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.

frontend/src/api/client.ts

@@ -229,6 +229,13 @@ export interface SimulateRequest {
   floor_gbp?: string | null;
   n_paths?: number;
   seed?: number;
+  life_events?: Array<{
+    year_start: number;
+    year_end?: number | null;
+    delta_gbp_per_year?: string;
+    one_time_amount_gbp?: string | null;
+    enabled?: boolean;
+  }>;
 }
 
 export interface SimulateResult {

frontend/src/pages/ScenarioDetail.tsx

@@ -7,7 +7,7 @@
 import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query';
 import { Link, useNavigate, useParams } from 'react-router-dom';
 
-import { api, type Scenario, type SimulateRequest } from '@/api/client';
+import { api, lifeEventsApi, type Scenario, type SimulateRequest } from '@/api/client';
 import { ApiError } from '@/api/client';
 import { FanChart } from '@/components/FanChart';
 import { GoalsSection } from '@/components/GoalsSection';
@@ -53,7 +53,9 @@ export function ScenarioDetail() {
     del.mutate();
   };
 
-  const onRunNow = (s: Scenario) =>
+  const onRunNow = async (s: Scenario) => {
+    // Pull events fresh so the run reflects whatever the user just edited.
+    const events = await lifeEventsApi.list(s.id);
     sim.mutate({
       jurisdiction: s.jurisdiction,
       strategy: s.strategy,
@@ -65,7 +67,15 @@ export function ScenarioDetail() {
       horizon_years: s.horizon_years,
       n_paths: 5000,
       seed: 42,
+      life_events: events.map((e) => ({
+        year_start: e.year_start,
+        year_end: e.year_end,
+        delta_gbp_per_year: e.delta_gbp_per_year,
+        one_time_amount_gbp: e.one_time_amount_gbp,
+        enabled: e.enabled,
+      })),
     });
+  };
 
   if (!Number.isFinite(id)) {
     return <p className="text-red-700">Invalid scenario id.</p>;
@@ -104,9 +114,10 @@ export function ScenarioDetail() {
         <div className="flex items-center gap-2">
           <button
             type="button"
-            onClick={() => onRunNow(s)}
+            onClick={() => void onRunNow(s)}
             disabled={sim.isPending}
             className="rounded-md border border-slate-300 bg-white text-sm px-3 py-1.5 hover:bg-slate-50 disabled:opacity-60"
+            title="Run a fresh MC including this scenario's life events"
           >
             {sim.isPending ? 'Running…' : 'Run now'}
           </button>

tests/test_api_simulate.py

@@ -113,6 +113,42 @@ async def test_compare_runs_two_scenarios(client: AsyncClient) -> None:
     assert all(len(r["yearly"]) == 20 for r in results)
 
 
+async def test_simulate_with_life_events_changes_outcome(client: AsyncClient) -> None:
+    """Same params with vs without a £500k inheritance at year 5 — the
+    inheritance run must end with strictly more median NW."""
+    base_req = {
+        "jurisdiction": "uk",
+        "strategy": "trinity",
+        "leave_uk_year": 0,
+        "glide_path": "static_60_40",
+        "spending_gbp": "60000",
+        "nw_seed_gbp": "1500000",
+        "horizon_years": 30,
+        "n_paths": 200,
+        "seed": 42,
+    }
+    base = await client.post("/simulate", json=base_req)
+    assert base.status_code == 200, base.text
+
+    enhanced = await client.post(
+        "/simulate",
+        json={
+            **base_req,
+            "life_events": [
+                {
+                    "year_start": 5,
+                    "one_time_amount_gbp": "500000",
+                }
+            ],
+        },
+    )
+    assert enhanced.status_code == 200, enhanced.text
+
+    base_p50 = float(base.json()["p50_ending_gbp"])
+    enhanced_p50 = float(enhanced.json()["p50_ending_gbp"])
+    assert enhanced_p50 > base_p50
+
+
 async def test_compare_rejects_single_scenario(client: AsyncClient) -> None:
     resp = await client.post(
         "/compare",

