Implementing Alpha Factors 26–64 from Price and Volume Data

Summary

This code implements a collection of cross-sectional and time-series equity alpha factors, mainly using close, open, high, low, volume, returns, and VWAP data. The factors combine operations such as rolling ranks, correlations, moving averages, extrema, and price changes. Some inputs are computed within the class, while many are expected as precomputed columns, and the implementation assembles the available outputs into a panel for multiple securities.

The comments identify the formulas and note that one factor requiring industry classification is omitted because the needed data is unavailable. Another factor is flagged by its author as potentially problematic after producing constant values in their own checks. The source includes a database retrieval example and a small stock sample, but it provides no factor-return analysis, portfolio construction rules, out-of-sample evaluation, or evidence of profitability. Several implementation choices and dependencies may also limit portability, so the formulas require independent verification before use.

Key ideas

• The factor set uses price, volume, returns, and VWAP through rolling ranks and correlations.
• Many factor inputs are expected to be precomputed and supplied as dataframe columns.
• An industry-neutralized factor is omitted because the required classification data is unavailable.
• The author flags one factor as possibly misconfigured after observing constant values.
• The code demonstrates data retrieval and factor assembly but reports no investment performance.

Tags

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