Merge branch 'dev' into main — AI 분석 목표가·손절가

CLAUDE.md §5.2 기준으로 계산해 매수 추천 금액 아래에 함께 표시.
  · 손절 = ATR(2.5×)과 -7% 중 타이트한 쪽(가격 높은 쪽), 적응형 모드면 그 값 사용
  · 목표 = 진입가 + 2R (R = 진입가 − 손절가) — +2R 50% 분할 매도 지점
ATR 은 stock_analysis.atr_14 우선, 없으면 DB 일봉으로 계산(KIS API 미사용). 테스트 4건 추가.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

backend/app/api/endpoints/market_data.py

@@ -580,6 +580,64 @@ async def get_orderbook(stock_code: str):
         return {"stock_code": stock_code, "asks": [], "bids": [], "error": "temporarily_unavailable"}
 
 
+def _suggest_targets(current_price: int, atr: float | None) -> dict:
+    """AI 분석 화면용 목표가·손절가 (CLAUDE.md §5.2 스윙 기준).
+
+      · 손절 = max(진입가 − ATR_STOP_MULTIPLIER×ATR, 진입가×(1−STOP_LOSS_PERCENT))
+        → 둘 중 '타이트한 쪽'(가격이 높은 쪽). ATR 없으면 퍼센트 기준만 사용.
+      · 목표 = 진입가 + 2R (R = 진입가 − 손절가) → +2R 도달 시 50% 분할 매도 지점
+    적응형 모드가 활성화되어 있으면 그 손절률·ATR 배수를 따른다.
+    """
+    from app.core.config import settings as _cfg
+
+    out = {
+        "target_price": 0,
+        "stop_loss_price": 0,
+        "target_pct": 0.0,
+        "stop_loss_pct": 0.0,
+        "risk_reward": 0.0,
+        "target_note": "",
+    }
+    if current_price <= 0:
+        return out
+
+    stop_pct = abs(float(getattr(_cfg, "STOP_LOSS_PERCENT", 7.0)))
+    atr_mult = float(getattr(_cfg, "ATR_STOP_MULTIPLIER", 2.5))
+    try:
+        from app.services.adaptive_strategy import get_current_adaptive_params
+        _adp = get_current_adaptive_params()
+        if not _adp.fallback_used:
+            stop_pct = abs(float(_adp.stop_loss_pct))
+            atr_mult = float(_adp.atr_stop_multiplier)
+    except Exception:
+        pass
+
+    pct_stop = current_price * (1 - stop_pct / 100)
+    if atr and atr > 0:
+        atr_stop = current_price - atr_mult * atr
+        stop = max(atr_stop, pct_stop)  # 타이트한 쪽 (CLAUDE.md 8.1-5: 항상 min 아닌 max=높은 가격)
+        basis = "ATR" if atr_stop > pct_stop else f"-{stop_pct:.0f}%"
+        note = f"손절 {basis} 기준(타이트한 쪽), 목표 +2R"
+    else:
+        stop = pct_stop
+        note = f"손절 -{stop_pct:.0f}% 기준(ATR 없음), 목표 +2R"
+
+    risk = current_price - stop
+    if risk <= 0:
+        return out
+    target = current_price + 2 * risk
+
+    out.update({
+        "target_price": int(round(target)),
+        "stop_loss_price": int(round(stop)),
+        "target_pct": round((target / current_price - 1) * 100, 2),
+        "stop_loss_pct": round((stop / current_price - 1) * 100, 2),
+        "risk_reward": 2.0,
+        "target_note": note,
+    })
+    return out
+
+
 def _suggest_buy_amount(
     current_price: int,
     recommendation: str,
@@ -693,6 +751,12 @@ async def get_ai_analysis(stock_code: str, db: AsyncSession = Depends(get_db)):
         analysis_record = result.scalar_one_or_none()
 
         indicators = {}
+        _atr_for_targets = None
+        if analysis_record and getattr(analysis_record, "atr_14", None):
+            try:
+                _atr_for_targets = float(analysis_record.atr_14)
+            except Exception:
+                _atr_for_targets = None
         if analysis_record:
             indicators = {
                 "rsi": float(analysis_record.rsi) if analysis_record.rsi else None,
@@ -852,6 +916,20 @@ async def get_ai_analysis(stock_code: str, db: AsyncSession = Depends(get_db)):
         else:
             analysis_text = "기술적 지표 데이터 없음"
 
