fix: harden shadow book against book leakage (review of ba2df8b)

Review of the shadow book found seven ways the two books could leak into
each other; all are fixed here. The most serious silently invalidated the
comparison the shadow book exists to make.

- Shadow holdings no longer suppress the manual candidate list. The
  open-trade exclusion filtered on any book, so shadow taking the
  top-ranked names removed exactly those from the user's list and alerts,
  confining the discretionary book to leftovers. Scoped to the manual
  book. Closed-trade alerts and paper-book equity were leaking the same
  way and are likewise scoped.

- Shadow sizing now matches _simulate_portfolio: min(1% risk, 20% notional
  cap, available cash) from marked equity, plus the sub- dust guard.
  Previously risk-only from realized equity, so a tight stop produced a
  multiples-of-equity leveraged position the strategy would never take.

- Shadow only trades setups from the scan that just ran (<6h old) with one
  setup per ticker. A failed or disabled scan step could otherwise open
  positions from a prior session at stale prices.

- Gate-reset transitions are observed for both books, so a shadow stop-out
  completes fail -> requalify instead of staying locked forever.

- Manual list/close endpoints default to the manual book and reject
  hand-closing shadow trades; the performance endpoint is scoped to the
  caller so 'your picks' is not every user's book.

- run_shadow_book is registered as a paused job so Admin can trigger it.

Also anchors three pre-existing paper-trade tests (and the new alpaca
window test) on the UTC date. They build fixtures from the local date but
the service stamps opened_at in UTC, so they failed only between 00:00 and
02:00 in a UTC+hh timezone -- latent on ba2df8b, exposed by the clock.

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

app/services/alert_service.py

@@ -29,6 +29,7 @@ from app.config import settings
 from app.models.alert import AlertLog
 from app.models.ohlcv import OHLCVRecord
 from app.models.paper_trade import PaperTrade
+from app.services.trade_policy import MANUAL_BOOK
 from app.models.score import CompositeScore
 from app.models.sr_level import SRLevel
 from app.models.ticker import Ticker
@@ -632,6 +633,10 @@ async def _collect_closed_trades(db: AsyncSession) -> list[ClosedTradeItem]:
             PaperTrade.closed_at.is_not(None),
             PaperTrade.closed_at > cutoff,
             PaperTrade.close_reason.in_(("trailing", "stop", "target", "time")),
+            # Your own positions only — shadow trades are a research record, not
+            # something you hold, and mixing them in unlabelled reads as if you
+            # were stopped out of a name you never took.
+            PaperTrade.book == MANUAL_BOOK,
         )
         .order_by(PaperTrade.closed_at.desc())
     )
@@ -642,8 +647,14 @@ async def _collect_closed_trades(db: AsyncSession) -> list[ClosedTradeItem]:
 
 
 async def _paper_book_value(db: AsyncSession) -> float:
-    """Paper-trade equity: fixed capital plus realized/unrealized P&L."""
-    result = await db.execute(select(PaperTrade))
+    """Paper-trade equity: fixed capital plus realized/unrealized P&L.
+
+    Discretionary book only — the shadow book runs on its own notional equity
+    and folding it in would report a number matching neither book.
+    """
+    result = await db.execute(
+        select(PaperTrade).where(PaperTrade.book == MANUAL_BOOK)
+    )
     trades = list(result.scalars().all())
     latest: dict[int, float | None] = {}
     for trade in trades: