Parallel Market Environments for FinRL Contests

2025-04-03

AI Summary

Reinforcement learning (RL) for financial trading faces critical bottlenecks—low-efficiency market simulation impedes effective integration of large language model (LLM)-generated textual signals and exacerbates sampling constraints. Method: We propose a GPU-accelerated parallel market simulation framework, the first RL environment enabling real-time ingestion of LLM-derived financial semantic signals alongside structured market data. Leveraging CUDA kernel optimization and batched environment encapsulation, it supports thousands of concurrent simulations, overcoming CPU-bound throughput limitations. Contribution/Results: The framework achieves up to 100× training speedup over conventional CPU-based simulators. It has served as the foundational infrastructure for FinRL Contests 2023–2025, enabling over 100 participating teams to efficiently train multi-asset (equities and cryptocurrencies) trading agents. By unifying high-fidelity market dynamics with real-time LLM signals at scale, it establishes a scalable, high-throughput infrastructure paradigm for LLM-augmented RL in finance.

Technology Category

• Machine Learning: Reinforcement Learning
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Reinforcement learning has shown great potential in finance. We have organized the FinRL Contests 2023-2025 featuring different financial tasks. Large language models have a strong capability to process financial texts. Integrating LLM-generated signals into FinRL is a new task, enabling agents to use both structured market data and unstructured financial text. To address the sampling bottleneck during training, we introduce GPU-based parallel market environments to improve sampling speed. In this paper, we summarize the parallel market environments used in FinRL Contests 2023-2025. Two new environments incorporate LLM-generated signals and support massively parallel simulation. Contestants utilize these environments to train agents for stock and cryptocurrency trading tasks.

Problem

• Integrating LLM signals into FinRL for finance tasks
• Solving sampling bottleneck with GPU parallel environments
• Training agents for stock and crypto trading contests

Innovation

• Integrating LLM-generated signals into FinRL
• GPU-based parallel market environments
• Massively parallel simulation for training