Financial Wind Tunnel: A Retrieval-Augmented Market Simulator

摘要

Online financial systems for stock prediction, portfolio optimization, and algorithmic trading must remain robust against rare and volatile market events, but historical data often fails to capture diverse unprecedented financial risks, creating a major bottleneck for systematic stress testing. To address this, we propose Financial Wind Tunnel (FWT), a deployable, retrieval-augmented market simulator that generates realistic, controllable, and adaptable financial dynamics for industrial-scale training and testing. FWT integrates both macro- and micro-level market patterns through a retrieval mechanism that conditions diffusion on relevant trends, supporting real-time and interpretable generation. Unlike existing methods that lack transferability across markets and scales, FWT supports large-scale cross-market pattern synthesis and what-if causal generation, enabling generalizable simulation beyond local historical regimes. We further introduce a simulation-driven optimizer for downstream models, enabling continuous improvement of online quantitative strategies. Deployed in production pipelines on CSI300 and HKSE components, FWT demonstrates measurable gains in stock prediction and portfolio optimization performance while maintaining scalability and operational reliability, offering a practical system for quantitative finance and stress testing.