AI Quant Research: Pattern Labels, Feature Importance, and Overfitting
Summary
This meetup Q&A discusses several practical questions in quantitative research. It explains that traders can label chart patterns for AI training in principle, but visual labels may vary with chart scale and between annotators. A model can only learn useful pattern features if the training labels are consistent and accurate. The discussion also notes that feature importance applies to the specific feature set, data, and model settings being evaluated; scores from separate runs are not directly comparable as a universal ranking.
For portfolio weights that change substantially across datasets or models, the document identifies overfitting as a possibility and suggests cross-validation and regularization to improve generalization. It also lists several routes to factor research: investment experience, reproducing published research, reusing existing factors, and automated discovery. Other Q&A covers a reported precision issue in a money-flow field and platform-specific Python and SQL workflows. The notes give conceptual guidance rather than a tested strategy, and provide no empirical evidence on model performance.
Key ideas
- Visual chart-pattern labels can be subjective because interpretation depends on chart scale and the person labeling the data.
- AI models need consistent, accurate training labels to learn the intended pattern features.
- Feature importance describes a model’s assessment within a particular dataset, feature set, and parameter configuration.
- Large changes in calculated portfolio weights across models or datasets can indicate overfitting risk.
- Cross-validation and regularization are suggested as ways to support model generalization.
- Factor research can draw on investment experience, published studies, existing factors, or automated methods.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.