Stratégie de réseau neuronal à double axe temporel


Date de création: 2023-09-14 16:10:26 Dernière modification: 2023-09-14 16:10:26
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Principe de stratégie

Cette stratégie utilise des modèles de prévision de réseaux neuronaux pour juger de la tendance des prix dans les deux périodes et pour effectuer des transactions en synchronisation des signaux sur les deux axes du temps.

La logique est la suivante:

  1. Obtenir deux variations de prix sur des axes de temps distincts, par exemple le jour et l’heure

  2. Entraîner les variations de prix dans un réseau de neurones pour obtenir une sortie de prévision pour chaque axe

  3. Lorsque les deux axes prédisent une sortie dans la même direction, c’est-à-dire au-delà de la barre, un signal de transaction est généré

  4. Plus d’opérations sont effectuées avec les prévisions horaires.

  5. Prévision de la ligne du jour pour faire une courte pause, prévision de la courte pause d’une heure pour faire une courte pause

  6. Si les deux axes ne s’accordent pas, la position est nulle.

Cette stratégie exploite pleinement l’information sur les deux axes du temps, détermine la direction de la tendance sur plusieurs dimensions, puis effectue des transactions, ce qui réduit efficacement les faux signaux.

Avantages stratégiques

  • Prévisions sur deux axes de temps pour une meilleure précision de jugement

  • Les réseaux neuronaux modélisent des données complexes

  • Évitez d’être piégé

Risque stratégique

  • La formation des réseaux nécessite de grandes quantités de données

  • La conception de la structure du réseau nécessite des tests répétés

  • Les signaux de combinaison de deux axes sont moins fréquents

Résumer

Cette stratégie utilise un réseau de neurones pour juger des tendances de prix à partir de deux axes temporels et effectuer des transactions en s’assurant de l’exactitude du jugement. Cependant, elle nécessite l’optimisation des paramètres du réseau et la définition d’une fréquence de transaction raisonnable.

Code source de la stratégie 
/*backtest
start: 2023-08-14 00:00:00
end: 2023-09-13 00:00:00
period: 3h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=2
strategy("ANN 2 signals", overlay=false, precision=4, calc_on_every_tick=true)

threshold = input(title="Threshold", type=float, defval=0.006, step=0.001)
largeTimeframe = input(title="Large timeframe",  defval='D')
smallTimeframe = input(title="Small timeframe",  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 + 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)
 
entryYesterday = security(syminfo.tickerid, largeTimeframe, ohlc4[2])
entryToday = security(syminfo.tickerid, largeTimeframe, ohlc4[1])
entry = (entryToday-entryYesterday)/entryYesterday
exitYesterday = security(syminfo.tickerid, smallTimeframe, ohlc4[2])
exitToday = security(syminfo.tickerid, smallTimeframe, ohlc4[1])
exit = (exitToday-exitYesterday)/exitYesterday

exitPrediction = ANN(exit)
entryPrediction = ANN(entry)
goLong = entryPrediction < -threshold and exitPrediction < -threshold
goShort = entryPrediction > threshold and exitPrediction > threshold
 
strategy.close("LONG", exitPrediction >= threshold or entryPrediction >= threshold)
strategy.close("SHORT", exitPrediction <= -threshold or entryPrediction <= threshold)

strategy.entry("LONG", strategy.long, when = goLong)
strategy.entry("SHORT", strategy.short, when = goShort) 

plot(entryPrediction, color=blue)
plot(exitPrediction, color=yellow)

bgcolor(goLong ? green : goShort ? red : gray, transp=20)
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