Combining Metaverse Membership, Turnover, Technical, and Valuation Screens

Summary

This post outlines a Chinese A-share screen combining membership in a metaverse-related category with a turnover-based condition, a technical signal, market-cap ranking, and a valuation filter using PEG. It also sketches a Python workflow that retrieves sector, price, and financial data, applies the conditions, and sorts the resulting names by recent price change.

The author notes that a narrow screen can omit company fundamentals and that chart patterns can be unreliable. The suggested additions are further valuation and size criteria, though the post does not present backtest results or establish that the filters improve performance. Some labels and formula descriptions are ambiguous, including the reference to a specific stock in the technical condition, so the implementation should be independently checked before use.

Key ideas

• The screen combines a sector category with turnover, technical, market-cap, and PEG conditions.
• The proposed workflow gathers sector membership, daily prices, capitalization ranks, and financial data.
• The post ranks qualifying stocks by recent price change and takes a subset of the list.
• The author cautions that technical patterns can produce false signals and that the screen omits broader company analysis.
• No performance evidence is supplied, and some formula descriptions require verification.

Tags

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