9cc781a8d6
All checks were successful
ci/woodpecker/push/woodpecker Pipeline was successful
income streams, Sankey cashflow, progress overlay, settings sub-pages
Wave 1 (9 features across 4 streams):
Stream A — dashboard skeleton
1.A.1 ScenarioShell with top tabs (Plan/Cash Flow/Tax Analytics/Compare/
Reports/Estate/Settings) + left Sidebar with Plans switcher.
1.A.2 GET /scenarios/{id}/year-stats?year=N returning per-year metrics
(NW, Δ NW, taxable income, taxes, eff. rate, spending, contribs,
investment growth). YearScrubber + YearStatsPanel render the
right-hand sidebar; URL ?year= preserves selection.
1.A.3 FanChart gains optional `milestones` prop (lib/milestone.ts maps
life_event.kind → emoji) + selectedYear marker line.
Stream B — goals + progress
1.B.1 New goals_eval module: target_nw_by_year / never_run_out /
target_real_income probability evaluation. Wired into POST
/simulate (exact, per-path) and GET /scenarios/{id}/projection
(approximated from persisted fan via percentile interpolation).
GoalsSection renders pass/fail badges.
1.B.2 GET /scenarios/{id}/progress overlays AccountSnapshot totals on
the projection fan; ProgressPage shows variance side-panel.
Stream C — income + cashflow
1.C.1 New IncomeStream model + alembic 0003 + CRUD endpoints. Engine
aggregates streams into per-year inflows + taxable arrays;
income tax routes through the jurisdiction tax engine.
IncomeStreamsSection on Plan tab.
1.C.2 GET /scenarios/{id}/cashflow?year=N returns sources/sinks for
an ECharts Sankey (sums conserve). CashflowTab body.
Stream D — settings
1.D.1 SettingsTab + sub-nav (Milestones/Rates/Dividends/Bonds/Tax/
Metrics/Other/Notes); placeholder cards for unbuilt sub-pages.
1.D.2 LifeEventsSection relocated to /scenarios/:id/settings.
1.D.3 RatesSettings (Fixed/Historical/Advanced segmented + per-asset
cards). SimulateRequest gains rates_mode, inflation_pct,
stocks/bonds growth + dividend, stocks_allocation. New
build_fixed_paths() in simulator. Real-return arithmetic
verified against (1+g+d)/(1+i)−1 ≈ 5.4%.
1.D.4 NotesSettings — markdown textarea, save-on-blur, stored in
scenario.config_json.notes.
Backend: 238 pytest pass (+19 new), mypy + ruff clean.
Frontend: typecheck + 7 unit tests + production build clean.
Roadmap for Wave 2-N is documented in the implementation plan.
126 lines
4 KiB
Python
126 lines
4 KiB
Python
"""Tests for fire_planner.goals_eval — parametrised over goal kinds."""
|
|
from __future__ import annotations
|
|
|
|
from dataclasses import dataclass
|
|
from decimal import Decimal
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from fire_planner.goals_eval import evaluate_goals
|
|
from fire_planner.simulator import SimulationResult
|
|
|
|
|
|
@dataclass
|
|
class _Goal:
|
|
kind: str
|
|
name: str
|
|
target_amount_gbp: Decimal | None = None
|
|
target_year: int | None = None
|
|
comparator: str = ">="
|
|
success_threshold: Decimal = Decimal("0.95")
|
|
enabled: bool = True
|
|
|
|
|
|
def _make_result(
|
|
portfolio_paths: list[list[float]],
|
|
withdrawal_paths: list[list[float]] | None = None,
|
|
) -> SimulationResult:
|
|
"""Build a SimulationResult from explicit per-path arrays."""
