engine+api: plumb life events into the simulator

Until now life events were stored but ignored by the engine — pure
metadata. Now they actually move portfolios.

Engine:
- simulator.simulate() takes optional cashflow_adjustments: a (n_years,)
  real-GBP array applied each year *after* savings + return but
  *before* withdrawal. Positive = inflow, negative = outflow.
- New fire_planner/life_events.py with EventInput dataclass +
  events_to_cashflow_array(events, horizon). Handles ranged deltas,
  one-time amounts, disabled events, year clipping past horizon,
  negative year_start (clipped to 0), and summing multiple events.

API:
- /simulate accepts optional life_events list. Server converts each
  to EventInput, builds cashflow_adjustments, passes to simulate().
- Frontend Run-now on scenario detail now fetches the scenario's
  life events and includes them in the request — projections finally
  reflect "retire at 50, kid born at y3, inheritance at y22".

Tests: 11 events helper + 4 end-to-end engine + 1 API integration =
16 new tests. 188 total (was 172). mypy strict + ruff clean.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

tests/test_simulator_events.py

@@ -0,0 +1,73 @@
+"""End-to-end: cashflow_adjustments make portfolios bigger or smaller."""
+from __future__ import annotations
+
+import numpy as np
+
+from fire_planner.glide_path import static
+from fire_planner.life_events import EventInput, events_to_cashflow_array
+from fire_planner.simulator import simulate
+from fire_planner.strategies.trinity import TrinityStrategy
+from fire_planner.tax.malaysia import MalaysiaTaxRegime
+from tests.test_simulator import fixed_paths
+
+
+def _baseline_kwargs() -> dict[str, object]:
+    """0% real returns, 25y, Trinity 4%, no taxes (Malaysia) — predictable."""
+    paths = fixed_paths(n_paths=1, n_years=25, stock_ret=0.0, bond_ret=0.0, cpi=0.0)
+    return dict(
+        paths=paths,
+        initial_portfolio=1_000_000.0,
+        spending_target=40_000.0,
+        glide=static(0.6),
+        strategy=TrinityStrategy(initial_rate=0.04),
+        regime=MalaysiaTaxRegime(),
+    )
+
+
+def test_no_adjustments_matches_baseline() -> None:
+    base = simulate(**_baseline_kwargs())  # type: ignore[arg-type]
+    with_zero = simulate(**_baseline_kwargs(), cashflow_adjustments=np.zeros(25))  # type: ignore[arg-type]
+    np.testing.assert_allclose(base.portfolio_real, with_zero.portfolio_real)
+
+
+def test_one_time_inheritance_lifts_portfolio() -> None:
+    kwargs = _baseline_kwargs()
+    adj = events_to_cashflow_array(
+        [EventInput(year_start=10, one_time_amount_gbp=250_000)],
+        horizon_years=25,
+    )
+    base = simulate(**kwargs)  # type: ignore[arg-type]
+    enhanced = simulate(**kwargs, cashflow_adjustments=adj)  # type: ignore[arg-type]
+    # Year 11 onward should be exactly £250k higher under 0% returns +
+    # constant Trinity withdrawal.
+    delta = enhanced.portfolio_real[0, 11:] - base.portfolio_real[0, 11:]
+    assert np.all(delta > 0)
+    # Year 11 specifically: +£250k landed at end of year 10, withdrawn
+    # nothing extra in y10. By y11 just propagated forward.
+    assert enhanced.portfolio_real[0, 11] - base.portfolio_real[0, 11] == 250_000
+
+
+def test_ongoing_expense_drains_portfolio() -> None:
+    kwargs = _baseline_kwargs()
+    adj = events_to_cashflow_array(
+        [EventInput(year_start=0, year_end=5, delta_gbp_per_year=-20_000)],
+        horizon_years=25,
+    )
+    base = simulate(**kwargs)  # type: ignore[arg-type]
+    drained = simulate(**kwargs, cashflow_adjustments=adj)  # type: ignore[arg-type]
+    # 6 years × £20k expense = £120k less by end of year 6, 0% growth.
+    delta = base.portfolio_real[0, 6] - drained.portfolio_real[0, 6]
+    assert delta == 120_000
+
+
+def test_event_can_force_failure() -> None:
+    """A massive expense early on can ruin an otherwise-successful run."""
+    kwargs = _baseline_kwargs()
+    adj = events_to_cashflow_array(
+        [EventInput(year_start=2, one_time_amount_gbp=-1_500_000)],
+        horizon_years=25,
+    )
+    base = simulate(**kwargs)  # type: ignore[arg-type]
+    ruined = simulate(**kwargs, cashflow_adjustments=adj)  # type: ignore[arg-type]
+    assert base.success_rate == 1.0
+    assert ruined.success_rate == 0.0