Calculating Cross-Sectional Dispersion Across Moving Averages

Summary

The post asks whether a feature expression can take the maximum of four moving averages and whether a similar expression can calculate their standard deviation for one day. The example uses 5-, 10-, 30-, and 60-day averages as four contemporaneous values, with the goal of turning their dispersion into a factor.

It also asks whether Python’s math module can be used inside the platform’s input-feature-list module. The document contains no answers or implementation details, so it establishes the questions but not which functions the expression language supports, how to compute the dispersion, or whether external Python modules are available. Researchers seeking to reproduce the proposed factor would need platform documentation or a follow-up response to confirm those details.

Key ideas

• The author asks whether the expression language supports taking a maximum across four moving-average features on the same date.
• The proposed dispersion factor is the standard deviation across the four moving-average values for a single date.
• The post asks whether Python’s math module is accessible from the input-feature-list module.
• The document does not provide answers or specify supported syntax.

Tags

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