Metrics

TVL, trading volume, fees, revenue, token incentives and active users. One row per thing per day, at two grains: app, and app and chain.

Tables

Daily DEX pool trading volume, Hyperliquid trading contributions and bridge transfer volumes cover closed UTC days and follow the availability cutoff of each contributing chain.

App
App × chain

One row per app per day. Where a table has a provenance column, ladder marks a figure summed from the app’s own markets and pools and projected the project’s series attributed to the app.

Table	Value column	What it holds
`metrics_apps.tvl_daily`	`tvl`	Total value locked across the app’s lending markets and vaults.
`metrics_apps.trading_volume_daily`	`trading_volume`	Value of trades executed by the app, measured on the quote leg at execution price.
`metrics_apps.active_loans_daily`	`active_loans`	USD value of open loans (outstanding borrows) across the app’s lending markets.
`metrics_apps.assets_staked_daily`	`assets_staked`	USD value of assets staked with the app.
`metrics_apps.fees_daily`	`fees`	Fees paid by users of the app across every chain it runs on.
`metrics_apps.fees_supply_side_daily`	`fees_supply_side`	Fees the app passed through to builders and, on HIP-3 markets, to market deployers.
`metrics_apps.revenue_daily`	`revenue`	Fees the app retained across every chain it runs on.
`metrics_apps.token_incentives_daily`	`token_incentives`	Value of tokens the app distributed as incentives.
`metrics_apps.outstanding_supply_daily`	`outstanding_supply`	USD value of borrowed principal outstanding.
`metrics_apps.user_dau_daily`	`user_dau`	Distinct users active on the app per day.
`metrics_apps.user_wau_daily`	`user_wau`	Distinct users active on the app over a trailing week.
`metrics_apps.user_mau_daily`	`user_mau`	Distinct users active on the app over a trailing month.

One row per app and chain per day. Each figure sits on the chain it comes from.

Table	Value column	What it holds
`metrics_app_chains.tvl_daily`	`tvl`	Total value locked across the app’s lending markets and vaults.
`metrics_app_chains.trading_volume_daily`	`trading_volume`	Value of trades executed by the app, measured on the quote leg at execution price.
`metrics_app_chains.active_loans_daily`	`active_loans`	USD value of open loans (outstanding borrows) across the app’s lending markets.
`metrics_app_chains.fees_daily`	`fees`	Fees paid by users of the app across every chain it runs on.
`metrics_app_chains.fees_supply_side_daily`	`fees_supply_side`	Fees the app passed through to builders and, on HIP-3 markets, to market deployers.
`metrics_app_chains.revenue_daily`	`revenue`	Fees the app retained across every chain it runs on.

Sample queries

App
App × chain
Apps of one project
Balance rollup

Each measure is its own table, so a row with all three is a join on app_id and timestamp. A full join keeps a day that only one of the tables has a figure for.

with fees as (
    select timestamp, fees
    from `metrics_apps.fees_daily`
    where app_id = 'uniswap-v3'
      and timestamp >= timestamp('2026-06-01')
),

revenue as (
    select timestamp, revenue
    from `metrics_apps.revenue_daily`
    where app_id = 'uniswap-v3'
      and timestamp >= timestamp('2026-06-01')
),

tvl as (
    select timestamp, tvl
    from `metrics_apps.tvl_daily`
    where app_id = 'uniswap-v3'
      and timestamp >= timestamp('2026-06-01')
)

select
    timestamp,
    fees.fees,
    revenue.revenue,
    tvl.tvl
from fees
full join revenue
    using (timestamp)
full join tvl
    using (timestamp)
order by timestamp

An app’s chain rows add back to its app row for the same day. tvl is a day-end balance, so read one day across chains rather than a range.

select
    chain_id,
    tvl
from `metrics_app_chains.tvl_daily`
where app_id = 'aave-v3'
  and timestamp = timestamp('2026-08-01')
order by tvl desc

The registry supplies the name and the daily table supplies the figure, joined on app_id. Filtering project_id on the daily table keeps the scan on one project.

select
    apps.app_id,
    apps.name,
    sum(daily.fees) as fees
from `metrics_apps.fees_daily` as daily
join `dimensions.apps` as apps
    using (app_id)
where daily.project_id = 'hyperliquid'
  and daily.timestamp >= timestamp('2026-08-01')
  and daily.timestamp < timestamp('2026-08-08')
group by apps.app_id, apps.name
order by fees desc

tvl stands at day end, so the week’s figure is the last day’s value and the summed column counts the same locked value seven times. user_dau is a distinct count and behaves the same way: read the day you want, or read user_wau for the week.

select
    array_agg(tvl order by timestamp desc limit 1)[offset(0)] as tvl,
    sum(tvl) as tvl_summed
from `metrics_apps.tvl_daily`
where app_id = 'aave-v3'
  and timestamp >= timestamp('2026-08-01')
  and timestamp < timestamp('2026-08-08')

Overview

Popular datasets

Market verticals

Chain ecosystems

Core entities

Tables

Sample queries

​Tables

​Sample queries

Tables

Sample queries