Jupyter-Style Analysis

Data Notebooks for prediction markets

Work in Python with live odds already connected. Query markets, test ideas, and backtest strategies without building your own data pipeline first.

Included in every plan, with live and historical data ready on day one.

Start Free Trial View Docs

Python & SQL support with live prediction market data

Live odds data from Kalshi, Polymarket, and more

Built-in historical datasets with settlement archives

Market-specific charting and visualization

Strategy backtesting utilities

Export to CSV, JSON, or Parquet

Capabilities

What you can do

Everything you need to analyze prediction markets in Python — without building infrastructure.

Query live prediction market odds with one line of Python

Backtest strategies against historical settlement data

Build custom probability models and visualize results

Compare implied probabilities across platforms side-by-side

Share notebooks with your team or publish publicly

Schedule notebooks to run on a cadence and alert on results

Use Cases

Built for prediction market analysis

Probability Modeling

Build and iterate on probability models using live prediction market data. Compare your model's estimates to market-implied probabilities across Kalshi, Polymarket, and sportsbooks.

Backtesting

Test trading strategies against historical settlement outcomes. Access tick-level odds history from every major prediction market platform since launch.

Cross-Platform Analysis

Compare implied probabilities across Kalshi, Polymarket, and sportsbooks in a single notebook. Spot cross-platform edges and price discrepancies.

Ready to backtest your first strategy?

14-day free trial. No credit card required.

Start Free Trial

Also from EVSignals

+EV Scanner → Market Data API → ParlayForYou → Prediction Insiders →

Start analyzing prediction markets

Get your first notebook running in minutes. Free 14-day trial on all plans.

Start Free Trial View Pricing

No credit card required · Documentation