dvcrn Stock Valuation Report Generator v3.0
Generate professional valuation reports with ONE command. The pipeline auto-detects peers, runs all 8 data scripts in parallel, and produces a polished HTML report.
Quick Start (One Prompt)
User: "Generate a valuation report for AAPL"
Agent steps:
Step 1: Run Data Pipeline
uv run --with yfinance,matplotlib,lxml python3 $SKILL_DIR/scripts/run_pipeline.py TICKER
# Outputs: /tmp/TICKER_data.json
Options:
# Specify peers manually
uv run --with yfinance,matplotlib,lxml python3 $SKILL_DIR/scripts/run_pipeline.py AAPL --peers MSFT GOOG META
Step 2: Qualitative Research (MANDATORY)
Run these searches in parallel and collect the results into /tmp/TICKER_research.json:
2a. Seeking Alpha Research
web_search "{TICKER} seekingalpha analysis 2025 2026"
web_search "seekingalpha {TICKER} strong buy OR turning bullish OR high growth"
Extract 3-5 articles: title, date, rating (Strong Buy/Buy/Hold/Sell), one-sentence thesis.
2b. X/Twitter Sentiment
# If bird CLI available:
bird search '$TICKER' -n 15 --plain
# Otherwise:
web_search "{TICKER} stock twitter sentiment price target"
Extract 3-5 notable posts: username, bull/bear stance, key argument, any specific price target.
2c. Analyst Consensus
web_search "{TICKER} analyst price target consensus 2026"
Extract: consensus rating, average/low/high price targets.
2d. Earnings & Revenue
web_search "{TICKER} latest earnings call revenue composition segments"
web_fetch "BEST_EARNINGS_URL" --maxChars 6000
Extract: revenue by segment (amount, % of total, YoY growth), geographic breakdown, funded accounts/AUM/key KPIs, management guidance.
2e. Catalysts & Risks
Synthesize from all research above. Aim for 5-7 catalysts and 5-7 risks (with mitigants).
Save as Research JSON
Save to /tmp/TICKER_research.json:
{
"sa_articles": [
{"title": "Article Title", "date": "Dec 2025", "rating": "Strong Buy", "summary": "Key thesis in one sentence"}
],
"twitter_sentiment": [
{"user": "FinanceGuy", "stance": "Bullish", "summary": "Key argument or price target"}
],
"analyst_consensus": {"rating": "Strong Buy", "avg_pt": 200.0, "low_pt": 150.0, "high_pt": 250.0},
"catalysts": [
"Q4 earnings beat could trigger re-rating (reporting Mar 19)",
"Geographic expansion into new markets reducing concentration risk"
],
"risks": [
"Regulatory risk in key markets. Mitigant: diversified across 6+ jurisdictions",
"Trading volume cyclicality. Mitigant: interest income provides stable base"
],
"revenue_composition": [
{"stream": "Product Sales", "amount": "$50B", "pct": "52%", "trend": "Strong growth", "notes": "Core hardware"}
],
"geographic_data": [
{"market": "Americas", "new_accounts_pct": "45%", "avg_deposit": "$50K", "highlights": "Largest market"}
]
}
Step 3: Generate Report
uv run python3 $SKILL_DIR/scripts/generate_report.py /tmp/TICKER_data.json --research /tmp/TICKER_research.json
# Outputs: /tmp/TICKER_report.html
Step 4: Convert to PDF & Deliver
/Applications/Google\ Chrome.app/Contents/MacOS/Google\ Chrome \
--headless --disable-gpu --print-to-pdf=/tmp/TICKER_report.pdf \
--no-pdf-header-footer /tmp/TICKER_report.html
Deliver the PDF (and optionally HTML) to the user.
Script Reference
| Script | Deps | Output |
|---|---|---|
run_pipeline.py TICKER [--peers P1 P2] [--output] |
yfinance,matplotlib,lxml | Merged JSON → file |
generate_report.py DATA.json [--output] [--research] |
(none) | HTML report |
fetch_fundamentals.py TICKER [PEERS...] |
yfinance | Financials, ratios, peer data |
fetch_technicals.py TICKER |
yfinance | SMAs, RSI, MACD, 52W range |
fetch_historical_valuation.py TICKER |
yfinance,lxml | 5yr P/E history, percentile |
dcf_model.py TICKER [--wacc] [--growth-*] |
yfinance | 10yr DCF bear/base/bull |
fetch_insiders.py TICKER |
yfinance | Insider txns + institutional holders |
fetch_options.py TICKER |
yfinance | P/C ratio, IV, unusual vol |
fetch_earnings_calendar.py TICKER |
yfinance | Next earnings date |
generate_charts.py TICKER |
yfinance,matplotlib | 4 PNGs → /tmp/ |
detect_peers.py TICKER [--count N] |
yfinance | Auto-detected peers |
filter_tweets.py (stdin) |
(none) | Filtered tweet JSON |
Report Sections
The generated report includes all of these (in order):
- Header (company name, ticker, date, data sources)
- Earnings Badge (next earnings date)
- KPI Cards (price, MCap, P/E, margins — 2 rows of 4)
- Quarterly Trends (with QoQ growth arrows ↑↑/↑/→/↓)
- Revenue Composition (segment breakdown table + insight callout)
- Geographic Expansion (market-by-market table)
- Technical Analysis (6-panel: RSI, SMAs, MACD, 52W range)
- Charts (2×2 grid: price+SMA, revenue, margins, PE history)
- Historical Valuation (5Y PE avg, range, percentile + mean reversion callout)
- Peer Comparison (full table with highlight row + discount callout)
- Options Sentiment (4 KPI cards + interpretation)
- Insider Activity
- Seeking Alpha Research (bullish/cautious grouping + consensus callout)
- X/Twitter Sentiment (notable takes + consensus callout)
- Catalysts (5-7 items, most important first)
- Risks (5-7 items with mitigants)
- Valuation Framework (P/E multiples, mean reversion, DCF)
- Price Target Scenarios (bear/base/bull with math)
- Investment Thesis (specific, opinionated verdict)
- Disclaimer
- Footer
Rules
- NEVER include personal position data, portfolio info, or user-identifiable information
- All scripts work with
uv run --with <deps>(no pip install needed) - Charts are embedded as base64 in HTML for portability
- If any data script fails, its section shows gracefully degraded (no broken HTML)