金融工程：AI识图关注石化、化工和有色

Quantitative Models and Construction Methods 1. Model Name: Convolutional Neural Network (CNN) for Price-Volume Data Modeling - Model Construction Idea: The model leverages convolutional neural networks to analyze standardized graphical representations of price-volume data, aiming to predict future price trends and map learned features to industry thematic indices[79][81] - Model Construction Process: - Standardize price-volume data into graphical formats for each stock within a specific time window[79] - Apply convolutional neural networks to extract features from these graphical representations[79] - Map the extracted features to thematic industry indices, such as the CSI Petrochemical Industry Index, CSI Subdivision Chemical Industry Theme Index, and others[81] - Model Evaluation: The model effectively identifies industry themes based on price-volume data and provides actionable insights for sector allocation[79][81] --- Model Backtesting Results 1. CNN Model - Thematic Indices Configured: - CSI Petrochemical Industry Index (h11057.CSI)[81] - CSI Subdivision Chemical Industry Theme Index (000813.CSI)[81] - CNI Oil & Gas Index (399439.SZ)[81] - CSI Oil & Gas Resources Index (931248.CSI)[81] - CNI Nonferrous Metals Index (399395.SZ)[81]