Trading BOT Using Reinforcement Learning Techniques

This study investigates the application of Deep Reinforcement Learning (DRL) methods to develop autonomous algorithmic trading strategies that dynamically adapt to market conditions. Using an Actor-Critic and policy-based framework within a custom trading environment built on OpenAI Gym, the AI agent learns optimal buy/sell actions from historical stock data spanning 2010 to 2025. Key financial constraints portfolio balance, transaction costs, and risk management are explicitly modeled to simulate realistic trading conditions. Multiple DRL algorithms, including Trust Region Policy Optimization (TRPO), Soft Actor-Critic (SAC), Proximal Policy Optimization (PPO), and REINFORCE, are implemented and comparatively evaluated using metrics such as total returns, Sharpe ratio, maximum drawdown, and average episodic rewards. Our results provide insights into each model’s strengths and practical viability, highlighting PPO’s stability and SAC’s exploration efficiency trade-offs. The system features visualization tools to elucidate portfolio dynamics and trading behavior. This comprehensive approach demonstrates DRL’s potential to advance autonomous trading systems capable of optimizing financial performance under real-world constraints.