Scoring Agent
Scoring Algorithm Overview
Component Architecture
The Scoring Agent represents the analytical core of Kaizen AI, responsible for transforming multi-dimensional project data into actionable risk assessments through the proprietary Kaizen Score (0-100). This agent employs a sophisticated hybrid approach that combines rule-based logic with advanced machine learning models to deliver accurate, real-time risk evaluations for cryptocurrency projects.
Core Responsibilities:
Multi-factor risk assessment across technical, economic, social, and governance dimensions
Real-time score calculation and updates based on changing project conditions
Machine learning model ensemble management and optimization
Historical pattern recognition and predictive analytics
Confidence interval calculation and uncertainty quantification
Dynamic threshold adjustment based on market conditions
Scoring Philosophy: The Kaizen Score reflects the principle of continuous improvement by adapting to evolving threat patterns and market dynamics. The scoring system prioritizes transparency, reproducibility, and actionable insights, ensuring users understand both the score rationale and the confidence level of each assessment.
Scoring Framework Architecture
Multi-Dimensional Scoring Model
// Core scoring interfaces and architecture
interface ScoringDimension {
readonly name: string;
readonly weight: number;
readonly factors: ScoringFactor[];
calculate(data: ProjectData): Promise<DimensionScore>;
}
interface ScoringFactor {
readonly id: string;
readonly name: string;
readonly weight: number;
readonly type: 'binary' | 'continuous' | 'categorical';
readonly validator: FactorValidator;
evaluate(data: ProjectData): Promise<FactorResult>;
}
interface KaizenScore {
readonly score: number; // 0-100
readonly confidence: number; // 0-1
readonly riskLevel: RiskLevel;
readonly breakdown: ScoreBreakdown;
readonly alerts: Alert[];
readonly metadata: ScoringMetadata;
}
export class ScoringEngine {
private dimensions: Map<string, ScoringDimension> = new Map();
private mlModels: MLModelEnsemble;
private ruleEngine: RuleEngine;
private scoreCache: ScoringCache;
private performanceTracker: PerformanceTracker;
constructor(
private config: ScoringConfig,
private dataSource: DataSourceInterface,
private modelRegistry: ModelRegistry
) {
this.mlModels = new MLModelEnsemble(config.models);
this.ruleEngine = new RuleEngine(config.rules);
this.scoreCache = new ScoringCache(config.cache);
this.performanceTracker = new PerformanceTracker();
this.initializeDimensions();
this.loadModels();
}
private initializeDimensions() {
// Technical Security Dimension (30% weight)
this.dimensions.set('technical', new TechnicalSecurityDimension({
weight: 0.30,
factors: [
new ContractVerificationFactor(0.25),
new AuditStatusFactor(0.30),
new OwnershipAnalysisFactor(0.20),
new HoneypotDetectionFactor(0.15),
new CodeComplexityFactor(0.10)
]
}));
// Economic Health Dimension (25% weight)
this.dimensions.set('economic', new EconomicHealthDimension({
weight: 0.25,
factors: [
new LiquidityAnalysisFactor(0.30),
new TokenDistributionFactor(0.25),
new MarketCapAnalysisFactor(0.20),
new TradingVolumeStabilityFactor(0.15),
new HolderAnalysisFactor(0.10)
]
}));
// Social Intelligence Dimension (25% weight)
this.dimensions.set('social', new SocialIntelligenceDimension({
weight: 0.25,
factors: [
new CommunityHealthFactor(0.30),
new SentimentAnalysisFactor(0.25),
new InfluencerActivityFactor(0.20),
new ManipulationDetectionFactor(0.15),
new EngagementAuthenticityFactor(0.10)
]
}));
// Governance Dimension (20% weight)
this.dimensions.set('governance', new GovernanceDimension({
weight: 0.20,
factors: [
new DecentralizationMeasureFactor(0.35),
new TeamTransparencyFactor(0.25),
new RoadmapExecutionFactor(0.20),
new ComplianceStatusFactor(0.20)
]
}));
}
async calculateScore(projectData: ProjectData): Promise<KaizenScore> {
const startTime = Date.now();
try {
// Check cache first
const cacheKey = this.generateCacheKey(projectData);
const cachedScore = await this.scoreCache.get(cacheKey);
if (cachedScore && this.isCacheValid(cachedScore, projectData)) {
return cachedScore;
}
// Calculate dimension scores in parallel
const dimensionPromises = Array.from(this.dimensions.values()).map(async dimension => {
const score = await dimension.calculate(projectData);
return { dimension: dimension.name, score };
});
const dimensionResults = await Promise.all(dimensionPromises);
// Apply machine learning adjustments
const mlAdjustments = await this.mlModels.predict(projectData, dimensionResults);
// Calculate final score
const finalScore = this.calculateFinalScore(dimensionResults, mlAdjustments);
// Generate alerts based on rules
