Using DeFi Portfolio Data for On-Chain Reputation

Detailed Instructions

The first step is to identify and connect to reliable data sources that provide a comprehensive view of a user's DeFi activity. On-chain data is immutable and verifiable, making it ideal for reputation scoring. You'll need to interact with multiple blockchains (Ethereum, Polygon, Arbitrum, etc.) and their respective DeFi protocols (Aave, Uniswap, Compound).

• Sub-step 1: Set up blockchain node connections or use a node provider like Alchemy or Infura. For Ethereum mainnet, your RPC endpoint might be https://eth-mainnet.g.alchemy.com/v2/YOUR_API_KEY.
• Sub-step 2: Identify key contract addresses for protocols you want to track. For example, the Aave V3 Pool contract on Ethereum mainnet is 0x87870Bca3F3fD6335C3F4ce8392D69350B4fA4E2.
• Sub-step 3: Use event listening or subgraphs to capture transactions. You can query The Graph for specific user interactions.

Tip: Prioritize protocols with high Total Value Locked (TVL) and user bases to capture the most significant reputation signals.

Detailed Instructions

Raw transaction data must be aggregated into a coherent portfolio for each wallet address. Portfolio aggregation involves summing holdings, debts, and yields across all connected protocols. Value normalization is critical, as assets have different decimals and prices; all values should be converted to a stable unit like USD.

• Sub-step 1: Parse transaction logs and event data to extract deposits, withdrawals, borrows, and repays for each user. Use libraries like ethers.js or web3.py.
• Sub-step 2: Calculate portfolio metrics such as total supplied assets, total borrowed assets, net worth, and health factor (for lending protocols).
• Sub-step 3: Fetch real-time price data from an oracle like Chainlink (0x5f4eC3Df9cbd43714FE2740f5E3616155c5b8419 for ETH/USD) or a DEX aggregator API to convert token amounts to USD.

javascriptCopy
// Example: Calculating net worth in USD using ethers.js and a price feed
const suppliedValueUSD = suppliedTokens * tokenPriceUSD;
const borrowedValueUSD = borrowedTokens * tokenPriceUSD;
const netWorthUSD = suppliedValueUSD - borrowedValueUSD;

Tip: Store normalized portfolio snapshots at regular intervals (e.g., daily) to track user behavior over time.

Detailed Instructions

Transform normalized portfolio data into quantifiable reputation signals. These signals measure trustworthiness, sophistication, and reliability. Key signals often include longevity (how long assets are held), diversity (spread across protocols), responsibility (maintaining safe health factors), and productivity (earning yield).

• Sub-step 1: Calculate time-weighted metrics. Determine the average duration of supplied liquidity or the age of the oldest active position. A user holding a position for over 180 days signals strong conviction.
• Sub-step 2: Assess risk management. Monitor the health factor on lending protocols; a consistent health factor above 2.0 indicates responsible borrowing.
• Sub-step 3: Measure protocol engagement. Count the number of unique, high-quality (TVL > $100M) protocols a user interacts with. Interaction with 5+ major protocols suggests sophistication.

Tip: Avoid signals that can be easily gamed, like one-time large deposits. Focus on sustained, complex behaviors.

Detailed Instructions

The final step is to synthesize the extracted signals into a single, usable reputation score. This involves applying weights to each signal and calculating a final value (e.g., 0-1000). The score must then be stored efficiently for querying, either in a centralized database or, for maximum composability, on-chain via a smart contract.

• Sub-step 1: Define a scoring algorithm. Assign weights to your signals (e.g., Longevity: 40%, Diversity: 30%, Health Factor: 30%). Use a formula like: Score = (Longevity_Score * 0.4) + (Diversity_Score * 0.3) + (Health_Score * 0.3).
• Sub-step 2: Implement storage. For off-chain, use a PostgreSQL database with a table user_reputation containing address, score, and timestamp. For on-chain, deploy a simple registry contract.

solidityCopy
// Example: Simplistic on-chain reputation registry contract snippet
contract ReputationRegistry {
    mapping(address => uint256) public reputationScore;
    function updateScore(address user, uint256 score) external {
        // Add access control in production
        reputationScore[user] = score;
    }
}

• Sub-step 3: Establish an update cadence. Scores should be recalculated and updated periodically (e.g., weekly) to reflect recent behavior.

Tip: Consider making the scoring logic and weights upgradeable to adapt to new insights and prevent score manipulation.

