# An intelligent financial portfolio trading strategy using deep Q-learning

arXiv: https://arxiv.org/abs/1907.03665 · HTML: https://mlsanity.com/paper/1907.03665/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

## Built on by (most cited first, 14 of 14)

- [Recent Advances in Reinforcement Learning in Finance](https://mlsanity.com/paper/2112.04553/) (2021)
- [The Evolution of Reinforcement Learning in Quantitative Finance: A Survey](https://mlsanity.com/paper/2408.10932/) (2024)
- [Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning](https://mlsanity.com/paper/2309.10129/) (2023)
- [Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting](https://mlsanity.com/paper/2209.05559/) (2022)
- [A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules](https://mlsanity.com/paper/2101.03867/) (2021)
- [High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning](https://mlsanity.com/paper/2112.04755/) (2021)
- [Reinforcement Learning Applied to Trading Systems: A Survey](https://mlsanity.com/paper/2212.06064/) (2022)
- [Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning](https://mlsanity.com/paper/2312.15385/) (2023)
- [A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning](https://mlsanity.com/paper/2501.06832/) (2025)
- [Risk-sensitive Actor-Critic with Static Spectral Risk Measures for Online and Offline Reinforcement Learning](https://mlsanity.com/paper/2507.03900/) (2025)
- [DDPG based on multi-scale strokes for financial time series trading strategy](https://mlsanity.com/paper/2207.10071/) (2022)
- [Deep Reinforcement Learning for Optimal Asset Allocation Using DDPG with TiDE](https://mlsanity.com/paper/2508.20103/) (2025)
- [An intelligent algorithmic trading based on a risk-return reinforcement learning algorithm](https://mlsanity.com/paper/2208.10707/) (2022)
- [Risk-averse policies for natural gas futures trading using distributional reinforcement learning](https://mlsanity.com/paper/2501.04421/) (2025)

## Abstract (arXiv)

Portfolio traders strive to identify dynamic portfolio allocation schemes so that their total budgets are efficiently allocated through the investment horizon. This study proposes a novel portfolio trading strategy in which an intelligent agent is trained to identify an optimal trading action by using deep Q-learning. We formulate a Markov decision process model for the portfolio trading process, and the model adopts a discrete combinatorial action space, determining the trading direction at prespecified trading size for each asset, to ensure practical applicability. Our novel portfolio trading strategy takes advantage of three features to outperform in real-world trading. First, a mapping function is devised to handle and transform an initially found but infeasible action into a feasible action closest to the originally proposed ideal action. Second, by overcoming the dimensionality problem, this study establishes models of agent and Q-network for deriving a multi-asset trading strategy in the predefined action space. Last, this study introduces a technique that has the advantage of deriving a well-fitted multi-asset trading strategy by designing an agent to simulate all feasible actions in each state. To validate our approach, we conduct backtests for two representative portfolios and demonstrate superior results over the benchmark strategies.