# Hyperliquid Historical Data Indexer Perpetual fills and wallet activity. Ready for your SQL. [\[ REQUEST ACCESS \]](https://stg.portal.supanode.xyz/)[Explore the dataset](https://supanode.xyz/services/hyperliquid/indexer#fit) ClickHouse SQLIllustrative query ``` SELECT wallet_address, count() AS fills FROM raw_node_fills_by_block PREWHERE utc_fill_dt = '2026-09-01' GROUP BY wallet_address ``` Perpetual fills → Typed tables → Your research ## Start with the trade. Investigate perpetual fills, wallets and execution context in ClickHouse without building your own fill warehouse. Query raw fill history from 27 July 2025. ### Trade flow and wallets Research trade activity, build wallet screens and compare realized PnL across a bounded historical window. ### Execution context Inspect liquidation and TWAP context, maker/taker activity and builder attribution alongside the fill. Raw fills and derived surfaces answer different questions and have different history rules. For the current book, explore the separate [Hyperliquid Orderbook streaming product](https://supanode.xyz/services/hyperliquid/websocket). ## Research fills with SQL. Use a ClickHouse client such as DBeaver or the Python client. This example ranks BTC wallets by traded notional within one UTC day. research.sql / illustrativeCopy SQL ``` SELECT wallet_address, count() AS fills, sum(price * size) AS notional_usd FROM hyperliquid.raw_node_fills_by_block PREWHERE utc_fill_dt = '2026-09-01' WHERE coin = 'BTC' GROUP BY wallet_address ORDER BY notional_usd DESC LIMIT 20 ``` Illustrative SQL. Results and query runtime depend on your workload. ### Filter the partition first `utc_fill_dt` is the raw fill partition key. Put your date bound in `PREWHERE`; filter `coin` for market-specific work. Set the date filter to your study window. `LIMIT` controls how many rows are returned; the date filter bounds the data scanned. `closed_pnl` and `fee` are already USD Float64 values. Do not divide them by token base units. [Read query patterns →](https://supanode.xyz/docs/hyperliquid/examples) ## Coverage, history and limits. Choose the raw table for individual fills, an order rollup for order-level totals, or a derived view for wallet and position research. HYPERLIQUID / CLICKHOUSEHistorical data COVERAGE Perpetual markets RAW FILL HISTORY From 27 July 2025 INTERFACE ClickHouse SQL DATABASE `hyperliquid` Scroll the table on a narrow screen to compare history and state → | Dataset | Research surface | History / state | | --- | --- | --- | | `raw_node_fills_by_block` | One row per fill: price, size, side, fees, position before the fill, realized PnL and execution context. | All perpetual markets, with history from 27 July 2025 and real-time refresh. | | `agg_fulfilled_order` | Fills rolled up by order. Read size, volume and PnL with `sum()`. | 90-day retention. | | `view_perpetual_wallet` | Derived wallet aggregates: realized PnL, volume, win counts, active days and ROI quantiles. | A computed view derived from its source tables. | | `view_wallet_position` | Latest position per wallet and market, with update time. | Latest position state per wallet and market. | **Documented snapshot, 1 September 2026:** 3,131,968,443 raw fills / 225.58 GB. ### Context around the trade - **Timing:** millisecond fill time plus nanosecond-resolution block time, block ID and transaction index. - **Execution:** maker/taker flag, start position, closed PnL, fee and liquidation fields. - **Attribution:** builder code, builder fee, client order ID, TWAP ID and priority gas. ### Interpret the fields correctly - **Fill-level coverage:** trades, liquidations, TWAP context, fees and position deltas. - **Timestamp resolution:** nanosecond resolution describes the stored block time. Measure delivery and query runtime for your workload. - **Wallet statistics:** use realized PnL and win rates to study past fills. Account for funding separately when calculating net returns. [Inspect full schemas and partition keys →](https://supanode.xyz/docs/hyperliquid/tables) ### Reconstruct the resting book. The SQL warehouse contains fills and derived views. To study the book around an event, request the separate order-level archive for your markets and date range. ### Query-ready fill warehouse Connect to ClickHouse and query tables. Use it for trade flow, wallet activity, liquidation context and builder attribution. ### Raw order-book archive MessagePack ABCI checkpoints plus hourly zstd streams for book diffs, order statuses and fills. Archive access is provisioned separately; confirm scope and terms with the team. 1 / Checkpoint Load the newest snapshot at or before the target time, including asset encoding and precision. 2 / Diffs Read hourly diff files covering the interval after the checkpoint. 3 / Replay Apply insert, amend and remove events in order, keyed by order ID. 4 / Aggregate Build price levels, then sample spread, midpoint and depth for the study. Use the raw archive to reconstruct order-book history from checkpoints and ordered diffs. [Read the archive workflow →](https://supanode.xyz/docs/hyperliquid/indexer#order-book-archive) ## Price & access. Hyperliquid ### Historical Indexer Separate subscription Historical perpetual-market data through SQL. - History since 27 July 2025 - Fills, liquidations and TWAP context - Order-level book archive and custom REST: scope confirmed separately $300 / month Free trial up to 24h on request; our team confirms availability and setup. Monthly, prepaid in crypto. [\[ REQUEST ACCESS \]](https://stg.portal.supanode.xyz/)[Documentation](https://supanode.xyz/docs/hyperliquid/indexer) Monthly, prepaid. Crypto-only billing: USDC, SOL, ETH or USDT. Confirm capacity and workload fit before subscribing. Separate archive access and any custom integration need an agreed scope. [Compare Hyperliquid pricing →](https://supanode.xyz/pricing/hyperliquid) ## Connect and validate. Start in the portal. Our team provisions your host, port, username and password for the `hyperliquid` database. 1. **Describe the workload.** Share markets, date range and expected queries in your access request. 2. **Confirm scope and access.** Our team arranges connection details per customer. Review the dataset and archive scope before connecting. 3. **Connect and validate.** Set database `hyperliquid`, test a bounded query and keep credentials out of source code. [Python and DBeaver connection guide →](https://supanode.xyz/docs/hyperliquid/indexer#connection) [Discuss access with support →](https://telegram.me/supanode_tgs) ## Before you connect. ### Does the plan include historical order-book SQL? The ClickHouse database covers fill activity and derived views. The order-level book archive is a separately provisioned surface with checkpoints and diffs. Agree markets, date coverage and terms with the team. ### Is the 90-day window the limit for all history? The 90-day retention applies to `agg_fulfilled_order`. Raw fill history starts 27 July 2025. Wallet views compute aggregates, while the latest-position view shows the latest state per wallet and market. ### Can you provide a custom REST integration? A custom REST endpoint around a specific query can be discussed on request. Agree its scope and price separately. ### How do I confirm trial access? Request a trial through the portal. Our team confirms availability and setup for up to 24 hours. ### Where are the complete schemas and connection examples? [Table reference](https://supanode.xyz/docs/hyperliquid/tables) lists columns, types and partition keys. [Connection Docs](https://supanode.xyz/docs/hyperliquid/indexer#connection) cover Python and DBeaver setup. --- Source: https://supanode.xyz/services/hyperliquid/indexer