ANN MACD Future Forecast (SPY 1D)

//@version=4

//MIT License

//Copyright (c) 2019 user-Noldo

//Permission is hereby granted, free of charge, to any person obtaining a copy
//of this software and associated documentation files (the "Software"), to deal
//in the Software without restriction, including without limitation the rights
//to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
//copies of the Software, and to permit persons to whom the Software is
//furnished to do so, subject to the following conditions:

//The above copyright notice and this permission notice shall be included in all
//copies or substantial portions of the Software.

//THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
//IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
//FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
//AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
//LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
//OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
//SOFTWARE.


study("ANN MACD Future Forecast (SPY 1D) ", max_bars_back=21)
src = close[0]

lights          = input(title="Barcolor I / 0 ? ", options=["ON", "OFF"], defval="OFF")


// Essential Functions

// Highest - Lowest Functions ( All efforts goes to RicardoSantos )

f_highest(_src, _length)=>
    _adjusted_length = _length < 1 ? 1 : _length
    _value = _src
    for _i = 0 to (_adjusted_length-1)
        _value := _src[_i] >= _value ? _src[_i] : _value
    _return = _value

f_lowest(_src, _length)=>
    _adjusted_length = _length < 1 ? 1 : _length
    _value = _src
    for _i = 0 to (_adjusted_length-1)
        _value := _src[_i] <= _value ? _src[_i] : _value
    _return = _value

// Function Sum  

f_sum(_src , _length) => 

    _output  = 0.00 
    
    _length_adjusted = _length < 1 ? 1 : _length
    
    for i = 0 to _length_adjusted-1
        _output := _output + _src[i]


// Unlocked Exponential Moving Average Function

f_ema(_src, _length)=>
    _length_adjusted = _length < 1 ? 1 : _length
    _multiplier = 2 / (_length_adjusted + 1)
    _return  = 0.00
    _return := na(_return[1]) ? _src : ((_src - _return[1]) * _multiplier) + _return[1]


// Unlocked Moving Average Function 

f_sma(_src, _length)=>
    
    _output = 0.00
    _length_adjusted = _length < 0 ? 0 : _length
    w = cum(_src)

    _output:= (w - w[_length_adjusted]) / _length_adjusted
   
    _output    


// Definition : Function Bollinger Bands

Multiplier = 2 
_length_bb = 20


e_r = f_sma(src,_length_bb)


// Function Standard Deviation : 

f_stdev(_src,_length) =>

    float _output = na 
    _length_adjusted = _length < 2 ? 2 : _length
    _avg  = f_ema(_src , _length_adjusted)
    evar  = (_src - _avg) * (_src - _avg)
    evar2 = ((f_sum(evar,_length_adjusted))/_length_adjusted)
    
    _output := sqrt(evar2)


std_r = f_stdev(src , _length_bb )


upband = e_r + (Multiplier * std_r)  // Upband
dnband = e_r - (Multiplier * std_r)  // Lowband
basis  = e_r                         // Midband

// Function : Squeeze Momentum Indicator (SQZMOM_LB) (LazyBear)

length = 20
mult = 2.0
lengthKC=20
multKC = 1.5

// Calculate BB

dev = multKC * f_stdev(src, length)
upperBB = e_r + dev
lowerBB = e_r - dev

// Calculate KC

ma = f_sma(src, lengthKC)
range = tr
rangema = f_sma(range, lengthKC)
upperKC = ma + rangema * multKC
lowerKC = ma - rangema * multKC

sqzOn  = (lowerBB > lowerKC) and (upperBB < upperKC)
sqzOff = (lowerBB < lowerKC) and (upperBB > upperKC)
noSqz  = (sqzOn == false) and (sqzOff == false)

val = linreg(src -  f_sma(f_sma(f_highest(f_highest(src,1), lengthKC), f_lowest(f_lowest(src,1), lengthKC)),f_sma(src,lengthKC)), lengthKC,0)


// 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 = tangentdiff(volume)
input_2 = tangentdiff(e_r)
input_3 = tangentdiff(upband)
input_4 = tangentdiff(dnband)
input_5 = tangentdiff(val)


// 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) 
n_4 = ActivationFunctionTanh(input_5 + 0) 


// Hidden Layers 

n_5  = ActivationFunctionTanh( -35.733   * n_0 + -86.718  * n_1 + -16.948 * n_2 +  -12.384  * n_3 + -38.803  * n_4 +  43.379) 
n_6  = ActivationFunctionTanh(  -2.394   * n_0 + -85.060  * n_1 +  -6.174 * n_2 +  -45.667  * n_3 +  26.925  * n_4 +  16.028) 
n_7  = ActivationFunctionTanh( -63.221   * n_0 +  -7.447  * n_1 +  34.106 * n_2 +  -28.880  * n_3 + -23.098  * n_4 +   9.914) 
n_8  = ActivationFunctionTanh( -59.117   * n_0 +  41.706  * n_1 +  19.044 * n_2 +  -13.141  * n_3 +  40.028  * n_4 + -50.017) 
n_9  = ActivationFunctionTanh( -9.193    * n_0 +  -64.825 * n_1 +  12.735 * n_2 +  11.168   * n_3 +  13.162  * n_4 +  -6.669) 


// Output Layer 

_output  = ActivationFunctionTanh(4.118 * n_5 + 36.306 * n_6 + -22.908 * n_7 + 11.143 * n_8 + -32.203 * n_9  + -0.263)

_chg_src = tangentdiff(src) * 100

_seed = (_output - _chg_src)


// MACD : SEED 


fastLength   = 12
slowlength   = 26
signalLength = 9


macd   = f_ema(_seed, fastLength) - f_ema(_seed, slowlength)
signal = f_ema(macd, signalLength)
       

hist = macd - signal

// Conditions : 

positive_condition = hist < 0 
negative_condition = hist > 0

col_hist() => positive_condition ? color.new(#F5FFFA,0) : negative_condition ? color.new(#FF355E,0) : color.new(color.yellow,0)
col_histo = col_hist()

//Plot data

hline(0, color=#FFB300, linewidth = 2)

plot(hist, color=(positive_condition ? #00A86B : #D40000), style=plot.style_columns,linewidth= 5 , title="Area", transp=30)
plot(hist, color=col_histo,style =plot.style_cross, title="Forecast", linewidth=3)



// Definition : Barcolor 

_lights = 0.00 


if (lights=="ON")

    _lights:= 1.00
    
if (lights=="OFF")

    _lights:= -1.00   


bcolor_on  = _lights ==  1.00
bcolor_off = _lights == -1.00


color_condition() => 

    (positive_condition and _lights == 1.00) ?  color.green  : (negative_condition and _lights == 1.00)  ?  color.red : na 
   
  
color barColor = na 
barColor := color_condition()

barcolor(color = barColor)