Selecting Metaverse Stocks by Prior-Day Rankings and Auction Value

Summary

This A-share stock-selection idea filters for companies associated with the metaverse theme that appeared on the prior day's trading leaderboard, then ranks eligible names by the current day's auction value and selects the highest-ranked group. The article also sketches a Python workflow using market data: retrieve spot listings, filter by industry, obtain auction information, apply a trading-volume condition, sort by auction value, and return the top five candidates.

The article cautions that the screen uses a narrow set of signals, omits many fundamental measures, and may miss other opportunities because it selects few names. It suggests adding measures such as return on equity, valuation, and financing-related liquidity information, as well as making the selection count configurable. No backtest or performance evidence is provided. The code's data source, field semantics, timing, and exception handling would need verification before relying on results in live trading.

Key ideas

• The screen focuses on metaverse-related A-share stocks that appeared on the prior day's leaderboard.
• Eligible stocks are ranked by current-day auction value, with the highest-ranked names selected.
• The example workflow also applies an auction trading-volume condition before ranking.
• The article identifies limited fundamental coverage and a small selection set as weaknesses.
• It proposes adding financial and liquidity measures, but offers no backtest evidence.

Tags

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