Financial Wind Tunnel: A Retrieval-Augmented Market Simulator

ArXiv ID: 2503.17909 “View on arXiv”

Authors: Unknown

Abstract

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

Keywords: Market simulator, Diffusion models, Retrieval-augmented generation, Synthetic data, Stress testing, Multi-asset

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced diffusion models and retrieval-augmented generation, indicating high mathematical complexity, while it provides experimental results, a code/data link, and discussion of downstream model optimization, suggesting substantial empirical rigor.

  flowchart TD
    A["Research Goal: Create Controllable &<br>Adaptable Market Simulator"] --> B["Core Methodology:<br>Retrieval-Augmented Diffusion"]
    B --> C["Key Processes:<br>1. Retrieve Cross-Sectional Info<br>2. Integrate Macro/Micro Patterns"]
    C --> D["Simulation Outcomes:<br>1. Controllable 'What-If' Generation<br>2. Stress Testing Scenarios"]
    D --> E["Downstream Impact:<br>1. Optimize Quant Models<br>2. Enhance Returns/Risk Control"]