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This is an implementation of an Artificial Neural Network (ANN) in pine. I made this as part of a bigger project and should be considered more as a proof of concept than a fully working indicator.

It was trained by a different program, using 3 years of bitcoin history. It's a 4 layer ANN that takes the percentual difference of the last few days as input. It was randomly generated (that's why the input is a bit funny :) ) and translated to pine.

It seems to be much better at trends and pump/dumps, because it keeps switching during sideways, so perhaps with a sideways indicator this could actually prove useful. In the chart above, I drew how you would trade with this indicator. Every red square is a short, every green square is a long.

The aqua plot line is the actual prediction. If this is above 0, the background is drawn green and the indicator is bullish . Otherwise, the background is drawn red and the indicator is bearish .

Any and all question can be asked by PM'ing me or emailing me at masterflappie@gmail.com
Script de código aberto

No verdadeiro espírito TradingView, o autor deste script o publicou com código aberto, para que os traders possam compreendê-lo e verificá-lo. Um brinde ao autor! Você pode usá-lo gratuitamente, mas a reutilização deste código em uma publicação é regida pelas Regras da Casa. Você pode favoritá-lo para utilizá-lo em um gráfico.

Quer usar esse script no gráfico?
study("ANN")

getDiff(offset) =>
    yesterday=close[offset+1]
    today=close[offset]
    delta=today-yesterday
    percentage=delta/yesterday
 
PineActivationFunctionLinear(v) => v
 
PineActivationFunctionTanh(v) => (exp(v) - exp(-v))/(exp(v) + exp(-v))

l0_0 = PineActivationFunctionLinear(getDiff(0))
l0_1 = PineActivationFunctionLinear(getDiff(0))
l0_2 = PineActivationFunctionLinear(getDiff(0))
l0_3 = PineActivationFunctionLinear(getDiff(0))
l0_4 = PineActivationFunctionLinear(getDiff(0))
l0_5 = PineActivationFunctionLinear(getDiff(0))
l0_6 = PineActivationFunctionLinear(getDiff(0))
l0_7 = PineActivationFunctionLinear(getDiff(0))
l0_8 = PineActivationFunctionLinear(getDiff(0))
l0_9 = PineActivationFunctionLinear(getDiff(0))
l0_10 = PineActivationFunctionLinear(getDiff(0))
l0_11 = PineActivationFunctionLinear(getDiff(0))
l0_12 = PineActivationFunctionLinear(getDiff(0))
l0_13 = PineActivationFunctionLinear(getDiff(0))
l0_14 = PineActivationFunctionLinear(getDiff(0))
 
