Correlation MCPs

Correlation MCPs

Overview

Correlation Model Context Protocols (MCPs) identify relationships between different data points, assets, and metrics. These specialized protocols detect patterns and connections that individual metric analysis might miss.

Available Correlation Protocols

Cross-Ecosystem Correlation MCP

Analyzes relationships between different market ecosystems (e.g., AI vs. DeFi, Layer-1 vs. Gaming):

const crossEcosystemMCP = new CrossEcosystemCorrelationMCP({
  ecosystems: ['ai', 'defi', 'gaming', 'layer1'],
  metrics: ['price', 'volume', 'social'],
  window: '30d',
  method: 'pearson'  // correlation method
});

const correlations = await crossEcosystemMCP.process();
// Returns: correlation matrix between ecosystems

Key capabilities:

• Ecosystem rotation detection

• Leading indicator identification

• Correlated market movements

• Divergence recognition

Temporal Correlation MCP

Focuses on time-delayed correlations between metrics, identifying leading and lagging relationships:

const temporalMCP = new TemporalCorrelationMCP({
  subject: 'BTC-price',
  targets: ['ETH-price', 'SOL-price', 'AVAX-price'],
  maxLag: 72,  // hours
  granularity: '1h',
  significance: 0.95  // statistical significance threshold
});

const lags = await temporalMCP.process();
// Returns: optimal lag time and correlation strength for each target

Key capabilities:

• Lead-lag relationship detection

• Price echo identification

• Temporal pattern recognition

• Predictive signal generation

Sentiment-Price Correlation MCP

Specializes in linking sentiment metrics to price movements with time-offset calibration:

const sentimentPriceMCP = new SentimentPriceCorrelationMCP({
  assets: ['BTC', 'ETH', 'SOL'],
  sentimentSources: ['twitter', 'reddit', 'discord'],
  timeOffset: [-48, 48],  // hours to check before/after
  granularity: '4h'
});

const impacts = await sentimentPriceMCP.process();
// Returns: sentiment impact factors and optimal time offsets

Key capabilities:

• Sentiment impact quantification

• Sentiment-price divergence detection

• Time-delayed impact assessment

• Source-specific correlation analysis

Volume-Engagement Correlation MCP

Analyzes trading volume in relation to social engagement metrics:

const volumeEngagementMCP = new VolumeEngagementCorrelationMCP({
  assets: ['BTC', 'ETH', 'SOL'],
  engagementMetrics: ['tweets', 'reddit-posts', 'discord-messages'],
  anomalyThreshold: 2.5,  // standard deviations
  window: '14d'
});

const anomalies = await volumeEngagementMCP.process();
// Returns: detected anomalies in volume-engagement relationship

Key capabilities:

• Volume-engagement divergence detection

• Manipulation pattern recognition

• Organic vs. inorganic activity differentiation

• Anomaly classification

Additional Specialized Correlation MCPs

• Whale Movement Correlation MCP: Tracks large holder actions across ecosystems

• Momentum Correlation MCP: Focuses on rate-of-change correlations between assets

• Market Dominance Correlation MCP: Analyzes shifts in market share between related ecosystems

• Volatility Correlation MCP: Measures correlation between volatility metrics across assets

• Developer Activity Correlation MCP: Links GitHub activity to market metrics

• Narrative Correlation MCP: Analyzes keywords and themes across social content

Integration Example

// Combining multiple correlation protocols
const multiCorrelation = new CompositeCorrelationMCP({
  protocols: [
    new SentimentPriceCorrelationMCP({ /* config */ }),
    new TemporalCorrelationMCP({ /* config */ }),
    new WhaleMovementCorrelationMCP({ /* config */ })
  ],
  integrationMethod: 'weighted',
  weights: [0.4, 0.4, 0.2]
});

const integratedSignals = await multiCorrelation.process();
// Returns: integrated correlation signals from multiple protocols

This compositional approach enables sophisticated correlation analysis through the combination of specialized protocols.