The problem Stake X gain Star Protocol Web3 solves

GainX aims to solve the common problems faced by NFT holders and users wanting some liquidity to earn leverage with their NFTs. Problems and their solutions via GainX.

Over-collateralized loans are the most common type of loans in today's market. But not everyone has so much liquidity or worth of assets. To solve this problem among the users, GainX sets up a reputation mechanism. The reputation majorly depends on transaction history, loan repayment history and staked amount in GainX liquidity pool. Also, a borrower can stake money to our protocol to leverage under-collateralized loans in some cases.

Poor hedging and risk management. NFTs and cryptocurrencies are very volatile at this point of time and need advanced risk management and tolerance mechanisms. Derivatives are an effective solution for this problem. Futures contract can be used to lock an APY return from the borrower and a Put Option can be used as an insurance for steep valuation drop in NFTs due to any reasons.

Less accurate and scalable machine learning model that can predict future prices for NFTs. State of the art model uses Recurrent Neural Network as base architecture which has proven results in various industrial applications .We also have a solution of using NEURAL PROPHET forecasting architecture by META (previously known as Facebook) . It uses combine power of both statistical algorithm and Neural Network architecture to make forecasting more generalized and robust It uses past NFT transaction as feed data and compute all underlying pattern . This process is training . Once the model is trained , it is now set to make predictions of NFT to any upcoming number of months . Provided sufficient data we can also analyze the future market condition trends such as CPI , NFT index which aim to facilitate valuation, portfolio tracking, lending and borrowing and collateralisation of non-fungible tokens.

Challenges I ran into

We lacked data, due to which we needed to reconsider model architecture . Expected data was a major challenge we ran through . Moreover rigorous tests were also a challenge to ensure that the outcome of our model should be more accurate. But to counter this problem we devised a ML model that would give accurate results despite that and we were able to achieve it. Our model works close to accurate and shows impressive results. We need more data to analyze the neural network architecture to make the model more generalized . Neural Prophet dependencies mismatch was a big challenge as newer versions of dependencies do not sync with older ones .

We have used SUI blockchian for more stability and scalability for this hackathon.
There were a lot of issues while SUI node setup, developing smart contracts, compiling, deploying and integrating as we are experienced in mostly EVM chains and SUI has it’s own learning curve