// hyperliquid · indexer

Hyperliquid Historical Data Indexer

Perpetual trading history since July 2025 - 3.13B fills with nanosecond block timing, liquidation and TWAP context - in a SQL-queryable ClickHouse warehouse.

Hyperliquid · indexer● live
timestamp
ns
markets
all
01.WHAT'S INCLUDED// Hyperliquid · indexer
  • Direct ClickHouse SQL over history since 2025-07-27
  • Fills · liquidations · TWAP, nanosecond block timing
  • Builder codes, priority gas, maker/taker flag
  • Per-wallet: positions · PnL · win-rate
  • All perp markets, real-time refresh
  • Order-level book archive on request
// factsSPEC
Timestamp precision
nanosecond block time
History since
2025-07-27
Fills indexed
3.13B (225.58 GB)
Coverage
all perpetual markets, real-time refresh
Interfaces
ClickHouse SQL + custom REST
Order-book archive
order-level, on request
Database name
hyperliquid
Access
credentials provisioned via Telegram
02.HOW IT WORKS// query it like a database
query.sqlclickhouse · hyperliquid
1SELECT wallet, SUM(size) AS vol
2FROM hyperliquid.fills
3WHERE ts > now() - INTERVAL 1 HOUR
4GROUP BY wallet
5ORDER BY vol DESC LIMIT 20
→ 200 OK · 82ms · joins, CTEs, window fns
// HOW TO QUERY
Connect with any ClickHouse client — DBeaver, DataGrip, psql, or the Python client — against the hyperliquid database, then write SQL with joins, CTEs, and window functions.
// INTERFACES
Direct ClickHouse SQL, or a custom REST endpoint shaped to a recurring query (quoted per scope).
// SCHEMA
Tables, column types, and example queries live in the indexer docs.
03.FAQ// 5 questions
What does the Hyperliquid indexer contain?

Perpetual trading data since 27 July 2025: 3.13 billion fills across all perpetual markets, each with execution price, size, position before the fill, realized PnL, fee, builder code, TWAP membership and the full liquidation triple. Block timing is nanosecond-resolution for latency and order-flow analysis. Funding rates are not part of this dataset.

How do I query it?

Through direct ClickHouse SQL using clients like DBeaver, DataGrip, or psql against the `hyperliquid` database. Supanode can also build a custom REST endpoint around a specific query on request.

How is access provisioned?

Connection details — host, port, username, password — are provisioned per customer over Telegram. The indexer is a flat monthly subscription.

Can I analyze individual wallets and build leaderboards?

Yes — fills are queryable per wallet, so positions, realized PnL, and win-rate roll up with plain SQL. Trader leaderboards, copy-trading research, and smart-money screens are the most common workloads on this dataset.

Why do nanosecond timestamps matter?

They preserve the exact ordering of fills, which is what order-flow analysis, latency studies, and execution backtests depend on. At second or millisecond precision, simultaneous fills collapse together and the sequencing information is gone.

Get Hyperliquid Historical Data Indexer.

Set up over Telegram with an engineer — endpoint, token, tuning.