260 lines
9.9 KiB
Python
260 lines
9.9 KiB
Python
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"""Simulator behaviour: deterministic short-horizon checks, then
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stochastic monotonicity + cFIREsim sanity calibration."""
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from __future__ import annotations
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import time
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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.returns.bootstrap import block_bootstrap
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from fire_planner.returns.shiller import ReturnsBundle, synthetic_returns
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from fire_planner.simulator import default_bucket_split, simulate
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from fire_planner.strategies.guyton_klinger import GuytonKlingerStrategy
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from fire_planner.strategies.trinity import TrinityStrategy
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from fire_planner.strategies.vpw import VpwStrategy
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from fire_planner.tax.bulgaria import BulgariaTaxRegime
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from fire_planner.tax.malaysia import MalaysiaTaxRegime
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from fire_planner.tax.uk import UkTaxRegime
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def fixed_paths(n_paths: int, n_years: int, stock_ret: float, bond_ret: float,
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cpi: float) -> np.ndarray:
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"""All-paths-identical returns — deterministic regression check."""
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out = np.zeros((n_paths, n_years, 3), dtype=np.float64)
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out[..., 0] = stock_ret
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out[..., 1] = bond_ret
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out[..., 2] = cpi
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return out
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def test_simulate_zero_returns_zero_inflation_drains_at_4pc() -> None:
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"""0% returns + 0% inflation, 4% Trinity, 25y horizon — withdraw
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£40k/y from £1M = drain to exactly £0 in year 25. Success because
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portfolio stays positive *during* every year (clipped to 0 at end)."""
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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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res = simulate(
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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(), # 0% to keep arithmetic clean
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)
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# Year 0 withdrawal is 40k, portfolio after = 960k
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assert res.portfolio_real[0, 1] == 960_000.0
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# 25y of £40k draws against zero growth = drain to 0 by end of y25.
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assert abs(res.portfolio_real[0, 25]) < 1.0
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def test_simulate_failing_path_marked_unsuccessful() -> None:
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"""6% Trinity rate against 0% real return for 25y — clearly fails."""
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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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res = simulate(
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paths=paths,
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initial_portfolio=1_000_000.0,
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spending_target=60_000.0,
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glide=static(1.0),
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strategy=TrinityStrategy(initial_rate=0.06),
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regime=MalaysiaTaxRegime(),
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)
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assert not res.success_mask[0]
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def test_simulate_growing_portfolio_succeeds() -> None:
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"""5% real return, 4% draw — classic surplus case."""
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paths = fixed_paths(n_paths=1, n_years=30, stock_ret=0.05, bond_ret=0.05, cpi=0.0)
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res = simulate(
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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(1.0),
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strategy=TrinityStrategy(initial_rate=0.04),
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regime=MalaysiaTaxRegime(),
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)
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assert res.success_mask[0]
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# Portfolio should grow above starting value
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assert res.portfolio_real[0, 30] > 1_000_000.0
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def test_savings_phase_increases_portfolio() -> None:
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"""5y of savings @ £100k / 0% return → portfolio grows."""
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paths = fixed_paths(n_paths=1, n_years=5, stock_ret=0.0, bond_ret=0.0, cpi=0.0)
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res = simulate(
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paths=paths,
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initial_portfolio=1_000_000.0,
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spending_target=0.0, # not drawing during accumulation
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glide=static(1.0),
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strategy=TrinityStrategy(initial_rate=0.0),
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regime=MalaysiaTaxRegime(),
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annual_savings=np.full(5, 100_000.0),
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)
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# 1M + 5×100k = 1.5M, no growth
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assert res.portfolio_real[0, 5] == 1_500_000.0
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def test_uk_tax_increases_failure_rate_vs_no_tax() -> None:
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"""Same scenario, UK regime should produce more or equal failures
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than Malaysia (zero tax) — paths are identical."""
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bundle = synthetic_returns(seed=1, n_years=120)
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rng = np.random.default_rng(0)
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paths = block_bootstrap(bundle, n_paths=200, n_years=30, block_size=5, rng=rng)
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common = dict(
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paths=paths,
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initial_portfolio=600_000.0, # tighter so tax matters
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spending_target=40_000.0,
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glide=static(0.7),
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strategy=TrinityStrategy(initial_rate=0.04),
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)
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msy = simulate(**common, regime=MalaysiaTaxRegime()) # type: ignore[arg-type]
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uk = simulate(**common, regime=UkTaxRegime()) # type: ignore[arg-type]
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assert uk.success_rate <= msy.success_rate
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assert uk.median_lifetime_tax() > msy.median_lifetime_tax()
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def test_vpw_never_runs_out() -> None:
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"""VPW scales withdrawal with portfolio — should never fully ruin."""
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bundle = synthetic_returns(seed=2, n_years=120)
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rng = np.random.default_rng(0)
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paths = block_bootstrap(bundle, n_paths=200, n_years=60, block_size=5, rng=rng)
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res = simulate(
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paths=paths,
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initial_portfolio=1_000_000.0,
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spending_target=50_000.0,
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glide=static(0.7),
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strategy=VpwStrategy(),
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regime=MalaysiaTaxRegime(),
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)
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# Every path should keep some portfolio > 0 throughout (until last year).
