11

2026-04-08 22:39:51 +08:00 · 2026-04-08 22:39:51 +08:00 · e37803de09
commit e37803de09
parent 9b79a433a4
1809 changed files with 1517 additions and 171764 deletions
--- a/backend/app/pycache/config.cpython-313.pyc
+++ b/backend/app/pycache/config.cpython-313.pyc
--- a/backend/app/analysis/pycache/intraday.cpython-313.pyc
+++ b/backend/app/analysis/pycache/intraday.cpython-313.pyc
--- a/backend/app/analysis/intraday.py
+++ b/backend/app/analysis/intraday.py
@ -29,41 +29,66 @@ logger = logging.getLogger(__name__)


 async def intraday_market_temperature(prev_temp: MarketTemperature) -> MarketTemperature:
-    """盘中市场温度：在前一日基线上叠加实时指数涨跌"""
+    """盘中市场温度：用腾讯实时行情计算真实涨跌数据"""
    index_data = await tencent_client.get_index_realtime()
    sh_index = index_data.get("000001.SH")

    if not sh_index:
        return prev_temp

-    # 盘中温度调整：基于上证实时涨跌幅微调
-    pct = sh_index.get("pct_chg", 0)
-    adjustment = 0
-    if pct > 1.5:
-        adjustment = 10
-    elif pct > 0.5:
-        adjustment = 5
-    elif pct > 0:
-        adjustment = 2
-    elif pct > -0.5:
-        adjustment = -2
-    elif pct > -1.5:
-        adjustment = -5
-    else:
-        adjustment = -10
+    # ── 用腾讯实时行情计算真实涨跌数量 ──
+    up_count = prev_temp.up_count
+    down_count = prev_temp.down_count
+    limit_up_count = prev_temp.limit_up_count
+    limit_down_count = prev_temp.limit_down_count

-    new_temp = min(max(prev_temp.temperature + adjustment, 0), 100)
+    try:
+        stock_basic = tushare_client.get_stock_basic()
+        if not stock_basic.empty:
+            # 过滤 ST 股票
+            all_codes = stock_basic[~stock_basic["name"].str.contains("ST", na=False)]["ts_code"].tolist()
+
+            if all_codes:
+                quotes = await tencent_client.get_realtime_quotes_batch(all_codes)
+                up_count = sum(1 for q in quotes.values() if q.pct_chg > 0)
+                down_count = sum(1 for q in quotes.values() if q.pct_chg < 0)
+                limit_up_count = sum(
+                    1 for q in quotes.values()
+                    if q.limit_up and q.price >= q.limit_up * 0.995
+                )
+                limit_down_count = sum(
+                    1 for q in quotes.values()
+                    if q.limit_down and q.price <= q.limit_down * 1.005
+                )
+                logger.info(
+                    f"盘中实时涨跌统计: 上涨={up_count} 下跌={down_count} "
+                    f"涨停={limit_up_count} 跌停={limit_down_count} (共{len(quotes)}只)"
+                )
+    except Exception as e:
+        logger.warning(f"获取盘中实时涨跌统计失败，使用上一日数据: {e}")
+
+    # ── 温度分数：基于实时涨跌比重新计算 ──
+    ratio = up_count / max(down_count, 1)
+    temp_from_ratio = min(ratio / 3.0 * 25, 25)  # 涨跌比维度 (0-25)
+    temp_from_limit_up = min(limit_up_count / 2, 25)  # 涨停数维度 (0-25)
+
+    # 指数方向调整 (0-25)
+    pct = sh_index.get("pct_chg", 0)
+    temp_from_index = min(max(pct * 8 + 12.5, 0), 25)
+
+    new_temp = round(temp_from_ratio + temp_from_limit_up + temp_from_index + 25, 1)
+    new_temp = min(max(new_temp, 0), 100)

    return MarketTemperature(
        trade_date=datetime.now().strftime("%Y%m%d"),
-        up_count=prev_temp.up_count,
-        down_count=prev_temp.down_count,
-        limit_up_count=prev_temp.limit_up_count,
-        limit_down_count=prev_temp.limit_down_count,
+        up_count=up_count,
+        down_count=down_count,
+        limit_up_count=limit_up_count,
+        limit_down_count=limit_down_count,
        max_streak=prev_temp.max_streak,
        broken_rate=prev_temp.broken_rate,
-        index_above_ma20=prev_temp.index_above_ma20,
-        temperature=round(new_temp, 1),
+        index_above_ma20=pct > 0 if sh_index else prev_temp.index_above_ma20,
+        temperature=new_temp,
    )


