ann_pyalgo.py

def getDiff(symbol,hours):
	""" function to calculate Daily Returns
      ohlc4 = (open+high+low+close)/4
  """

  delta = ohlc4_today(symbol)-ohlc_yesterday(hours)
	return delta/ohlc_yesterday(hours)


'''
PineActivationFunctionLinear(v) => v
PineActivationFunctionTanh(v) => (exp(v) - exp(-v))/(exp(v) + exp(-v))
l0_0 = PineActivationFunctionLinear(getDiff())
l1_0 = PineActivationFunctionTanh(l0_0*0.8446488687)
'''

# l0_0 is single input Value, and we create 4 layers l1_0,l1_1,l1_2,l1_3,l1_4

def strategy(l0_0):
	
	# input layers for neural
	
	l1_0 = np.tanh(l0_0*0.8446488687)
	l1_1 = np.tanh(l0_0*-0.5674069006)
	l1_2 = np.tanh(l0_0*0.8676766445)
	l1_3 = np.tanh(l0_0*0.5200611473)
	l1_4 = np.tanh(l0_0*-0.2215499554)

	# hidden layers for neural
	
	l2_0 = np.tanh(l1_0*0.3341657935 + l1_1*-2.0060003664 + l1_2*0.8606354375 +l1_3*0.9184846912 + l1_4*-0.8531172267)
	l2_1 = np.tanh(l1_0*-0.0394076437 + l1_1*-0.4720374911 + l1_2*0.2900968524 +l1_3*1.0653326022 + l1_4*0.3000188806)
	l2_2 = np.tanh(l1_0*-0.559307785 + l1_1*-0.9353655177 + l1_2*1.2133832962 + l1_3*0.1952686024+ l1_4*0.8552068166)
	l2_3 = np.tanh(l1_0*-0.4293220754 + l1_1*0.8484259409 + l1_2*-0.7154087313 +l1_3*0.1102971055 + l1_4*0.2279392724)
	l2_4 = np.tanh(l1_0*0.9111779155 + l1_1*0.2801691115 + l1_2*0.0039982713 + l1_3*-0.5648257117 + l1_4*0.3281705155)


	l2_5 = np.tanh(l1_0*-0.2963954503 + l1_1*0.4046532178 + l1_2*0.2460580977 +l1_3*0.6608675819 + l1_4*-0.8732022547)
	l2_6 = np.tanh(l1_0*0.8810811932 + l1_1*0.6903706878 + l1_2*-0.5953059103 + l1_3*-0.3084040686 + l1_4*-0.4038498853)
	l2_7 = np.tanh(l1_0*-0.5687101164 + l1_1*0.2736758588 + l1_2*-0.2217360382 +l1_3*0.8742950972 + l1_4*0.2997583987)
	l2_8 = np.tanh(l1_0*0.0708459913 + l1_1*0.8221730616 + l1_2*-0.7213265567 + l1_3*-0.3810462836 + l1_4*0.0503867753)
	l2_9 = np.tanh(l1_0*0.4880140595 + l1_1*0.9466627196 + l1_2*1.0163097961 + l1_3*-0.9500386514 + l1_4*-0.6341709382)
	l2_10 = np.tanh(l1_0*1.3402207103 + l1_1*0.0013395288 + l1_2*3.4813009133 + l1_3*-0.8636814677 + l1_4*41.3171047132)
	l2_11 = np.tanh(l1_0*1.2388217292 + l1_1*-0.6520886912 + l1_2*0.3508321737 +l1_3*0.6640560714 + l1_4*1.5936220597)

