Algorithmic Trading Strategies: Enhancing Performance with Reinforcement Learning Techniques
Keywords:
Reinforcement Learning, Deep Q-Learning, Proximal Policy Optimization, Financial Markets, Adaptive Strategies, Market Environment Modeling.Abstract
Algorithmic trading has transformed financial markets by automating decision-making processes, enhancing trading speed, and increasing market efficiency. The integration of Reinforcement Learning (RL) techniques offers new opportunities for developing adaptive and self-learning trading strategies. This paper explores the application of RL in algorithmic trading, highlighting key methods, frameworks, and challenges. We demonstrate how RL can enhance the performance of trading strategies by enabling models to learn from market dynamics and optimize decision-making in real-time. The study also discusses the impact of different RL algorithms, such as Deep Q-Learning and Proximal Policy Optimization, on trading performance across various market conditions.
