A-Share Screening with Weekly MACD, Volatility, and Value Filters

Summary

This A-share screen combines daily amplitude above 1 with a positive weekly MACD reading and a listing history longer than one year. The article then adds valuation and profitability constraints: price-to-earnings below 20, price-to-book below 3, and return on equity above 10%. It presents the filter as a way to identify volatile stocks with upward momentum while excluding newer listings and applying basic quality checks.

The post explains the intended role of each condition and gives example implementations for a stock screener and Python workflow. It provides no backtest, performance statistics, or evidence that these thresholds predict returns. The source also has an implementation mismatch: its Python example uses ROA while the stated rule calls for ROE, and its MACD checks are not clearly equivalent to a weekly red histogram. It cautions that the original technical filters alone omit broader market and sector context, and recommends incorporating fundamentals and industry characteristics.

Key ideas

• The screen requires daily amplitude above 1, positive weekly MACD, and more than one year since listing.
• The expanded rules add price-to-earnings below 20, price-to-book below 3, and return on equity above 10%.
• The proposed filters combine volatility and trend signals with valuation and profitability measures.
• The post provides example screening logic but no backtest or performance evidence.
• Its Python example uses a return-on-assets field where the written rule specifies return on equity.

Tags

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