mlsanityData to 7 Oct 2026

Dynamic Portfolio Optimization via Augmented DDPG with Quantum Price Levels-Based Trading Strategy

arXiv 2501.08528 ↗First posted 15 Jan 2025cs.CE cs.AIcited by 0

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Abstract (arXiv)

With the development of deep learning, Dynamic Portfolio Optimization (DPO) problem has received a lot of attention in recent years, not only in the field of finance but also in the field of deep learning. Some advanced research in recent years has proposed the application of Deep Reinforcement Learning (DRL) to the DPO problem, which demonstrated to be more advantageous than supervised learning in solving the DPO problem. However, there are still certain unsolved issues: 1) DRL algorithms usually have the problems of slow learning speed and high sample complexity, which is especially problematic when dealing with complex financial data. 2) researchers use DRL simply for the purpose of obtaining high returns, but pay little attention to the problem of risk control and trading strategy, which will affect the stability of model returns. In order to address these issues, in this study we revamped the intrinsic structure of the model based on the Deep Deterministic Policy Gradient (DDPG) and proposed the Augmented DDPG model. Besides, we also proposed an innovative risk control strategy based on Quantum Price Levels (QPLs) derived from Quantum Finance Theory (QFT). Our experimental results revealed that our model has better profitability as well as risk control ability with less sample complexity in the DPO problem compared to the baseline models.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 28 references that are in the map. First posted on arXiv 15 Jan 2025.

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References · 8

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 123
  5. 2020Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction Statescited by 55
  6. 2020Deep Stock Trading: A Hierarchical Reinforcement Learning Framework for Portfolio Optimization and Order Executioncited by 20
  7. 2019Reinforcement Learning for Portfolio Managementcited by 12
  8. 2020Application of Deep Q-Network in Portfolio Managementcited by 11

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