block_timestamp::Date AS date,tx_hash,

amount_in_usd AS amountUSD,

origin_from_address as user

from optimism.defi.ez_dex_swaps

where amount_in_usd<power(10,6)

and tx_hash in (select distinct(tx_hash) from optimism.core.ez_decoded_event_logs where origin_to_address in ('0xcb1355ff08ab38bbce60111f1bb2b784be25d7e8','0x1ba16f6bb1ca9d9b32346e9d51966a606ef21c60','0xb555edf5dcf85f42ceef1f3630a52a108e55a654' ,'0xec8b0f7ffe3ae75d7ffab09429e3675bb63503e4'))

and date>='2023-01-01'

case when date>='2023-10-17' then 'after fee changes' else 'before fee changes' end as type,

count(distinct(user)) as "Users",

count(distinct tx_hash) as "Swaps",

avg(amountUSD) as "Amount Avg",

sum(amountUSD) as "Volume",

date_trunc('{{ time }}', date) as dt,

sum("Swaps") over (partition by type order by date_trunc('{{ time }}', date)) as "Swaps(cum)",

sum("Volume") over (partition by type order by date_trunc('{{ time }}', date)) as "Volume(cum)",

(("Swaps" - lag("Swaps", 1) over (partition by type order by date_trunc('{{ time }}', date)))/lag("Swaps", 1) over (partition by type order by date_trunc('{{ time }}', date))) * 100 as "swap changes(%)",

(("Volume" - lag("Volume", 1) over (partition by type order by date_trunc('{{ time }}', date)))/lag("Volume", 1) over (partition by type order by date_trunc('{{ time }}', date))) * 100 as "volume changes(%)",

(("Users" - lag("Users", 1) over (partition by type order by date_trunc('{{ time }}', date)))/lag("Users", 1) over (partition by type order by date_trunc('{{ time }}', date))) * 100 as "user changes(%)"

from optimism

group by type,dt