Screening Metaverse Stocks by Long-Term Trend and Turnover

Summary

The post describes a China equity screen that selects stocks in the metaverse theme when the prior close is above the 250-day moving average and prior-day turnover exceeds 8%. It frames the moving-average condition as a long-term price-trend filter and turnover as a sign of recent trading activity. The post also gives implementation references in Supermind, JavaScript, and Python, though these include platform-specific data handling and extra filters beyond the stated core logic.

The author warns that the screen is simple, may miss quality companies, and can overemphasize popular themes and recently active stocks. Market sentiment and timing may affect its results. Suggested refinements include adding valuation and financial measures, company and industry fundamentals, and liquidity trends. No backtest, returns, or risk statistics are provided, so the conditions should be understood as a screening rule rather than evidence of a profitable strategy.

Key ideas

• The core screen requires metaverse-sector membership, price above the 250-day moving average, and prior-day turnover above 8%.
• The moving-average condition acts as a long-term trend filter, while turnover targets recently active stocks.
• The post cautions that theme and activity filters can neglect company fundamentals and long-term value.
• It suggests adding financial, industry, and liquidity measures, but supplies no performance validation.

Tags

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