Advanced Stock Trading Strategies Utilizing Deep Q-Network and Deep Reinforcement Learning: A Dynamic Ensemble Approach

DRL has the potential to be applied successfully to stock market forecasting and automated trading. In this paper, a dynamic stock-decision ensemble strategy based on DRL is introduced to reduce the forecast error using Multi-DQN and Deep Q-learning agents. In this context, our approach is designed to provide a consistent, comprehensive description of customers and markets, supplementing other analysis techniques to enhance decision-making in the trading environment. As evidenced by experimental outcomes, the herein proposed ensemble model achieves better overall performances than conventional trading systems based on different financial evaluation criteria: ROI, Sharpe ratio and, recently, risk-adjusted returns. All these give a strong indication that the DRL-based ensemble approach is a reliable and malleable framework for stock market prediction. This work develops a stock investing system using DRL ensembles to handle market instabilities and improve processing time. Our IDQN model proves better at predicting financial prices and resisting market shakes than traditional techniques.