strategies: spending input is honoured + new "Custom" preset with guardrails

The user noticed the "Annual spending" field was a no-op for Trinity,
GK, VPW, VPW+floor — the strategies internally hardcoded the year-0
withdrawal as `initial_portfolio × initial_rate` (4% / 5.5%) and
ignored what the user typed. Two fixes:

(1) Trinity + GK now use state.initial_withdrawal (= the user's
    spending_target) as the year-0 draw. GK's guardrail anchor
    becomes the implied initial rate (initial_withdrawal /
    initial_portfolio), so the rule shape adapts to the user's
    chosen rate. Both strategies still fall back to their preset
    rate × initial_portfolio when initial_withdrawal isn't set
    (test paths). VPW and VPW+floor stay algorithmic — they're
    "withdraw-what's-sustainable" by design and don't take a
    spending input.

(2) New "custom" preset (SpendingPlanStrategy) exposing all the
    knobs:
    - initial_spend = "Annual spending" input
    - annual_real_adjust_pct = scale last year's withdrawal by N%
      each year (0 = constant real £, +0.02 = 2%/yr healthcare
      creep, -0.005 = -0.5%/yr slow-down with age)
    - guardrail_threshold_pct = if portfolio falls below X% of
      starting NW, trigger a cut (None = disabled)
    - guardrail_cut_pct = cut last year's withdrawal by Y% each
      triggered year

Adjust applies first, then guardrail cut — so a triggered year in
+2% adjust mode goes 40k → 40.8k → 36.7k.

UI: "custom" added to the strategy dropdown; when selected, three
extra fields appear (annual real adjustment %, guardrail trigger
threshold, guardrail cut size) with hints. The existing inputs
(spending, NW seed) drive year 0 across all strategies that use
them. About-the-model panel updated.

10 new tests on SpendingPlanStrategy + adjusted GK tests for the
new spending_target-aware behaviour. 209 backend tests + 7
frontend tests. mypy + ruff + tsc all pass.

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

fire_planner/api/simulate.py

@@ -105,13 +105,22 @@ def _project(req: SimulateRequest, paths: np.ndarray) -> tuple[SimulationResult,
         ]
         cashflow_adjustments = events_to_cashflow_array(engine_events, req.horizon_years)
 
+    strategy = build_strategy(
+        req.strategy,
+        floor=floor,
+        annual_real_adjust_pct=float(req.annual_real_adjust_pct),
+        guardrail_threshold_pct=(float(req.guardrail_threshold_pct)
+                                 if req.guardrail_threshold_pct is not None else None),
+        guardrail_cut_pct=float(req.guardrail_cut_pct),
+    )
+
     started = time.perf_counter()
     result = simulate(
         paths=paths,
         initial_portfolio=float(req.nw_seed_gbp),
         spending_target=float(req.spending_gbp),
         glide=get_glide(req.glide_path),
-        strategy=build_strategy(req.strategy, floor=floor),
+        strategy=strategy,
         regime=build_regime_schedule(req.jurisdiction, req.leave_uk_year),
         horizon_years=req.horizon_years,
         annual_savings=annual_savings,