Generating Controllable Order Flow with a Diffusion Guided Meta Agent

Summary

This paper proposes a generative framework for creating financial market order flow with control over market behavior. Its motivation is that existing order-flow generation methods may reproduce market data poorly and offer limited control, restricting their usefulness in research and trading applications.

The proposed Diffusion Guided Meta Agent combines a conditional diffusion model with a meta agent informed by financial economic priors. The diffusion model represents changing market conditions through time-varying distribution parameters for mid-price returns and order arrival rates. The meta agent then uses those distributions to generate orders. The paper reports improved generation fidelity and controllability in experiments, and tests the generated environment for downstream high-frequency trading tasks, also reporting computational efficiency. The supplied description does not specify datasets, baselines, metric values, or the range of market conditions tested, so it is not enough to judge how well the approach generalizes beyond those experiments.

Key ideas

• Order-flow generation aims to model the sequence of orders that forms a financial market.
• A conditional diffusion model represents evolving distributions for mid-price returns and order arrival rates.
• A meta agent uses financial economic priors to generate orders from those distributions.
• The authors report gains in controllability, fidelity, and efficiency, and evaluate use in high-frequency trading tasks.

Tags

# 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.

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