Stock Screening by Daily Range, Price, and Trading Activity

Summary

This note describes a stock screen that selects shares with a daily high-low range above 1% and a closing price below 20, then ranks candidates by a trading-activity proxy calculated from volume and price. It presents the rules as a way to combine short-term movement with market attention and includes formula and Python examples for calculating and sorting candidates.

The article acknowledges that attention-based rankings may be unstable and that price and trading activity do not establish company quality or investment value. It proposes adding fundamental measures such as valuation and return on equity, or applying machine-learning methods, but gives no implementation or evidence for those extensions. No backtest or realized performance is reported. The examples also leave some details unclear: the “K-line below 20” condition is rendered as closing price below 20, and the formula snippet appears to treat the ranking score as a boolean condition rather than only as a sort key. The screen therefore needs specification and testing before it can support an investment conclusion.

Key ideas

• The screen filters for a daily high-low range greater than 1% and a closing price below 20.
• Candidates are ranked by a volume-and-price measure intended to represent trading activity.
• The note warns that attention rankings can be unstable and do not measure company fundamentals.
• It suggests adding fundamental variables or machine-learning methods without specifying or evaluating them.
• The article provides no backtest, and its examples leave the ranking logic ambiguous.

Tags

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