Metaverse Stock Screening with Turnover and Moving-Average Confluence

Summary

This document proposes screening stocks in the metaverse industry for prior-day actual turnover between 3% and 28%, alongside a condition described as at least five moving averages converging. It names the 5-, 10-, 20-, 30-, and 60-period averages. The screen is framed as combining an industry theme, a trading-activity filter, and a technical price condition. Formula and Python examples are included, but the formula shown multiplies the five moving averages and checks whether the product is positive; that condition does not, by itself, demonstrate that the averages are close together. The examples therefore leave ambiguity about how confluence is meant to be measured.

The author notes that the screen omits other technical and fundamental factors and says turnover and moving-average criteria alone cannot establish value or overall quality. Suggested additions include valuation and profitability measures, other technical indicators, and risk controls. The document provides no backtest, selection results, or evidence that the industry theme or filters predict returns, so it is best read as a screen concept with implementation details that need careful validation.

Key ideas

• The proposed universe is metaverse-related stocks with prior-day actual turnover between 3% and 28%.
• The technical condition references the 5-, 10-, 20-, 30-, and 60-period moving averages.
• The example formula checks whether the product of the averages is positive, which does not establish that they are converging.
• The screen omits broader fundamentals and risk controls, and the document gives no performance evidence.

Tags

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