Stock Narrative Explorer

Interactive equity-narrative tool that generates confidence-tiered, day-by-day AI explanations for stock movements across a configurable watchlist, combining yfinance, Finnhub, SEC EDGAR, and Google News with daily drift detection.

Python FastAPI Anthropic API React/Vite Docker Railway

Problem

When a stock moves sharply, the explanations available in real time are usually low-quality: rumor-driven, single-source, or written hours after the move with the benefit of hindsight. I wanted a tool that could explain a day’s price action with the same standard I’d hold an internal incident review to: cite sources, separate signal from rumor, and be explicit about what it doesn’t know.

Approach

A daily pipeline pulls price data, news, regulatory filings, and macro context for a configurable watchlist (NVDA, MSFT and TSLA by default, plus any US ticker on demand). An AI layer generates a per-day narrative against a strict schema: the explanation must include a confidence tier, supporting sources, and an explicit “what’s missing” section. A drift-detection job re-evaluates older narratives when new information lands so they don’t go stale silently.

Architecture

Data layer: yfinance for prices, Finnhub for company news, SEC EDGAR for filings, Google News for surrounding press. All sources cached at the day level — re-runs are cheap.
AI layer: Anthropic API. Structured output enforced server-side; confidence tiers are not optional fields.
Backend: FastAPI, deployed via Docker on Railway. Daily cron triggers the pipeline; results land in a small SQLite store.
Frontend: React + Vite. Per-ticker timeline view, with the narrative, sources, and confidence tier visible together — no hidden citations.

Outcomes / What I learned

A good narrative requires forcing the model to admit ignorance — confidence tiers and “what’s missing” did more for quality than any prompt iteration.
The 30-day baseline + drift-detection job catches stale narratives more reliably than threshold alerts on raw price.
Caching at the day level kept Anthropic API costs low enough to run the full watchlist daily without budget anxiety.