Market Atlas

App Screenshot
App Screenshot
App Screenshot
App Screenshot

Inspiration

Markets are flooded with confident narratives — headlines that sound convincing, trend strongly on social media, and often move sentiment indicators. But as investors, we kept asking a simpler question:

When does the story stop matching reality?

Most sentiment tools measure how positive or negative the news is. Very few measure whether that sentiment is actually reliable when compared to real price movement. We were inspired to build a tool that doesn’t just summarize narratives, but tests them against market outcomes.

What it does

Market Atlas detects moments of false conviction — periods where headlines and sentiment are confident, but price action disagrees.

For each stock, the app:

Aggregates daily news sentiment
Compares it to actual price returns
Computes a Narrative Reliability Score over 7 / 14 / 30 days
Surfaces misalignment days, where bullish narratives coincided with falling prices (or vice versa)
Allows users to drill down into the actual headlines behind those failures

The result is not just a score, but an auditable story: Score → Failure → Evidence

How we built it

Backend

Python + FastAPI for the API layer
Supabase (Postgres) for structured storage
Background worker pipeline for ingestion and computation
Daily jobs to:
- Ingest news articles
- Score sentiment using an ML model (with chunking to handle token limits)
- Aggregate daily sentiment
- Ingest historical and daily prices
- Compute rolling alignment metrics

Frontend

Next.js (App Router)
TypeScript
Material UI for fast, consistent layout
A narrative-driven UI that prioritizes insight over charts

Key Metric

Narrative alignment is computed using:

Directional agreement between sentiment and returns
Strength-weighted misalignment detection
Rolling windows instead of single-day noise

All external data fetching happens outside the UI. The frontend only reads from the database, ensuring consistency and speed.

Challenges we ran into

Data coverage limits: Free news APIs only provide limited historical depth, forcing us to explicitly track and surface sentiment coverage.
Token limits in ML models: Articles often exceeded model limits, requiring chunking and aggregation without biasing results.
Avoiding noisy metrics: Simple correlation alone was misleading; we had to combine direction, magnitude, and confidence.
UI clarity: The hardest part was deciding what not to show and framing the data as a clear narrative.

Accomplishments that we're proud of

Turning abstract sentiment analysis into a testable, falsifiable signal
Making misalignment auditable through real headlines
Building a full ingestion → scoring → aggregation → visualization pipeline during a hackathon
Designing a UI that tells a story instead of overwhelming users with charts
Explicitly communicating uncertainty and data coverage

What we learned

Sentiment alone is cheap; sentiment reliability is hard
Alignment matters more than raw positivity
Clear narratives beat complex metrics
Precomputing metrics is far more scalable than calculating them on every page load
Strong framing can make existing data feel entirely new