i have historical data of crypto
i have following code to train a lstm model on my data 
check and see if it has any problems:
# %%
import numpy as np
import pandas as pd
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler
import os

# %%
def load_and_process_data(file_path, target_col, scaler, sequence_length, batch_size = 32):
    # Load data
    data = pd.read_csv(file_path)
    
    # Split features and labels
    X = data.drop([
        'Date', 'Symbol',
        'y_High_1d', 'y_Low_1d', 'y_Priority_1d',
         'y_High_2d', 'y_Low_2d', 'y_Priority_2d',
          'y_High_3d', 'y_Low_3d', 'y_Priority_3d',
           'y_High_5d', 'y_Low_5d', 'y_Priority_5d'], axis=1).values

    y_high_1d = data['y_High_1d'].values
    y_low_1d = data['y_Low_1d'].values
    y_priority_1d = data['y_Priority_1d'].values

    y_high_2d = data['y_High_2d'].values
    y_low_2d = data['y_Low_2d'].values
    y_priority_2d = data['y_Priority_2d'].values

    y_high_3d = data['y_High_3d'].values
    y_low_3d = data['y_Low_3d'].values
    y_priority_3d = data['y_Priority_3d'].values

    y_high_5d = data['y_High_5d'].values
    y_low_5d = data['y_Low_5d'].values
    y_priority_5d = data['y_Priority_5d'].values
    
    # Optionally, apply preprocessing here (e.g., Scaler transform)
    X = scaler.fit_transform(X)
    X = scaler.transform(X)

    # # Create sequences
    # X_seq, y_seq = [], []
    # for i in range(len(X) - sequence_length):
    #     X_seq.append(X[i:i+sequence_length])
    #     y_seq.append(y[i+sequence_length])
    # return np.array(X_seq), np.array(y_seq)

    # Splitting data into batches
    for i in range(0, len(data), batch_size):
        end = i + batch_size
        X_batch = X[i:end]

        y_high_batch_1d = y_high_1d[i:end]
        y_low_batch_1d = y_low_1d[i:end]
        y_priority_batch_1d = y_priority_1d[i:end]

        y_high_batch_2d = y_high_2d[i:end]
        y_low_batch_2d = y_low_2d[i:end]
        y_priority_batch_2d = y_priority_2d[i:end]

        y_high_batch_3d = y_high_3d[i:end]
        y_low_batch_3d = y_low_3d[i:end]
        y_priority_batch_3d = y_priority_3d[i:end]

        y_high_batch_5d = y_high_5d[i:end]
        y_low_batch_5d = y_low_5d[i:end]
        y_priority_batch_5d = y_priority_5d[i:end]
        
        yield X_batch, [y_high_batch_1d, y_low_batch_1d,y_priority_batch_1d,
            y_high_batch_2d, y_low_batch_2d,y_priority_batch_2d,
            y_high_batch_3d, y_low_batch_3d, y_priority_batch_3d,
            y_high_batch_5d, y_low_batch_5d, y_priority_batch_5d]

# %%
scaler = MinMaxScaler(feature_range=(0, 1))
# Normally, you'd fit the scaler on your dataset. You'll need to adapt this to your scenario.
# For demonstration, let's assume all features are within 0 and 100.


# %%
def train_model(files, batch_size, epochs):
    target_cols =[
                'y_High_1d', 'y_Low_1d', 'y_Priority_1d',
                 'y_High_2d', 'y_Low_2d', 'y_Priority_2d',
                  'y_High_3d', 'y_Low_3d', 'y_Priority_3d',
                   'y_High_5d', 'y_Low_5d', 'y_Priority_5d']

    for epoch in range(epochs):
        data_generator = load_and_process_data(file_path, target_cols, scaler, sequence_length=30, batch_size = batch_size)
        for X_batch, y_batch in data_generator:  # Here’s how you iterate over the generator
            model.train_on_batch(X_batch, y_batch)

# %%
from tensorflow.keras.layers import Input, Dense, LSTM, concatenate
from tensorflow.keras.models import Model

# Input layer
input_layer = Input(shape=(30, 6427))  # Adjust accordingly

# Shared LSTM layer
lstm_out = LSTM(50, activation='relu')(input_layer)

# Defining three separate outputs

out_high_1d = Dense(1, name='high_output_1d')(lstm_out)  # No activation, linear output
out_low_1d = Dense(1, name='low_output_1d')(lstm_out)    # No activation, linear output
out_priority_1d = Dense(1, activation='sigmoid', name='priority_output_1d')(lstm_out)

out_high_2d = Dense(1, name='high_output_2d')(lstm_out)  # No activation, linear output
out_low_2d = Dense(1, name='low_output_2d')(lstm_out)    # No activation, linear output
out_priority_2d = Dense(1, activation='sigmoid', name='priority_output_2d')(lstm_out)

out_high_3d = Dense(1, name='high_output_3d')(lstm_out)  # No activation, linear output
out_low_3d = Dense(1, name='low_output_3d')(lstm_out)    # No activation, linear output
out_priority_3d = Dense(1, activation='sigmoid', name='priority_output_3d')(lstm_out)

out_high_5d = Dense(1, name='high_output_5d')(lstm_out)  # No activation, linear output
out_low_5d = Dense(1, name='low_output_5d')(lstm_out)    # No activation, linear output
out_priority_5d = Dense(1, activation='sigmoid', name='priority_output_5d')(lstm_out)

# Constructing the model
model = Model(inputs=input_layer, outputs=[
    out_high_1d, out_low_1d, out_priority_1d,out_high_2d, out_low_2d, out_priority_2d,
    out_high_3d, out_low_3d, out_priority_3d,out_high_5d, out_low_5d, out_priority_5d])


model.compile(optimizer='adam', 
            loss={
                'high_output_1d': 'mse', 'low_output_1d': 'mse', 'priority_output_1d': 'binary_crossentropy',
                'high_output_2d': 'mse', 'low_output_2d': 'mse', 'priority_output_2d': 'binary_crossentropy',
                'high_output_3d': 'mse', 'low_output_3d': 'mse', 'priority_output_3d': 'binary_crossentropy',
                'high_output_5d': 'mse', 'low_output_5d': 'mse', 'priority_output_5d': 'binary_crossentropy'
            },
            metrics={
                'high_output_1d': ['mae'], 'low_output_1d': ['mae'], 'priority_output_1d': ['accuracy'],
                'high_output_2d': ['mae'], 'low_output_2d': ['mae'], 'priority_output_2d': ['accuracy'],
                'high_output_3d': ['mae'], 'low_output_3d': ['mae'], 'priority_output_3d': ['accuracy'],
                'high_output_5d': ['mae'], 'low_output_5d': ['mae'], 'priority_output_5d': ['accuracy']
            }
)

# %%
csv_folder_path = r"E:\01_calculate_talib\New folder (2)"
file_paths = []  # Update this list with actual file paths

for csv_file in os.listdir(csv_folder_path):
    file_path = os.path.join(csv_folder_path, csv_file)
    file_paths.append(file_path)
batch_size = 32
epochs = 1000  # Set the number of epochs

train_model(file_paths, batch_size, epochs)

# %%