# python libraries
import MetaTrader5 as mt5
import tensorflow as tf
import numpy as np
import pandas as pd
import tf2onnx
from datetime import timedelta, datetime
# input parameters

symbol1 = "EURJPY"
symbol2 = "USDJPY"
symbol3 = "EURUSD"
sample_size1 = 200000
optional = "_M1_test"
timeframe = mt5.TIMEFRAME_M1

#end_date = datetime.now()
end_date = datetime(2024, 3, 4, 0)

inp_history_size = 120

sample_size = sample_size1
symbol = symbol1
optional = optional
inp_model_name = str(symbol)+"_"+str(optional)+".onnx" 

if not mt5.initialize():
    print("initialize() failed, error code =",mt5.last_error())
    quit()

# we will save generated onnx-file near the our script to use as resource
from sys import argv
data_path=argv[0]
last_index=data_path.rfind("\\")+1
data_path=data_path[0:last_index]
print("data path to save onnx model",data_path)

# and save to MQL5\Files folder to use as file
terminal_info=mt5.terminal_info()
file_path=terminal_info.data_path+"\\MQL5\\Files\\"
print("file path to save onnx model",file_path)

# set start and end dates for history data

#end_date = datetime.now()
#end_date = datetime(2024, 5, 1, 0)
start_date = end_date - timedelta(days=inp_history_size*20)

# print start and end dates
print("data start date =",start_date)
print("data end date =",end_date)

# get rates
eurusd_rates = mt5.copy_rates_from(symbol, timeframe , end_date, sample_size )

# create dataframe
df=pd.DataFrame()
df = pd.DataFrame(eurusd_rates)
print(df)
# Extraer los precios de cierre directamente
datas = df['close'].values

"""# Calcular la inversa de cada valor
inverted_data = 1 / datas

# Convertir los datos invertidos a un array de numpy si es necesario
data = inverted_data.values"""

data = datas.reshape(-1,1)
# Imprimir los resultados
"""data = datas"""
# scale data
from sklearn.preprocessing import MinMaxScaler
scaler=MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(data)

# training size is 80% of the data
training_size = int(len(scaled_data)*0.80) 
print("Training_size:",training_size)
train_data_initial = scaled_data[0:training_size,:]
test_data_initial = scaled_data[training_size:,:1]

# split a univariate sequence into samples
def split_sequence(sequence, n_steps):
    X, y = list(), list()
    for i in range(len(sequence)):
       # find the end of this pattern
       end_ix = i + n_steps
       # check if we are beyond the sequence
       if end_ix > len(sequence)-1:
          break
       # gather input and output parts of the pattern
       seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]
       X.append(seq_x)
       y.append(seq_y)
    return np.array(X), np.array(y)

# split into samples
time_step = inp_history_size
x_train, y_train = split_sequence(train_data_initial, time_step)
x_test, y_test = split_sequence(test_data_initial, time_step)

# reshape input to be [samples, time steps, features] which is required for LSTM
x_train =x_train.reshape(x_train.shape[0],x_train.shape[1],1)
x_test = x_test.reshape(x_test.shape[0],x_test.shape[1],1)



# define model
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv1D, MaxPooling1D, Dropout, Flatten, LSTM
from keras.metrics import RootMeanSquaredError as rmse
from tensorflow.keras import callbacks
model = Sequential()
model.add(Conv1D(filters=256, kernel_size=2, activation='relu',padding = 'same',input_shape=(inp_history_size,1)))
model.add(MaxPooling1D(pool_size=2))
model.add(LSTM(100, return_sequences = True))
model.add(Dropout(0.3))
model.add(LSTM(100, return_sequences = False))
model.add(Dropout(0.3))
model.add(Dense(units=1, activation = 'sigmoid'))
model.compile(optimizer='adam', loss= 'mse' , metrics = [rmse()])

# Set up early stopping
early_stopping = callbacks.EarlyStopping(
    monitor='val_loss',
    patience=5,
    restore_best_weights=True,
)

# model training for 300 epochs
history = model.fit(x_train, y_train, epochs = 300 , validation_data = (x_test,y_test), batch_size=32, callbacks=[early_stopping], verbose=2)

