 do

$Inverse[i+j*degree] = $cofactor[i*degree+j]*(1/determinant)

init = 0

Endif

Khaled wrote:

Lucas, can i suggest that you replace this inverse matrix by whatever pleases you to get over this step (for now until we find a solution) and continue the rest with an alternative option to this inverse matrix? Thanks

Ok, i did not replace, but i found a solution for calculating the inverse of the matrix, until degree 4… Now i can finish and will found a solution later for inversing bigger square matrix

1 user thanked author for this post.

Nicolas

LucasBest

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Topics: 16

Replies: 208

Been thanked: 72 times

08/28/2023 at 9:16 AM #219918

LucasBest

It is working now, but there is still some bugs…

defparam drawonlastbaronly = true
Maxlength = 500
long = Min(barindex,Min(Maxlength,length))
// degree = 0-8 Polynomial Degree
src = customclose
// lock = Lock Forecast
once init = 1

n = barindex

If init=1 and long=length then
For k = 0 to degree*long-1 do
$design[k]=0
Next
For i = 0 to degree-1 do
For j = 0 to long-1 do
P = pow(j,i)
$designT[i*long+j]= P
$design[j*degree+i]= P
Next
Next
//Matrix.mult
For i = 0 to degree-1 do
For j = 0 to degree-1 do
sum = 0
For k = 0 to long-1 do
sum=sum+$designT[i*long+k]*$design[k*degree+j]
Next
$produit[i*degree+j]=sum
Next
Next

//Extracting Minors Matrix i,j
Minordegree = degree-1
For i = 0 to Minordegree do
For j = 0 to Minordegree do
m = 0
For k = 0 to degree*degree-1 do
If (j<>k mod degree) and (i<>floor(k/degree)) then
$Minor[m]=$produit[k]
m=m+1
Endif
Next
//Calculation Determinant
If Minordegree = 1 then
determinant = $Minor[0]
Elsif Minordegree = 2 then
determinant = $Minor[0]*$Minor[3]-$Minor[1]*$Minor[2]
Elsif Minordegree = 3 then
determinant = $Minor[0]*($Minor[4]*$Minor[8]-$Minor[5]*$Minor[7])-$Minor[1]*($Minor[3]*$Minor[8]-$Minor[5]*$Minor[6])+$Minor[2]*($Minor[3]*$Minor[7]-$Minor[4]*$Minor[6])
Endif
//Setting cofactor Matrix
$cofactor[i*degree+j]=pow(-1,i+j)*determinant
Next
Next
//Calculating overall determinant
determinant = 0
For k = 0 to degree-1 do
determinant = determinant + $produit[k]*$cofactor[k]
Next
//Setting Inverse Matrix => $a
For k = 0 to degree*degree-1 do
$a[k]=0
Next
For i = 0 to degree-1 do
For j = 0 to degree-1 do
$a[i+j*degree] = $cofactor[i*degree+j]*(1/determinant)
Next
Next

For i = 0 to degree-1 do
For j = 0 to degree-1 do
$a[i+j*degree] = $cofactor[i*degree+j]*(1/determinant)
Next
Next
//Setting Mult matrice => $b
For i = 0 to degree-1 do
For j = 0 to long-1 do
sum = 0
For k = 0 to degree-1 do
sum=sum+$a[i*degree+k]*$designT[k*long+j]
Next
$b[i*long+j]=sum
Next
Next
init = 0
Endif

once pass = 1
once x = -1 * Extrapolate
once forecast = 0

If IsLastBarUpdate then
If pass = 1 then
For i = 0 to long-1 do
$response[i] = src[i]
Next
//Setting Mult matrice => $coefficients
For i = 0 to degree-1 do
j = 0
sum = 0
For k = 0 to long-1 do
sum=sum+$b[i*long+k]*$response[k]
Next
$coefficients[i]=sum
Next
once y1 = 0
idx = 0
For i = -Extrapolate to long-1 do
y2 = 0
for j = 0 to degree-1 do
y2 = y2+pow(i,j)*$coefficients[j]
Next
If idx = 0 then
forecast = y2
Endif
If y2 < y1 then
r = 10
g = 255
b = 10
else
r = 255
g = 10
b = 10
Endif
DRAWSEGMENT(n-i, y2, n-i+1, y1)style(line,5)coloured(r,g,b,100)
y1 = y2
idx = idx+1

If lock then
pass = 0
Endif
Next
else
y2 = 0
x = x -1
For j = 0 to degree do
y2 = y2+pow(x,j)*$coefficients[j]
Next
forecast = y2
Endif

If pass = 0 then
DRAWPOINT(n+extrapolate, forecast, 5)
Endif
Endif

Return

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defparam drawonlastbaronly = true

Maxlength = 500

long = Min(barindex,Min(Maxlength,length))

