Demo: Finding Edge in Bitcoin Prediction Markets (Part 1)

This is the first in a series of demos exploring how I built an automated system to identify mispriced positions in cryptocurrency prediction markets.

The problem: Prediction markets are theoretically efficient, but in practice, pricing on lower-probability outcomes often lags behind real market movements — especially during volatile periods.

What I built:

• Automated data pipeline pulling real-time pricing from Polymarket and Bitcoin spot/futures data
• Analysis framework using Claude to surface positions where market odds appear to undervalue actual probability
• Performance tracking to measure and refine the strategy over time

Results so far: Achieved a 39% win rate on "longshot" positions (implied probability <15%), significantly outperforming the break-even threshold.

Skills demonstrated: Quantitative analysis, Python automation, API integration, working with LLMs for data analysis, systematic hypothesis testing.

Future parts will go deeper into the data pipeline, the analysis logic, and what I've learned about where prediction markets break down.esting.