# evaluate training data
train_loss, train_rmse = model.evaluate(x_train,y_train, batch_size = 32)
print(f"train_loss={train_loss:.3f}")
print(f"train_rmse={train_rmse:.3f}")

# evaluate testing data
test_loss, test_rmse = model.evaluate(x_test,y_test, batch_size = 32)
print(f"test_loss={test_loss:.3f}")
print(f"test_rmse={test_rmse:.3f}")

# save model to ONNX
output_path = data_path+inp_model_name
onnx_model = tf2onnx.convert.from_keras(model, output_path=output_path)
print(f"saved model to {output_path}")

output_path = file_path+inp_model_name
onnx_model = tf2onnx.convert.from_keras(model, output_path=output_path)
print(f"saved model to {output_path}")

# finish
mt5.shutdown()
#prediction using testing data

#prediction using testing data
test_predict = model.predict(x_test)
print(test_predict)
print("longitud total de la prediccion: ", len(test_predict))
print("longitud total del sample: ", sample_size)

plot_y_test = np.array(y_test).reshape(-1, 1)  # Selecciona solo el último elemento de cada muestra de prueba
plot_y_train = y_train.reshape(-1,1)
train_predict = model.predict(x_train)
#print(plot_y_test)

#calculate metrics
from sklearn import metrics
from sklearn.metrics import r2_score
#transform data to real values
value1=scaler.inverse_transform(plot_y_test)
#print(value1)
# Escala las predicciones inversas al transformarlas a la escala original
value2 = scaler.inverse_transform(test_predict.reshape(-1, 1))
#print(value2)
#calc score
score = np.sqrt(metrics.mean_squared_error(value1,value2))

print("RMSE         : {}".format(score))
print("MSE          :", metrics.mean_squared_error(value1,value2))
print("R2 score     :",metrics.r2_score(value1,value2))


#sumarize model
model.summary()

#Print error
value11=pd.DataFrame(value1)
value22=pd.DataFrame(value2)
#print(value11)
#print(value22)



value111=value11.iloc[:,:]
value222=value22.iloc[:,:]

print("longitud salida (tandas de 1 minuto): ",len(value111) )
#print("en horas son " + str((len(value111))*60*24)+ " minutos")
print("en horas son " + str(((len(value111)))/60)+ " horas")
print("en horas son " + str(((len(value111)))/60/24)+ " dias")


# Calculate error
error = value111 - value222

import matplotlib.pyplot as plt
# Plot error
plt.figure(figsize=(10, 6))
plt.scatter(range(len(error)), error, color='blue', label='Error')
plt.axhline(y=0, color='red', linestyle='--', linewidth=1)  # Línea horizontal en y=0
plt.title('Error de Predicción ' + str(symbol))
plt.xlabel('Índice de la muestra')
plt.ylabel('Error')
plt.legend()
plt.grid(True)
plt.savefig(str(symbol)+str(optional)+'.png') 

rmse_ = format(score)
mse_ = metrics.mean_squared_error(value1,value2)
r2_ = metrics.r2_score(value1,value2)

resultados= [rmse_,mse_,r2_]

# Abre un archivo en modo escritura
with open(str(symbol)+str(optional)+"results.txt", "w") as archivo:
    # Escribe cada resultado en una línea separada
    for resultado in resultados:
        archivo.write(str(resultado) + "\n")

# finish
mt5.shutdown()

#show iteration-rmse graph for training and validation
plt.figure(figsize = (18,10))
plt.plot(history.history['root_mean_squared_error'],label='Training RMSE',color='b')
plt.plot(history.history['val_root_mean_squared_error'],label='Validation-RMSE',color='g')
plt.xlabel("Iteration")
plt.ylabel("RMSE")
plt.title("RMSE" + str(symbol))
plt.legend()
plt.savefig(str(symbol)+str(optional)+'1.png') 

#show iteration-loss graph for training and validation
plt.figure(figsize = (18,10))
plt.plot(history.history['loss'],label='Training Loss',color='b')
plt.plot(history.history['val_loss'],label='Validation-loss',color='g')
plt.xlabel("Iteration")
plt.ylabel("Loss")
plt.title("LOSS" + str(symbol))
plt.legend()
plt.savefig(str(symbol)+str(optional)+'2.png') 

