Prediction Market Analysis
A framework for analyzing prediction market data, including the largest publicly available dataset of Polymarket and Kalshi market and trade data. Provides tools for data collection, storage, and running analysis scripts that generate figures and statistics.
Overview
This project enables research and analysis of prediction markets by providing:
- Pre-collected datasets from Polymarket and Kalshi
- Data collection indexers for gathering new data
- Analysis framework for generating figures and statistics
Currently supported features:
- Market metadata collection (Kalshi & Polymarket)
- Trade history collection via API and blockchain
- Parquet-based storage with automatic progress saving
- Extensible analysis script framework
Installation & Usage
Requires Python 3.9+. Install dependencies with uv:
uv sync
Download and extract the pre-collected dataset (36GiB compressed):
make setup
This downloads data.tar.zst from Cloudflare R2 Storage and extracts it to data/.
Data Collection
Collect market and trade data from prediction market APIs:
make index
This opens an interactive menu to select which indexer to run. Data is saved to data/kalshi/ and data/polymarket/ directories. Progress is saved automatically, so you can interrupt and resume collection.
Running Analyses
make analyze
This opens an interactive menu to select which analysis to run. You can run all analyses or select a specific one. Output files (PNG, PDF, CSV, JSON) are saved to output/.
Packaging Data
To compress the data directory for storage/distribution:
make package
This creates a zstd-compressed tar archive (data.tar.zst) and removes the data/ directory.
Project Structure
├── src/
│ ├── analysis/ # Analysis scripts
│ │ ├── kalshi/ # Kalshi-specific analyses
│ │ └── polymarket/ # Polymarket-specific analyses
│ ├── indexers/ # Data collection indexers
│ │ ├── kalshi/ # Kalshi API client and indexers
│ │ └── polymarket/ # Polymarket API/blockchain indexers
│ └── common/ # Shared utilities and interfaces
├── data/ # Data directory (extracted from data.tar.zst)
│ ├── kalshi/
│ │ ├── markets/
│ │ └── trades/
│ └── polymarket/
│ ├── blocks/
│ ├── markets/
│ └── trades/
├── docs/ # Documentation
└── output/ # Analysis outputs (figures, CSVs)
Documentation
Contributing
If you'd like to contribute to this project, please open a pull-request with your changes, as well as detailed information on what is changed, added, or improved.
For more information, see the contributing guide.
Issues
If you've found an issue or have a question, please open an issue here.
Research & Citations
- Becker, J. (2026). The Microstructure of Wealth Transfer in Prediction Markets. Jbecker. https://jbecker.dev/research/prediction-market-microstructure
- Becker, J. (2026). The Microstructure of Wealth Transfer in Prediction Markets. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7217640
- Le, N. A. (2026). Decomposing Crowd Wisdom: Domain-Specific Calibration Dynamics in Prediction Markets. arXiv. https://arxiv.org/abs/2602.19520
- Akey P., Gregoire, V., Harvie, N., Martineau, C. (2026). Who Wins and Who Loses In Prediction Markets? Evidence from Polymarket. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6443103
- Vedova, J. (2026). Who Profits from Prediction Markets? Execution, not Information. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6191618
- Brown, A. (2026). Cassandra Or the Boy Who Cried Wolf? Are Prediction Markets Effective Early Warning Systems?. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6381538
- Cao, D. (2026). Retail-Adjusted Expected Value in Prediction Markets: Calibration, Longshot Bias, and Consumer Welfare. SSRN. https://papers.ssrn.com/sol3/Delivery.cfm/7049119.pdf?abstractid=7049119&mirid=1
- Adamczewski, M. (2026). Integration Without Leadership: Cross-Venue Price Discovery in UFC Prediction Markets. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7194218
- Reichenbach, F., Walther, M. (2025). Exploring Decentralized Prediction Markets: Accuracy, Skill, and Bias on Polymarket. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5910522
- Bartlett, R., O'Hara, M. (2026). Adverse Selection in Prediction Markets: Evidence from Kalshi. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6615739
- Luong, K. L., Heesen, G. (2026). The Wisdom of the Few: Skilled Traders and Prediction Market Accuracy. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6758662
- Adegbenro, A. (2026). What Prediction Markets Can See: Market Formation, Settlement Legibility, and the Geography of Tradable Uncertainty in Africa and Latin America. arXiv. https://arxiv.org/abs/2606.17503
- Mauboussin, M. J., Callahan, D. (2026). The Wisdom of Crowds in Markets: Crowd Behavior in Prediction, Betting, and Stock Markets. Morgan Stanley. https://www.morganstanley.com/content/dam/im/assets/publication/thought-leadership/consilient-observer/article_thewisdomofcrowds_ltr.pdf?1786370649511
If you have used or plan to use this dataset in your research, please reach out via email or Twitter -- i'd love to hear about what you're using the data for! Additionally, feel free to open a PR and update this section with a link to your paper.
这是一个用于收集和分析 Polymarket 与 Kalshi 预测市场数据的框架，并附带了目前公开可得的规模最大的市场和交易历史数据集。