Rolling Time-Series and Ranking Helpers for Quantitative Factor Research

Summary

This code collection provides helper functions commonly used when building quantitative factors from time-series data. It wraps TA-Lib operations for rolling sums, standard deviations, minima, maxima, momentum differences, and weighted moving averages. It also includes routines for rolling extrema locations, shifting observations, products, time-series ranks, cross-sectional ranks with tie-handling options, and scaling an array by its absolute-value sum. The covariance helper reconstructs covariance from correlation and standard deviations.

The examples show implementation choices rather than a complete factor model or trading strategy, and they provide no empirical evaluation. Several details need review before use: a shift based on a circular roll wraps values from the end to the beginning, which can leak invalid observations; percentile ranking can divide by zero for a one-element array; and the tie-handling branches deserve validation. The extrema routines also rely on a one-dimensional sliding-window layout despite accepting shapes that appear more general. Users should check edge cases, missing values, alignment, and library conventions against their intended data pipeline.

Key ideas

• The helpers cover common rolling statistics and transformations used in time-series factor construction.\nRolling minima and maxima include routines that report the position of an extreme within each window.\nThe rank function supports several tie policies and an optional percentile scale.\nThe scaling helper normalizes values by their total absolute magnitude and returns zeros for a zero-sum input.\nCircular shifting and edge-case handling require careful validation to avoid invalid alignment or numerical errors.

Tags

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