/gf-dma-health-index

name: gf-dma-health-index
description: Score a stock's current valuation/trend health using the GF-DMA Health Index, combining fundamental growth speed, 20/50/100/200DMA trend speed, price-to-DMA divergence, ATR divergence, escape ratio, and estimate revisions. Use when the user provides a ticker or asks for GF-DMA scoring, valuation health, trend health, healthy momentum, overheated/escape risk, or whether a rising/falling stock is fundamentally supported.

GF-DMA Health Index

Core Idea

Evaluate whether a stock's current price trend is supported by fundamental speed and moving-average structure.

Use the index to answer:

Is the current price trend supported by revenue growth, profit growth, estimate revisions, and the 20/50/100/200DMA system?

Treat results as research analysis, not investment advice. For latest/current scoring, verify data from current sources before calculating.

Required Inputs

Collect the newest available data before scoring:

• Price/technical data: latest price, 20DMA, 50DMA, 100DMA, 200DMA, ATR20, 5-day price change, and 20/50/100/200-day price changes or historical prices.
• Fundamental data: latest quarterly revenue, EPS, gross margin or gross profit, next-quarter company guidance, consensus revenue/EPS estimates, and 30-day estimate revisions.
• Preferred sources: company IR releases/presentations, earnings calls, Yahoo Finance historical prices/analysis, TradingView technicals/estimates, Barchart technical analysis, Seeking Alpha estimates, Koyfin, FactSet, Bloomberg, TIKR, or Visible Alpha.

For U.S.-listed companies, SEC filings can improve the fundamental side of the score. `edgartools` is an optional helper for retrieving the latest 10-K, 10-Q, 8-K, XBRL financial statements, filing text, insider transactions, and ownership filings.

If the environment does not already have it, install with `pip install edgartools` or `uv pip install edgartools`. The import package is `edgar`, not `edgartools`. SEC access requires an identity; set `EDGAR_IDENTITY="Name email@example.com"` in the environment or call `from edgar import set_identity; set_identity("name@example.com")` before requests.

Minimal usage pattern:

from edgar import Company

company = Company("AAPL")
financials = company.get_financials()
income = financials.income_statement()
filings = company.get_filings(form="8-K")

Use SEC data for:

• reported quarterly revenue, gross profit, EPS, cash flow, balance sheet, share count, and historical trend baselines
• management language on demand, backlog, pricing, capacity, inventory, customer concentration, and risks
• 8-K earnings releases or guidance disclosures when they contain the newest company-provided numbers

Do not use SEC data for the technical module or revision module. Price, 20/50/100/200DMA, ATR20, 5-day price slope, consensus estimates, and 30-day estimate revisions still require market-data and estimate sources. When SEC data is used, state the filing form and filing date so the user can judge freshness.

If a required field is unavailable, say which field is missing and use the simplified formula only when appropriate.

Calculation Workflow

1. Fundamental Speed

Calculate:

G_f = 0.35G_Revenue + 0.25G_GrossProfit + 0.30G_EPS + 0.10G_Revision

Where:

• `G_Revenue = next-quarter revenue guidance / latest-quarter revenue - 1`
• `G_GrossProfit = next-quarter gross profit / latest-quarter gross profit - 1`
• `G_EPS = next-quarter EPS guidance / latest-quarter EPS - 1`
• `G_Revision = 30-day consensus estimate revision`

Fallbacks:

• If gross profit or EPS is missing: `G_f = 0.5G_Revenue + 0.5G_EPS`
• If only revenue guidance is available: `G_f = G_Revenue`

2. DMA Speed

Calculate quarterly annualized-equivalent moving-average speed for each DMA:

G_DMAx = ((SMA_x(t) - SMA_x(t-k)) / SMA_x(t-k)) * (63 / k)

Use `k = 5` or `10` trading days by default. If only price-change data is available, approximate:

DailySlope_x ~= (P_t - P_t-x) / x
G_DMAx ~= DailySlope_x * 63 / P_t

Compute `G_DMA20`, `G_DMA50`, `G_DMA100`, and `G_DMA200`.

3. Fundamental-DMA Match

Calculate:

R_x = G_DMAx / G_f

Interpret `R_50` and `R_100` first:

| R_x | Status | | ---: | --- | | < 0.5 | Trend clearly below fundamental speed | | 0.5-0.8 | Under-reflected or cheap versus trend | | 0.8-1.3 | Healthy match | | 1.3-2.0 | Hot but potentially explainable | | > 2.0 | Overheated / FOMO escape risk |

Core DMA emphasis:

| Stock type | Key DMA | | --- | --- | | Mega-cap growth leaders like NVDA, AVGO, MSFT | 50DMA | | Memory/cyclical semis like MU, SNDK | 100DMA | | High-elasticity optical names like LITE, AAOI | 20DMA + 50DMA | | Industrial AI/power names like ETN, VRT, TEL | 100DMA + 200DMA | | Small-cap hard-manufacturing names like SIVE, CPSH | 20DMA + ATR divergence | | Semiconductor ETFs like SOXX, SMH | 50DMA + 100DMA |

4. Price-DMA Divergence

Calculate:

D_x = P_t / SMA_x(t) - 1
Z_x = (P_t - SMA_x) / ATR20

Interpretation:

| Signal | Status | | --- | --- | | 0%-5% above 20DMA | Healthy close-to-line trend | | 5%-12% above 20DMA | Strong trend, mild valuation stretch | | 12%-20% above 20DMA | Hot; divergence score should fall | | >20% above 20DMA | Short-term escape; divergence score should fall sharply | | >30% above 50DMA | Medium-term overheat | | >50% above 100DMA | Major repricing | | >100% above 200DMA | Extreme long-cycle repricing | | 0%-5% below 20/50DMA with stable fundamentals | Healthy pullback; divergence score can rise | | 5%-15% below 50DMA with stable/improving fundamentals | Better valuation entry, but verify trend damage separately | | Below 100/200DMA with deteriorating fundamentals | Trend damage; do not treat as cheap automatically |

ATR divergence is asymmetric:

| Z_x | Status | | ---: | --- | | 0 to 2 | Healthy | | 2 to 3 | Hot | | 3 to 4 | Very hot | | >4 | Escape; reduce divergence score sharply | | -1 to 0 with stab