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>

models/listing.py

@@ -7,6 +7,7 @@ import json
 from typing import Any, Dict, List
 from pydantic import BaseModel, Field as PydanticField, model_validator
 from rec import routing
+from sqlalchemy import UniqueConstraint
 from sqlmodel import JSON, TEXT, SQLModel, Field
 
 
@@ -92,6 +93,14 @@ class Listing(SQLModel, table=False):
         sa_type=TEXT, nullable=True, default=None
     )  # Store as JSON string for simplicity
 
+    # Per-listing price-trend snapshot maintained by the daily aggregator.
+    # `price_14d_ago` is the historical price ~14 days before the most recent
+    # aggregator run (sourced from price_history_json). `price_change_pct_14d`
+    # is the % change from that to the current `price` (positive=up, neg=down).
+    # Both are null when the listing has no entry that old in its history.
+    price_14d_ago: float | None = Field(default=None, nullable=True)
+    price_change_pct_14d: float | None = Field(default=None, nullable=True)
+
     @property
     def is_removed(self) -> bool:
         if not self.additional_info:
@@ -176,6 +185,34 @@ class BuyListing(Listing, table=True):
     )  # in years, e.g., 90, 80, etc.
 
 
+class DailyListingAggregate(SQLModel, table=True):
+    """One row per (snapshot_date, listing_type, bedroom band).
+
+    Written daily by `compute_daily_market_aggregates_task` after the scrape
+    settles. Drives the `MarketTrendStrip` UI ("get a vibe of the market").
+    The (date, listing_type, min_bedrooms, max_bedrooms) tuple is unique;
+    the aggregator upserts rather than appends so re-running on the same day
+    refreshes the snapshot instead of duplicating it.
+    """
+    __table_args__ = (
+        UniqueConstraint(
+            "snapshot_date", "listing_type", "min_bedrooms", "max_bedrooms",
+            name="uq_aggregate_date_filter",
+        ),
+    )
+
+    id: int | None = Field(default=None, primary_key=True)
+    snapshot_date: datetime = Field(nullable=False, index=True)
+    listing_type: str = Field(nullable=False)  # "RENT" or "BUY"
+    min_bedrooms: int = Field(nullable=False)
+    max_bedrooms: int = Field(nullable=False)
+    listing_count: int = Field(nullable=False)
+    median_total_price: float | None = Field(default=None, nullable=True)
+    median_qmprice: float | None = Field(default=None, nullable=True)
+    mean_total_price: float | None = Field(default=None, nullable=True)
+    mean_qmprice: float | None = Field(default=None, nullable=True)
+
+
 @dataclass(frozen=True)
 class DestinationMode:
     destination_address: str