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 + l0_9*1.1994484991 + l0_10*-0.9879081404 + l0_11*-0.3564970612 + l0_12*1.5814717823 + l0_13*-0.9614804676 + l0_14*0.9204822346)
l1_7 = PineActivationFunctionTanh(l0_0*-4.2700957175 + l0_1*9.4328591157 + l0_2*-4.3045548 + l0_3*5.0616868842 + l0_4*3.3388781058 + l0_5*-2.1885073225 + l0_6*-6.506301518 + l0_7*3.8429000108 + l0_8*-1.6872237349 + l0_9*2.4107095799 + l0_10*-3.0873985314 + l0_11*-2.8358325447 + l0_12*2.4044366491 + l0_13*0.636779082 + l0_14*-13.2173215035)
l1_8 = PineActivationFunctionTanh(l0_0*-8.3224697492 + l0_1*-9.4825530183 + l0_2*3.5294389835 + l0_3*0.1538618049 + l0_4*-13.5388631898 + l0_5*-0.1187936017 + l0_6*-8.4582741139 + l0_7*5.1566299292 + l0_8*10.345519938 + l0_9*2.9211759333 + l0_10*-5.0471804233 + l0_11*4.9255989983 + l0_12*-9.9626142544 + l0_13*23.0043143258 + l0_14*20.9391809343)
l1_9 = PineActivationFunctionTanh(l0_0*-0.9120518654 + l0_1*0.4991807488 + l0_2*-1.877244586 + l0_3*3.1416466525 + l0_4*1.063709676 + l0_5*0.5210126835 + l0_6*-4.9755780108 + l0_7*2.0336532347 + l0_8*-1.1793121093 + l0_9*-0.730664855 + l0_10*-2.3515987428 + l0_11*-0.1916546514 + l0_12*-2.2530340504 + l0_13*-0.2331829119 + l0_14*0.7216218149)
l1_10 = PineActivationFunctionTanh(l0_0*-5.2139618683 + l0_1*1.0663790028 + l0_2*1.8340834959 + l0_3*1.6248173447 + l0_4*-0.7663740145 + l0_5*0.1062788171 + l0_6*2.5288021501 + l0_7*-3.4066549066 + l0_8*-4.9497988755 + l0_9*-2.3060668143 + l0_10*-1.3962486274 + l0_11*0.6185583427 + l0_12*0.2625299576 + l0_13*2.0270246444 + l0_14*0.6372015811)
l1_11 = PineActivationFunctionTanh(l0_0*0.2020072665 + l0_1*0.3885852709 + l0_2*-0.1830248843 + l0_3*-1.2408598444 + l0_4*-0.6365798088 + l0_5*1.8736534268 + l0_6*0.656206442 + l0_7*-0.2987482678 + l0_8*-0.2017485963 + l0_9*-1.0604095303 + l0_10*0.239793356 + l0_11*-0.3614172938 + l0_12*0.2614678044 + l0_13*1.0083551762 + l0_14*-0.5473833797)
l1_12 = PineActivationFunctionTanh(l0_0*-0.4367517149 + l0_1*-10.0601304934 + l0_2*1.9240604838 + l0_3*-1.3192184047 + l0_4*-0.4564760159 + l0_5*-0.2965270368 + l0_6*-1.1407423613 + l0_7*2.0949647291 + l0_8*-5.8212599297 + l0_9*-1.3393321939 + l0_10*7.6624548265 + l0_11*1.1309391851 + l0_12*-0.141798054 + l0_13*5.1416736187 + l0_14*-1.8142503125)
l1_13 = PineActivationFunctionTanh(l0_0*1.103948336 + l0_1*-1.4592033032 + l0_2*0.6146278432 + l0_3*0.5040966421 + l0_4*-2.4276090772 + l0_5*-0.0432902426 + l0_6*-0.0044259999 + l0_7*-0.5961347308 + l0_8*0.3821026107 + l0_9*0.6169102373 +l0_10*-0.1469847611 + l0_11*-0.0717167683 + l0_12*-0.0352403695 + l0_13*1.2481310788 + l0_14*0.1339628411)
l1_14 = PineActivationFunctionTanh(l0_0*-9.8049980534 + l0_1*13.5481068519 + l0_2*-17.1362809025 + l0_3*0.7142100864 + l0_4*4.4759163422 + l0_5*4.5716161777 + l0_6*1.4290884628 + l0_7*8.3952862712 + l0_8*-7.1613700432 + l0_9*-3.3249489518+ l0_10*-0.7789587912 + l0_11*-1.7987628873 + l0_12*13.364752545 + l0_13*5.3947219678 + l0_14*12.5267547127)
l1_15 = PineActivationFunctionTanh(l0_0*0.9869461803 + l0_1*1.9473351905 + l0_2*2.032925759 + l0_3*7.4092080633 + l0_4*-1.9257741399 + l0_5*1.8153585328 + l0_6*1.1427866392 + l0_7*-0.3723167449 + l0_8*5.0009927384 + l0_9*-0.2275103411 + l0_10*2.8823012914 + l0_11*-3.0633141934 + l0_12*-2.785334815 + l0_13*2.727981E-4 + l0_14*-0.1253009512)
l1_16 = PineActivationFunctionTanh(l0_0*4.9418118585 + l0_1*-2.7538199876 + l0_2*-16.9887588104 + l0_3*8.8734475297 + l0_4*-16.3022734814 + l0_5*-4.562496601 + l0_6*-1.2944373699 + l0_7*-9.6022946986 + l0_8*-1.018393866 + l0_9*-11.4094515429 + l0_10*24.8483091382 + l0_11*-3.0031522277 + l0_12*0.1513114555 + l0_13*-6.7170487021 + l0_14*-14.7759227576)
l1_17 = PineActivationFunctionTanh(l0_0*5.5931454656 + l0_1*2.22272078 + l0_2*2.603416897 + l0_3*1.2661196599 + l0_4*-2.842826446 + l0_5*-7.9386099121 + l0_6*2.8278849111 + l0_7*-1.2289445238 + l0_8*4.571484248 + l0_9*0.9447425595 + l0_10*4.2890688351 + l0_11*-3.3228258483 + l0_12*4.8866215526 + l0_13*1.0693412194 + l0_14*-1.963203112)