#show actual vs predicted (training) graph
plt.figure(figsize=(18,10))
plt.plot(scaler.inverse_transform(plot_y_train),color = 'b', label = 'Original')
plt.plot(scaler.inverse_transform(train_predict),color='red', label = 'Predicted')
plt.title("Prediction Graph Using Training Data" + str(symbol))
plt.xlabel("Hours")
plt.ylabel("Price")
plt.legend()
plt.savefig(str(symbol)+str(optional)+'3.png') 

#show actual vs predicted (testing) graph
plt.figure(figsize=(18,10))
plt.plot(scaler.inverse_transform(plot_y_test),color = 'b',  label = 'Original')
plt.plot(scaler.inverse_transform(test_predict),color='g', label = 'Predicted')
plt.title("Prediction Graph Using Testing Data" + str(symbol))
plt.xlabel("Hours")
plt.ylabel("Price")
plt.legend()
plt.savefig(str(symbol)+str(optional)+'4.png') 


################################################################################################ EURJPY 1



# python libraries
import MetaTrader5 as mt5
import tensorflow as tf
import numpy as np
import pandas as pd
import tf2onnx

# input parameters

inp_history_size = 120

sample_size = sample_size1
symbol = symbol2
optional = optional
inp_model_name = str(symbol)+"_"+str(optional)+".onnx" 

if not mt5.initialize():
    print("initialize() failed, error code =",mt5.last_error())
    quit()

# we will save generated onnx-file near the our script to use as resource
from sys import argv
data_path=argv[0]
last_index=data_path.rfind("\\")+1
data_path=data_path[0:last_index]
print("data path to save onnx model",data_path)

# and save to MQL5\Files folder to use as file
terminal_info=mt5.terminal_info()
file_path=terminal_info.data_path+"\\MQL5\\Files\\"
print("file path to save onnx model",file_path)

# set start and end dates for history data
from datetime import timedelta, datetime
#end_date = datetime.now()
#end_date = datetime(2024, 5, 1, 0)
start_date = end_date - timedelta(days=inp_history_size*20)

# print start and end dates
print("data start date =",start_date)
print("data end date =",end_date)

# get rates
eurusd_rates2 = mt5.copy_rates_from(symbol, timeframe ,  end_date, sample_size)
# create dataframe
df=pd.DataFrame()
df2 = pd.DataFrame(eurusd_rates2)
print(df2)
# Extraer los precios de cierre directamente
datas2 = df2['close'].values

"""inverted_data = 1 / datas

# Convertir los datos invertidos a un array de numpy si es necesario
data = inverted_data.values"""
data2 = datas2.reshape(-1,1)


# Convertir los datos invertidos a un array de numpy si es necesario
#data = datas.values

# Imprimir los resultados

# scale data
from sklearn.preprocessing import MinMaxScaler
scaler2=MinMaxScaler(feature_range=(0,1))
scaled_data2 = scaler2.fit_transform(data2)

# training size is 80% of the data
training_size2 = int(len(scaled_data2)*0.80) 
print("Training_size:",training_size2)
train_data_initial2 = scaled_data2[0:training_size2,:]
test_data_initial2 = scaled_data2[training_size2:,:1]

# split a univariate sequence into samples
def split_sequence(sequence, n_steps):
    X, y = list(), list()
    for i in range(len(sequence)):
       # find the end of this pattern
       end_ix = i + n_steps
       # check if we are beyond the sequence
       if end_ix > len(sequence)-1:
          break
       # gather input and output parts of the pattern
       seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]
       X.append(seq_x)
       y.append(seq_y)
    return np.array(X), np.array(y)

# split into samples
time_step = inp_history_size
x_train2, y_train2 = split_sequence(train_data_initial2, time_step)
x_test2, y_test2 = split_sequence(test_data_initial2, time_step)

# reshape input to be [samples, time steps, features] which is required for LSTM
x_train2 =x_train2.reshape(x_train2.shape[0],x_train2.shape[1],1)
x_test2 = x_test2.reshape(x_test2.shape[0],x_test2.shape[1],1)



