Repaints ANN Next Coming Candlestick Forecast SPX 1D For ThinkOrSwim

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Tootall

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Need to also convert this pinescript to thinkscript as well

https://www.tradingview.com/v/50zVVT8N/

Code: 
// This source code is subject to the terms of the Mozilla Public License 2.0 at [URL]https://mozilla.org/MPL/2.0/[/URL]
// © Noldo

//@version=4
study("ANN Next Coming Candlestick Forecast SPX 1D" , overlay = true)

src = close

o = open
h = high
l = low
c = close

_indicator1 = ((o - o[1] ) / (o[1]))
_indicator2 = ((h - h[1] ) / (h[1]))
_indicator3 = ((l - l[1] ) / (l[1]))
_indicator4 = ((c - c[1] ) / (c[1]))
_indicator5 = (src - src[1]) / src[1]

// Inputs on Tangent Function :

tangentdiff(_src) => nz((_src - _src[1]) / _src[1] )


// Deep Learning Activation Function (Tanh) :

ActivationFunctionTanh(v) => (1 - exp(-2 * v))/( 1 + exp(-2 * v))


// DEEP LEARNING

// INPUTS :

input_1 = (_indicator1)
input_2 = (_indicator2)
input_3 = (_indicator3)
input_4 = (_indicator4)


// LAYERS :

// Input Layers

n_0 = ActivationFunctionTanh(input_1 + 0)  
n_1 = ActivationFunctionTanh(input_2 + 0)
n_2 = ActivationFunctionTanh(input_3 + 0)
n_3 = ActivationFunctionTanh(input_4 + 0)


//


fun_open() =>

    float _output = na
   
    n_4  = ActivationFunctionTanh(0.030535   * n_0  + 5.113012  * n_1  + -26.085717  * n_2  + -5.320280   * n_3  + 7.354752 )

    n_5  = ActivationFunctionTanh(4.167948   * n_0  + 7.225875  * n_1  + -0.871215   * n_2  + -8.894535   * n_3  + -7.064751 )        

    n_6  = ActivationFunctionTanh(-0.806293  * n_0  + -0.304470 * n_1  + -3.909741   * n_2  + -5.009985   * n_3  + 5.127558 )
   
    n_7  = ActivationFunctionTanh(-29.736063 * n_0  + 28.668433 * n_1  + 0.138417    * n_2  + -57.588543  * n_3  + 2.824914 )
   
    n_8  = ActivationFunctionTanh(-0.429393  * n_0  + 0.482744  * n_1  + -0.789797   * n_2  + -2.987460   * n_3  + -4.310747 )
   
    n_9  = ActivationFunctionTanh(1.758357   * n_0  + -0.618090 * n_1  + 2.449362    * n_2  + -1.583126   * n_3  + 1.165846 )
   
    _output :=  ActivationFunctionTanh(-0.653030 * n_4  + -4.646999 * n_5  + -1.678999  * n_6  + -17.077652  * n_7  + 0.875426 * n_8 + -6.672465 * n_9 + 6.940722)
   
   
fun_high() =>

    float _output = na
   
    n_4  = ActivationFunctionTanh(10.186543  * n_0  + -30.964897 * n_1  + 21.672385  * n_2  + -40.895894  * n_3  + 7.957443 )

    n_5  = ActivationFunctionTanh(-15.252332 * n_0  + 14.845403  * n_1  + 10.621491  * n_2  + -23.817824  * n_3  + 2.947530 )        

    n_6  = ActivationFunctionTanh(-15.179010 * n_0  + -30.011878 * n_1  + 35.650459  * n_2  + -61.480486  * n_3  + 3.898503 )
   
    n_7  = ActivationFunctionTanh(35.656454  * n_0  + -11.134354 * n_1  + -28.071578 * n_2  + 2.923959    * n_3  + -1.805703 )
   
    n_8  = ActivationFunctionTanh(3.462374   * n_0  + -13.644019 * n_1  + -30.226394 * n_2  + -1.083953   * n_3  + 23.032872 )
   
    n_9  = ActivationFunctionTanh(-47.265829 * n_0  + 19.021801  * n_1  + 10.565216  * n_2  + -27.520789  * n_3  + 6.947500 )
   