l1_18 = PineActivationFunctionTanh(l0_0*0.2705520264 + l0_1*0.4002328199 + l0_2*0.1592515845 + l0_3*0.371893552 + l0_4*-1.6639467871 + l0_5*2.2887318884 + l0_6*-0.148633664 + l0_7*-0.6517792263 + l0_8*-0.0993032992 + l0_9*-0.964940376 + l0_10*0.1286342935 + l0_11*0.4869943595 + l0_12*1.4498648166 + l0_13*-0.3257333384 + l0_14*-1.3496419812)
l1_19 = PineActivationFunctionTanh(l0_0*-1.3223200798 + l0_1*-2.2505204324 + l0_2*0.8142804525 + l0_3*-0.848348177 + l0_4*0.7208860589 + l0_5*1.2033423756 + l0_6*-0.1403005786 + l0_7*0.2995941644 + l0_8*-1.1440473062 + l0_9*1.067752916 + l0_10*-1.2990534679 + l0_11*1.2588583869 + l0_12*0.7670409455 + l0_13*2.7895972983 + l0_14*-0.5376152512)
l1_20 = PineActivationFunctionTanh(l0_0*0.7382351572 + l0_1*-0.8778865631 + l0_2*1.0950766363 + l0_3*0.7312146997 + l0_4*2.844781386 + l0_5*2.4526730903 + l0_6*-1.9175165077 + l0_7*-0.7443755288 + l0_8*-3.1591419438 + l0_9*0.8441602697 + l0_10*1.1979484448 + l0_11*2.138098544 + l0_12*0.9274159536 + l0_13*-2.1573448803 + l0_14*-3.7698356464)
l1_21 = PineActivationFunctionTanh(l0_0*5.187120117 + l0_1*-7.7525670576 + l0_2*1.9008346975 + l0_3*-1.2031603996 + l0_4*5.917669142 + l0_5*-3.1878682719 + l0_6*1.0311747828 + l0_7*-2.7529484612 + l0_8*-1.1165884578 + l0_9*2.5524942323 + l0_10*-0.38623241 + l0_11*3.7961317445 + l0_12*-6.128820883 + l0_13*-2.1470707709 + l0_14*2.0173792965)
l1_22 = PineActivationFunctionTanh(l0_0*-6.0241676562 + l0_1*0.7474455584 + l0_2*1.7435724844 + l0_3*0.8619835076 + l0_4*-0.1138406797 + l0_5*6.5979359352 + l0_6*1.6554154348 + l0_7*-3.7969458806 + l0_8*1.1139097376 + l0_9*-1.9588417 + l0_10*3.5123392221 + l0_11*9.4443103128 + l0_12*-7.4779291395 + l0_13*3.6975940671 + l0_14*8.5134262747)
l1_23 = PineActivationFunctionTanh(l0_0*-7.5486576471 + l0_1*-0.0281420865 + l0_2*-3.8586839454 + l0_3*-0.5648792233 + l0_4*-7.3927282026 + l0_5*-0.3857538046 + l0_6*-2.9779885698 + l0_7*4.0482279965 + l0_8*-1.1522499578 + l0_9*-4.1562500212 + l0_10*0.7813134307 + l0_11*-1.7582667612 + l0_12*1.7071109988 + l0_13*6.9270873208 + l0_14*-4.5871357362)
l1_24 = PineActivationFunctionTanh(l0_0*-5.3603442228 + l0_1*-9.5350611629 + l0_2*1.6749984422 + l0_3*-0.6511065892 + l0_4*-0.8424823239 + l0_5*1.9946675213 + l0_6*-1.1264361638 + l0_7*0.3228676616 + l0_8*5.3562230396 + l0_9*-1.6678168952+ l0_10*1.2612580068 + l0_11*-3.5362671399 + l0_12*-9.3895191366 + l0_13*2.0169228673 + l0_14*-3.3813191557)
l1_25 = PineActivationFunctionTanh(l0_0*1.1362866429 + l0_1*-1.8960071702 + l0_2*5.7047307243 + l0_3*-1.6049785053 + l0_4*-4.8353898931 + l0_5*-1.4865381145 + l0_6*-0.2846893475 + l0_7*2.2322095997 + l0_8*2.0930488668 + l0_9*1.7141411002 + l0_10*-3.4106032176 + l0_11*3.0593289612 + l0_12*-5.0894813904 + l0_13*-0.5316299133 + l0_14*0.4705265416)
l1_26 = PineActivationFunctionTanh(l0_0*-0.9401400975 + l0_1*-0.9136086957 + l0_2*-3.3808688582 + l0_3*4.7200776773 + l0_4*3.686296919 + l0_5*14.2133723935 + l0_6*1.5652940954 + l0_7*-0.2921139433 + l0_8*1.0244504511 + l0_9*-7.6918299134 + l0_10*-0.594936135 + l0_11*-1.4559914156 + l0_12*2.8056435224 + l0_13*2.6103905733 + l0_14*2.3412348872)
l1_27 = PineActivationFunctionTanh(l0_0*1.1573980186 + l0_1*2.9593661909 + l0_2*0.4512594325 + l0_3*-0.9357210858 + l0_4*-1.2445804495 + l0_5*4.2716471631 + l0_6*1.5167912375 + l0_7*1.5026853293 + l0_8*1.3574772038 + l0_9*-1.9754386842 + l0_10*6.727671436 + l0_11*8.0145772889 + l0_12*7.3108970663 + l0_13*-2.5005627841 + l0_14*8.9604502277)
l1_28 = PineActivationFunctionTanh(l0_0*6.3576350212 + l0_1*-2.9731672725 + l0_2*-2.7763558082 + l0_3*-3.7902984555 + l0_4*-1.0065574585 + l0_5*-0.7011836061 + l0_6*-1.0298068578 + l0_7*1.201007784 + l0_8*-0.7835862254 + l0_9*-3.9863597435 + l0_10*6.7851825502 + l0_11*1.1120256721 + l0_12*-2.263287351 + l0_13*1.8314374104 + l0_14*-2.279102097)
l1_29 = PineActivationFunctionTanh(l0_0*-7.8741911036 + l0_1*-5.3370618518 + l0_2*11.9153868964 + l0_3*-4.1237170553 + l0_4*2.9491152758 + l0_5*1.0317132502 + l0_6*2.2992199883 + l0_7*-2.0250502364 + l0_8*-11.0785995839 + l0_9*-6.3615588554 + l0_10*-1.1687644976 + l0_11*6.3323478015 + l0_12*6.0195076962 + l0_13*-2.8972208702 + l0_14*3.6107747183)
 
