Controllable Financial Market Generation with Diffusion Guided Meta Agent

TL;DR

This paper introduces DigMA, a diffusion-based meta agent that generates controllable and high-fidelity financial market data, improving over existing methods and supporting downstream trading applications.

Contribution

The paper proposes a novel diffusion-guided meta agent model for controllable financial market generation, incorporating economic priors for enhanced fidelity and practical utility.

Findings

DigMA achieves superior controllability and fidelity in market data generation.

The model effectively supports downstream high-frequency trading tasks.

It demonstrates computational efficiency in generating market scenarios.

Abstract

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.