	l2_12 = np.tanh(l1_0*-0.1800525171 + l1_1*-0.2620989752 + l1_2*0.056675277 + l1_3*-0.5045395315 + l1_4*0.2732553554)
	l2_13 = np.tanh(l1_0*-0.7776331454 + l1_1*0.1895231137 + l1_2*0.5384918862 +l1_3*0.093711904 + l1_4*-0.3725627758)
	l2_14 = np.tanh(l1_0*-0.3181583022 + l1_1*0.2467979854 + l1_2*0.4341718676 + l1_3*-0.7277619935 + l1_4*0.1799381758)
	l2_15 = np.tanh(l1_0*-0.5558227731 + l1_1*0.3666152536 + l1_2*0.1538243225 + l1_3*-0.8915928174 + l1_4*-0.7659355684)
	l2_16 = np.tanh(l1_0*0.6111516061 + l1_1*-0.5459495224 + l1_2*-0.5724238425 + l1_3*-0.8553500765 + l1_4*-0.8696190472)
	l2_17 = np.tanh(l1_0*0.6843667454 + l1_1*0.408652181 + l1_2*-0.8830470112 + l1_3*-0.8602324935 + l1_4*0.1135462621)
	l2_18 = np.tanh(l1_0*-0.1569048216 + l1_1*-1.4643247888 + l1_2*0.5557152813 +l1_3*1.0482791924 + l1_4*1.4523116833)
	l2_19 = np.tanh(l1_0*0.5207514017 + l1_1*-0.2734444192 + l1_2*-0.3328660936 + l1_3*-0.7941515963 + l1_4*-0.3536051491)
	l2_20 = np.tanh(l1_0*-0.4097807954 + l1_1*0.3198619826 + l1_2*0.461681627 + l1_3*-0.1135575498 + l1_4*0.7103339851)
	l2_21 = np.tanh(l1_0*-0.8725014237 + l1_1*-1.0312091401 + l1_2*0.2267643037 + l1_3*-0.6814258121 + l1_4*0.7524828703)
	l2_22 = np.tanh(l1_0*-0.3986855003 + l1_1*0.4962556631 + l1_2*-0.7330224516 +l1_3*0.7355772164 + l1_4*0.3180141739)
	l2_23 = np.tanh(l1_0*-1.083080442 + l1_1*1.8752543187 + l1_2*0.3623326265 + l1_3*-0.348145191+ l1_4*0.1977935038)
	l2_24 = np.tanh(l1_0*-0.0291290625 + l1_1*0.0612906199 + l1_2*0.1219696687 + l1_3*-1.0273685429 + l1_4*0.0872219768)
	l2_25 = np.tanh(l1_0*0.931791094 + l1_1*-0.313753684 + l1_2*-0.3028724837 + l1_3*0.7387076712+ l1_4*0.3806140391)
	l2_26 = np.tanh(l1_0*0.2630619402 + l1_1*-1.9827996702 + l1_2*-0.7741413496 +l1_3*0.1262957444 + l1_4*0.2248777886)


	l2_27 = np.tanh(l1_0*-0.2666322362 + l1_1*-1.124654664 + l1_2*0.7288282621 + l1_3*-0.1384289204 + l1_4*0.2395966188)
	l2_28 = np.tanh(l1_0*0.6611845175 + l1_1*0.0466048937 + l1_2*-0.1980999993 +l1_3*0.8152350927 + l1_4*0.0032723211)
	l2_29 = np.tanh(l1_0*-0.3150344751 + l1_1*0.1391754608 + l1_2*0.5462816249 + l1_3*-0.7952302364 + l1_4*-0.7520712378)
	l2_30 = np.tanh(l1_0*-0.0576916066 + l1_1*0.3678415302 + l1_2*0.6802537378 +l1_3*1.1437036331 + l1_4*-0.8637405666)
	l2_31 = np.tanh(l1_0*0.7016273068 + l1_1*0.3978601709 + l1_2*0.3157049654 + l1_3*-0.2528455662 + l1_4*-0.8614146703)
	l2_32 = np.tanh(l1_0*1.1741126834 + l1_1*-1.4046408959 + l1_2*1.2914477803 +l1_3*0.9904052964 + l1_4*-0.6980155826)



# output layer for neural

	l3_0 = np.tanh(l2_0*-0.1366382003 + l2_1*0.8161960822 + l2_2*-0.9458773183 + \
	l2_3*0.4692969576 + l2_4*0.0126710629 + l2_5*-0.0403001012 + l2_6*-0.0116244898 + l2_7*-0.4874816289 + l2_8*\
	-0.6392241448 + l2_9*-0.410338398 + l2_10*-0.1181027081 + l2_11*0.1075562037 + l2_12*-0.5948728252 \
	+l2_13*0.5593677345 + l2_14*-0.3642935247 + l2_15*-0.2867603217 + l2_16*0.142250271 + l2_17*-0.0535698019 \
	+l2_18*-0.034007685 + l2_19*-0.3594532426 + l2_20*0.2551095195 + l2_21*0.4214344983 + l2_22*0.8941621336 \
	+l2_23*0.6283377368 + l2_24*-0.7138020667 + l2_25*-0.1426738249 + l2_26*0.172671223 + l2_27*0.0714824385 \
	+l2_28*-0.3268182144 + l2_29*-0.0078989755 + l2_30*-0.2032828145 + l2_31*-0.0260631534 + l2_32*0.4918037012)
	return l3_0