Tokens

Four surfaces cover token activity on Gnosis.

facts_tokens.transfers: one row per token transfer.
facts_tokens.balance_changes: one row per account per transfer, so a transfer produces a negative row for the sender and a positive one for the receiver.
functions.calculate_latest_token_balances and its historical sibling: holder balances at any date, computed at query time.
metrics_tokens.price_daily: one USD price per token per day.

Every column and both function signatures are documented at Tokens.

Sample queries

The event tables split by day on block_timestamp and hold billions of rows. Bound that column in every query; without a bound the query reads the whole table.

Count the transfers and the accounts behind them on one day. Counts of distinct accounts across longer windows cannot be added up day by day; Assets ▸ Senders covers the surface built for that question.

select
    count(*) as transfers,
    count(distinct from_address) as senders,
    count(distinct to_address) as recipients
from `facts_tokens.transfers`
where block_timestamp >= timestamp('2026-08-20')
  and block_timestamp < timestamp('2026-08-21')
  and chain_id = 'gnosis'
  and token_type = 'erc20'

Rank the busiest Gnosis tokens on one day. Joining dimensions.tokens on token_id adds the symbol and name of each contract.

select
    tokens.symbol,
    transfers.token_address,
    count(*) as transfer_count,
    count(distinct transfers.from_address) as senders
from `facts_tokens.transfers` as transfers
join `dimensions.tokens` as tokens
    using (token_id)
where transfers.block_timestamp >= timestamp('2026-08-20')
  and transfers.block_timestamp < timestamp('2026-08-21')
  and transfers.chain_id = 'gnosis'
group by tokens.symbol, transfers.token_address
order by transfer_count desc
limit 20

Find the accounts that gained and lost the most of one token on one day. Summing balance_change_raw per account nets the incoming and outgoing legs; dividing by 10^decimals converts to token amounts. The token is named by token_address and chain_id, which is what holds the read to one token’s blocks.

select
    changes.account_address,
    sum(changes.balance_change_raw) / pow(10, tokens.decimals) as net_change
from `facts_tokens.balance_changes` as changes
join `dimensions.tokens` as tokens
    using (token_id)
where changes.block_timestamp >= timestamp('2026-08-20')
  and changes.block_timestamp < timestamp('2026-08-21')
  and changes.chain_id = 'gnosis'
  and changes.token_address = '0xedbc7449a9b594ca4e053d9737ec5dc4cbccbfb2'
group by changes.account_address, tokens.decimals
order by net_change desc
limit 20

List the largest current holders of one Gnosis token. The returned balance is already converted out of raw units.

select
    account_address,
    balance
from `functions.calculate_latest_token_balances`(
    'gnosis',
    '0xedbc7449a9b594ca4e053d9737ec5dc4cbccbfb2',
    'erc20'
)
order by balance desc
limit 20

Trace one account’s balance in one token over time. The function returns a row only on the days the balance moved; the balance on any other date is the latest row at or before it.

select
    balance_date,
    balance
from `functions.calculate_historical_eod_token_balances`(
    'gnosis',
    '0xedbc7449a9b594ca4e053d9737ec5dc4cbccbfb2',
    'erc20'
)
where account_address = '0x9008d19f58aabd9ed0d60971565aa8510560ab41'
order by balance_date desc
limit 20

Read one Gnosis token’s daily price. Each row is the midnight UTC price in USD.

select
    prices.timestamp,
    tokens.symbol,
    prices.price
from `metrics_tokens.price_daily` as prices
join `dimensions.tokens` as tokens
    using (token_id)
where prices.timestamp >= timestamp('2026-08-16')
  and prices.timestamp < timestamp('2026-08-23')
  and prices.token_id = '0xedbc7449a9b594ca4e053d9737ec5dc4cbccbfb2-gnosis'
order by prices.timestamp

Notes

The token list is everything we see onchain, which reaches far past the tokens anyone would want: a ranking of the busiest tokens returns contracts whose symbols imitate well-known ones. Nothing has gone wrong when those appear. Joining dimensions.asset_tokens keeps the tokens tied to a named asset with a named issuer; Assets covers that layer.

Overview

Core entities

Core datasets

Market verticals

Chain verticals

Sample queries

Notes

​Sample queries

​Notes

Sample queries

Notes