标题： StockSim：一种双模式订单级模拟器，用于评估金融市场中的多智能体大语言模型 显示英文标题

标题： StockSim: A Dual-Mode Order-Level Simulator for Evaluating Multi-Agent LLMs in Financial Markets

摘要： 我们提出StockSim，一个开源的仿真平台，用于在现实的金融决策场景中对大型语言模型（LLMs）进行系统评估。 与之前提供的工具包相比，其范围有限， StockSim提供了一个全面的系统，完全模拟市场动态，并支持不同粒度的多样化仿真模式。 它包含了关键的现实世界因素，如延迟、滑点和订单簿微观结构，这些因素之前被忽视了，从而能够更真实和深入地评估基于LLM的交易代理。 一个可扩展的基于角色的代理框架支持异构交易策略和多代理协调，使StockSim成为NLP研究在不确定性和顺序决策推理方面的独特测试平台。 我们在https: //github.com/harrypapa2002/StockSim公开所有代码。 显示英文摘要

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.