Real-world financial decision-making is a challenging problem that requires reasoning over heterogeneous signals, including company fundamentals derived from regulatory filings and trading signals computed from price dynamics. Recently, with the advancement of Large Language Models (LLMs), financial analysts have begun to use them for financial decision-making tasks. However, existing financial question answering benchmarks for testing these models primarily focus on company balance sheet data and rarely evaluate reasoning over how company stocks trade in the market or their interactions with fundamentals. To take advantage of the strengths of both approaches, we introduce FinTradeBench, a benchmark for evaluating financial reasoning that integrates company fundamentals and trading signals. FinTradeBench contains 1,400 questions grounded in NASDAQ-100 companies over a ten-year historical window. The benchmark is organized into three reasoning categories: fundamentals-focused, trading-signal-focused, and hybrid questions requiring cross-signal reasoning. To ensure reliability at scale, we adopt a calibration-then-scaling framework that combines expert seed questions, multi-model response generation, intra-model self-filtering, numerical auditing, and human-LLM judge alignment. We evaluate 14 LLMs under zero-shot prompting and retrieval-augmented settings and witness a clear performance gap. Retrieval substantially improves reasoning over textual fundamentals, but provides limited benefit for trading-signal reasoning. These findings highlight fundamental challenges in the numerical and time-series reasoning for current LLMs and motivate future research in financial intelligence.

翻译：现实世界的金融决策是一项富有挑战性的问题，需要对异构信号进行推理，包括从监管申报文件中提取的公司基本面信息以及从价格动态计算得出的交易信号。近年来，随着大语言模型（LLMs）的进步，金融分析师已开始将其用于金融决策任务。然而，现有的用于测试这些模型的金融问答基准主要关注公司资产负债表数据，很少评估模型对股票市场交易行为或其与基本面互动的推理能力。为充分利用两种方法的优势，我们提出了FinTradeBench——一个整合公司基本面与交易信号的金融推理评估基准。FinTradeBench包含基于纳斯达克100指数成分股十年历史窗口生成的1400个问题。该基准按推理类别分为三组：聚焦基本面问题、聚焦交易信号问题及需要跨信号推理的混合问题。为确保大规模可靠性，我们采用"标定-扩展"框架，该框架结合了专家种子问题、多模型响应生成、模型内自过滤、数值审计及人机判据对齐。我们在零样本提示和检索增强设置下评估了14个LLM，并观察到显著的性能差距。检索显著提升了基于文本的基本面推理能力，但对交易信号推理的改善有限。这些发现揭示了当前LLM在数值推理与时间序列推理方面的根本性挑战，并为未来金融智能研究提供了方向。