# define model
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv1D, MaxPooling1D, Dropout, Flatten, LSTM
from keras.metrics import RootMeanSquaredError as rmse
from tensorflow.keras import callbacks
model = Sequential()
model.add(Conv1D(filters=256, kernel_size=2, activation='relu',padding = 'same',input_shape=(inp_history_size,1)))
model.add(MaxPooling1D(pool_size=2))
model.add(LSTM(100, return_sequences = True))
model.add(Dropout(0.3))
model.add(LSTM(100, return_sequences = False))
model.add(Dropout(0.3))
model.add(Dense(units=1, activation = 'sigmoid'))
model.compile(optimizer='adam', loss= 'mse' , metrics = [rmse()])

# Set up early stopping
early_stopping = callbacks.EarlyStopping(
    monitor='val_loss',
    patience=5,
    restore_best_weights=True,
)

# model training for 300 epochs
history2 = model.fit(x_train2, y_train2, epochs = 300 , validation_data = (x_test2,y_test2), batch_size=32, callbacks=[early_stopping], verbose=2)

# evaluate training data
train_loss2, train_rmse2 = model.evaluate(x_train2,y_train2, batch_size = 32)
print(f"train_loss={train_loss2:.3f}")
print(f"train_rmse={train_rmse2:.3f}")

# evaluate testing data
test_loss2, test_rmse2 = model.evaluate(x_test2,y_test2, batch_size = 32)
print(f"test_loss={test_loss2:.3f}")
print(f"test_rmse={test_rmse2:.3f}")

# save model to ONNX
output_path = data_path+inp_model_name
onnx_model = tf2onnx.convert.from_keras(model, output_path=output_path)
print(f"saved model to {output_path}")

output_path = file_path+inp_model_name
onnx_model = tf2onnx.convert.from_keras(model, output_path=output_path)
print(f"saved model to {output_path}")

# finish
mt5.shutdown()
#prediction using testing data

#prediction using testing data
test_predict2 = model.predict(x_test2)
print(test_predict2)
print("longitud total de la prediccion: ", len(test_predict2))
print("longitud total del sample: ", sample_size)

plot_y_test2 = np.array(y_test2).reshape(-1, 1)  # Selecciona solo el último elemento de cada muestra de prueba
plot_y_train2 = y_train2.reshape(-1,1)
train_predict2 = model.predict(x_train2)
#print(plot_y_test)

#calculate metrics
from sklearn import metrics
from sklearn.metrics import r2_score
#transform data to real values
value12=scaler2.inverse_transform(plot_y_test2)
#print(value1)
# Escala las predicciones inversas al transformarlas a la escala original
value22 = scaler2.inverse_transform(test_predict2.reshape(-1, 1))
#print(value2)
#calc score
score2 = np.sqrt(metrics.mean_squared_error(value12,value22))

print("RMSE         : {}".format(score2))
print("MSE          :", metrics.mean_squared_error(value12,value22))
print("R2 score     :",metrics.r2_score(value12,value22))


#sumarize model
model.summary()

#Print error
value112=pd.DataFrame(value12)
value222=pd.DataFrame(value22)
#print(value11)
#print(value22)



value1112=value112.iloc[:,:]
value2222=value222.iloc[:,:]

print("longitud salida (tandas de 1 min): ",len(value1112) )
#print("en horas son " + str((len(value1112))*60*24)+ " minutos")
print("en horas son " + str(((len(value1112)))/60)+ " horas")
print("en horas son " + str(((len(value1112)))/60/24)+ " dias")


# Calculate error
error2 = value1112 - value2222

import matplotlib.pyplot as plt
# Plot error
plt.figure(figsize=(10, 6))
plt.scatter(range(len(error2)), error2, color='blue', label='Error')
plt.axhline(y=0, color='red', linestyle='--', linewidth=1)  # Línea horizontal en y=0
plt.title('Error de Predicción ' + str(symbol))
plt.xlabel('Índice de la muestra')
plt.ylabel('Error')
plt.legend()
plt.grid(True)
plt.savefig(str(symbol)+str(optional)+'.png') 

rmse_2 = format(score2)
mse_2 = metrics.mean_squared_error(value12,value22)
r2_2 = metrics.r2_score(value12,value22)

resultados2= [rmse_2,mse_2,r2_2]

