Learning Prediction Markets

From Concepts to Strategies

A comprehensive guide to understanding, building, and trading in prediction markets

Prediction markets sit at the intersection of finance, data science, and collective intelligence. They transform uncertain questions about the future into tradeable contracts, harnessing the wisdom of crowds to produce remarkably accurate forecasts. This book takes you from the foundational principles of probability and market microstructure through advanced trading strategies, platform engineering, and the emerging regulatory landscape — equipping you to participate in one of the most exciting developments in modern forecasting.

Across 42 chapters organized in 7 parts, you will learn how prediction markets work, why they aggregate information so effectively, and how to apply quantitative techniques to build models, construct portfolios, and design platforms of your own. Every concept is accompanied by working Python code, real-world case studies, and hands-on exercises so that you can move from theory to practice with confidence.

Whether you are a data scientist curious about forecasting, a trader exploring new asset classes, a researcher studying information aggregation, or a developer building the next generation of prediction platforms, this book provides the depth and breadth you need.

First Edition

2026

Learning Prediction Markets: From Concepts to Strategies First Edition

All code examples are written in Python 3.9+ and are available in the accompanying repository. Mathematical notation follows standard conventions used in probability theory, statistics, and financial economics.

This book is intended for educational purposes. Nothing contained herein constitutes financial advice, investment advice, or a solicitation to buy or sell any financial instrument. Prediction market participation may be subject to legal restrictions depending on your jurisdiction. Readers are responsible for understanding and complying with all applicable laws and regulations.

Page count: approximately 1,400 pages Chapters: 42 Parts: 7 Primary language: Python Mathematical intensity: Intermediate