Analysis MCPs

Analysis MCPs

Overview

Analysis Model Context Protocols (MCPs) apply advanced statistical and mathematical methods to market data, uncovering complex patterns and relationships beyond simple metrics and correlations.

Available Analysis Protocols

Non-Linear Correlation MCP

Implements advanced statistical methods for detecting complex, non-linear relationships:

const nonLinearMCP = new NonLinearCorrelationMCP({
  assets: ['BTC', 'ETH', 'SOL', 'AVAX'],
  methods: ['spearman', 'kendall-tau', 'mutual-information'],
  significance: 0.95,
  windowSize: '30d'
});

const nonLinearRelationships = await nonLinearMCP.process();
// Returns: Non-linear relationship metrics

Key capabilities:

• Rank correlation analysis

• Mutual information calculation

• Non-parametric relationship detection

• Power law relationship identification

Multi-Factor Correlation MCP

Combines multiple metrics into composite factors for higher-level analysis:

const multiFactorMCP = new MultiFactorCorrelationMCP({
  factors: [
    {
      name: 'momentum',
      metrics: ['price-change', 'volume-change', 'social-sentiment']
    },
    {
      name: 'fundamentals',
      metrics: ['active-addresses', 'transaction-value', 'fees']
    },
    {
      name: 'risk',
      metrics: ['volatility', 'liquidity', 'drawdown']
    }
  ],
  normalization: 'z-score',
  dimensionReduction: 'pca'
});

const factorAnalysis = await multiFactorMCP.process();
// Returns: Factor analysis with principal components

Key capabilities:

• Composite factor construction

• Principal component analysis

• Factor significance testing

• Cross-factor correlation analysis

Anomaly Detection MCP

Identifies statistical outliers and unusual patterns across multiple metrics:

const anomalyMCP = new AnomalyDetectionMCP({
  metrics: ['price', 'volume', 'social-sentiment', 'on-chain-activity'],
  methods: ['isolation-forest', 'one-class-svm', 'mahalanobis-distance'],
  sensitivity: 0.85,
  ensembleMethod: 'voting'
});

const anomalies = await anomalyMCP.process();
// Returns: Detected anomalies with confidence scores

Key capabilities:

• Multi-method anomaly detection

• Confidence scoring

• Anomaly classification

• Historical pattern matching

Pattern Recognition MCP

Identifies recurring market patterns and historical precedents:

const patternMCP = new PatternRecognitionMCP({
  patterns: ['head-and-shoulders', 'double-bottom', 'bull-flag', 'wyckoff-accumulation'],
  timeframes: ['1h', '4h', '1d'],
  minimumConfidence: 0.75,
  includeHiddenPatterns: true
});

const detectedPatterns = await patternMCP.process();
// Returns: Identified patterns with confidence metrics

Key capabilities:

• Technical pattern recognition

• Pattern completion projection

• Historical success rate analysis

• Multi-timeframe confirmation

Causality Analysis MCP

Goes beyond correlation to analyze potential causal relationships:

const causalityMCP = new CausalityAnalysisMCP({
  variables: ['btc-price', 'eth-price', 'defi-tvl', 'market-sentiment'],
  method: 'granger',
  maxLag: 10,
  significance: 0.95
});

const causalRelationships = await causalityMCP.process();
// Returns: Causal relationship graph with confidence metrics

Key capabilities:

• Granger causality testing

• Causal graph construction

• Driver/follower classification

• Intervention analysis

Additional Analysis MCPs

• Time Series Forecasting MCP: Implements predictive models for time series data

• Regime Change Detection MCP: Identifies market phase transitions

• Attribution Analysis MCP: Performs factor performance breakdown

• Risk Decomposition MCP: Analyzes multiple sources of market risk

• Structural Break Detection MCP: Identifies fundamental changes in market behavior

Integration Example

// Advanced analytical pipeline
const advancedAnalysisMCP = new AnalysisPipelineMCP({
  stages: [
    new AnomalyDetectionMCP({ /* config */ }),
    new NonLinearCorrelationMCP({ /* config */ }),
    new CausalityAnalysisMCP({ /* config */ })
  ],
  feedbackLoops: true,
  persistIntermediateResults: true
});

const analysisResults = await advancedAnalysisMCP.process();
// Returns: Multi-stage analytical results

This pipeline approach enables sophisticated analytical workflows through the sequential application of specialized analysis protocols.