# Abre un archivo en modo escritura
with open(str(symbol)+str(optional)+"results.txt", "w") as archivo:
    # Escribe cada resultado en una línea separada
    for resultado in resultados2:
        archivo.write(str(resultado) + "\n")

# finish
mt5.shutdown()

#show iteration-rmse graph for training and validation
plt.figure(figsize = (18,10))
plt.plot(history2.history['root_mean_squared_error'],label='Training RMSE',color='b')
plt.plot(history2.history['val_root_mean_squared_error'],label='Validation-RMSE',color='g')
plt.xlabel("Iteration")
plt.ylabel("RMSE")
plt.title("RMSE" + str(symbol))
plt.legend()
plt.savefig(str(symbol)+str(optional)+'1.png') 

#show iteration-loss graph for training and validation
plt.figure(figsize = (18,10))
plt.plot(history2.history['loss'],label='Training Loss',color='b')
plt.plot(history2.history['val_loss'],label='Validation-loss',color='g')
plt.xlabel("Iteration")
plt.ylabel("Loss")
plt.title("LOSS" + str(symbol))
plt.legend()
plt.savefig(str(symbol)+str(optional)+'2.png') 

#show actual vs predicted (training) graph
plt.figure(figsize=(18,10))
plt.plot(scaler2.inverse_transform(plot_y_train2),color = 'b', label = 'Original')
plt.plot(scaler2.inverse_transform(train_predict2),color='red', label = 'Predicted')
plt.title("Prediction Graph Using Training Data" + str(symbol))
plt.xlabel("Hours")
plt.ylabel("Price")
plt.legend()
plt.savefig(str(symbol)+str(optional)+'3.png') 

#show actual vs predicted (testing) graph
plt.figure(figsize=(18,10))
plt.plot(scaler2.inverse_transform(plot_y_test2),color = 'b',  label = 'Original')
plt.plot(scaler2.inverse_transform(test_predict2),color='g', label = 'Predicted')
plt.title("Prediction Graph Using Testing Data" + str(symbol))
plt.xlabel("Hours")
plt.ylabel("Price")
plt.legend()
plt.savefig(str(symbol)+str(optional)+'4.png') 




##############################################################################################  JPYUSD



# python libraries
import MetaTrader5 as mt5
import tensorflow as tf
import numpy as np
import pandas as pd
import tf2onnx

# input parameters

inp_history_size = 120

sample_size = sample_size1
symbol = symbol3
optional = optional
inp_model_name = str(symbol)+"_"+str(optional)+".onnx" 

if not mt5.initialize():
    print("initialize() failed, error code =",mt5.last_error())
    quit()

# we will save generated onnx-file near the our script to use as resource
from sys import argv
data_path=argv[0]
last_index=data_path.rfind("\\")+1
data_path=data_path[0:last_index]
print("data path to save onnx model",data_path)

# and save to MQL5\Files folder to use as file
terminal_info=mt5.terminal_info()
file_path=terminal_info.data_path+"\\MQL5\\Files\\"
print("file path to save onnx model",file_path)

# set start and end dates for history data
from datetime import timedelta, datetime
#end_date = datetime.now()
#end_date = datetime(2024, 5, 1, 0)
start_date = end_date - timedelta(days=inp_history_size*20)

# print start and end dates
print("data start date =",start_date)
print("data end date =",end_date)

# get rates
eurusd_rates3 = mt5.copy_rates_from(symbol, timeframe ,  end_date, sample_size)
# create dataframe
df3=pd.DataFrame()
df3 = pd.DataFrame(eurusd_rates3)
print(df3)
# Extraer los precios de cierre directamente
datas3 = df3['close'].values

"""# Calcular la inversa de cada valor
inverted_data = 1 / datas

# Convertir los datos invertidos a un array de numpy si es necesario
data = inverted_data.values"""
data3 = datas3.reshape(-1,1)
# Imprimir los resultados
"""data = datas"""
# scale data
from sklearn.preprocessing import MinMaxScaler
scaler3=MinMaxScaler(feature_range=(0,1))
scaled_data3 = scaler3.fit_transform(data3)

