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>

bot/data/db.py

@@ -348,6 +348,24 @@ class Database:
             )
             return [dict(r) for r in rows]
 
+    async def get_daily_pnl_closes(self) -> list[float]:
+        """Return the closing total_pnl of every observed UTC day, oldest first.
+
+        One value per calendar day with at least one metrics_daily snapshot
+        (the day's last snapshot, same collapse rule as get_metrics_history).
+        This is the input series for the Sharpe ratio: len() = days observed,
+        consecutive deltas = daily PnL changes.
+        """
+        async with self._pool.acquire() as conn:
+            rows = await conn.fetch(
+                """
+                SELECT DISTINCT ON (timestamp::date) total_pnl
+                FROM metrics_daily
+                ORDER BY timestamp::date ASC, timestamp DESC
+                """
+            )
+            return [float(r["total_pnl"] or 0) for r in rows]
+
     async def backfill_feature_columns(self) -> int:
         """Back-populate feat_*_lo for trades created before Phase 6.