fix(metrics): replace inflated PnL formula; drop fake calibration_score

total_pnl now uses edge_net × net_cost instead of (0.5 - entry_price) × shares.
The old formula overestimated BUY_NO trades at low entry prices by 3–10× because
buying at price 0.158 yields 3164 shares — any exit-at-0.5 assumption produced
$1072 PnL on $500 deployed. edge_net × net_cost is bounded by net_cost per trade
and uses the model's own signal, giving $122 for the same position.

calibration_score is now None (null in API) instead of 1 - 2×|avg_edge|. That
formula was not a real calibration: it requires knowing market resolutions
(YES=1/NO=0) which we do not store yet. Returning null is more honest than
returning 0.0 or a meaningless proxy. Fix 3 will compute it from closed trades.

check_promotion_thresholds updated to handle None calibration (null → not ready).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

api/main.py

@@ -112,14 +112,20 @@ async def get_summary():
         "cash_available": max(0.0, paper_bankroll - total_deployed),
         "legacy_incomplete_count": legacy_count,
         "reentry_guard_blocks_24h": len(inverted),
+        # Metrics from latest metrics_daily snapshot (computed by MetricsTracker).
+        # total_pnl: estimated unrealized PnL for open trades in the current bot
+        #            session — uses edge_net × net_cost (model edge on deployed
+        #            capital). Resets to 0 on pod restart until Fix 3 is applied.
+        # calibration_score: null until market resolution data is available
+        #            (requires close_price / outcome per closed trade).
         "total_pnl": latest.get("total_pnl", 0),
         "win_rate": latest.get("win_rate", 0),
         "sharpe_ratio": latest.get("sharpe_ratio", 0),
-        "calibration_score": latest.get("calibration_score", 0),
+        "calibration_score": latest.get("calibration_score"),  # null if unavailable
         "promotion_ready": (
             latest.get("sharpe_ratio", 0) >= 0.5
             and latest.get("win_rate", 0) >= 0.52
-            and latest.get("calibration_score", 0) >= 0.7
+            and (latest.get("calibration_score") or 0) >= 0.7  # null → not ready
             and len(all_trades) >= 50
         ),
     }