Polymarket_data

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 Wang^1,2, Leiyu Chao^1,3, Yu Bao^1,4, Lian Cheng^1,3, Jianhan Liao^1,5, Yikang Li^1,†

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

^†Corresponding author

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

# Using pip
pip install pandas pyarrow

# Optional: for faster parquet reading
pip install fastparquet

Load Data with Pandas

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

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

# 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:

{
    '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:

{
    '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

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

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

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

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:

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)
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 - 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

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:

Report Issues: Found data quality issues? Open an issue
Suggest Features: Ideas for new data transformations? Let us know!
Contribute Code: Improve our collection pipeline via pull requests

License

MIT License - Free for commercial and research use.

See LICENSE file for details.

Contact & Support

Email: wangzhengjie@sii.edu.cn
Issues: GitHub Issues
Dataset: HuggingFace
Code: GitHub Repository

Citation

If you use this dataset in your research, please cite:

@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 • GitHub • Documentation