    _output :=  ActivationFunctionTanh(-0.696537 * n_4  + -1.349433 * n_5  + 27.262956  * n_6  + -1.042353  * n_7  + -0.540196 * n_8 + -10.735585 * n_9 + 1.303216)


fun_low() =>

    float _output = na
   
    n_4  = ActivationFunctionTanh(4.363108   * n_0  + -18.301472 * n_1  + -15.376884  * n_2  + 21.208559  * n_3  + -0.458119 )

    n_5  = ActivationFunctionTanh(-2.651826  * n_0  + 5.205410   * n_1  + -5.920993   * n_2  + -4.847458  * n_3  + 8.315580 )        

    n_6  = ActivationFunctionTanh(13.885322  * n_0  + -5.517922  * n_1  + -15.241118  * n_2  + -8.673229  * n_3  + -4.954015 )
   
    n_7  = ActivationFunctionTanh(10.490466  * n_0  + -25.201536 * n_1  + 10.262121   * n_2  + -1.116144  * n_3  + -5.254103 )
   
    n_8  = ActivationFunctionTanh(-14.687736 * n_0  +   9.030202 * n_1  + -17.332462  * n_2  + 8.068070   * n_3  + 0.755134 )
   
    n_9  = ActivationFunctionTanh( 0.895168  * n_0  +  -1.737740 * n_1  + 4.899143    * n_2  + -7.718495  * n_3  + 5.493688 )
   
    _output :=  ActivationFunctionTanh(4.132907 * n_4  + -17.501595 * n_5  + 4.617443  * n_6  + -28.476857  * n_7  + -5.888234 * n_8 + -24.434500 * n_9 + 41.318760)


fun_close() =>

    float _output = na
   
    n_4  = ActivationFunctionTanh(22.427157  * n_0  + -26.691701 * n_1  + 4.937141    * n_2  + 9.034960    * n_3  + -10.692978 )

    n_5  = ActivationFunctionTanh(-38.288087 * n_0  + 10.050028  * n_1  + -44.706345  * n_2  + -17.816354  * n_3  + 30.566226 )        

    n_6  = ActivationFunctionTanh(-33.995444 * n_0  + 14.501766  * n_1  + -43.286508  * n_2  + -13.387415  * n_3  + 24.708075 )
   
    n_7  = ActivationFunctionTanh(-14.392948 * n_0  + 28.483095  * n_1  + -22.979338  * n_2  + -7.658263   * n_3  + -5.650564  )
   
    n_8  = ActivationFunctionTanh(28.837901  * n_0  + -26.354494 * n_1  + 0.520683    * n_2  + 25.004913   * n_3  + -17.883236 )
   
    n_9  = ActivationFunctionTanh(-4.811354  * n_0  + -4.036420  * n_1  + -8.332775   * n_2  + -1.157164   * n_3  + 0.466793 )
   
    _output :=  ActivationFunctionTanh(-22.053311 * n_4  + 3.652552 * n_5  + -4.390465  * n_6  +  2.103060  * n_7  + 20.027285 * n_8 + 11.510129 * n_9 + -0.415015)


// Current Open Values

_chg_open = tangentdiff(o) * 100

_seed_open = (fun_open() - _chg_open) / 100

f_open =  o * (1 - _seed_open)

// Current High Values

_chg_high = tangentdiff(h) * 100

_seed_high = (fun_high() - _chg_high) / 100

f_high =  h * (1 - _seed_high)

// Current Low Values

_chg_low = tangentdiff(l) * 100

_seed_low = (fun_low() - _chg_low) / 100

f_low =  l * (1 - _seed_low)

// Current Close Values

_chg_c = tangentdiff(c) * 100

_seed_c = (fun_close() - _chg_c) / 100

f_close=  c * (1 - _seed_c)


plotcandle(f_close,f_high,f_low,f_open,color=f_close>f_close[1]?color.teal:color.maroon,wickcolor=#5d606b )
 
