financial-analysis-stock-screening

name

financial-analysis-stock-screening

description

Stock screening and ranking for US equities using SEC EDGAR data. Use when asked to: find stocks by sector or theme; rank stocks by valuation, growth, or quality metrics; surface investment ideas; screen for stocks meeting specific criteria; find the best growth/value/quality stocks in a group. Triggers: "best AI stocks", "cheap healthcare stocks", "fastest growing semiconductors", "find me value stocks", "screen for high-growth tech", "rank these stocks by quality", "which of these is the best growth stock". Note: For side-by-side peer benchmarking with statistical analysis, use comps-analysis instead.

Stock Screening

Quantitative stock screener with composite scoring. Discovers candidates via web search, filters by thresholds, scores on growth/value/quality dimensions, and returns a ranked list with actionable picks. Data from SEC EDGAR (financials), Yahoo Finance (market data), and web search (universe discovery).

IMPORTANT: This skill requires running bash run.sh to produce scores. You MUST execute the script and use its JSON output — do not skip it or compute metrics manually. The script returns growth/value/quality scores (0-100) and composite rankings that must appear in the output.

Setup

No dependencies required. All scripts use Python standard library only.

Workflow

Step 1 — Clarify criteria

Before running any script, understand:

• Universe: sector, theme, or broad market?
• Style: growth (high revenue/earnings growth), value (cheap multiples), or quality (high margins)?
• Thresholds: e.g. "revenue growth >20%", "P/E below 25x", "margin >30%"

If the user gives a vague request like "find me good tech stocks", default to growth style and state the assumption. If the user remains vague after clarification (no specific style or thresholds), default to growth + quality: rank by revenue growth first, then net margin as tiebreaker. State this explicitly so the user can adjust.

Step 2 — Build the candidate universe

Use web search to identify the relevant stock universe:

• top [sector] stocks by market cap [year]
• [theme] stocks list [year] (e.g. "AI stocks list 2026")
• S&P 500 [sector] constituents

Common universes for reference:

Theme	Example symbols
Magnificent 7	AAPL, MSFT, GOOGL, AMZN, NVDA, META, TSLA
Semiconductors	NVDA, AMD, INTC, AVGO, QCOM, TXN, MRVL, MU
AI concept	NVDA, MSFT, GOOGL, META, AMZN, CRM, PLTR, SNOW
EV / Clean Energy	TSLA, RIVN, LCID, NIO, ENPH, FSLR, PLUG

These are reference examples — always verify via web search for the current year, as index constituents and thematic groupings change over time.

Narrow to max 10 symbols before running the screener. State which symbols were excluded and why.

Step 3 — Run the screener

Always run the screener script — do not compute scores or filter manually. The script produces standardized scores, rankings, and filter results that must be used in Step 4.

bash run.sh <SYM1> <SYM2> ... <SYM10> --style <growth|value|quality>
# With filters:
bash run.sh <SYMS> --style growth --min-growth 10 --min-margin 15 --max-pe 40

Scoring system:

Each company is scored 0-100 on three dimensions:

• Growth score (60% revenue growth + 40% net income growth)
• Value score (50% P/E + 50% P/S — lower multiples score higher)
• Quality score (50% net margin + 50% operating margin)

Composite score is weighted by style:

• growth: 50% growth + 30% quality + 20% value
• value: 50% value + 30% quality + 20% growth
• quality: 50% quality + 30% growth + 20% value

Threshold filters (optional):

• --min-growth N: exclude companies with revenue growth < N%
• --min-margin N: exclude companies with net margin < N%
• --max-pe N: exclude companies with P/E > Nx

Step 4 — Present ranked results

Use the JSON output from get_screen.py directly — present the scores, rank, and filtered_out fields as-is. Do not invent your own scoring system (no star ratings, no PEG-based rankings). The script's composite score is the authoritative ranking.

Lead with screen summary:

Screen: [Style] — [Sector/Theme]
Universe: [N] candidates → [M] passed filters
Ranked by: composite score ([style] weighted)

Then ranked table (sorted by composite score):

Rank	Symbol	Revenue	Rev Growth	Net Margin	P/E	Growth	Value	Quality	Composite
1	NVDA	$130B	+114%	55.8%	35.8x	98.2	42.1	89.5	84.7
2	META	$162B	+22%	35.6%	25.4x	72.1	68.3	72.0	71.2

Then Top 3 picks:

1. [TICKER] — [One-line thesis]  (Composite: XX.X)
   [Why it ranks highest — which scores drive the result]
   [Key risk or caveat]

Filtered out (if any):

Excluded: [TICKER] (rev growth 5.2% < 10% threshold)

Step 5 — Deep dive (if user wants)

For top picks, validate with historical trend:

bash run.sh <SYMBOL> --style quality  # re-run with single symbol for detail

Check: is the metric improving over time or a one-time event?

Optionally, validate the pick against its sector peers using the comps-analysis skill for full statistical benchmarking.

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Output Format

Sections in order:

1. Screen summary box
2. Ranked table with scores
3. Top 3 picks with thesis
4. Filtered out / excluded (if any)
5. Caveats

Close with caveats:

• Scores are relative within this peer group — adding/removing a company changes all scores
• Screens surface candidates, not conclusions — each pick needs further validation
• SEC data is annual (10-K); recent quarterly shifts may not be reflected

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Formatting Rules

• Revenue: B or M, e.g. "$416B"
• Margins and growth: one decimal, e.g. "26.9%", "+15.7%"
• Multiples: one decimal, e.g. "28.4x"
• Scores: one decimal, e.g. "84.7"

Limitations

• Relative scoring: scores are only meaningful within the screened group, not absolute
• No real-time price data: P/E and P/S depend on Yahoo Finance availability
• US stocks only: SEC EDGAR covers US-listed equities
• Annual data: 10-K by default; quarterly shifts may not be reflected
• No dividend data: For income-style screening, dividend yield must come from web search