Rising Lows, High Amplitude, and Float Filters for Stock Selection

Summary

This post outlines a Chinese equity screen that combines price behavior with a share-float constraint. It selects stocks with amplitude above 1, a rising bottom pattern, and a circulating share count no greater than 5.5 billion shares. It gives example implementations for a screening formula and a Python workflow, including comparisons of successive bottom measures and a fundamental-data filter.

The post offers no backtest results or evidence that the screen predicts returns. It cautions that share float can change, while rising-bottom and rounded-base patterns take time to form and may be obscured by short-term price fluctuations. It suggests adding financial or operating measures and potentially using machine-learning models, then adapting conditions to market context and investment objectives. The code is explicitly illustrative: the Python example uses high-price standard deviation as its amplitude test and market capitalization for the size condition, which do not exactly match the stated amplitude and circulating-share definitions. Implementations should therefore verify the data fields and indicator meanings before use.

Key ideas

• The proposed screen combines amplitude above 1, rising lows, and a circulating float cap of 5.5 billion shares.
• The post supplies formula and Python examples, but their field definitions do not perfectly match the verbal criteria.
• Changing share float and short-term price fluctuations can make screening results unstable.
• The post recommends considering additional fundamentals and adapting criteria to market conditions, without presenting performance evidence.

Tags

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