Reactive Publishing Python for Prediction Markets, Political Risk, and Event Contracts offers a practical, code-focused guide to applying Python in the rapidly evolving world of prediction markets, event contracts, and political risk analysis.
This book bridges programming and real-world forecasting by showing how to collect, analyze, and model data related to election outcomes, macroeconomic events, sports results, and geopolitical developments. Through clear explanations and working examples, you'll learn how to build scrapers, work with APIs from major prediction platforms, clean and structure event data, create probabilistic models, and evaluate forecast accuracy.
What's Inside:
- Data acquisition techniques for prediction markets and event contracts
- Working with election polling data, betting odds, and geopolitical indicators
- Building statistical and machine learning models for event probability forecasting
- Risk assessment frameworks using Python for political and macroeconomic uncertainty
- Practical case studies across elections, sports, and global events
- Code examples using popular libraries such as pandas, NumPy, scikit-learn, and requests
Whether you're a data analyst, quantitative researcher, developer interested in forecasting, or professional navigating political and economic risk, this book provides the technical foundation to turn raw event data into structured, actionable insights.
Note: This is a technical programming book focused on methods and implementation. No prior experience with prediction markets is required, but basic Python knowledge is recommended.
