chore: cleanup duplicate trade save, misleading cycle counters, and /api/summary inconsistencies

Bug #5: metrics.record_trade() only delegated to save_trade(), which
executor.execute() already calls — every trade was written twice (deduped
only by ON CONFLICT DO NOTHING). Remove the redundant call and the now-dead
method. RealExecutor.execute() raises NotImplementedError, so real mode is
unaffected.

Bug #6 (CYCLE SUMMARY): manifold accepted/rejected counters only increment
on the active-signal path, so with MANIFOLD_SIGNAL_ENABLED=false they always
printed 0/0 — print 'manifold_signal: disabled' instead.
family_conflicts_prevented duplicated blocked_by_family (same counter
printed twice); removed. gnews_cap was a dead variable with a misleading
comment; removed.

Bug #7 (/api/summary): total_trades was len() over a LIMIT-500 query —
capped once history grows; counts now come from COUNT(*) via
compute_metrics_from_db. cash_available was reimplemented in the API;
extract cash_available() in paper.py (same formula, unchanged) and feed it
from get_open_position_data() — the exact source/helper
PaperExecutor.initialize() uses. Test asserts API and executor report
identical cash for the same DB state.

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

api/main.py

@@ -11,6 +11,7 @@ from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 
 from bot.data.db import Database
+from bot.executor.paper import cash_available
 
 # 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"
@@ -280,27 +281,31 @@ async def get_summary():
     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.
     """
-    latest_metrics, open_trades, all_trades, inverted, legacy_count = await asyncio.gather(
+    latest_metrics, counts, position_data, inverted, legacy_count = await asyncio.gather(
         db.get_metrics_history(days=1),
-        db.get_recent_trades(limit=500, status="open"),
-        db.get_recent_trades(limit=500),
+        db.compute_metrics_from_db(),
+        db.get_open_position_data(),
         db.get_recently_closed_inverted(hours=24),
         db.get_legacy_incomplete_count(),
     )
 
     latest = latest_metrics[0] if latest_metrics else {}
     paper_bankroll = float(os.getenv("PAPER_BANKROLL", "10000"))
-    total_deployed = sum(t.get("net_cost", 0) for t in open_trades)
+    total_trades = int(counts["total_trades"] 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
 
     return {
         # ── Portfolio state (live from DB) ──────────────────────────────────
         "paper_mode":              os.getenv("PAPER_MODE", "true") == "true",
         "paper_bankroll":          paper_bankroll,
-        "total_trades":            len(all_trades),           # exact, from DB
-        "open_trades_count":       len(open_trades),          # exact, from DB
-        "closed_trades_count":     len(all_trades) - len(open_trades),  # exact
+        "total_trades":            total_trades,              # COUNT(*), uncapped
+        "open_trades_count":       int(counts["open_count"] or 0),    # COUNT(*), uncapped
+        "closed_trades_count":     int(counts["closed_count"] or 0),  # COUNT(*), uncapped
         "total_deployed":          total_deployed,            # exact, from DB
-        "cash_available":          max(0.0, paper_bankroll - total_deployed),  # exact
+        "cash_available":          cash_available(paper_bankroll, total_net_cost_open),
         "legacy_incomplete_count": legacy_count,              # exact, from DB
         "reentry_guard_blocks_24h": len(inverted),            # exact, from DB
 
@@ -333,6 +338,6 @@ async def get_summary():
             (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
-            and len(all_trades) >= 50
+            and total_trades >= 50
         ),
     }