Quantum-Inspired Convolutional Reinforcement Learning for Interpretable and Optimized Multi-Stock Trading Investment Strategies and Portfolio Management

Abstract

Traditional financial models often rely solely on historical price data, failing to account for real-time market complexity and sentiment. This research addresses this gap by proposing a quantum-inspired reinforcement learning framework that integrates sentiment indicators and market conditions for optimized multi-stock trading decisions. A quantum-inspired financial modeling methodology is used to maximize trading decisions, with Rewards and Sentiment indicators based on Convolutional Revolution Reinforcement Learning (RMS-CRRL) and Quantum Finance Theory (QFT). This research suggests a multi-stock trading strategy with Dow Jones 50 component stocks, where the financial information is obtained from the Yahoo Finance and Alpha Vantage APIs. The model leverages data preprocessing methods such as missing value interpolation, outlier detection, and consistency enhancements to provide high-quality data.  The novelty of the model lies in its integration of market conditions and consumer sentiment, setting it apart from conventional models that rely solely on historical price data. The RMS-CRRL structure utilizes convolutional neural networks for stock price forecasting to minimize uncertainty in the financial market. The revised revolution optimization algorithm is applied to model stability and the best choice of hyperparameters. In addition, the interpretability of the convolutional neural networks-based predictions is guaranteed using rectangular constraints for local interpretable model-agnostic explanations providing explainability in trade decisions. Comparisons for performance show that the suggested model outperforms baseline methods such as traditional DDPG and A3C models, achieving cumulative returns of 22.4%, which reflects a 7.2% improvement in annual returns. The RMS-CRRL approach also demonstrates an average portfolio value benefit of 10-15% compared to the conventional approach.