--- a/backend/app/analysis/potential_scanner.py
+++ b/backend/app/analysis/potential_scanner.py
@ -0,0 +1,416 @@
+"""潜在启动股扫描器（Channel B）
+
+从全市场中寻找底部蓄势、即将启动的股票。
+不依赖板块热度，独立于 Channel A（强中选强）。
+
+筛选逻辑：
+1. 基本面预筛：PE/PB 合理、市值适中、非 ST 非次新
+2. 底部形态：股价在 60 日低点附近，近期不再创新低
+3. 缩量蓄势：成交量萎缩至地量水平
+4. 技术早期信号：MACD 底部金叉、RSI 从超卖回升等
+"""
+
+import logging
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+
+from app.data.tushare_client import tushare_client
+from app.analysis.technical import add_all_indicators
+from app.analysis.capital_flow import _score_valuation
+from app.config import settings
+
+logger = logging.getLogger(__name__)
+
+
+def scan_potential_breakout(
+    trade_date: str = None,
+    exclude_codes: set[str] = None,
+) -> list[dict]:
+    """扫描全市场潜在启动股票
+
+    Args:
+        trade_date: 交易日期，默认最新
+        exclude_codes: 需要排除的股票代码集合（Channel A 已推荐的）
+
+    Returns:
+        list[dict]: [{ts_code, name, sector, capital_score, ...}]
+    """
+    if not trade_date:
+        trade_date = tushare_client.get_latest_trade_date()
+
+    exclude_codes = exclude_codes or set()
+
+    # ── 第一步：基本面预筛 ──
+    basic_df = tushare_client.get_daily_basic(trade_date)
+    if basic_df.empty:
+        logger.warning("Channel B: daily_basic 数据为空")
+        return []
+
+    stock_basic = tushare_client.get_stock_basic()
+
+    # 过滤 ST
+    st_codes = set()
+    if not stock_basic.empty:
+        st_codes = set(stock_basic[stock_basic["name"].str.contains("ST", na=False)]["ts_code"])
+
+    # 过滤次新（上市不足 min_list_days 天）
+    new_codes = set()
+    if not stock_basic.empty:
+        cutoff = (datetime.now() - timedelta(days=settings.min_list_days)).strftime("%Y%m%d")
+        new_codes = set(stock_basic[stock_basic["list_date"] > cutoff]["ts_code"])
+
+    # 基本面硬性条件：仅沪深主板(00/60) + 创业板(30)
+    # 自动排除：北证(xxxxx.BJ)、科创板(68xxxx.SH)、ST、次新
+    eligible = basic_df[
+        (basic_df["ts_code"].str.startswith(("00", "30", "60"))) &
+        (~basic_df["ts_code"].isin(st_codes)) &
+        (~basic_df["ts_code"].isin(new_codes)) &
+        (~basic_df["ts_code"].isin(exclude_codes))
+    ].copy()
+
+    # PE 筛选：0-60（排除亏损和泡沫）
+    if "pe_ttm" in eligible.columns:
+        eligible = eligible[
+            eligible["pe_ttm"].notna() &
+            (eligible["pe_ttm"] > 0) &
+            (eligible["pe_ttm"] <= 60)
+        ]
+
+    # PB 筛选：0-8
+    if "pb" in eligible.columns:
+        eligible = eligible[
+            eligible["pb"].notna() &
+            (eligible["pb"] > 0) &
+            (eligible["pb"] <= 8)
+        ]
+
+    # 流通市值：30-500 亿
+    if "circ_mv" in eligible.columns:
+        eligible["circ_mv_yi"] = eligible["circ_mv"] / 10000  # 万 → 亿
+        eligible = eligible[
+            (eligible["circ_mv_yi"] >= 30) &
+            (eligible["circ_mv_yi"] <= 500)
+        ]
+
+    # 换手率 > 1%（有一定活跃度）
+    if "turnover_rate" in eligible.columns:
+        eligible = eligible[eligible["turnover_rate"].notna() & (eligible["turnover_rate"] > 1)]
+
+    # 当日跌幅或微涨（底部区域：涨幅 < 3%，排除已启动的）
+    if "pct_chg" in eligible.columns:
+        eligible = eligible[
+            eligible["pct_chg"].notna() &
+            (eligible["pct_chg"] < 3)
+        ]
+
+    if eligible.empty:
+        logger.warning("Channel B: 基本面预筛后无股票")
+        return []
+
+    # 按换手率排序取 Top N（活跃度高的优先，避免扫描过多）
+    if "turnover_rate" in eligible.columns:
+        eligible = eligible.sort_values("turnover_rate", ascending=False)
+
+    max_candidates = 300  # 最多扫描 300 只，控制 K 线请求量
+    candidate_codes = eligible["ts_code"].head(max_candidates).tolist()
+    logger.info(f"Channel B: 基本面预筛后 {len(eligible)} 只，取 Top {len(candidate_codes)} 只进行技术扫描")
+
+    # ── 第二步：技术面扫描 ──
+    results = []
+    processed = 0
+
+    for ts_code in candidate_codes:
+        try:
+            df = tushare_client.get_stock_daily(ts_code, days=120)
+            if df.empty or len(df) < 30:
+                continue
+
+            df = df.sort_values("trade_date").reset_index(drop=True)
+            df = add_all_indicators(df)
+
+            # 底部形态检测
+            if not _check_bottom_reversal(df):
+                continue
+
+            # 缩量蓄势检测
+            volume_shrink = _check_volume_shrink(df)
+
+            # 技术早期信号（至少满足一个）
+            early_signal = _check_technical_early_signal(df)
+            if not early_signal:
+                continue
+
+            # 计算评分
+            score = _score_potential(df, volume_shrink)
+
+            # 估值评分
+            row_data = eligible[eligible["ts_code"] == ts_code]
+            pe = None
+            pb = None
+            circ_mv = None
+            turnover_rate = 0
+            if not row_data.empty:
+                r = row_data.iloc[0]
+                pe = float(r["pe_ttm"]) if pd.notna(r.get("pe_ttm")) else None
+                pb = float(r["pb"]) if pd.notna(r.get("pb")) else None
+                circ_mv = float(r.get("circ_mv_yi", 0)) or None
+                turnover_rate = float(r.get("turnover_rate", 0) or 0)
+
+            valuation_score = _score_valuation(pe, pb)
+
+            # 股票名称
+            name = ts_code
+            if not stock_basic.empty:
+                name_row = stock_basic[stock_basic["ts_code"] == ts_code]
+                if not name_row.empty:
+                    name = name_row.iloc[0]["name"]
+
+            # 行业作为 sector
+            sector = ""
+            if not stock_basic.empty:
+                ind_row = stock_basic[stock_basic["ts_code"] == ts_code]
+                if not ind_row.empty:
+                    sector = str(ind_row.iloc[0].get("industry", ""))
+
+            results.append({
+                "ts_code": ts_code,
+                "name": name,
+                "sector": sector,
+                "sectors": [sector] if sector else [],
+                "capital_score": round(score, 1),
+                "valuation_score": round(valuation_score, 1),
+                "main_net_inflow": 0,
+                "inflow_ratio": 0,
+                "turnover_rate": round(turnover_rate, 2),
+                "volume_ratio": None,
+                "circ_mv": circ_mv,
+                "pe": round(pe, 2) if pe else None,
+                "pb": round(pb, 2) if pb else None,
+            })
+
+            processed += 1
+
+        except Exception as e:
+            logger.debug(f"Channel B 扫描 {ts_code} 异常: {e}")
+            continue
+
+    # 按评分排序
+    results.sort(key=lambda x: x["capital_score"], reverse=True)
+    top = results[:settings.top_stock_count]
+
+    logger.info(f"Channel B: 扫描完成，{len(results)} 只底部股票，取 Top {len(top)}")
