Screening Metaverse Stocks with Institutional Flow and Large-Order Activity

Summary

The post outlines a Chinese equity screening idea focused on metaverse-related stocks. It combines three filters: membership in the selected industry, a positive institutional activity measure, and a threshold condition involving price movement and large-order net activity. The stated rationale is to focus on industry exposure, institutional interest, and trading activity together. It also provides indicator references and a Python example, though the code does not clearly implement the stated description throughout and includes apparent inconsistencies.

The author flags sector instability and the lag in institutional activity as risks, and notes that the combined activity measure may behave differently across stocks. Suggested refinements include adding indicators from other industries, adjusting rules as conditions change, and exploring machine learning. No backtest, return data, benchmark, or threshold validation is presented. The screen should therefore be read as an unvalidated selection concept, not evidence of profitability; the metric definition and code need careful review before use.

Key ideas

• The proposed screen targets stocks classified in the metaverse industry.
• It adds a positive institutional activity condition and a threshold based on price change and large-order activity.
• The post identifies sector instability and delayed institutional signals as risks.
• It suggests combining other indicators or adapting the screen to changing markets.
• No empirical performance evidence is supplied, and the code has apparent inconsistencies.

Tags

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