+        # 목표가·손절가용 ATR — 분석 레코드에 없으면 DB 일봉으로 계산
+        if _atr_for_targets is None:
+            try:
+                from app.services.mean_reversion_strategy import calculate_atr
+                _db_chart = await _fetch_chart_from_db(stock_code, 30)
+                if len(_db_chart) >= 15:
+                    _atr_for_targets = calculate_atr(
+                        [c["high"] for c in _db_chart],
+                        [c["low"] for c in _db_chart],
+                        [c["close"] for c in _db_chart],
+                    )
+            except Exception as _atr_err:
+                logger.debug(f"[AI분석] ATR 계산 실패 ({stock_code}): {_atr_err}")
+
         # 추세 최종 결정 (이동평균선 없으면 가격 변동으로)
         if trend == "SIDEWAYS" and high_price and low_price and current_price:
             daily_change_pct = ((current_price - low_price) / low_price) * 100 if low_price > 0 else 0
@@ -875,6 +953,8 @@ async def get_ai_analysis(stock_code: str, db: AsyncSession = Depends(get_db)):
             "trend": trend,
             # 매수 추천 금액 (실제 자동매매 사이징과 동일 기준)
             **_suggest_buy_amount(current_price, recommendation, confidence, stock_code),
+            # 목표가·손절가 (CLAUDE.md §5.2)
+            **_suggest_targets(current_price, _atr_for_targets),
             "analyzed_at": datetime.now(_KST).isoformat(),  # KST +09:00 포함
         }
 

backend/tests/test_ai_analysis_buy_amount.py

@@ -66,3 +66,39 @@ def test_no_balance_info_reports_reason():
     portfolio_cache.summary = None
     out = _suggest_buy_amount(70_000, "BUY", 0.8, "005930")
     assert out["suggested_amount"] == 0 and "잔고" in out["sizing_note"]
+
+
+# ── 목표가 · 손절가 ─────────────────────────────────────────────────────────
+from app.api.endpoints.market_data import _suggest_targets  # noqa: E402
+
+
+def test_targets_use_tighter_stop_and_2r():
+    """ATR 손절(2.5×)과 -7% 중 타이트한 쪽(가격 높은 쪽), 목표는 +2R."""
+    with patch("app.services.adaptive_strategy.get_current_adaptive_params", side_effect=Exception):
+        # ATR 1,000 → ATR손절 100,000-2,500 = 97,500 / -7% = 93,000 → 97,500 채택
+        out = _suggest_targets(100_000, atr=1_000.0)
+    assert out["stop_loss_price"] == 97_500
+    assert out["target_price"] == 105_000          # 100,000 + 2×2,500
+    assert out["stop_loss_pct"] == pytest.approx(-2.5, abs=0.01)
+    assert out["target_pct"] == pytest.approx(5.0, abs=0.01)
+    assert out["risk_reward"] == 2.0
+    assert "ATR" in out["target_note"]
+
+
+def test_percent_stop_wins_when_atr_is_wide():
+    with patch("app.services.adaptive_strategy.get_current_adaptive_params", side_effect=Exception):
+        # ATR 5,000 → ATR손절 87,500 < -7% 93,000 → 93,000(타이트) 채택
+        out = _suggest_targets(100_000, atr=5_000.0)
+    assert out["stop_loss_price"] == 93_000
+    assert out["target_price"] == 114_000
+    assert "-7%" in out["target_note"]
+
+
+def test_targets_without_atr_fall_back_to_percent():
+    with patch("app.services.adaptive_strategy.get_current_adaptive_params", side_effect=Exception):
+        out = _suggest_targets(100_000, atr=None)
+    assert out["stop_loss_price"] == 93_000 and "ATR 없음" in out["target_note"]
+
+
+def test_targets_zero_price():
+    assert _suggest_targets(0, atr=1000.0)["target_price"] == 0

frontend/src/components/ChartPanel.tsx

@@ -169,6 +169,28 @@ export const AiAnalysisPanel = React.memo(({ aiAnalysisData }: AiAnalysisPanelPr
                     {aiAnalysisData.data.suggested_quantity > 0 && aiAnalysisData.data.sizing_note && (
                         <div className="text-[9px] text-[#3a5570] mt-0.5">{aiAnalysisData.data.sizing_note}</div>
                     )}
+
+                    {/* 목표가 · 손절가 (CLAUDE.md §5.2: 손절은 ATR/퍼센트 중 타이트한 쪽, 목표는 +2R) */}
+                    {aiAnalysisData.data.target_price > 0 && (
+                        <div className="flex items-baseline gap-3 mt-1">
+                            <span className="text-[9px] uppercase tracking-wider text-[#4a6580]">목표</span>
+                            <span className="text-[11px] font-mono text-[#ef4444]">
+                                {Number(aiAnalysisData.data.target_price).toLocaleString('ko-KR')}원
+                                <span className="text-[9px] ml-1">(+{aiAnalysisData.data.target_pct}%)</span>
+                            </span>
+                            <span className="text-[9px] uppercase tracking-wider text-[#4a6580]">손절</span>
+                            <span className="text-[11px] font-mono text-[#3b82f6]">
+                                {Number(aiAnalysisData.data.stop_loss_price).toLocaleString('ko-KR')}원
+                                <span className="text-[9px] ml-1">({aiAnalysisData.data.stop_loss_pct}%)</span>
+                            </span>
+                            <span className="ml-auto text-[9px] font-mono text-[#4a6580]">
+                                R:R 1:{aiAnalysisData.data.risk_reward}
+                            </span>
+                        </div>
+                    )}
+                    {aiAnalysisData.data.target_note && (
+                        <div className="text-[9px] text-[#3a5570] mt-0.5">{aiAnalysisData.data.target_note}</div>
+                    )}
                 </div>
             )}