+    for r in top:
+        logger.info(
+            f"Channel B: {r['name']}({r['ts_code']}) "
+            f"评分={r['capital_score']} 估值={r['valuation_score']}"
+        )
+
+    return top
+
+
+def _check_bottom_reversal(df: pd.DataFrame) -> bool:
+    """底部反转形态检测
+
+    条件（满足任一即可）：
+    1. 股价距 60 日低点 < 10%（深度底部）
+    2. 股价距 60 日低点 < 20% 且近 5 日止跌企稳
+    """
+    if len(df) < 20:
+        return False
+
+    lookback = min(60, len(df))
+    recent = df.tail(lookback)
+    last = df.iloc[-1]
+
+    low_60d = recent["low"].min()
+    dist_from_low = (last["close"] - low_60d) / low_60d * 100
+
+    # 深度底部：距低点 < 10%，直接通过
+    if dist_from_low < 10:
+        return True
+
+    # 一般底部：距低点 < 20% 且近期止跌
+    if dist_from_low > 20:
+        return False
+
+    # 止跌检测：近 5 日最低价 vs 之前 5 日最低价
+    last_5 = df.tail(5)
+    if len(df) >= 10:
+        prev_5 = df.iloc[-10:-5]
+        recent_min = last_5["low"].min()
+        prev_min = prev_5["low"].min()
+        return recent_min >= prev_min * 0.97  # 允许 3% 误差
+    return False
+
+
+def _check_volume_shrink(df: pd.DataFrame) -> bool:
+    """缩量蓄势检测
+
+    条件：近 3 日均量 < vol_ma20 * 0.7
+    """
+    if len(df) < 20:
+        return False
+
+    last = df.iloc[-1]
+    vol_ma20 = last.get("vol_ma10", last["vol"])  # 用 vol_ma10 近似
+
+    recent_3_vol = df["vol"].tail(3).mean()
+    return recent_3_vol < vol_ma20 * 0.7
+
+
+def _check_technical_early_signal(df: pd.DataFrame) -> bool:
+    """技术早期信号检测（满足任一即可）
+
+    1. MACD 底部金叉（DIF 从负区上穿 DEA）
+    2. MACD 柱状线由负转正或缩短（底部动能转换）
+    3. RSI 从超卖区回升（5 日前 RSI < 40，当前 > 40）
+    4. 底部放量长阳（近 10 日内有涨幅 >3% 且量 > vol_ma5*1.3）
+    5. MA5 开始向上拐头（短期趋势转变）
+    6. 布林带下轨支撑（触及下轨后反弹）
+    """
+    if len(df) < 10:
+        return False
+
+    last = df.iloc[-1]
+    prev = df.iloc[-2]
+
+    # 信号 1：MACD 底部金叉
+    if (prev["dif"] <= prev["dea"] and last["dif"] > last["dea"]
+            and last["dif"] < 0):
+        return True
+
+    # 信号 2：MACD 柱状线缩短（绿柱变短，动能衰减）
+    if len(df) >= 3:
+        hist_1 = last["macd_hist"]
+        hist_2 = prev["macd_hist"]
+        hist_3 = df.iloc[-3]["macd_hist"]
+        if hist_1 < 0 and hist_2 < 0 and hist_1 > hist_2 and hist_2 > hist_3:
+            return True  # 绿柱连续缩短
+
+    # 信号 3：RSI 从超卖区回升
+    if len(df) >= 6:
+        rsi_5d_ago = df.iloc[-6].get("rsi14", 50)
+        rsi_now = last.get("rsi14", 50)
+        if rsi_5d_ago < 40 and rsi_now > 40:
+            return True
+
+    # 信号 4：底部放量长阳
+    recent_10 = df.tail(10)
+    for _, row in recent_10.iterrows():
+        pct = row.get("pct_chg", 0) or 0
+        vol = row["vol"]
+        vol_ma = row.get("vol_ma5", vol)
+        if pct > 3 and vol > vol_ma * 1.3:
+            return True
+
+    # 信号 5：MA5 向上拐头（3 日前下降，现在上升）
+    if len(df) >= 4:
+        ma5_now = last["ma5"]
+        ma5_1d = prev["ma5"]
+        ma5_3d = df.iloc[-4]["ma5"]
+        if ma5_1d < ma5_3d and ma5_now > ma5_1d:  # 先降后升，拐头
+            return True
+
+    # 信号 6：布林带下轨支撑后反弹
+    if "boll_lower" in df.columns:
+        recent_5 = df.tail(5)
+        touched = any(row["low"] <= row["boll_lower"] * 1.01 for _, row in recent_5.iterrows())
+        if touched and last["close"] > last["boll_lower"]:
+            return True
+
+    return False
+
+
+def _score_potential(df: pd.DataFrame, volume_shrink: bool) -> float:
+    """潜在启动股评分（0-100）
+
+    评分维度：
+    - 底部位置得分 (35%): 距 60 日低点越近越好
+    - 技术信号强度 (30%): MACD/RSI/量价共振
+    - 缩量蓄势 (20%): 成交量萎缩程度
+    - 均线形态 (15%): 均线收敛或即将突破
+    """
+    score = 0.0
+    last = df.iloc[-1]
+
+    lookback = min(60, len(df))
+    low_60d = df["low"].tail(lookback).min()
+    dist_from_low = (last["close"] - low_60d) / low_60d * 100
+
+    # 1) 底部位置得分 (35 分)
+    if dist_from_low < 5:
+        score += 35  # 几乎在最低点
+    elif dist_from_low < 8:
+        score += 28
+    elif dist_from_low < 12:
+        score += 20
+    elif dist_from_low <= 15:
+        score += 12
+    elif dist_from_low <= 20:
+        score += 6   # 距低点较远但仍属底部区域
+
+    # 2) 技术信号强度 (30 分)
+    signal_count = 0
+    prev = df.iloc[-2] if len(df) >= 2 else last
+
+    # MACD 金叉
+    if prev["dif"] <= prev["dea"] and last["dif"] > last["dea"] and last["dif"] < 0:
+        signal_count += 1
+
+    # RSI 回升
+    if len(df) >= 6:
+        rsi_5d_ago = df.iloc[-6].get("rsi14", 50)
+        rsi_now = last.get("rsi14", 50)
+        if rsi_5d_ago < 35 and rsi_now > 40:
+            signal_count += 1
+
+    # 放量长阳
+    recent_10 = df.tail(10)
+    for _, row in recent_10.iterrows():
+        pct = row.get("pct_chg", 0) or 0
+        vol = row["vol"]
+        vol_ma = row.get("vol_ma5", vol)
+        if pct > 3 and vol > vol_ma * 1.5:
+            signal_count += 1
+            break
+
+    if signal_count >= 3:
+        score += 30
+    elif signal_count == 2:
+        score += 22
+    elif signal_count == 1:
+        score += 14
+
+    # 3) 缩量蓄势 (20 分)
+    if volume_shrink:
+        recent_3_vol = df["vol"].tail(3).mean()
+        vol_ma10 = last.get("vol_ma10", df["vol"].mean())
+        shrink_ratio = recent_3_vol / vol_ma10 if vol_ma10 > 0 else 1
+        if shrink_ratio < 0.4:
+            score += 20  # 极度缩量
+        elif shrink_ratio < 0.55:
+            score += 15
+        elif shrink_ratio < 0.7:
+            score += 10
+    else:
+        score += 3
+
+    # 4) 均线形态 (15 分): 短期均线开始上翘
+    if len(df) >= 20:
+        ma5_now = last["ma5"]
+        ma5_3d = df.iloc[-3]["ma5"] if len(df) >= 3 else ma5_now
+        ma10_now = last["ma10"]
+        ma20_now = last["ma20"]
+
+        # MA5 上穿 MA10
+        if ma5_now > ma10_now and df.iloc[-2]["ma5"] <= df.iloc[-2]["ma10"]:
+            score += 15  # 金叉
+        # MA5 向上
+        elif ma5_now > ma5_3d:
+            score += 10
+        # 均线收敛（MA5 接近 MA20）
+        elif abs(ma5_now - ma20_now) / ma20_now < 0.02:
+            score += 8
+
+    return score
--- a/backend/app/api/pycache/market.cpython-313.pyc
+++ b/backend/app/api/pycache/market.cpython-313.pyc
--- a/backend/app/api/pycache/recommendations.cpython-313.pyc
+++ b/backend/app/api/pycache/recommendations.cpython-313.pyc
--- a/backend/app/api/pycache/sectors.cpython-313.pyc
+++ b/backend/app/api/pycache/sectors.cpython-313.pyc
--- a/backend/app/api/market.py
+++ b/backend/app/api/market.py
@ -5,7 +5,7 @@ from fastapi import APIRouter
 from app.data.tushare_client import tushare_client
 from app.data import tencent_client
 from app.engine.recommender import get_latest_recommendations
-from app.config import is_trading_hours
+from app.config import is_trading_hours, is_market_session

