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>

tasks/listing_tasks.py

@@ -656,3 +656,15 @@ def setup_periodic_tasks(sender, **kwargs):
             dump_listings_task.s(schedule.to_query_parameters().model_dump_json()),
             name=schedule.name,
         )
+
+    # Daily market aggregator — fires after the 03:00 RENT scrape so the
+    # snapshot reflects today's freshly-pulled data. Imported lazily to
+    # avoid a circular import (market_tasks imports celery_app, which
+    # imports listing_tasks via the include list).
+    from tasks.market_tasks import compute_daily_market_aggregates_task
+    celery_logger.info("Registering periodic task: daily-market-aggregator at 4:00")
+    sender.add_periodic_task(
+        crontab(minute="0", hour="4", day_of_week="*"),
+        compute_daily_market_aggregates_task.s(),
+        name="daily-market-aggregator",
+    )

tasks/market_tasks.py

@@ -0,0 +1,57 @@
+"""Daily market-trend aggregator Celery task.
+
+Fires daily at 04:00 UTC — one hour after the 03:00 RENT scrape so the
+data is fresh. Calls into `services.market_aggregator` to:
+  1. Recompute per-listing `price_14d_ago` / `price_change_pct_14d`.
+  2. Upsert the per-(listing_type, bedroom-band) row in
+     `dailylistingaggregate` for today's snapshot.
+
+Idempotent: re-running on the same day refreshes both surfaces in place.
+"""
+from __future__ import annotations
+
+import logging
+from typing import Any
+
+from celery_app import app
+from database import engine
+from services import market_aggregator
+
+celery_logger = logging.getLogger("celery_app")
+
+
+@app.task(
+    bind=True,
+    name="tasks.market_tasks.compute_daily_market_aggregates_task",
+    time_limit=3600,
+    soft_time_limit=3500,
+    acks_late=True,
+)
+def compute_daily_market_aggregates_task(self: Any) -> dict[str, Any]:
+    """Run both stages of the daily market aggregator."""
+    celery_logger.info("Starting daily market aggregator (task=%s)", self.request.id)
+    per_listing = market_aggregator.update_per_listing_trend(engine)
+    aggregates = market_aggregator.compute_aggregate_snapshot(engine)
+    result = {
+        "status": "ok",
+        "per_listing": per_listing,
+        "aggregates": [
+            {
+                "snapshot_date": a.snapshot_date.isoformat(),
+                "listing_type": a.listing_type,
+                "min_bedrooms": a.min_bedrooms,
+                "max_bedrooms": a.max_bedrooms,
+                "listing_count": a.listing_count,
+                "median_total_price": a.median_total_price,
+                "median_qmprice": a.median_qmprice,
+            }
+            for a in aggregates
+        ],
+    }
+    celery_logger.info(
+        "Daily market aggregator complete: rent_updated=%s buy_updated=%s aggregates=%s",
+        per_listing.get("rent_updated"),
+        per_listing.get("buy_updated"),
+        len(aggregates),
+    )
+    return result