l2_0 = PineActivationFunctionTanh(l1_0*-0.590546797 + l1_1*0.6608304658 + l1_2*-0.3358268839 + l1_3*-0.748530283 + l1_4*-0.333460383 + l1_5*-0.3409307681 + l1_6*0.1916558198 + l1_7*-0.1200399453 + l1_8*-0.5166151854 + l1_9*-0.8537164676 +l1_10*-0.0214448647 + l1_11*-0.553290271 + l1_12*-1.2333302892 + l1_13*-0.8321813811 + l1_14*-0.4527761741 + l1_15*0.9012545631 + l1_16*0.415853215 + l1_17*0.1270548319 + l1_18*0.2000460279 + l1_19*-0.1741942671 + l1_20*0.419830522 + l1_21*-0.059839291 + l1_22*-0.3383001769 + l1_23*0.1617814073 + l1_24*0.3071848006 + l1_25*-0.3191182045 + l1_26*-0.4981831822 + l1_27*-1.467478375 + l1_28*-0.1676432563 + l1_29*1.2574849126)
l2_1 = PineActivationFunctionTanh(l1_0*-0.5514235841 + l1_1*0.4759190049 + l1_2*0.2103576983 + l1_3*-0.4754377924 + l1_4*-0.2362941295 + l1_5*0.1155082119 + l1_6*0.7424215794 + l1_7*-0.3674198672 + l1_8*0.8401574461 + l1_9*0.6096563193 + l1_10*0.7437935674 + l1_11*-0.4898638101 + l1_12*-0.4168668092 + l1_13*-0.0365111095 + l1_14*-0.342675224 + l1_15*0.1870268765 + l1_16*-0.5843050987 + l1_17*-0.4596547471 + l1_18*0.452188522 + l1_19*-0.6737126684 + l1_20*0.6876072741 + l1_21*-0.8067776704 + l1_22*0.7592979467 + l1_23*-0.0768239468 + l1_24*0.370536097 + l1_25*-0.4363884671 + l1_26*-0.419285676 + l1_27*0.4380251141 + l1_28*0.0822528948 + l1_29*-0.2333910809)
l2_2 = PineActivationFunctionTanh(l1_0*-0.3306539521 + l1_1*-0.9382247194 + l1_2*0.0746711276 + l1_3*-0.3383838985 + l1_4*-0.0683232217 + l1_5*-0.2112358049 + l1_6*-0.9079234054 + l1_7*0.4898595603 + l1_8*-0.2039825863 + l1_9*1.0870698641+ l1_10*-1.1752901237 + l1_11*1.1406403923 + l1_12*-0.6779626786 + l1_13*0.4281048906 + l1_14*-0.6327670055 + l1_15*-0.1477678844 + l1_16*0.2693637584 + l1_17*0.7250738509 + l1_18*0.7905904504 + l1_19*-1.6417250883 + l1_20*-0.2108095534 +l1_21*-0.2698557472 + l1_22*-0.2433656685 + l1_23*-0.6289943273 + l1_24*0.436428207 + l1_25*-0.8243825184 + l1_26*-0.8583496686 + l1_27*0.0983131026 + l1_28*-0.4107462518 + l1_29*0.5641683087)
l2_3 = PineActivationFunctionTanh(l1_0*1.7036869992 + l1_1*-0.6683507666 + l1_2*0.2589197112 + l1_3*0.032841148 + l1_4*-0.4454796342 + l1_5*-0.6196149423 + l1_6*-0.1073622976 + l1_7*-0.1926393101 + l1_8*1.5280232458 + l1_9*-0.6136527036 +l1_10*-1.2722934357 + l1_11*0.2888655811 + l1_12*-1.4338638512 + l1_13*-1.1903556863 + l1_14*-1.7659663905 + l1_15*0.3703086867 + l1_16*1.0409140889 + l1_17*0.0167382209 + l1_18*0.6045646461 + l1_19*4.2388788116 + l1_20*1.4399738234 + l1_21*0.3308571935 + l1_22*1.4501137667 + l1_23*0.0426123724 + l1_24*-0.708479795 + l1_25*-1.2100800732 + l1_26*-0.5536278651 + l1_27*1.3547250573 + l1_28*1.2906250286 + l1_29*0.0596007114)
