Fixing Missing Rolling-Factor Data by Extending the Lookback Window

Summary

This brief Chinese-language support note explains a prediction-data problem traced to missing values in a rolling price-range factor. The example factor compares a short moving average of the high-low range with a longer moving average. According to the response, many missing observations caused a later module to remove all rows during missing-value handling, leaving the prediction set unusable.

The suggested remedy is to increase the number of historical days fetched in an earlier module so the rolling calculations have enough prior observations. This addresses missing values caused by insufficient lookback at the start of a rolling window. The note does not provide code, quantify how much additional history is needed, or confirm that the change resolved the issue. It also does not discuss other sources of missing data or recommend validation checks after changing the lookback.

Key ideas

• Rolling factors can produce missing values when the available history is shorter than the calculation window.
• Excess missing rows may be removed during preprocessing and leave a prediction set unusable.
• Increasing the upstream data lookback can provide observations needed for rolling calculations.
• The suggested fix is specific to insufficient historical coverage and is not shown to resolve other missing-data causes.

Tags

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