Last edited by a moderator:
Tootall said:
Need to also convert this pinescript to thinkscript as well

https://www.tradingview.com/v/50zVVT8N/

Code: 
// This source code is subject to the terms of the Mozilla Public License 2.0 at [URL]https://mozilla.org/MPL/2.0/[/URL]
// © Noldo

//@version=4
study("ANN Next Coming Candlestick Forecast SPX 1D" , overlay = true)

src = close

o = open
h = high
l = low
c = close

_indicator1 = ((o - o[1] ) / (o[1]))
_indicator2 = ((h - h[1] ) / (h[1]))
_indicator3 = ((l - l[1] ) / (l[1]))
_indicator4 = ((c - c[1] ) / (c[1]))
_indicator5 = (src - src[1]) / src[1]

// Inputs on Tangent Function :

tangentdiff(_src) => nz((_src - _src[1]) / _src[1] )


// Deep Learning Activation Function (Tanh) :

ActivationFunctionTanh(v) => (1 - exp(-2 * v))/( 1 + exp(-2 * v))


// DEEP LEARNING

// INPUTS :

input_1 = (_indicator1)
input_2 = (_indicator2)
input_3 = (_indicator3)
input_4 = (_indicator4)


// LAYERS :

// Input Layers

n_0 = ActivationFunctionTanh(input_1 + 0)
n_1 = ActivationFunctionTanh(input_2 + 0)
n_2 = ActivationFunctionTanh(input_3 + 0)
n_3 = ActivationFunctionTanh(input_4 + 0)


//


fun_open() =>

    float _output = na
 
    n_4  = ActivationFunctionTanh(0.030535   * n_0  + 5.113012  * n_1  + -26.085717  * n_2  + -5.320280   * n_3  + 7.354752 )

    n_5  = ActivationFunctionTanh(4.167948   * n_0  + 7.225875  * n_1  + -0.871215   * n_2  + -8.894535   * n_3  + -7.064751 )      

    n_6  = ActivationFunctionTanh(-0.806293  * n_0  + -0.304470 * n_1  + -3.909741   * n_2  + -5.009985   * n_3  + 5.127558 )
 
    n_7  = ActivationFunctionTanh(-29.736063 * n_0  + 28.668433 * n_1  + 0.138417    * n_2  + -57.588543  * n_3  + 2.824914 )
 
    n_8  = ActivationFunctionTanh(-0.429393  * n_0  + 0.482744  * n_1  + -0.789797   * n_2  + -2.987460   * n_3  + -4.310747 )
 
    n_9  = ActivationFunctionTanh(1.758357   * n_0  + -0.618090 * n_1  + 2.449362    * n_2  + -1.583126   * n_3  + 1.165846 )
 
    _output :=  ActivationFunctionTanh(-0.653030 * n_4  + -4.646999 * n_5  + -1.678999  * n_6  + -17.077652  * n_7  + 0.875426 * n_8 + -6.672465 * n_9 + 6.940722)
 
 
fun_high() =>

    float _output = na
 
    n_4  = ActivationFunctionTanh(10.186543  * n_0  + -30.964897 * n_1  + 21.672385  * n_2  + -40.895894  * n_3  + 7.957443 )

    n_5  = ActivationFunctionTanh(-15.252332 * n_0  + 14.845403  * n_1  + 10.621491  * n_2  + -23.817824  * n_3  + 2.947530 )      

    n_6  = ActivationFunctionTanh(-15.179010 * n_0  + -30.011878 * n_1  + 35.650459  * n_2  + -61.480486  * n_3  + 3.898503 )
 
    n_7  = ActivationFunctionTanh(35.656454  * n_0  + -11.134354 * n_1  + -28.071578 * n_2  + 2.923959    * n_3  + -1.805703 )
 
    n_8  = ActivationFunctionTanh(3.462374   * n_0  + -13.644019 * n_1  + -30.226394 * n_2  + -1.083953   * n_3  + 23.032872 )
 
    n_9  = ActivationFunctionTanh(-47.265829 * n_0  + 19.021801  * n_1  + 10.565216  * n_2  + -27.520789  * n_3  + 6.947500 )
 
