A Metaverse Stock Screen Using Auction Activity and Prior Turnover

Summary

The document proposes screening Chinese stocks in the metaverse industry by ranking them on the current day's opening-auction amount and focusing on the top five. It then applies a second filter: yesterday's turnover rate multiplied by the ratio of current auction volume to the previous day's volume must fall between 0.5 and 2. The post also gives indicator expressions and a Python example intended to illustrate the selection process.

This is a rule-based screening concept, not a demonstrated profitable strategy. The article provides no backtest or return evidence and cautions that short-term activity measures may overlook company fundamentals and volatile market conditions. There is also an implementation mismatch: the Python example ultimately sorts qualifying stocks by circulating market capitalization, rather than ranking them by auction amount as the stated method specifies. The sample dates and data fields may therefore need adjustment before the code can represent the described screen.

Key ideas

• The screen targets stocks classified in the metaverse industry and ranks them by current opening-auction amount.
• It keeps the top five auction-amount names, then applies a turnover and volume ratio filter between 0.5 and 2.
• The article supplies platform expressions and a Python example, but no performance testing.
• The Python example sorts the final results by circulating market capitalization, which differs from the stated auction ranking.
• The post warns that activity-based screening omits fundamental and broader market considerations.

Tags

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