• NFT trading volume returned to pre-LUNA crash levels in February, reaching $2 billion for the first time since May 2022.
• Ethereum (ETH) remained the top blockchain by NFT trading volume, with $1.8 billion in February – a 174% increase from January’s $659 million.
• Blur was the busiest NFT marketplace in February, accounting for 64.8% of the entire market trading volume, while OpenSea followed with 28.7%.

NFT Trading Volume Returns to Pre-Luna Crash Levels

In February, Non-fungible token (NFT) market’s trading volume increased to $2 billion, reaching its pre-LUNA crash levels according to DappRadar’s Industry Report. This marks a 117% spike from January’s $956 million in trading volume. While sales count recorded a 31.46% decrease falling to 6.3 million from January’s 9.2 million.

Ethereum Remains Top Blockchain By NFT Trading Volume

Ethereum (ETH) maintained its status as the top blockchain by NFT trading volume in February with $1.8 billion – a 174% increase from January’s $659 million and representing 83.36% of the entire NFT market. Solana (SOL) and Polygon (MATIC) followed ETH as second and third chain respectively; SOL recorded a 12% decrease from January’s $86 million and MATIC marked a 147% increase in February reaching $39 million from the previous month’s$16 million.

Blur Vs OpenSea

In terms of trading volume, Blur triumphed over OpenSea facilitating over $1.3 billion throughout the month compared to OpenSea’s 587$, accounting for 64.8% of the whole NFT market trading volume respectively while X2Y2 and LooksRare followed with 1.9 % and 1 7 % respectfully . However ,OpenSea still holds more users than Blur; currently 316 199 compared to 96 856 .

Profit Chasers Vs Art Lovers

Based on different metrics regarding user numbers it appears that while profit chasers are drawn more towards Blur art lovers seems favouring OpenSea due to their higher user base .

Conclusion

The data indicates that even though there is an upward trend in overall non fungible token volumes each marketplace is favored by different types of users based on their metrics .