 router = APIRouter(prefix="/api/market", tags=["market"])

@ -48,7 +48,7 @@ async def get_overview():

    盘中用腾讯实时行情，盘后用 Tushare 日线（有缓存）。
    """
-    if is_trading_hours():
+    if is_market_session():
        return await _overview_realtime()
    return _overview_daily()

--- a/backend/app/api/recommendations.py
+++ b/backend/app/api/recommendations.py
@ -50,6 +50,8 @@ async def get_latest():
                "reasons": r.reasons,
                "risk_note": r.risk_note,
                "llm_analysis": r.llm_analysis,
+                "llm_score": r.llm_score,
+                "strategy": r.strategy,
                "scan_session": r.scan_session,
                "created_at": r.created_at.isoformat() if r.created_at else None,
            }
--- a/backend/app/api/sectors.py
+++ b/backend/app/api/sectors.py
@ -4,7 +4,7 @@ import logging

 from fastapi import APIRouter

-from app.config import is_trading_hours
+from app.config import is_market_session
 from app.data.tushare_client import tushare_client
 from app.data.tencent_client import get_realtime_quotes_batch
 from app.engine.recommender import get_latest_sectors
@ -15,7 +15,7 @@ router = APIRouter(prefix="/api/sectors", tags=["sectors"])

 async def _enrich_sectors_realtime(sectors_data: list[dict]) -> list[dict]:
    """盘中时，用腾讯实时行情补充板块涨幅和涨停数"""
-    if not is_trading_hours():
+    if not is_market_session():
        for s in sectors_data:
            s["realtime_pct_change"] = None
            s["realtime_limit_up_count"] = None
@ -72,6 +72,9 @@ async def _enrich_sectors_realtime(sectors_data: list[dict]) -> list[dict]:

        s["is_realtime"] = True

+    # 盘中按实时涨幅重新排序（涨幅高的排前面）
+    sectors_data.sort(key=lambda s: s.get("realtime_pct_change") or 0, reverse=True)
+
    return sectors_data


--- a/backend/app/config.py
+++ b/backend/app/config.py
@ -79,6 +79,21 @@ def is_trading_hours() -> bool:
    return (930 <= t <= 1130) or (1300 <= t <= 1500)


