Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization

TL;DR

This paper introduces a hierarchical deep reinforcement learning framework called M3T for optimizing VWAP trading strategies, effectively reducing transaction costs by capturing multi-scale market patterns.

Contribution

It proposes a novel hierarchical architecture combining macro, meta, and micro traders with LSTM forecasting to improve VWAP strategy performance.

Findings

Outperforms baseline strategies in VWAP slippage reduction

Achieves an average cost saving of 1.16 basis points

Demonstrates effectiveness on Shanghai stock exchange data

Abstract

Designing an intelligent volume-weighted average price (VWAP) strategy is a critical concern for brokers, since traditional rule-based strategies are relatively static that cannot achieve a lower transaction cost in a dynamic market. Many studies have tried to minimize the cost via reinforcement learning, but there are bottlenecks in improvement, especially for long-duration strategies such as the VWAP strategy. To address this issue, we propose a deep learning and hierarchical reinforcement learning jointed architecture termed Macro-Meta-Micro Trader (M3T) to capture market patterns and execute orders from different temporal scales. The Macro Trader first allocates a parent order into tranches based on volume profiles as the traditional VWAP strategy does, but a long short-term memory neural network is used to improve the forecasting accuracy. Then the Meta Trader selects a short-term subgoal appropriate to instant liquidity within each tranche to form a mini-tranche. The Micro Trader consequently extracts the instant market state and fulfils the subgoal with the lowest transaction cost. Our experiments over stocks listed on the Shanghai stock exchange demonstrate that our approach outperforms baselines in terms of VWAP slippage, with an average cost saving of 1.16 base points compared to the optimal baseline.