Sudoswap explorer
I recently submitted a project for the Macro Hacks NFT Financialization hackathon (github repo, app) and secured a prize from NFTBank.
Main findings:
- Sudoswap has been gaining lots of traction since their launch in 2022, reaching 120k NFTs traded as well as 28M USD in total volume.
- Although there is already an excellent dashboard available on Dune for sudoswap, a few indicators (like wash trading and additional meta data from NFT collections, such as estimated vs floor prices time dependency and past volumes) were not yet available.
- Having this additional data could help NFT collectors/traders make more informed decisions.
- I gathered sudoswap trading data (minting data was also available but not used for this use-case) from Flipside Crypto using their SDK. A data fetching job was packaged as a cronjob to continously fetch data and store in a DB instance in the cloud.
- Additional metadata for each NFT collection was gathered using NFTBank API, including price estimates for a large number of NFT projects.
- I used a rather simple methodology for defining “wash trading” activity: if wallet A sold an item from an NFT collection to wallet B and, within 1 hour (this was set rather arbitrarily) wallet B sells the NFT back to wallet A, then this is considered 1 wash trade. This is of course a simplistic approach, since 1) there might be valid reasons for such a transaction to be occur and 2) more ellaborate approaches, such as having transactions of the form A -> B, B -> C and finally C -> A would not be labelled as “wash trades” in the current methodology.
- Next steps could potentially include gathering additional data from NFTBank for generating more insights of each collection, and also expanding the wash trading methodology (as described in the previous item).