Create cow_amm_lp_and_tvl_4047078.sql (#17)

* Create cow_amm_lp_and_tvl_4047078

The query find the relevant cow amm pool, get lp token balance based on the incoming and outgoing transfers. 

Then we use the exciting materialized table to calculate TVL for the same pool.

* add gitignore for .env to present for pushing secret

* Adding fixes to the CoW AMM query

* Adding .sql to the query name

* Addressing comments

* Addressing sqlfluff issues

* Addressing sqlfluff issues

* Addressing additional comments

* Alligning sqlfluff file with the uniswap pr

* Adding comments

* Calculation fix

* Only one pool per token pair

* Additional condition

* wip

* use tvl_by_tx subquery and other simplification/performance improvements

* improve lookback

---------

Co-authored-by: Felix Leupold <[email protected]>

.gitignore

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+.env

cow_amm/.sqlfluff

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+[sqlfluff:templater:jinja:context]
+start='2024-08-08 00:00'
+token_a='0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48'
+token_b='0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2'

cowamm/profitability/10k_growth/cow_4047078.sql

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+-- Computes the TVL, lp token total supply and lp token price of a CoW AMM pool over time
+-- Parameters
+--  {{token_a}} - either token of the desired uni pool
+--  {{token_b}} - other token of the desired uni pool
+--  {{start}} - date as of which the analysis should run
+
+-- Given that we might not have records every day in the source data (e.g. not every day the lp supply may change), 
+-- but still want to visualize development on a per day basis,  we create an auxiliary table with one record per 
+-- day between `start` and `now`
+with date_range as (
+    select t.day
+    from
+        unnest(sequence(
+            date(timestamp '{{start}}'),
+            date(now())
+        )) t (day) --noqa: AL01
+),
+
+-- Finds the CoW AMM pool address given tokens specified in query parameters (regardless of order) 
+cow_amm_pool as (
+    select
+        created_at,
+        address
+    from query_3959044
+    where ((token_1_address = {{token_a}} and token_2_address = {{token_b}}) or (token_2_address = {{token_a}} and token_1_address = {{token_b}}))
+    order by 1 desc
+    limit 1
+),
+
+-- per day lp token total supply changes of the CoW AMM pool by looking at burn/mint events
+lp_balance_delta as (
+    select
+        date(evt_block_time) as "day",
+        sum(case when "from" = 0x0000000000000000000000000000000000000000 then value else -value end) as lp_supply
+    from erc20_ethereum.evt_transfer
+    where
+        contract_address in (select address from cow_amm_pool)
+        and ("from" = 0x0000000000000000000000000000000000000000 or "to" = 0x0000000000000000000000000000000000000000)
+    group by 1
+),
+
+lp_total_supply_incomplete as (
+    select
+        day,
+        lp_supply,
+        sum(lp_supply) over (order by day) as total_lp
+    from lp_balance_delta
+),
+
+-- performance optimisation: reduce the total range of the join below to the last value before the start period
+lp_total_supply_start as (
+    select max(day) as "start"
+    from lp_total_supply_incomplete
+    where day <= date(timestamp '{{start}}')
+),
+
+-- lp token total supply without date gaps
+lp_total_supply as (
+    select
+        day,
+        total_lp
+    from (
+        -- join full date range with potentially incomplete data. This results in many rows per day (all total supplies on or before that day)
+        -- rank() is then used to order join candidates by recency (rank = 1 is the latest lp supply)
+        select
+            date_range.day,
+            total_lp,
+            rank() over (partition by (date_range.day) order by lp.day desc) as latest
+        from date_range
+        inner join lp_total_supply_incomplete as lp
+            on date_range.day >= lp.day
+            -- performance optimisation: this assumes one week prior to start there was at least one lp supply change event
+            and lp.day >= (select start from lp_total_supply_start)
+    )
+    where latest = 1
+),
+
+tvl_by_tx as (
+    select
+        *,
+        rank() over (partition by date(block_time) order by block_time desc) as latest
+    from "query_4059700(token_a='{{token_a}}', token_b='{{token_b}}')"
+    where pool = (select address from cow_amm_pool)
+    -- performance optimisation: this assumes one week prior to start there was at least one tvl change event
+    and block_time >= timestamp '{{start}}' - interval '7' day
+),
+
+tvl as (
+    select
+        day,
+        tvl
+    from (
+        -- join full date range with potentially incomplete data. This results in many rows per day (all total tvl on or before that day)
+        -- rank() is then used to order join candidates by recency (rank = 1 is the latest tvl)
+        select
+            date_range.day,
+            tvl,
+            rank() over (partition by (date_range.day) order by tvl.block_time desc) as latest
+        from date_range
+        inner join tvl_by_tx as tvl
+            on date_range.day >= date(tvl.block_time)
+            -- performance optimisation: only look at the last update of the day
+            and tvl.latest = 1
+    )
+    where latest = 1
+),
+
+-- With this we can plot the lp token price (tvl/lp total supply) over time
+final as (
+    select
+        tvl.day,
+        tvl,
+        total_lp,
+        tvl / total_lp as lp_token_price
+    from tvl
+    inner join lp_total_supply as lp
+        on tvl.day = lp.day
+)
+
+-- Compute current value of initial investment together with other relevant output columns
+select
+    day,
+    tvl,
+    total_lp,
+    lp_token_price,
+    (
+        -- Assess initial investment in lp tokens
+        select 10000 / lp_token_price as investment
+        from final
+        where day = timestamp '{{start}}'
+    ) * lp_token_price as current_value_of_investment
+from final
+where day >= timestamp '{{start}}'
+order by 1 desc