Quantum-Enhanced Reinforcement Learning with LSTM Forecasting Signals for Optimizing Fintech Trading Decisions
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908454287835136 |
|---|---|
| author | Liu, Yen-Ku Pan, Yun-Huei Lu, Pei-Fan Tsai, Yun-Cheng Chen, Samuel Yen-Chi |
| author_facet | Liu, Yen-Ku Pan, Yun-Huei Lu, Pei-Fan Tsai, Yun-Cheng Chen, Samuel Yen-Chi |
| contents | Financial trading environments are characterized by high volatility, numerous macroeconomic signals, and dynamically shifting market regimes, where traditional reinforcement learning methods often fail to deliver breakthrough performance. In this study, we design a reinforcement learning framework tailored for financial systems by integrating quantum circuits. We compare (1) the performance of classical A3C versus quantum A3C algorithms, and (2) the impact of incorporating LSTM-based predictions of the following week's economic trends on learning outcomes. The experimental framework adopts a custom Gymnasium-compatible trading environment, simulating discrete trading actions and evaluating rewards based on portfolio feedback. Experimental results show that quantum models - especially when combined with predictive signals - demonstrate superior performance and stability under noisy financial conditions, even with shallow quantum circuit depth. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12835 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum-Enhanced Reinforcement Learning with LSTM Forecasting Signals for Optimizing Fintech Trading Decisions Liu, Yen-Ku Pan, Yun-Huei Lu, Pei-Fan Tsai, Yun-Cheng Chen, Samuel Yen-Chi Computational Engineering, Finance, and Science Financial trading environments are characterized by high volatility, numerous macroeconomic signals, and dynamically shifting market regimes, where traditional reinforcement learning methods often fail to deliver breakthrough performance. In this study, we design a reinforcement learning framework tailored for financial systems by integrating quantum circuits. We compare (1) the performance of classical A3C versus quantum A3C algorithms, and (2) the impact of incorporating LSTM-based predictions of the following week's economic trends on learning outcomes. The experimental framework adopts a custom Gymnasium-compatible trading environment, simulating discrete trading actions and evaluating rewards based on portfolio feedback. Experimental results show that quantum models - especially when combined with predictive signals - demonstrate superior performance and stability under noisy financial conditions, even with shallow quantum circuit depth. |
| title | Quantum-Enhanced Reinforcement Learning with LSTM Forecasting Signals for Optimizing Fintech Trading Decisions |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2507.12835 |