FinRL Contests: Benchmarking Data-driven Financial Reinforcement Learning Agents
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912483879419904 |
|---|---|
| author | Wang, Keyi Holzer, Nikolaus Xia, Ziyi Cao, Yupeng Gao, Jiechao Walid, Anwar Xiao, Kairong Yanglet, Xiao-Yang Liu |
| author_facet | Wang, Keyi Holzer, Nikolaus Xia, Ziyi Cao, Yupeng Gao, Jiechao Walid, Anwar Xiao, Kairong Yanglet, Xiao-Yang Liu |
| contents | Financial reinforcement learning (FinRL) is now a practical paradigm for financial engineering. However, applying RL strategies to real-world trading tasks remains a challenge for individuals, as it is error-prone and engineering-heavy. The non-stationarity of financial data, low signal-to-noise ratios, and various market frictions require deep accumulations. Although numerous FinRL methods have been developed for tasks such as stock/crypto trading and portfolio management, the lack of standardized task definitions, real-time high-quality datasets, close-to-real market environments, and robust baselines has hindered consistent reproduction in both open-source community and FinTech industry. To bridge this gap, we organized a series of FinRL Contests from 2023 to 2025, covering a diverse range of financial tasks such as stock trading, order execution, crypto trading, and the use of large language model (LLM)-engineered signals. These contests attracted 200+ participants from 100+ institutions over 20+ countries. To encourage participations, we provided starter kits featuring GPU-optimized parallel market environments, ensemble learning, and comprehensive instructions. In this paper, we summarize these benchmarking efforts, detailing task formulations, data curation pipelines, environment implementations, evaluation protocols, participant performance, and organizational insights. It guides our follow-up FinRL contests, and also provides a reference for FinAI contests alike. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02281 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FinRL Contests: Benchmarking Data-driven Financial Reinforcement Learning Agents Wang, Keyi Holzer, Nikolaus Xia, Ziyi Cao, Yupeng Gao, Jiechao Walid, Anwar Xiao, Kairong Yanglet, Xiao-Yang Liu Computational Engineering, Finance, and Science Financial reinforcement learning (FinRL) is now a practical paradigm for financial engineering. However, applying RL strategies to real-world trading tasks remains a challenge for individuals, as it is error-prone and engineering-heavy. The non-stationarity of financial data, low signal-to-noise ratios, and various market frictions require deep accumulations. Although numerous FinRL methods have been developed for tasks such as stock/crypto trading and portfolio management, the lack of standardized task definitions, real-time high-quality datasets, close-to-real market environments, and robust baselines has hindered consistent reproduction in both open-source community and FinTech industry. To bridge this gap, we organized a series of FinRL Contests from 2023 to 2025, covering a diverse range of financial tasks such as stock trading, order execution, crypto trading, and the use of large language model (LLM)-engineered signals. These contests attracted 200+ participants from 100+ institutions over 20+ countries. To encourage participations, we provided starter kits featuring GPU-optimized parallel market environments, ensemble learning, and comprehensive instructions. In this paper, we summarize these benchmarking efforts, detailing task formulations, data curation pipelines, environment implementations, evaluation protocols, participant performance, and organizational insights. It guides our follow-up FinRL contests, and also provides a reference for FinAI contests alike. |
| title | FinRL Contests: Benchmarking Data-driven Financial Reinforcement Learning Agents |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2504.02281 |