const alerts = await this.ruleEngine.evaluateAlerts(projectData, finalScore);
// Calculate confidence level
const confidence = this.calculateConfidence(projectData, dimensionResults, mlAdjustments);
const kaizenScore: KaizenScore = {
score: Math.round(finalScore.weightedScore),
confidence,
riskLevel: this.determineRiskLevel(finalScore.weightedScore),
breakdown: finalScore.breakdown,
alerts,
metadata: {
calculatedAt: new Date().toISOString(),
processingTime: Date.now() - startTime,
dataQuality: projectData.qualityScore || 0.8,
modelVersions: this.mlModels.getVersions(),
cacheKey
}
};
// Cache the result
await this.scoreCache.set(cacheKey, kaizenScore);
// Track performance
this.performanceTracker.recordScoring({
processingTime: Date.now() - startTime,
score: kaizenScore.score,
confidence: kaizenScore.confidence,
alertCount: alerts.length
});
return kaizenScore;
} catch (error) {
logger.error('Scoring calculation failed:', error, { projectData });
throw new ScoringError('Failed to calculate Kaizen Score', error);
}
}
}Technical Security Dimension
Contract Security Analysis
// Technical security evaluation implementation
export class TechnicalSecurityDimension implements ScoringDimension {
readonly name = 'technical';
readonly weight = 0.30;
readonly factors: ScoringFactor[];
constructor(config: TechnicalSecurityConfig) {
this.factors = config.factors;
}
async calculate(data: ProjectData): Promise<DimensionScore> {
const factorResults: FactorResult[] = [];
for (const factor of this.factors) {
try {
const result = await factor.evaluate(data);
factorResults.push(result);
} catch (error) {
logger.warn(`Factor evaluation failed: ${factor.id}`, error);
// Use default safe score for failed evaluations
factorResults.push({
factorId: factor.id,
score: 50, // Neutral score
confidence: 0.1, // Low confidence
explanation: 'Evaluation failed - using safe default'
});
}
}
// Calculate weighted average
const weightedScore = this.calculateWeightedScore(factorResults);
const averageConfidence = this.calculateAverageConfidence(factorResults);
return {
dimension: this.name,
score: weightedScore,
confidence: averageConfidence,
factors: factorResults,
alerts: this.generateDimensionAlerts(factorResults)
};
}
private calculateWeightedScore(results: FactorResult[]): number {
let weightedSum = 0;
let totalWeight = 0;
for (let i = 0; i < results.length; i++) {
const factor = this.factors[i];
const result = results[i];
// Adjust weight based on confidence
const adjustedWeight = factor.weight * result.confidence;
weightedSum += result.score * adjustedWeight;
totalWeight += adjustedWeight;
}
return totalWeight > 0 ? weightedSum / totalWeight : 50;
}
}
// Contract verification factor implementation
export class ContractVerificationFactor implements ScoringFactor {
readonly id = 'contract_verification';
readonly name = 'Contract Verification Status';
readonly weight = 0.25;
readonly type = 'binary' as const;
constructor(private contractAnalyzer: ContractAnalyzer) {}
async evaluate(data: ProjectData): Promise<FactorResult> {
try {
const contractInfo = await this.contractAnalyzer.getContractInfo(
data.contractAddress,
data.network
);
if (!contractInfo) {
return {
factorId: this.id,
score: 0,
confidence: 0.9,
explanation: 'Contract not found or inaccessible',
details: { status: 'not_found' }
};
}
// Evaluate verification status
let score = 0;
let explanation = '';
const details: any = {
isVerified: contractInfo.isVerified,
verificationSource: contractInfo.verificationSource
};
if (contractInfo.isVerified) {
// Base score for verification
score = 70;
explanation = 'Contract is verified';
// Bonus points for quality verification
if (contractInfo.verificationSource === 'etherscan' ||
contractInfo.verificationSource === 'sourcify') {
score += 20;
explanation += ' on reputable platform';
}
// Additional points for complete verification
if (contractInfo.hasConstructorArgs && contractInfo.hasLibraries) {
score += 10;
explanation += ' with complete implementation details';
}
details.verificationQuality = 'high';
} else {
score = 20; // Low score for unverified contracts
explanation = 'Contract is not verified - high risk';
details.verificationQuality = 'none';
}
return {
factorId: this.id,
score: Math.min(100, score),
confidence: 0.95,
explanation,
details
};
} catch (error) {
logger.error('Contract verification analysis failed:', error);
return {
factorId: this.id,
score: 30, // Conservative score on error
confidence: 0.3,
explanation: 'Unable to verify contract status',
details: { error: error.message }
};
}
}
}
// Honeypot detection factor
export class HoneypotDetectionFactor implements ScoringFactor {
readonly id = 'honeypot_detection';
readonly name = 'Honeypot Risk Assessment';
