A-Share Screening with Metaverse Theme, Turnover, and Market Capitalization

Summary

This document presents an A-share selection rule combining a metaverse-sector classification, elevated prior-day turnover, and a stock-code prefix. Its final stated logic also ranks candidates by market value and selects the top portion. The accompanying explanation treats theme exposure as a way to target a market narrative and turnover as a liquidity and attention filter, while acknowledging the limits of restricting stocks by code and sector.

The post offers indicator formulas and a Python example as implementation references, but the example includes a fixed historical year and does not establish strategy performance. The text also suggests adding technical and fundamental filters, such as relative strength and valuation measures, and checking broader historical data. It cautions that sentiment-driven selection can lose effectiveness over time and that smaller companies may carry greater risk. No backtest results or evidence of profitability are supplied.

Key ideas

• The screen combines a metaverse classification, high prior-day turnover, and a stock-code prefix.
• The final rule further ranks candidates by market value and keeps the top share of the group.
• The stated rationale is to focus on thematic attention and trading activity.
• Theme dependence, code restrictions, and smaller-company risk can limit the screen.
• The implementation examples do not provide performance evidence.

Tags

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