The Monte Carlo used to compare jurisdictions at a flat London-equivalent
spend, which silently overstated the cost-of-living for any move to a
cheaper region. Now every cross-jurisdiction simulation auto-scales
spending_gbp by the real Numbeo/Expatistan ratio between the user's
baseline city and the target city.
Architecture:
- fire_planner/col/baseline.py — 22 cities with headline Numbeo data
(source URLs + snapshot dates embedded) — fallback when scraper fails
- col/numbeo.py + col/expatistan.py — httpx async scrapers, regex-parsed,
polite 1.1s rate-limit, EUR/USD anchored
- col/cache.py — PG-backed cache (col_snapshot table, 1-year TTL)
- col/service.py — sync compute_col_ratio() for the simulator; async
lookup_city_cached() with source reconciliation for the refresh CronJob
- alembic 0005 — col_snapshot table, UNIQUE(city_slug, source_name)
Simulator wiring:
- SimulateRequest gains col_auto_adjust=True (default), col_baseline_city,
col_target_city. Defaults pick the jurisdiction's representative city.
- _resolve_col_adjustment scales spending_gbp before path-building.
- SimulateResult surfaces col_multiplier_applied + col_adjusted_spending_gbp.
CLIs:
- python -m fire_planner col-seed — loads BASELINES into col_snapshot
(post-migration seed step)
- python -m fire_planner col-refresh-stale --within-days 7 — used by the
weekly fire-planner-col-refresh CronJob
268 tests pass. Mypy strict + ruff clean.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
events, interactive Visx Gantt + spending-profile chart
Charts are now the primary editor for life events. The Plan-tab body
re-orders to make charts ~80% of viewport real-estate; legacy form
sections are collapsed into a drawer.
Backend:
- alembic 0004: life_event.category enum (essential / discretionary /
not_spending). Defaults to essential so existing rows keep their
full spending impact.
- Simulator gains discretionary_outflows + flex_rules params. Tracks
per-path running ATH, applies the deepest applicable cut to
discretionary outflows when portfolio drops vs ATH (PLab-style flex
spending). Cut amount stays in the portfolio (refund pattern).
- New flex_spending module with FlexRule + applicable_cut +
cuts_per_year (vectorised). Sortable rules; "deepest cut wins" so
users specify cumulative cuts at each tier.
- New /scenarios/{id}/spending-profile endpoint returning per-year
base / essential / discretionary / flex_cut / total breakdown.
- SimulateRequest gains flex_rules + life_event.category roundtrip.
- 8 new tests; 246 total pytest pass; mypy + ruff clean.
Frontend (Visx + ECharts):
- Installed @visx/{scale,shape,group,axis,event,responsive,tooltip}
for native SVG drag interactions.
- New <SpendingProfileChart> — Visx stacked-area of base/essential/
discretionary with red flex-cut overlay, hover tooltip, click-to-
scrub-year.
- New <EventGantt> — interactive Visx Gantt:
* Click empty space → popover create at that year (default
essential spending event)
* Click a bar → inline edit popover (name, kind, range, £/y,
category) with delete button
* Drag bar middle → moves the whole event (year-resolution snap)
* Drag bar edges → resizes year_start / year_end
* All gestures persist via PATCH /life-events/{id}
- New <FlexRulesEditor> — list of {from_ath_pct, cut} tiers, save-on-
change to scenario.config_json.flex_rules.
- Plan-tab redesign: NW fan dominant top with floating stat badges
(Year/Age/NW/Δ NW/Spending/Eff. tax) over the chart; spending-
profile chart middle; Gantt bottom; flex-rules editor; legacy form
sections in a collapsed <details> drawer.
- Frontend typecheck + 7 vitest tests + production build all clean.
Two new tables and three new columns on `scenario` to give the
ProjectionLab-style UI a place to land:
- `scenario` gains `kind` (cartesian | user), `name`, `description`,
`parent_scenario_id`. Existing Cartesian flow keeps `kind='cartesian'`
by default; user-defined scenarios point `parent_scenario_id` at the
base they cloned from (NULL for root).
- `life_event` — timed events on a scenario timeline: retirement, kid
born, mortgage payoff, sabbatical, inheritance, etc. `year_start` and
`year_end` are scenario-relative (year 0 = today).
`delta_gbp_per_year` covers ranged effects; `one_time_amount_gbp`
covers one-shot impacts. `enabled` lets the UI toggle without delete.
- `retirement_goal` — user-defined success criteria (target_nw,
never_run_out, inheritance, ...). `comparator` + `success_threshold`
let the goal say "≥ £2M at year 25 in ≥ 90% of paths".
Migration 0002 adds the columns + tables idempotently.
145 tests; mypy strict + ruff clean.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
