用强化学习优化交易策略，降低市场冲击和成本。
Right Place, Right Time: Market Simulation-based RL for Execution Optimisation
- 在可响应的市场模拟器中训练强化学习代理，分解滑点来源。
- 策略表现接近阿尔姆格伦-克里斯效率前沿，优于基线方法。
- 适合量化交易员和算法开发人员参考应用。
执行算法对现代交易至关重要，使市场参与者能够在最小化市场影响和交易成本的前提下完成大额订单。随着算法日趋复杂，优化难度不断上升。本文提出一种基于强化学习（RL）的框架，用于发现最优执行策略，并在反应式代理驱动的市场模拟器中进行评估。该模拟器生成动态订单流，帮助我们将滑点分解为市场影响与执行风险两部分。我们采用阿尔姆格伦与克里斯提出的效率前沿作为评估标准，衡量代理在风险与成本之间的权衡能力。结果表明，基于RL的策略持续优于基准方法，且运行接近效率前沿，展现出优秀的风险与冲击优化能力。研究验证了强化学习在交易优化中的强大潜力。
原文摘要 · Abstract (English)
Execution algorithms are vital to modern trading, they enable market participants to execute large orders while minimising market impact and transaction costs. As these algorithms grow more sophisticated, optimising them becomes increasingly challenging. In this work, we present a reinforcement learning (RL) framework for discovering optimal execution strategies, evaluated within a reactive agent-based market simulator. This simulator creates reactive order flow and allows us to decompose slippage into its constituent components: market impact and execution risk. We assess the RL agent's performance using the efficient frontier based on work by Almgren and Chriss, measuring its ability to balance risk and cost. Results show that the RL-derived strategies consistently outperform baselines and operate near the efficient frontier, demonstrating a strong ability to optimise for risk and impact. These findings highlight the potential of reinforcement learning as a powerful tool in the trader's toolkit.
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