readonly weight = 0.15;
readonly type = 'continuous' as const;
constructor(
private honeypotDetector: HoneypotDetector,
private simulationEngine: TransactionSimulator
) {}
async evaluate(data: ProjectData): Promise<FactorResult> {
try {
// Multiple honeypot detection methods
const detectionResults = await Promise.all([
this.honeypotDetector.analyzeBytecode(data.contractAddress, data.network),
this.honeypotDetector.checkKnownPatterns(data.contractAddress),
this.simulationEngine.simulateTransactions(data.contractAddress, data.network),
this.honeypotDetector.analyzeTokenomics(data)
]);
const [bytecodeAnalysis, patternAnalysis, simulationResults, tokenomicsAnalysis] = detectionResults;
// Calculate composite risk score
let riskScore = 0;
const riskFactors: string[] = [];
// Bytecode analysis (40% weight)
if (bytecodeAnalysis.suspiciousFunctions.length > 0) {
riskScore += 40;
riskFactors.push(`Suspicious functions: ${bytecodeAnalysis.suspiciousFunctions.join(', ')}`);
}
// Known pattern matching (30% weight)
if (patternAnalysis.matchedPatterns.length > 0) {
riskScore += 30;
riskFactors.push(`Known malicious patterns detected`);
}
// Transaction simulation (20% weight)
if (simulationResults.sellSimulation.success === false) {
riskScore += 20;
riskFactors.push('Sell transaction simulation failed');
}
// Tokenomics red flags (10% weight)
if (tokenomicsAnalysis.hasHighTax || tokenomicsAnalysis.hasBlacklist) {
riskScore += 10;
riskFactors.push('Suspicious tokenomics detected');
}
// Convert risk score to safety score (inverse)
const safetyScore = Math.max(0, 100 - riskScore);
return {
factorId: this.id,
score: safetyScore,
confidence: 0.85,
explanation: riskScore === 0
? 'No honeypot indicators detected'
: `Honeypot risk detected: ${riskFactors.join('; ')}`,
details: {
riskScore,
riskFactors,
bytecodeAnalysis,
simulationResults: {
buySuccess: simulationResults.buySimulation.success,
sellSuccess: simulationResults.sellSimulation.success,
gasEstimate: simulationResults.gasEstimate
}
}
};
} catch (error) {
logger.error('Honeypot detection failed:', error);
return {
factorId: this.id,
score: 60, // Neutral score on error
confidence: 0.4,
explanation: 'Honeypot analysis incomplete - data insufficient',
details: { error: error.message }
};
}
}
}Machine Learning Models
Ensemble Model Implementation
// Advanced ML model ensemble for score prediction and adjustment
export class MLModelEnsemble {
private models: Map<string, MLModel> = new Map();
private ensembleStrategy: EnsembleStrategy;
private featureEngine: FeatureEngine;
private modelValidator: ModelValidator;
constructor(private config: MLConfig) {
this.ensembleStrategy = new WeightedVotingStrategy(config.ensemble);
this.featureEngine = new FeatureEngine(config.features);
this.modelValidator = new ModelValidator(config.validation);
this.initializeModels();
}
private async initializeModels() {
// Primary classification models
this.models.set('xgboost_classifier', new XGBoostModel({
objective: 'binary:logistic',
max_depth: 6,
learning_rate: 0.1,
n_estimators: 100,
subsample: 0.8,
colsample_bytree: 0.8,
random_state: 42
}));
this.models.set('random_forest', new RandomForestModel({
n_estimators: 200,
max_depth: 8,
min_samples_split: 5,
min_samples_leaf: 2,
random_state: 42
}));
this.models.set('neural_network', new NeuralNetworkModel({
layers: [
{ units: 128, activation: 'relu', dropout: 0.3 },
{ units: 64, activation: 'relu', dropout: 0.2 },
{ units: 32, activation: 'relu', dropout: 0.1 },
{ units: 1, activation: 'sigmoid' }
],
optimizer: 'adam',
learning_rate: 0.001,
batch_size: 32,
epochs: 100
}));
// Specialized models
this.models.set('lstm_time_series', new LSTMModel({
sequence_length: 24, // 24 hours of data
hidden_units: 50,
dropout: 0.2,
objective: 'regression'
}));
this.models.set('isolation_forest', new IsolationForestModel({
n_estimators: 100,
contamination: 0.1,
random_state: 42
}));
// Load pre-trained weights
await this.loadModelWeights();
}
async predict(
projectData: ProjectData,
dimensionResults: DimensionResult[]
): Promise<MLAdjustments> {
try {
// Extract features for ML models
const features = await this.featureEngine.extractFeatures(projectData, dimensionResults);
// Get predictions from each model
const predictions: Map<string, ModelPrediction> = new Map();
for (const [modelName, model] of this.models) {
try {
const prediction = await model.predict(features);
predictions.set(modelName, prediction);
} catch (error) {
logger.warn(`Model prediction failed: ${modelName}`, error);
// Continue with other models
}
}
// Ensemble the predictions
const ensemblePrediction = await this.ensembleStrategy.combine(predictions);
// Generate adjustments based on ensemble prediction
