engine+api: plumb life events into the simulator
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>
2026-05-09 22:30:33 +00:00
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"""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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2026-05-10 19:17:57 +00:00
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import pytest
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engine+api: plumb life events into the simulator
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>
2026-05-09 22:30:33 +00:00
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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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2026-05-10 19:17:57 +00:00
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def test_extra_outflows_show_up_in_withdrawal_trace() -> None:
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"""A £100k spending bump in years 5-10 should be visible on the
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withdrawal trace — not just silently drained from the portfolio."""
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kwargs = _baseline_kwargs()
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adj = np.zeros(25, dtype=np.float64)
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extras = np.zeros(25, dtype=np.float64)
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adj[5:11] = -100_000.0 # drains the portfolio
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extras[5:11] = 100_000.0 # surfaces on the chart
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base = simulate(**kwargs) # type: ignore[arg-type]
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bumped = simulate(**kwargs, cashflow_adjustments=adj, extra_outflows=extras) # type: ignore[arg-type]
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# Year 0–4 unchanged (no extra outflow)
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np.testing.assert_allclose(base.withdrawal_real[:, :5], bumped.withdrawal_real[:, :5])
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# Years 5–10 should be ~100k higher than baseline (clipped only when
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# the portfolio was already drained — checked by spot-test).
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assert (bumped.withdrawal_real[:, 5:11] > base.withdrawal_real[:, 5:11]).all()
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# Year 5 specifically: strategy w (~40k) + 100k extra ≈ 140k.
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assert bumped.withdrawal_real[0, 5] == pytest.approx(140_000.0, rel=0.05)
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engine+api: plumb life events into the simulator
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>
2026-05-09 22:30:33 +00:00
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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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