Stochastic Process Analyzer
Uses Cloudflare CDN for serving external stylesheets and scripts in the stochastic process visualization components
Supports containerized deployment with Docker for scalable stochastic process analysis server instances
Offers integration with Jupyter notebooks for advanced stochastic process analysis and financial modeling workflows
Supports Redis caching for improved performance of stochastic process simulations and financial calculations
Uses SQLite database for persistent storage of simulation data, financial metrics, and model configurations
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Stochastic Process Analyzersimulate a GBM model with 5% drift and 20% volatility for 1 year"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
{
"name": "stochastic-process-analyzer",
"version": "2.0.0",
"description": "Advanced stochastic process analysis and financial modeling MCP server",
"author": "Stochastic Process Analysis Team",
"license": "MIT",
"server": {
"host": "localhost",
"port": 3001,
"protocol": "http",
"baseUrl": "http://localhost:3001",
"timeout": 30000,
"keepAlive": true,
"maxConnections": 100
},
"capabilities": {
"tools": [
{
"name": "simulate_stochastic_process",
"description": "Generate comprehensive stochastic process simulations",
"version": "2.0.0",
"category": "simulation",
"tags": ["stochastic", "finance", "modeling"],
"complexity": "high",
"estimatedTime": "5-30s"
},
{
"name": "calculate_financial_metrics",
"description": "Calculate comprehensive financial risk and performance metrics",
"version": "2.0.0",
"category": "analysis",
"tags": ["finance", "risk", "metrics"],
"complexity": "medium",
"estimatedTime": "1-5s"
},
{
"name": "export_analysis",
"description": "Export analysis results in various formats",
"version": "2.0.0",
"category": "export",
"tags": ["export", "data", "formats"],
"complexity": "low",
"estimatedTime": "1-3s"
},
{
"name": "render_gbm_component",
"description": "Render interactive GBM analyzer component",
"version": "2.0.0",
"category": "visualization",
"tags": ["gbm", "component", "interactive"],
"complexity": "medium",
"estimatedTime": "2-5s"
}
],
"resources": [
{
"name": "simulation_data",
"description": "Access to generated simulation data",
"type": "data",
"format": "json",
"access": "read"
},
{
"name": "financial_metrics",
"description": "Calculated financial and risk metrics",
"type": "metrics",
"format": "json",
"access": "read"
},
{
"name": "model_configurations",
"description": "Available stochastic model configurations",
"type": "config",
"format": "json",
"access": "read"
},
{
"name": "component_templates",
"description": "HTML component templates",
"type": "template",
"format": "html",
"access": "read"
}
],
"models": [
{
"name": "gbm",
"fullName": "Geometric Brownian Motion",
"description": "Classic model for asset price movements",
"equation": "dS(t) = μS(t)dt + σS(t)dW(t)",
"parameters": {
"drift": {
"type": "number",
"range": [-0.5, 0.5],
"default": 0.05,
"description": "Annual drift rate (μ)"
},
"volatility": {
"type": "number",
"range": [0.01, 2.0],
"default": 0.2,
"description": "Annual volatility (σ)"
},
"initialValue": {
"type": "number",
"range": [0.1, 10000],
"default": 100,
"description": "Initial asset value (S₀)"
},
"timeHorizon": {
"type": "number",
"range": [0.1, 10],
"default": 1,
"description": "Time horizon in years"
},
"steps": {
"type": "integer",
"range": [50, 10000],
"default": 252,
"description": "Number of time steps"
},
"numPaths": {
"type": "integer",
"range": [100, 10000],
"default": 1000,
"description": "Number of simulation paths"
}
},
"applications": ["finance", "options", "risk"],
"complexity": "medium",
"available": true
},
{
"name": "ou",
"fullName": "Ornstein-Uhlenbeck Process",
"description": "Mean-reverting stochastic process",
"equation": "dX(t) = θ(μ - X(t))dt + σdW(t)",
"parameters": {
"meanReversion": {
"type": "number",
"range": [0.01, 10],
"default": 1,
"description": "Mean reversion speed (θ)"
},
"longTermMean": {
"type": "number",
"range": [-100, 100],
"default": 0,
"description": "Long-term mean (μ)"
},
"volatility": {
"type": "number",
"range": [0.01, 2.0],
"default": 0.2,
"description": "Volatility (σ)"
},
"initialValue": {
"type": "number",
"range": [-100, 100],
"default": 0,
"description": "Initial value (X₀)"
}
},
"applications": ["interest-rates", "commodities", "volatility"],
"complexity": "medium",
"available": false
},
{
"name": "jump",
"fullName": "Jump Diffusion Process",
"description": "GBM with random jumps",
"equation": "dS(t) = μS(t)dt + σS(t)dW(t) + S(t-)dJ(t)",
"applications": ["crisis-modeling", "event-risk", "derivatives"],
"complexity": "high",
"available": false
},
{
"name": "heston",
"fullName": "Heston Stochastic Volatility",
"description": "Model with stochastic volatility",
"applications": ["options", "volatility-surface", "exotic-derivatives"],
