Metaverse Stock Screening with Auction Turnover and KDJ

Summary

This Chinese-language post proposes screening mainland Chinese stocks associated with the metaverse. It ranks candidates by the day’s auction amount, keeps the top five, and combines that liquidity or activity filter with a condition on the K value of the KDJ indicator. The post also gives a platform-specific screening expression and a Python example intended to illustrate data retrieval and filtering.

The stated rationale is to combine current market activity with a short-term technical signal. The article warns that this approach omits company fundamentals and industry quality, can be exposed to market volatility or tight funding, and relies on a short-horizon indicator. It suggests adding financial, management, industry, and longer-term value factors, as well as tuning technical parameters. The code is only a reference and appears inconsistent with the stated method: it assigns closing prices to the K field and uses ranking and indicator logic that do not clearly implement the described auction-amount and KDJ filters. No backtest results or evidence of profitability are provided.

Key ideas

• The proposed universe is metaverse-related mainland Chinese equities.
• The screen combines top-five ranking by daily auction amount with a KDJ K-value condition.
• The article identifies missing fundamentals, industry assessment, and long-term valuation as limitations.
• Its Python example does not clearly calculate the stated KDJ signal or auction-amount ranking.
• No backtest evidence is supplied to establish whether the screen performs well.

Tags

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