feat(metrics): real Sharpe ratio from daily PnL curve with minimum-sample gate

sharpe_ratio was hardcoded to 0.0 in MetricsTracker and exposed as
'or 0' in /api/summary. With only 1 resolved trade (~40 flat days plus
one +299 jump) any computed Sharpe is statistically meaningless, so:

- bot/metrics/sharpe.py: annualized Sharpe (sqrt(365)) from daily
  total_pnl closes, normalized by bankroll; sharpe_with_gate() returns
  None + status until >=30 days observed AND >=10 resolved trades.
- Database.get_daily_pnl_closes(): last metrics_daily snapshot per UTC
  day, oldest first — the return series input.
- MetricsTracker: stores the real (gated) Sharpe in the snapshot, NULL
  below the gate; log line now includes sharpe.
- /api/summary: live Sharpe + sharpe_status/days_observed/min_* fields
  explaining why it is null; resolved_count now live from COUNT(*).
- promotion_ready: requires resolved>=10, days>=30, and non-null
  win_rate/calibration/sharpe plus existing thresholds — a single lucky
  resolved trade can no longer promote.
- Dashboard Sharpe card shows the insufficient-sample explanation when
  null instead of a bare em dash.

Tests: 13 new in tests/test_sharpe_gate.py (formula, gate, API contract,
tracker snapshot); verified failing pre-fix. Suite: 62 passed.

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

tests/test_api_summary_consistency.py

@@ -44,6 +44,7 @@ class FakeDB:
             "total_trades": self._total,
             "open_count": self._open,
             "closed_count": self._total - self._open,
+            "resolved_count": 0,
         }
 
     async def get_recently_closed_inverted(self, hours=24):
@@ -52,6 +53,9 @@ class FakeDB:
     async def get_legacy_incomplete_count(self):
         return 0
 
+    async def get_daily_pnl_closes(self):
+        return []
+
 
 def _run(db: FakeDB, monkeypatch) -> tuple[dict, PaperExecutor]:
     monkeypatch.setattr(api_main, "db", db)