Controllable Financial Market Generation with Diffusion Guided Meta Agent

ArXiv ID: 2408.12991 “View on arXiv”

Authors: Unknown

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.

Keywords: Conditional Diffusion Model, Order-Flow Modeling, Meta Agent, Market State Generation, High-Frequency Trading, Equities

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper introduces advanced generative modeling with conditional diffusion models and a meta-agent framework, involving sophisticated mathematical formulations; empirical validation includes experiments on real stock market data, ablation studies, and downstream task evaluation, though the primary validation is computational efficiency rather than live trading performance.

  flowchart TD
    A["Research Goal<br>Controllable Financial Market Generation"] --> B["Data Input<br>High-Frequency Equity Market Data"]
    B --> C["Methodology: Conditional Diffusion Model<br>Generates Market State Dynamics"]
    C --> D["Methodology: Meta Agent with Financial Priors<br>Generates Order-Flow from Distributions"]
    D --> E["Computational Process<br>Simulate Order Execution & Market Mechanics"]
    E --> F["Outcomes: Superior Fidelity & Controllability"]
    E --> G["Outcomes: Effective Environment for HFT"]