# /// script
# requires-python = ">=3.12"
# dependencies = []
# exclude-newer = "2026-08-08T00:00:00Z"
# ///
"""Readable APBO, benefit-cost, OCI, and sensitivity lab for Harbor Light."""

from __future__ import annotations


def money(amount: float) -> str:
    return f"({abs(amount):,.2f})" if amount < 0 else f"{amount:,.2f}"


def show(title: str, rows: list[tuple[str, float]]) -> None:
    print(f"\n{title}\n{'-' * len(title)}")
    width = max(len(label) for label, _ in rows)
    for label, value in rows:
        print(f"{label:<{width}}  {money(value):>18}")


# The actuary supplies opening APBO after applying the plan's attribution method.
opening_apbo = 7_500_000.00
service_cost = 500_000.00
interest_cost = 450_000.00
obligation_loss = 300_000.00
benefits_paid = 250_000.00
ending_apbo = (
    opening_apbo + service_cost + interest_cost + obligation_loss - benefits_paid
)
show(
    "Accumulated postretirement benefit obligation",
    [
        ("Opening APBO", opening_apbo),
        ("Service cost", service_cost),
        ("Interest cost", interest_cost),
        ("Actuarial loss", obligation_loss),
        ("Benefits paid", -benefits_paid),
        ("Ending APBO", ending_apbo),
    ],
)

opening_assets = 2_000_000.00
actual_return = 120_000.00
contribution = 400_000.00
ending_assets = opening_assets + actual_return + contribution - benefits_paid
funded_status = ending_assets - ending_apbo
show(
    "Plan assets and funded status",
    [
        ("Opening plan assets", opening_assets),
        ("Actual return", actual_return),
        ("Employer contribution", contribution),
        ("Benefits paid", -benefits_paid),
        ("Ending plan assets", ending_assets),
        ("Ending funded status", funded_status),
    ],
)

expected_return = 100_000.00
loss_amortization = 50_000.00
benefit_cost = service_cost + interest_cost - expected_return + loss_amortization
asset_gain = actual_return - expected_return
current_oci_loss = obligation_loss - asset_gain - loss_amortization
opening_aoci_loss = 800_000.00
ending_aoci_loss = opening_aoci_loss + current_oci_loss
opening_funded_status = opening_assets - opening_apbo
liability_increase = -(funded_status - opening_funded_status)
entry_difference = benefit_cost + current_oci_loss - contribution - liability_increase
show(
    "Cost, OCI, and employer-entry control",
    [
        ("Postretirement benefit cost", benefit_cost),
        ("Current OCI loss", current_oci_loss),
        ("Ending AOCI loss", ending_aoci_loss),
        ("Cash contribution", -contribution),
        ("Liability increase", -liability_increase),
        ("Entry difference", entry_difference),
    ],
)

# Conditional actuarial outputs; this script does not choose the trend assumption.
higher_trend_obligation = 9_200_000.00
lower_trend_obligation = 8_000_000.00
show(
    "Healthcare-trend sensitivity",
    [
        ("Base APBO", ending_apbo),
        ("Higher-trend APBO", higher_trend_obligation),
        ("Higher-trend change", higher_trend_obligation - ending_apbo),
        ("Lower-trend APBO", lower_trend_obligation),
        ("Lower-trend change", lower_trend_obligation - ending_apbo),
    ],
)

assert abs(ending_apbo - 8_500_000.00) < 0.01
assert abs(ending_assets - 2_270_000.00) < 0.01
assert abs(funded_status + 6_230_000.00) < 0.01
assert abs(entry_difference) < 0.01

print("\nBoundary: EPBO, attribution, APBO, and trend scenarios are actuarial inputs.")
print("The script does not forecast claims or determine legal funding or benefit security.")