DAI SQL Windows, Factor Neutralization, and Missing-Value Handling
Summary
This FAQ explains three data-processing tasks in BigQuant’s DAI SQL environment. It outlines how to define rolling aggregate macros and date-partitioned cross-sectional ranking or averaging macros, including a workaround for choosing sort direction when SQL keywords cannot be passed as macro arguments. It also describes industry and market-cap neutralization as a cross-sectional regression, with the factor as the response and industry indicators plus log market capitalization as explanatory variables.
The FAQ warns that missing values in either the factor or regression inputs can make neutralization results invalid, and recommends filtering them before fitting. It gives an example of robustly clipping and standardizing factor values before computing residuals. For missing-value handling, it shows forward filling within each instrument, replacing nulls with a constant, or filling selected daily price fields with a cross-sectional median. These are implementation examples, not an evaluation of resulting signals; users still need to consider whether each fill method is appropriate for their data and research question.
Key ideas
- Rolling SQL macros can use ordered window frames partitioned by instrument, while cross-sectional macros can partition observations by date.
- A Boolean parameter can select ascending or descending rank order when SQL ordering keywords cannot be macro arguments.
- Industry and market-cap neutralization regresses a factor on industry indicators and log market capitalization, then uses residuals.
- Missing values in the factor or regression inputs can invalidate neutralization, so they should be handled first.
- The FAQ demonstrates forward fill, constant replacement, and daily cross-sectional median imputation without assessing their effects on research results.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.