Web3 User Scoring System

Overview

This scoring system calculates a comprehensive score for Web3 users, taking into account their Ethereum holdings, NFTs, activity, ERC20 tokens, and transaction behavior. The aim is to create a fair and dynamic score that reflects a user's trustworthiness and financial health in the Web3 ecosystem.

Components

1. Transaction-based Scoring Calculates a score based on the user's interactions with other addresses. It considers the frequency of transactions and the score difference between interacting addresses.

2. Holdings-based Scoring Evaluates Ethereum balance, NFT holdings, account activity, and ERC20 token holdings to compute a score that reflects the user's asset strength.

3. Overall Scoring Combines transaction-based and holdings-based scores to produce a final score between 0 and 1000.

4. LSTM Model for Liquidation Risk Prediction: designed to predict the likelihood of loan liquidation in decentralized finance (DeFi) platforms

Transaction-based Scoring Formulae:

• Influence Factor (IF): $A \times (1 - e^{-k \times |S_{\text{current}} - S_{\text{other}}|})$
• Frequency Modifier (FM): $\dfrac{B} {(1 + e^{-C \times (n_{transactions} - 1)})}$
• Transaction Score: Varies based on the transaction type (sending or receiving) and the score difference between interacting addresses.

Holdings-based Scoring Formulae:

• Ethereum Balance Score: $(\dfrac{balance} {1000000}) \times 300$

• Ethereum NFTs Held Score: $(\dfrac{nfts}{20}) \times 300$

• Ethereum Activity Score: $(\dfrac{age} {10}) \times 150$

• ERC20 Tokens Score: $(\dfrac{balance_{erc20}}{10000}) \times 100$

LSTM Model for Liquidation Risk Prediction

This LSTM (Long Short-Term Memory) model is specifically designed to predict the likelihood of loan liquidation in decentralized finance (DeFi) platforms, using data from the Aave lending protocol.

Liquidation Process in DeFi

• Liquidation occurs when a borrower's collateral value falls below a certain threshold, making the loan risky for lenders.
• The model predicts this risk by analyzing patterns in loan transactions and borrower behaviors.

Model Architecture

• Multiple LSTM layers capture temporal dependencies in transaction data, crucial for understanding borrower's financial behavior over time.
• Dropout layers follow each LSTM layer, reducing overfitting and enhancing model generalization.
• The output layer predicts the probability of liquidation, helping platforms and users to proactively manage risk.

Application

• The model aids in assessing the liquidation risk of loans on DeFi platforms.
• It provides insights for both lenders and borrowers, enabling informed decision-making and risk management in the dynamic DeFi ecosystem.

HOW TO RUN THE PROJECT:

1. Build docker image with:

docker build -t serpro-web3 .

2. Run the server image with:

docker run -p 8000:80 -it serpro-web3

3. Install the packages on the frontend with:

npm install

4. Run the local frontend with:

npm run dev

NOTES:

To run the project you will need a postgres instance as well an alchemy sdk api key

use the following env as starting point:

DATABASE_URL=
# the next env is optional and you should use to run the project migrations
ALEMBIC_DATABASE_URL=
ALCHEMY_API_KEY=
NFT_CONTRACT_ADDRESS=0x0d4c5f4c0b5dabf5f0e2c6dcbf0b4c0b91d0d1f4