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FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents

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  • Mostapha Benhenda

    (LAGA)

Abstract

This paper presents a novel risk-sensitive trading agent combining reinforcement learning and large language models (LLMs). We extend the Conditional Value-at-Risk Proximal Policy Optimization (CPPO) algorithm, by adding risk assessment and trading recommendation signals generated by a LLM from financial news. Our approach is backtested on the Nasdaq-100 index benchmark, using financial news data from the FNSPID dataset and the DeepSeek V3, Qwen 2.5 and Llama 3.3 language models. The code, data, and trading agents are available at: https://github.com/benstaf/FinRL_DeepSeek

Suggested Citation

  • Mostapha Benhenda, 2025. "FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents," Papers 2502.07393, arXiv.org.
    • Handle: RePEc:arx:papers:2502.07393
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    References listed on IDEAS

    as
    1. Qianggang Ding & Haochen Shi & Bang Liu, 2024. "TradExpert: Revolutionizing Trading with Mixture of Expert LLMs," Papers 2411.00782, arXiv.org.
    2. Alejandro Lopez-Lira & Yuehua Tang, 2023. "Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models," Papers 2304.07619, arXiv.org, revised Sep 2024.
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