    _output :=  ActivationFunctionTanh(-0.696537 * n_4  + -1.349433 * n_5  + 27.262956  * n_6  + -1.042353  * n_7  + -0.540196 * n_8 + -10.735585 * n_9 + 1.303216)


fun_low() =>

    float _output = na
 
    n_4  = ActivationFunctionTanh(4.363108   * n_0  + -18.301472 * n_1  + -15.376884  * n_2  + 21.208559  * n_3  + -0.458119 )

    n_5  = ActivationFunctionTanh(-2.651826  * n_0  + 5.205410   * n_1  + -5.920993   * n_2  + -4.847458  * n_3  + 8.315580 )      

    n_6  = ActivationFunctionTanh(13.885322  * n_0  + -5.517922  * n_1  + -15.241118  * n_2  + -8.673229  * n_3  + -4.954015 )
 
    n_7  = ActivationFunctionTanh(10.490466  * n_0  + -25.201536 * n_1  + 10.262121   * n_2  + -1.116144  * n_3  + -5.254103 )
 
    n_8  = ActivationFunctionTanh(-14.687736 * n_0  +   9.030202 * n_1  + -17.332462  * n_2  + 8.068070   * n_3  + 0.755134 )
 
    n_9  = ActivationFunctionTanh( 0.895168  * n_0  +  -1.737740 * n_1  + 4.899143    * n_2  + -7.718495  * n_3  + 5.493688 )
 
    _output :=  ActivationFunctionTanh(4.132907 * n_4  + -17.501595 * n_5  + 4.617443  * n_6  + -28.476857  * n_7  + -5.888234 * n_8 + -24.434500 * n_9 + 41.318760)


fun_close() =>

    float _output = na
 
    n_4  = ActivationFunctionTanh(22.427157  * n_0  + -26.691701 * n_1  + 4.937141    * n_2  + 9.034960    * n_3  + -10.692978 )

    n_5  = ActivationFunctionTanh(-38.288087 * n_0  + 10.050028  * n_1  + -44.706345  * n_2  + -17.816354  * n_3  + 30.566226 )      

    n_6  = ActivationFunctionTanh(-33.995444 * n_0  + 14.501766  * n_1  + -43.286508  * n_2  + -13.387415  * n_3  + 24.708075 )
 
    n_7  = ActivationFunctionTanh(-14.392948 * n_0  + 28.483095  * n_1  + -22.979338  * n_2  + -7.658263   * n_3  + -5.650564  )
 
    n_8  = ActivationFunctionTanh(28.837901  * n_0  + -26.354494 * n_1  + 0.520683    * n_2  + 25.004913   * n_3  + -17.883236 )
 
    n_9  = ActivationFunctionTanh(-4.811354  * n_0  + -4.036420  * n_1  + -8.332775   * n_2  + -1.157164   * n_3  + 0.466793 )
 
    _output :=  ActivationFunctionTanh(-22.053311 * n_4  + 3.652552 * n_5  + -4.390465  * n_6  +  2.103060  * n_7  + 20.027285 * n_8 + 11.510129 * n_9 + -0.415015)


// Current Open Values

_chg_open = tangentdiff(o) * 100

_seed_open = (fun_open() - _chg_open) / 100

f_open =  o * (1 - _seed_open)

// Current High Values

_chg_high = tangentdiff(h) * 100

_seed_high = (fun_high() - _chg_high) / 100

f_high =  h * (1 - _seed_high)

// Current Low Values

_chg_low = tangentdiff(l) * 100

_seed_low = (fun_low() - _chg_low) / 100

f_low =  l * (1 - _seed_low)

// Current Close Values

_chg_c = tangentdiff(c) * 100

_seed_c = (fun_close() - _chg_c) / 100

f_close=  c * (1 - _seed_c)


plotcandle(f_close,f_high,f_low,f_open,color=f_close>f_close[1]?color.teal:color.maroon,wickcolor=#5d606b )
Click to expand...
find below. (May repaint - pls test and confirm)

