no code implementations • 12 Jan 2025 • Ruoyu Sun, Yue Xi, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su
As a result, the DRL agents cannot explore the dynamic portfolio optimization policy to improve the risk-adjusted profitability in the training process.
no code implementations • 30 Mar 2024 • Wen Sheng, Zhong Zheng, Jiajun Liu, Han Lu, Hanyuan Zhang, Zhengyong Jiang, Zhihong Zhang, Daoping Zhu
Concurrently, we utilized Dice coefficient as the metric for assessing the segmentation outcomes produced by YNetr, having advantage of capturing different frequency information.
no code implementations • 23 Feb 2024 • Ruoyu Sun, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su
However, typical DRL agents for portfolio optimization cannot learn a policy that is aware of the dynamic correlation between portfolio asset returns.
no code implementations • 2 Oct 2023 • Zhengyong Jiang, Jeyan Thiayagalingam, Jionglong Su, Jinjun Liang
To the best of our knowledge, our approach is the first to combine clustering methods and reinforcement learning methods for portfolio management in the context of multi-period trading.
no code implementations • 21 Mar 2021 • Huanming Zhang, Zhengyong Jiang, Jionglong Su
We compare the compound annual return rate of our strategy against seven other strategies, e. g., Uniform Buy and Hold, Exponential Gradient and Universal Portfolios.
no code implementations • 13 Mar 2020 • Ziming Gao, Yuan Gao, Yi Hu, Zhengyong Jiang, Jionglong Su
This paper will introduce a strategy based on the classic Deep Reinforcement Learning algorithm, Deep Q-Network, for portfolio management in stock market.