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用扩散引导元智能体生成可控订单流

文章 arXiv papers · 作者: Yu-Hao Huang et al.
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总结

本文提出一种生成框架，用于创建可控制市场行为的金融市场订单流。其出发点是，现有订单流生成方法可能无法很好地复现市场数据，而且控制能力有限，因而限制了其在研究和交易应用中的用途。

所提出的扩散引导元智能体结合条件扩散模型和受金融经济先验信息指导的元智能体。扩散模型通过随时间变化的分布参数表示市场条件变化，这些参数针对中间价收益和订单到达率。随后，元智能体利用这些分布生成订单。论文报告称，实验中的生成保真度和可控性有所提高，并测试了生成环境在后续高频交易任务中的应用，也报告了计算效率。所提供的描述未说明数据集、基准、指标值或测试的市场条件范围，因此不足以判断该方法能否推广到这些实验以外的情形。

核心观点

  • 订单流生成旨在模拟构成金融市场的订单序列。
  • 条件扩散模型表示中间价收益和订单到达率随时间演变的分布。
  • 元智能体利用金融经济先验信息根据这些分布生成订单。
  • 作者报告称，可控性、保真度和效率均有所提升，并评估了该方法在高频交易任务中的应用。

标签

机器学习高频交易市场微观结构
全文
# Controllable Financial Market Generation with Diffusion Guided Meta Agent


# Controllable Financial Market Generation with Diffusion Guided Meta Agent









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。