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# Year `n-1` end may be tiny but >= 0 since VPW caps drain at 100% in y=H-1.
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assert res.portfolio_real[:, 1:-1].min() > 0
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def test_simulator_deterministic_with_same_paths() -> None:
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paths = fixed_paths(n_paths=10, n_years=30, stock_ret=0.05, bond_ret=0.03, cpi=0.02)
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common = 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(0.7),
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strategy=GuytonKlingerStrategy(),
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regime=BulgariaTaxRegime(),
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)
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a = simulate(**common) # type: ignore[arg-type]
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b = simulate(**common) # type: ignore[arg-type]
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np.testing.assert_array_equal(a.portfolio_real, b.portfolio_real)
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def test_success_rate_monotone_in_portfolio() -> None:
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"""More starting wealth → higher (or equal) success rate."""
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bundle = synthetic_returns(seed=3, n_years=120)
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rng = np.random.default_rng(0)
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paths = block_bootstrap(bundle, n_paths=300, n_years=30, block_size=5, rng=rng)
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common = dict(
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paths=paths,
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spending_target=40_000.0,
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glide=static(0.7),
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strategy=TrinityStrategy(initial_rate=0.04),
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regime=MalaysiaTaxRegime(),
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)
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low = simulate(**common, initial_portfolio=600_000.0) # type: ignore[arg-type]
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high = simulate(**common, initial_portfolio=1_500_000.0) # type: ignore[arg-type]
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assert high.success_rate >= low.success_rate
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def test_success_rate_monotone_in_spending() -> None:
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"""Less spending → higher success rate."""
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bundle = synthetic_returns(seed=4, n_years=120)
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rng = np.random.default_rng(0)
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paths = block_bootstrap(bundle, n_paths=300, n_years=30, block_size=5, rng=rng)
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common = dict(
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paths=paths,
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initial_portfolio=1_000_000.0,
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glide=static(0.7),
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strategy=TrinityStrategy(initial_rate=0.04),
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regime=MalaysiaTaxRegime(),
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)
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cheap = simulate(**common, spending_target=30_000.0) # type: ignore[arg-type]
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fat = simulate(**common, spending_target=80_000.0) # type: ignore[arg-type]
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assert cheap.success_rate >= fat.success_rate
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def test_fan_quantiles_shape() -> None:
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bundle = synthetic_returns(seed=5, n_years=120)
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paths = block_bootstrap(bundle,
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n_paths=100,
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n_years=20,
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block_size=5,
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rng=np.random.default_rng(0))
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res = simulate(
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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(0.7),
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strategy=TrinityStrategy(),
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regime=MalaysiaTaxRegime(),
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)
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p10 = res.fan_quantiles(10)
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assert p10.shape == (21, ) # n_years + 1
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def test_perf_under_60s_for_10k_paths_60y() -> None:
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"""Stretch goal — at 10k paths × 60y the simulator should finish
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in well under a minute on commodity hardware. Test allows 60s
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(generous; CI can vary)."""
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bundle = synthetic_returns(seed=6, n_years=150)
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paths = block_bootstrap(bundle,
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n_paths=10_000,
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n_years=60,
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block_size=5,
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rng=np.random.default_rng(0))
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start = time.perf_counter()
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simulate(
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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(0.7),
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strategy=TrinityStrategy(),
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regime=MalaysiaTaxRegime(),
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)
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elapsed = time.perf_counter() - start
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assert elapsed < 60, f"too slow: {elapsed:.2f}s"
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def test_convergence_5k_vs_50k_paths() -> None:
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"""Success rate should be stable to within ±1.5% between 5k and
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50k paths (Monte Carlo SE ~0.5% at 10k samples)."""
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bundle = synthetic_returns(seed=7, n_years=150)
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paths_small = block_bootstrap(bundle,
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n_paths=5_000,
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n_years=30,
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block_size=5,
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rng=np.random.default_rng(0))
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paths_large = block_bootstrap(bundle,
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n_paths=50_000,
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n_years=30,
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block_size=5,
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rng=np.random.default_rng(0))
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common = dict(
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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(0.7),
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strategy=TrinityStrategy(),
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regime=MalaysiaTaxRegime(),
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)
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small = simulate(paths=paths_small, **common) # type: ignore[arg-type]
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large = simulate(paths=paths_large, **common) # type: ignore[arg-type]
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assert abs(small.success_rate - large.success_rate) < 0.015
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def test_default_bucket_split_smoke() -> None:
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inputs = default_bucket_split(50_000.0, year_idx=5)
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assert inputs.capital_gains == 50000
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def test_returns_bundle_supplies_ie_data_columns() -> None:
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"""Sanity: the bundle has stock/bond/cpi correctly aligned."""
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b = synthetic_returns(seed=8, n_years=10)
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assert isinstance(b, ReturnsBundle)
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assert len(b.stock_nominal) == 10
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