import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import load_model

InputNet1=pd.read_csv('EURUSDInputNet1Min.csv', delimiter=';',header=None)
InputNet2OutNet1=pd.read_csv('EURUSDInputNet2OutNet1Min.csv', delimiter=';',header=None)
OutNet2=pd.read_csv('EURUSDOutNet2Min.csv', delimiter=';',header=None)

mean = InputNet1.mean(axis=0)
std = InputNet1.std(axis=0)
InputNet1 -= mean
InputNet1 /= std

mean = InputNet2OutNet1.mean(axis=0)
std = InputNet2OutNet1.std(axis=0)
InputNet2OutNet1 -= mean
InputNet2OutNet1 /= std

Net1Min = Sequential()
Net1Min.add(Dense(22, activation='relu', input_shape=(InputNet1.shape[1],)))
Net1Min.add(Dense(11))
Net1Min.add(Dense(11))
Net1Min.add(Dense(60))

Net1Min.compile(optimizer='adam', loss='mse', metrics=['mse'])
print(Net1Min.summary())

Net1Min.fit(InputNet1, InputNet2OutNet1, epochs=5, batch_size=10,verbose=2,validation_split=0.3)#validation_split=0.3
Net1Min.save('net1Min.h5')

mean = OutNet2.mean(axis=0)
std = OutNet2.std(axis=0)
OutNet2 -= mean
OutNet2 /= std

Net2Min = Sequential()
Net2Min.add(Dense(60, activation='relu', input_shape=(InputNet2OutNet1.shape[1],)))
Net2Min.add(Dense(1))

Net2Min.compile(optimizer='adam', loss='mse', metrics=['mae'])
print(Net2Min.summary())

Net2Min.fit(InputNet2OutNet1, OutNet2, epochs=5, batch_size=10,verbose=2,validation_split=0.3)
Net2Min.save('net2Min.h5')

InputNet1=pd.read_csv('EURUSDInputNet1Max.csv', delimiter=';',header=None)
InputNet2OutNet1=pd.read_csv('EURUSDInputNet2OutNet1Max.csv', delimiter=';',header=None)
OutNet2=pd.read_csv('EURUSDOutNet2Max.csv', delimiter=';',header=None)

mean = InputNet1.mean(axis=0)
std = InputNet1.std(axis=0)
InputNet1 -= mean
InputNet1 /= std

mean = InputNet2OutNet1.mean(axis=0)
std = InputNet2OutNet1.std(axis=0)
InputNet2OutNet1 -= mean
InputNet2OutNet1 /= std

Net1Max = Sequential()
Net1Max.add(Dense(22, activation='relu', input_shape=(InputNet1.shape[1],)))
Net1Max.add(Dense(11))
Net1Max.add(Dense(11))
Net1Max.add(Dense(60))

Net1Max.compile(optimizer='adam', loss='mse', metrics=['mse'])
print(Net1Max.summary())

Net1Max.fit(InputNet1, InputNet2OutNet1, epochs=5, batch_size=10,verbose=2,validation_split=0.3)#validation_split=0.3
Net1Max.save('net1Max.h5')

mean = OutNet2.mean(axis=0)
std = OutNet2.std(axis=0)
OutNet2 -= mean
OutNet2 /= std

Net2Max = Sequential()
Net2Max.add(Dense(60, activation='relu', input_shape=(InputNet2OutNet1.shape[1],)))
Net2Max.add(Dense(30))
Net2Max.add(Dense(30))
Net2Max.add(Dense(1))

Net2Max.compile(optimizer='adam', loss='mse', metrics=['mae'])
print(Net2Max.summary())

Net2Max.fit(InputNet2OutNet1, OutNet2, epochs=5, batch_size=10,verbose=2,validation_split=0.3)
Net2Max.save('net2Max.h5')

NetTest=pd.read_csv('EURUSDTest.csv', delimiter=';',header=None)
Date=pd.read_csv('EURUSDDate.csv', delimiter=';',header=None)

Net1Min = load_model('net1Min.h5')
Net2Min=  load_model('net2Min.h5')
Net1Max = load_model('net1Max.h5')
Net2Max=  load_model('net2Max.h5')
Net1Min = Net1Min.predict(NetTest)
Net2Min = Net2Min.predict(Net1Min)
Net1Max = Net1Max.predict(NetTest)
Net2Max = Net2Max.predict(Net1Max)

Date=pd.DataFrame(Date)
Date['0'] = Net2Min
Date.to_csv('IndicatorMin.csv',index=False, header=False,sep=';')
Date['0'] = Net2Max
Date.to_csv('IndicatorMax.csv',index=False, header=False,sep=';')

Date['0'] = Net2Min
Date['1'] = Net2Max
Date.to_csv('Indicator.csv',index=False, header=False,sep=';')

input('Press ENTER to exit')