Generative modeling has transformed many fields, such as language and visual modeling, while its application in financial markets remains under-explored. As the minimal unit within a financial market is an order, order-flow modeling represents a fundamental generative financial task. However, current approaches often yield unsatisfactory fidelity in generating order flow, and their generation lacks controllability, thereby limiting their practical applications. In this paper, we formulate the challenge of controllable financial market generation, and propose a Diffusion Guided Meta Agent (DigMA) model to address it. Specifically, we employ a conditional diffusion model to capture the dynamics of the market state represented by time-evolving distribution parameters of the mid-price return rate and the order arrival rate, and we define a meta agent with financial economic priors to generate orders from the corresponding distributions. Extensive experimental results show that DigMA achieves superior controllability and generation fidelity. Moreover, we validate its effectiveness as a generative environment for downstream high-frequency trading tasks and its computational efficiency.

翻译：生成建模已彻底改变了语言和视觉建模等诸多领域，但其在金融市场中的应用仍待深入探索。由于金融市场中的最小单元是订单，订单流建模代表了一项基础的生成式金融任务。然而，当前方法在生成订单流时往往保真度不足，且其生成过程缺乏可控性，从而限制了实际应用。本文提出了可控金融市场生成的挑战，并提出了扩散引导元代理模型以应对此挑战。具体而言，我们采用条件扩散模型来捕捉市场状态的动态，该状态由中间价格回报率和订单到达率的时变分布参数表示，并定义了一个具备金融经济学先验的元代理，以从相应分布中生成订单。大量实验结果表明，DigMA 实现了卓越的可控性和生成保真度。此外，我们验证了其作为下游高频交易任务的生成环境的有效性及其计算效率。