+def is_market_session() -> bool:
+    """判断当前是否在 A 股交易日内（含午休 11:30-13:00）
+
+    午休期间腾讯实时行情仍返回上午收盘价，可用于展示。
+    与 is_trading_hours() 的区别：午休时返回 True。
+    """
+    from zoneinfo import ZoneInfo
+    now = datetime.now(ZoneInfo("Asia/Shanghai"))
+    weekday = now.weekday()
+    if weekday >= 5:
+        return False
+    t = now.hour * 100 + now.minute
+    return 930 <= t <= 1500
+
+
 def is_pre_close() -> bool:
    """判断是否在收盘后、数据更新前（15:00-15:30 数据尚未完全更新）"""
    from zoneinfo import ZoneInfo
--- a/backend/app/data/pycache/models.cpython-313.pyc
+++ b/backend/app/data/pycache/models.cpython-313.pyc
--- a/backend/app/data/models.py
+++ b/backend/app/data/models.py
@ -119,6 +119,8 @@ class Recommendation(BaseModel):
    reasons: list[str] = []
    risk_note: str = ""
    level: str = ""                 # 强烈推荐/推荐/观望/回避
+    strategy: str = "momentum"      # momentum(强中选强) / potential(潜在启动)
    llm_analysis: str = ""          # LLM 深度分析
+    llm_score: float | None = None  # AI 评分 1-10
    scan_session: str = ""
    created_at: datetime | None = None
--- a/backend/app/db/pycache/database.cpython-313.pyc
+++ b/backend/app/db/pycache/database.cpython-313.pyc
--- a/backend/app/db/pycache/tables.cpython-313.pyc
+++ b/backend/app/db/pycache/tables.cpython-313.pyc
--- a/backend/app/db/database.py
+++ b/backend/app/db/database.py
@ -29,6 +29,8 @@ async def init_db():
            "ALTER TABLE recommendations ADD COLUMN position_score REAL",
            "ALTER TABLE recommendations ADD COLUMN valuation_score REAL",
            "ALTER TABLE recommendations ADD COLUMN llm_analysis TEXT DEFAULT ''",
+            "ALTER TABLE recommendations ADD COLUMN strategy TEXT DEFAULT 'momentum'",
+            "ALTER TABLE recommendations ADD COLUMN llm_score REAL",
            "ALTER TABLE market_temperature ADD COLUMN max_streak INTEGER",
            "ALTER TABLE market_temperature ADD COLUMN broken_rate REAL",
        ]:
--- a/backend/app/db/tables.py
+++ b/backend/app/db/tables.py
@ -26,6 +26,8 @@ recommendations_table = Table(
    Column("stop_loss", Float),
    Column("reasons", Text),
    Column("llm_analysis", Text, default=""),
+    Column("strategy", Text, default="momentum"),
+    Column("llm_score", Float, default=None),
    Column("scan_session", Text),
    Column("created_at", DateTime, server_default=func.now()),
 )
--- a/backend/app/engine/pycache/recommender.cpython-313.pyc
+++ b/backend/app/engine/pycache/recommender.cpython-313.pyc
--- a/backend/app/engine/pycache/screener.cpython-313.pyc
+++ b/backend/app/engine/pycache/screener.cpython-313.pyc
--- a/backend/app/engine/recommender.py
+++ b/backend/app/engine/recommender.py
@ -34,11 +34,11 @@ async def refresh_recommendations(trade_date: str = None, scan_session: str = "m
    # 持久化到数据库
    await _save_to_db(result)

-    # 异步 LLM 增强（不阻塞返回）
+    # 异步 AI 深度分析（不阻塞返回）
    if settings.deepseek_api_key:
        import asyncio
-        from app.llm.enhancer import enhance_recommendations
-        asyncio.create_task(enhance_recommendations(result))
+        from app.llm.analysis_agent import analyze_recommendations
+        asyncio.create_task(analyze_recommendations(result))

    return result

@ -138,6 +138,8 @@ async def _save_to_db(result: dict):
                    stop_loss=rec.stop_loss,
                    reasons=json.dumps(rec.reasons, ensure_ascii=False),
                    llm_analysis=rec.llm_analysis,
+                    strategy=rec.strategy,
+                    llm_score=rec.llm_score,
                    scan_session=rec.scan_session,
                )
                await db.execute(stmt)
@ -201,6 +203,8 @@ async def _load_today_from_db() -> dict:
                    stop_loss=r["stop_loss"],
                    reasons=json.loads(r["reasons"]) if r["reasons"] else [],
                    llm_analysis=r.get("llm_analysis") or "",
+                    strategy=r.get("strategy") or "momentum",
+                    llm_score=r.get("llm_score"),
                    scan_session=r["scan_session"] or "",
                ))

--- a/backend/app/engine/screener.py
+++ b/backend/app/engine/screener.py
@ -1,7 +1,7 @@
-"""四层漏斗筛选器
+"""双通道漏斗筛选器

-串联市场温度 → 板块热度 → 资金筛选 → 技术信号，
-输出最终推荐列表。
+Channel A（强中选强）：市场温度 → 板块热度 → 资金筛选 → 技术信号
+Channel B（潜在启动）：全市场技术扫描 → 底部形态 → 估值筛选

 自动检测是否在交易时段：
  - 盘中模式：用前一日 Tushare 数据 + 腾讯实时行情混合筛选
@ -13,6 +13,7 @@ import logging
 from app.analysis.market_temp import calculate_market_temperature
 from app.analysis.sector_scanner import scan_hot_sectors
 from app.analysis.capital_flow import filter_stocks_by_capital
+from app.analysis.potential_scanner import scan_potential_breakout
 from app.analysis.signals import generate_signals
 from app.analysis.intraday import intraday_market_temperature, intraday_filter_stocks
 from app.data.models import MarketTemperature, SectorInfo, TechnicalSignal, Recommendation
@ -22,7 +23,7 @@ logger = logging.getLogger(__name__)


 async def run_screening(trade_date: str = None) -> dict:
-    """执行完整的四层漏斗筛选流程
+    """执行双通道筛选流程

    自动检测交易时段：
    - 盘中 → 用前一日板块+实时行情筛选
@ -31,7 +32,6 @@ async def run_screening(trade_date: str = None) -> dict:
    返回: {
        "market_temp": MarketTemperature,
        "hot_sectors": [SectorInfo],
-        "capital_filtered": [dict],
        "recommendations": [Recommendation],
        "scan_mode": "intraday" | "post_market",
    }
@ -40,13 +40,11 @@ async def run_screening(trade_date: str = None) -> dict:
    scan_mode = "intraday" if intraday else "post_market"
    logger.info(f"=== 筛选模式: {'盘中实时' if intraday else '盘后'} ===")

