用流匹配模型学习多种专家策略，动态优化交易执行。
FlowOE: Imitation Learning with Flow Policy from Ensemble RL Experts for Optimal Execution under Heston Volatility and Concave Market Impacts
- 基于流匹配的模仿学习框架，融合多专家策略
- 在不同市场条件下收益更高、风险更低
- 首次将流匹配用于随机最优执行问题
金融市场的最优执行指在一定时间内以战略方式完成大额资产交易，以平衡市场冲击成本与时机或波动性风险。传统方法如静态Almgren-Chriss模型在动态市场中表现不佳。本文提出FlowOE，一种基于流匹配模型的新型模仿学习框架，从多样化的专家策略中学习，并根据当前市场条件自适应选择最合适的策略。其关键创新在于在模仿过程中引入精炼损失函数，使FlowOE不仅能模仿专家行为，还能改进其动作。据我们所知，这是首个将流匹配模型应用于随机最优执行问题的工作。在多种市场条件下的实证评估表明，FlowOE显著优于特定校准的专家模型和其他传统基准，实现更高收益并降低风险。结果凸显了FlowOE在增强自适应最优执行方面的实用价值与潜力。
原文摘要 · Abstract (English)
Optimal execution in financial markets refers to the process of strategically transacting a large volume of assets over a period to achieve the best possible outcome by balancing the trade-off between market impact costs and timing or volatility risks. Traditional optimal execution strategies, such as static Almgren-Chriss models, often prove suboptimal in dynamic financial markets. This paper propose flowOE, a novel imitation learning framework based on flow matching models, to address these limitations. FlowOE learns from a diverse set of expert traditional strategies and adaptively selects the most suitable expert behavior for prevailing market conditions. A key innovation is the incorporation of a refining loss function during the imitation process, enabling flowOE not only to mimic but also to improve upon the learned expert actions. To the best of our knowledge, this work is the first to apply flow matching models in a stochastic optimal execution problem. Empirical evaluations across various market conditions demonstrate that flowOE significantly outperforms both the specifically calibrated expert models and other traditional benchmarks, achieving higher profits with reduced risk. These results underscore the practical applicability and potential of flowOE to enhance adaptive optimal execution.
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