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
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
{
"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
Production MCP server for US equity and options intelligence: real-time IV radar, Monte Carlo simulation, options pressure, strategy backtesting, AI prediction, pre-trade risk analysis, and automated stock research reports.
Compound interest and Monte Carlo simulators for investing, retirement and past asset returns.
MCP server for Financial Modeling Prep API with 250+ financial data tools
Research-only MCP server: your AI as a quant research desk. 90 tools, no trades, no brokers.
