feat(stock): technical_posture (스크리너 노드 보유종목 적용)

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

stock/app/test_holdings_intel.py

@@ -1,5 +1,9 @@
+import datetime as dt
+import pandas as pd
+
 from app import holdings_intel as hi
 
+
 def test_get_holdings_merges_price_and_pnl(monkeypatch):
     monkeypatch.setattr(hi.db, "get_all_portfolio", lambda: [
         {"id": 1, "broker": "kis", "ticker": "005930", "name": "삼성전자",
@@ -54,3 +58,86 @@ def test_get_holdings_price_missing(monkeypatch):
     hs = hi.get_holdings()
     assert hs[0]["current_price"] is None
     assert hs[0]["pnl_rate"] is None
+
+
+# ---- Phase 2 tests ----
+
+def _toy_ctx(tickers=("005930",), n=300):
+    """결정적 일봉으로 ScreenContext 유사 객체 구성."""
+    from app.screener.engine import ScreenContext
+    rows = []
+    base = dt.date(2025, 1, 1)
+    for t in tickers:
+        price = 1000
+        for i in range(n):
+            price = int(price * 1.002)  # 완만한 상승 → 정배열
+            d = (base + dt.timedelta(days=i)).isoformat()
+            rows.append({"ticker": t, "date": d, "open": price, "high": price,
+                         "low": price, "close": price, "volume": 1000, "value": price*1000})
+    prices = pd.DataFrame(rows)
+    master = pd.DataFrame({"name": [f"n{t}" for t in tickers],
+                           "market": ["KOSPI"]*len(tickers),
+                           "market_cap": [1e12]*len(tickers)},
+                          index=pd.Index(tickers, name="ticker"))
+    flow = pd.DataFrame(columns=["ticker","date","foreign_net","institution_net"])
+    return ScreenContext(master=master, prices=prices, flow=flow,
+                         kospi=pd.Series(dtype=float), asof=base+dt.timedelta(days=n-1))
+
+
+def test_technical_posture_returns_scores():
+    ctx = _toy_ctx(("005930",))
+    scores = hi.technical_posture(ctx, ["005930"])
+    assert "005930" in scores
+    assert 0.0 <= scores["005930"] <= 100.0     # 상승추세 → 양수 점수
+
+
+# ---- Task 2.2 tests ----
+
+def _ticker_prices(closes, vols=None):
+    n = len(closes)
+    base = dt.date(2025, 1, 1)
+    vols = vols or [1000]*n
+    return pd.DataFrame({
+        "ticker": ["005930"]*n,
+        "date": [(base+dt.timedelta(days=i)).isoformat() for i in range(n)],
+        "open": closes, "high": closes, "low": closes, "close": closes, "volume": vols,
+    })
+
+
+DEFAULT_EXIT = {"stop_pct": 0.08, "take_pct": 0.25, "climax_vol_x": 3.0}
+
+
+def test_exit_rules_stop_and_ma():
+    closes = [1000]*60 + [1100]*200  # 충분한 길이, 최근 평탄
+    df = _ticker_prices(closes)
+    # 현재가가 평단(2000) 대비 -45% → stop_loss
+    flags = hi.exit_rules({"avg_price": 2000, "current_price": 1100}, df, DEFAULT_EXIT)
+    assert flags["stop_loss"] is True
+    # 종가 1100 > MA50≈1100, MA200은 더 낮음 → ma 이탈 아님
+    assert flags["ma200_break"] is False
+
+
+def test_exit_rules_take_profit():
+    df = _ticker_prices([1000]*260)
+    flags = hi.exit_rules({"avg_price": 1000, "current_price": 1300}, df, DEFAULT_EXIT)
+    assert flags["take_profit"] is True   # +30% ≥ 25%
+
+
+# ---- Task 2.3 tests ----
+
+def test_decide_action_matrix():
+    # 강건 + 이탈 없음 + 높은 강도 → add
+    a, r = hi.decide_action(tech_score=80, exit_flags={}, pnl=5)
+    assert a == "add"
+    # ma200 이탈 → sell
+    a, r = hi.decide_action(70, {"ma200_break": True}, 2)
+    assert a == "sell"
+    # stop_loss → sell
+    a, _ = hi.decide_action(70, {"stop_loss": True}, -10)
+    assert a == "sell"
+    # ma50 이탈만 → trim
+    a, _ = hi.decide_action(60, {"ma50_break": True}, 3)
+    assert a == "trim"
+    # 이탈 없음 보통 강도 → hold
+    a, _ = hi.decide_action(50, {}, 1)
+    assert a == "hold"