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>

alembic/versions/a8b9c0d1e2f3_add_price_trend_columns_and_aggregate.py

@@ -0,0 +1,68 @@
+"""add price trend columns and daily market aggregate table
+
+Revision ID: a8b9c0d1e2f3
+Revises: f7a8b9c0d1e2
+Create Date: 2026-05-16 12:00:00.000000
+
+Wires the price-monitoring feature:
+- Per-listing trend columns (`price_14d_ago`, `price_change_pct_14d`) on
+  RentListing and BuyListing. Both are nullable — they stay empty for
+  listings with no entry that old in their price_history_json.
+- A new `dailylistingaggregate` table keyed on
+  (snapshot_date, listing_type, min_bedrooms, max_bedrooms) with median /
+  mean / count for the daily-filter scope. One row per day per band.
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = 'a8b9c0d1e2f3'
+down_revision: Union[str, None] = 'f7a8b9c0d1e2'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    for table in ("rentlisting", "buylisting"):
+        op.add_column(table, sa.Column("price_14d_ago", sa.Float(), nullable=True))
+        op.add_column(
+            table, sa.Column("price_change_pct_14d", sa.Float(), nullable=True)
+        )
+
+    op.create_table(
+        "dailylistingaggregate",
+        sa.Column("id", sa.Integer(), nullable=False),
+        sa.Column("snapshot_date", sa.DateTime(), nullable=False),
+        sa.Column("listing_type", sa.String(length=8), nullable=False),
+        sa.Column("min_bedrooms", sa.Integer(), nullable=False),
+        sa.Column("max_bedrooms", sa.Integer(), nullable=False),
+        sa.Column("listing_count", sa.Integer(), nullable=False),
+        sa.Column("median_total_price", sa.Float(), nullable=True),
+        sa.Column("median_qmprice", sa.Float(), nullable=True),
+        sa.Column("mean_total_price", sa.Float(), nullable=True),
+        sa.Column("mean_qmprice", sa.Float(), nullable=True),
+        sa.PrimaryKeyConstraint("id"),
+        sa.UniqueConstraint(
+            "snapshot_date", "listing_type", "min_bedrooms", "max_bedrooms",
+            name="uq_aggregate_date_filter",
+        ),
+    )
+    op.create_index(
+        "ix_dailylistingaggregate_snapshot_date",
+        "dailylistingaggregate",
+        ["snapshot_date"],
+    )
+
+
+def downgrade() -> None:
+    op.drop_index(
+        "ix_dailylistingaggregate_snapshot_date",
+        table_name="dailylistingaggregate",
+    )
+    op.drop_table("dailylistingaggregate")
+    for table in ("rentlisting", "buylisting"):
+        op.drop_column(table, "price_change_pct_14d")
+        op.drop_column(table, "price_14d_ago")