Polymarket Data

Bitquery provides Polymarket data dumps in Parquet format, designed for large-scale analytics, historical backfills, and data lake integrations. These datasets can be hosted directly in your own cloud storage (for example, AWS S3) and queried using engines like Snowflake, BigQuery, Athena, Spark, etc.

Polymarket runs on Polygon (Matic), so all Polymarket datasets live under the matic/ prefix.

Available Polymarket Topics

For Polymarket, Bitquery currently provides the following datasets:

• Prediction Trades – Outcome-token trades with market question, outcome label, price, and collateral amounts

• Prediction Settlements – Market resolution events such as payout redemptions

• DEX Trades – Polymarket trades in the standard EVM DEX trades schema

Sample Polymarket Cloud Dataset

You can explore schemas and validate your tooling using the public Polymarket sample datasets:

GitHub reference (schemas & examples)
https://github.com/bitquery/blockchain-cloud-data-dump-sample/tree/main/polymarket

Sample Parquet downloads (public S3)

• Prediction Trades – Download

• Prediction Settlements – Download

• DEX Trades – Download

Polymarket Dataset Directory Structure

bitquery-blockchain-dataset/
└── matic/
    ├── polymarket/
    │   ├── prediction_trades/
    │   │   ├── 84735000_84735049.parquet
    │   │   ├── 84735050_84735099.parquet
    │   │   └── ...
    │   └── PredictionSettlements/
    │       ├── 85230000_85230049.parquet
    │       ├── 85230050_85230099.parquet
    │       └── ...
    └── dex_trades/
        └── polymarket/
            ├── 83713800_83713849.parquet
            ├── 83713850_83713899.parquet
            └── ...

Block Range Naming Convention

Each Parquet file name follows this format:

<start_block>_<end_block>.parquet

Example:

84735000_84735049.parquet

Dataset Fields

Prediction Trades records an outcome-token trade together with the market it belongs to:

• Block_Number, Block_Time, Transaction_Hash, Transaction_From

• Trade_OutcomeTrade_* – buyer, seller, order id, amount, collateral amount, price, IsOutcomeBuy, plus USD equivalents

• Trade_Prediction_Question_* – market question title, id, market id, resolution source, image, creation time

• Trade_Prediction_Outcome_* – outcome id, index, and label (for example Down)

• Trade_Prediction_OutcomeToken_* / Trade_Prediction_CollateralToken_* – ERC-1155 outcome token and ERC-20 collateral token (for example USDC) details

• Trade_Prediction_Marketplace_* – protocol name, family (Gnosis_CTF), version, and contract

Prediction Settlements records how a market resolves for a holder:

• Settlement_EventType (for example Redemption), Settlement_Holder, Settlement_OutcomeTokenIds

• Settlement_Amounts_* – amount and collateral amount, with USD equivalents

• Settlement_Prediction_* – same question, outcome, token, and marketplace structure as trades

DEX Trades uses the standard EVM DEX trades schema documented on the EVM Data page.

Get Full Access

The full dataset is delivered into your own cloud storage (S3, GCS) or warehouse share (Snowflake, BigQuery). To buy or trial it, fill the API form or contact sales@bitquery.io.

Reading Files with DuckDB

No key, no client library — point DuckDB at the public sample directly:

SELECT *
FROM read_parquet('https://bitquery-blockchain-dataset.s3.us-east-1.amazonaws.com/matic/polymarket/prediction_trades/84735000_84735049.parquet')
LIMIT 10;

Reading Files in Python

import pandas as pd

df = pd.read_parquet("https://bitquery-blockchain-dataset.s3.us-east-1.amazonaws.com/matic/polymarket/prediction_trades/84735000_84735049.parquet")
print(df.info())
df.head()

Real-Time vs Batch Data Access

Cloud data dumps are optimized for batch analytics and historical workloads.

If you require low-latency or streaming Polymarket data, Bitquery also provides:

• Kafka streams

• GraphQL subscriptions