Advanced Financial Portfolio Management Using a Deep Reinforcement Learning Framework

The integration of advanced machine learning techniques, particularly deep reinforcement learning (DRL), in financial portfolio management has gained significant attention for its potential to optimize trading strategies. This paper introduces a comprehensive methodology for developing a DRL-based framework aimed at enhancing investment decision-making and overall portfolio performance. Using a robust dataset from Kaggle that includes historical price data, trading volumes, and technical indicators, the proposed approach addresses the complexities of trading in volatile markets. Key stages of the methodology include data preprocessing, feature engineering, environment setup, and the development of a DRL agent, followed by rigorous back testing and evaluation against traditional portfolio management techniques. The DRL agent, implemented in Python using prominent machine learning libraries, achieved an impressive accuracy of 98%, demonstrating its effectiveness in predicting profitable trades while managing risk. Back testing results show substantial improvements in portfolio returns compared to conventional methods, highlighting the framework's potential for real-world trading applications. This research not only contributes to the existing literature on financial machine learning but also offers practical insights for practitioners interested in applying advanced methodologies in trading operations. By combining cutting-edge algorithms with real-time market data, the framework aims to enhance the efficiency and robustness of financial portfolio management.