wrongmove: daily price-trend monitoring (per-listing badge + macro strip)

Two surfaces wired up so the user can "get a vibe of the market":

**Per-listing** — each PropertyCard now shows a small pill next to the
price when the listing's total_price moved >=1% over a 14-day lookback
(e.g. "↓ £200 (-4%) in 14d"). Drops render green, rises render red.
Computed from `price_history_json` by the daily aggregator and
denormalised onto the listing row so the streaming endpoint just
passes it through.

**Macro** — new always-visible inline strip above the chip strip
showing today's median total price, median £/m², and listing count
for the current filter's bedroom band, each with a 30-day % delta:
"Rent · 1-2 bed · 30d: Median £2,500 ↓ -4% · £/m² £50 ↓ -2% · Listings 4,200 ↑ +5%".

Both data sources are populated daily at 04:00 UTC by a new Celery
beat task that fires 1h after the 03:00 RENT scrape and feeds two
sinks: a per-listing update pass and an upsert to a new
`dailylistingaggregate` table keyed on
(snapshot_date, listing_type, min_bedrooms, max_bedrooms).

## Backend
- `models/listing.py`: Listing parent gains `price_14d_ago` + `price_
  change_pct_14d` nullable floats (inherited by RentListing/BuyListing).
  New `DailyListingAggregate` table model with unique constraint on
  (date, type, min_bed, max_bed).
- Alembic `a8b9c0d1e2f3`: adds the two columns to both listing tables
  and creates the aggregate table + date index.
- `services/market_aggregator.py` (new): `compute_trend_for_listing`,
  `update_per_listing_trend` (batched, idempotent), `_stats` (median
  + mean filtered to positive finite values), `compute_aggregate_
  snapshot` (dialect-aware MySQL / SQLite upsert), `fetch_trend_
  series` (range query for the API).
- `tasks/market_tasks.py` (new): `compute_daily_market_aggregates_task`
  Celery task wrapping both stages.
- `tasks/listing_tasks.py:setup_periodic_tasks`: registers the daily
  task at 04:00 UTC alongside the existing scrape schedules.
- `celery_app.py`: includes the new tasks module.
- `api/app.py`: new `GET /api/market_trend?listing_type=&min_bedrooms=&
  max_bedrooms=&days=` endpoint returning the daily series.
- `ui_exporter.py`: GeoJSON feature properties now carry
  `price_14d_ago` and `price_change_pct_14d` so the frontend can
  render the badge without an extra round-trip.

## Frontend
- `types/index.ts`: new `MarketTrendPoint`; `PropertyProperties` gains
  the two optional trend fields.
- `components/PropertyCard.tsx`: derived `trendBadge` (>=1% threshold,
  null-safe) rendered as a small pill on both card variants.
- `hooks/useMarketTrend.ts` (new): fetches the trend series, derives
  current-vs-oldest deltas per metric (% change rounded to 1dp).
- `components/MarketTrendStrip.tsx` (new): compact inline strip with
  three metric cells. Hidden when the aggregator hasn't produced any
  rows yet (graceful start during the first week post-launch).
- `App.tsx`: renders the strip above the chip strip whenever the
  active queryParameters are known.

## Tests
- pytest: 10 new (trend math edge cases including null history,
  malformed JSON, only-recent entries, drops, rises, zero current
  price; _stats empty / nonpositive filtering; upsert idempotency on
  an in-memory SQLite seed). 34 decision + aggregator tests pass.
- vitest: 8 new (useMarketTrend fetch URL, two-point delta,
  single-point null delta, empty series; PropertyCard trend badge
  arrow direction + sign for drops/rises, noise threshold, null
  guard). 229 tests pass total, tsc clean.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

api/app.py

@@ -698,6 +698,53 @@ async def get_districts(
     return district_service.get_all_districts()
 
 
+class MarketTrendPoint(BaseModel):
+    """One day of aggregated market stats for the (listing_type, bed-band)."""
+    snapshot_date: str
+    listing_count: int
+    median_total_price: float | None
+    median_qmprice: float | None
+    mean_total_price: float | None
+    mean_qmprice: float | None
+
+
+@app.get("/api/market_trend", response_model=list[MarketTrendPoint])
+async def get_market_trend(
+    user: Annotated[User, Depends(get_current_user)],
+    listing_type: str = Query("RENT", description="RENT or BUY"),
+    min_bedrooms: int = Query(1, ge=0),
+    max_bedrooms: int = Query(2, ge=0),
+    days: int = Query(30, ge=1, le=365, description="Lookback window in days"),
+) -> list[MarketTrendPoint]:
+    """Daily aggregate snapshots for the requested (type × bed-band) over
+    the last N days. Powers the MarketTrendStrip UI."""
+    from services.market_aggregator import fetch_trend_series  # noqa: PLC0415
+
+    if listing_type not in {"RENT", "BUY"}:
+        raise HTTPException(status_code=400, detail="listing_type must be RENT or BUY")
+    if min_bedrooms > max_bedrooms:
+        raise HTTPException(status_code=400, detail="min_bedrooms must be <= max_bedrooms")
+
+    rows = fetch_trend_series(
+        engine,
+        listing_type=listing_type,
+        min_bedrooms=min_bedrooms,
+        max_bedrooms=max_bedrooms,
+        days=days,
+    )
+    return [
+        MarketTrendPoint(
+            snapshot_date=r.snapshot_date.isoformat(),
+            listing_count=r.listing_count,
+            median_total_price=r.median_total_price,
+            median_qmprice=r.median_qmprice,
+            mean_total_price=r.mean_total_price,
+            mean_qmprice=r.mean_qmprice,
+        )
+        for r in rows
+    ]
+
+
 class ListingDetailResponse(BaseModel):
     id: int
     price: float