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>

fire_planner/simulator.py

@@ -155,6 +155,7 @@ def simulate(
     regime: TaxRegime | RegimeFn,
     horizon_years: int | None = None,
     annual_savings: npt.NDArray[np.float64] | None = None,
+    cashflow_adjustments: npt.NDArray[np.float64] | None = None,
     bucket_split: _BucketSplit = default_bucket_split,
 ) -> SimulationResult:
     """Run the MC simulation. `paths` shape: (n_paths, n_years, 3).
@@ -163,6 +164,12 @@ def simulate(
     decided by the strategy. `annual_savings`, if given, is a (n_years,)
     real-GBP array — added at the start of each year while accumulating.
 
+    `cashflow_adjustments`, if given, is a (n_years,) real-GBP array of
+    per-year deltas applied **after** savings + return but **before**
+    withdrawal. Positive = inflow (e.g. inheritance, rental income),
+    negative = extra outflow (e.g. childcare, sabbatical). Used to plumb
+    `life_event` rows into the projection.
+
     `regime` may be a single `TaxRegime` (constant for all years) or a
     callable `(year_idx) -> TaxRegime` to model jurisdiction switches —
     e.g. UK for years 0..N-1, then Cyprus from year N onward.
@@ -181,6 +188,8 @@ def simulate(
 
     if annual_savings is None:
         annual_savings = np.zeros(n_years, dtype=np.float64)
+    if cashflow_adjustments is None:
+        cashflow_adjustments = np.zeros(n_years, dtype=np.float64)
 
     for y in range(n_years):
         alloc = glide(y)
@@ -192,8 +201,11 @@ def simulate(
         real_bond = (1 + nominal_bond) / (1 + cpi) - 1
         port_return = alloc * real_stock + (1 - alloc) * real_bond
 
-        # Add savings at year start, then apply year's return.
+        # Add savings at year start, apply year's return, then apply
+        # life-event cashflow adjustments. Adjustments don't compound
+        # this year's returns (they're treated as end-of-year events).
         portfolio = (portfolio + annual_savings[y]) * (1 + port_return)
+        portfolio = portfolio + cashflow_adjustments[y]
 
         # Strategy is per-path Python — 600k iterations at 60y × 10k paths.
         # Profiled: ~3 seconds for the full Trinity / GK / VPW set.