Add goddid.money, used Entropy in credit score calculation

entropy/polymarket_credit_calculation/creditContract/CreditScore.sol

@@ -0,0 +1,290 @@
+// SPDX-License-Identifier: MIT
+pragma solidity ^0.8.20;
+
+import "@pythnetwork/entropy-sdk-solidity/IEntropyV2.sol";
+import "@pythnetwork/entropy-sdk-solidity/IEntropyConsumer.sol";
+import "@pythnetwork/entropy-sdk-solidity/EntropyStructsV2.sol";
+
+contract CreditScore is IEntropyConsumer {
+    // Structs to hold Polymarket data
+    struct ClosedPosition {
+        int256 realizedPnl;
+        uint256 totalBought;
+        bytes32 asset;
+    }
+
+    struct CurrentPosition {
+        uint256 size;
+        uint256 avgPrice;
+        int256 initialValue;
+        int256 currentValue;
+        int256 cashPnl;
+        int256 percentPnl;
+        uint256 totalBought;
+        int256 realizedPnl;
+        int256 percentRealizedPnl;
+        uint256 curPrice;
+    }
+
+    struct UserData {
+        string name;
+        uint256 value;
+        ClosedPosition[] closedPositions;
+        CurrentPosition[] currentPositions;
+    }
+
+    struct CreditScoreData {
+        uint256 score;
+        uint256 timestamp;
+        bytes32 entropyUsed;
+        bool isCalculated;
+    }
+
+    // Events
+    event CreditScoreRequested(address indexed user, uint64 sequenceNumber);
+    event CreditScoreCalculated(address indexed user, uint256 score, bytes32 entropyUsed);
+
+    // State variables
+    IEntropyV2 private entropy;
+    address private entropyProvider;
+
+    mapping(address => UserData) public userData;
+    mapping(address => CreditScoreData) public creditScores;
+    mapping(uint64 => address) private sequenceToUser;
+    mapping(address => uint64) private userToSequence;
+
+    // Constants for score calculation
+    uint256 private constant MAX_SCORE = 999;
+    uint256 private constant MIN_SCORE = 0;
+    uint256 private constant ENTROPY_VARIANCE_WEIGHT = 50; // Max 50 points of variance from entropy
+
+    constructor(address _entropy, address _entropyProvider) {
+        entropy = IEntropyV2(_entropy);
+        entropyProvider = _entropyProvider;
+    }
+
+    // Submit user data and request credit score calculation
+    function submitUserDataAndRequestScore(
+        string memory name,
+        uint256 value,
+        ClosedPosition[] memory closedPositions,
+        CurrentPosition[] memory currentPositions
+    ) external payable {
+        // Clear previous data
+        delete userData[msg.sender].closedPositions;
+        delete userData[msg.sender].currentPositions;
+
+        // Store user data
+        userData[msg.sender].name = name;
+        userData[msg.sender].value = value;
+
+        // Store closed positions
+        for (uint i = 0; i < closedPositions.length; i++) {
+            userData[msg.sender].closedPositions.push(closedPositions[i]);
+        }
+
+        // Store current positions
+        for (uint i = 0; i < currentPositions.length; i++) {
+            userData[msg.sender].currentPositions.push(currentPositions[i]);
+        }
+
+        // Request entropy for variance
+        uint256 fee = entropy.getFeeV2();
+        require(msg.value >= fee, "Insufficient fee for entropy");
+
+        uint64 sequenceNumber = entropy.requestV2{ value: fee }();
+        sequenceToUser[sequenceNumber] = msg.sender;
+        userToSequence[msg.sender] = sequenceNumber;
+
+        emit CreditScoreRequested(msg.sender, sequenceNumber);
+    }
+
+    // Calculate base credit score without entropy (for preview purposes)
+    function calculateBaseScore(address user) public view returns (uint256) {
+        UserData storage data = userData[user];
+
+        if (data.closedPositions.length == 0 && data.currentPositions.length == 0) {
+            return 500; // Default middle score for new users
+        }
+
+        uint256 score = 0;
+        uint256 weightSum = 0;
+
+        // 1. Calculate profit/loss metrics from closed positions (40% weight)
+        if (data.closedPositions.length > 0) {
+            int256 totalPnl = 0;
+            uint256 totalInvested = 0;
+            uint256 winCount = 0;
+
+            for (uint i = 0; i < data.closedPositions.length; i++) {
+                totalPnl += data.closedPositions[i].realizedPnl;
+                totalInvested += data.closedPositions[i].totalBought;
+                if (data.closedPositions[i].realizedPnl > 0) {
+                    winCount++;
+                }
+            }
+
+            // Win rate (0-200 points)
+            uint256 winRate = (winCount * 200) / data.closedPositions.length;
+            score += winRate;
+
+            // Profit ratio (0-200 points)
+            if (totalInvested > 0) {
+                if (totalPnl > 0) {
+                    uint256 profitRatio = (uint256(totalPnl) * 200) / totalInvested;
+                    if (profitRatio > 200) profitRatio = 200; // Cap at 200
+                    score += profitRatio;
+                } else {
+                    // Negative PnL reduces score
+                    uint256 lossRatio = (uint256(-totalPnl) * 100) / totalInvested;
+                    if (lossRatio > 200) lossRatio = 200;
+                    score += 0; // No additional score for losses
+                }
+            }
+            weightSum += 400;
+        }
+
