Polymarket Data

Complete Data Infrastructure for Polymarket — Fetch, Process, Analyze

A comprehensive dataset of 1.9 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 **163GB of historical on-chain trading data** from Polymarket, containing **1.9 billion records** across 538K+ 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 structured datasets at different abstraction levels — raw blockchain events, processed trades with market linkage, market metadata, and derived quantitative views — serving diverse research needs. - **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 | |------|------|---------|-------------| | `trades.parquet` | 28GB | 418.3M | **Recommended.** Processed trades with market metadata linkage | | `orderfilled.parquet` | 84GB | 689.0M | Raw blockchain events from OrderFilled logs | | `markets.parquet` | 85MB | 538,587 | Market information and metadata | | `quant.parquet` | 28GB | 418.2M | Derived: unified YES perspective (for quant research) | | `users.parquet` | 23GB | 340.6M | Derived: user-level split by maker/taker (for quant research) | **Total**: 163GB, 1.9 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 trades (recommended for most users) df = pd.read_parquet('trades.parquet') print(f"Total trades: {len(df):,}") # 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 trades dataset = load_dataset( "SII-WANGZJ/Polymarket_data", data_files="trades.parquet" ) # Load multiple files dataset = load_dataset( "SII-WANGZJ/Polymarket_data", data_files=["trades.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 ``` ## File Selection Guide > **We recommend `trades.parquet` as the primary dataset for most use cases.** It preserves all original trade semantics with market metadata linked, requiring no assumptions about token normalization. `quant.parquet` and `users.parquet` are derived datasets designed for our internal quantitative research. They apply specific transformations — normalizing all trades to the YES (token1) perspective — which may not be suitable for every analysis scenario. Detailed transformation logic is documented below. ## Data Structure ### trades.parquet - Processed Trades (Recommended) Complete trade records with market metadata linkage. Preserves all original blockchain semantics — no normalization or filtering applied. **Best for:** General-purpose analysis, custom research, building your own pipelines. **Schema:** | Column | Type | Description | |--------|------|-------------| | `timestamp` | uint64 | Unix timestamp (seconds) | | `block_number` | uint64 | Polygon block number | | `transaction_hash` | string | Blockchain transaction hash | | `log_index` | uint32 | Log index within the transaction | | `contract` | string | Exchange contract address | | `market_id` | string | Polymarket market identifier | | `condition_id` | string | CTF condition ID | | `event_id` | string | Event group identifier | | `maker` | string | Maker wallet address | | `taker` | string | Taker wallet address | | `price` | float64 | Trade price (0–1) | | `usd_amount` | float64 | USD (USDC) value of the trade | | `token_amount` | float64 | Number of outcome tokens traded | | `maker_direction` | string | Maker's direction: `BUY` or `SELL` | | `taker_direction` | string | Taker's direction: `BUY` or `SELL` | | `nonusdc_side` | string | Which outcome token was traded: `token1` (YES) or `token2` (NO) | | `asset_id` | string | The non-USDC token's asset ID | ### orderfilled.parquet - Raw Blockchain Events Unprocessed `OrderFilled` events directly from Polygon blockchain logs. No decoding, no market linkage — pure on-chain data. **Best for:** Blockchain research, data verification, building custom processing pipelines from scratch. **Schema:** | Column | Type | Description | |--------|------|-------------| | `timestamp` | uint64 | Unix timestamp (seconds) | | `block_number` | uint64 | Polygon block number | | `transaction_hash` | string | Blockchain transaction hash | | `log_index` | uint32 | Log index within the transaction | | `contract` | string | Exchange contract address | | `order_hash` | string | Unique order hash | | `maker` | string | Maker wallet address | | `taker` | string | Taker wallet address | | `maker_asset_id` | string | Asset ID of maker's token | | `taker_asset_id` | string | Asset ID of taker's token | | `maker_amount_filled` | string | Amount filled for maker (wei, uint256 as string) | | `taker_amount_filled` | string | Amount filled for taker (wei, uint256 as string) | | `maker_fee` | string | Maker fee (wei, uint256 as string) | | `taker_fee` | string | Taker fee (wei, uint256 as string) | | `protocol_fee` | string | Protocol fee (wei, uint256 as string) | > Note: Amount and fee fields are stored as strings because they are uint256 values from the blockchain that exceed standard integer range. ### markets.parquet - Market Metadata Market information, outcome token details, and event grouping. **Best for:** Linking trades to market context, filtering by market attributes, understanding market outcomes. **Schema:** | Column | Type | Description | |--------|------|-------------| | `id` | string | Market identifier (join key with `market_id` in other