Hyperliquid indexer

Complete perpetual trading data with nanosecond precision. Query every fill, funding payment, and TWAP execution from the full Hyperliquid history in a SQL-queryable ClickHouse warehouse.

What's indexed

Table	Description
raw_node_fills_by_block	Raw, per-fill records as read from the node, ordered by block
agg_fulfilled_order	Aggregated, per-order rollup of fills
view_perpetual_wallet	Per-wallet aggregate view across all positions
view_wallet_position	Latest position per wallet and market

Coverage: all perpetual markets, updated in real time. For exact row counts and the full column schema, see /hyperliquid/tables.

Key features

Nanosecond timestamps

Every fill includes the exact nanosecond it occurred — not just block time. Useful for latency analysis, cross-venue arbitrage timing, and order-flow pattern detection.

Complete position context

Each trade includes entry/exit price, position size before and after, realized PnL, liquidation flags, and TWAP correlation.

Smart wallet discovery

Find wallets with consistent profitability patterns.

SELECT
    user,
    count(*)                                        AS trades,
    sum(closedPnl) / 1e6                            AS realized_pnl,
    sum(closedPnl) / nullIf(sum(abs(closedPnl)), 0) AS win_rate
FROM fills
WHERE time > now() - INTERVAL 30 DAY
GROUP BY user
HAVING trades > 100
ORDER BY realized_pnl DESC
LIMIT 50

Sample queries

Recent large fills

SELECT
    time,
    user,
    coin,
    side,
    px,
    sz,
    closedPnl / 1e6 AS pnl_usd
FROM fills
WHERE time > now() - INTERVAL 1 HOUR
  AND sz * px > 100000  -- > $100k notional
ORDER BY sz * px DESC
LIMIT 20

Funding rate history

SELECT
    toStartOfHour(time) AS hour,
    coin,
    avg(fundingRate)    AS avg_funding,
    sum(fundingRate)    AS cumulative_funding
FROM fundings
WHERE time > now() - INTERVAL 7 DAY
  AND coin = 'BTC'
GROUP BY hour, coin
ORDER BY hour

TWAP execution analysis

SELECT
    user,
    coin,
    count(*)  AS twap_fills,
    sum(sz)   AS total_size,
    avg(px)   AS avg_price,
    min(time) AS started,
    max(time) AS ended
FROM twaps
WHERE time > now() - INTERVAL 24 HOUR
GROUP BY user, coin
ORDER BY total_size DESC
LIMIT 20

Connection details

The Indexer is exposed over ClickHouse. Connect with standard tooling and run your own SQL.

Python

import clickhouse_connect

client = clickhouse_connect.get_client(
    host='<your-host>',
    port=<your-port>,
    username='<your-username>',
    password='<your-password>',
    database='hyperliquid'
)

df = client.query_df("""
    SELECT * FROM fills
    WHERE time > now() - INTERVAL 1 HOUR
    LIMIT 1000
""")

DBeaver

1. 1
   New connection

   Create a New Connection and select the ClickHouse driver.

2. 2
   Host and port

   Enter your provisioned host (<your-host>) and port (<your-port>).

3. 3
   Database

   Set the database to hyperliquid.

4. 4
   Credentials

   Enter your provisioned username and password, then test the connection.

Next steps

• Browse the full schema: /hyperliquid/tables
• Explore query patterns: /hyperliquid/examples
• Provision access or start a trial: @supanode_tgs