// degree = 0-8 Polynomial Degree

src = customclose

// lock = Lock Forecast

once init = 1

n = barindex

If init=1 and long=length then

For k = 0 to degree*long-1 do

$design[k]=0

For i = 0 to degree-1 do

For j = 0 to long-1 do

P = pow(j,i)

$designT[i*long+j]= P

$design[j*degree+i]= P

//Matrix.mult

For i = 0 to degree-1 do

For j = 0 to degree-1 do

sum = 0

For k = 0 to long-1 do

sum=sum+$designT[i*long+k]*$design[k*degree+j]

$produit[i*degree+j]=sum

//Extracting Minors Matrix i,j

Minordegree = degree-1

For i = 0 to Minordegree do

For j = 0 to Minordegree do

m = 0

For k = 0 to degree*degree-1 do

If (j<>k mod degree) and (i<>floor(k/degree)) then

$Minor[m]=$produit[k]

m=m+1

Endif

//Calculation Determinant

If Minordegree = 1 then

determinant = $Minor[0]

Elsif Minordegree = 2 then

determinant = $Minor[0]*$Minor[3]-$Minor[1]*$Minor[2]

Elsif Minordegree = 3 then

determinant = $Minor[0]*($Minor[4]*$Minor[8]-$Minor[5]*$Minor[7])-$Minor[1]*($Minor[3]*$Minor[8]-$Minor[5]*$Minor[6])+$Minor[2]*($Minor[3]*$Minor[7]-$Minor[4]*$Minor[6])

Endif

//Setting cofactor Matrix

$cofactor[i*degree+j]=pow(-1,i+j)*determinant

//Calculating overall determinant

determinant = 0

For k = 0 to degree-1 do

determinant = determinant + $produit[k]*$cofactor[k]

//Setting Inverse Matrix => $a

For k = 0 to degree*degree-1 do

$a[k]=0

For i = 0 to degree-1 do

For j = 0 to degree-1 do

$a[i+j*degree] = $cofactor[i*degree+j]*(1/determinant)

For i = 0 to degree-1 do

For j = 0 to degree-1 do

$a[i+j*degree] = $cofactor[i*degree+j]*(1/determinant)

//Setting Mult matrice => $b

For i = 0 to degree-1 do

For j = 0 to long-1 do

sum = 0

For k = 0 to degree-1 do

sum=sum+$a[i*degree+k]*$designT[k*long+j]

$b[i*long+j]=sum

init = 0

Endif

once pass = 1

once x = -1 * Extrapolate

once forecast = 0

If IsLastBarUpdate then

If pass = 1 then

For i = 0 to long-1 do

$response[i] = src[i]

//Setting Mult matrice => $coefficients

For i = 0 to degree-1 do

j = 0

sum = 0

For k = 0 to long-1 do

sum=sum+$b[i*long+k]*$response[k]

$coefficients[i]=sum

once y1 = 0

idx = 0

For i = -Extrapolate to long-1 do

y2 = 0

for j = 0 to degree-1 do

y2 = y2+pow(i,j)*$coefficients[j]

If idx = 0 then

forecast = y2

Endif

If y2 < y1 then

r = 10

g = 255

b = 10

else

r = 255

g = 10

b = 10

Endif

DRAWSEGMENT(n-i, y2, n-i+1, y1)style(line,5)coloured(r,g,b,100)

y1 = y2

idx = idx+1

If lock then

pass = 0

Endif

else

y2 = 0

x = x -1

For j = 0 to degree do

y2 = y2+pow(x,j)*$coefficients[j]

forecast = y2

Endif

If pass = 0 then

DRAWPOINT(n+extrapolate, forecast, 5)

Endif

Return

LucasBest

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08/28/2023 at 9:34 AM #219923

LucasBest

With 3 and 4 degree and a different lngth… forecast can be completly different while increasing/decreasing degree or length.
I think this kind of indicator, that repaint, and only shows what we want to see are completly useless for trading.

LucasBest

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08/28/2023 at 9:37 AM #219927

LucasBest

There is still some bugs/issues, but i add here the last itf i have. I will try to found the bugs later wheni will have time.

2 users thanked author for this post.

robert123,

Khaled

LucasBest

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08/28/2023 at 9:47 AM #219930

Khaled

Bravo Lucas. Yes, it repaints like the Center of Gravity. But anyway, bravo for having solved such a complex mathematical/coding problem. We can have separate discussion on the relevance of Indicators in general for a Retail Trader. After a few yers of observation of the Futures markets (never trader Forex), the Indicators do offer very limited success for day trading. They might be uselful on higher timeframe, but up to 30 minutes, what matters is Order Flow and Price Action, which unless you have access to sophisticated tools and an army of quants and programmers can be a huge task for a Retail Trader. The HFT Algos use the information available in the DOM to grab a few ticks and play games of manipulation and spoofing. Merci encore!

Khaled

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08/28/2023 at 3:36 PM #219955

Aragorna

Hi, I tried to put it on but it doesn’ show anything. Very strange!

Aragorna

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