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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>
73 lines
3 KiB
Python
73 lines
3 KiB
Python
"""End-to-end: cashflow_adjustments make portfolios bigger or smaller."""
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from __future__ import annotations
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import numpy as np
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from fire_planner.glide_path import static
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from fire_planner.life_events import EventInput, events_to_cashflow_array
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from fire_planner.simulator import simulate
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from fire_planner.strategies.trinity import TrinityStrategy
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from fire_planner.tax.malaysia import MalaysiaTaxRegime
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from tests.test_simulator import fixed_paths
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def _baseline_kwargs() -> dict[str, object]:
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"""0% real returns, 25y, Trinity 4%, no taxes (Malaysia) — predictable."""
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paths = fixed_paths(n_paths=1, n_years=25, stock_ret=0.0, bond_ret=0.0, cpi=0.0)
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return dict(
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paths=paths,
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initial_portfolio=1_000_000.0,
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spending_target=40_000.0,
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glide=static(0.6),
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strategy=TrinityStrategy(initial_rate=0.04),
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regime=MalaysiaTaxRegime(),
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)
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def test_no_adjustments_matches_baseline() -> None:
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base = simulate(**_baseline_kwargs()) # type: ignore[arg-type]
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with_zero = simulate(**_baseline_kwargs(), cashflow_adjustments=np.zeros(25)) # type: ignore[arg-type]
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np.testing.assert_allclose(base.portfolio_real, with_zero.portfolio_real)
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def test_one_time_inheritance_lifts_portfolio() -> None:
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kwargs = _baseline_kwargs()
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adj = events_to_cashflow_array(
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[EventInput(year_start=10, one_time_amount_gbp=250_000)],
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horizon_years=25,
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)
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base = simulate(**kwargs) # type: ignore[arg-type]
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enhanced = simulate(**kwargs, cashflow_adjustments=adj) # type: ignore[arg-type]
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# Year 11 onward should be exactly £250k higher under 0% returns +
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# constant Trinity withdrawal.
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delta = enhanced.portfolio_real[0, 11:] - base.portfolio_real[0, 11:]
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assert np.all(delta > 0)
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# Year 11 specifically: +£250k landed at end of year 10, withdrawn
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# nothing extra in y10. By y11 just propagated forward.
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assert enhanced.portfolio_real[0, 11] - base.portfolio_real[0, 11] == 250_000
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def test_ongoing_expense_drains_portfolio() -> None:
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kwargs = _baseline_kwargs()
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adj = events_to_cashflow_array(
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[EventInput(year_start=0, year_end=5, delta_gbp_per_year=-20_000)],
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horizon_years=25,
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)
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base = simulate(**kwargs) # type: ignore[arg-type]
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drained = simulate(**kwargs, cashflow_adjustments=adj) # type: ignore[arg-type]
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# 6 years × £20k expense = £120k less by end of year 6, 0% growth.
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delta = base.portfolio_real[0, 6] - drained.portfolio_real[0, 6]
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assert delta == 120_000
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def test_event_can_force_failure() -> None:
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"""A massive expense early on can ruin an otherwise-successful run."""
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kwargs = _baseline_kwargs()
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adj = events_to_cashflow_array(
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[EventInput(year_start=2, one_time_amount_gbp=-1_500_000)],
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horizon_years=25,
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)
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base = simulate(**kwargs) # type: ignore[arg-type]
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ruined = simulate(**kwargs, cashflow_adjustments=adj) # type: ignore[arg-type]
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assert base.success_rate == 1.0
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assert ruined.success_rate == 0.0
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