Polymarket Data

Complete Data Infrastructure for Polymarket — Fetch, Process, Analyze

A comprehensive dataset of 1.1 billion trading records from Polymarket, processed into multiple analysis-ready formats. Features cleaned data, unified token perspectives, and user-level transformations — ready for market research, behavioral studies, and quantitative analysis.

Zhengjie Wang1,2, Leiyu Chao1,3, Yu Bao1,4, Lian Cheng1,3, Jianhan Liao1,5, Yikang Li1,†

1Shanghai Innovation Institute    2Westlake University    3Shanghai Jiao Tong University
4Harbin Institute of Technology    5Fudan University

†Corresponding author

HuggingFace Dataset GitHub Repository License Data Quality

--- ## TL;DR We provide **107GB of historical on-chain trading data** from Polymarket, containing **1.1 billion records** across 268K+ markets. The dataset is directly fetched from Polygon blockchain, fully verified, and ready for analysis. Perfect for market research, behavioral studies, data science projects, and academic research. ## Highlights - **Complete Blockchain History**: All OrderFilled events from Polymarket's two exchange contracts, with no missing blocks or gaps. Every single trade from the platform's inception is included. - **Multiple Analysis Perspectives**: 5 carefully curated datasets serving different research needs - from raw blockchain events to user-level behavior analysis, with unified data transformations for easy analysis. - **Production Ready**: Clean, validated data with proper schema documentation. All trades are verified against blockchain RPC, with market metadata linked and ready to use. - **Open Source Pipeline**: Fully reproducible data collection process. Our open-source tools allow you to verify, update, or extend the dataset independently. ## Dataset Overview | File | Size | Records | Description | |------|------|---------|-------------| | `orderfilled.parquet` | 31GB | 293.3M | Raw blockchain events from OrderFilled logs | | `trades.parquet` | 32GB | 293.3M | Processed trades with market metadata linkage | | `markets.parquet` | 68MB | 268,706 | Market information and metadata | | `quant.parquet` | 21GB | 170.3M | Clean market data with unified YES perspective | | `users.parquet` | 23GB | 340.6M | User behavior data split by maker/taker roles | **Total**: 107GB, 1.1 billion records ## Use Cases ### Market Research & Analysis - Study prediction market dynamics and price discovery mechanisms - Analyze market efficiency and information aggregation - Research crowd wisdom and forecasting accuracy ### Behavioral Studies - Track individual user trading patterns and decision-making - Study market participant behavior under different conditions - Analyze risk preferences and trading strategies ### Data Science & Machine Learning - Train models for price prediction and market forecasting - Feature engineering for time-series analysis - Develop algorithms for market analysis ### Academic Research - Economics and finance research on prediction markets - Social science studies on collective intelligence - Computer science research on blockchain data analysis ## Quick Start ### Installation ```bash # Using pip pip install pandas pyarrow # Optional: for faster parquet reading pip install fastparquet ``` ### Load Data with Pandas ```python import pandas as pd # Load clean market data df = pd.read_parquet('quant.parquet') print(f"Total trades: {len(df):,}") # Load user behavior data users = pd.read_parquet('users.parquet') print(f"Total user actions: {len(users):,}") # Load market metadata markets = pd.read_parquet('markets.parquet') print(f"Total markets: {len(markets):,}") ``` ### Load from HuggingFace Datasets ```python from datasets import load_dataset # Load specific file dataset = load_dataset( "SII-WANGZJ/Polymarket_data", data_files="quant.parquet" ) # Load multiple files dataset = load_dataset( "SII-WANGZJ/Polymarket_data", data_files=["quant.parquet", "markets.parquet"] ) ``` ### Download Specific Files ```bash # Download using HuggingFace CLI pip install huggingface_hub # Download a specific file hf download SII-WANGZJ/Polymarket_data quant.parquet --repo-type dataset # Download all files hf download SII-WANGZJ/Polymarket_data --repo-type dataset ``` ## Data Structure ### quant.parquet - Clean Market Data Filtered and normalized trade data with unified token perspective (YES token). **Key Features:** - Unified perspective: All trades normalized to YES token (token1) - Clean data: Contract trades filtered out, only real user trades - Complete information: Maker/taker roles preserved - Best for: Market analysis, price studies, time-series forecasting **Schema:** ```python { 'transaction_hash': str, # Blockchain transaction hash 'block_number': int, # Block number 'datetime': datetime, # Transaction timestamp 'market_id': str, # Market identifier 'maker': str, # Maker wallet address 'taker': str, # Taker wallet address 'token_amount': float, # Amount of tokens traded 'usd_amount': float, # USD value 'price': float, # Trade price (0-1) } ``` ### users.parquet - User Behavior Data Split maker/taker records with unified buy direction for user analysis. **Key Features:** - Split records: Each trade becomes 2 records (one maker, one taker) - Unified direction: All converted to BUY (negative amounts = selling) - User sorted: Ordered by user for trajectory analysis - Best for: User profiling, PnL calculation, wallet analysis **Schema:** ```python { 'transaction_hash': str, # Transaction hash 'block_number': int, # Block number 'datetime': datetime, # Timestamp 'market_id': str, # Market identifier 'user': str, # User wallet address 'role': str, # 'maker' or 'taker' 'token_amount': float, # Signed amount (+ buy, - sell) 'usd_amount': float, # USD value 'price': float, # Trade price } ``` ### markets.parquet - Market Metadata Market information and outcome token details. **Best for:** Linking trades to market context, filtering by market attributes ### trades.parquet - Processed Blockchain Data Raw OrderFilled events with market linkage but no transformations. **Best for:** Custom analysis requiring original blockchain data ### orderfilled.parquet - Raw Blockchain Events Unprocessed OrderFilled events directly from blockchain logs. **Best for:** Blockchain research, verification, custom processing pipelines ## Example Analysis ### 1. Calculate Market Statistics ```python import pandas as pd df = pd.read_parquet('quant.parquet') # Market-level statistics market_stats = df.groupby('market_id').agg({ 'usd_amount': ['sum', 'mean'], # Total volume and average trade size 'price': ['mean', 'std', 'min', 'max'], # Price statistics 'transaction_hash': 'count' # Number of trades }).round(4) print(market_stats.head()) ``` ### 2. Track Price Evolution ```python import pandas as pd import matplotlib.pyplot as plt df = pd.read_parquet('quant.parquet') df['datetime'] = pd.to_datetime(df['datetime']) # Select a specific market market_id = 'your-market-id' market_data = df[df['market_id'] == market_id].sort_values('datetime') # Plot price over time plt.figure(figsize=(12, 6)) plt.plot(market_data['datetime'], market_data['price']) plt.title(f'Price Evolution - Market {market_id}') plt.xlabel('Date') plt.ylabel('Price') plt.show() ``` ### 3. Analyze User Behavior ```python import pandas as pd df = pd.read_parquet('users.parquet') # Calculate net position per user per market user_positions = df.groupby(['user', 'market_id']).agg({ 'token_amount': 'sum', # Net position (positive = long, negative = short) 'usd_amount': 'sum', # Total USD traded 'transaction_hash': 'count' # Number of trades }).reset_index() # Find most active users active_users = user_positions.groupby('user').agg({ 'market_id': 'count', # Number of markets traded 'usd_amount': 'sum' # Total volume }).sort_values('usd_amount', ascending=False) print(active_users.head(10)) ``` ### 4. Market Volume Analysis ```python import pandas as pd df = pd.read_parquet('quant.parquet') markets = pd.read_parquet('markets.parquet') # Join with market metadata df = df.merge(markets[['market_id', 'question']], on='market_id', how='left') # Top