Quantitative trading strategy optimization in the complex and dynamic financial markets presents good challenges due to market non-stationarity, bounded rationality of participants, and the lack of adaptability in existing algorithms. To address these challenges, we propose a novel adaptive quantitative trading strategy optimization framework that seamlessly integrates meta reinforcement learning, cognitive game theory, and automated strategy generation. Our framework achieves superior adaptability, robustness, and profitability, with annualized returns of 51.9%, 49.3%, 46.5%, and 53.7% and Sharpe ratios of 2.37, 2.21, 2.08, and 2.45 in the Chinese, US, European, and Japanese stock markets, respectively, outperforming traditional methods and state-of-the-art machine learning algorithms. The maximum drawdowns are limited to -10.2%, -11.4%, -12.1%, and -10.8%, and the Sortino ratios reach 3.54, 3.28, 3.07, and 3.68, demonstrating effective downside risk management. However, challenges remain in terms of computational complexity, the need for more extensive out-of-sample validation, the incorporation of advanced NLP techniques, and the extension to other markets and asset classes. These limitations call for further research efforts. Overall, this research makes notable contributions to quantitative trading, meta reinforcement learning, and cognitive game theory, opening up new avenues for the development of adaptive, robust, and high-performing trading strategies.
