Stock Screening with Converging Moving Averages and Positive Returns

Summary

This note proposes screening stocks for convergence among five moving averages: 5, 10, 20, 30, and 60 days. It combines that condition with a positive but capped 10-day price gain and an upward-sloping 30-day average. The explanation treats clustered averages as a possible sign of a developing trend and the return filter as a way to avoid stocks with either negative or very rapid recent gains. The stated final selection logic also adds price-to-earnings and price-to-book limits.

The document warns that volatile markets can quickly disrupt moving-average relationships and that a sharp price rise can reverse the 30-day average. It suggests testing longer averages and adding valuation filters. No backtest or performance evidence is provided. The Python example appears inconsistent with the described method: its convergence test checks repeated values within each moving-average series rather than convergence between the averages, and its later filtering steps reset the selected list. These issues make the example unsuitable as a faithful implementation without correction.

Key ideas

• The proposed screen looks for convergence among 5-, 10-, 20-, 30-, and 60-day moving averages.
• It also requires a positive 10-day return below the stated upper bound and a rising 30-day average.
• The final written criteria add price-to-earnings and price-to-book limits.
• The note identifies volatility and trend reversal as risks and provides no evidence of historical performance.
• The sample code does not reliably implement the described convergence test.

Tags

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