Xi-Jing Ou

Reinforcement Learning-Driven Dynamic Trading Strategies for Financial Markets

This paper presents the RL-DynTrade framework by using a cutting-edge deep reinforcement learning method, Proximal Policy Optimization (PPO), with a Deep Q Network (DQN) agent to dynamically adapt to changing risk-reward dynamics. PPO enables real-time, fine-grained, risk-reward adaptation via an actor-critic design wi...