What is Cross Ecosystem Correlation Mcp

The cross-ecosystem-correlation-mcp is a Model Context Protocol (MCP) designed to analyze correlations between various crypto ecosystems and asset categories, providing insights into capital flows, sentiment contagion, and market interdependencies.

Use cases

Use cases include analyzing the correlation between DeFi and AI ecosystems, detecting capital flows between gaming and other sectors, and identifying leading indicators for investment opportunities.

How to use

To use the cross-ecosystem-correlation-mcp, install it via npm and initialize it with necessary adapters. You can then analyze correlations between different ecosystems by calling the analyzeEcosystemCorrelations method with specified parameters such as ecosystems and timeframe.

Key features

Key features include multi-ecosystem analysis, correlation matrix generation, network mapping, flow detection, leading indicators identification, correlation breakdowns by various metrics, and temporal analysis of correlations.

Where to use

This MCP can be used in financial analysis, investment strategy development, market research, and any domain that requires understanding of interconnections between different crypto ecosystems.

INTUE Cross-Ecosystem Correlation MCP

A sophisticated Model Context Protocol (MCP) for analyzing correlations between crypto ecosystems and asset categories.

Overview

The Cross-Ecosystem Correlation MCP enables deep analysis of relationships between different crypto ecosystems, revealing insights into capital flows, sentiment contagion, and market interdependencies. This MCP powers data-driven investment strategies by identifying emerging patterns across ecosystem boundaries.

Installation

npm install @intue/cross-ecosystem-correlation-mcp

Features

• Multi-Ecosystem Analysis: Compare relationships between defined crypto ecosystems (DeFi, AI, Gaming, etc.)
• Correlation Matrix: Generate complete correlation matrices across ecosystems and assets
• Network Mapping: Visualize ecosystem relationships as directed network graphs
• Flow Detection: Identify capital and sentiment flows between ecosystems
• Leading Indicators: Discover which ecosystems lead or lag others
• Correlation Breakdowns: Analyze by volume, price movement, social sentiment, and more
• Temporal Analysis: Track how correlations evolve over different timeframes

Usage

Basic Ecosystem Correlation

const { EcosystemCorrelator } = require('@intue/cross-ecosystem-correlation-mcp');
const LunarCrushAdapter = require('@intue/lunarcrush-adapter');

// Initialize with adapters
const lunarcrush = new LunarCrushAdapter({ apiKey: 'YOUR_API_KEY' });
const correlator = new EcosystemCorrelator({
  adapters: { lunarcrush }
});

// Analyze correlations between ecosystems
async function analyzeEcosystems() {
  const correlations = await correlator.analyzeEcosystemCorrelations([
    'ai-agents', 
    'defi', 
    'gaming'
  ], { 
    timeframe: '30d',
    metric: 'price'
  });
  
  console.log('Correlation matrix:', correlations.matrix);
  console.log('Strongest correlation:', correlations.strongestPair);
  console.log('Network graph data:', correlations.networkData);
}

analyzeEcosystems();

Leading/Lagging Analysis

// Analyze which ecosystems lead or lag others
async function analyzeLeadLag() {
  const leadLag = await correlator.analyzeLeadLagRelationships([
    'ai-agents', 
    'defi', 
    'layer1'
  ], {
    timeframe: '90d',
    interval: '1d',
    metric: 'price'
  });
  
  console.log('Leading ecosystems:', leadLag.leaders);
  console.log('Lagging ecosystems:', leadLag.laggers);
  console.log('Lead/lag matrix (days):', leadLag.lagMatrix);
}

analyzeLeadLag();

Capital Flow Analysis

// Analyze capital flows between ecosystems
async function analyzeCapitalFlows() {
  const flows = await correlator.analyzeCapitalFlows([
    'ai-agents', 
    'defi', 
    'gaming',
    'layer1'
  ], {
    timeframe: '14d',
    minFlowPercentage: 5 // Only show significant flows (>5%)
  });
  
  console.log('Flow matrix:', flows.flowMatrix);
  console.log('Net inflows by ecosystem:', flows.netInflows);
  console.log('Strongest flows:', flows.significantFlows);
}

analyzeCapitalFlows();

API Reference

EcosystemCorrelator Constructor

• adapters: Object containing data adapters (lunarcrush, etc.)
• cache: Optional cache instance
• ttl: Cache TTL in milliseconds

Main Methods

• analyzeEcosystemCorrelations(ecosystems, options): Generate correlation matrix
• analyzeAssetCorrelations(assets, options): Generate asset-to-asset correlations
• analyzeLeadLagRelationships(ecosystems, options): Find leading/lagging relationships
• analyzeCapitalFlows(ecosystems, options): Track capital movement between ecosystems
• generateCorrelationMatrix(data, options): Create correlation matrix from any dataset
• generateNetworkGraph(correlationMatrix, options): Convert matrix to network graph

Options

• timeframe: Time window for analysis (‘1d’, ‘7d’, ‘30d’, etc.)
• interval: Data resolution (‘1h’, ‘1d’, ‘1w’)
• metric: Metric to correlate (‘price’, ‘volume’, ‘sentiment’, ‘combined’)
• minCorrelation: Minimum correlation strength to include (0-1)
• lagMax: Maximum lead/lag days to check

Related Packages

• @intue/core - Core utilities for the INTUE ecosystem
• @intue/lunarcrush-adapter - LunarCrush API adapter
• @intue/sentiment-analysis-mcp - Sentiment analysis MCP

License

MIT