Stock Market Prediction through Ensemble of Deep Learning and Machine Learning Models along with Technical Indicators

Introducing a novel ensemble methodology for stock market prediction that seamlessly combines well-known traditional machine learning algorithms like Support Vector Machines (SVM) and Random Forest with cutting-edge deep learning models like Long Short-Term Memory Networks (LSTM) and Recurrent Neural Networks (RNN). The addition of crucial technical indicators, such as Relative Strength Index (RSI), Moving Averages, Bollinger Bands, and Money Flow Index (MFI), further enhances this comprehensive ensemble framework. Through the combination of these several models and the use of the enlightening data that market indicators offer, the method overcomes the constraints of single algorithms. Combining major technical indicators with deep learning and conventional machine learning approaches yields an ensemble model that performs very well in identifying subtle market patterns and making accurate forecasts. Thorough real-world testing demonstrates the efficacy of the ensemble technique. The all-inclusive tool shows its ability to accurately predict stock market movements and offers a strong foundation for negotiating the complexities of financial markets. These findings advance the area of finance by giving academics, investors, and practitioners seeking better decision support in the dynamic and complex world of the stock market a sophisticated and reliable method.