gf-dma-health-index

To evaluate stock trend support, you compare fundamental growth metrics like revenue and EPS against the moving average structure to determine if an upward price trend is fundamentally supported or at risk of a breakdown.

Price divergence analysis identifies stock overvaluation or undervaluation by measuring the gap between the current stock price and its moving averages, checking whether the price movement aligns with the underlying DMA structure.

Calculating a stock health index requires fundamental data on revenue, EPS, and gross margin, alongside technical inputs including moving averages, ATR, trend parallelism, and estimate revisions.

Yes, you can use Python with pandas and numpy for technical stock analysis and DMA scoring, utilizing these libraries to compute moving averages, ATR, and fundamental growth metric alignments.

The best way to check alignment between a stock's moving average trend and estimate revisions is to score trend parallelism against the consistency of fundamental estimate revisions using a structured health index analysis.