const adjustments = this.generateAdjustments(ensemblePrediction, dimensionResults);
return adjustments;
} catch (error) {
logger.error('ML prediction failed:', error);
// Return neutral adjustments on error
return {
scoreAdjustment: 0,
confidenceAdjustment: 0,
riskFactorAdjustments: {},
anomalyScore: 0.5,
explanation: 'ML analysis unavailable'
};
}
}
private generateAdjustments(
prediction: EnsemblePrediction,
dimensionResults: DimensionResult[]
): MLAdjustments {
const baseScore = this.calculateBaseScore(dimensionResults);
// ML adjustment based on prediction confidence and value
let scoreAdjustment = 0;
if (prediction.confidence > 0.8) {
// High confidence predictions get more weight
const predictionScore = prediction.probability * 100;
const scoreDifference = predictionScore - baseScore;
// Apply up to 15% adjustment based on ML prediction
scoreAdjustment = Math.max(-15, Math.min(15, scoreDifference * 0.3));
}
// Anomaly detection adjustment
const anomalyScore = prediction.anomalyScore || 0.5;
if (anomalyScore > 0.8) {
scoreAdjustment -= 10; // Penalize highly anomalous projects
}
// Risk factor specific adjustments
const riskFactorAdjustments: Record<string, number> = {};
if (prediction.featureImportance) {
for (const [feature, importance] of Object.entries(prediction.featureImportance)) {
if (importance > 0.1) { // Significant features
riskFactorAdjustments[feature] = importance * scoreAdjustment;
}
}
}
return {
scoreAdjustment,
confidenceAdjustment: prediction.confidence > 0.7 ? 0.1 : -0.05,
riskFactorAdjustments,
anomalyScore,
explanation: this.generateMLExplanation(prediction, scoreAdjustment)
};
}
private generateMLExplanation(prediction: EnsemblePrediction, adjustment: number): string {
if (Math.abs(adjustment) < 2) {
return 'ML analysis confirms rule-based assessment';
}
if (adjustment > 0) {
return `ML models suggest lower risk than initial assessment (confidence: ${(prediction.confidence * 100).toFixed(1)}%)`;
} else {
return `ML models indicate higher risk factors detected (confidence: ${(prediction.confidence * 100).toFixed(1)}%)`;
}
}
}
// XGBoost model implementation
export class XGBoostModel implements MLModel {
private model: any; // XGBoost model instance
private featureNames: string[] = [];
private isLoaded = false;
constructor(private config: XGBoostConfig) {}
async predict(features: FeatureVector): Promise<ModelPrediction> {
if (!this.isLoaded) {
await this.loadModel();
}
try {
// Prepare features in correct format
const featureArray = this.prepareFeatures(features);
// Get prediction
const prediction = await this.model.predict(featureArray);
const probability = Array.isArray(prediction) ? prediction[0] : prediction;
// Get feature importance
const featureImportance = await this.model.getFeatureImportance();
return {
probability,
confidence: this.calculateConfidence(probability, featureArray),
featureImportance: this.mapFeatureImportance(featureImportance),
modelName: 'xgboost_classifier'
};
} catch (error) {
logger.error('XGBoost prediction failed:', error);
throw new ModelPredictionError('XGBoost prediction failed', error);
}
}
private prepareFeatures(features: FeatureVector): number[] {
const featureArray: number[] = [];
for (const featureName of this.featureNames) {
const value = features[featureName];
if (typeof value === 'number') {
featureArray.push(value);
} else if (typeof value === 'boolean') {
featureArray.push(value ? 1 : 0);
} else if (value === null || value === undefined) {
featureArray.push(0); // Default value for missing features
} else {
// Handle categorical features
featureArray.push(this.encodeCategorical(value.toString()));
}
}
return featureArray;
}
private calculateConfidence(probability: number, features: number[]): number {
// Confidence based on prediction certainty and feature completeness
const predictionCertainty = Math.abs(probability - 0.5) * 2; // 0 to 1
// Feature completeness (non-zero features / total features)
const nonZeroFeatures = features.filter(f => f !== 0).length;
const featureCompleteness = nonZeroFeatures / features.length;
// Combined confidence score
return (predictionCertainty * 0.7) + (featureCompleteness * 0.3);
}
async train(trainingData: TrainingDataset): Promise<TrainingResult> {
try {
const { features, labels, validationFeatures, validationLabels } = trainingData;
// Prepare training data
const trainMatrix = this.prepareTrainingMatrix(features, labels);
const validMatrix = this.prepareTrainingMatrix(validationFeatures, validationLabels);
// Train model
const startTime = Date.now();
this.model = await this.trainXGBoost(trainMatrix, validMatrix);
const trainingTime = Date.now() - startTime;
// Validate model performance
const validationResults = await this.validateModel(validationFeatures, validationLabels);
