feat(tickers): record delisting instead of deleting the symbol

Retiring a symbol meant delete_ticker or bootstrap_universe(prune_missing),
both of which cascade through OHLCV, setups and scores. That destroys exactly
the history four research documents already apologise for: today's tracked
universe projected backward is survivorship-biased, and hard-deleting every
delisted name is what causes it. Keeping the rows preserves the option to fix
that — it does not fix it, which needs the replay to model a delisting as an
exit event.

tickers gains delisted_on / delisted_reason (migration 032). NULL means
actively traded.

The filter is opt-in via ticker_service.active_only rather than folded into a
shared getter: the registry and admin views deliberately keep delisted rows so
the delisting is visible, and a silent default would undo that. Applied to the
live path only — scanner, momentum ranking, scoring, breadth, fundamentals
candidates, SEC universe, earnings import, ingestion loops. run_backtest keeps
them on purpose.

Detection runs off OHLCV staleness, not off the SEC fundamentals import: that
importer stalls for days on unrelated Company-Facts gaps and would take
detection down with it. On a stale symbol the scheduler asks SEC for a Form
25/25-NSE/15 and retires it only on a hit, so a halt or a rename (SATS->ECHO)
keeps the existing warning. The probe waits 3 stale days so a market-data
outage cannot turn into one SEC request per symbol per run.

Safe to automate because it is reversible: clear_delisted un-retires a false
positive, where a delete had already taken the history.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

app/services/momentum_service.py

@@ -18,6 +18,7 @@ from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.models.ticker import Ticker
+from app.services import ticker_service
 from app.services.price_service import query_ohlcv
 
 logger = logging.getLogger(__name__)
@@ -169,7 +170,9 @@ async def compute_activation_ranks(db: AsyncSession) -> dict[str, dict[str, floa
     before scanning; the research backtest ranked each weekly setup-candidate
     cross-section, so this is the deliberate production approximation.
     """
-    result = await db.execute(select(Ticker).order_by(Ticker.symbol))
+    result = await db.execute(
+        ticker_service.active_only(select(Ticker).order_by(Ticker.symbol))
+    )
     tickers = list(result.scalars().all())
 
     benchmark_closes = await _load_activation_benchmark(db)