fix(stock): Phase 2 결정엔진 견고화 (빈노드 제외·cur=0 손절·params기본값·NaN MA·테스트)

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

stock/app/holdings_intel.py

@@ -7,6 +7,7 @@ import pandas as pd
 
 from . import db
 from . import price_fetcher
+from .screener.engine import combine
 
 
 def _krx_tickers() -> set:
@@ -36,8 +37,6 @@ def get_holdings() -> list[dict]:
 
 # ---- Task 2.1: technical_posture ----
 
-from .screener.engine import combine
-
 
 def _score_nodes_and_weights():
     """NODE_REGISTRY에서 보유종목 매수강도 계산용 노드 인스턴스화."""
@@ -59,16 +58,24 @@ def technical_posture(ctx, tickers: list[str]) -> dict[str, float]:
             scores[n.name] = n.compute(scoped, {})
         except Exception:
             scores[n.name] = pd.Series(0.0, index=scoped.master.index)
-    total = combine(scores, weights)
+    scores_ne = {k: s for k, s in scores.items() if not s.empty}
+    weights_ne = {k: w for k, w in weights.items() if k in scores_ne}
+    if not weights_ne:
+        return {}
+    total = combine(scores_ne, weights_ne)
     return {t: float(total.get(t, 0.0)) for t in tickers if t in total.index}
 
 
 # ---- Task 2.2: exit_rules ----
 
+_DEFAULT_EXIT_PARAMS = {"stop_pct": 0.08, "take_pct": 0.25, "climax_vol_x": 3.0}
+
+
 def _ma(closes: "pd.Series", window: int) -> Optional[float]:
     if len(closes) < window:
         return None
-    return float(closes.rolling(window).mean().iloc[-1])
+    val = closes.rolling(window).mean().iloc[-1]
+    return float(val) if pd.notna(val) else None
 
 
 def exit_rules(holding: dict, ticker_prices: "pd.DataFrame", params: dict) -> dict:
@@ -76,6 +83,7 @@ def exit_rules(holding: dict, ticker_prices: "pd.DataFrame", params: dict) -> di
 
     Note: momentum_loss는 compute_and_store 단계에서 집계하므로 여기서 설정하지 않는다.
     """
+    p = {**_DEFAULT_EXIT_PARAMS, **(params or {})}
     flags = {"stop_loss": False, "ma50_break": False, "ma200_break": False,
              "take_profit": False, "climax": False}
     avg = holding.get("avg_price")
@@ -87,10 +95,10 @@ def exit_rules(holding: dict, ticker_prices: "pd.DataFrame", params: dict) -> di
     last_close = float(closes.iloc[-1]) if len(closes) else cur
     if cur is None:
         cur = last_close
-    if cur and avg:
-        if cur < avg * (1 - params["stop_pct"]):
+    if cur is not None and avg:
+        if cur < avg * (1 - p["stop_pct"]):
             flags["stop_loss"] = True
-        if (cur - avg) / avg >= params["take_pct"]:
+        if avg > 0 and (cur - avg) / avg >= p["take_pct"]:
             flags["take_profit"] = True
     ma50 = _ma(closes, 50)
     ma200 = _ma(closes, 200)
@@ -106,7 +114,7 @@ def exit_rules(holding: dict, ticker_prices: "pd.DataFrame", params: dict) -> di
         last_vol = vol.iloc[-1]
         hi_ = float(tp["high"].astype(float).iloc[-1])
         cl_ = float(tp["close"].astype(float).iloc[-1])
-        if avg_vol and last_vol >= avg_vol * params["climax_vol_x"] and hi_ > 0 and cl_ < hi_ * 0.97:
+        if avg_vol and last_vol >= avg_vol * p["climax_vol_x"] and hi_ > 0 and cl_ < hi_ * 0.97:
             flags["climax"] = True
     return flags
 
@@ -116,7 +124,8 @@ def exit_rules(holding: dict, ticker_prices: "pd.DataFrame", params: dict) -> di
 ADD_SCORE = 70.0   # 이 이상이면 추가매수 후보
 
 
-def decide_action(tech_score: float, exit_flags: dict, pnl: float | None) -> tuple[str, str]:
+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).
 
     Returns:
@@ -142,6 +151,6 @@ def decide_action(tech_score: float, exit_flags: dict, pnl: float | None) -> tup
     if reasons:
         return "trim", " · ".join(reasons)
     # 추가매수
-    if tech_score is not None and tech_score >= ADD_SCORE:
+    if tech_score is not None and tech_score >= add_score:
         return "add", f"기술적 강도 양호({tech_score:.0f})"
     return "hold", "특이 신호 없음"

stock/app/test_holdings_intel.py

@@ -141,3 +141,50 @@ def test_decide_action_matrix():
     # 이탈 없음 보통 강도 → hold
     a, _ = hi.decide_action(50, {}, 1)
     assert a == "hold"
+
+
+# ---- Phase 2 hardening tests (m3) ----
+
+def _ticker_prices_hl(closes, highs, vols):
+    n = len(closes)
+    base = dt.date(2025, 1, 1)
+    return pd.DataFrame({
+        "ticker": ["005930"] * n,
+        "date": [(base + dt.timedelta(days=i)).isoformat() for i in range(n)],
+        "open": closes,
+        "high": highs,
+        "low": closes,
+        "close": closes,
+        "volume": vols,
+    })
+
+
+def test_exit_rules_climax():
+    closes = [1000] * 30
+    highs = [1000] * 29 + [1100]  # 마지막날 상단꼬리(종가1000 < 고가1100*0.97)
+    vols = [1000] * 29 + [5000]   # 거래량 5x
+    flags = hi.exit_rules({"avg_price": 900, "current_price": 1000},
+                          _ticker_prices_hl(closes, highs, vols), {})
+    assert flags["climax"] is True
+
+
+def test_exit_rules_ma200_break():
+    closes = list(range(1000, 1000 + 260))[::-1]  # 하락 추세 → 종가 < MA200
+    df = _ticker_prices(closes)
+    flags = hi.exit_rules({"avg_price": 2000, "current_price": closes[-1]}, df, {})
+    assert flags["ma200_break"] is True
+
+
+def test_technical_posture_short_history_returns_low_not_crash():
+    ctx = _toy_ctx(("005930",), n=100)   # <252 → MA 노드 NaN→0, but no crash
+    scores = hi.technical_posture(ctx, ["005930"])
+    assert "005930" in scores
+    assert 0.0 <= scores["005930"] <= 100.0
+
+
+def test_technical_posture_empty_kospi_not_penalized():
+    # rs_rating는 빈 kospi에서 빈 Series → combine에서 제외되어야 (C1)
+    ctx = _toy_ctx(("005930",), n=300)   # kospi 빈 fixture
+    scores = hi.technical_posture(ctx, ["005930"])
+    # ma_alignment+momentum만으로 정규화 → 상승추세면 충분히 높은 점수
+    assert scores["005930"] > 50.0