In this code The problem im facing with my code is that the loss value is becoming "nan" (not a number) during the training process. This can happen due to several reasons, such as incorrect data preprocessing, inappropriate choice of activation functions or loss function, or numerical instability in the model.

Here are a few suggestions to help you overcome this problem:

Check the data preprocessing: Ensure that the data preprocessing steps are correct and consistent. Make sure you are handling missing values appropriately and scaling the data properly. Double-check the input and output dimensions to the model.

Use appropriate activation functions: The choice of activation functions can have an impact on the stability of the model during training. In your code, you are using the "softmax" activation function in the last layer, which is suitable for multi-class classification problems. However, since you are using "sparse_categorical_crossentropy" as the loss function, make sure your labels are integers and not one-hot encoded. If your labels are already one-hot encoded, consider changing the loss function to "categorical_crossentropy".

Adjust learning rate and optimizer: Experiment with different learning rates and optimizers to find the optimal combination for your model. You can try reducing the learning rate or using different optimizers such as RMSprop or SGD to see if it helps stabilize the training process.

Regularize the model: Overfitting can also lead to instability during training. You can try adding regularization techniques such as dropout or L2 regularization to the LSTM layers to prevent overfitting and improve the stability of the training process.

Increase the number of training samples: If you have a small dataset, it can lead to unstable training. Consider gathering more data if possible or apply data augmentation techniques to increase the size of your training set.

Check for numerical instability: Large or unnormalized values in the input data can lead to numerical instability. Apply appropriate scaling techniques such as normalization or standardization to ensure the input values are within a reasonable range.

Debug step by step: Start by training the model with a small number of epochs and check if the loss value remains stable or becomes "nan". If it remains stable, gradually increase the number of epochs to identify at which point the loss becomes "nan". This can help identify if there is a specific issue with the data or model architecture.|

Code:
import yfinance as yf
import pandas as pd
import numpy as np

nifty_df = yf.download('^NSEI', start='2009-01-01', end='2021-12-31')

# Clean the data
nifty_df = nifty_df.dropna()

# Calculate various percent differences
nifty_df['PctOpenClose'] = (nifty_df['Close'] - nifty_df['Open']) / nifty_df['Open'] * 100
nifty_df['PctHighLow'] = (nifty_df['High'] - nifty_df['Low']) / nifty_df['Low'] * 100
nifty_df['PctChange'] = nifty_df['Close'].pct_change() * 100

# Remove the first row with NaN in 'PctChange'
nifty_df = nifty_df.iloc[1:]

# Export cleaned dataset
nifty_df.to_csv('nifty_cleaned.csv')

def create_labels(df, col_name, threshold=0.5):
    labels = []
    for index, row in df.iterrows():
        future_price = row[col_name]
        if future_price >= threshold:
            labels.append(1)    # Buy
        elif future_price <= -threshold:
            labels.append(-1)   # Sell
        else:
            labels.append(0)    # Hold

    return labels

nifty_df['Labels'] = create_labels(nifty_df, 'PctChange')

import matplotlib.pyplot as plt
import seaborn as sns

sns.countplot(nifty_df['Labels'])

plt.title('Count of Buy, Hold, and Sell')
plt.xlabel('Trade Decisions')
plt.ylabel('Count')
plt.show()

from sklearn.ensemble import ExtraTreesClassifier

X = nifty_df.drop(['Labels'], axis=1)
y = nifty_df['Labels']

# Fit the ExtraTreesClassifier
et_clf = ExtraTreesClassifier()
et_clf.fit(X, y)

# Visualize feature importances
feature_importances = pd.Series(et_clf.feature_importances_, index=X.columns)
feature_importances.nlargest(10).plot(kind='barh')
plt.show()


important_features = feature_importances.nlargest(8).index.tolist()
X_filtered = X[important_features]
X_filtered

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
scaled_X = scaler.fit_transform(X_filtered)

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(scaled_X, y, test_size=0.2, random_state=42)

from keras.models import Sequential
from keras.layers import LSTM, Dense, Dropout

look_back = 20

def create_dataset(X, y, look_back=100):
    dataX, dataY = [], []
    for i in range(len(X)-look_back-1):
        dataX.append(X[i:(i+look_back), :])
        dataY.append(y[i + look_back])
    return np.array(dataX), np.array(dataY)

X_train_lstm, y_train_lstm = create_dataset(X_train, y_train, look_back)
X_test_lstm, y_test_lstm = create_dataset(X_test, y_test, look_back)


from keras.models import Sequential
from keras.layers import LSTM, Dense

model = Sequential()
model.add(LSTM(100, activation='relu', return_sequences=True, input_shape=(look_back, 8)))
model.add(Dropout(0.2))
model.add(LSTM(100, activation='relu', return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(100, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(3, activation='softmax'))
from keras.optimizers import Adam
optimizer = Adam(clipvalue=1.0)
model.compile(optimizer='SGD', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

model.fit(X_train_lstm, y_train_lstm, epochs=200, verbose=1)

from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, accuracy_score

y_pred = model.predict(X_test_lstm)

y_pred_labels = np.argmax(y_pred, axis=1) - 1

print("Confusion Matrix:")
print(confusion_matrix(y_test_lstm, y_pred_labels))

print("\n Evaluation Metrics:")
print("Accuracy: ", accuracy_score(y_test_lstm, y_pred_labels))
print("Precision: ", precision_score(y_test_lstm, y_pred_labels, average='macro'))
print("Recall: ", recall_score(y_test_lstm, y_pred_labels, average='macro'))
print("F1-Score: ", f1_score(y_test_lstm, y_pred_labels, average='macro'))

model.save('nifty_lstm_model.h5') 

# Import the necessary libraries
from keras.models import load_model

# Load the trained model
trained_model = load_model('nifty_lstm_model.h5')

# Load the new dataset for prediction
new_data = pd.read_csv('new_data.csv')

# Preprocess the new dataset
preprocessed_new_data = preprocess_data(new_data)  # Assumes the same preprocessing is applied as before

# Predict
predicted_labels = trained_model.predict(preprocessed_new_data)