1

backend/app/engine/recommender.py

@@ -284,6 +284,18 @@ async def get_performance_stats() -> dict:
     """获取推荐胜率统计"""
     try:
         from sqlalchemy import text
+        latest_tracked_sql = (
+            "WITH latest_tracked AS ("
+            "  SELECT r.id AS recommendation_id, t.pct_from_entry, t.max_return_pct, "
+            "         t.max_drawdown_pct, t.hit_target, t.hit_stop_loss "
+            "  FROM recommendations r "
+            "  INNER JOIN recommendation_tracking t ON t.recommendation_id = r.id "
+            "  INNER JOIN ("
+            "    SELECT recommendation_id, MAX(id) as max_id "
+            "    FROM recommendation_tracking GROUP BY recommendation_id"
+            "  ) latest ON t.id = latest.max_id"
+            ") "
+        )
         async with get_db() as db:
             # 总推荐数
             result = await db.execute(
@@ -294,8 +306,8 @@ async def get_performance_stats() -> dict:
             # 有跟踪记录的推荐
             result = await db.execute(
                 text(
-                    "SELECT COUNT(DISTINCT r.id) FROM recommendations r "
-                    "INNER JOIN recommendation_tracking t ON t.recommendation_id = r.id"
+                    latest_tracked_sql +
+                    "SELECT COUNT(*) FROM latest_tracked"
                 )
             )
             tracked = result.scalar() or 0
@@ -303,14 +315,8 @@ async def get_performance_stats() -> dict:
             # 胜率基于最新跟踪日的最终 pct（正值=盈利，负值=亏损）
             result = await db.execute(
                 text(
-                    "SELECT COUNT(*) FROM ("
-                    "  SELECT t.recommendation_id, t.pct_from_entry as latest_pct "
-                    "  FROM recommendation_tracking t "
-                    "  INNER JOIN ("
-                    "    SELECT recommendation_id, MAX(id) as max_id "
-                    "    FROM recommendation_tracking GROUP BY recommendation_id"
-                    "  ) latest ON t.id = latest.max_id"
-                    ") WHERE latest_pct > 0"
+                    latest_tracked_sql +
+                    "SELECT COUNT(*) FROM latest_tracked WHERE pct_from_entry > 0"
                 )
             )
             winning = result.scalar() or 0
@@ -318,14 +324,8 @@ async def get_performance_stats() -> dict:
             # 平均收益（基于最新跟踪日的 pct）
             result = await db.execute(
                 text(
-                    "SELECT AVG(latest_pct) FROM ("
-                    "  SELECT t.pct_from_entry as latest_pct "
-                    "  FROM recommendation_tracking t "
-                    "  INNER JOIN ("
-                    "    SELECT recommendation_id, MAX(id) as max_id "
-                    "    FROM recommendation_tracking GROUP BY recommendation_id"
-                    "  ) latest ON t.id = latest.max_id"
-                    ")"
+                    latest_tracked_sql +
+                    "SELECT AVG(pct_from_entry) FROM latest_tracked"
                 )
             )
             avg_return = result.scalar()
@@ -334,8 +334,8 @@ async def get_performance_stats() -> dict:
             # 达到目标价的推荐
             result = await db.execute(
                 text(
-                    "SELECT COUNT(DISTINCT recommendation_id) FROM recommendation_tracking "
-                    "WHERE hit_target = 1"
+                    latest_tracked_sql +
+                    "SELECT COUNT(*) FROM latest_tracked WHERE hit_target = 1"
                 )
             )
             hit_target_count = result.scalar() or 0
@@ -343,8 +343,8 @@ async def get_performance_stats() -> dict:
             # 触发止损的推荐
             result = await db.execute(
                 text(
-                    "SELECT COUNT(DISTINCT recommendation_id) FROM recommendation_tracking "
-                    "WHERE hit_stop_loss = 1"
+                    latest_tracked_sql +
+                    "SELECT COUNT(*) FROM latest_tracked WHERE hit_stop_loss = 1"
                 )
             )
             hit_stop_count = result.scalar() or 0
@@ -364,14 +364,8 @@ async def get_performance_stats() -> dict:
             # 最大浮盈/最大回撤统计
             result = await db.execute(
                 text(
-                    "SELECT AVG(max_return_pct), AVG(max_drawdown_pct) FROM ("
-                    "  SELECT t.recommendation_id, t.max_return_pct, t.max_drawdown_pct "
-                    "  FROM recommendation_tracking t "
-                    "  INNER JOIN ("
-                    "    SELECT recommendation_id, MAX(id) as max_id "
-                    "    FROM recommendation_tracking GROUP BY recommendation_id"
-                    "  ) latest ON t.id = latest.max_id"
-                    ")"
+                    latest_tracked_sql +
+                    "SELECT AVG(max_return_pct), AVG(max_drawdown_pct) FROM latest_tracked"
                 )
             )
             avg_extremes = result.fetchone()
@@ -425,6 +419,7 @@ async def get_performance_stats() -> dict:
                     "created_at": str(r["created_at"])[:10] if r["created_at"] else "",
                 })
 
