Screening Stocks by Moving Average Confluence, Recent Activity, and Opening Gain

Summary

This proposed stock screen combines three filters: at least five moving averages converging, appearance on the prior day’s trading activity list, and a 9:25 a.m. gain below six percent. The article frames moving average confluence as a way to identify potential trend alignment, uses the activity list as a proxy for participation, and limits the opening gain to avoid selecting shares that have already surged sharply. It also suggests adding volume, valuation, and market-capitalization conditions.

The article presents sample Python snippets but no backtest, returns, benchmark, or execution rules. The code does not clearly implement the stated multi-average convergence or verify membership in the prior day’s activity list; it instead applies turnover and rolling-price checks, so the examples should not be treated as a faithful implementation. The author notes that short-term filters can miss longer trends, activity-list data may be unreliable, and the opening threshold can exclude stocks that continue rising.

Key ideas

• The proposed screen requires at least five moving averages to converge.
• It also filters for prior-day trading activity and an opening gain below six percent.
• The article proposes volume and valuation filters as possible refinements.
• The sample code does not clearly implement the stated convergence or activity-list conditions.
• No evidence of historical or live performance is provided.

Tags

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