Metaverse Stock Screening with Positive Auction Net Buying and Heat Ranking

Summary

This Chinese stock-screening note describes selecting companies in the metaverse concept group when auction-period main-fund net buying is positive, then ranking qualifying names by stock heat. It frames the concept membership as a way to focus on a popular theme and the auction flow filter as a demand signal. Its accompanying examples outline an indicator formula and a Python-style workflow that retrieves listed stocks, filters for the concept, estimates net buying, and sorts candidates.

The document provides no performance results, validation procedure, or evidence that heat predicts returns or that positive auction net buying persists after the open. It also describes the aim as finding high-return, low-risk stocks without supporting that claim. Theme classification, flow data quality, and subjective or lagging heat rankings can all affect selections; the note recommends broader risk assessment and periodic strategy updates but does not specify a tested implementation.

Key ideas

• The screen focuses on stocks labeled as part of the metaverse theme.
• It requires positive net main-fund buying during the auction period.
• Qualifying stocks are ranked from highest to lowest by stock heat.
• The note gives example indicator and data-processing approaches but reports no tested returns.

Tags

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