-    # ── 第一层：市场温度 ──
-    # 盘中和盘后都先计算基线温度（盘中用前一日数据）
-    logger.info("=== 第一层：市场温度计 ===")
+    # ── 市场温度（共享） ──
+    logger.info("=== 市场温度计 ===")
    market_temp = calculate_market_temperature(trade_date)

    if intraday:
-        # 盘中叠加实时指数涨跌调整温度
        market_temp = await intraday_market_temperature(market_temp)
        logger.info(f"盘中市场温度(实时调整): {market_temp.temperature}")
    else:
@ -54,54 +52,94 @@ async def run_screening(trade_date: str = None) -> dict:

    market_temp_score = market_temp.temperature

-    # ── 第二层：板块热度 ──
-    # 盘中用前一日的板块热度作为基线（哪些板块近期是热点）
-    logger.info("=== 第二层：板块热度扫描 ===")
+    # ── 板块热度（Channel A 需要） ──
+    logger.info("=== 板块热度扫描 ===")
    all_sectors = scan_hot_sectors(trade_date)
    hot_sectors = all_sectors[:settings.top_sector_count]

-    if not hot_sectors:
-        logger.warning("未找到热门板块")
-        return {
-            "market_temp": market_temp,
-            "hot_sectors": [],
-            "capital_filtered": [],
-            "recommendations": [],
-            "scan_mode": scan_mode,
-        }
+    # ── Channel A：强中选强 ──
+    recommendations_a = []
+    capital_filtered = []

-    # ── 第三层：资金/实时筛选 ──
-    if intraday:
-        logger.info("=== 第三层：盘中实时个股筛选 ===")
-        capital_filtered = await intraday_filter_stocks(hot_sectors)
-    else:
-        logger.info("=== 第三层：个股资金筛选 ===")
-        capital_filtered = await filter_stocks_by_capital(hot_sectors, trade_date)
+    if hot_sectors:
+        if intraday:
+            logger.info("=== Channel A：盘中实时个股筛选 ===")
+            capital_filtered = await intraday_filter_stocks(hot_sectors)
+        else:
+            logger.info("=== Channel A：个股资金筛选 ===")
+            capital_filtered = await filter_stocks_by_capital(hot_sectors, trade_date)

-    if not capital_filtered:
-        logger.warning("筛选后无符合条件的个股")
-        return {
-            "market_temp": market_temp,
-            "hot_sectors": hot_sectors,
-            "capital_filtered": [],
-            "recommendations": [],
-            "scan_mode": scan_mode,
-        }
+        if capital_filtered:
+            recommendations_a = _build_recommendations(
+                capital_filtered, market_temp, hot_sectors,
+                market_temp_score=market_temp_score,
+                strategy="momentum", intraday=intraday,
+            )

-    # ── 第四层：技术面信号 ──
-    logger.info("=== 第四层：技术面买卖信号 ===")
+    logger.info(f"Channel A（强中选强）: {len(recommendations_a)} 只")
+
+    # ── Channel B：潜在启动 ──
+    logger.info("=== Channel B：潜在启动扫描 ===")
+    exclude_codes = {r.ts_code for r in recommendations_a}
+    potential_filtered = scan_potential_breakout(trade_date, exclude_codes)
+
+    recommendations_b = []
+    if potential_filtered:
+        recommendations_b = _build_recommendations(
+            potential_filtered, market_temp, hot_sectors,
+            market_temp_score=market_temp_score,
+            strategy="potential", intraday=intraday,
+        )
+
+    logger.info(f"Channel B（潜在启动）: {len(recommendations_b)} 只")
+
+    # 合并，按评分排序
+    all_recommendations = recommendations_a + recommendations_b
+    all_recommendations.sort(key=lambda x: x.score, reverse=True)
+
+    # 过滤掉低质量推荐
+    all_recommendations = [r for r in all_recommendations if r.score >= 40]
+
+    logger.info(f"=== 筛选完成: {len(all_recommendations)} 只股票 ({scan_mode}) ===")
+    for r in all_recommendations[:5]:
+        strategy_label = "强中选强" if r.strategy == "momentum" else "潜在启动"
+        logger.info(f"  [{strategy_label}] {r.name}({r.ts_code}) {r.level} 评分={r.score} 信号={r.signal}")
+
+    return {
+        "market_temp": market_temp,
+        "hot_sectors": hot_sectors,
+        "capital_filtered": capital_filtered,
+        "recommendations": all_recommendations,
+        "scan_mode": scan_mode,
+    }
+
+
+def _build_recommendations(
+    stocks: list[dict],
+    market_temp: MarketTemperature,
+    hot_sectors: list[SectorInfo],
+    market_temp_score: float = 0,
+    strategy: str = "momentum",
+    intraday: bool = False,
+) -> list[Recommendation]:
+    """从筛选结果构建推荐列表（Channel A/B 共用）"""
    recommendations = []

-    for stock in capital_filtered:
+    for stock in stocks:
        ts_code = stock["ts_code"]
        name = stock["name"]
        sector = stock["sector"]

        tech_signal = generate_signals(ts_code, name)

-        # 板块得分（含阶段调整）
-        sector_score = _get_sector_score(stock["sector"], hot_sectors)
-        sector_stage = _get_sector_stage(stock["sector"], hot_sectors)
+        # 板块得分
+        if strategy == "momentum":
+            sector_score = _get_sector_score(sector, hot_sectors)
+            sector_stage = _get_sector_stage(sector, hot_sectors)
+        else:
+            # Channel B：不在热门板块中，给基础分
+            sector_score = 30.0
+            sector_stage = "mid"

        # 估值安全得分
        valuation_score = stock.get("valuation_score", 50)
@ -109,37 +147,56 @@ async def run_screening(trade_date: str = None) -> dict:
        # 位置安全得分
        position_score = tech_signal.position_score