+            winning = min(winning, tracked)
             win_rate = round(winning / tracked * 100, 1) if tracked > 0 else 0
 
             return {

backend/app/llm/chat_agent.py

@@ -27,6 +27,7 @@ TOOL_LABELS = {
     "get_user_watchlist_snapshot": "读取自选股作战池",
     "get_stock_kline": "查询K线数据",
     "get_stock_capital_flow": "查询资金流向",
+    "diagnose_stock": "生成个股会诊",
     "search_stock": "搜索股票",
 }
 

backend/app/llm/prompts.py

@@ -34,7 +34,7 @@ ENHANCE_USER_TEMPLATE = """\
 请对该股票进行 2-3 句话的深度分析："""
 
 CHAT_SYSTEM_PROMPT = """\
-你是 A 股投研作战台里的 AI 作战助理，不是泛化闲聊机器人。你的核心任务是解释系统已经生成的结果，并帮助用户把市场、板块、推荐和自选股串成可执行判断。
+你是 A 股投研作战台里的系统智能体，不是泛化闲聊机器人。你的核心任务是回答所有与本系统有关的问题，并把市场、板块、推荐、自选股、个股诊断和系统校准串成可执行判断。
 
 你的能力：
 1. 查询今日作战结论，包括市场状态、今日打法、建议仓位、重点板块和规避规则
@@ -42,12 +42,15 @@ CHAT_SYSTEM_PROMPT = """\
 3. 查询当前用户的自选股池与最新建议
 4. 查询个股K线、技术面、资金流向数据
 5. 搜索股票代码，并把结果放回当前交易语境中分析
+6. 对单只股票生成系统化会诊，输出结论、触发条件、失效条件、仓位边界和风险清单
 
 重要提醒：
 - 回答用户关于"今天市场怎么样"之类的问题时，必须调用 get_realtime_indices 获取实时指数数据
 - 回答用户关于"今天该怎么做"、"当前推荐怎么看"、"自选股该怎么处理"这类问题时，优先调用 get_strategy_board、get_latest_recommendations、get_user_watchlist_snapshot
+- 回答用户关于某只股票能不能看、是否该买、持仓怎么处理、为什么涨跌、是否要复盘时，必须先 search_stock（如果用户没给标准 ts_code），再调用 diagnose_stock；必要时补充 get_stock_capital_flow、get_stock_technical_signal
 - 盘中时段（9:30-15:00）必须使用实时数据，盘后时段使用当日收盘或最近一次系统生成的数据
 - 不要脱离系统上下文泛泛而谈，必须先调用工具获取最新结果再回答
+- A 股优先看资金顺势、主线板块、量价承接和位置；RSI/MACD/KDJ 只做节奏与风控确认，不能因超买超卖本身直接否决或买入
 
 回答要求：
 1. 使用工具获取最新数据后再回答，不要凭空编造数据

backend/app/llm/tool_executor.py

@@ -44,6 +44,8 @@ async def execute_tool(name: str, arguments: dict) -> str:
             return await _search_stock(arguments["keyword"])
         elif name == "get_stock_technical_signal":
             return await _get_stock_technical_signal(arguments["ts_code"])
+        elif name == "diagnose_stock":
+            return await _diagnose_stock(arguments["ts_code"], arguments.get("mode", "entry"))
         elif name == "get_sector_performance":
             return await _get_sector_performance(arguments["sector_name"])
         elif name == "get_realtime_indices":
@@ -227,6 +229,126 @@ async def _get_stock_technical_signal(ts_code: str) -> str:
     return json.dumps(data, ensure_ascii=False, default=str)
 
 
+async def _diagnose_stock(ts_code: str, mode: str = "entry") -> str:
+    """生成系统化个股会诊，供作战问答智能体调用。"""
+    from sqlalchemy import text
+    from app.db.database import get_db
+    from app.db import tables
+    from app.llm.client import chat_completion
+
+    mode_map = {
+        "entry": "建仓前诊断",
+        "holding": "持仓复核",
+        "review": "回撤复盘",
+        "tracking": "继续跟踪",
+    }
+    mode = mode if mode in mode_map else "entry"
+
+    strategy_board = await _get_strategy_board()
+    latest_recommendations = await _get_latest_recommendations()
+    hot_sectors = await _get_hot_sectors(8)
+    kline = await _get_stock_kline(ts_code, 80)
+    capital_flow = await _get_stock_capital_flow(ts_code, 15)
+    technical_signal = await _get_stock_technical_signal(ts_code)
+
+    latest_rec = None
+    stock_name = ts_code
+    try:
+        recs = json.loads(latest_recommendations)
+        latest_rec = next((item for item in recs if item.get("ts_code") == ts_code), None)
+    except Exception:
+        latest_rec = None
+    if latest_rec and latest_rec.get("name"):
+        stock_name = latest_rec["name"]
+
+    recent_diagnoses = []
+    try:
+        async with get_db() as db:
+            rows = (await db.execute(
+                text(
+                    "SELECT name, diagnosis_mode, diagnosis, created_at "
+                    "FROM stock_diagnoses WHERE ts_code = :ts_code "
+                    "ORDER BY created_at DESC, id DESC LIMIT 3"
+                ),
+                {"ts_code": ts_code},
+            )).fetchall()
+            recent_diagnoses = [dict(row._mapping) for row in rows]
+            if recent_diagnoses and recent_diagnoses[0].get("name"):
+                stock_name = recent_diagnoses[0]["name"]
+    except Exception:
+        recent_diagnoses = []
+
+    prompt = f"""请在 A 股作战台语境下，对 {ts_code} 做一次系统化个股会诊。
+
+诊断模式: {mode_map[mode]}
+
+今日作战结论:
+{strategy_board}
+
+最新推荐池中该股记录:
+{json.dumps(latest_rec, ensure_ascii=False, default=str) if latest_rec else "不在最新推荐池"}
+
+热门板块:
+{hot_sectors}
+
+K线与技术指标:
+{kline}
+
+资金流:
+{capital_flow}
+
+技术信号:
+{technical_signal}
+
+最近诊断:
+{json.dumps(recent_diagnoses, ensure_ascii=False, default=str)}
+
+输出要求:
+- 先给明确结论，只能是「可操作 / 重点关注 / 观察 / 回避」
+- 明确当前动作、触发条件、失效条件、仓位边界、下一步观察点
+- A 股优先看资金顺势、主线板块、量价承接和位置；技术指标只做节奏与风控确认
+- RSI、MACD、KDJ 的超买超卖不能单独决定买卖
+- 不写传统研报，不堆原始数据，不承诺收益
+- 用 Markdown 输出，保持简洁"""
+
+    resp = await chat_completion([
+        {
+            "role": "system",
+            "content": (
+                "你是 A 股投研作战台的个股会诊智能体。"
+                "你必须融合系统作战结论、板块、推荐池、资金流、K线和技术信号，"
+                "输出可执行但带风险边界的会诊结论。"
+            ),
+        },
+        {"role": "user", "content": prompt},
+    ])
+    if not resp or not resp.content:
+        return json.dumps({"error": "个股会诊生成失败，LLM 未返回内容"}, ensure_ascii=False)
+
+    diagnosis = resp.content.strip()
+    try:
+        async with get_db() as db:
+            await db.execute(
+                tables.stock_diagnoses_table.insert().values(
+                    ts_code=ts_code,
+                    name=stock_name,
+                    diagnosis_mode=mode,
+                    diagnosis=diagnosis,
+                )
+            )
+            await db.commit()
+    except Exception as e:
+        logger.warning(f"保存聊天会诊结果失败 {ts_code}: {e}")
+
+    return json.dumps({
+        "ts_code": ts_code,
+        "name": stock_name,
+        "mode": mode,
+        "diagnosis": diagnosis,
+        "saved": True,
+    }, ensure_ascii=False, default=str)
+
+
 async def _get_sector_performance(sector_name: str) -> str:
     """获取板块表现数据"""
     from app.engine.recommender import get_latest_sectors

backend/app/llm/tools.py

@@ -145,6 +145,28 @@ CHAT_TOOLS = [
             },
         },
     },
+    {
+        "type": "function",
+        "function": {
+            "name": "diagnose_stock",
+            "description": "对单只 A 股做系统化个股会诊，融合今日作战结论、推荐池、板块、K线、资金流和技术信号，输出结论、触发条件、失效条件和风险边界",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "ts_code": {
+                        "type": "string",
+                        "description": "股票代码，如 '000001.SZ'。如果用户只给名称，应先调用 search_stock 找到 ts_code",
+                    },
+                    "mode": {
+                        "type": "string",
+                        "enum": ["entry", "holding", "review", "tracking"],
+                        "description": "诊断模式：entry 建仓前；holding 持仓复核；review 回撤复盘；tracking 继续跟踪。默认 entry",
+                    },
+                },
+                "required": ["ts_code"],
+            },
+        },
+    },
     {
         "type": "function",
         "function": {

frontend/src/app/(auth)/chat/page.tsx

@@ -12,23 +12,23 @@ interface DisplayMessage {
 
 const QUICK_QUESTIONS = [
   "结合今日作战结论，告诉我今天应该重点看什么。",
-  "把当前推荐池分成可操作、重点关注和仅观察三层讲给我。",
+  "诊断一下 300750.SZ，给出触发条件和失效条件。",
   "看看我的自选股里哪些需要明天优先盯盘。",
-  "如果今天只允许做一个方向，你建议我盯哪个主线，为什么？",
+  "复盘当前推荐池，哪些信号最近更有效？",
 ];
 
 const CHAT_SCENES = [
   {
-    title: "问今日打法",
-    description: "进攻 / 试错 / 防守",
+    title: "市场",
+    description: "打法 / 仓位 / 风险",
   },
   {
-    title: "问推荐池",
-    description: "进池原因 / 触发 / 放弃",
+    title: "个股",
+    description: "诊断 / 触发 / 失效",
   },
   {
-    title: "问自选股",
-    description: "观察池 / 候选池 / 持仓池",
+    title: "系统",
+    description: "推荐池 / 自选股 / 校准",
   },
 ];
 
@@ -108,15 +108,13 @@ export default function ChatPage() {
         <aside className="hidden xl:flex xl:flex-col xl:gap-4">
           <div className="glass-card-static p-5 animate-fade-in-up">
             <div className="text-[10px] font-semibold uppercase tracking-[0.22em] text-cyan-400">
-              Combat Chat
+              System Agent
             </div>
-            <h1 className="mt-2 text-xl font-bold tracking-tight">AI 作战问答</h1>
+            <h1 className="mt-2 text-xl font-bold tracking-tight">系统智能体</h1>
           </div>
 
           <div className="glass-card-static p-5 animate-fade-in-up">
-            <div className="text-[10px] font-semibold uppercase tracking-[0.22em] text-amber-400">
-              适合拿来问
-            </div>
+            <div className="text-[10px] font-semibold uppercase tracking-[0.22em] text-amber-400">能力</div>
             <div className="mt-4 space-y-3">
               {CHAT_SCENES.map((scene) => (
                 <div key={scene.title} className="rounded-2xl border border-border-subtle bg-surface-1/70 px-4 py-3">
@@ -137,9 +135,9 @@ export default function ChatPage() {
                 </svg>
               </div>
               <div>
-                <h2 className="text-sm font-semibold">围绕作战结果继续追问</h2>
+                <h2 className="text-sm font-semibold">A 股作战智能体</h2>
                 <p className="text-xs text-text-muted">
-                  今日作战 / 推荐池 / 自选股 / 主线板块
+                  市场 / 推荐 / 自选 / 个股诊断 / 系统校准
                 </p>
               </div>
             </div>
@@ -163,7 +161,7 @@ export default function ChatPage() {
                       <path d="M21 11.5a8.38 8.38 0 01-.9 3.8 8.5 8.5 0 01-7.6 4.7 8.38 8.38 0 01-3.8-.9L3 21l1.9-5.7a8.38 8.38 0 01-.9-3.8 8.5 8.5 0 014.7-7.6 8.38 8.38 0 013.8-.9h.5a8.48 8.48 0 018 8v.5z" />
                     </svg>
                   </div>
-                  <h3 className="text-lg font-semibold">追问作战结论</h3>
+                  <h3 className="text-lg font-semibold">问市场、系统或个股</h3>
                 </div>
 
                 <div className="mx-auto mt-8 grid w-full max-w-3xl gap-3 md:grid-cols-2">
@@ -230,7 +228,7 @@ export default function ChatPage() {
                 value={input}
                 onChange={(event) => setInput(event.target.value)}
                 onKeyDown={handleKeyDown}
-                placeholder="比如：结合我的自选股和今日主线，明天我应该先看哪几只？"
+                placeholder="比如：诊断一下 300750.SZ，结合今日主线给触发和失效条件"
                 rows={1}
                 disabled={streaming}
                 className="min-h-[46px] flex-1 resize-none rounded-2xl border border-border-subtle bg-surface-2 px-4 py-3 text-sm transition-all duration-200 placeholder:text-text-muted/40 focus:outline-none focus:ring-1 focus:ring-accent-cyan/30"

frontend/src/app/(auth)/dashboard/page.tsx

@@ -153,7 +153,7 @@ export default function DashboardPage() {
     setRefreshResult(null);
     try {
       const result = await postAPI<{ tracked?: number; win_rate?: number }>("/api/recommendations/update-tracking");
-      setRefreshResult(`跟踪已更新，样本 ${result.tracked ?? 0}，胜率 ${(result.win_rate ?? 0).toFixed(1)}%`);
+      setRefreshResult(`跟踪已更新，样本 ${result.tracked ?? 0}，胜率 ${clampPercent(result.win_rate ?? 0).toFixed(1)}%`);
       await loadData();
       await loadSecondaryData();
       setTimeout(() => setRefreshResult(null), 5000);
@@ -752,6 +752,11 @@ function buildMarketSummary(
   };
 }
 
+function clampPercent(value: number) {
+  if (!Number.isFinite(value)) return 0;
+  return Math.max(0, Math.min(100, value));
+}
+
 function buildActionGuides(
   strategyProfile: LatestResult["strategy_profile"],
   board: StrategyBoard | null,

frontend/src/app/(auth)/strategy/page.tsx

@@ -91,6 +91,7 @@ export default function StrategyPage() {
   }, [authLoading, loadData, user]);
 
   const diagnosis = useMemo(() => buildCalibrationDiagnosis(iteration, performance), [iteration, performance]);
+  const safeWinRate = clampPercent(performance?.win_rate ?? 0);
 
   if (authLoading || user?.role !== "admin" || loading) {
     return (
@@ -137,7 +138,7 @@ export default function StrategyPage() {
 
           <div className="grid grid-cols-2 gap-2 self-start">
             <MetricCard label="复盘样本" value={iteration?.sample_size ?? 0} />
-            <MetricCard label="整体胜率" value={`${(performance?.win_rate ?? 0).toFixed(1)}%`} tone={(performance?.win_rate ?? 0) >= 50 ? "up" : "down"} />
+            <MetricCard label="整体胜率" value={`${safeWinRate.toFixed(1)}%`} tone={safeWinRate >= 50 ? "up" : "down"} />
             <MetricCard label="平均收益" value={`${(performance?.avg_return ?? 0) > 0 ? "+" : ""}${(performance?.avg_return ?? 0).toFixed(2)}%`} tone={(performance?.avg_return ?? 0) >= 0 ? "up" : "down"} />
             <MetricCard label="平均回撤" value={`${(performance?.avg_max_drawdown ?? 0).toFixed(2)}%`} tone="down" />
             <MetricFact label="已跟踪" value={`${performance?.tracked ?? 0} 只`} />
@@ -238,7 +239,7 @@ function buildCalibrationDiagnosis(
   iteration: StrategyIterationReport | null,
   performance: PerformanceStats | null
 ) {
-  const winRate = performance?.win_rate ?? 0;
+  const winRate = clampPercent(performance?.win_rate ?? 0);
   const avgReturn = performance?.avg_return ?? 0;
   const tracked = performance?.tracked ?? 0;
   const headline =
@@ -269,6 +270,11 @@ function buildCalibrationDiagnosis(
   return { headline, detail, useFor, notFor };
 }
 
+function clampPercent(value: number) {
+  if (!Number.isFinite(value)) return 0;
+  return Math.max(0, Math.min(100, value));
+}
+
 function DecisionList({
   title,
   items,

frontend/src/components/nav.tsx

@@ -118,8 +118,8 @@ export function SidebarNav() {
       <SideNavItem href="/recommendations" icon={<TargetIcon />} label="推荐池" />
       <SideNavItem href="/sectors" icon={<FireIcon />} label="板块主线" />
       <SideNavItem href="/watchlists" icon={<WatchlistIcon />} label="自选股" />
+      <SideNavItem href="/chat" icon={<ChatIcon />} label="系统智能体" />
       <SideNavItem href="/diagnose" icon={<DiagnoseIcon />} label="个股诊断" />
-      <SideNavItem href="/chat" icon={<ChatIcon />} label="作战问答" />
       {user?.role === "admin" && (
         <>
           <SideNavItem href="/strategy" icon={<StrategyIcon />} label="系统校准" />
@@ -159,6 +159,9 @@ export function MobileBottomNav() {
         <MobileNavItem href="/recommendations" label="推荐池">
           <TargetIcon />
         </MobileNavItem>
+        <MobileNavItem href="/chat" label="智能体">
+          <ChatIcon />
+        </MobileNavItem>
         <MobileNavItem href="/watchlists" label="自选">
           <WatchlistIcon />
         </MobileNavItem>