Sample notebooks + pre-rendered demo artifacts — zero-friction portfolio preview

[DogInfantry]

Summary

Add two Jupyter notebooks and a pre-rendered sample HTML note committed to the repo, so that any reader can evaluate the output quality of the engine in under 60 seconds without installing anything.

Motivation

Finance recruiters and hiring managers are not CLI users. Pre-rendered output artifacts mean someone can assess the repo's quality from GitHub without cloning or running anything. This is the single highest-leverage packaging change for portfolio impact, and it costs zero engineering effort relative to actual feature work.

Deliverables

Notebooks

notebooks/01_full_note_walkthrough.ipynb

• Runs the full pipeline for AAPL as-of a fixed date
• Uses fixture/cached data so no API keys needed
• Shows each pipeline stage with inline commentary
• Exports the HTML note at the end

notebooks/02_dcf_and_peer_analysis.ipynb

• Standalone DCF (and Monte Carlo once Monte Carlo DCF + EV bridge — scenario engine with sector-specific assumption packs #23 is merged) walkthrough
• Peer CCA table and percentile band chart
• Designed as a teaching artifact: shows methodology, not just output

Demo Artifacts

outputs/demo/
├── AAPL_research_note_demo.html     # Full pre-rendered 12-section note
├── AAPL_exec_summary_demo.json      # Exec summary artifact
└── AAPL_screening_report_demo.html  # Screening output

Acceptance Criteria

• notebooks/01_full_note_walkthrough.ipynb runs end-to-end using cached fixture data (no live API keys)
• notebooks/02_dcf_and_peer_analysis.ipynb produces at least 3 charts inline
• Pre-rendered AAPL_research_note_demo.html committed to outputs/demo/
• README links directly to the demo HTML (via GitHub Pages or raw HTML link)
• Notebooks include a one-line setup cell: pip install -r requirements.txt
• Demo artifacts regenerated and recommitted on each major release