Risk Assessment Framework

Liquidity Pool Analysis

Core Metrics

Liquidity Depth Assessment

• Total Value Locked (TVL) analysis

• Liquidity distribution across DEXs

• Pool concentration risk evaluation

• Impermanent loss potential calculation

Liquidity Lock Analysis

// Example liquidity lock evaluation
const liquidityRisk = {
  lockDuration: "6 months", // Duration of liquidity lock
  lockPercentage: 85, // Percentage of liquidity locked
  lockContract: "verified", // Lock contract verification status
  emergencyUnlock: false, // Emergency unlock functions present
  riskScore: calculateLiquidityRisk(lockDuration, lockPercentage)
}

Key Indicators:

• Lock Duration: Longer locks indicate higher commitment

• Lock Percentage: Higher percentages reduce rug pull risk

• Lock Mechanism: Team-controlled vs. third-party locks

• Unlock Schedule: Gradual vs. cliff unlocking patterns

Analysis Framework

Low Risk Indicators:

• 80%+ liquidity locked for 6+ months

• Multiple DEX liquidity distribution

• Third-party lock mechanisms

• No emergency unlock functions

High Risk Indicators:

• <50% liquidity locked

• Single DEX concentration

• Team-controlled locks

• Short lock durations (<30 days)

Ownership Structure Evaluation

Token Distribution Analysis

Ownership Concentration Metrics

• Top 10 holder concentration

• Developer wallet allocations

• Exchange wallet identification

• Burn address verification

Distribution Health Score

def calculate_distribution_score(top_holders):
    # Gini coefficient for ownership distribution
    concentration_penalty = calculate_gini(top_holders)
    dev_allocation = get_dev_wallet_percentage()
    burn_percentage = get_burn_percentage()
    
    score = 100 - (concentration_penalty * 50) - (dev_allocation * 30) + (burn_percentage * 10)
    return max(0, min(100, score))

Risk Categories

Centralization Risks:

• Single entity holding >50% of supply

• Development team controlling >20%

• Lack of token burning mechanisms

• Concentrated voting power

Decentralization Indicators:

• Wide holder distribution

• Significant burned supply

• Community-controlled governance

• Transparent allocation schedules

Developer Activity Monitoring

Activity Metrics

Code Development Tracking

• GitHub commit frequency

• Repository update patterns

• Code quality improvements

• Bug fix responsiveness

Communication Assessment

• Social media activity levels

• Community engagement frequency

• Transparency in communications

• Response to community concerns

Evaluation Framework

Positive Indicators:

• Regular code updates (weekly+)

• Active community engagement

• Transparent roadmap execution

• Responsive support channels

Warning Signs:

• Decreasing commit activity

• Reduced communication frequency

• Missed milestone deliveries

• Unresponsive to critical issues

Code Quality Assessment

Static Analysis Metrics

Security Vulnerabilities

• Known vulnerability patterns

• Access control implementations

• Input validation mechanisms

• External call safety measures

Code Complexity Analysis

// Example: Analyzing function complexity
contract SecurityAnalysis {
    function analyzeComplexity(address contractAddr) public view returns (uint256) {
        // Cyclomatic complexity calculation
        // Function count and interaction patterns
        // Dependency depth analysis
        return complexityScore;
    }
}

Quality Indicators

High Quality Markers:

• Comprehensive test coverage (>90%)

• Well-documented code

• Standard security patterns

• Professional development practices

Quality Concerns:

• Minimal or no testing

• Undocumented functions

• Non-standard implementations

• Copy-paste code patterns

Market Manipulation Detection

Detection Algorithms

Pump and Dump Patterns

• Unusual volume spikes

• Coordinated trading patterns

• Social media manipulation signals

• Price action anomalies

Wash Trading Detection

// Wash trading detection algorithm
function detectWashTrading(transactions) {
    const patterns = analyzeTransactionPatterns(transactions);
    const volumeAnomalies = detectVolumeAnomalies(patterns);
    const addressClusters = identifyRelatedAddresses(patterns);
    
    return {
        suspiciousVolume: volumeAnomalies.percentage,
        relatedAddresses: addressClusters.length,
        manipulationScore: calculateManipulationRisk(patterns)
    };
}

Red Flag Indicators

Market Manipulation Signs:

• Sudden volume increases (>500% above average)

• Coordinated buy/sell patterns

• Bot-like trading behavior

• Artificial price supports

Organic Growth Indicators:

• Gradual volume increases

• Diverse trader participation

• Natural price discovery

• Sustained community interest