用检索增强生成市场数据，让模型测试更真实可控。
Financial Wind Tunnel: A Retrieval-Augmented Market Simulator
- 通过检索跨市场信息作为条件，融合宏观与微观动态
- 支持'假设性'生成和前所未有的跨市场趋势模拟
- 适配复杂波动市场，提升下游模型性能与风险控制
市场模拟器旨在生成高质量的合成金融数据以模仿真实市场动态，这对模型开发和稳健评估至关重要。尽管模拟方法持续进步，但市场波动在规模和成因上差异显著，现有框架往往仅在特定任务中表现优异。为此，我们提出金融风洞（Financial Wind Tunnel, FWT），一种检索增强型市场模拟器，可生成可控、合理且适应性强的市场动态，用于模型测试。FWT在不同数据频率下具备更全面系统的生成能力。通过检索方法获取跨截面信息作为增强条件，基于扩散模型的模拟器无缝整合了宏观与微观市场模式。此外，该框架支持广泛可控性，包括通过‘假设’提示实现因果生成或合成前所未有的跨市场趋势。我们还开发了自动化优化器，利用FWT对模拟场景进行压力测试，以提升下游量化模型收益并控制风险。实验表明，该方法实现了可泛化、可靠的市场模拟，显著提升下游模型在高度复杂和波动市场中的性能与适应性。代码与数据样本已公开于 https://anonymous.4open.science/r/fwt_-E852。
原文摘要 · Abstract (English)
Market simulator tries to create high-quality synthetic financial data that mimics real-world market dynamics, which is crucial for model development and robust assessment. Despite continuous advancements in simulation methodologies, market fluctuations vary in terms of scale and sources, but existing frameworks often excel in only specific tasks. To address this challenge, we propose Financial Wind Tunnel (FWT), a retrieval-augmented market simulator designed to generate controllable, reasonable, and adaptable market dynamics for model testing. FWT offers a more comprehensive and systematic generative capability across different data frequencies. By leveraging a retrieval method to discover cross-sectional information as the augmented condition, our diffusion-based simulator seamlessly integrates both macro- and micro-level market patterns. Furthermore, our framework allows the simulation to be controlled with wide applicability, including causal generation through "what-if" prompts or unprecedented cross-market trend synthesis. Additionally, we develop an automated optimizer for downstream quantitative models, using stress testing of simulated scenarios via FWT to enhance returns while controlling risks. Experimental results demonstrate that our approach enables the generalizable and reliable market simulation, significantly improve the performance and adaptability of downstream models, particularly in highly complex and volatile market conditions. Our code and data sample is available at https://anonymous.4open.science/r/fwt_-E852
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