- The report introduces the "Alpha Factor Database" developed by the Guangfa Financial Engineering team, which is based on MySQL 8.0 and integrates over a decade of research experience. The database includes fundamental factors, Level-1 medium-frequency factors, Level-2 high-frequency factors, machine learning factors, and alternative data factors, supporting strategies such as long-short, index enhancement, ETF rotation, asset allocation, and derivatives[1][9][11] - The "agru_dailyquote" factor, a deep learning factor, is analyzed for its performance across various indices and timeframes. For the entire market with monthly rebalancing, its RankIC averages are 5.30% (1 week), -3.44% (1 month), 11.41% (1 year), and 13.63% (historical). Its historical win rate is 90.85%[4][54][55] - The "DL_1" factor, another deep learning factor, shows RankIC averages of 8.44% (1 week), -4.38% (1 month), 13.69% (1 year), and 13.66% (historical) in the entire market with monthly rebalancing. Its historical win rate is 86.80%[4][54][55] - The "fimage" factor, also a deep learning factor, has RankIC averages of 6.14% (1 week), 2.47% (1 month), 3.80% (1 year), and 5.06% (historical) in the entire market with monthly rebalancing. Its historical win rate is 77.44%[4][54][55] - The "keyperiod_ret_zero" factor, a Level-2 high-frequency factor, demonstrates negative RankIC averages of -8.25% (1 week), -6.39% (1 month), -5.32% (1 year), and -5.39% (historical) in the entire market with monthly rebalancing. Its historical win rate is 85.69%[4][54][55] - The "real_var" factor, a minute-frequency factor, shows negative RankIC averages of -5.14% (1 week), -3.61% (1 month), -7.94% (1 year), and -8.87% (historical) in the entire market with monthly rebalancing. Its historical win rate is 73.73%[4][54][55] - The "bigbuy_bigsell" factor, a Level-2 high-frequency factor, achieves positive RankIC averages of 5.71% (1 week), -3.56% (1 month), 6.80% (1 year), and 9.63% (historical) in the entire market with monthly rebalancing. Its historical win rate is 77.85%[4][54][55] - The "Amihud_illiq" factor, a minute-frequency factor, shows positive RankIC averages of 5.82% (1 week), -7.52% (1 month), 10.48% (1 year), and 10.70% (historical) in the entire market with monthly rebalancing. Its historical win rate is 73.59%[4][54][55]

Core Viewpoint - The article discusses the development and capabilities of the GF Quantitative Alpha Factor Database, which supports various investment strategies through a comprehensive factor library built on extensive research and data accumulation by the GF Quantitative team [1]. Group 1: Database Overview - The GF Quantitative Alpha Factor Database is established on MySQL 8.0 and encompasses over a decade of research experience, integrating fundamental factors, Level-1 and Level-2 high-frequency factors, machine learning factors, and alternative data factors [1]. - The database supports strategies such as long-short strategies, index enhancement, ETF rotation, asset allocation, and derivatives [1]. - The GF Quantitative team possesses a data storage capacity of over 100TB and high-performance CPU/GPU computing servers, collaborating with reliable data providers like Wind, Tianruan, and Tonglian for efficient factor development and dynamic updates [1]. Group 2: Factor Types and Performance - The article lists various factors categorized by type, including deep learning factors, trading volume factors, and market order ratios, each with specific definitions and performance metrics [3]. - For instance, the "agr_dailyquote" factor has a historical average of 14.22% and a historical win rate of 91.97% [3]. - The "bigbuy" factor shows a historical average of 7.85% with a win rate of 66.74% [3]. Group 3: Research Reports - A series of research reports are available for download, covering topics such as style factor-driven quantitative stock selection, industry selection, and macroeconomic observations related to Alpha factor trends [4][5]. - The reports include analyses on the application of factors in the CSI 300 index and various strategies for capturing industry alpha drivers [4].