Datathons Solutions

Datathon 2022 NFT Nexo case

Posted kostadinovk, owliie, apewithacape, gencho Mentors: iliya
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1. Business Understanding

There are multiple platforms for cryptocurrency-backed loans where you provide crypto as collateral and in return, you receive a loan in the form of another token. For such scenarios, there are present multiple solutions such as Aave and Compound. The problem grows exponentially when it comes to non-fungible tokens (NFTs). Up until now, a lending provider for NFTs does not exist due to its volatility in interest, trends and social media influence.

What is an NFT?

NFT stands for ‘non-fungible token’. When something is fungible, like a dollar bill, it is equivalent to, and can thus be exchanged for, any other dollar bill. In contrast, a non-fungible token is a unique asset in digital form that cannot be exchanged for any other NFT. This means that every NFT is a ‘one-of-a-kind’ item. NFTs are transferred from one owner to another using blockchain technology, which creates a digital trail from seller to buyer that verifies the transaction. This encodes the unique ownership rights to the buyer (new owner).

The problem?

The market price of a unique NFT is not easily observable. We want to predict it.

2. Data Understanding

There are two datasets that we are using. The first one contains NFT sales:

blockNumber timeStamp hash from contractAddress to tokenID tokenName tokenSymbol gasPrice currency amount amountUsd
13988541 1641961048 0xc208fdb2f133bda64522fececd6518a565aaa6e8801b0a776f2f93c922fe9420 0x0000000000000000000000000000000000000000 0xed5af388653567af2f388e6224dc7c4b3241c544 0xd45058bf25bbd8f586124c479d384c8c708ce23a 0 Azuki AZUKI 1.45E+11 ETH 0 0
13988541 1641961048 0xc208fdb2f133bda64522fececd6518a565aaa6e8801b0a776f2f93c922fe9420 0x0000000000000000000000000000000000000000 0xed5af388653567af2f388e6224dc7c4b3241c544 0xd45058bf25bbd8f586124c479d384c8c708ce23a 1 Azuki AZUKI 1.45E+11 ETH 0 0
13988541 1641961048 0xc208fdb2f133bda64522fececd6518a565aaa6e8801b0a776f2f93c922fe9420 0x0000000000000000000000000000000000000000 0xed5af388653567af2f388e6224dc7c4b3241c544 0xd45058bf25bbd8f586124c479d384c8c708ce23a 2 Azuki AZUKI 1.45E+11 ETH 0 0
13988541 1641961048 0xc208fdb2f133bda64522fececd6518a565aaa6e8801b0a776f2f93c922fe9420 0x0000000000000000000000000000000000000000 0xed5af388653567af2f388e6224dc7c4b3241c544 0xd45058bf25bbd8f586124c479d384c8c708ce23a 3 Azuki AZUKI 1.45E+11 ETH 0 0
13988541 1641961048 0xc208fdb2f133bda64522fececd6518a565aaa6e8801b0a776f2f93c922fe9420 0x0000000000000000000000000000000000000000 0xed5af388653567af2f388e6224dc7c4b3241c544 0xd45058bf25bbd8f586124c479d384c8c708ce23a 4 Azuki AZUKI 1.45E+11 ETH 0 0
13988541 1641961048 0xc208fdb2f133bda64522fececd6518a565aaa6e8801b0a776f2f93c922fe9420 0x0000000000000000000000000000000000000000 0xed5af388653567af2f388e6224dc7c4b3241c544 0xd45058bf25bbd8f586124c479d384c8c708ce23a 5 Azuki AZUKI 1.45E+11 ETH 0 0
13988541 1641961048 0xc208fdb2f133bda64522fececd6518a565aaa6e8801b0a776f2f93c922fe9420 0x0000000000000000000000000000000000000000 0xed5af388653567af2f388e6224dc7c4b3241c544 0xd45058bf25bbd8f586124c479d384c8c708ce23a 6 Azuki AZUKI 1.45E+11 ETH 0 0
13988541 1641961048 0xc208fdb2f133bda64522fececd6518a565aaa6e8801b0a776f2f93c922fe9420 0x0000000000000000000000000000000000000000 0xed5af388653567af2f388e6224dc7c4b3241c544 0xd45058bf25bbd8f586124c479d384c8c708ce23a 7 Azuki AZUKI 1.45E+11 ETH 0 0

We have the NFT collection and token id, sender’s address, receiver’s address, amount and currency.