Detailed Instructions

First, identify the key DeFi protocols and on-chain activities that contribute to a user's financial reputation. This includes data from lending platforms (like Aave, Compound), decentralized exchanges (like Uniswap, Curve), and yield aggregators. The core logic involves aggregating metrics such as total value locked (TVL), historical transaction volume, loan repayment history, and length of engagement. For example, a user's reputation could be a weighted score where timely loan repayments on Aave's Ethereum mainnet pool (address: 0x7d2768dE32b0b80b7a3454c06BdAc94A69DDc7A9) contribute 40%, while consistent liquidity provision on Uniswap V3 contributes 30%.

• Sub-step 1: Map relevant smart contract addresses for data extraction, such as Aave's LendingPool and Uniswap's NonfungiblePositionManager.
• Sub-step 2: Design the aggregation formula, e.g., Reputation Score = (0.4 * LoanScore) + (0.3 * LiquidityScore) + (0.2 * VolumeScore) + (0.1 * AgeScore).
• Sub-step 3: Determine the blockchain networks to support, starting with Ethereum mainnet and expanding to Layer 2s like Arbitrum.

Tip: Use subgraph queries from The Graph for efficient historical data fetching instead of direct RPC calls for complex metrics.

Detailed Instructions

Create a reputation oracle smart contract that receives signed data from off-chain aggregators and makes it available on-chain. The contract must have functions to update scores in a permissioned manner and allow dApps to query them. Implement a multi-signature or decentralized oracle network pattern for data updates to ensure security. The contract should store a mapping from user address to a struct containing the score, timestamp, and a nonce to prevent replay attacks. For example:

solidityCopy
struct ReputationData {
    uint256 score; // 0-1000 scale
    uint256 updatedAt;
    uint256 nonce;
}
mapping(address => ReputationData) public reputationOf;

• Sub-step 1: Write and test the contract in Solidity using Foundry, implementing critical functions like updateReputation(address user, uint256 score, uint256 nonce, bytes signature).
• Sub-step 2: Add access control using OpenZeppelin's Ownable or a multisig contract like Gnosis Safe for the updateReputation function.
• Sub-step 3: Deploy the contract to a testnet (e.g., Sepolia) first, verifying it on Etherscan with constructor arguments.

Tip: Include an event emission for each update (e.g., ReputationUpdated) so dApps can efficiently track changes.

Detailed Instructions

Build a serverless function or microservice (using AWS Lambda or a similar framework) that periodically calculates reputation scores. This service must query blockchain data via RPC providers (like Alchemy or Infura) and subgraphs, apply the aggregation logic, and sign the results with a private key whose public key is whitelisted in the oracle contract. The service should run on a schedule (e.g., every 24 hours) and handle thousands of addresses efficiently. Key implementation details include using the ethers.js library to generate signatures and managing nonces to ensure each update is unique.

• Sub-step 1: Set up a Node.js service that fetches raw data for a batch of addresses using batch RPC calls or multicall contracts.
• Sub-step 2: Compute the score for each address, normalizing values to a standard range (e.g., 0-1000).
• Sub-step 3: Sign the message keccak256(abi.encodePacked(userAddress, score, nonce)) using the oracle's private key and submit the transaction via ethers.

Tip: Store the current nonce for each user in a database (like PostgreSQL) to maintain state between runs and prevent conflicts.

Detailed Instructions

dApp integration involves reading the oracle contract to retrieve reputation scores and using them to gate features like loan limits, voting power, or access to premium services. Developers must call the reputationOf view function and implement client-side logic to interpret the score. For security, dApps should verify the score's freshness by checking the updatedAt timestamp and consider it stale if older than a defined threshold (e.g., 7 days). Additionally, implement a fallback mechanism in case the oracle is unresponsive, perhaps using a cached value or a default score.

• Sub-step 1: In your dApp's frontend (using a framework like React), use a library like wagmi or ethers to read the score: const score = await contract.reputationOf(userAddress);.
• Sub-step 2: Design UI components that display the reputation tier (e.g., "Gold" for scores > 800) based on the retrieved value.
• Sub-step 3: Write smart contract functions in your dApp that require a minimum reputation score, reverting if reputationOf(msg.sender) < requiredScore.

Tip: To reduce gas costs for users, consider having your dApp contract store a local cached copy of the score updated via a relayed meta-transaction system.