Diagnosing NaT Errors in Factor Analysis Date Filtering

Summary

The document reports a pandas error raised when a factor-analysis workflow formats the minimum date with strftime. The shown failing expression takes the minimum of a date column and immediately formats it; pandas raises the error when that value is NaT, its missing datetime value. This points to missing or unparseable dates, or a filtered dataset with no valid dates, as issues to investigate before date formatting.

The report includes the surrounding workflow: stock-pool filtering, a factor-data filtering function, and subsequent date-bound extraction. It does not provide a confirmed diagnosis or a working fix, and the linked source is not reproduced. A practical debugging approach is to inspect the date column’s parsed values and missing count, and verify that filtering leaves valid rows before computing date bounds. This is a narrow data-validation issue in a factor research pipeline, not evidence about a factor’s predictive value or investment performance.

Key ideas

• The error occurs when strftime is called on a missing datetime value represented as NaT.
• The failing code formats the minimum and maximum dates in the factor data after filtering.
• Missing or invalid dates, or an empty filtered dataset, are possible causes to check.
• Validate parsed dates and confirm valid rows remain before extracting date boundaries.
• The report supplies no confirmed root cause, tested fix, or factor performance results.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.