A-Share Screening with Positive MACD, Stock Heat, and Company Quality

Summary

This A-share screening proposal combines a positive MACD reading with ranking by stock popularity and a qualitative preference for high-quality companies. Screening is intended to run after each daily close. The document gives the standard exponential-average construction for MACD and sketches a Python workflow that retrieves price, indicator, popularity, and financial data before filtering and sorting candidates.

The write-up argues that trend, investor attention, and company quality may complement one another, then identifies limits: company quality is difficult to quantify, MACD is a single technical measure, popularity may reflect speculation, and the screen lacks a full fundamental review. It suggests adding technical and financial measures such as return on equity and leverage. The code does not clearly implement the stated popularity ranking, uses a price condition absent from the written rule, and sorts by closing price rather than heat. No backtest or investment results are supplied, so the idea needs careful data validation and evaluation.

Key ideas

• The proposed screen selects stocks with a positive MACD reading and favors more popular names and companies judged to be high quality.
• The selection is intended to run after the daily close.
• The example defines MACD from exponential moving averages of closing prices.
• The supplied code includes a price filter and sorting step that do not fully match the stated screening logic.
• The author notes that popularity and qualitative company classifications can be unreliable and recommends adding broader analysis.

Tags

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