Diagnosing Empty Backtest Data After NaN Filtering

Summary

This short BigQuant forum post concerns a backtest error indicating that no data remains after missing values are dropped. The suggested diagnosis is that stock-filter settings may be too restrictive, removing every candidate before the backtest proceeds. In particular, the post points to the A-share stock-filter module and recommends relaxing its constraints, including selecting the full market when appropriate.

The discussion also suggests changing the backtest and training date ranges. These adjustments offer practical places to investigate when preprocessing leaves an empty dataset: verify that the selected universe contains securities and that the chosen dates overlap available data. The post is only a brief troubleshooting note and supplies no code, detailed debugging sequence, or confirmation that any one change resolves the error. It does not establish whether missing values themselves, data coverage, or filtering constraints were the root cause in every case.

Key ideas

• The error means no rows remain after missing values are removed.
• Restrictive stock-universe filters can leave a backtest with no data.
• Relaxing A-share filters or selecting the full market may help restore candidates.
• Changing training and backtest date ranges is another suggested diagnostic step.
• The post offers suggestions but no confirmed fix or detailed debugging procedure.

Tags

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