-        # 六维综合评分（降低动量权重，增加安全维度）
-        # 市场10% + 板块20% + 资金20% + 技术20% + 位置安全15% + 估值安全15%
-        final_score = (
-            market_temp_score * 0.10 +
-            sector_score * 0.20 +
-            stock["capital_score"] * 0.20 +
-            tech_signal.score * 0.20 +
-            position_score * 0.15 +
-            valuation_score * 0.15
-        )
+        # 综合评分（根据策略调整权重）
+        if strategy == "momentum":
+            # 强中选强：板块和资金权重高
+            # 市场10% + 板块20% + 资金20% + 技术15% + 位置安全15% + 估值安全20%
+            final_score = (
+                market_temp_score * 0.10 +
+                sector_score * 0.20 +
+                stock["capital_score"] * 0.20 +
+                tech_signal.score * 0.15 +
+                position_score * 0.15 +
+                valuation_score * 0.20
+            )
+        else:
+            # 潜在启动：技术面和估值权重高
+            # 市场10% + 技术25% + 资金(potential_score)15% + 位置安全25% + 估值安全25%
+            final_score = (
+                market_temp_score * 0.10 +
+                tech_signal.score * 0.25 +
+                stock["capital_score"] * 0.15 +
+                position_score * 0.25 +
+                valuation_score * 0.25
+            )

-        # 板块尾声阶段额外惩罚（防止追板块尾部）
-        if sector_stage == "end":
-            final_score *= 0.85
-        elif sector_stage == "late":
-            final_score *= 0.92
+        # 板块尾声阶段额外惩罚（仅 Channel A）
+        if strategy == "momentum":
+            if sector_stage == "end":
+                final_score *= 0.85
+            elif sector_stage == "late":
+                final_score *= 0.92

        # 确定信号和等级
        signal = "HOLD"
-        if (tech_signal.score >= settings.buy_score_threshold
-                and tech_signal.signal_count >= settings.buy_min_signals
-                and position_score >= 30):     # 位置太危险不给买入信号
-            signal = "BUY"
+        if strategy == "momentum":
+            if (tech_signal.score >= settings.buy_score_threshold
+                    and tech_signal.signal_count >= settings.buy_min_signals
+                    and position_score >= 30):
+                signal = "BUY"
+        else:
+            # Channel B：技术面要求稍低，但位置安全要求更高
+            if (tech_signal.score >= settings.buy_score_threshold * 0.7
+                    and position_score >= 40):
+                signal = "BUY"

        level = _score_to_level(final_score)

        # 生成推荐理由
-        reasons = _generate_reasons(stock, tech_signal, market_temp, intraday)
+        reasons = _generate_reasons(stock, tech_signal, market_temp, intraday, strategy)

        # 风险提示
-        risk_note = _generate_risk_note(market_temp, tech_signal, intraday)
+        risk_note = _generate_risk_note(market_temp, tech_signal, intraday, strategy)

        rec = Recommendation(
            ts_code=ts_code,
@ -159,26 +216,11 @@ async def run_screening(trade_date: str = None) -> dict:
            reasons=reasons,
            risk_note=risk_note,
            level=level,
+            strategy=strategy,
        )
        recommendations.append(rec)

-    # 按综合评分排序
-    recommendations.sort(key=lambda x: x.score, reverse=True)
-
-    # 过滤掉低质量推荐（综合分 < 40 为"回避"级别，不展示）
-    recommendations = [r for r in recommendations if r.score >= 40]
-
-    logger.info(f"=== 筛选完成: {len(recommendations)} 只股票 ({scan_mode}) ===")
-    for r in recommendations[:5]:
-        logger.info(f"  {r.name}({r.ts_code}) {r.level} 评分={r.score} 信号={r.signal}")
-
-    return {
-        "market_temp": market_temp,
-        "hot_sectors": hot_sectors,
-        "capital_filtered": capital_filtered,
-        "recommendations": recommendations,
-        "scan_mode": scan_mode,
-    }
+    return recommendations


 def _get_sector_score(sector_name: str, hot_sectors: list[SectorInfo]) -> float:
@ -186,7 +228,7 @@ def _get_sector_score(sector_name: str, hot_sectors: list[SectorInfo]) -> float:
    for s in hot_sectors:
        if s.sector_name == sector_name:
            return s.heat_score
-    return 30.0  # 不在热门板块中，给一个基础分
+    return 30.0


 def _get_sector_stage(sector_name: str, hot_sectors: list[SectorInfo]) -> str:
@ -210,12 +252,26 @@ def _score_to_level(score: float) -> str:

 def _generate_reasons(
    stock: dict, tech: TechnicalSignal, market: MarketTemperature,
-    intraday: bool = False,
+    intraday: bool = False, strategy: str = "momentum",
 ) -> list[str]:
    reasons = []

-    if intraday:
-        # 盘中理由：侧重实时行情指标
+    if strategy == "potential":
+        # Channel B 理由：侧重底部形态和估值
+        if tech.position_score >= 70:
+            reasons.append("位置处于低位，距高点回调充分")
+        if tech.macd_golden:
+            reasons.append("MACD底部金叉，反转信号初现")
+        if tech.pullback_support:
+            reasons.append("缩量回踩支撑位，蓄势充分")
+        if tech.big_yang:
+            reasons.append("底部出现放量长阳，资金介入")
+        if stock.get("valuation_score", 0) >= 60:
+            reasons.append("估值安全，下行空间有限")
+        if not reasons:
+            reasons.append("技术面底部信号显现，关注启动时机")
+    elif intraday:
+        # Channel A 盘中理由
        pct = stock.get("pct_chg", 0)
        vr = stock.get("volume_ratio")
        if vr and vr > 2:
@ -224,31 +280,41 @@ def _generate_reasons(
            reasons.append(f"盘中涨幅{pct:.1f}%，走势强劲")
        elif pct > 0:
            reasons.append(f"盘中涨幅{pct:.1f}%，温和上攻")
+
+        reasons.append(f"所属板块【{stock['sector']}】为当前热门概念")
+
+        tech_reasons = []
+        if tech.ma_bullish:
+            tech_reasons.append("均线多头排列")
+        if tech.volume_breakout:
+            tech_reasons.append("放量突破")
+        if tech.macd_golden:
+            tech_reasons.append("MACD金叉")
+        if tech_reasons:
+            reasons.append("技术面: " + "、".join(tech_reasons))
    else:
-        # 盘后理由：侧重资金流向
+        # Channel A 盘后理由
        inflow = stock.get("main_net_inflow", 0)
        if inflow > 5000:
            reasons.append(f"主力资金大幅流入{inflow:.0f}万元")
        elif inflow > 1000:
            reasons.append(f"主力资金持续流入{inflow:.0f}万元")

