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🧠 NFT data: how to evaluate NFT data

Today, we'll show you how to find NFT trends and analyze NFT projects across crypto, starting with an example on MadLads, a Solana NFT project.

Watch this video for a detailed tutorial, or scroll down to read a quick overview👇

 ✏️ Tutorial overview: 

1. How to find NFT data

Because NFTs vary by chain and multiple chains support NFTs, there’s no one table for NFTs. In the 'Explore Data' tab in your query engine, select the chain you want to check for NFT data.

In this case we’ll select Solana, and from the drop down list we can already tell we'll mostly want what's in the nft_sales or nft_mints tables.

(Reminder: dim tables describe entities, fact tables contain pure chain data, and ez tables include added context with chain data for human-readability.)

2. Refine your SQL query

We can write a query to quickly filter through the data and find the information we actually want. So, if we want to find the daily volume of MadLads NFTs, we'll need a few things:

with t1 as (select a.*, b.label from solana.core.fact_nft_sales a             

(selects one table and wraps it in the name t1)

left join solana.core.dim_labels b                                                         

(selects a second table and joins it with our first)

on a.mint = b.address                                                                         

(matches the mint column to the address column across tables)

where b.label = 'mad lads'                                                                   

(filters for only MadLads NFTs)

and a.succeeded = TRUE                                                                   

(filters for only successful transactions)

and block_timestamp::date > '2023-04-19')                                       

(Sets a one-month time range from today)

From here, we can add the data that will end up filling the axes on our chart:

SELECT

date_trunc(day, block_timestamp) as date,                                     

(changes block timestamp to date)

count(tx_id) as sales_count,                                                           

(counts unique transactions as sales)

count(distinct purchaser) as unique_buyer,                                     

(counts distinct purchasers as buyers)

marketplace,                                                                                   

(includes marketplace data)

sum(sales_amount) as volume_in_sol                                             

(gets the sum of sales as a total volume in $SOL)

FROM t1                                                                                                   

(tells the query to find this data in our original select)

where block_timestamp::date > '2023-04-19'

and succeeded = TRUE

and sales_amount > 0

and marketplace like '%tensor%'                                                   

(filters for Tensor marketplace)

group by date, marketplace order by date desc;

In the end, hit the play/run button, and we should end up with a query and table looking like this: