diff --git a/backend/app/api/endpoints/market_data.py b/backend/app/api/endpoints/market_data.py
index 7d1d6c8..6985dd1 100644
--- a/backend/app/api/endpoints/market_data.py
+++ b/backend/app/api/endpoints/market_data.py
@@ -580,6 +580,88 @@ async def get_orderbook(stock_code: str):
         return {"stock_code": stock_code, "asks": [], "bids": [], "error": "temporarily_unavailable"}
 
 
+def _suggest_buy_amount(
+    current_price: int,
+    recommendation: str,
+    confidence: float,
+    stock_code: str,
+) -> dict:
+    """AI 분석 화면에 표시할 매수 추천 금액·수량 산출.
+
+    실제 자동매매 사이징과 같은 기준을 쓴다(CLAUDE.md §5.1):
+      · 종목당 상한 = 총자산 × MAX_POSITION_SIZE (적응형 모드면 그 값)
+      · 현금 여력 = 예수금 − 최소 현금보유(총자산 × (1 − MAX_TOTAL_CAPITAL_USAGE))
+      · 스윙 합산 상한(SWING_MAX_CAPITAL_RATIO) 잔여 한도
+      · 신뢰도 보정 = 예산 × (confidence / 0.7), 최대 1.2배
+    BUY 가 아니거나 잔고 정보를 모르면 금액 0 과 사유를 반환한다.
+    """
+    from app.core.config import settings as _cfg
+
+    out = {
+        "suggested_amount": 0,
+        "suggested_quantity": 0,
+        "position_ratio": 0.0,
+        "sizing_note": "",
+    }
+    if current_price <= 0:
+        out["sizing_note"] = "현재가 없음"
+        return out
+    if recommendation != "BUY":
+        out["sizing_note"] = f"{recommendation} 신호 — 신규 매수 제안 없음"
+        return out
+
+    try:
+        from app.api.endpoints.portfolio import portfolio_cache
+        summary = portfolio_cache.get_summary_copy() or {}
+        total_assets = float(summary.get("total_assets", 0) or 0)
+        available_cash = float(summary.get("available_cash", 0) or 0)
+        stock_value = float(summary.get("total_value", 0) or 0)
+    except Exception:
+        total_assets = available_cash = stock_value = 0.0
+
+    if total_assets <= 0 or available_cash <= 0:
+        out["sizing_note"] = "잔고 조회 전 — 금액 계산 불가"
+        return out
+
+    position_cap = float(getattr(_cfg, "MAX_POSITION_SIZE", 0.06))
+    try:
+        from app.services.adaptive_strategy import get_current_adaptive_params
+        _adp = get_current_adaptive_params()
+        if not _adp.fallback_used:
+            position_cap = float(_adp.position_size)
+    except Exception:
+        pass
+
+    budget = total_assets * position_cap
+    # 신뢰도 보정 (기준 0.7)
+    if confidence > 0:
+        budget *= min(1.2, max(0.5, confidence / 0.7))
+    # 현금 여력
+    reserve = total_assets * (1 - float(getattr(_cfg, "MAX_TOTAL_CAPITAL_USAGE", 0.95)))
+    budget = min(budget, max(0.0, available_cash - reserve))
+    # 스윙 합산 상한 잔여
+    swing_cap = float(getattr(_cfg, "SWING_MAX_CAPITAL_RATIO", 1.0) or 1.0)
+    if 0 < swing_cap < 1:
+        budget = min(budget, max(0.0, total_assets * swing_cap - stock_value))
+
+    qty = int(budget // current_price)
+    if qty <= 0:
+        out["sizing_note"] = "가용 예산이 1주 미만 — 매수 제안 없음"
+        return out
+
+    amount = qty * current_price
+    out.update({
+        "suggested_amount": amount,
+        "suggested_quantity": qty,
+        "position_ratio": round(amount / total_assets * 100, 2),
+        "sizing_note": (
+            f"총자산 {total_assets:,.0f}원 × 종목당 {position_cap * 100:.0f}%"
+            f"(신뢰도 {confidence * 100:.0f}% 반영) 기준"
+        ),
+    })
+    return out
+
+
 @router.get("/ai-analysis/{stock_code}")
 async def get_ai_analysis(stock_code: str, db: AsyncSession = Depends(get_db)):
     """AI 기반 종목 분석 엔드포인트 (기술적 지표 + 규칙 기반 분석)"""
@@ -791,6 +873,8 @@ async def get_ai_analysis(stock_code: str, db: AsyncSession = Depends(get_db)):
             "recommendation": recommendation,
             "confidence": confidence,
             "trend": trend,
+            # 매수 추천 금액 (실제 자동매매 사이징과 동일 기준)
+            **_suggest_buy_amount(current_price, recommendation, confidence, stock_code),
             "analyzed_at": datetime.now(_KST).isoformat(),  # KST +09:00 포함
         }
 
diff --git a/backend/tests/test_ai_analysis_buy_amount.py b/backend/tests/test_ai_analysis_buy_amount.py
new file mode 100644
index 0000000..d96c8de
--- /dev/null
+++ b/backend/tests/test_ai_analysis_buy_amount.py
@@ -0,0 +1,68 @@
+"""AI 투자 분석 — 매수 추천 금액 산출 검증 (자동매매 사이징과 동일 기준)."""
+from unittest.mock import patch
+
+import pytest
+
+from app.api.endpoints.market_data import _suggest_buy_amount
+from app.api.endpoints.portfolio import portfolio_cache
+
+
+def _summary(total_assets=10_000_000, cash=5_000_000, stock_value=3_000_000):
+    return {"total_assets": total_assets, "available_cash": cash, "total_value": stock_value}
+
+
+@pytest.fixture(autouse=True)
+def _cache():
+    portfolio_cache.summary = _summary()
+    yield
+    portfolio_cache.summary = None
+
+
+class _Adp:
+    fallback_used = False
+    position_size = 0.06
+
+
+def test_buy_amount_uses_position_cap_and_confidence():
+    with patch("app.services.adaptive_strategy.get_current_adaptive_params", return_value=_Adp()):
+        out = _suggest_buy_amount(current_price=70_000, recommendation="BUY",
+                                  confidence=0.7, stock_code="005930")
+    # 1,000만 × 6% = 60만, 신뢰도 0.7 → 보정 1.0 → 70,000원 × 8주 = 560,000
+    assert out["suggested_quantity"] == 8
+    assert out["suggested_amount"] == 560_000
+    assert out["position_ratio"] == pytest.approx(5.6, abs=0.01)
+    assert "6%" in out["sizing_note"]
+
+
+def test_high_confidence_scales_up_capped_at_120pct():
+    with patch("app.services.adaptive_strategy.get_current_adaptive_params", return_value=_Adp()):
+        out = _suggest_buy_amount(70_000, "BUY", 1.0, "005930")
+    # 60만 × min(1.2, 1.0/0.7=1.43) = 72만 → 10주
+    assert out["suggested_quantity"] == 10
+
+
+def test_non_buy_recommendation_returns_zero():
+    for rec in ("HOLD", "SELL"):
+        out = _suggest_buy_amount(70_000, rec, 0.8, "005930")
+        assert out["suggested_amount"] == 0 and rec in out["sizing_note"]
+
+
+def test_limited_by_available_cash_and_reserve():
+    portfolio_cache.summary = _summary(cash=600_000)   # 예수금 60만, 최소보유 5% = 50만
+    with patch("app.services.adaptive_strategy.get_current_adaptive_params", return_value=_Adp()):
+        out = _suggest_buy_amount(70_000, "BUY", 0.7, "005930")
+    assert out["suggested_quantity"] == 1   # 사용 가능 10만 → 1주
+
+
+def test_limited_by_swing_capital_cap():
+    # 스윙 상한 50% = 500만, 이미 490만 보유 → 잔여 10만
+    portfolio_cache.summary = _summary(stock_value=4_900_000)
+    with patch("app.services.adaptive_strategy.get_current_adaptive_params", return_value=_Adp()):
+        out = _suggest_buy_amount(70_000, "BUY", 0.7, "005930")
+    assert out["suggested_quantity"] == 1
+
+
+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"]
diff --git a/frontend/src/components/ChartPanel.tsx b/frontend/src/components/ChartPanel.tsx
index 0d83845..81ac8b8 100644
--- a/frontend/src/components/ChartPanel.tsx
+++ b/frontend/src/components/ChartPanel.tsx
@@ -143,6 +143,35 @@ export const AiAnalysisPanel = React.memo(({ aiAnalysisData }: AiAnalysisPanelPr
                     </span>
                 )}
             </div>
+            {/* 매수 추천 금액 — 실제 자동매매 사이징과 동일 기준 (총자산×종목당 비중, 신뢰도 반영) */}
+            {aiAnalysisData?.data && (
+                <div className="px-3 py-1.5 border-b border-[#152035] bg-[#0a1120] flex-shrink-0">
+                    <div className="flex items-baseline gap-2">
+                        <span className="text-[9px] uppercase tracking-wider text-[#4a6580]">매수 추천</span>
+                        {aiAnalysisData.data.suggested_quantity > 0 ? (
+                            <>
+                                <span className="text-[13px] font-mono text-[#22c55e]">
+                                    {Number(aiAnalysisData.data.suggested_amount).toLocaleString('ko-KR')}원
+                                </span>
+                                <span className="text-[10px] font-mono text-[#8faabf]">
+                                    {aiAnalysisData.data.suggested_quantity}주
+                                </span>
+                                <span className="ml-auto text-[9px] font-mono text-[#4a6580]">
+                                    총자산 {aiAnalysisData.data.position_ratio}%
+                                </span>
+                            </>
+                        ) : (
+                            <span className="text-[10px] text-[#4a6580]">
+                                {aiAnalysisData.data.sizing_note || '매수 제안 없음'}
+                            </span>
+                        )}
+                    </div>
+                    {aiAnalysisData.data.suggested_quantity > 0 && aiAnalysisData.data.sizing_note && (
+                        <div className="text-[9px] text-[#3a5570] mt-0.5">{aiAnalysisData.data.sizing_note}</div>
+                    )}
+                </div>
+            )}
+
             {/* Content */}
             <div className="flex-1 overflow-y-auto custom-scrollbar p-3">
                 {aiAnalysisText ? (