Equity Screening with Amplitude, Control Shareholding, Relative Return, and Valuation

Summary

This post starts with a proposed stock screen based on amplitude above 1, a control-related measure above 21, and positive return. Its expanded version adds relative performance against a benchmark over roughly three months and valuation and profitability filters: positive, bounded price-to-earnings, price-to-book, and price-to-sales ratios, plus gross margin above 20%. It supplies formula and Python examples that turn several conditions into scores and use a threshold to select stocks.

The post identifies risks from relying on a few short-term signals and recommends broader fundamental inputs. It also suggests machine learning as a possible way to build a risk model, but provides no trained model, backtest, or performance evidence. Terms and implementation details are inconsistent: the control measure is described differently across sections, and the examples do not implement the stated initial amplitude condition in the same way. The proposed filters and scoring should therefore be treated as an unvalidated screening recipe; the document does not show that they improve returns or control risk.

Key ideas

• The expanded screen combines amplitude and a control-related measure with relative stock performance.
• It adds valuation ratios and gross margin as fundamental filters.
• The examples use a score assembled from several conditions to select stocks.
• The post acknowledges short-term bias and missing fundamentals as weaknesses of the simpler screen.
• No backtest or measured evidence supports the proposed improvements.

Tags

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