And the second one contains all traits of each NFT:

collection contract token_id nft_url trait_type value
azuki 0xed5af388653567af2f388e6224dc7c4b3241c544 0 https://ikzttp.mypinata.cloud/ipfs/QmQFkLSQysj94s5GvTHPyzTxrawwtjgiiYS2TBLgrvw8CW/0 Type Human
azuki 0xed5af388653567af2f388e6224dc7c4b3241c544 0 https://ikzttp.mypinata.cloud/ipfs/QmQFkLSQysj94s5GvTHPyzTxrawwtjgiiYS2TBLgrvw8CW/0 Hair Water
azuki 0xed5af388653567af2f388e6224dc7c4b3241c544 0 https://ikzttp.mypinata.cloud/ipfs/QmQFkLSQysj94s5GvTHPyzTxrawwtjgiiYS2TBLgrvw8CW/0 Clothing Pink Oversized Kimono
azuki 0xed5af388653567af2f388e6224dc7c4b3241c544 0 https://ikzttp.mypinata.cloud/ipfs/QmQFkLSQysj94s5GvTHPyzTxrawwtjgiiYS2TBLgrvw8CW/0 Eyes Striking
azuki 0xed5af388653567af2f388e6224dc7c4b3241c544 0 https://ikzttp.mypinata.cloud/ipfs/QmQFkLSQysj94s5GvTHPyzTxrawwtjgiiYS2TBLgrvw8CW/0 Mouth Frown
azuki 0xed5af388653567af2f388e6224dc7c4b3241c544 0 https://ikzttp.mypinata.cloud/ipfs/QmQFkLSQysj94s5GvTHPyzTxrawwtjgiiYS2TBLgrvw8CW/0 Offhand Monkey King Staff
azuki 0xed5af388653567af2f388e6224dc7c4b3241c544 0 https://ikzttp.mypinata.cloud/ipfs/QmQFkLSQysj94s5GvTHPyzTxrawwtjgiiYS2TBLgrvw8CW/0 Background Off White A
azuki 0xed5af388653567af2f388e6224dc7c4b3241c544 1 https://ikzttp.mypinata.cloud/ipfs/QmQFkLSQysj94s5GvTHPyzTxrawwtjgiiYS2TBLgrvw8CW/1 Type Human
azuki 0xed5af388653567af2f388e6224dc7c4b3241c544 1 https://ikzttp.mypinata.cloud/ipfs/QmQFkLSQysj94s5GvTHPyzTxrawwtjgiiYS2TBLgrvw8CW/1 Hair Pink Hairband

3. The solution

We want to predict the price of an NFT based on the market prices and sale history of similar NFTs.

Finding similar NFTs

To find similar NFTs on which we base our prediction we use clustering. We cluster all NFTs by traits. We tried KMeans and GMM.

This is the elbow graph for KMeans. We can see that the optimal value for k looks to be 100 but the error is very high.
No description available.
And here it is for GMM. We can see that by increasing the number of clusters the error gets higher. This is due to the fact that the data probably isn’t easily clusterable and that BIC penalizes the addition of more clusters.
No description available.

With clustering we can also predict confidence for which collection the NFT would be in – we find its cluster. The confidence % for each collection is equal to the % of all the NFTs from that collection in that cluster.

Since in this context outliers are very important we split our data into outliers and non-outliers for clustering, so essentially we have separate clusters for regulars and outliers. We define if it’s an outlier or not based on it’s price – if it’s way above the average price of all NFTs.

** TODO: Add distribution of NTF transaction prices **

Predicting the price

In order to predict the price of an NFT we:

  1. Find the cluster it is closest to (either a regular or an outlier one).
  2. We get the % score of each collection & determine the most likely collection
  3. We get all NFTs from that collection
  4. Predict the price based on the prices of all NFTs above mentioned

For predicting the price we can:

  1. Simply average
  2. Do a weighted average based on how easy the sale of the NFT was. NFTs that sold more often in the last 30 days have a higher weight than those that sold less often.

For 2. we can do that either linearly or exponentially.

The above mentioned solution achieves a MAE of: TODO

We compared it to a baseline method of predicting the price – picking a random price out of all the NFT price records.
The baseline method achieves an MAE of: TODO

Method 2

In order to predict the price of an NFT we:

  1. Get all latest prices (last trade price) of each token in the last 30 days
  2. Get the wanted token’s traits
  3. For each trait find all tokens which share it and get the mean of all their prices
  4. If a given trait doesn’t have any value drawn from the other tokens it is excluded
  5. Calculate weighted average of the prices and the rarity of each trait

For example, from collection BAYC token #2121 has the following traits with respective rarity and average price for each cluster of tokens with the same trait:

Background: Blue – 12.42% – 92.91 ETH

Fur: Cream – 6.36% – 84.13 ETH

Clothes: Lab Coat – 1.44% – 109.14 ETH

Mouth: Phoneme Vuh – 3.33% – 83.12 ETH

Eyes: Holographic – 1.51% – 117.25 ETH

Hat: Halo – 3.24% – 96.60 ETH

Open photo

The resulting price from the calculations is 97.43 ETH for the period Feb 25 2022 – Mar 28 2022

4. Future work

The prices also depend on a lot of other factors. We would like to experiment with them in the following order:

  1. Take into consideration market data for the prices of cryptocurrencies at the time of each transaction
  2. Take into consideration data of the owners of NFTs
  3. Map an NFT with its owner in Twitter, fetch his tweets and engineer some features based on sentiment analysis

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3 thoughts on “Datathon 2022 NFT Nexo case”

  2. 0
    votes

    I appreciate very much your attempt to find segments of similar nft-s and believe that after appropriate data prep the results would be better. 
    After that additional modelling step could be made within each segment.

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