StockSim：一种用于评估金融市场中多智能体LLM的双模式订单级模拟器
StockSim: A Dual-Mode Order-Level Simulator for Evaluating Multi-Agent LLMs in Financial Markets
AI总结：
StockSim是一个开源的双模式订单级金融模拟平台，通过建模延迟、滑点等现实因素并提供可扩展的多智能体框架，系统评估LLM在真实金融决策中的表现。
摘要：
我们提出了StockSim，一个开源模拟平台，用于在真实金融决策场景中系统评估大型语言模型（LLMs）。不同于以往范围有限的工具包，StockSim提供了一个全面系统，完整建模市场动态并支持不同粒度的多样模拟模式。它纳入了此前被忽视的关键现实因素，如延迟、滑点和订单簿微观结构，从而能够对基于LLM的交易智能体进行更忠实、更具洞察力的评估。一个可扩展的、基于角色的智能体框架支持异构交易策略和多智能体协调，使StockSim成为研究不确定性下推理与序贯决策的NLP研究的独特能力测试平台。我们在https://github.com/harrypapa2002/StockSim开源了所有代码。
英文摘要：
We present StockSim, an open-source simulation platform for systematic evaluation of large language models (LLMs) in realistic financial decision-making scenarios. Unlike previous toolkits that offer limited scope, StockSim delivers a comprehensive system that fully models market dynamics and supports diverse simulation modes of varying granularity. It incorporates critical real-world factors, such as latency, slippage, and order-book microstructure, that were previously neglected, enabling more faithful and insightful assessment of LLM-based trading agents. An extensible, role-based agent framework supports heterogeneous trading strategies and multi-agent coordination, making StockSim a uniquely capable testbed for NLP research on reasoning under uncertainty and sequential decision-making. We open-source all our code at https: //github.com/harrypapa2002/StockSim.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