# training size is 80% of the data
training_size3 = int(len(scaled_data3)*0.80) 
print("Training_size:",training_size3)
train_data_initial3 = scaled_data3[0:training_size3,:]
test_data_initial3 = scaled_data3[training_size3:,:1]

# split a univariate sequence into samples
def split_sequence(sequence, n_steps):
    X, y = list(), list()
    for i in range(len(sequence)):
       # find the end of this pattern
       end_ix = i + n_steps
       # check if we are beyond the sequence
       if end_ix > len(sequence)-1:
          break
       # gather input and output parts of the pattern
       seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]
       X.append(seq_x)
       y.append(seq_y)
    return np.array(X), np.array(y)

# split into samples
time_step = inp_history_size
x_train3, y_train3 = split_sequence(train_data_initial3, time_step)
x_test3, y_test3 = split_sequence(test_data_initial3, time_step)

# reshape input to be [samples, time steps, features] which is required for LSTM
x_train3 =x_train3.reshape(x_train3.shape[0],x_train3.shape[1],1)
x_test3 = x_test3.reshape(x_test3.shape[0],x_test3.shape[1],1)



# define model
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv1D, MaxPooling1D, Dropout, Flatten, LSTM
from keras.metrics import RootMeanSquaredError as rmse
from tensorflow.keras import callbacks
model = Sequential()
model.add(Conv1D(filters=256, kernel_size=2, activation='relu',padding = 'same',input_shape=(inp_history_size,1)))
model.add(MaxPooling1D(pool_size=2))
model.add(LSTM(100, return_sequences = True))
model.add(Dropout(0.3))
model.add(LSTM(100, return_sequences = False))
model.add(Dropout(0.3))
model.add(Dense(units=1, activation = 'sigmoid'))
model.compile(optimizer='adam', loss= 'mse' , metrics = [rmse()])

# Set up early stopping
early_stopping = callbacks.EarlyStopping(
    monitor='val_loss',
    patience=5,
    restore_best_weights=True,
)

# model training for 300 epochs
history3 = model.fit(x_train3, y_train3, epochs = 300 , validation_data = (x_test3,y_test3), batch_size=32, callbacks=[early_stopping], verbose=2)

# evaluate training data
train_loss3, train_rmse3 = model.evaluate(x_train3,y_train3, batch_size = 32)
print(f"train_loss={train_loss3:.3f}")
print(f"train_rmse={train_rmse3:.3f}")

# evaluate testing data
test_loss3, test_rmse3 = model.evaluate(x_test3,y_test3, batch_size = 32)
print(f"test_loss={test_loss3:.3f}")
print(f"test_rmse={test_rmse3:.3f}")

# save model to ONNX
output_path = data_path+inp_model_name
onnx_model = tf2onnx.convert.from_keras(model, output_path=output_path)
print(f"saved model to {output_path}")

output_path = file_path+inp_model_name
onnx_model = tf2onnx.convert.from_keras(model, output_path=output_path)
print(f"saved model to {output_path}")

# finish
mt5.shutdown()
#prediction using testing data

#prediction using testing data
test_predict3 = model.predict(x_test3)
print(test_predict3)
print("longitud total de la prediccion: ", len(test_predict3))
print("longitud total del sample: ", sample_size)

plot_y_test3 = np.array(y_test3).reshape(-1, 1)  # Selecciona solo el último elemento de cada muestra de prueba
plot_y_train3 = y_train3.reshape(-1,1)
train_predict3 = model.predict(x_train3)
#print(plot_y_test)

#calculate metrics
from sklearn import metrics
from sklearn.metrics import r2_score
#transform data to real values
value13=scaler3.inverse_transform(plot_y_test3)
#print(value1)
# Escala las predicciones inversas al transformarlas a la escala original
value23 = scaler3.inverse_transform(test_predict3.reshape(-1, 1))
#print(value2)
#calc score
score3 = np.sqrt(metrics.mean_squared_error(value13,value23))

print("RMSE         : {}".format(score3))
print("MSE          :", metrics.mean_squared_error(value13,value23))
print("R2 score     :",metrics.r2_score(value13,value23))


#sumarize model
model.summary()

