col: simulator auto-adjusts spending to local prices via Numbeo+Expatistan

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>

fire_planner/col/models.py

@@ -0,0 +1,64 @@
+"""Pydantic models for per-city cost-of-living data.
+
+Every category figure is monthly GBP for a single person — the
+denomination the simulator expects when scaling `spending_gbp`. The
+source object retains the original currency, FX rate, and snapshot
+date so we can re-validate or update a stale baseline.
+"""
+from __future__ import annotations
+
+from datetime import date
+from decimal import Decimal
+from typing import Literal
+
+from pydantic import BaseModel, ConfigDict, Field
+
+SourceName = Literal["numbeo", "expatistan", "baseline", "manual"]
+
+
+class ColSource(BaseModel):
+    """Provenance for a CityCostIndex entry — where did the numbers come
+    from and when. The simulator surfaces this in the SimulateResult so
+    the user can audit which baseline was applied."""
+
+    model_config = ConfigDict(frozen=True)
+
+    name: SourceName
+    url: str | None = None
+    snapshot_date: date
+    raw_currency: str = "GBP"
+    gbp_per_unit: Decimal = Decimal("1")
+
+
+class CategoryBreakdown(BaseModel):
+    """Per-category monthly costs in GBP for a single person."""
+
+    model_config = ConfigDict(frozen=True)
+
+    rent_1bed_center: Decimal
+    rent_1bed_outside: Decimal | None = None
+    groceries: Decimal
+    restaurants: Decimal
+    transport: Decimal
+    utilities: Decimal
+    leisure: Decimal
+
+
+class CityCostIndex(BaseModel):
+    """One city's headline cost-of-living snapshot."""
+
+    model_config = ConfigDict(frozen=True)
+
+    city: str
+    city_slug: str = Field(min_length=1)
+    country: str
+    total_single_no_rent_gbp: Decimal
+    total_single_with_rent_gbp: Decimal
+    breakdown: CategoryBreakdown
+    source: ColSource
+
+    @property
+    def total_monthly_gbp(self) -> Decimal:
+        """The number the simulator uses for ratios — `with rent` is the
+        right anchor because moving location changes rent too."""
+        return self.total_single_with_rent_gbp