tables) | | `question` | string | Market question text | | `slug` | string | URL slug | | `condition_id` | string | CTF condition ID | | `token1` | string | Asset ID of outcome token 1 (YES) | | `token2` | string | Asset ID of outcome token 2 (NO) | | `answer1` | string | Label for token1 outcome (e.g., "Yes") | | `answer2` | string | Label for token2 outcome (e.g., "No") | | `closed` | uint8 | 0 = active, 1 = settled | | `active` | uint8 | Whether the market is currently active | | `archived` | uint8 | Whether the market is archived | | `outcome_prices` | string | JSON array of final prices, e.g. `["0.99", "0.01"]` means answer1 won | | `volume` | float64 | Total traded volume (USD) | | `event_id` | string | Parent event identifier | | `event_slug` | string | Parent event URL slug | | `event_title` | string | Parent event title | | `created_at` | datetime | Market creation time | | `end_date` | datetime | Market end / resolution time | | `updated_at` | datetime | Last metadata update time | ### quant.parquet - Unified YES Perspective (For Quantitative Research) > **Note:** This is a derived dataset built for our own quantitative research. It normalizes all trades to the YES (token1) perspective: for trades originally on token2 (NO), the price is converted to `1 - price`, and the buy/sell direction is flipped. Contract-address trades are filtered out, keeping only real user trades. **If you need the original trade semantics, use `trades.parquet` instead.** **Schema:** | Column | Type | Description | |--------|------|-------------| | `timestamp` | uint64 | Unix timestamp (seconds) | | `block_number` | uint64 | Polygon block number | | `transaction_hash` | string | Blockchain transaction hash | | `log_index` | uint32 | Log index within the transaction | | `market_id` | string | Market identifier | | `condition_id` | string | CTF condition ID | | `event_id` | string | Event group identifier | | `price` | float64 | YES token price (0–1). For original token2 trades: `1 - original_price` | | `usd_amount` | float64 | USD value | | `token_amount` | float64 | Token amount | | `side` | string | `BUY` or `SELL` (from YES token perspective). For original token2 trades: direction is flipped | | `maker` | string | Maker wallet address | | `taker` | string | Taker wallet address | ### users.parquet - User-Level Behavior Data (For Quantitative Research) > **Note:** This is a derived dataset built for our own research. Each trade is split into two records (one for maker, one for taker), with the same token1 normalization as `quant.parquet`. All records are converted to a unified BUY direction — negative `token_amount` indicates selling. **If you need the original trade semantics, use `trades.parquet` instead.** **Schema:** | Column | Type | Description | |--------|------|-------------| | `timestamp` | uint64 | Unix timestamp (seconds) | | `block_number` | uint64 | Polygon block number | | `transaction_hash` | string | Blockchain transaction hash | | `log_index` | uint32 | Log index within the transaction | | `market_id` | string | Market identifier | | `condition_id` | string | CTF condition ID | | `event_id` | string | Event group identifier | | `user` | string | User wallet address | | `role` | string | `maker` or `taker` | | `price` | float64 | YES token price (normalized, same as quant) | | `usd_amount` | float64 | USD value | | `token_amount` | float64 | Signed amount: positive = buy, negative = sell | ## Example Analysis ### 1. Calculate Market Statistics ```python import pandas as pd df = pd.read_parquet('trades.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('trades.parquet') df['datetime'] = pd.to_datetime(df['timestamp'], unit='s') # Select a specific market market_id = 'your-market-id' market_data = df[df['market_id'] == market_id].sort_values('timestamp') # 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. Market Volume Analysis ```python import pandas as pd df = pd.read_parquet('trades.parquet') markets = pd.read_parquet('markets.parquet') # Join with market metadata (markets uses 'id', trades uses 'market_id') df = df.merge(markets[['id', 'question']], left_on='market_id', right_on='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) ``` ### 4. Analyze by Token Side ```python import pandas as pd df = pd.read_parquet('trades.parquet') # Compare YES vs NO token trading activity side_stats = df.groupby('nonusdc_side').agg({ 'usd_amount': ['sum', 'mean'], 'transaction_hash': 'count' }) print(side_stats) # Filter for only YES token trades on a specific market market_id = 'your-market-id' yes_trades = df[(df['market_id'] == market_id) & (df['nonusdc_side'] == 'token1')] print(f"YES trades: {len(yes_trades):,}") ``` ## 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: 418.2M records (from 418.3M trades) 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 ## 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**: 2026-03-05 **Coverage**: - Time Range: Polymarket inception to 2026-03-04 - Total Markets: 538,587 - Total Trades: 418.3 million (processed), 689.0 million (raw OrderFilled) - Unique Users: [To be calculated] **Data Freshness**: Updated periodically 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)