Projects

The top of the catalog breaks activity down by who ran it. A project is the entity behind a protocol, such as Hyperliquid itself or a builder listing its own markets; its apps’ figures on HyperCore sit on Apps.

metrics_project_chains: one table per measure, one row per project per chain per day.

A project figure for one chain and one day is the sum of its apps on that chain and day, worked out once and served at both levels. The same holds across chains: a project’s chain rows for one day sum to what metrics_projects serves without the chain breakout, so use these tables when the chain breakdown is the question. Notional volume comes in two lenses. exchange_notional_trading_volume_daily credits the project whose matching engine settled the trade, and interface_notional_trading_volume_daily credits the project whose interface it was placed through. Each is a complete account of the same trades, so pick one lens and stay in it. Every column is documented at Projects ▸ Metrics.

Sample queries

One project's series
Builders on HyperCore

Read one project’s daily HyperCore fees.

select
    daily.timestamp,
    daily.fees
from `metrics_project_chains.fees_daily` as daily
where daily.timestamp >= timestamp('2026-08-16')
  and daily.timestamp < timestamp('2026-08-23')
  and daily.chain_id = 'hypercore'
  and daily.project_id = 'hyperliquid'
order by daily.timestamp

Rank the projects listing markets on HyperCore by the notional traded through their interfaces. The interface lens is the one that separates the builders, since the matching engine behind them is the same one.

select
    projects.name,
    sum(daily.notional_trading_volume) as notional_trading_volume
from `metrics_project_chains.interface_notional_trading_volume_daily` as daily
join `dimensions.projects` as projects
    using (project_id)
where daily.timestamp >= timestamp('2026-08-16')
  and daily.timestamp < timestamp('2026-08-23')
  and daily.chain_id = 'hypercore'
group by projects.name
order by notional_trading_volume desc
limit 20

Overview

Popular datasets

Market verticals

Chain ecosystems

Core entities

Sample queries

​Sample queries

Sample queries