snip3r

This project evaluates and scans through various queries to identify promising NFTs for an NFT buyer to make an investment. These NFT collections are identified by monitoring whale buyer activity. If several whales are making purchases in various collections, this identifies a collection as promising. On the other hand, if the whales are starting to sell the NFT collection our trader would want to sell. We aggregate available NFTs from all NFT marketplaces using the Reservoir API.

Reservoir API was used behind the scenes to pickup any data regarding NFTs. This API made it significantly easier to determine data like the rarity of a certain NFT instantly, instead of having to manually calculate that amount. This means our code ran more efficiently, and there was less time spent on trying to figure out how to get the wanted NFT data.

Next.JS was a framework used during the creation of the front-end of the application. Using figma, our team created the user design for the front end of the application.

Using the data from the Reservoir API, we used Python and Pandas in order to get the data into a workable structure by using a dataframe. With the dataframe, removing null values was more straightforward and it was easy to filter through the data to determine what NFTs certain whales were buying or selling, to create the sentiment analysis. To determine the whales that were worth tracking, the platform NFTGo was used to determine the most profitable whales that were actively trading in the last 30 days. In the future, the code will dynamically determine what whales best meet his criteria.