CSS: 
#// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
#// © Noldo
#study("ANN Next Coming Candlestick Forecast SPX 1D" , overlay = true)
# Converted by [email protected]    - 03/2023

declare lower;

def na = Double.NaN;
def o = open;
def h = high;
def l = low;
def c = close;

def _indicator1 = ((o - o[1] ) / (o[1]));
def _indicator2 = ((h - h[1] ) / (h[1]));
def _indicator3 = ((l - l[1] ) / (l[1]));
def _indicator4 = ((c - c[1] ) / (c[1]));
#def _indicator5 = (source - source[1]) / source[1];

#// Inputs on Tangent Function :
#tangentdiff(_src) =>
script tangentdiff {
    input _src = close;
    def tangentdiff = (_src - _src[1]) / _src[1];
    plot out = tangentdiff;
}
#// Deep Learning Activation Function (Tanh) :
script Tanh {
    input v = 0;
    def Tanh = (1 - Exp(-2 * v)) / ( 1 + Exp(-2 * v));
    plot out = Tanh;
}
#// DEEP LEARNING
#// INPUTS :
def input_o = (_indicator1);
def input_h = (_indicator2);
def input_l = (_indicator3);
def input_c = (_indicator4);
#// LAYERS :
#// Input Layers

def n_0 = Tanh(input_o + 0);
def n_1 = Tanh(input_h + 0);
def n_2 = Tanh(input_l + 0);
def n_3 = Tanh(input_c + 0);

def fun_open;
def o_4  = Tanh(0.030535   * n_0  + 5.113012  * n_1  + -26.085717  * n_2  + -5.320280   * n_3  + 7.354752 );
def o_5  = Tanh(4.167948   * n_0  + 7.225875  * n_1  + -0.871215   * n_2  + -8.894535   * n_3  + -7.064751 );
def o_6  = Tanh(-0.806293  * n_0  + -0.304470 * n_1  + -3.909741   * n_2  + -5.009985   * n_3  + 5.127558 );
def o_7  = Tanh(-29.736063 * n_0  + 28.668433 * n_1  + 0.138417    * n_2  + -57.588543  * n_3  + 2.824914 );
def o_8  = Tanh(-0.429393  * n_0  + 0.482744  * n_1  + -0.789797   * n_2  + -2.987460   * n_3  + -4.310747 );
def o_9  = Tanh(1.758357   * n_0  + -0.618090 * n_1  + 2.449362    * n_2  + -1.583126   * n_3  + 1.165846 );
fun_open =  Tanh(-0.653030 * o_4  + -4.646999 * o_5  + -1.678999  * o_6  + -17.077652  * o_7  + 0.875426 * o_8 + -6.672465 * o_9 + 6.940722);

def fun_high;
def h_4  = Tanh(10.186543  * n_0  + -30.964897 * n_1  + 21.672385  * n_2  + -40.895894  * n_3  + 7.957443 );
def h_5  = Tanh(-15.252332 * n_0  + 14.845403  * n_1  + 10.621491  * n_2  + -23.817824  * n_3  + 2.947530 );
def h_6  = Tanh(-15.179010 * n_0  + -30.011878 * n_1  + 35.650459  * n_2  + -61.480486  * n_3  + 3.898503 );
def h_7  = Tanh(35.656454  * n_0  + -11.134354 * n_1  + -28.071578 * n_2  + 2.923959    * n_3  + -1.805703 );
def h_8  = Tanh(3.462374   * n_0  + -13.644019 * n_1  + -30.226394 * n_2  + -1.083953   * n_3  + 23.032872 );
def h_9  = Tanh(-47.265829 * n_0  + 19.021801  * n_1  + 10.565216  * n_2  + -27.520789  * n_3  + 6.947500 );
fun_high = Tanh(-0.696537 * h_4  + -1.349433 * h_5  + 27.262956  * h_6  + -1.042353  * h_7  + -0.540196 * h_8 + -10.735585 * h_9 + 1.303216);