this.isLoaded = true;
return {
modelName: 'xgboost_classifier',
trainingTime,
accuracy: validationResults.accuracy,
precision: validationResults.precision,
recall: validationResults.recall,
f1Score: validationResults.f1Score,
auc: validationResults.auc,
featureImportance: validationResults.featureImportance
};
} catch (error) {
logger.error('XGBoost training failed:', error);
throw new ModelTrainingError('XGBoost training failed', error);
}
}
private async trainXGBoost(trainMatrix: any, validMatrix: any): Promise<any> {
// Implementation would use actual XGBoost library
// This is a placeholder for the training logic
const model = {
predict: async (features: number[]) => {
// Simplified prediction logic
const sum = features.reduce((a, b) => a + b, 0);
return Math.min(1, Math.max(0, sum / features.length));
},
getFeatureImportance: async () => {
// Return mock feature importance
const importance: Record<string, number> = {};
this.featureNames.forEach((name, index) => {
importance[name] = Math.random() * 0.1; // Mock importance
});
return importance;
}
};
return model;
}
}Pattern Recognition
Advanced Pattern Detection System
// Pattern recognition for fraud detection and risk assessment
export class PatternRecognitionEngine {
private patternDetectors: Map<string, PatternDetector> = new Map();
private anomalyDetector: AnomalyDetector;
private timeSeriesAnalyzer: TimeSeriesAnalyzer;
private behaviorAnalyzer: BehaviorAnalyzer;
constructor(private config: PatternRecognitionConfig) {
this.anomalyDetector = new AnomalyDetector(config.anomaly);
this.timeSeriesAnalyzer = new TimeSeriesAnalyzer(config.timeSeries);
this.behaviorAnalyzer = new BehaviorAnalyzer(config.behavior);
this.initializePatternDetectors();
}
private initializePatternDetectors() {
// Rug pull pattern detector
this.patternDetectors.set('rug_pull', new RugPullPatternDetector({
liquidityThreshold: 0.1, // 10% liquidity removal threshold
timeWindow: 3600, // 1 hour window
volumeSpike: 5.0, // 5x normal volume
priceDropThreshold: 0.5 // 50% price drop
}));
// Pump and dump detector
this.patternDetectors.set('pump_dump', new PumpDumpPatternDetector({
priceIncrease: 3.0, // 300% price increase
volumeIncrease: 10.0, // 10x volume increase
timeWindow: 86400, // 24 hours
sellPressure: 0.8 // 80% of trades are sells after pump
}));
// Wash trading detector
this.patternDetectors.set('wash_trading', new WashTradingDetector({
repetitivePattern: 0.7, // 70% similarity threshold
volumeConcentration: 0.3, // 30% volume from few addresses
timePattern: 'regular' // Regular time intervals
}));
// Sybil attack detector
this.patternDetectors.set('sybil_attack', new SybilAttackDetector({
addressSimilarity: 0.8, // 80% transaction pattern similarity
creationPattern: 'batch', // Batch creation pattern
fundingSource: 'single' // Single funding source
}));
// MEV bot detector
this.patternDetectors.set('mev_bot', new MEVBotDetector({
frontRunning: true,
sandwichAttacks: true,
arbitragePattern: 0.9, // 90% profitable trades
gasOptimization: true
}));
}
async analyzePatterns(projectData: ProjectData): Promise<PatternAnalysisResult> {
const startTime = Date.now();
try {
// Run all pattern detectors in parallel
const detectionPromises = Array.from(this.patternDetectors.entries()).map(
async ([patternName, detector]) => {
try {
const result = await detector.detect(projectData);
return { patternName, result };
} catch (error) {
logger.warn(`Pattern detection failed: ${patternName}`, error);
return {
patternName,
result: {
detected: false,
confidence: 0,
riskScore: 50,
explanation: 'Detection failed'
}
};
}
}
);
const detectionResults = await Promise.all(detectionPromises);
// Analyze anomalies
const anomalyResult = await this.anomalyDetector.analyzeAnomalies(projectData);
// Time series analysis
const timeSeriesResult = await this.timeSeriesAnalyzer.analyze(projectData);
// Behavioral analysis
const behaviorResult = await this.behaviorAnalyzer.analyze(projectData);
// Combine all results
const combinedResult = this.combineAnalysisResults(
detectionResults,
anomalyResult,
timeSeriesResult,
behaviorResult
);
const processingTime = Date.now() - startTime;
return {
...combinedResult,
metadata: {
processingTime,
patternsAnalyzed: detectionResults.length,
analysisTimestamp: new Date().toISOString()
}
};
} catch (error) {
logger.error('Pattern analysis failed:', error);
throw new PatternAnalysisError('Pattern analysis failed', error);
}
}
private combineAnalysisResults(
detectionResults: Array<{ patternName: string; result: PatternDetectionResult }>,
anomalyResult: AnomalyAnalysisResult,
timeSeriesResult: TimeSeriesAnalysisResult,
behaviorResult: BehaviorAnalysisResult
): PatternAnalysisResult {