tests/test_life_events.py

@@ -0,0 +1,107 @@
+"""Tests for the life-events → cashflow-array helper."""
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from fire_planner.life_events import EventInput, events_to_cashflow_array
+
+
+def test_empty_events_yield_zero_array() -> None:
+    arr = events_to_cashflow_array([], horizon_years=5)
+    np.testing.assert_array_equal(arr, np.zeros(5))
+
+
+def test_one_time_event_lands_at_year_start() -> None:
+    arr = events_to_cashflow_array(
+        [EventInput(year_start=3, one_time_amount_gbp=250_000)],
+        horizon_years=10,
+    )
+    expected = np.zeros(10)
+    expected[3] = 250_000
+    np.testing.assert_array_equal(arr, expected)
+
+
+def test_ranged_delta_applied_inclusive() -> None:
+    arr = events_to_cashflow_array(
+        [EventInput(year_start=2, year_end=5, delta_gbp_per_year=-10_000)],
+        horizon_years=10,
+    )
+    expected = np.zeros(10)
+    expected[2:6] = -10_000  # 2,3,4,5 inclusive
+    np.testing.assert_array_equal(arr, expected)
+
+
+def test_year_end_none_is_one_time() -> None:
+    """Ranged events default year_end == year_start."""
+    arr = events_to_cashflow_array(
+        [EventInput(year_start=4, year_end=None, delta_gbp_per_year=-5_000)],
+        horizon_years=10,
+    )
+    expected = np.zeros(10)
+    expected[4] = -5_000
+    np.testing.assert_array_equal(arr, expected)
+
+
+def test_disabled_events_skipped() -> None:
+    arr = events_to_cashflow_array(
+        [
+            EventInput(year_start=0, one_time_amount_gbp=1_000_000, enabled=False),
+            EventInput(year_start=1, delta_gbp_per_year=-50_000, year_end=3, enabled=False),
+        ],
+        horizon_years=5,
+    )
+    np.testing.assert_array_equal(arr, np.zeros(5))
+
+
+def test_events_past_horizon_clipped() -> None:
+    """Events starting at or beyond the horizon don't apply at all;
+    ranged events that overlap the horizon get clipped to the last year."""
+    arr = events_to_cashflow_array(
+        [
+            EventInput(year_start=10, one_time_amount_gbp=100_000),
+            EventInput(year_start=8, year_end=15, delta_gbp_per_year=-5_000),
+        ],
+        horizon_years=10,
+    )
+    # First event: year 10 is outside (horizon 0..9), so nothing.
+    # Second event: clipped to years 8..9.
+    expected = np.zeros(10)
+    expected[8] = -5_000
+    expected[9] = -5_000
+    np.testing.assert_array_equal(arr, expected)
+
+
+def test_multiple_events_sum() -> None:
+    arr = events_to_cashflow_array(
+        [
+            EventInput(year_start=0, year_end=4, delta_gbp_per_year=-12_000),
+            EventInput(year_start=2, one_time_amount_gbp=50_000),
+            EventInput(year_start=3, delta_gbp_per_year=20_000, year_end=10),
+        ],
+        horizon_years=10,
+    )
+    expected = np.zeros(10)
+    expected[0:5] += -12_000  # event 1: years 0..4
+    expected[2] += 50_000  # event 2: year 2 lump sum
+    expected[3:10] += 20_000  # event 3: years 3..9 (clipped from 3..10)
+    np.testing.assert_array_equal(arr, expected)
+
+
+def test_negative_year_start_clipped_to_zero() -> None:
+    arr = events_to_cashflow_array(
+        [EventInput(year_start=-2, year_end=2, delta_gbp_per_year=-1_000)],
+        horizon_years=5,
+    )
+    expected = np.zeros(5)
+    expected[0:3] = -1_000  # 0,1,2
+    np.testing.assert_array_equal(arr, expected)
+
+
+@pytest.mark.parametrize("amount", [0, 0.0, None])
+def test_zero_or_none_one_time_amount_skipped(amount: float | None) -> None:
+    arr = events_to_cashflow_array(
+        [EventInput(year_start=2, one_time_amount_gbp=amount)],
+        horizon_years=5,
+    )
+    np.testing.assert_array_equal(arr, np.zeros(5))

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