"complexity": "high",
"available": false
}
]
},
"authentication": {
"type": "api_key",
"required": false,
"headerName": "X-API-Key",
"description": "Optional API key for rate limiting and analytics"
},
"rateLimit": {
"enabled": true,
"requests": 100,
"window": 3600,
"skipSuccessfulRequests": false,
"skipFailedRequests": true
},
"limits": {
"maxSimulationSteps": 10000,
"maxSimulationPaths": 10000,
"maxConcurrentRequests": 10,
"requestTimeout": 30000,
"maxPayloadSize": "10mb",
"maxExportSize": "50mb"
},
"logging": {
"level": "info",
"format": "combined",
"maxSize": "10mb",
"maxFiles": 5,
"destinations": ["console", "file", "rotating-file"]
},
"monitoring": {
"enabled": true,
"metrics": {
"requests": true,
"errors": true,
"performance": true,
"memory": true,
"cpu": true
},
"healthCheck": {
"path": "/health",
"interval": 30000,
"timeout": 5000
}
},
"cache": {
"enabled": true,
"type": "memory",
"ttl": 300000,
"maxSize": 100,
"redis": {
"host": "localhost",
"port": 6379,
"password": null,
"db": 0
}
},
"database": {
"type": "sqlite",
"path": "data/stochastic.db",
"options": {
"synchronous": "NORMAL",
"journal_mode": "WAL"
},
"migrations": {
"enabled": true,
"path": "migrations/"
}
},
"security": {
"helmet": {
"contentSecurityPolicy": {
"directives": {
"defaultSrc": ["'self'"],
"styleSrc": ["'self'", "'unsafe-inline'", "https://cdnjs.cloudflare.com"],
"scriptSrc": ["'self'", "https://cdnjs.cloudflare.com"],
"imgSrc": ["'self'", "data:", "https:"]
}
}
},
"cors": {
"origin": ["http://localhost:3000", "https://claude.ai"],
"credentials": true,
"methods": ["GET", "POST", "PUT", "DELETE", "OPTIONS"],
"allowedHeaders": ["Content-Type", "Authorization", "X-MCP-Version", "X-API-Key"]
}
},
"features": {
"simulationBatching": true,
"componentCaching": true,
"exportFormats": ["json", "csv", "xml", "pdf"],
"realTimeUpdates": false,
"multiDimensional": true,
"customModels": false,
"distributedComputing": false
},
"integrations": {
"claude": {
"enabled": true,
"version": "4.0",
"endpoints": {
"completions": "https://api.anthropic.com/v1/completions"
}
},
"jupyter": {
"enabled": false,
"kernelPath": "/usr/local/share/jupyter/kernels/python3"
},
"r": {
"enabled": false,
"rscriptPath": "/usr/bin/Rscript"
}
},
"deployment": {
"environment": "development",
"containers": {
"docker": {
"image": "stochastic-mcp:latest",
"ports": ["3001:3001"],
"volumes": ["./data:/app/data", "./logs:/app/logs"],
"environment": {
"NODE_ENV": "production",
"PORT": "3001",
"LOG_LEVEL": "info"
}
}
},
"scaling": {
"minReplicas": 1,
"maxReplicas": 5,
"targetCPU": 70,
"targetMemory": 80
}
},
"documentation": {
"openapi": {
"enabled": true,
"path": "/docs/openapi.json",
"ui": "/docs"
},
"examples": {
"enabled": true,
"path": "/examples"
}
},
"testing": {
"enabled": true,
"coverage": {
"threshold": 80,
"reportFormats": ["text", "html", "json"]
},
"e2e": {
"enabled": false,
"browserless": false
}
},
"version": "2.0.0",
"lastUpdated": "2024-07-09T00:00:00Z",
"maintainer": {
"name": "Stochastic Process Analysis Team",
"email": "support@stochastic-mcp.com",
"url": "https://github.com/your-org/stochastic-process-mcp"
}
}
This server cannot be deployed
Maintenance
Related MCP Connectors
HPSILab Quant finance MCP for US stocks, ETFs, options, Monte Carlo, backtesting, and risk analysis.
Research-only MCP server: your AI as a quant research desk. 90 tools, no trades, no brokers.
7-factor stock scoring MCP server. US/HK/CN, 74 stocks. Free + Premium (USDC/Base). x402 ready.
Deterministic what-if & scenario simulation for AI agents: projections, sensitivity & break-even.
Related MCP Servers
- AlicenseCqualityDmaintenanceA server that provides advanced mathematical and financial calculation capabilities for AI code assistants, enabling them to perform complex calculations like symbolic calculus, numerical methods, and financial analysis without implementing algorithms directly.189 npm8-
- FlicenseNot gradedqualityDmaintenanceA comprehensive Model Context Protocol (MCP) server that transforms Yahoo Finance into a powerful financial analysis platform, providing real-time market data, advanced analytics, and visualizations for professional traders and AI assistants.-
- AlicenseNot gradedqualityDmaintenanceA comprehensive quantitative finance MCP server providing live financial analysis, including price/risk metrics, HMM market regime detection, backtesting, options analytics, fundamentals, sentiment, Monte Carlo simulation, pairs trading, factor models, and portfolio optimization. It integrates with Claude via natural language commands and uses only free data sources.2MIT
- AlicenseNot gradedqualityBmaintenanceA quantitative finance MCP server providing 24 tools for option pricing, portfolio optimization, risk measurement, fixed income analysis, and utility functions, enabling AI clients to perform professional financial calculations.MIT