Building Factor and Label Datasets for Quantitative Models
Summary
The article explains how a multi-asset daily market-data panel can be turned into model-ready factor and label tables. It separates the workflow into defining train, validation, and test date ranges; adding feature expressions and a future-return label; preparing data to calculate expressions, merge external factors, and optionally restrict observations to index membership; and processing the resulting table for learning or inference. It emphasizes aligning the label horizon with the intended holding period and rebalance schedule.
It describes common preprocessing choices, including dropping or filling missing values, cross-sectional normalization or ranking, and robust scaling with outlier clipping. Built-in Alpha158 and Alpha101 collections are presented as runnable starting points, while a factor-analysis view can inspect forward returns by factor groups. The article is a software workflow tutorial, not empirical proof that the example factors predict returns. It also distinguishes factor diagnostics from a portfolio backtest that includes construction, turnover, costs, and execution.
Key ideas
- The dataset workflow separates raw market data, feature and label definitions, preparation, preprocessing, and segment-based retrieval.
- Training, validation, and test periods should be explicitly defined for model development and evaluation.
- Labels should reflect the strategy’s intended holding period and rebalance frequency.
- Preprocessing options include missing-value handling, cross-sectional scaling or ranking, and robust normalization.
- Factor performance analysis can inform research but does not replace a portfolio backtest with costs and execution.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.