用强化学习优化交易策略，提升买卖效率。
Optimal Execution with Reinforcement Learning
- 基于订单簿状态设计强化学习模型，高频决策
- 在模拟环境中表现优于传统执行策略
- 适合量化交易与算法交易研究者参考
本研究通过强化学习开发最优交易策略，旨在帮助交易员在有限时间内高效买入或卖出持仓。所提模型利用订单簿当前状态作为输入特征，以高频率进行决策控制。为克服依赖历史数据的局限性，采用多智能体市场仿真平台ABIDES，生成多样化的订单簿深度环境。提出定制化的马尔可夫决策过程（MDP）框架，并评估方法性能，与标准执行策略进行对比。结果表明，强化学习代理在交易成本控制上显著优于传统策略，具备实际应用潜力。
原文摘要 · Abstract (English)
This study investigates the development of an optimal execution strategy through reinforcement learning, aiming to determine the most effective approach for traders to buy and sell inventory within a finite time horizon. Our proposed model leverages input features derived from the current state of the limit order book and operates at a high frequency to maximize control. To simulate this environment and overcome the limitations associated with relying on historical data, we utilize the multi-agent market simulator ABIDES, which provides a diverse range of depth levels within the limit order book. We present a custom MDP formulation followed by the results of our methodology and benchmark the performance against standard execution strategies. Results show that the reinforcement learning agent outperforms standard strategies and offers a practical foundation for real-world trading applications.
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