def fun_low;
def l_4  = Tanh(4.363108   * n_0  + -18.301472 * n_1  + -15.376884  * n_2  + 21.208559  * n_3  + -0.458119 );
def l_5  = Tanh(-2.651826  * n_0  + 5.205410   * n_1  + -5.920993   * n_2  + -4.847458  * n_3  + 8.315580 );
def l_6  = Tanh(13.885322  * n_0  + -5.517922  * n_1  + -15.241118  * n_2  + -8.673229  * n_3  + -4.954015 );
def l_7  = Tanh(10.490466  * n_0  + -25.201536 * n_1  + 10.262121   * n_2  + -1.116144  * n_3  + -5.254103 );
def l_8  = Tanh(-14.687736 * n_0  +   9.030202 * n_1  + -17.332462  * n_2  + 8.068070   * n_3  + 0.755134 );
def l_9  = Tanh( 0.895168  * n_0  +  -1.737740 * n_1  + 4.899143    * n_2  + -7.718495  * n_3  + 5.493688 );
fun_low =  Tanh(4.132907 * l_4  + -17.501595 * l_5  + 4.617443  * l_6  + -28.476857  * l_7  + -5.888234 * l_8 + -24.434500 * l_9 + 41.318760);

def fun_close;
def c_4  = Tanh(22.427157  * n_0  + -26.691701 * n_1  + 4.937141    * n_2  + 9.034960    * n_3  + -10.692978 );
def c_5  = Tanh(-38.288087 * n_0  + 10.050028  * n_1  + -44.706345  * n_2  + -17.816354  * n_3  + 30.566226 );
def c_6  = Tanh(-33.995444 * n_0  + 14.501766  * n_1  + -43.286508  * n_2  + -13.387415  * n_3  + 24.708075 );
def c_7  = Tanh(-14.392948 * n_0  + 28.483095  * n_1  + -22.979338  * n_2  + -7.658263   * n_3  + -5.650564  );
def c_8  = Tanh(28.837901  * n_0  + -26.354494 * n_1  + 0.520683    * n_2  + 25.004913   * n_3  + -17.883236 );
def c_9  = Tanh(-4.811354  * n_0  + -4.036420  * n_1  + -8.332775   * n_2  + -1.157164   * n_3  + 0.466793 );
fun_close = Tanh(-22.053311 * c_4  + 3.652552 * c_5  + -4.390465  * c_6  +  2.103060  * c_7  + 20.027285 * c_8 + 11.510129 * c_9 + -0.415015);

#// Current Open Values
def _chg_open = tangentdiff(o) * 100;
def _seed_open = (fun_open - _chg_open) / 100;
def f_open =  o * (1 - _seed_open);
#// Current High Values
def _chg_high = tangentdiff(h) * 100;
def _seed_high = (fun_high - _chg_high) / 100;
def f_high =  h * (1 - _seed_high);
#// Current Low Values
def _chg_low = tangentdiff(l) * 100;
def _seed_low = (fun_low - _chg_low) / 100;
def f_low =  l * (1 - _seed_low);
#// Current Close Values
def _chg_c = tangentdiff(c) * 100;
def _seed_c = (fun_close - _chg_c) / 100;
def f_close =  c * (1 - _seed_c);

def up = f_close > f_close[1];

# Plot the new Chart
AddChart(high = if up then f_high else na , low = f_low , open = f_open,  close = f_close,
         type = ChartType.CANDLE, growcolor =  CreateColor(0,137,123));

AddChart(high = if up then na else f_high , low = f_low , open = f_open,  close = f_close,
         type = ChartType.CANDLE, growcolor =  CreateColor(136,14,79));


plot upperBand = f_open + ATR(LENGTH=1);
plot lowerBand = f_low - ATR(LENGTH=1);

upperBand.AssignValueColor(if up then CreateColor(0,137,123) else CreateColor(136,14,79));
lowerBand.AssignValueColor(if up then CreateColor(0,137,123) else CreateColor(136,14,79));
upperBand.SetPaintingStrategy(PaintingStrategy.POINTS);
lowerBand.SetPaintingStrategy(PaintingStrategy.POINTS);

