Prediction Market Analysis provides a comprehensive framework for researchers to investigate prediction markets. It offers pre-collected datasets from Polymarket and Kalshi, alongside tools to collect new data and perform statistical analysis. This project addresses the challenge of accessing and analyzing large-scale prediction market data, enabling deeper understanding of market dynamics.
This project distinguishes itself by offering the largest publicly available dataset of Polymarket and Kalshi data and a flexible framework for custom analysis. It features automatic data saving, an extensible analysis script system, and clear documentation on data schemas and usage. The modular design allows for easy addition of new data sources and analysis techniques.
- Data Collection: Supports pre-collected datasets from Kalshi and Polymarket, and provides tools to collect new data via APIs and blockchain.
- Analysis Framework: Offers an extensible framework for running statistical analyses and generating visualizations.
- Data Storage: Utilizes Parquet format with automatic progress saving for efficient storage and resumption of data collection.
- Extensibility: Allows users to create and run custom analysis scripts tailored to their specific research questions.
- Command-line Interface: Provides a user-friendly command-line interface for data collection, analysis, and packaging.
- Documentation: Includes detailed documentation on data schemas and writing custom analysis scripts.
- Modularity: Features a modular structure with separate directories for data collection, analysis, and shared utilities.
The project is in an active development phase with a well-defined structure, functional data collection and analysis tools, and supporting documentation. Recent commits and issue responses indicate ongoing maintenance and improvement.
Researchers, data scientists, and analysts benefit from Prediction Market Analysis by providing easy access to large prediction market datasets and tools for in-depth statistical analysis. It facilitates understanding market dynamics, evaluating prediction accuracy, and exploring wealth transfer mechanisms. Compared to manual data collection and analysis, this framework offers significant efficiency and scalability.