# FinRL Contests: Benchmarking Data-driven Financial Reinforcement Learning Agents

arXiv: https://arxiv.org/abs/2504.02281 · HTML: https://mlsanity.com/paper/2504.02281/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2025.

## Built on by (most cited first, 2 of 2)

- [KellyBench: A Benchmark for Long-Horizon Sequential Decision Making](https://mlsanity.com/paper/2604.27865/) (2026)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)

## Abstract (arXiv)

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.