|
|
portfolio = np.asarray(portfolio_paths, dtype=np.float64)
|
|
n_paths, ncols = portfolio.shape
|
|
n_years = ncols - 1
|
|
if withdrawal_paths is None:
|
|
wd = np.zeros((n_paths, n_years), dtype=np.float64)
|
|
else:
|
|
wd = np.asarray(withdrawal_paths, dtype=np.float64)
|
|
tax = np.zeros((n_paths, n_years), dtype=np.float64)
|
|
success_mask = portfolio[:, 1:-1].min(axis=1) > 0.0 if ncols >= 3 else np.ones(
|
|
n_paths, dtype=bool)
|
|
return SimulationResult(
|
|
portfolio_real=portfolio,
|
|
withdrawal_real=wd,
|
|
tax_real=tax,
|
|
success_mask=success_mask,
|
|
)
|
|
|
|
|
|
def test_target_nw_by_year_exact_count() -> None:
|
|
# 4 paths, 3 years. At year 2: [200, 1500, 2500, 3000]. Target ≥ £2M
|
|
# → 2/4 hit → probability 0.5.
|
|
portfolio = [
|
|
[1000, 500, 200, 100],
|
|
[1000, 1200, 1500, 1700],
|
|
[1000, 2000, 2500, 2800],
|
|
[1000, 2400, 3000, 3500],
|
|
]
|
|
result = _make_result(portfolio)
|
|
goal = _Goal(kind="target_nw_by_year",
|
|
name="≥ £2M at y2",
|
|
target_amount_gbp=Decimal("2000"),
|
|
target_year=2,
|
|
comparator=">=",
|
|
success_threshold=Decimal("0.4"))
|
|
[eval_] = evaluate_goals(result, [goal])
|
|
assert eval_.probability == pytest.approx(0.5)
|
|
assert eval_.passed is True
|
|
|
|
|
|
def test_never_run_out_full_horizon() -> None:
|
|
# 4 paths over 4 years. Path 0 hits 0 at year 2. Path 1 hits 0 at
|
|
# year 3. Path 2 + 3 stay positive throughout.
|
|
portfolio = [
|
|
[1000, 500, 0, 0, 0],
|
|
[1000, 800, 600, 0, 0],
|
|
[1000, 900, 800, 700, 600],
|
|
[1000, 1100, 1200, 1300, 1400],
|
|
]
|
|
result = _make_result(portfolio)
|
|
goal = _Goal(kind="never_run_out",
|
|
name="don't ruin",
|
|
target_year=None,
|
|
success_threshold=Decimal("0.5"))
|
|
[eval_] = evaluate_goals(result, [goal])
|
|
assert eval_.probability == pytest.approx(0.5)
|
|
assert eval_.passed is True
|
|
|
|
|
|
def test_target_real_income_uses_path_median() -> None:
|
|
portfolio = [
|
|
[1000, 1000, 1000],
|
|
[1000, 1000, 1000],
|
|
[1000, 1000, 1000],
|
|
]
|
|
withdrawals = [
|
|
[40_000, 40_000],
|
|
[60_000, 60_000],
|
|
[80_000, 80_000],
|
|
]
|
|
result = _make_result(portfolio, withdrawals)
|
|
goal = _Goal(kind="target_real_income",
|
|
name="≥ £50k income",
|
|
target_amount_gbp=Decimal("50000"),
|
|
target_year=0,
|
|
comparator=">=",
|
|
success_threshold=Decimal("0.5"))
|
|
[eval_] = evaluate_goals(result, [goal])
|
|
assert eval_.probability == pytest.approx(2 / 3)
|
|
assert eval_.passed is True
|
|
|
|
|
|
def test_disabled_goals_skipped() -> None:
|
|
portfolio = [[1000, 500, 0]]
|
|
result = _make_result(portfolio)
|
|
enabled = _Goal(kind="never_run_out", name="active")
|
|
disabled = _Goal(kind="never_run_out", name="muted", enabled=False)
|
|
evals = evaluate_goals(result, [enabled, disabled])
|
|
assert [e.name for e in evals] == ["active"]
|
|
|
|
|
|
def test_unknown_kind_returns_zero() -> None:
|
|
portfolio = [[1000, 1500, 2000]]
|
|
result = _make_result(portfolio)
|
|
goal = _Goal(kind="not_implemented", name="???")
|
|
[eval_] = evaluate_goals(result, [goal])
|
|
assert eval_.probability == 0.0
|
|
assert eval_.passed is False
|