refactor: CB 게이지 저장소를 context tree → system_metrics 별도 테이블로 분리

대시보드 표시 전용 데이터를 AI 의사결정용 context tree에 저장하는 것은
관심사 분리 위반. system_metrics 경량 테이블을 신설하여 완전히 분리. (PR #197 코드리뷰 반영)

- db.py: system_metrics 테이블 추가 (key/value/updated_at)
- main.py: context_store.set_context(L6_DAILY) → db_conn.execute(system_metrics)
- app.py: contexts 쿼리 → system_metrics 쿼리
- tests: _seed_cb_context를 system_metrics 삽입으로 변경

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

src/dashboard/app.py

@@ -84,14 +84,11 @@ def create_dashboard_app(db_path: str) -> FastAPI:
             pnl_pct_rows = conn.execute(
                 """
                 SELECT key, value
-                FROM contexts
+                FROM system_metrics
-                WHERE layer = 'L6_DAILY'
+                WHERE key LIKE 'portfolio_pnl_pct_%'
-                  AND timeframe = ?
-                  AND key LIKE 'portfolio_pnl_pct_%'
                 ORDER BY updated_at DESC
                 LIMIT 20
-                """,
+                """
-                (today,),
             ).fetchall()
             current_pnl_pct: float | None = None
             if pnl_pct_rows:

src/db.py

@@ -138,6 +138,18 @@ def init_db(db_path: str) -> sqlite3.Connection:
         " ON trades (stock_code, market, timestamp DESC)"
     )
 
+    # Lightweight key-value store for trading system runtime metrics (dashboard use only)
+    # Intentionally separate from the AI context tree to preserve separation of concerns.
+    conn.execute(
+        """
+        CREATE TABLE IF NOT EXISTS system_metrics (
+            key        TEXT PRIMARY KEY,
+            value      TEXT NOT NULL,
+            updated_at TEXT NOT NULL
+        )
+        """
+    )
+
     conn.commit()
     return conn
 

src/main.py

@@ -430,13 +430,16 @@ async def trading_cycle(
             {"volume_ratio": candidate.volume_ratio},
         )
 
-    # Store latest pnl_pct in L6 (daily P&L layer) so the dashboard can display the CB gauge
+    # Write pnl_pct to system_metrics (dashboard-only table, separate from AI context tree)
-    context_store.set_context(
+    db_conn.execute(
-        ContextLayer.L6_DAILY,
+        "INSERT OR REPLACE INTO system_metrics (key, value, updated_at) VALUES (?, ?, ?)",
-        datetime.now(UTC).date().isoformat(),
+        (
-        f"portfolio_pnl_pct_{market.code}",
+            f"portfolio_pnl_pct_{market.code}",
-        {"pnl_pct": round(pnl_pct, 4)},
+            json.dumps({"pnl_pct": round(pnl_pct, 4)}),
+            datetime.now(UTC).isoformat(),
+        ),
     )
+    db_conn.commit()
 
     # Build portfolio data for global rule evaluation
     portfolio_data = {

tests/test_dashboard.py

@@ -355,20 +355,12 @@ def test_positions_empty_when_no_trades(tmp_path: Path) -> None:
 
 def _seed_cb_context(conn: sqlite3.Connection, pnl_pct: float, market: str = "KR") -> None:
     import json as _json
-    from datetime import UTC, datetime
-    today = datetime.now(UTC).date().isoformat()
     conn.execute(
-        """
+        "INSERT OR REPLACE INTO system_metrics (key, value, updated_at) VALUES (?, ?, ?)",
-        INSERT OR REPLACE INTO contexts (layer, timeframe, key, value, created_at, updated_at)
-        VALUES (?, ?, ?, ?, ?, ?)
-        """,
         (
-            "L6_DAILY",
-            today,
             f"portfolio_pnl_pct_{market}",
             _json.dumps({"pnl_pct": pnl_pct}),
-            f"{today}T10:00:00+00:00",
+            "2026-02-22T10:00:00+00:00",
-            f"{today}T10:00:00+00:00",
         ),
     )
     conn.commit()