Price and Volume Signals in a Partial Alpha Factor Library

Summary

This document presents a partial Python implementation of an Alpha101-style factor library. Its functions combine price and volume data using rolling ranks, correlations, covariance, moving averages, standard deviations, price changes, and volume averages. Several methods return component series separately, while others produce a single signal; the code also replaces missing and infinite values in many calculations.

The excerpt shows implementation choices rather than a trading strategy or validated factor set. It includes no performance results, investment universe, signal interpretation, or backtest methodology. Some formulas are explicitly marked as potentially problematic, and the code is incomplete in the supplied excerpt. The returned factors therefore need review for correctness, data alignment, and intended use before research conclusions can be drawn.

Key ideas

• The code builds signals from rolling price and volume statistics, including ranks and correlations.
• Many functions return intermediate components instead of a final combined factor.
• Missing and infinite values are handled inconsistently across the displayed methods.
• The excerpt provides no backtest results or evidence that the signals predict returns.

Tags

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