const detectedPatterns: DetectedPattern[] = [];
let overallRiskScore = 0;
let totalConfidence = 0;
let patternCount = 0;
// Process individual pattern detection results
for (const { patternName, result } of detectionResults) {
if (result.detected && result.confidence > 0.6) {
detectedPatterns.push({
pattern: patternName,
confidence: result.confidence,
riskScore: result.riskScore,
explanation: result.explanation,
evidence: result.evidence || {}
});
overallRiskScore += result.riskScore * result.confidence;
totalConfidence += result.confidence;
patternCount++;
}
}
// Factor in anomaly detection
if (anomalyResult.isAnomalous) {
detectedPatterns.push({
pattern: 'statistical_anomaly',
confidence: anomalyResult.confidence,
riskScore: anomalyResult.riskScore,
explanation: anomalyResult.explanation,
evidence: anomalyResult.anomalies
});
overallRiskScore += anomalyResult.riskScore * anomalyResult.confidence;
totalConfidence += anomalyResult.confidence;
patternCount++;
}
// Factor in time series anomalies
if (timeSeriesResult.hasAnomalies) {
detectedPatterns.push({
pattern: 'time_series_anomaly',
confidence: timeSeriesResult.confidence,
riskScore: timeSeriesResult.riskScore,
explanation: timeSeriesResult.explanation,
evidence: timeSeriesResult.anomalies
});
overallRiskScore += timeSeriesResult.riskScore * timeSeriesResult.confidence;
totalConfidence += timeSeriesResult.confidence;
patternCount++;
}
// Calculate overall scores
const averageRiskScore = patternCount > 0 ? overallRiskScore / totalConfidence : 50;
const averageConfidence = patternCount > 0 ? totalConfidence / patternCount : 0;
return {
detectedPatterns,
overallRiskScore: averageRiskScore,
confidence: averageConfidence,
hasHighRiskPatterns: detectedPatterns.some(p => p.riskScore > 80),
anomalyScore: anomalyResult.anomalyScore,
behaviorScore: behaviorResult.behaviorScore,
recommendations: this.generateRecommendations(detectedPatterns)
};
}
private generateRecommendations(patterns: DetectedPattern[]): string[] {
const recommendations: string[] = [];
for (const pattern of patterns) {
switch (pattern.pattern) {
case 'rug_pull':
recommendations.push('Immediate exit recommended - rug pull pattern detected');
break;
case 'pump_dump':
recommendations.push('Avoid during price spike - pump and dump pattern detected');
break;
case 'wash_trading':
recommendations.push('Volume metrics unreliable - wash trading detected');
break;
case 'sybil_attack':
recommendations.push('Community metrics compromised - sybil attack detected');
break;
case 'mev_bot':
recommendations.push('High MEV extraction - consider alternative execution strategies');
break;
default:
recommendations.push(`Monitor for ${pattern.pattern} risk factors`);
}
}
return recommendations;
}
}
// Rug pull pattern detector implementation
export class RugPullPatternDetector implements PatternDetector {
constructor(private config: RugPullConfig) {}
async detect(projectData: ProjectData): Promise<PatternDetectionResult> {
try {
const riskFactors: string[] = [];
let riskScore = 0;
let confidence = 0;
// Check liquidity removal patterns
const liquidityAnalysis = await this.analyzeLiquidityChanges(projectData);
if (liquidityAnalysis.hasRapidRemoval) {
riskScore += 40;
confidence += 0.3;
riskFactors.push('Rapid liquidity removal detected');
}
// Check developer wallet behavior
const developerAnalysis = await this.analyzeDeveloperBehavior(projectData);
if (developerAnalysis.hasSuspiciousActivity) {
riskScore += 30;
confidence += 0.25;
riskFactors.push('Suspicious developer wallet activity');
}
// Check price movement patterns
const priceAnalysis = await this.analyzePriceMovements(projectData);
if (priceAnalysis.hasRapidDrop) {
riskScore += 20;
confidence += 0.2;
riskFactors.push('Rapid price decline detected');
}
// Check contract modifications
const contractAnalysis = await this.analyzeContractChanges(projectData);
if (contractAnalysis.hasRecentChanges) {
riskScore += 10;
confidence += 0.15;
riskFactors.push('Recent contract modifications');
}
// Social media abandonment
const socialAnalysis = await this.analyzeSocialActivity(projectData);
if (socialAnalysis.hasAbandonment) {
riskScore += 15;
confidence += 0.1;
riskFactors.push('Social media abandonment');
}
const detected = riskScore > 50 && confidence > 0.6;
return {
detected,
confidence: Math.min(1, confidence),
riskScore: Math.min(100, riskScore),
explanation: detected
? `Rug pull pattern detected: ${riskFactors.join(', ')}`
: 'No significant rug pull indicators found',
evidence: {
liquidityAnalysis,
developerAnalysis,
priceAnalysis,
contractAnalysis,
socialAnalysis,
riskFactors
}
};
} catch (error) {
logger.error('Rug pull detection failed:', error);
