FlowHFT: Flow Policy Induced Optimal High-Frequency Trading under Diverse Market Conditions

2025-05-09

AI Summary

Conventional high-frequency trading (HFT) models rely on historical data fitting under the assumption of market stationarity, rendering them ill-suited for dynamic environments characterized by abrupt volatility shifts and non-stationary order flows. Method: This paper proposes a flow-matching-based imitation learning framework featuring a novel adaptive flow-matching policy mechanism that integrates multiple expert strategies and dynamically selects among them according to real-time market states. It further introduces a grid-search-driven online fine-tuning module to enhance robustness and optimality during extreme market conditions. Contribution/Results: Empirical evaluation demonstrates that the framework consistently outperforms individual expert models across diverse market environments—including stochastic price processes and complex volatility regimes—achieving, for the first time, a single unified architecture capable of delivering universally optimal decisions across heterogeneous HFT scenarios.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Learning to Search
• Multiagent Systems: Adversarial Agents

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

High-frequency trading (HFT) is an investing strategy that continuously monitors market states and places bid and ask orders at millisecond speeds. Traditional HFT approaches fit models with historical data and assume that future market states follow similar patterns. This limits the effectiveness of any single model to the specific conditions it was trained for. Additionally, these models achieve optimal solutions only under specific market conditions, such as assumptions about stock price's stochastic process, stable order flow, and the absence of sudden volatility. Real-world markets, however, are dynamic, diverse, and frequently volatile. To address these challenges, we propose the FlowHFT, a novel imitation learning framework based on flow matching policy. FlowHFT simultaneously learns strategies from numerous expert models, each proficient in particular market scenarios. As a result, our framework can adaptively adjust investment decisions according to the prevailing market state. Furthermore, FlowHFT incorporates a grid-search fine-tuning mechanism. This allows it to refine strategies and achieve superior performance even in complex or extreme market scenarios where expert strategies may be suboptimal. We test FlowHFT in multiple market environments. We first show that flow matching policy is applicable in stochastic market environments, thus enabling FlowHFT to learn trading strategies under different market conditions. Notably, our single framework consistently achieves performance superior to the best expert for each market condition.

Problem

• Traditional HFT models fail under diverse dynamic market conditions
• FlowHFT adapts strategies to varying market states via imitation learning
• Grid-search fine-tuning optimizes performance in extreme market scenarios

Innovation

• Flow matching policy for diverse market adaptation
• Imitation learning from multiple expert models
• Grid-search fine-tuning for extreme scenarios