l2_4 = PineActivationFunctionTanh(l1_0*-0.462165126 + l1_1*-1.0996742176 + l1_2*1.0928262999 + l1_3*1.806407067 + l1_4*0.9289147669 + l1_5*0.8069022793 + l1_6*0.2374237802 + l1_7*-2.7143979019 + l1_8*-2.7779203877 + l1_9*0.214383903 + l1_10*-1.3111536623 + l1_11*-2.3148813568 + l1_12*-2.4755355804 + l1_13*-0.6819733236 + l1_14*0.4425615226 + l1_15*-0.1298218043 + l1_16*-1.1744832824 + l1_17*-0.395194848 + l1_18*-0.2803397703 + l1_19*-0.4505071197 + l1_20*-0.8934956598 + l1_21*3.3232916348 + l1_22*-1.7359534851 + l1_23*3.8540421743 + l1_24*1.4424032523 + l1_25*0.2639823693 + l1_26*0.3597053634 + l1_27*-1.0470693728 + l1_28*1.4133480357 + l1_29*0.6248098695)
l2_5 = PineActivationFunctionTanh(l1_0*0.2215807411 + l1_1*-0.5628295071 + l1_2*-0.8795982905 + l1_3*0.9101585104 + l1_4*-1.0176831976 + l1_5*-0.0728884401 + l1_6*0.6676331658 + l1_7*-0.7342174108 + l1_8*9.4428E-4 + l1_9*0.6439774272 + l1_10*-0.0345236026 + l1_11*0.5830977027 + l1_12*-0.4058921837 + l1_13*-0.3991888077 + l1_14*-1.0090426973 + l1_15*-0.9324780698 + l1_16*-0.0888749165 + l1_17*0.2466351736 + l1_18*0.4993304601 + l1_19*-1.115408696 + l1_20*0.9914246705 + l1_21*0.9687743445 + l1_22*0.1117130875 + l1_23*0.7825109733 + l1_24*0.2217023612 + l1_25*0.3081256411 + l1_26*-0.1778007966 + l1_27*-0.3333287743 + l1_28*1.0156352461 + l1_29*-0.1456257813)
l2_6 = PineActivationFunctionTanh(l1_0*-0.5461783383 + l1_1*0.3246015999 + l1_2*0.1450605434 + l1_3*-1.3179944349 + l1_4*-1.5481775261 + l1_5*-0.679685633 + l1_6*-0.9462335139 + l1_7*-0.6462399371 + l1_8*0.0991658683 + l1_9*0.1612892194 +l1_10*-1.037660602 + l1_11*-0.1044778824 + l1_12*0.8309203243 + l1_13*0.7714766458 + l1_14*0.2566767663 + l1_15*0.8649416329 + l1_16*-0.5847461285 + l1_17*-0.6393969272 + l1_18*0.8014049359 + l1_19*0.2279568228 + l1_20*1.0565217821 + l1_21*0.134738029 + l1_22*0.3420395576 + l1_23*-0.2417397219 + l1_24*0.3083072038 + l1_25*0.6761739059 + l1_26*-0.4653817053 + l1_27*-1.0634057566 + l1_28*-0.5658892281 + l1_29*-0.6947283681)
l2_7 = PineActivationFunctionTanh(l1_0*-0.5450410944 + l1_1*0.3912849372 + l1_2*-0.4118641117 + l1_3*0.7124695074 + l1_4*-0.7510266122 + l1_5*1.4065673913 + l1_6*0.9870731545 + l1_7*-0.2609363107 + l1_8*-0.3583639958 + l1_9*0.5436375706 +l1_10*0.4572450099 + l1_11*-0.4651538878 + l1_12*-0.2180218212 + l1_13*0.5241262959 + l1_14*-0.8529323253 + l1_15*-0.4200378937 + l1_16*0.4997885721 + l1_17*-1.1121528189 + l1_18*0.5992411048 + l1_19*-1.0263270781 + l1_20*-1.725160642 + l1_21*-0.2653995722 + l1_22*0.6996703032 + l1_23*0.348549086 + l1_24*0.6522482482 + l1_25*-0.7931928436 + l1_26*-0.5107994359 + l1_27*0.0509642698 + l1_28*0.8711187423 + l1_29*0.8999449627)
l2_8 = PineActivationFunctionTanh(l1_0*-0.7111081522 + l1_1*0.4296245062 + l1_2*-2.0720732038 + l1_3*-0.4071818684 + l1_4*1.0632721681 + l1_5*0.8463224325 + l1_6*-0.6083948423 + l1_7*1.1827669608 + l1_8*-0.9572307844 + l1_9*-0.9080517673 + l1_10*-0.0479029057 + l1_11*-1.1452853213 + l1_12*0.2884352688 + l1_13*0.1767851586 + l1_14*-1.089314461 + l1_15*1.2991763966 + l1_16*1.6236630806 + l1_17*-0.7720263697 + l1_18*-0.5011541755 + l1_19*-2.3919413568 + l1_20*0.0084018338 + l1_21*0.9975216139 + l1_22*0.4193541029 + l1_23*1.4623834571 + l1_24*-0.6253069691 + l1_25*0.6119677341 + l1_26*0.5423948388 + l1_27*1.0022450377 + l1_28*-1.2392984069 + l1_29*1.5021529822)
 
