SII-WANGZJ/Polymarket_data

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 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

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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 CLOB Trading History: Every OrderFilled event from Polymarket's two CLOB exchange contracts, with no missing blocks or gaps, from the CLOB launch (first record 2022-11-21 UTC) onward. Note: this covers the on-chain CLOB era only — earlier FPMM/AMM-era trades (2020–Nov 2022), which predate these contracts, are not 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

# Using pip
pip install pandas pyarrow

# Optional: for faster parquet reading
pip install fastparquet

Load Data with Pandas

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

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

# 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

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

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

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

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 - 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
• Reproducible Pipeline: Fully open-source collection process; refreshes are run on an as-needed basis rather than a fixed schedule
• 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: 2026-03-05

Coverage:

• Time Range: CLOB launch (2022-11-21 UTC) to 2026-03-04 (CLOB OrderFilled events only; pre-CLOB FPMM/AMM-era trades not included)
• Total Markets: 538,587
• Total Trades: 418.3 million (processed), 689.0 million (raw OrderFilled)
• Unique Users: [To be calculated]

Data Freshness: Refreshed on an as-needed basis (no fixed schedule); the pipeline is open-source, so you can also update or extend the dataset yourself

Contributing

We welcome contributions to improve the dataset and tools:

1. Report Issues: Found data quality issues? Open an issue
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 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

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Built for the research and data science community

HuggingFace • GitHub • Documentation