A New DAPO Algorithm for Stock Trading
Abstract
Recent advances in reinforcement learning, like Dynamic sAmpling Policy Optimization (DAPO) algorithm, have demonstrated strong performance when combined with Large Language Models (LLMs). Motivated by this, we investigate whether similar benefits can be achieved in financial trading. We develop a novel trading agent that integrates an improved Group Relative Policy Optimization (GRPO) algorithm, enhanced with insights from DAPO, and incorporates LLM-based signals of risk and sentiment from financial news. We evaluate our method on the NASDAQ-100 index using the FNSPID dataset. Our improved DAPO algorithm achieves a cumulative return of 230.49% and an Information Ratio of 0.37, outperforming the CPPO-DeepSeek baseline. Additionally, our approach reduces training time from approximately 8 hours to 2.5 hours over 100 epochs, while significantly decreasing RAM usage. Our approach addresses limitations of existing RL-LLM frameworks, offering a scalable solution for building financial trading agents. Code is available at: https://github.com/Ruijian-Zha/FinRL-DAPO-SR/
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