+        // 2. Current positions performance (30% weight)
+        if (data.currentPositions.length > 0) {
+            int256 currentTotalPnl = 0;
+            uint256 currentTotalInvested = 0;
+
+            for (uint i = 0; i < data.currentPositions.length; i++) {
+                currentTotalPnl += data.currentPositions[i].cashPnl;
+                currentTotalInvested += data.currentPositions[i].totalBought;
+            }
+
+            // Current position health (0-300 points)
+            if (currentTotalInvested > 0) {
+                if (currentTotalPnl >= 0) {
+                    uint256 currentRatio = (uint256(currentTotalPnl) * 150) / currentTotalInvested;
+                    if (currentRatio > 150) currentRatio = 150;
+                    score += 150 + currentRatio; // Base 150 + up to 150 bonus
+                } else {
+                    // Losses reduce from base
+                    uint256 lossRatio = (uint256(-currentTotalPnl) * 150) / currentTotalInvested;
+                    if (lossRatio > 150) lossRatio = 150;
+                    score += (150 - lossRatio);
+                }
+            } else {
+                score += 150; // Neutral if no current positions
+            }
+            weightSum += 300;
+        }
+
+        // 3. Portfolio value consideration (20% weight)
+        if (data.value > 0) {
+            // Scale based on value (logarithmic scale)
+            uint256 valueScore = 0;
+            if (data.value >= 1000000) {
+                // 1M+
+                valueScore = 200;
+            } else if (data.value >= 500000) {
+                // 500k+
+                valueScore = 180;
+            } else if (data.value >= 100000) {
+                // 100k+
+                valueScore = 150;
+            } else if (data.value >= 50000) {
+                // 50k+
+                valueScore = 120;
+            } else if (data.value >= 10000) {
+                // 10k+
+                valueScore = 100;
+            } else {
+                valueScore = (data.value * 100) / 10000; // Linear scale below 10k
+            }
+            score += valueScore;
+            weightSum += 200;
+        }
+
+        // 4. Activity bonus (10% weight)
+        uint256 totalTrades = data.closedPositions.length + data.currentPositions.length;
+        uint256 activityScore = 0;
+        if (totalTrades >= 20) {
+            activityScore = 100;
+        } else if (totalTrades >= 10) {
+            activityScore = 80;
+        } else if (totalTrades >= 5) {
+            activityScore = 60;
+        } else if (totalTrades > 0) {
+            activityScore = (totalTrades * 60) / 5;
+        }
+        score += activityScore;
+        weightSum += 100;
+
+        // Normalize to 0-949 range (leaving room for entropy variance)
+        if (weightSum > 0) {
+            score = (score * 949) / weightSum;
+        }
+
+        return score;
+    }
+
+    // Entropy callback implementation
+    function entropyCallback(uint64 sequenceNumber, address, bytes32 randomNumber) internal override {
+        address user = sequenceToUser[sequenceNumber];
+        require(user != address(0), "Invalid sequence number");
+
+        // Calculate base score
+        uint256 baseScore = calculateBaseScore(user);
+
+        // Add entropy-based variance (±ENTROPY_VARIANCE_WEIGHT points)
+        uint256 entropyFactor = uint256(randomNumber) % (ENTROPY_VARIANCE_WEIGHT * 2 + 1);
+        int256 variance = int256(entropyFactor) - int256(ENTROPY_VARIANCE_WEIGHT);
+
+        // Calculate final score with bounds checking
+        int256 finalScoreInt = int256(baseScore) + variance;
+        uint256 finalScore;
+
+        if (finalScoreInt < 0) {
+            finalScore = 0;
+        } else if (finalScoreInt > int256(MAX_SCORE)) {
+            finalScore = MAX_SCORE;
+        } else {
+            finalScore = uint256(finalScoreInt);
+        }
+
+        // Store the calculated score
+        creditScores[user] = CreditScoreData({
+            score: finalScore,
+            timestamp: block.timestamp,
+            entropyUsed: randomNumber,
+            isCalculated: true
+        });
+
+        emit CreditScoreCalculated(user, finalScore, randomNumber);
+
+        // Clean up mappings
+        delete sequenceToUser[sequenceNumber];
+        delete userToSequence[user];
+    }
+
+    // Required by IEntropyConsumer
+    function getEntropy() internal view override returns (address) {
+        return address(entropy);
+    }
+
+    // Get the fee required for entropy
+    function getEntropyFee() public view returns (uint256) {
+        return entropy.getFeeV2();
+    }
+
+    // Get user's credit score
+    function getCreditScore(address user) external view returns (uint256 score, uint256 timestamp, bool isCalculated) {
+        CreditScoreData memory data = creditScores[user];
+        return (data.score, data.timestamp, data.isCalculated);
+    }
+
+    // Get user's pending sequence number
+    function getPendingSequence(address user) external view returns (uint64) {
+        return userToSequence[user];
+    }
+
+    // Check if user has pending score calculation
+    function hasPendingCalculation(address user) external view returns (bool) {
+        return userToSequence[user] != 0;
+    }
+
+    receive() external payable {}
+}