用深度强化学习优化金融交易策略，自动适应市场变化。
Deep Reinforcement Learning for Online Optimal Execution Strategies
- 基于DDPG设计新算法，处理非马尔可夫的交易策略问题。
- 在多种价格影响衰减核下逼近最优执行策略。
- 能自适应动态市场，减少人工干预需求。
本文解决动态金融市场中学习非马尔可夫最优执行策略的挑战。提出一种基于深度确定性策略梯度（DDPG）的新型演员-评论家算法，重点建模由一般衰减核表示的瞬时价格影响。通过多种衰减核的数值实验，验证该算法能有效逼近最优执行策略。此外，所提算法表现出对随时间变化的市场参数的良好适应性。研究结果表明，现代强化学习算法可显著降低最优执行任务中频繁且低效的人工干预需求。
原文摘要 · Abstract (English)
This paper tackles the challenge of learning non-Markovian optimal execution strategies in dynamic financial markets. We introduce a novel actor-critic algorithm based on Deep Deterministic Policy Gradient (DDPG) to address this issue, with a focus on transient price impact modeled by a general decay kernel. Through numerical experiments with various decay kernels, we show that our algorithm successfully approximates the optimal execution strategy. Additionally, the proposed algorithm demonstrates adaptability to evolving market conditions, where parameters fluctuate over time. Our findings also show that modern reinforcement learning algorithms can provide a solution that reduces the need for frequent and inefficient human intervention in optimal execution tasks.
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