MonteCarlo

Monte Carlo Simulation

Security Level 1 (Enabled)

SindByte tool execution flow: voice command, AI parse, execute, validate, result

Description

Runs statistical simulations with random numbers. Ideal for complex problems with many uncertainties, stochastic processes, risk analyses and probabilistic calculations. Monte Carlo methods deliver approximate solutions through repeated random experiments.

JSON Parameters

{
  "simulationType": "pi",
  "iterations": 1000000,
  "parameters": {
    "radius": 1.0
  },
  "seed": 42,
  "parallel": true
}

Parameter	Type	Required	Description
`simulationType`	String	Yes	Simulation type: pi, integration, optimization, custom
`iterations`	Number	Yes	Number of simulation runs
`parameters`	Object	No	Simulation-specific parameters
`seed`	Number	No	Random seed for reproducible results
`parallel`	Boolean	No	Parallel computation (default: true)

Example Usage

API call:

POST /api/v1/tools/montecarlo
Content-Type: application/json

{
  "simulationType": "pi",
  "iterations": 1000000,
  "seed": 42
}

Examples

Pi calculation:

{
  "simulationType": "pi",
  "iterations": 10000000,
  "parameters": {"radius": 1.0},
  "seed": 314159
}

Numerical integration:

{
  "simulationType": "integration",
  "iterations": 1000000,
  "parameters": {
    "function": "sin(x) * exp(-x)",
    "xMin": 0,
    "xMax": 3.14159
  }
}

Optimization:

{
  "simulationType": "optimization",
  "iterations": 500000,
  "parameters": {
    "objective": "minimize",
    "function": "(x-2)^2 + (y+3)^2",
    "bounds": {"x": [-10, 10], "y": [-10, 10]}
  }
}

Return Values

{
  "result": 3.141592,
  "iterations": 1000000,
  "durationMs": 1250,
  "statistics": {
    "mean": 3.141592,
    "stdDev": 0.00052,
    "min": 3.1401,
    "max": 3.1432
  },
  "convergence": 0.9999,
  "success": true
}

Field	Type	Description
`result`	Number	Simulation result
`iterations`	Number	Number of iterations performed
`durationMs`	Number	Computation time in milliseconds
`statistics`	Object	Statistical measures
`convergence`	Number	Degree of convergence (0-1)
`success`	Boolean	True if successful

Related Tools

Why This Tool Stands Out

Stochastic Power Solve problems that deterministic methods cannot handle - uncertainty becomes an advantage!
Parallel Execution Use all CPU cores - millions of iterations in seconds instead of minutes!
Reproducible Identical results with seed values - important for scientific validation!
Scalable From a quick prototype to a production simulation - one million or one billion!

Perfect for:

Risk Analysts VaR calculation
Researchers Physical simulation
Quants Option pricing models

Time Savings

No measured time-saving or performance figures are available here. Results depend on the task and environment.

Ready to Get Started?

Start your Monte Carlo simulations today.

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