Flipside World Cup Round of 16: NFT Sales Comparison By Chain

NFT Sales Comparison By Chain

The following code was used to extract the data→

select
    date_trunc('{{Date_Grouping}}',block_timestamp)::date as date 
    ,count(DISTINCT tx_hash) as sales
    ,count(DISTINCT SELLER_ADDRESS) as  sellers
    ,count(DISTINCT BUYER_ADDRESS) as  buyers
    ,count(DISTINCT NFT_ADDRESS) as NFTs
    ,sum(PRICE_USD) as sales_volume
    ,avg(PRICE_USD) as avg_sales_volume
    ,sum(sales) over(order by date) as growth_sales
    ,sum(sellers) over(order by date) as growth_sellers
  	,sum(buyers) over(order by date) as growth_buyers
    ,sum(sales_volume) over(order by date) as growth_sales_volume
  from ethereum.core.ez_nft_sales
  where block_timestamp::date &gt;=CURRENT_DATE-{{Days}}
  group by 1
  order by 1

The following code was used to extract the data→

 select
    date_trunc('{{Date_Grouping}}',block_timestamp)::date as date 
    ,count(DISTINCT TX_GROUP_ID) as sales
    ,count(DISTINCT PURCHASER) as buyers
    ,count(1) as sellers
    ,count(DISTINCT NFT_ASSET_ID) as NFTs
    ,sum(TOTAL_SALES_AMOUNT*avg_price) as sales_volume
    ,avg(TOTAL_SALES_AMOUNT*avg_price) as avg_sales_volume
    ,sum(sales) over(order by date) as growth_sales
    ,sum(sellers) over(order by date) as growth_sellers
  	,sum(buyers) over(order by date) as growth_buyers
    ,sum(sales_volume) over(order by date) as growth_sales_volume
  from algorand.nft.ez_nft_sales
    join algo_price on algo_price.day_price = block_timestamp::date
  where block_timestamp::date &gt;=CURRENT_DATE-{{Days}}
  group by 1
  order by 1

The following code was used to extract the data→

select 
      date_trunc('{{Date_Grouping}}',block_timestamp)::date as date 
      ,count(DISTINCT TX_ID) as sales
      ,count(DISTINCT buyer) as buyers
      ,count(DISTINCT seller) as sellers
      ,count(DISTINCT nft_id) as NFTs
      ,sum(PRICE*avg_price) as sales_volume
      ,avg(PRICE*avg_price) as avg_sales_volume
      ,sum(sales) over(order by date) as growth_sales
      ,sum(sellers) over(order by date) as growth_sellers
  	  ,sum(buyers) over(order by date) as growth_buyers
      ,sum(sales_volume) over(order by date) as growth_sales_volume
  from flow.core.ez_nft_sales
    join flow_price on TOKEN_CONTRACT = CURRENCY and block_timestamp::date = day_price
  where block_timestamp::date &gt;=CURRENT_DATE-{{Days}}
  group by 1
  order by 1

The following code was used to extract the data→

 select
    date_trunc('{{Date_Grouping}}',block_timestamp)::date as date 
    ,count(DISTINCT TX_ID) as sales
    ,count(DISTINCT PURCHASER) as buyers
    ,count(DISTINCT seller) as sellers
    ,count(DISTINCT MINT) as NFTs
    ,sum(SALES_AMOUNT*avg_price) as sales_volume
    ,avg(SALES_AMOUNT*avg_price) as avg_sales_volume
    ,sum(sales) over(order by date) as growth_sales
    ,sum(sellers) over(order by date) as growth_sellers
  	,sum(buyers) over(order by date) as growth_buyers
    ,sum(sales_volume) over(order by date) as growth_sales_volume
  from solana.core.fact_nft_sales
    join sol_price on day_price = block_timestamp::date
  where block_timestamp::date &gt;=CURRENT_DATE-{{Days}}
    and SUCCEEDED=true
  group by 1
  order by 1

