feat(stock): portfolio_health (집중도·현금·손익)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

stock/app/holdings_intel.py

@@ -209,6 +209,35 @@ def news_issues(tickers: list[str], date: str, use_llm: bool = True) -> dict[str
     return out
 
 
+# ---- Task 3.3: portfolio_health ----
+
+
+def portfolio_health(holdings: list[dict], total_cash: int = 0) -> dict:
+    """비중 집중도(최대비중·HHI) + 현금비중 + 총손익 요약."""
+    evals, buys = [], []
+    for h in holdings:
+        cur = h.get("current_price") or h.get("avg_price") or 0
+        ev = cur * h.get("quantity", 0)
+        bu = (h.get("avg_price") or 0) * h.get("quantity", 0)
+        evals.append(ev)
+        buys.append(bu)
+    total_eval = sum(evals)
+    total_buy = sum(buys)
+    weights = [e / total_eval for e in evals] if total_eval else []
+    hhi = sum(w * w for w in weights)
+    total_assets = total_eval + (total_cash or 0)
+    return {
+        "positions": len(holdings),
+        "total_eval": total_eval,
+        "total_buy": total_buy,
+        "total_pnl": total_eval - total_buy,
+        "total_pnl_rate": ((total_eval - total_buy) / total_buy * 100.0) if total_buy else 0.0,
+        "max_weight": max(weights) if weights else 0.0,
+        "hhi": round(hhi, 4),
+        "cash_ratio": ((total_cash or 0) / total_assets) if total_assets else 0.0,
+    }
+
+
 def decide_action(tech_score: float, exit_flags: dict, pnl: float | None,
                   add_score: float = ADD_SCORE) -> tuple[str, str]:
     """액션 결정 매트릭스: sell > trim > add > hold (우선순위 순).

stock/app/test_holdings_intel.py

@@ -213,3 +213,16 @@ def test_news_issues_flags_negative_sentiment(monkeypatch):
     assert "005930" in issues
     assert issues["005930"][0]["type"] == "news"
     assert issues["005930"][0]["severity"] in ("med", "high")
+
+
+def test_portfolio_health():
+    holdings = [
+        {"ticker": "005930", "quantity": 10, "avg_price": 70000, "current_price": 77000,
+         "is_krx": True},
+        {"ticker": "000660", "quantity": 5, "avg_price": 100000, "current_price": 90000,
+         "is_krx": True},
+    ]
+    h = hi.portfolio_health(holdings, total_cash=1000000)
+    assert h["positions"] == 2
+    assert 0 <= h["max_weight"] <= 1.0
+    assert "total_eval" in h and "total_pnl" in h and "cash_ratio" in h