#-- END of CODE

and this Upper plot to forecast next candle

CSS: 
#// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
#// © Noldo
#study("ANN Next Coming Candlestick Forecast SPX 1D" , overlay = true)
# Converted by [email protected]    - 03/2023

input UseChartTimeframe = yes;
input Resolution    = AggregationPeriod.FIVE_MIN;    # 'Resolution'
input NonRepainting    = no;     # 'Non-Repainting'

def na = Double.NaN;
def agg = GetAggregationPeriod();
def Rep = if NonRepainting then 1 else 0;
def last = IsNaN(close[-Rep]);# and isNaN(close[-2]);
def TF = if UseChartTimeframe then agg else Resolution;
def h = if UseChartTimeframe then high[Rep] else high(Period = TF)[Rep];
def l = if UseChartTimeframe then low[Rep] else low(Period = TF)[Rep];
def c = if UseChartTimeframe then close[Rep] else close(Period = TF)[Rep];
def o = if UseChartTimeframe then open[Rep] else open(Period = TF)[Rep];

def _indicator1 = ((o - o[1] ) / (o[1]));
def _indicator2 = ((h - h[1] ) / (h[1]));
def _indicator3 = ((l - l[1] ) / (l[1]));
def _indicator4 = ((c - c[1] ) / (c[1]));
#def _indicator5 = (source - source[1]) / source[1];

#// Inputs on Tangent Function :
#tangentdiff(_src) =>
script tangentdiff {
    input _src = close;
    def tangentdiff = (_src - _src[1]) / _src[1];
    plot out = tangentdiff;
}
#// Deep Learning Activation Function (Tanh) :
script Tanh {
    input v = 0;
    def Tanh = (1 - Exp(-2 * v)) / ( 1 + Exp(-2 * v));
    plot out = Tanh;
}
#// DEEP LEARNING
#// INPUTS :
def input_o = (_indicator1);
def input_h = (_indicator2);
def input_l = (_indicator3);
def input_c = (_indicator4);
#// LAYERS :
#// Input Layers

def n_0 = Tanh(input_o + 0);
def n_1 = Tanh(input_h + 0);
def n_2 = Tanh(input_l + 0);
def n_3 = Tanh(input_c + 0);

def fun_open;
def o_4  = Tanh(0.030535   * n_0  + 5.113012  * n_1  + -26.085717  * n_2  + -5.320280   * n_3  + 7.354752 );
def o_5  = Tanh(4.167948   * n_0  + 7.225875  * n_1  + -0.871215   * n_2  + -8.894535   * n_3  + -7.064751 );
def o_6  = Tanh(-0.806293  * n_0  + -0.304470 * n_1  + -3.909741   * n_2  + -5.009985   * n_3  + 5.127558 );
def o_7  = Tanh(-29.736063 * n_0  + 28.668433 * n_1  + 0.138417    * n_2  + -57.588543  * n_3  + 2.824914 );
def o_8  = Tanh(-0.429393  * n_0  + 0.482744  * n_1  + -0.789797   * n_2  + -2.987460   * n_3  + -4.310747 );
def o_9  = Tanh(1.758357   * n_0  + -0.618090 * n_1  + 2.449362    * n_2  + -1.583126   * n_3  + 1.165846 );
fun_open =  Tanh(-0.653030 * o_4  + -4.646999 * o_5  + -1.678999  * o_6  + -17.077652  * o_7  + 0.875426 * o_8 + -6.672465 * o_9 + 6.940722);

def fun_high;
def h_4  = Tanh(10.186543  * n_0  + -30.964897 * n_1  + 21.672385  * n_2  + -40.895894  * n_3  + 7.957443 );
def h_5  = Tanh(-15.252332 * n_0  + 14.845403  * n_1  + 10.621491  * n_2  + -23.817824  * n_3  + 2.947530 );
def h_6  = Tanh(-15.179010 * n_0  + -30.011878 * n_1  + 35.650459  * n_2  + -61.480486  * n_3  + 3.898503 );
def h_7  = Tanh(35.656454  * n_0  + -11.134354 * n_1  + -28.071578 * n_2  + 2.923959    * n_3  + -1.805703 );
def h_8  = Tanh(3.462374   * n_0  + -13.644019 * n_1  + -30.226394 * n_2  + -1.083953   * n_3  + 23.032872 );
def h_9  = Tanh(-47.265829 * n_0  + 19.021801  * n_1  + 10.565216  * n_2  + -27.520789  * n_3  + 6.947500 );
fun_high = Tanh(-0.696537 * h_4  + -1.349433 * h_5  + 27.262956  * h_6  + -1.042353  * h_7  + -0.540196 * h_8 + -10.735585 * h_9 + 1.303216);

