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>

api/main.py

@@ -12,6 +12,11 @@ from fastapi.middleware.cors import CORSMiddleware
 
 from bot.data.db import Database
 from bot.executor.paper import cash_available
+from bot.metrics.sharpe import (
+    MIN_DAYS_OBSERVED,
+    MIN_RESOLVED_TRADES,
+    sharpe_with_gate,
+)
 
 # Phase 6 format (Phase 6+): values already in log-odds space.
 # "fg_lo=+0.1200 mom_lo=+0.0000 news_lo=+0.0000 mfld_lo=-0.7483 btc_dom_lo=+0.0000"
@@ -280,23 +285,40 @@ async def get_summary():
     PnL and performance metrics come from the latest metrics_daily snapshot,
     which is written by the bot every cycle via MetricsTracker.update_daily_summary().
     After Fix 3, that snapshot is also DB-computed — not dependent on pod restarts.
+    sharpe_ratio is the exception: it is recomputed live here from the daily
+    PnL-close series (same sharpe_with_gate the tracker uses), so the
+    explanation fields (sharpe_status, days_observed) always match the value.
     """
-    latest_metrics, counts, position_data, inverted, legacy_count = await asyncio.gather(
-        db.get_metrics_history(days=1),
-        db.compute_metrics_from_db(),
-        db.get_open_position_data(),
-        db.get_recently_closed_inverted(hours=24),
-        db.get_legacy_incomplete_count(),
+    latest_metrics, counts, position_data, inverted, legacy_count, daily_closes = (
+        await asyncio.gather(
+            db.get_metrics_history(days=1),
+            db.compute_metrics_from_db(),
+            db.get_open_position_data(),
+            db.get_recently_closed_inverted(hours=24),
+            db.get_legacy_incomplete_count(),
+            db.get_daily_pnl_closes(),
+        )
     )
 
     latest = latest_metrics[0] if latest_metrics else {}
     paper_bankroll = float(os.getenv("PAPER_BANKROLL", "10000"))
     total_trades = int(counts["total_trades"] or 0)
+    resolved_count = int(counts.get("resolved_count") or 0)
     # Same source PaperExecutor.initialize() uses to reconstruct cash:
     # total_net_cost_open = SUM(net_cost) over open trades, uncapped.
     _, total_net_cost_open = position_data
     total_deployed = total_net_cost_open
 
+    # Sharpe: computed live from the daily PnL curve (same function the
+    # tracker uses for the snapshot). None + status while the minimum-sample
+    # gate (>=30 days observed, >=10 resolved trades) is not met — a Sharpe
+    # over 1 resolved trade is statistically meaningless.
+    days_observed = len(daily_closes)
+    sharpe, sharpe_status = sharpe_with_gate(daily_closes, paper_bankroll, resolved_count)
+
+    win_rate = latest.get("win_rate")
+    calibration = latest.get("calibration_score")
+
     return {
         # ── Portfolio state (live from DB) ──────────────────────────────────
         "paper_mode":              os.getenv("PAPER_MODE", "true") == "true",
@@ -319,25 +341,35 @@ async def get_summary():
         "realized_pnl":       latest.get("realized_pnl") or 0,
         "total_pnl":          latest.get("total_pnl") or 0,
 
-        # ── Performance metrics (from latest metrics_daily snapshot) ─────────
+        # ── Performance metrics ──────────────────────────────────────────────
         # win_rate: fraction of resolved closed trades where close_pnl > 0.
         #   null if fewer than 5 resolved trades. Source: closed+resolved trades.
-        # sharpe_ratio: 0.0 — requires daily-return time series (not yet tracked).
+        # sharpe_ratio: annualized Sharpe of the daily total_pnl curve, computed
+        #   live from metrics_daily. null while the minimum-sample gate fails
+        #   (sharpe_status explains why). Source: bot/metrics/sharpe.py.
         # calibration_score: 1 − Brier score on resolved trades (higher = better).
         #   null if fewer than 10 resolved trades. Source: closed+resolved trades.
-        "win_rate":           latest.get("win_rate"),         # null if < 5 resolved
-        "sharpe_ratio":       latest.get("sharpe_ratio") or 0,  # 0.0 until tracked
-        "calibration_score":  latest.get("calibration_score"),  # null if < 10 resolved
+        "win_rate":           win_rate,                       # null if < 5 resolved
+        "sharpe_ratio":       sharpe,                         # null if gate fails
+        "sharpe_status":      sharpe_status,  # ok | insufficient_sample | zero_variance
+        "days_observed":      days_observed,
+        "min_days_required":  MIN_DAYS_OBSERVED,
+        "min_resolved_required": MIN_RESOLVED_TRADES,
+        "calibration_score":  calibration,                    # null if < 10 resolved
 
-        # ── Counters from snapshot ───────────────────────────────────────────
-        "resolved_count":     latest.get("resolved_count") or 0,
+        # ── Counters (live from DB) ──────────────────────────────────────────
+        "resolved_count":     resolved_count,
 
         # ── Promotion gate ───────────────────────────────────────────────────
-        # All thresholds must pass; null metrics count as not-ready.
+        # Never promote on a tiny sample: requires the resolved/days minimums
+        # AND non-null metrics AND all thresholds. A single lucky resolved
+        # trade must not flip this to true.
         "promotion_ready": (
-            (latest.get("sharpe_ratio") or 0) >= 0.5
-            and (latest.get("win_rate") or 0) >= 0.52
-            and (latest.get("calibration_score") or 0) >= 0.7
+            resolved_count >= MIN_RESOLVED_TRADES
+            and days_observed >= MIN_DAYS_OBSERVED
+            and win_rate is not None and win_rate >= 0.52
+            and calibration is not None and calibration >= 0.7
+            and sharpe is not None and sharpe >= 0.5
             and total_trades >= 50
         ),
     }