f43322e5ce
All checks were successful
ci/woodpecker/push/woodpecker Pipeline was successful
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>
66 lines
2.9 KiB
Python
66 lines
2.9 KiB
Python
"""Guyton-Klinger 4-rule guardrails (FPA Journal, 2006).
|
||
|
||
Decision rules applied each year, in order:
|
||
|
||
1. **Portfolio-Management Rule** — choose which asset class to draw from
|
||
(we delegate to the simulator's rebalance logic; ignored here).
|
||
2. **Inflation Rule** — skip the inflation uplift on the prior year's
|
||
withdrawal if both:
|
||
a. the prior year's nominal portfolio return was negative, AND
|
||
b. the current withdrawal rate would exceed the initial rate.
|
||
3. **Capital-Preservation Rule** — cut the withdrawal by 10% if the
|
||
current rate exceeds 120% of the initial rate AND there are more
|
||
than 15 years left in the horizon.
|
||
4. **Prosperity Rule** — increase the withdrawal by 10% if the current
|
||
rate is below 80% of the initial rate.
|
||
|
||
This implementation operates in real GBP, so the inflation-skip rule
|
||
has no effect (real values don't drift with inflation). The other three
|
||
rules apply normally. Trade-off: simplifies the math at the cost of
|
||
slightly under-cutting in nominal-stress scenarios.
|
||
|
||
Initial rate baseline: 5.5% of starting portfolio (per Guyton-Klinger
|
||
paper, allows higher sustainable spend than Trinity by tolerating
|
||
guardrail cuts).
|
||
"""
|
||
from __future__ import annotations
|
||
|
||
from fire_planner.strategies.base import StrategyState, WithdrawalStrategy
|
||
|
||
DEFAULT_INITIAL_RATE = 0.055
|
||
CAPITAL_PRESERVATION_RATIO = 1.20
|
||
PROSPERITY_RATIO = 0.80
|
||
ADJUSTMENT = 0.10
|
||
MIN_HORIZON_FOR_CUT = 15
|
||
|
||
|
||
class GuytonKlingerStrategy(WithdrawalStrategy):
|
||
name = "guyton_klinger"
|
||
|
||
def __init__(self, initial_rate: float = DEFAULT_INITIAL_RATE) -> None:
|
||
self.initial_rate = initial_rate
|
||
|
||
def propose_withdrawal(self, state: StrategyState) -> float:
|
||
# Year 0 = the user's target spending; the implied initial rate
|
||
# (initial_withdrawal / initial_portfolio) becomes the anchor
|
||
# the guardrails compare against. Falls back to the preset rate
|
||
# × initial_portfolio when no target was given.
|
||
target_initial = (state.initial_withdrawal
|
||
if state.initial_withdrawal > 0 else
|
||
state.initial_portfolio * self.initial_rate)
|
||
if state.year_idx == 0:
|
||
return target_initial
|
||
if state.portfolio <= 0:
|
||
return 0.0
|
||
implied_initial_rate = (target_initial / state.initial_portfolio
|
||
if state.initial_portfolio > 0 else self.initial_rate)
|
||
last_w = state.last_withdrawal
|
||
current_rate = last_w / state.portfolio
|
||
years_left = state.horizon_years - state.year_idx
|
||
# Capital-preservation cut: only if more than 15 years remain.
|
||
if (current_rate > implied_initial_rate * CAPITAL_PRESERVATION_RATIO
|
||
and years_left > MIN_HORIZON_FOR_CUT):
|
||
return last_w * (1 - ADJUSTMENT)
|
||
if current_rate < implied_initial_rate * PROSPERITY_RATIO:
|
||
return last_w * (1 + ADJUSTMENT)
|
||
return last_w
|