Datasets:

SII-WANGZJ
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Polymarket_data

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-<div align="center">
-<h1>Polymarket Data</h1>
-<h3>Complete Data Infrastructure for Polymarket — Fetch, Process, Analyze</h3>
-<p style="max-width: 750px; margin: 0 auto;">
-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.
-</p>
-<p>
-<b>Zhengjie Wang</b><sup>1,2</sup>, <b>Leiyu Chao</b><sup>1,3</sup>, <b>Yu Bao</b><sup>1,4</sup>, <b>Lian Cheng</b><sup>1,3</sup>, <b>Jianhan Liao</b><sup>1,5</sup>, <b>Yikang Li</b><sup>1,†</sup>
-</p>
-<p>
-<sup>1</sup>Shanghai Innovation Institute &nbsp;&nbsp; <sup>2</sup>Westlake University &nbsp;&nbsp; <sup>3</sup>Shanghai Jiao Tong University
-<br>
-<sup>4</sup>Harbin Institute of Technology &nbsp;&nbsp; <sup>5</sup>Fudan University
-</p>
-<p>
-<sup>†</sup>Corresponding author
-</p>
-</div>
-<p align="center">
-  <a href="https://huggingface.co/datasets/SII-WANGZJ/Polymarket_data">
-    <img src="https://img.shields.io/badge/Hugging%20Face-Dataset-yellow.svg" alt="HuggingFace Dataset"/>
-  </a>
-  <a href="https://github.com/SII-WANGZJ/Polymarket_data">
-    <img src="https://img.shields.io/badge/GitHub-Code-black.svg?logo=github" alt="GitHub Repository"/>
-  </a>
-  <a href="https://github.com/SII-WANGZJ/Polymarket_data/blob/main/LICENSE">
-    <img src="https://img.shields.io/badge/License-MIT-blue.svg" alt="License"/>
-  </a>
-  <a href="#data-quality">
-    <img src="https://img.shields.io/badge/Data-Verified-green.svg" alt="Data Quality"/>
-  </a>
-</p>
----
-## 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
----
-<div align="center">
-**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)
-</div>