QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction (2408.03088v1)

Abstract: Financial market prediction and optimal trading strategy development remain challenging due to market complexity and volatility. Our research in quantum finance and reinforcement learning for decision-making demonstrates the approach of quantum-classical hybrid algorithms to tackling real-world financial challenges. In this respect, we corroborate the concept with rigorous backtesting and validate the framework's performance under realistic market conditions, by including fixed transaction cost per trade. This paper introduces a Quantum Attention Deep Q-Network (QADQN) approach to address these challenges through quantum-enhanced reinforcement learning. Our QADQN architecture uses a variational quantum circuit inside a traditional deep Q-learning framework to take advantage of possible quantum advantages in decision-making. We gauge the QADQN agent's performance on historical data from major market indices, including the S&P 500. We evaluate the agent's learning process by examining its reward accumulation and the effectiveness of its experience replay mechanism. Our empirical results demonstrate the QADQN's superior performance, achieving better risk-adjusted returns with Sortino ratios of 1.28 and 1.19 for non-overlapping and overlapping test periods respectively, indicating effective downside risk management.

• The paper presents a novel QADQN that integrates a variational quantum circuit with deep Q-learning to efficiently process high-dimensional financial data.
• It employs a POMDP framework and imitative learning strategies, validated on S&P 500 data, achieving total returns up to 78.91% and a maximum drawdown of 19.24%.
• These findings highlight the potential of quantum-enhanced reinforcement learning to improve risk-adjusted decision-making in volatile financial markets.

Quantum Attention Deep Q-Network for Financial Market Prediction

The paper "QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction" introduces a novel approach to tackling the complex and volatile nature of financial markets through quantum-enhanced reinforcement learning (RL). Recognizing the limitations of traditional computational finance methods and the challenges posed by market volatility, this work proposes a Quantum Attention Deep Q-Network (QADQN) that integrates a Variational Quantum Circuit (VQC) within a traditional deep Q-learning framework.

Core Contributions

The key contributions of this work can be distilled into several focal points:

1. Quantum Attention Layers: The incorporation of quantum attention layers within deep RL agents enhances the framework's ability to focus on relevant market features, thereby improving decision-making efficiency. This is achieved by leveraging the computational power of quantum computing to handle the high-dimensional financial data more effectively.
2. POMDP Framework with Deterministic Policy Gradient Method: The QADQN is designed within a Partially Observable Markov Decision Process (POMDP) framework, utilizing an attention-based deterministic policy gradient method. This framework is particularly suited for dealing with the uncertainty and partial observability inherent in financial markets, helping the agent to better manage the complex dynamics and noise present in the data.
3. Imitative Learning Strategies: Through modified Q-learning and the initialization of a replay buffer pre-populated with actions derived from the established Dual Thrust methodology, the approach aims to maintain an optimal balance between exploration and exploitation of available resources, thereby improving the learning efficiency of the RL agent.
4. Rigorous Backtesting and Validation: The framework has been thoroughly backtested and validated against historical market data, including major indices like the S&P 500. The validation process includes considerations for transaction costs and evaluates performance metrics such as total return rate, Maximum Drawdown, Sharpe ratio, and Sortino ratio, providing a comprehensive assessment of the framework's reliability and effectiveness.

Methodology

Quantum Attention Mechanism

The core component of the QADQN framework is the Quantum Multihead Self-Attention (QMSA) mechanism. This mechanism projects the input state into a quantum state using parameterized quantum circuits, computes attention scores, and aggregates the results to enhance the decision-making process. The use of quantum circuits allows the model to efficiently handle the high-dimensional parameter spaces associated with self-attention, crucial for contemporary deep-learning architectures.

Agent Architecture

The QADQN agent's architecture consists of several key components: an LSTM network for feature extraction, quantum attention layers for enhanced focus on relevant features, a Quantum Post-Net layer for final decision-making, and a replay memory for experience replay. The agent employs an Upper Confidence Bound (UCB) policy for action selection and integrates the Dual Thrust strategy for pre-populated actions, ensuring a robust balance between exploration and exploitation.

Results and Discussion

Empirical Evaluation

The empirical results demonstrate the QADQN model's superior performance in financial trading tasks. On historical data from the S&P 500, the QADQN framework achieved significant positive returns during both overlapping and non-overlapping test periods, outperforming the traditional Buy & Hold strategy and the Deep Deterministic Policy Gradient (DDPG) method. Key metrics such as Sharpe and Sortino ratios indicate that the QADQN framework not only provides higher returns but also effectively manages downside risk.

Quantitative Performance

• Total Return: The QADQN framework recorded returns of 78.91% and 71.26% for overlapping and non-overlapping test periods, respectively.
• Risk-Adjusted Performance: Sharpe ratios of 0.82 and 0.77, and Sortino ratios of 1.19 and 1.28 for overlapping and non-overlapping periods, respectively, underscore the framework's ability to generate superior risk-adjusted returns.
• Maximum Drawdown: The maximum drawdown was contained to 19.24%, reflecting the framework's robustness in managing significant market downturns.

Conclusions

This research demonstrates the potential of integrating quantum computing with RL to address the complexities of financial market prediction. The QADQN framework, by incorporating quantum attention mechanisms, achieves notable improvements in decision-making efficiency and risk management. The empirical results validate its robustness and efficacy in real-world applications.

Future Directions

The paper acknowledges the need for further research to enhance the scalability of the QADQN framework, incorporate more complex quantum circuits, and expand its application to different asset classes. Addressing challenges related to quantum noise and exploring quantum error correction within the framework could significantly advance the practical deployment of quantum-enhanced RL in financial markets.

Acknowledgment

This work was supported by the NYUAD Center for Quantum and Topological Systems (CQTS), funded by Tamkeen under the NYUAD Research Institute grant.

In summary, "QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction" provides a compelling approach to leveraging quantum-enhanced RL for financial trading, reflecting a substantial advancement in the field of quantum finance.