Screening Stocks by Daily Range, Turnover, and Trading-Activity Rank

Summary

This stock screen selects shares with daily high-low amplitude of at least 1% and turnover above 2% but no greater than 9%, then ranks candidates by a measure of trading activity or investor attention. The article's example formula uses traded amount as the ranking input, while its sample Python workflow sketches filtering and ranking with stock and money-flow data.

The intended rationale is to find actively traded, volatile stocks receiving market attention. The post cautions that its heat measure may be noisy and that higher volatility brings risk, and suggests refining the attention metric and adding other technical or broader valuation analysis. It provides no backtest, performance statistics, or evidence that the ranking predicts returns. The sample implementation also does not clearly reconcile the stated turnover thresholds with the percentage scale used in its data filter, so the screen requires careful field and unit validation before use.

Key ideas

• The screen filters for daily amplitude of at least 1% and turnover between 2% and 9%.
• Candidates are ordered by a trading-activity proxy, with traded amount used in the example formula.
• The strategy aims to surface volatile stocks attracting market attention, but no predictive edge is demonstrated.
• The article flags uncertainty in the heat measure and the risk associated with volatile shares.
• The turnover units in the example should be checked against the source data before implementation.

Tags

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