mlsanityData to 7 Oct 2026

Dynamic Datasets and Market Environments for Financial Reinforcement Learning

arXiv 2304.13174 ↗First posted 25 Apr 2023cs.LG cs.AIcited by 15

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Abstract (arXiv)

The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets. Building high-quality market environments for training financial reinforcement learning (FinRL) agents is difficult due to major factors such as the low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting. In this paper, we present FinRL-Meta, a data-centric and openly accessible library that processes dynamic datasets from real-world markets into gym-style market environments and has been actively maintained by the AI4Finance community. First, following a DataOps paradigm, we provide hundreds of market environments through an automatic data curation pipeline. Second, we provide homegrown examples and reproduce popular research papers as stepping stones for users to design new trading strategies. We also deploy the library on cloud platforms so that users can visualize their own results and assess the relative performance via community-wise competitions. Third, we provide dozens of Jupyter/Python demos organized into a curriculum and a documentation website to serve the rapidly growing community. The open-source codes for the data curation pipeline are available at https://github.com/AI4Finance-Foundation/FinRL-Meta

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 41 of its 113 references that are in the map. First posted on arXiv 25 Apr 2023.

Builds on

41 of 113 references.

References · 41

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29 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 6; 2026: 2.

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  1. 2024FinBen: A Holistic Financial Benchmark for Large Language Modelscited by 140
  2. 2023FinGPT: Democratizing Internet-scale Data for Financial Large Language Modelscited by 65
  3. 2023Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learningcited by 41
  4. 2024FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Modelscited by 39
  5. 2025Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?cited by 19
  6. 2025FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generationcited by 17
  7. 2024MUSO: Achieving Exact Machine Unlearning in Over-Parameterized Regimescited by 3
  8. 2025Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Informationcited by 2
  9. 2025LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboardcited by 2
  10. 2024Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Executioncited by 1
  11. 2024Customized FinGPT Search Agents Using Foundation Modelscited by 1
  12. 2026Janus-Q: End-to-End Event-Driven Trading via Hierarchical-Gated Reward Modelingcited by 1
3 more
  1. 2025Revisiting Ensemble Methods for Stock Trading and Crypto Trading Tasks at ACM ICAIF FinRL Contest 2023-2024cited by 0
  2. 2025Directly Learning Stock Trading Strategies Through Profit Guided Loss Functionscited by 0
  3. 2026Compositional Behavioral Semantics for State Abstraction in Reinforcement Learningcited by 0