Screening Metaverse Stocks by Institutional Activity and Recent Lows

Summary

The post outlines a Chinese equity screening rule that combines metaverse-sector membership, positive institutional flow, and a condition based on rolling 20-period lows and closes. It describes the price condition as a way to find comparatively low prices, then presents formula and Python examples that join company, investor-flow, and daily price data. The code illustrates one possible data workflow, including filtering candidates and calculating rolling lows.

The post warns that sector classification and institutional activity can be imperfect signals, and that a low-price condition does not establish sound fundamentals or protect against a broad market decline. It suggests adding technical or fundamental filters, such as valuation measures, and considering company quality. No backtest, benchmark comparison, transaction-cost analysis, or evidence of profitability is provided. The example uses particular data fields and dates, so its implementation depends on data availability and may not match the screening description exactly. The rule is best read as a candidate-generation idea requiring validation.

Key ideas

• The screen combines metaverse-sector membership, positive institutional-flow data, and a rolling-low price condition.
• The examples show how company, investor-flow, and daily price data can be combined for screening.
• Sector labels and institutional-flow measures may be incomplete or noisy.
• A low-price signal does not establish fundamental value or protect against market-wide losses.
• The post provides no backtest or evidence that the screen is profitable.

Tags

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