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The purpose of this research is to devise a tactic that can closely track thedaily cumulative volume-weighted average price (VWAP) using reinforcementlearning. Previous studies often choose a relatively short trading horizon toimplement their models, making it difficult to accurately track the dailycumulative VWAP since the variations of financial data are often insignificantwithin the short trading horizon. In this paper, we aim to develop a strategythat can accurately track the daily cumulative VWAP while minimizing thedeviation from the VWAP. We propose a method that leverages the U-shapedpattern of intraday stock trade volumes and use Proximal Policy Optimization(PPO) as the learning algorithm. Our method follows a dual-level approach: aTransformer model that captures the overall(global) distribution of dailyvolumes in a U-shape, and a LSTM model that handles the distribution of orderswithin smaller(local) time intervals. The results from our experiments suggestthat this dual-level architecture improves the accuracy of approximating thecumulative VWAP, when compared to previous reinforcement learning-based models.

An Adaptive Dual-level Reinforcement Learning Approach for Optimal Trade Execution