Deep Stock Trading: A Hierarchical Reinforcement Learning Framework for Portfolio Optimization and Order Execution

2020-12-23 · Rundong Wang, Hongxin Wei, Bo An, Zhouyan Feng, Jun Yao

Portfolio management via reinforcement learning is at the forefront of fintech research, which explores how to optimally reallocate a fund into different financial assets over the long term by trial-and-error. Existing methods are impractical since they usually assume each reallocation can be finished immediately and thus ignoring the price slippage as part of the trading cost. To address these issues, we propose a hierarchical reinforced stock trading system for portfolio management (HRPM). Concretely, we decompose the trading process into a hierarchy of portfolio management over trade execution and train the corresponding policies. The high-level policy gives portfolio weights at a lower frequency to maximize the long term profit and invokes the low-level policy to sell or buy the corresponding shares within a short time window at a higher frequency to minimize the trading cost. We train two levels of policies via pre-training scheme and iterative training scheme for data efficiency. Extensive experimental results in the U.S. market and the China market demonstrate that HRPM achieves significant improvement against many state-of-the-art approaches.

📄 PDF Abstract BibTeX arXiv:2012.12620

Code (0)

등록된 구현이 없습니다.

Tasks

Hierarchical Reinforcement Learning Management Portfolio Optimization Reinforcement Learning (RL)

Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Execution

2024-10-19 · Zijie Zhao, Roy E. Welsch

Leveraging Deep Reinforcement Learning (DRL) in automated stock trading has shown promising results, yet its application faces significant challenges, including the curse of dimensionality, inertia in trading actions, an…

Deep Reinforcement Learning Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning

CAD: Clustering And Deep Reinforcement Learning Based Multi-Period Portfolio Management Strategy

2023-10-02 · Zhengyong Jiang, Jeyan Thiayagalingam, Jionglong Su, Jinjun Liang

In this paper, we present a novel trading strategy that integrates reinforcement learning methods with clustering techniques for portfolio management in multi-period trading. Specifically, we leverage the clustering meth…

Clustering Deep Reinforcement Learning Management reinforcement-learning

High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning

2021-12-09 · Uta Pigorsch, Sebastian Schäfer

This paper proposes a Deep Reinforcement Learning algorithm for financial portfolio trading based on Deep Q-learning. The algorithm is capable of trading high-dimensional portfolios from cross-sectional datasets of any s…

Deep Reinforcement Learning Q-Learning reinforcement-learning Reinforcement Learning+2

Taxable Stock Trading with Deep Reinforcement Learning

2019-07-28 · Shan Huang

In this paper, we propose stock trading based on the average tax basis. Recall that when selling stocks, capital gain should be taxed while capital loss can earn certain tax rebate. We learn the optimal trading strategie…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Practical Deep Reinforcement Learning Approach for Stock Trading

2018-11-19 · Xiao-Yang Liu, Zhuoran Xiong, Shan Zhong, Hongyang Yang 외

Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning …