"""Custom user-defined spending plan.

A flexible strategy where the user chooses every knob:

- `initial_spend` — year 0 withdrawal in real GBP (taken from
  `state.initial_withdrawal` if not overridden).
- `annual_real_adjust_pct` — fraction by which last year's withdrawal
  scales each subsequent year, on top of inflation. 0.0 = constant
  real GBP (Trinity-shape). +0.02 = 2%/yr above-inflation creep
  (e.g. healthcare). -0.005 = -0.5%/yr decreasing spend (slowing
  down with age).
- `guardrail_threshold_pct` — if portfolio drops below this fraction
  of the starting NW, apply a cut. None = no guardrail.
- `guardrail_cut_pct` — fraction by which to cut last year's
  withdrawal when triggered. Applied multiplicatively each triggered
  year — not "snap to threshold-implied rate", just a soft cut.

The cut is checked AFTER the annual adjustment, so a cut + an
increase don't double-apply: cut wins.

Compared to Guyton-Klinger this is simpler — one threshold, one
cut size, no prosperity rule. If the user wants the prosperity rule
behaviour they can pick the GK preset.
"""
from __future__ import annotations

from fire_planner.strategies.base import StrategyState, WithdrawalStrategy


class SpendingPlanStrategy(WithdrawalStrategy):
    name = "custom"

    def __init__(
        self,
        initial_spend: float | None = None,
        annual_real_adjust_pct: float = 0.0,
        guardrail_threshold_pct: float | None = None,
        guardrail_cut_pct: float = 0.10,
    ) -> None:
        self.initial_spend = initial_spend
        self.annual_real_adjust_pct = annual_real_adjust_pct
        self.guardrail_threshold_pct = guardrail_threshold_pct
        self.guardrail_cut_pct = guardrail_cut_pct

    def propose_withdrawal(self, state: StrategyState) -> float:
        if state.year_idx == 0:
            # Explicit override wins; otherwise take the user's target.
            return (self.initial_spend
                    if self.initial_spend is not None and self.initial_spend > 0 else
                    state.initial_withdrawal)
        if state.portfolio <= 0:
            return 0.0

        proposed = state.last_withdrawal * (1.0 + self.annual_real_adjust_pct)

        if (self.guardrail_threshold_pct is not None
                and state.initial_portfolio > 0):
            trigger_at = state.initial_portfolio * self.guardrail_threshold_pct
            if state.portfolio < trigger_at:
                proposed = proposed * (1.0 - self.guardrail_cut_pct)

        return max(0.0, proposed)