ANN Strategy Indicator 3.2 (Rebuilt By Kevin Manrrique)So I rebuilt the ANN Strategy with my own codes I added. It took me a long time to get this far, the first 2 weeks it wasn't repainting but then it started repainting slowly. Not hard like the original script. Please feel free to edit the script and post it at the bottom. Thank you everyone! I hope this script I rebuilt can help people! That's why were all here for, a community!
This script is meant to be use in hourly time frames!
If possible use with USD pairs.
Works great with news also!
//@version=2
study("ANN Strategy Indicator 3.2 (Rebuilt By Kevin Manrrique)", overlay=false, precision=777)
threshold = input(title="Threshold", type=float, defval=0.001, step=0.001)
largeTimeframe = input(title="Large timeframe", type=resolution, defval='D')
smallTimeframe = input(title="Small timeframe", type=resolution, defval='60')
PineActivationFunctionLinear(v) => v
PineActivationFunctionTanh(v) =>
(exp(v) - exp(-v))/(exp(v) + exp(-v))
ANN(input) =>
l0_0 = PineActivationFunctionLinear(input)
l0_1 = PineActivationFunctionLinear(input)
l0_2 = PineActivationFunctionLinear(input)
l0_3 = PineActivationFunctionLinear(input)
l0_4 = PineActivationFunctionLinear(input)
l0_5 = PineActivationFunctionLinear(input)
l0_6 = PineActivationFunctionLinear(input)
l0_7 = PineActivationFunctionLinear(input)
l0_8 = PineActivationFunctionLinear(input)
l0_9 = PineActivationFunctionLinear(input)
l0_10 = PineActivationFunctionLinear(input)
l0_11 = PineActivationFunctionLinear(input)
l0_12 = PineActivationFunctionLinear(input)
l0_13 = PineActivationFunctionLinear(input)
l0_14 = PineActivationFunctionLinear(input)
l1_0 = PineActivationFunctionTanh(l0_0*5.040340774 + l0_1*-1.3025994088 + l0_2*19.4225543981 + l0_3*1.1796960423 + l0_4*2.4299395823 + l0_5*3.159003445 + l0_6*4.6844527551 + l0_7*-6.1079267196 + l0_8*-2.4952869198 + l0_9*-4.0966081154 + l0_10*-2.2432843111 + l0_11*-0.6105764807 + l0_12*-0.0775684605 + l0_13*-0.7984753138 + l0_14*3.4495907342)
l1_1 = PineActivationFunctionTanh(l0_0*5.9559031982 + l0_1*-3.1781960056 + l0_2*-1.6337491061 + l0_3*-4.3623166512 + l0_4*0.9061990402 + l0_5*-0.731285093 + l0_6*-6.2500232251 + l0_7*0.1356087758 + l0_8*-0.8570572885 + l0_9*-4.0161353298 + l0_10*1.5095552083 + l0_11*1.324789197 + l0_12*-0.1011973878 + l0_13*-2.3642090162 + l0_14*-0.7160862442)
l1_2 = PineActivationFunctionTanh(l0_0*4.4350881378 + l0_1*-2.8956461034 + l0_2*1.4199762607 + l0_3*-0.6436844261 + l0_4*1.1124274281 + l0_5*-4.0976954985 + l0_6*2.9317456342 + l0_7*0.0798318393 + l0_8*-5.5718144311 + l0_9*-0.6623352208 +l0_10*3.2405203222 + l0_11*-10.6253384513 + l0_12*4.7132919253 + l0_13*-5.7378151597 + l0_14*0.3164836695)
l1_3 = PineActivationFunctionTanh(l0_0*-6.1194605467 + l0_1*7.7935605604 + l0_2*-0.7587522153 + l0_3*9.8382495905 + l0_4*0.3274314734 + l0_5*1.8424796541 + l0_6*-1.2256355427 + l0_7*-1.5968600758 + l0_8*1.9937700922 + l0_9*5.0417809111 + l0_10*-1.9369944654 + l0_11*6.1013201778 + l0_12*1.5832910747 + l0_13*-2.148403244 + l0_14*1.5449437366)
l1_4 = PineActivationFunctionTanh(l0_0*3.5700040028 + l0_1*-4.4755892733 + l0_2*0.1526702072 + l0_3*-0.3553664401 + l0_4*-2.3777962662 + l0_5*-1.8098849587 + l0_6*-3.5198449134 + l0_7*-0.4369370497 + l0_8*2.3350169623 + l0_9*1.9328960346 + l0_10*1.1824141812 + l0_11*3.0565148049 + l0_12*-9.3253401534 + l0_13*1.6778555498 + l0_14*-3.045794332)
l1_5 = PineActivationFunctionTanh(l0_0*3.6784907623 + l0_1*1.1623683715 + l0_2*7.1366362145 + l0_3*-5.6756546585 + l0_4*12.7019884334 + l0_5*-1.2347823331 + l0_6*2.3656619827 + l0_7*-8.7191778213 + l0_8*-13.8089238753 + l0_9*5.4335943836 + l0_10*-8.1441181338 + l0_11*-10.5688113287 + l0_12*6.3964140758 + l0_13*-8.9714236223 + l0_14*-34.0255456929)
l1_6 = PineActivationFunctionTanh(l0_0*-0.4344517548 + l0_1*-3.8262167437 + l0_2*-0.2051098003 + l0_3*0.6844201221 + l0_4*1.1615893422 + l0_5*-0.404465314 + l0_6*-0.1465747632 + l0_7*-0.006282458 + l0_8*0.1585655487
