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/api/schemas.py

@@ -203,6 +203,15 @@ class GoalCreate(BaseModel):
 # ── simulate / compare ───────────────────────────────────────────────
 
 
+class LifeEventInput(BaseModel):
+    """Engine-level event shape — same as the DB row's relevant fields."""
+    year_start: int = Field(ge=0, le=100)
+    year_end: int | None = Field(default=None, ge=0, le=100)
+    delta_gbp_per_year: Decimal = Decimal("0")
+    one_time_amount_gbp: Decimal | None = None
+    enabled: bool = True
+
+
 class SimulateRequest(BaseModel):
     """Sync, non-persisted simulate. Used by the React UI for what-if."""
     jurisdiction: str
@@ -216,6 +225,7 @@ class SimulateRequest(BaseModel):
     floor_gbp: Decimal | None = None
     n_paths: int = Field(ge=100, le=50_000, default=5_000)
     seed: int = 42
+    life_events: list[LifeEventInput] = Field(default_factory=list)
 
 
 class SimulateResult(BaseModel):

fire_planner/api/simulate.py

@@ -26,6 +26,7 @@ from fire_planner.api.schemas import (
     SimulateResult,
 )
 from fire_planner.glide_path import get as get_glide
+from fire_planner.life_events import EventInput, events_to_cashflow_array
 from fire_planner.returns.bootstrap import block_bootstrap
 from fire_planner.returns.shiller import load_from_csv, synthetic_returns
 from fire_planner.scenarios import build_regime_schedule, build_strategy
@@ -47,6 +48,21 @@ def _project(req: SimulateRequest) -> tuple[SimulationResult, float]:
     annual_savings = (np.full(req.horizon_years, float(req.savings_per_year_gbp), dtype=np.float64)
                       if req.savings_per_year_gbp > 0 else None)
     floor = float(req.floor_gbp) if req.floor_gbp is not None else None
+
+    cashflow_adjustments = None
+    if req.life_events:
+        engine_events = [
+            EventInput(
+                year_start=ev.year_start,
+                year_end=ev.year_end,
+                delta_gbp_per_year=float(ev.delta_gbp_per_year),
+                one_time_amount_gbp=(float(ev.one_time_amount_gbp)
+                                     if ev.one_time_amount_gbp is not None else None),
+                enabled=ev.enabled,
+            ) for ev in req.life_events
+        ]
+        cashflow_adjustments = events_to_cashflow_array(engine_events, req.horizon_years)
+
     started = time.perf_counter()
     result = simulate(
         paths=paths,
@@ -57,6 +73,7 @@ def _project(req: SimulateRequest) -> tuple[SimulationResult, float]:
         regime=build_regime_schedule(req.jurisdiction, req.leave_uk_year),
         horizon_years=req.horizon_years,
         annual_savings=annual_savings,
+        cashflow_adjustments=cashflow_adjustments,
     )
     elapsed = time.perf_counter() - started
     return result, elapsed

fire_planner/life_events.py

@@ -0,0 +1,58 @@
+"""Convert life-event records into per-year cashflow adjustments.
+
+Two event shapes the engine understands:
+
+- **Ranged delta**: `delta_gbp_per_year != 0` applied each year in
+  `[year_start, year_end]` (inclusive). Use a negative delta for
+  expenses (childcare, sabbatical), positive for income (rental,
+  pension that hasn't started yet).
+
+- **One-time amount**: `one_time_amount_gbp` applied once at
+  `year_start`. Inheritance, house sale proceeds, lump-sum gift.
+
+Disabled events (`enabled=False`) are skipped. Year ranges that
+extend past the simulation horizon are clipped — events beyond year
+H simply don't happen in this run.
+"""
+from __future__ import annotations
+
+from collections.abc import Iterable
+from dataclasses import dataclass
+
+import numpy as np
+import numpy.typing as npt
+
+
+@dataclass(frozen=True)
+class EventInput:
+    """Engine-level event shape — decoupled from the SQLAlchemy ORM and
+    the API Pydantic schema so callers can construct them however."""
+    year_start: int
+    year_end: int | None = None
+    delta_gbp_per_year: float = 0.0
+    one_time_amount_gbp: float | None = None
+    enabled: bool = True
+
+
+def events_to_cashflow_array(
+    events: Iterable[EventInput],
+    horizon_years: int,
+) -> npt.NDArray[np.float64]:
+    """Sum a list of events into a `(horizon_years,)` real-GBP array."""
+    out = np.zeros(horizon_years, dtype=np.float64)
+    for ev in events:
+        if not ev.enabled:
+            continue
+        start = max(0, int(ev.year_start))
+        if start >= horizon_years:
+            continue
+
+        if ev.delta_gbp_per_year:
+            end = ev.year_end if ev.year_end is not None else ev.year_start
+            end = min(int(end), horizon_years - 1)
+            if end >= start:
+                out[start:end + 1] += float(ev.delta_gbp_per_year)
+
+        if ev.one_time_amount_gbp:
+            out[start] += float(ev.one_time_amount_gbp)
+    return out

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.