Monte Carlo

Monte Carlo Simulation & Sensitivity Analysis

Uncertainty analysis, tornado charts, and decision trees.

Monte Carlo Valuation

Sample from input distributions to generate valuation distribution.

from mcp_server.tools import monte_carlo_valuation
result = monte_carlo_valuation(
    input_distributions=[
        {"name": "revenue", "distribution": "normal", "params": {"mean": 1000000, "std": 100000}},
        {"name": "cost", "distribution": "uniform", "params": {"low": 200000, "high": 400000}},
    ],
    iterations=10000, seed=42,
)

Tornado Sensitivity Analysis

One-at-a-time sensitivity ranked by impact magnitude.

Decision Tree Analysis

Backward induction for valuing assets with contingent outcomes.

from mcp_server.tools import decision_tree_valuation
tree = {
    "nodes": [
        {"id": "root", "type": "decision", "label": "Invest?", "value": 0},
        {"id": "success", "type": "terminal", "label": "Success", "value": 1000000},
        {"id": "failure", "type": "terminal", "label": "Failure", "value": 0},
    ],
    "edges": [
        {"from": "root", "to": "success", "probability": 0.6, "cost": 200000},
        {"from": "root", "to": "failure", "probability": 0.4, "cost": 200000},
    ],
}
result = decision_tree_valuation(tree=tree)

MCP Tools

monte_carlo_valuation, decision_tree_valuation