markets by volume top_markets = df.groupby(['market_id', 'question']).agg({ 'usd_amount': 'sum' }).sort_values('usd_amount', ascending=False).head(20) print(top_markets) ``` ## Data Processing Pipeline ``` Polygon Blockchain (RPC) ↓ orderfilled.parquet (Raw events) ↓ trades.parquet (+ Market linkage) ↓ ├─→ quant.parquet (Trade-level, unified YES perspective) │ └─→ Filter contracts + Normalize tokens │ └─→ users.parquet (User-level, split maker/taker) └─→ Split records + Unified BUY direction ``` **Key Transformations:** 1. **quant.parquet**: - Filter out contract trades (keep only user trades) - Normalize all trades to YES token perspective - Preserve maker/taker information - Result: 170.3M records (from 293.3M) 2. **users.parquet**: - Split each trade into 2 records (maker + taker) - Convert all to BUY direction (signed amounts) - Sort by user for easy querying - Result: 340.6M records (from 293.3M × 2, some filtered) ## Documentation - **[DATA_DESCRIPTION.md](DATA_DESCRIPTION.md)** - Comprehensive documentation - Detailed schema for all 5 files - Data cleaning and transformation process - Usage examples and best practices - Comparison between different files ## Data Quality - **Complete History**: No missing blocks or gaps in blockchain data - **Verified Sources**: All OrderFilled events from 2 official exchange contracts - **Blockchain Verified**: Cross-checked against Polygon RPC nodes - **Regular Updates**: Automated daily pipeline for fresh data - **Open Source**: Fully reproducible collection process **Contracts Tracked:** - Exchange Contract 1: `0x4bFb41d5B3570DeFd03C39a9A4D8dE6Bd8B8982E` - Exchange Contract 2: `0xC5d563A36AE78145C45a50134d48A1215220f80a` ## Collection Tools Data collected using our open-source toolkit: [polymarket-data](https://github.com/SII-WANGZJ/Polymarket_data) **Features:** - Direct blockchain RPC integration - Efficient batch processing - Automatic retry and error handling - Data validation and verification ## Dataset Statistics **Last Updated**: 2026-01-01 **Coverage**: - Time Range: [Polymarket inception] to [Latest update] - Total Markets: 268,706 - Total Trades: 293.3 million - Total Volume: $[To be calculated] billion - Unique Users: [To be calculated] **Data Freshness**: Updated daily via automated pipeline ## Contributing We welcome contributions to improve the dataset and tools: 1. **Report Issues**: Found data quality issues? [Open an issue](https://github.com/SII-WANGZJ/Polymarket_data/issues) 2. **Suggest Features**: Ideas for new data transformations? Let us know! 3. **Contribute Code**: Improve our collection pipeline via pull requests ## License MIT License - Free for commercial and research use. See [LICENSE](LICENSE) file for details. ## Contact & Support - **Email**: [wangzhengjie@sii.edu.cn](mailto:wangzhengjie@sii.edu.cn) - **Issues**: [GitHub Issues](https://github.com/SII-WANGZJ/Polymarket_data/issues) - **Dataset**: [HuggingFace](https://huggingface.co/datasets/SII-WANGZJ/Polymarket_data) - **Code**: [GitHub Repository](https://github.com/SII-WANGZJ/Polymarket_data) ## Citation If you use this dataset in your research, please cite: ```bibtex @misc{polymarket_data_2026, title={Polymarket Data: Complete Data Infrastructure for Polymarket}, author={Wang, Zhengjie and Chao, Leiyu and Bao, Yu and Cheng, Lian and Liao, Jianhan and Li, Yikang}, year={2026}, howpublished={\url{https://huggingface.co/datasets/SII-WANGZJ/Polymarket_data}}, note={A comprehensive dataset and toolkit for Polymarket prediction markets} } ``` ## Acknowledgments - **Polymarket** for building the leading prediction market platform - **Polygon** for providing reliable blockchain infrastructure - **HuggingFace** for hosting and distributing large datasets - The open-source community for tools and libraries ---
**Built for the research and data science community** [HuggingFace](https://huggingface.co/datasets/SII-WANGZJ/Polymarket_data) • [GitHub](https://github.com/SII-WANGZJ/Polymarket_data) • [Documentation](DATA_DESCRIPTION.md)