//@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)
.
