{"ID":2895428,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.09255","arxiv_id":"2507.09255","title":"StockSim: A Dual-Mode Order-Level Simulator for Evaluating Multi-Agent LLMs in Financial Markets","abstract":"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.","short_abstract":"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 simulatio...","url_abs":"https://arxiv.org/abs/2507.09255","url_pdf":"https://arxiv.org/pdf/2507.09255v1","authors":"[\"Charidimos Papadakis\",\"Giorgos Filandrianos\",\"Angeliki Dimitriou\",\"Maria Lymperaiou\",\"Konstantinos Thomas\",\"Giorgos Stamou\"]","published":"2025-07-12T11:29:44Z","proceeding":"cs.CE","tasks":"[\"cs.CE\",\"cs.MA\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}