The following code was used to extract the data→

---*****************************************************NEAR Section*******************************************
,t1 as (
  select
    blocK_timestamp,
    tx_hash,
    tx:receipt as receipt,
    tx:public_key as public_key,
    tx:signer_id as signer_id,
    tx:receiver_id as receiver_id
  from near.core.fact_transactions
  where block_timestamp::date &gt;=CURRENT_DATE-{{Days}}
),
t2 as (
  select
    block_timestamp,
    tx_hash,
    public_key, 
    signer_id, 
    receiver_id,
    seq,key,path,index,
    replace(value:outcome:logs[0], '\\') as logs,
    check_json(logs) as checks
  from t1,
  	table(flatten(input =&gt; receipt))
  where block_timestamp::date &gt;=CURRENT_DATE-{{Days}}
),
t3 as (
  select
    block_timestamp,
    tx_hash,
    public_key, 
    signer_id, 
    receiver_id,
    try_parse_json(logs) as parse_logs,
    parse_logs:type as type,
    parse_logs:params:buyer_id as buyer_id,
    parse_logs:params:owner_id as owner_id,
    parse_logs:params:is_offer as is_offer,
    parse_logs:params:is_auction as is_auction,
    parse_logs:params:nft_contract_id as nft_contract_id,
    parse_logs:params:token_id as token_id,
    parse_logs:params:ft_token_id as ft_token_id,
    parse_logs:params:price/pow(10,24) as near
  from t2 
  where checks is null and logs is not null 
    and type is not null 
    and block_timestamp::date &gt;=CURRENT_DATE-{{Days}}
),
sales as (
  select 
    date_trunc('{{Date_Grouping}}',block_timestamp)::date as date,
    count(distinct tx_hash) as n_sales,
    count(distinct owner_id) as n_sellers,
    count(distinct buyer_id) as n_buyers,
    sum(near) as volume,
    case when min(near) =0 then 1/(pow(10,9))
    else min(near) end as min_price,
    max(near) as max_price,
    avg(near) as avg_price
  from t3 
  where type='resolve_purchase'
  	and block_timestamp::date &gt;=CURRENT_DATE-{{Days}}
  group by 1
  order by 1 asc
),
marketplaces as (
  select
    date_trunc('{{Date_Grouping}}',block_timestamp)::date as date,
    receiver_id as marketplace,
    count(distinct tx_hash) as n_sales,
    count(distinct owner_id) as n_sellers,
    count(distinct buyer_id) as n_buyers,
    sum(near) as volume,
    case when min(near) =0 then 1/(pow(10,9))
    else min(near) end as min_price,
    max(near) as max_price,
    avg(near) as avg_price
  from t3 where type='resolve_purchase'
  	and block_timestamp::date &gt;=CURRENT_DATE-{{Days}}
  group by 1,2
  order by 1 asc
),
projects as (
  select
    distinct parse_logs:params:token_series_id as token_series_id,
    parse_logs:params:creator_id as nft_project
  from t3 where type='nft_create_series'
  --group by 1
)
--***********************************************************End NEAR Section********************************************
select
    date_trunc('{{Date_Grouping}}',block_timestamp)::date as date
    -- nft_project,
    ,count(distinct tx_hash) as sales
    ,count(distinct owner_id) as sellers
    ,count(distinct buyer_id) as buyers
    ,count(1) as NFTs
    ,sum(near*avg_price) as sales_volume
    ,avg(near*avg_price) as avg_sales_volume
    ,sum(sales) over(order by date) as growth_sales
    ,sum(sellers) over(order by date) as growth_sellers
  	,sum(buyers) over(order by date) as growth_buyers
    ,sum(sales_volume) over(order by date) as growth_sales_volume
  from t3
  	join projects on split_part(t3.token_id,':',0) like projects.token_series_id
    join near_price on day_price = block_timestamp::date
  where type='resolve_purchase'
  	and block_timestamp::date &gt;=CURRENT_DATE-{{Days}}
  group by 1
  order by 1

The following code was used to extract the data→

 select
    date_trunc('{{Date_Grouping}}',block_timestamp)::date as date 
    ,count(DISTINCT TX_HASH) as sales
    ,count(DISTINCT BUYER_ADDRESS) as buyers
    ,count(DISTINCT SELLER_ADDRESS) as sellers
    ,count(DISTINCT NFT_ADDRESS) as NFTs
    ,sum(PRICE_USD) as sales_volume
    ,avg(PRICE_USD) as avg_sales_volume
    ,sum(sales) over(order by date) as growth_sales
    ,sum(sellers) over(order by date) as growth_sellers
  	,sum(buyers) over(order by date) as growth_buyers
    ,sum(sales_volume) over(order by date) as growth_sales_volume
  from optimism.core.ez_nft_sales
  where block_timestamp::date &gt;=CURRENT_DATE-{{Days}}
  group by 1
  order by 1

Methodology

The following 9 chains were analyzed→

To change the analysis time and the type of analysis in terms of time separation, the parameters above the dashboard can be used

1-9 chains were compared in the first part →

The number of NFT sales transactions
Number of sellers
Number of buyers
The number of NFTs sold
Sales volume
Average sales volume Comparing the number of sales, the number of buyers, the number of sellers, and the number of NFs sold per day
Growth in the number of sales transactions over time
Growing the number of sellers over time
Growth in the number of buyers over time
Sales volume growth over time

2- Each chain was examined in depth →

The number of NFT sales
The number of NFT sales traction
Number of buyers
Number of sellers
Sales volume
Average sales volume
Average sales volume, number of sales, number of buyers, and number of sellers per day
Dispersion of the number of sales transactions over time
The growth of the number of buyers and sellers and the volume of sales over time
Sales price distribution in terms of the number of sales transactions
Sales price distribution in terms of the number of buyers
Comparison of the native token price of the chain with the number of sales transactions
Comparison of the chain's native token price with sales volume
Comparison of sales platforms or marketplaces in each chain
Comparison of top 10 collections or top 10 projects in each chain