"""DCF valuation with WACC/terminal growth sensitivity."""
from __future__ import annotations
from datetime import datetime, timezone
from statistics import median
from typing import Any
TOOL_META: dict[str, Any] = {
"name": "dcf_sensitivity_matrix",
"description": "Builds a DCF table across WACC and terminal growth assumptions.",
"inputSchema": {
"type": "object",
"properties": {
"free_cash_flows": {"type": "array", "items": {"type": "number"}},
"wacc_range": {"type": "array", "items": {"type": "number"}},
"terminal_growth_range": {"type": "array", "items": {"type": "number"}},
"shares_outstanding": {"type": "number"},
"net_debt": {"type": "number"},
},
"required": [
"free_cash_flows",
"wacc_range",
"terminal_growth_range",
"shares_outstanding",
"net_debt",
],
},
"outputSchema": {
"type": "object",
"properties": {
"status": {"type": "string"},
"data": {"type": "object"},
"timestamp": {"type": "string"},
},
},
}
def dcf_sensitivity_matrix(
free_cash_flows: list[float],
wacc_range: list[float],
terminal_growth_range: list[float],
shares_outstanding: float,
net_debt: float,
**_: Any,
) -> dict[str, Any]:
"""Return base case and sensitivity matrix for DCF valuation."""
try:
matrix: list[list[float]] = []
prices = []
for growth in terminal_growth_range:
row = []
for wacc in wacc_range:
price = _dcf_price(free_cash_flows, wacc, growth, shares_outstanding, net_debt)
row.append(round(price, 2))
prices.append(price)
matrix.append(row)
base_case = {
"wacc": wacc_range[len(wacc_range) // 2],
"terminal_growth": terminal_growth_range[len(terminal_growth_range) // 2],
"price": matrix[len(matrix) // 2][len(wacc_range) // 2],
}
data = {
"base_case": base_case,
"sensitivity_matrix": matrix,
"wacc_labels": wacc_range,
"growth_labels": terminal_growth_range,
"min_price": round(min(prices), 2),
"max_price": round(max(prices), 2),
"median_price": round(median(prices), 2),
}
return {
"status": "success",
"data": data,
"timestamp": datetime.now(timezone.utc).isoformat(),
}
except Exception as exc: # noqa: BLE001
_log_lesson("dcf_sensitivity_matrix", str(exc))
return {
"status": "error",
"data": {"error": str(exc)},
"timestamp": datetime.now(timezone.utc).isoformat(),
}
def _dcf_price(cashflows: list[float], wacc: float, growth: float, shares: float, net_debt: float) -> float:
discount = 0.0
for idx, fcf in enumerate(cashflows, start=1):
discount += fcf / ((1 + wacc) ** idx)
terminal = cashflows[-1] * (1 + growth) / (wacc - growth)
present_value = discount + terminal / ((1 + wacc) ** len(cashflows))
equity_value = present_value - net_debt
return equity_value / shares if shares else 0.0
def _log_lesson(skill_name: str, error: str) -> None:
with open("logs/lessons.md", "a", encoding="utf-8") as handle:
handle.write(f"- [{datetime.now(timezone.utc).isoformat()}] {skill_name}: {error}\n")
