[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-72302":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":23,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":34,"readmeContent":35,"aiSummary":36,"trendingCount":16,"starSnapshotCount":16,"syncStatus":15,"lastSyncTime":37,"discoverSource":38},72302,"prediction-market-analysis","Jon-Becker\u002Fprediction-market-analysis","Jon-Becker","A framework for collecting and analyzing prediction market data, including the largest publicly available dataset of Polymarket and Kalshi market and trade data.","https:\u002F\u002Fjbecker.dev\u002Fresearch\u002Fprediction-market-microstructure",null,"Python",3483,487,32,2,0,17,37,139,51,108.57,"MIT License",false,"main",true,[27,28,29,30,31,32,33],"data","data-science","datasets","kalshi","polymarket","prediction-markets","statistics","2026-06-12 04:01:04","# Prediction Market Analysis\n\nA 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.\n\n## Overview\n\nThis project enables research and analysis of prediction markets by providing:\n- Pre-collected datasets from Polymarket and Kalshi\n- Data collection indexers for gathering new data\n- Analysis framework for generating figures and statistics\n\nCurrently supported features:\n- Market metadata collection (Kalshi & Polymarket)\n- Trade history collection via API and blockchain\n- Parquet-based storage with automatic progress saving\n- Extensible analysis script framework\n\n## Installation & Usage\n\nRequires Python 3.9+. Install dependencies with [uv](https:\u002F\u002Fgithub.com\u002Fastral-sh\u002Fuv):\n\n```bash\nuv sync\n```\n\nDownload and extract the pre-collected dataset (36GiB compressed):\n\n```bash\nmake setup\n```\n\nThis downloads `data.tar.zst` from [Cloudflare R2 Storage](https:\u002F\u002Fs3.jbecker.dev\u002Fdata.tar.zst) and extracts it to `data\u002F`.\n\n### Data Collection\n\nCollect market and trade data from prediction market APIs:\n\n```bash\nmake index\n```\n\nThis opens an interactive menu to select which indexer to run. Data is saved to `data\u002Fkalshi\u002F` and `data\u002Fpolymarket\u002F` directories. Progress is saved automatically, so you can interrupt and resume collection.\n\n### Running Analyses\n\n```bash\nmake analyze\n```\n\nThis 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\u002F`.\n\n### Packaging Data\n\nTo compress the data directory for storage\u002Fdistribution:\n\n```bash\nmake package\n```\n\nThis creates a zstd-compressed tar archive (`data.tar.zst`) and removes the `data\u002F` directory.\n\n## Project Structure\n\n```\n├── src\u002F\n│   ├── analysis\u002F           # Analysis scripts\n│   │   ├── kalshi\u002F         # Kalshi-specific analyses\n│   │   └── polymarket\u002F     # Polymarket-specific analyses\n│   ├── indexers\u002F           # Data collection indexers\n│   │   ├── kalshi\u002F         # Kalshi API client and indexers\n│   │   └── polymarket\u002F     # Polymarket API\u002Fblockchain indexers\n│   └── common\u002F             # Shared utilities and interfaces\n├── data\u002F                   # Data directory (extracted from data.tar.zst)\n│   ├── kalshi\u002F\n│   │   ├── markets\u002F\n│   │   └── trades\u002F\n│   └── polymarket\u002F\n│       ├── blocks\u002F\n│       ├── markets\u002F\n│       └── trades\u002F\n├── docs\u002F                   # Documentation\n└── output\u002F                 # Analysis outputs (figures, CSVs)\n```\n\n## Documentation\n\n- [Data Schemas](docs\u002FSCHEMAS.md) - Parquet file schemas for markets and trades\n- [Writing Analyses](docs\u002FANALYSIS.md) - Guide for writing custom analysis scripts\n\n## Contributing\n\nIf 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.\n\nFor more information, see the [contributing guide](CONTRIBUTING.md).\n\n## Issues\n\nIf you've found an issue or have a question, please open an issue [here](https:\u002F\u002Fgithub.com\u002Fjon-becker\u002Fprediction-market-analysis\u002Fissues).\n\n## Research & Citations\n\n- Becker, J. (2026). _The Microstructure of Wealth Transfer in Prediction Markets_. Jbecker. https:\u002F\u002Fjbecker.dev\u002Fresearch\u002Fprediction-market-microstructure\n- Le, N. A. (2026). _Decomposing Crowd Wisdom: Domain-Specific Calibration Dynamics in Prediction Markets_. arXiv. https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.19520\n- Akey P., Gregoire, V., Harvie, N., Martineau, C. (2026). _Who Wins and Who Loses In Prediction Markets? Evidence from Polymarket_. SSRN. https:\u002F\u002Fpapers.ssrn.com\u002Fsol3\u002Fpapers.cfm?abstract_id=6443103\n- Vedova, J. (2026). _Who Profits from Prediction Markets? Execution, not Information_. SSRN. https:\u002F\u002Fpapers.ssrn.com\u002Fsol3\u002Fpapers.cfm?abstract_id=6191618\n\nIf you have used or plan to use this dataset in your research, please reach out via [email](mailto:jonathan@jbecker.dev) or [Twitter](https:\u002F\u002Fx.com\u002FBeckerrJon) -- 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.\n","该项目是一个用于收集和分析预测市场数据的框架，包括Polymarket和Kalshi市场上最大的公开可用数据集。核心功能包括从这两个平台自动收集市场元数据及交易历史，并通过API和区块链技术实现数据更新；采用Parquet格式存储数据，支持自动保存进度以确保数据收集过程中的稳定性；提供可扩展的分析脚本框架，便于生成图表和统计报告。适用于金融研究者、数据科学家以及对预测市场感兴趣的专业人士进行深入的数据挖掘与趋势分析。","2026-06-11 03:41:14","high_star"]