def fun_low;
def l_4  = Tanh(4.363108   * n_0  + -18.301472 * n_1  + -15.376884  * n_2  + 21.208559  * n_3  + -0.458119 );
def l_5  = Tanh(-2.651826  * n_0  + 5.205410   * n_1  + -5.920993   * n_2  + -4.847458  * n_3  + 8.315580 );
def l_6  = Tanh(13.885322  * n_0  + -5.517922  * n_1  + -15.241118  * n_2  + -8.673229  * n_3  + -4.954015 );
def l_7  = Tanh(10.490466  * n_0  + -25.201536 * n_1  + 10.262121   * n_2  + -1.116144  * n_3  + -5.254103 );
def l_8  = Tanh(-14.687736 * n_0  +   9.030202 * n_1  + -17.332462  * n_2  + 8.068070   * n_3  + 0.755134 );
def l_9  = Tanh( 0.895168  * n_0  +  -1.737740 * n_1  + 4.899143    * n_2  + -7.718495  * n_3  + 5.493688 );
fun_low =  Tanh(4.132907 * l_4  + -17.501595 * l_5  + 4.617443  * l_6  + -28.476857  * l_7  + -5.888234 * l_8 + -24.434500 * l_9 + 41.318760);

def fun_close;
def c_4  = Tanh(22.427157  * n_0  + -26.691701 * n_1  + 4.937141    * n_2  + 9.034960    * n_3  + -10.692978 );
def c_5  = Tanh(-38.288087 * n_0  + 10.050028  * n_1  + -44.706345  * n_2  + -17.816354  * n_3  + 30.566226 );
def c_6  = Tanh(-33.995444 * n_0  + 14.501766  * n_1  + -43.286508  * n_2  + -13.387415  * n_3  + 24.708075 );
def c_7  = Tanh(-14.392948 * n_0  + 28.483095  * n_1  + -22.979338  * n_2  + -7.658263   * n_3  + -5.650564  );
def c_8  = Tanh(28.837901  * n_0  + -26.354494 * n_1  + 0.520683    * n_2  + 25.004913   * n_3  + -17.883236 );
def c_9  = Tanh(-4.811354  * n_0  + -4.036420  * n_1  + -8.332775   * n_2  + -1.157164   * n_3  + 0.466793 );
fun_close = Tanh(-22.053311 * c_4  + 3.652552 * c_5  + -4.390465  * c_6  +  2.103060  * c_7  + 20.027285 * c_8 + 11.510129 * c_9 + -0.415015);

#// Current Open Values
def _chg_open = tangentdiff(o) * 100;
def _seed_open = (fun_open - _chg_open) / 100;
def f_open =  o * (1 - _seed_open);
#// Current High Values
def _chg_high = tangentdiff(h) * 100;
def _seed_high = (fun_high - _chg_high) / 100;
def f_high =  h * (1 - _seed_high);
#// Current Low Values
def _chg_low = tangentdiff(l) * 100;
def _seed_low = (fun_low - _chg_low) / 100;
def f_low =  l * (1 - _seed_low);
#// Current Close Values
def _chg_c = tangentdiff(c) * 100;
def _seed_c = (fun_close - _chg_c) / 100;
def f_close =  c * (1 - _seed_c);

def up = f_close > f_close[1];

plot Predc = if last and last[Rep + 1] then ohlc4[2] else na;
Predc.AssignValueColor(if up[2] > 0 then CreateColor(0,137,123) else CreateColor(136,14,79));
Predc.SetPaintingStrategy(PaintingStrategy.POINTS);
Predc.SetLineWeight(4);



#-- END of CODE
 
Last edited:
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