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

TL;DR

This paper introduces an augmented DDPG model combined with Quantum Price Levels for improved dynamic portfolio optimization, enhancing profitability and risk control with reduced sample complexity.

Contribution

It proposes a novel augmented DDPG framework and a quantum finance-based risk control strategy for more efficient and stable portfolio optimization.

Findings

Better profitability compared to baseline models

Enhanced risk control capabilities

Reduced sample complexity in training

Abstract

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.