Core Viewpoint - The article discusses the potential of Reinforcement Learning (RL) in quantitative investment, particularly in developing timing strategies that can maximize cumulative returns through trial and error learning mechanisms [1][2]. Summary by Sections 1. Introduction to Reinforcement Learning - Reinforcement Learning (RL) is a machine learning method that enables decision-making systems to learn optimal actions in specific situations to maximize cumulative rewards. This method is particularly suitable for environments with clear goals but no direct guidance on achieving them [6][12]. 2. Timing Strategy - The article focuses on the Double Deep Q-Network (DDQN) model, which uses 10-minute frequency price and volume data as input. The goal is for the model to learn to provide buy/sell/hold signals at various time points to maximize end-period returns. The backtesting phase outputs timing signals every 10 minutes, adhering to a t+1 trading rule [2][3]. 3. Empirical Analysis - The strategy was tested on various liquid ETFs and stocks from January 1, 2023, to May 31, 2025. The results showed that the strategy generated 72, 30, 73, and 188 timing signals for different assets, with average win rates of 52.8%, 53.3%, 54.8%, and 51.6%, respectively. Cumulative returns outperformed benchmark assets by 10.9%, 35.5%, 64.9%, and 37.8% [3][74][80]. 4. Summary and Outlook - Despite the impressive performance of RL in various fields, challenges such as stability issues remain in the quantitative investment domain. Future reports will explore more RL algorithms to develop superior strategies [5]. 5. Data Description - The timing strategy was applied to the CSI 300 Index, CSI 500 Index, CSI 1000 Index, and a specific stock, utilizing liquid ETFs corresponding to these indices. The training data spanned from January 1, 2014, to December 31, 2019, with validation and testing periods defined [74][75]. 6. Performance Metrics - The performance metrics for the RL timing strategy included total returns, annualized returns, maximum drawdown, annualized volatility, Sharpe ratio, information ratio, and return-to-drawdown ratio, demonstrating the strategy's effectiveness compared to benchmark assets [77][80].