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>

tests/test_spending_plan.py

@@ -0,0 +1,87 @@
+"""SpendingPlanStrategy: user-customisable initial spend + annual adjust + guardrail."""
+from fire_planner.strategies.base import StrategyState
+from fire_planner.strategies.spending_plan import SpendingPlanStrategy
+
+
+def state(**overrides: float | int) -> StrategyState:
+    base = dict(
+        portfolio=1_000_000.0,
+        initial_portfolio=1_000_000.0,
+        initial_withdrawal=40_000.0,
+        year_idx=0,
+        horizon_years=60,
+        last_withdrawal=40_000.0,
+        expected_real_return=0.04,
+    )
+    base.update(overrides)
+    return StrategyState(**base)  # type: ignore[arg-type]
+
+
+def test_year_zero_takes_initial_withdrawal() -> None:
+    s = SpendingPlanStrategy()
+    assert s.propose_withdrawal(state()) == 40_000.0
+
+
+def test_year_zero_explicit_override() -> None:
+    s = SpendingPlanStrategy(initial_spend=72_000.0)
+    assert s.propose_withdrawal(state()) == 72_000.0
+
+
+def test_constant_real_with_zero_adjust() -> None:
+    s = SpendingPlanStrategy(annual_real_adjust_pct=0.0)
+    assert s.propose_withdrawal(state(year_idx=10, last_withdrawal=40_000.0)) == 40_000.0
+
+
+def test_positive_annual_adjust_grows_spending() -> None:
+    """+2% real adjust → year 5 spend is last_w × 1.02 (one step from year 4)."""
+    s = SpendingPlanStrategy(annual_real_adjust_pct=0.02)
+    out = s.propose_withdrawal(state(year_idx=5, last_withdrawal=40_000.0))
+    assert abs(out - 40_800.0) < 1e-6
+
+
+def test_negative_annual_adjust_shrinks_spending() -> None:
+    s = SpendingPlanStrategy(annual_real_adjust_pct=-0.005)
+    out = s.propose_withdrawal(state(year_idx=10, last_withdrawal=40_000.0))
+    assert abs(out - 39_800.0) < 1e-6
+
+
+def test_guardrail_not_triggered_when_portfolio_above_threshold() -> None:
+    s = SpendingPlanStrategy(guardrail_threshold_pct=0.80, guardrail_cut_pct=0.10)
+    # portfolio at 90% of starting → above threshold → no cut
+    out = s.propose_withdrawal(state(year_idx=3, portfolio=900_000.0,
+                                     last_withdrawal=40_000.0))
+    assert out == 40_000.0
+
+
+def test_guardrail_triggered_cuts_by_pct() -> None:
+    s = SpendingPlanStrategy(guardrail_threshold_pct=0.80, guardrail_cut_pct=0.10)
+    # portfolio at 70% of starting → below threshold → 10% cut
+    out = s.propose_withdrawal(state(year_idx=3, portfolio=700_000.0,
+                                     last_withdrawal=40_000.0))
+    assert abs(out - 36_000.0) < 1e-6
+
+
+def test_guardrail_combines_with_annual_adjust() -> None:
+    """Adjust applies first, then cut — both fire."""
+    s = SpendingPlanStrategy(
+        annual_real_adjust_pct=0.02,
+        guardrail_threshold_pct=0.80,
+        guardrail_cut_pct=0.10,
+    )
+    out = s.propose_withdrawal(state(year_idx=3, portfolio=700_000.0,
+                                     last_withdrawal=40_000.0))
+    # 40_000 * 1.02 = 40_800; trigger; 40_800 * 0.90 = 36_720
+    assert abs(out - 36_720.0) < 1e-6
+
+
+def test_guardrail_disabled_when_threshold_none() -> None:
+    s = SpendingPlanStrategy(guardrail_threshold_pct=None)
+    out = s.propose_withdrawal(state(year_idx=3, portfolio=10_000.0,
+                                     last_withdrawal=40_000.0))
+    assert out == 40_000.0  # no cut despite tiny portfolio
+
+
+def test_returns_zero_when_portfolio_drained() -> None:
+    s = SpendingPlanStrategy()
+    out = s.propose_withdrawal(state(year_idx=5, portfolio=0.0, last_withdrawal=40_000.0))
+    assert out == 0.0

tests/test_strategies.py

@@ -36,17 +36,30 @@ def test_trinity_doesnt_increase_with_portfolio_growth() -> None:
                                       last_withdrawal=40_000.0)) == 40_000.0
 
 
-def test_gk_year_zero_uses_initial_rate() -> None:
+def test_gk_year_zero_uses_initial_withdrawal() -> None:
+    """Year-0 honours the user's target spending (state.initial_withdrawal),
+    not the strategy's preset rate. The preset rate now only matters as a
+    fallback when initial_withdrawal isn't set."""
     s = GuytonKlingerStrategy(initial_rate=0.055)
-    # 5.5% of 1M = 55,000
-    assert s.propose_withdrawal(state()) == 55_000.0
+    # state default has initial_withdrawal=40_000 → year 0 returns 40_000.
+    assert s.propose_withdrawal(state()) == 40_000.0
+
+
+def test_gk_year_zero_falls_back_to_preset_when_no_target() -> None:
+    s = GuytonKlingerStrategy(initial_rate=0.055)
+    # Override initial_withdrawal=0 → fall back to 5.5% × 1M = 55_000.
+    assert s.propose_withdrawal(state(initial_withdrawal=0)) == 55_000.0
 
 
 def test_gk_capital_preservation_cut() -> None:
-    """Portfolio crashed: current rate now > 120% of 5.5% = 6.6%; > 15y left → cut 10%."""
+    """Portfolio crashed: current rate now > 120% of the implied initial rate (5.5%);
+    > 15y left → cut 10%. Implied rate = initial_withdrawal / initial_portfolio."""
     s = GuytonKlingerStrategy(initial_rate=0.055)
-    # last_w = 55,000; portfolio = 700,000 → rate = 7.86% > 6.6%
-    out = s.propose_withdrawal(state(year_idx=5, portfolio=700_000.0, last_withdrawal=55_000.0))
+    # initial_withdrawal=55k, initial_portfolio=1M → implied rate = 5.5%.
+    # last_w = 55k; portfolio = 700k → current rate = 7.86% > 6.6% guardrail.
+    out = s.propose_withdrawal(state(year_idx=5, portfolio=700_000.0,
+                                     last_withdrawal=55_000.0,
+                                     initial_withdrawal=55_000.0))
     assert abs(out - 49_500.0) < 0.01