l3_0 = PineActivationFunctionTanh(l2_0*0.3385061186 + l2_1*0.6218531956 + l2_2*-0.7790340983 + l2_3*0.1413078332 + l2_4*0.1857010624 + l2_5*-0.1769456351 + l2_6*-0.3242337911 + l2_7*-0.503944883 + l2_8*0.1540568869)
 
hline(0, title="base line")
bgcolor(l3_0 > 0 ? green : red, transp=20)
plot(l3_0, color=aqua, title="prediction")

Comentários


@sirolf2009 Thank you. Interesting indicator.
1) It repaints the current candle. Do you take action on candle close?
2) How do you suggest to take away the choppiness during sideways periods?
Thank you in advance!
+21 Resposta
Hi, what is the loss-function you used? I'm currenctly testing some stacked LSTM layers.
+8 Resposta
does the indis repaint ??
+7 Resposta
how to apply this indicator to chart
+7 Resposta
Strategy version available!
+7 Resposta
mrxdaviepoo sirolf2009
@sirolf2009, hey i'm kind of new to pine script and I was looking at the source code. I noticed that it really comes down to l3_0 compared to the threshold. I see that there is a default value of 0.0014 with a step of 0.0001. Where does 0.0014 come from? What are the dependencies and when would we step up or down? Thanks, I really appreciate any help!
+10 Resposta
pookNast sirolf2009
Wow, so glad I found this, it is glorious! Very much appreciated@sirolf2009,
I think what would really take this 'Strategy Version' to level II would be by integrating 'alerts' with the signals!
Keep up the good work, cheers!
+1 Resposta
Kivilcimli sirolf2009
@sirolf2009, hello. how can add alert for this indikator?
+2 Resposta
Nicee....
+2 Resposta
thank you, it will help
+2 Resposta