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, and insufficient portfolio diversification. Addressing these challenges, we introduce the Hierarchical Reinforced Trader (HRT), a novel trading strategy employing a bi-level Hierarchical Reinforcement Learning framework. The HRT integrates a Proximal Policy Optimization (PPO)-based High-Level Controller (HLC) for strategic stock selection with a Deep Deterministic Policy Gradient (DDPG)-based Low-Level Controller (LLC) tasked with optimizing trade executions to enhance portfolio value. In our empirical analysis, comparing the HRT agent with standalone DRL models and the S&P 500 benchmark during both bullish and bearish market conditions, we achieve a positive and higher Sharpe ratio. This advancement not only underscores the efficacy of incorporating hierarchical structures into DRL strategies but also mitigates the aforementioned challenges, paving the way for designing more profitable and robust trading algorithms in complex markets.

📄 PDF Abstract BibTeX arXiv:2410.14927

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement LearningHierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning

TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution

2021-02-16 · Karush Suri, Xiao Qi Shi, Konstantinos Plataniotis, Yuri Lawryshyn

Advances in Reinforcement Learning (RL) span a wide variety of applications which motivate development in this area. While application tasks serve as suitable benchmarks for real world problems, RL is seldomly used in pr…

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

Forecasting the Performance of US Stock Market Indices During COVID-19: RF vs LSTM

2023-06-06 · Reza Nematirad, Amin Ahmadisharaf, Ali Lashgari

The US stock market experienced instability following the recession (2007-2009). COVID-19 poses a significant challenge to US stock traders and investors. Traders and investors should keep up with the stock market. This …

Time Series

Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization

2022-12-11 · XiaoDong Li, Pangjing Wu, Chenxin Zou, Qing Li

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 dy…

Deep Reinforcement Learning Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Corporate Governance, Noise Trading and Liquidity of Stocks

2020-01-17 · Jianhao Su

Our main task is to study the effect of corporate governance on the market liquidity of listed companies' stocks. We establish a theoretical model that contains the heterogeneity of investors' beliefs to explain the mech…

Two approaches to modeling the interaction of small and medium price-taking traders with a stock exchange by mathematical programming techniques

2016-10-18

The paper presents two new approaches to modeling the interaction of small and medium pricetaking traders with a stock exchange. In the framework of these approaches, the traders can form and manage their portfolios of f…