#Print error
value113=pd.DataFrame(value13)
value223=pd.DataFrame(value23)
#print(value11)
#print(value22)



value1113=value113.iloc[:,:]
value2223=value223.iloc[:,:]

print("longitud salida (tandas de 1 hora): ",len(value1113) )
#print("en horas son " + str((len(value1113))*60*24)+ " minutos")
print("en horas son " + str(((len(value1113)))/60)+ " horas")
print("en horas son " + str(((len(value1113)))/60/24)+ " dias")


# Calculate error
error3 = value1113 - value2223

import matplotlib.pyplot as plt
# Plot error
plt.figure(figsize=(10, 6))
plt.scatter(range(len(error3)), error3, color='blue', label='Error')
plt.axhline(y=0, color='red', linestyle='--', linewidth=1)  # Línea horizontal en y=0
plt.title('Error de Predicción ' + str(symbol))
plt.xlabel('Índice de la muestra')
plt.ylabel('Error')
plt.legend()
plt.grid(True)
plt.savefig(str(symbol)+str(optional)+'.png') 

rmse_3 = format(score3)
mse_3 = metrics.mean_squared_error(value13,value23)
r2_3 = metrics.r2_score(value13,value23)

resultados3= [rmse_3,mse_3,r2_3]

# Abre un archivo en modo escritura
with open(str(symbol)+str(optional)+"results.txt", "w") as archivo:
    # Escribe cada resultado en una línea separada
    for resultado in resultados3:
        archivo.write(str(resultado) + "\n")

# finish
mt5.shutdown()

#show iteration-rmse graph for training and validation
plt.figure(figsize = (18,10))
plt.plot(history3.history['root_mean_squared_error'],label='Training RMSE',color='b')
plt.plot(history3.history['val_root_mean_squared_error'],label='Validation-RMSE',color='g')
plt.xlabel("Iteration")
plt.ylabel("RMSE")
plt.title("RMSE" + str(symbol))
plt.legend()
plt.savefig(str(symbol)+str(optional)+'1.png') 

#show iteration-loss graph for training and validation
plt.figure(figsize = (18,10))
plt.plot(history3.history['loss'],label='Training Loss',color='b')
plt.plot(history3.history['val_loss'],label='Validation-loss',color='g')
plt.xlabel("Iteration")
plt.ylabel("Loss")
plt.title("LOSS" + str(symbol))
plt.legend()
plt.savefig(str(symbol)+str(optional)+'2.png') 

#show actual vs predicted (training) graph
plt.figure(figsize=(18,10))
plt.plot(scaler3.inverse_transform(plot_y_train3),color = 'b', label = 'Original')
plt.plot(scaler3.inverse_transform(train_predict3),color='red', label = 'Predicted')
plt.title("Prediction Graph Using Training Data" + str(symbol))
plt.xlabel("Hours")
plt.ylabel("Price")
plt.legend()
plt.savefig(str(symbol)+str(optional)+'3.png') 

#show actual vs predicted (testing) graph
plt.figure(figsize=(18,10))
plt.plot(scaler3.inverse_transform(plot_y_test3),color = 'b',  label = 'Original')
plt.plot(scaler3.inverse_transform(test_predict3),color='g', label = 'Predicted')
plt.title("Prediction Graph Using Testing Data" + str(symbol))
plt.xlabel("Hours")
plt.ylabel("Price")
plt.legend()
plt.savefig(str(symbol)+str(optional)+'4.png') 

################################################################################################
##############################################################################################
"""
import onnxruntime as ort
import numpy as np

# Cargar el modelo ONNX
sesion = ort.InferenceSession("EURUSD_M1_inverse_test.onnx")

# Obtener el nombre de la entrada y la salida del modelo
input_name = sesion.get_inputs()[0].name
output_name = sesion.get_outputs()[0].name

# Crear datos de entrada de prueba como un array de numpy
# Asegúrate de que los datos de entrada coincidan con la forma y el tipo esperado por el modelo
input_data = [1,120] #np.random.rand(1, 10).astype(np.float32)  # Ejemplo: entrada de tamaño [1, 10]

# Realizar la inferencia
result = sesion.run([output_name], {input_name: input_data})

# Imprimir el resultado
print(result)
"""