Polymarket indexer

Every on-chain Polymarket fill since the exchange opened, joined to the market and event metadata that makes it readable. Already collected and structured in ClickHouse - you write SQL.

$300 / mo flat.

What's indexed

History: fills from 21 November 2022 to now, continuously refreshed. Full column lists are in the Table reference.

How the pieces fit

asset is the outcome token - one market has one row per outcome, each with its own clob_token_id. That join turns a raw fill into "someone bought YES on this question at this probability".

What this makes easy

• Executed price series for any outcome, at any resolution, straight from fills.
• Calibration studies - bucket outcomes by traded probability and check how often they actually resolved true.
• Trader analysis - net position and cost basis per wallet per outcome, maker versus taker mix, fees paid.
• Event rollups - volume and unique wallets across every market belonging to one event.
• Resolution edge - what people paid versus how the market settled.
• Polygon transaction costs - gas, base fee and priority fee travel with every fill.

Order-book archive

Fills tell you what traded. For what the book looked like, we keep a separate CLOB capture archive - full snapshots, price-level changes, and explicit best-bid / best-ask transitions per outcome token.

1. 1
   Anchor on a snapshot

   Find the book event for the outcome token you want. It gives you both sides of the book at that instant.

2. 2
   Apply the changes

   Walk the price_change records forward. Each sets a price level to the given size, or removes the level when the size is zero.

3. 3
   Read the book

   At any point in the replay you have best bid, best ask, and level counts on both sides.

Interfaces

• ClickHouse SQL - connect with DBeaver, DataGrip, the Python client, or anything speaking the HTTP or native protocol.
• Custom REST endpoints - a recurring query deployed as a stable URL, quoted per scope.

Connection

The database is named polymarket.

Python

import os
import clickhouse_connect

client = clickhouse_connect.get_client(
    host=os.environ['CH_HOST'],
    port=int(os.environ['CH_PORT']),
    username=os.environ['CH_USER'],
    password=os.environ['CH_PASSWORD'],
    database='polymarket',
    secure=True,
)

df = client.query_df("""
    SELECT block_timestamp, wallet, asset, side, is_maker,
           amount_token / 1e6 AS shares,
           amount_usdc  / 1e6 AS usdc,
           amount_usdc / nullIf(amount_token, 0) AS implied_probability
    FROM polymarket_order_filled_v3
    PREWHERE block_timestamp >= now() - INTERVAL 1 HOUR
    ORDER BY amount_usdc DESC
    LIMIT 100
""")
print(df.head())

DBeaver

1. 1
   New connection

   Create a New Connection and select the ClickHouse driver.

2. 2
   Host and port

   Enter the host and port we provisioned for you.

3. 3
   Database

   Set the database to polymarket.

4. 4
   Credentials

   Enter your username and password, then test the connection.

Access

Provisioning is manual, over Telegram: @supanode_tgs.

Next steps