Core Insights - The article focuses on optimizing a timing model based on price and volume factors, addressing issues of instability, signal volatility, and the reliability of single signals [1][2]. Factor Selection - The model employs a dual standard of group IC and threshold settings to tackle the challenge of unstable effectiveness, ensuring that selected factors can consistently predict outcomes across different value ranges [2]. Model Training and Signal Generation - A strategy of random grouping and independent training is used to filter noise and balance signal robustness. The signal generation process involves rolling smoothing and multi-group voting to ensure accurate and stable timing signals [3]. - Backtesting from 2019 to September 2025 shows significant improvements over benchmarks, with a 1-day signal yielding an annualized return of 3.61% and a Sharpe ratio of 1.12, outperforming the benchmark [3].