Duplicate from SII-WANGZJ/Polymarket_data

Co-authored-by: SII-WANGZJ <SII-WANGZJ@users.noreply.huggingface.co>

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+*.pcm filter=lfs diff=lfs merge=lfs -text
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README.md

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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 1.9 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.
+</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>

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