In recent years, there has been a growing interest in applying reinforcement learning (RL) techniques to order execution owing to RL’s strong sequential decision-making ability. However, realistic order execution tasks usually involve a large fine-grained action space and a long trading duration. The former hinders the RL agents from efficient exploration. The latter increases the task complexity, since the agent must capture price advantages throughout the day as well as micro changes within a few seconds on the limited order books. In addressing these challenges, we propose MacMic, a novel Hierarchical RL-based order execution approach that captures market patterns and executes orders from different temporal scales. MacMic employs a high-level agent to split the parent order into smaller slices at coarse-grained time steps. Then a low-level agent is adopted to execute these slices by placing fixed-size sub-orders at a continuous time. Besides, to balance the multifaceted objectives of the two tasks, MacMic pretrains a causal stacking hidden Markov model (SHMM) to obtain both effective macro-level and micro-level market states. Comprehensive experimental results on 200 stocks across the US and China A-share markets validate the effectiveness of the proposed method.