Quantitative Factor Discovery Methods: A comprehensive comparison of traditional approaches and advanced genetic algorithm techniques, highlighting their strengths, limitations, and comparative performance in financial factor identification.

Shark GPLearn Architecture: An in-depth exploration of the architecture of the CPU-GPU heterogeneous computing platform, detailing the innovative GPLearn algorithm designed for automatic and intelligent factor discovery in complex financial datasets.

Factor Discovery Workflow: Practical usage examples demonstrating the streamlined discovery process, with detailed comparisons and performance benchmarks against existing Python gplearn implementations.

Application Scenarios: Comprehensive coverage of data preparation, preprocessing methodologies, model training, and evaluation strategies using real-world financial datasets. The section also explores multi-factor backtesting and optimization techniques to validate the approach's practical effectiveness.