throw new PatternDetectionError('Rug pull detection failed', error);
}
}
private async analyzeLiquidityChanges(projectData: ProjectData): Promise<LiquidityAnalysis> {
// Implementation would analyze liquidity pool changes over time
return {
hasRapidRemoval: false,
removalPercentage: 0,
timeframe: 0,
currentLiquidity: projectData.liquidity?.totalUSD || 0
};
}
private async analyzeDeveloperBehavior(projectData: ProjectData): Promise<DeveloperAnalysis> {
// Implementation would analyze developer wallet transactions
return {
hasSuspiciousActivity: false,
largeTransfers: [],
unusualPatterns: []
};
}
}Real-Time Score Updates
Dynamic Score Recalculation System
// Real-time score updating and event-driven recalculation
export class RealTimeScoreUpdater {
private scoreSubscriptions: Map<string, ScoreSubscription> = new Map();
private updateQueue: PriorityQueue<ScoreUpdateTask>;
private eventProcessors: Map<string, EventProcessor> = new Map();
private updateThresholds: UpdateThresholds;
private scoringEngine: ScoringEngine;
constructor(
private config: RealTimeConfig,
scoringEngine: ScoringEngine,
private eventBus: EventBus,
private notificationService: NotificationService
) {
this.scoringEngine = scoringEngine;
this.updateQueue = new PriorityQueue<ScoreUpdateTask>();
this.updateThresholds = new UpdateThresholds(config.thresholds);
this.initializeEventProcessors();
this.setupEventListeners();
this.startUpdateProcessor();
}
private initializeEventProcessors() {
// Blockchain event processors
this.eventProcessors.set('liquidity_change', new LiquidityChangeProcessor());
this.eventProcessors.set('large_transfer', new LargeTransferProcessor());
this.eventProcessors.set('contract_interaction', new ContractInteractionProcessor());
this.eventProcessors.set('social_spike', new SocialSpikeProcessor());
this.eventProcessors.set('price_movement', new PriceMovementProcessor());
}
private setupEventListeners() {
// Listen for blockchain events
this.eventBus.on('blockchain_event', (event: BlockchainEvent) => {
this.handleBlockchainEvent(event);
});
// Listen for social events
this.eventBus.on('social_event', (event: SocialEvent) => {
this.handleSocialEvent(event);
});
// Listen for market events
this.eventBus.on('market_event', (event: MarketEvent) => {
this.handleMarketEvent(event);
});
}
async subscribeToScoreUpdates(
contractAddress: string,
network: string,
userId: string,
callback: ScoreUpdateCallback
): Promise<string> {
const subscriptionId = this.generateSubscriptionId(contractAddress, network, userId);
const subscription: ScoreSubscription = {
id: subscriptionId,
contractAddress,
network,
userId,
callback,
threshold: 5, // Minimum score change to trigger update
lastUpdate: Date.now(),
isActive: true
};
this.scoreSubscriptions.set(subscriptionId, subscription);
// Get current score
const currentScore = await this.scoringEngine.getCachedScore(contractAddress, network);
if (currentScore) {
callback({
type: 'initial',
contractAddress,
network,
oldScore: null,
newScore: currentScore,
timestamp: Date.now()
});
}
return subscriptionId;
}
private async handleBlockchainEvent(event: BlockchainEvent) {
try {
const processor = this.eventProcessors.get(event.type);
if (!processor) {
return;
}
const impactAssessment = await processor.assessImpact(event);
if (impactAssessment.shouldTriggerUpdate) {
await this.queueScoreUpdate({
contractAddress: event.contractAddress,
network: event.network,
priority: impactAssessment.priority,
reason: event.type,
triggerEvent: event,
estimatedImpact: impactAssessment.estimatedImpact
});
}
} catch (error) {
logger.error('Failed to handle blockchain event:', error, { event });
}
}
private async queueScoreUpdate(task: ScoreUpdateTask) {
// Check if update is necessary based on thresholds
if (!await this.shouldUpdateScore(task)) {
return;
}
// Add to priority queue
this.updateQueue.enqueue(task, task.priority);
logger.debug('Queued score update', {
contractAddress: task.contractAddress,
network: task.network,
reason: task.reason,
priority: task.priority
});
}
private async shouldUpdateScore(task: ScoreUpdateTask): Promise<boolean> {
const key = `${task.network}:${task.contractAddress}`;
// Check minimum time interval
const lastUpdate = await this.getLastUpdateTime(key);
const minInterval = this.updateThresholds.getMinInterval(task.reason);
if (Date.now() - lastUpdate < minInterval) {
return false;
}
// Check estimated impact threshold
const impactThreshold = this.updateThresholds.getImpactThreshold(task.reason);
return Math.abs(task.estimatedImpact) >= impactThreshold;
}
private startUpdateProcessor() {
// Process updates continuously
setInterval(async () => {
await this.processUpdateQueue();
}, 1000); // Check every second
// Batch process lower priority updates
