Metaverse Stock Screening by Auction Turnover and Net Buying

Summary

This article proposes screening Chinese A-shares in the metaverse industry by ranking them on the day’s opening-auction turnover and retaining the top five with positive net buying attributed to major auction participants. It frames turnover as a market-activity measure and the net-buying condition as a way to narrow the candidates toward stocks with stronger apparent demand. The article also outlines corresponding platform filters and presents sample Python intended to illustrate implementation.

No historical results or performance comparisons are supplied. The article itself warns that auction-flow data may be incomplete or asymmetric, that a single day’s auction does not establish a company’s value or prospects, and that the filters are simple. Its sample Python does not clearly reproduce the stated ranking: it sorts by company name after checking a field presented as net buying. The screen should therefore be treated as an initial selection idea, with data definitions and implementation checked before use; the article suggests adding fundamental, company, and other market information.

Key ideas

• The proposed universe is metaverse-related Chinese A-shares.
• The screen selects stocks among the top five by current-day opening-auction turnover.
• It requires positive net buying attributed to major participants in the auction.
• The article cautions that auction data alone cannot assess company value or prospects.
• The sample implementation appears inconsistent with the stated turnover ranking and should be checked.

Tags

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