setInterval(async () => {
await this.processBatchUpdates();
}, 30000); // Every 30 seconds
}
private async processUpdateQueue() {
try {
const batchSize = this.config.batchSize || 5;
const tasks: ScoreUpdateTask[] = [];
// Dequeue high priority tasks
for (let i = 0; i < batchSize && !this.updateQueue.isEmpty(); i++) {
const task = this.updateQueue.dequeue();
if (task && task.priority >= 7) { // High priority threshold
tasks.push(task);
} else if (task) {
// Re-queue lower priority tasks
this.updateQueue.enqueue(task, task.priority);
break;
}
}
if (tasks.length > 0) {
await this.processBatchScoreUpdates(tasks);
}
} catch (error) {
logger.error('Update queue processing failed:', error);
}
}
private async processBatchScoreUpdates(tasks: ScoreUpdateTask[]) {
const updatePromises = tasks.map(async (task) => {
try {
return await this.performScoreUpdate(task);
} catch (error) {
logger.error('Individual score update failed:', error, { task });
return null;
}
});
const results = await Promise.all(updatePromises);
// Notify subscribers of successful updates
for (let i = 0; i < results.length; i++) {
const result = results[i];
const task = tasks[i];
if (result) {
await this.notifySubscribers(task, result);
}
}
}
private async performScoreUpdate(task: ScoreUpdateTask): Promise<ScoreUpdateResult> {
const startTime = Date.now();
try {
// Get current score
const currentScore = await this.scoringEngine.getCachedScore(
task.contractAddress,
task.network
);
// Get fresh project data
const projectData = await this.getProjectData(task.contractAddress, task.network);
// Recalculate score
const newScore = await this.scoringEngine.calculateScore(projectData);
// Calculate change
const scoreChange = currentScore ? newScore.score - currentScore.score : 0;
// Update cache and database
await this.updateScoreStorage(task.contractAddress, task.network, newScore);
const processingTime = Date.now() - startTime;
return {
contractAddress: task.contractAddress,
network: task.network,
oldScore: currentScore,
newScore,
scoreChange,
reason: task.reason,
processingTime,
timestamp: Date.now()
};
} catch (error) {
logger.error('Score update calculation failed:', error, { task });
throw error;
}
}
private async notifySubscribers(task: ScoreUpdateTask, result: ScoreUpdateResult) {
// Find relevant subscriptions
const relevantSubscriptions = Array.from(this.scoreSubscriptions.values())
.filter(sub =>
sub.contractAddress === task.contractAddress &&
sub.network === task.network &&
sub.isActive &&
Math.abs(result.scoreChange) >= sub.threshold
);
// Notify each subscriber
for (const subscription of relevantSubscriptions) {
try {
await subscription.callback({
type: 'update',
contractAddress: result.contractAddress,
network: result.network,
oldScore: result.oldScore,
newScore: result.newScore,
reason: result.reason,
timestamp: result.timestamp
});
// Update last notification time
subscription.lastUpdate = Date.now();
} catch (error) {
logger.error('Subscriber notification failed:', error, {
subscriptionId: subscription.id
});
}
}
// Send notifications for significant changes
if (Math.abs(result.scoreChange) >= 15) {
await this.notificationService.sendScoreChangeAlert({
contractAddress: result.contractAddress,
network: result.network,
oldScore: result.oldScore?.score || 0,
newScore: result.newScore.score,
change: result.scoreChange,
reason: result.reason
});
}
}
}
// Event impact processors
export class LiquidityChangeProcessor implements EventProcessor {
async assessImpact(event: BlockchainEvent): Promise<ImpactAssessment> {
const liquidityData = event.data;
if (!liquidityData.oldValue || !liquidityData.newValue) {
return {
shouldTriggerUpdate: false,
priority: 1,
estimatedImpact: 0
};
}
const changePercentage = Math.abs(
(liquidityData.newValue - liquidityData.oldValue) / liquidityData.oldValue
);
let priority = 1;
let estimatedImpact = 0;
if (changePercentage > 0.5) { // 50% liquidity change
priority = 9; // High priority
estimatedImpact = -20; // Significant negative impact
} else if (changePercentage > 0.2) { // 20% liquidity change
priority = 6; // Medium priority
estimatedImpact = -10; // Moderate negative impact
} else if (changePercentage > 0.05) { // 5% liquidity change
priority = 3; // Low priority
estimatedImpact = -5; // Minor negative impact
}
return {
shouldTriggerUpdate: changePercentage > 0.05,
priority,
estimatedImpact,
explanation: `Liquidity changed by ${(changePercentage * 100).toFixed(1)}%`
};
}
}This comprehensive Scoring Agent documentation provides the technical foundation for understanding and implementing the sophisticated risk assessment system that generates Kaizen AI's signature 0-100 scores through advanced machine learning and rule-based analysis.
Last updated