-    # 板块
-    reasons.append(f"所属板块【{stock['sector']}】为当前热门概念")
+        reasons.append(f"所属板块【{stock['sector']}】为当前热门概念")

-    # 技术面
-    tech_reasons = []
-    if tech.ma_bullish:
-        tech_reasons.append("均线多头排列")
-    if tech.volume_breakout:
-        tech_reasons.append("放量突破")
-    if tech.macd_golden:
-        tech_reasons.append("MACD金叉")
-    if tech.pullback_support:
-        tech_reasons.append("缩量回踩支撑")
-    if tech.big_yang:
-        tech_reasons.append("底部放量长阳")
-    if tech_reasons:
-        reasons.append("技术面: " + "、".join(tech_reasons))
+        tech_reasons = []
+        if tech.ma_bullish:
+            tech_reasons.append("均线多头排列")
+        if tech.volume_breakout:
+            tech_reasons.append("放量突破")
+        if tech.macd_golden:
+            tech_reasons.append("MACD金叉")
+        if tech.pullback_support:
+            tech_reasons.append("缩量回踩支撑")
+        if tech.big_yang:
+            tech_reasons.append("底部放量长阳")
+        if tech_reasons:
+            reasons.append("技术面: " + "、".join(tech_reasons))

    # 位置安全
    if tech.position_score >= 70:
@ -256,16 +322,18 @@ def _generate_reasons(
    elif tech.position_score < 30:
        reasons.append("注意：短期涨幅较大，追高风险")

-    return reasons[:3]  # 最多3条
+    return reasons[:3]


 def _generate_risk_note(
    market: MarketTemperature, tech: TechnicalSignal,
-    intraday: bool = False,
+    intraday: bool = False, strategy: str = "momentum",
 ) -> str:
    notes = []
    if intraday:
        notes.append("盘中数据参考，需结合尾盘确认")
+    if strategy == "potential":
+        notes.append("底部股票可能继续盘整，注意时间成本")
    if market.temperature < 30:
        notes.append("市场情绪偏冷，系统性风险较高")
    elif market.temperature < 50:
--- a/backend/app/llm/pycache/prompts.cpython-313.pyc
+++ b/backend/app/llm/pycache/prompts.cpython-313.pyc
--- a/backend/app/llm/pycache/tool_executor.cpython-313.pyc
+++ b/backend/app/llm/pycache/tool_executor.cpython-313.pyc
--- a/backend/app/llm/pycache/tools.cpython-313.pyc
+++ b/backend/app/llm/pycache/tools.cpython-313.pyc
--- a/backend/app/llm/analysis_agent.py
+++ b/backend/app/llm/analysis_agent.py
@ -0,0 +1,249 @@
+"""AI 深度分析
+
+预先获取 K 线、资金流、技术信号等数据，一次性传入 LLM 生成结构化分析报告。
+不依赖 tool calling，避免 DeepSeek DSML 标签问题。
+"""
+
+import asyncio
+import json
+import logging
+import re
+
+from app.llm.client import chat_completion
+from app.llm.prompts import (
+    MOMENTUM_ANALYSIS_PROMPT,
+    POTENTIAL_ANALYSIS_PROMPT,
+)
+from app.config import settings
+
+logger = logging.getLogger(__name__)
+
+
+async def analyze_recommendations(result: dict) -> None:
+    """对所有推荐股票执行 AI 深度分析"""
+    recommendations = result.get("recommendations", [])
+    if not recommendations or not settings.deepseek_api_key:
+        return
+
+    try:
+        await _do_analyze(result, recommendations)
+    except Exception as e:
+        logger.error(f"AI 分析任务异常: {e}")
+        for rec in recommendations:
+            if not rec.llm_analysis:
+                rec.llm_analysis = "AI 分析暂时不可用"
+        await _broadcast_llm_ready(recommendations)
+
+
+async def _do_analyze(result: dict, recommendations: list) -> None:
+    """分析核心逻辑"""
+    market_temp = result.get("market_temp")
+    hot_sectors = result.get("hot_sectors", [])
+
+    # 构建板块文本
+    sectors_text = "\n".join(
+        f"- {s.sector_name}: 涨幅{s.pct_change}%, 资金流入{s.capital_inflow}万, "
+        f"涨停{s.limit_up_count}家, 热度{s.heat_score}分, 阶段={s.stage}"
+        for s in hot_sectors[:5]
+    ) if hot_sectors else "暂无板块数据"
+
+    # 温度等级
+    temp_val = market_temp.temperature if market_temp else 0
+    if temp_val >= 60:
+        temp_level = "积极"
+    elif temp_val >= 30:
+        temp_level = "谨慎"
+    else:
+        temp_level = "低迷"
+
+    enhanced_count = 0
+    for rec in recommendations:
+        try:
+            # 预先获取该股票的详细数据
+            stock_data = await _fetch_stock_data(rec.ts_code, rec.sector)
+
+            strategy_label = "强中选强" if rec.strategy == "momentum" else "潜在启动"
+            system_prompt = (
+                MOMENTUM_ANALYSIS_PROMPT
+                if rec.strategy == "momentum"
+                else POTENTIAL_ANALYSIS_PROMPT
+            )
+
+            user_msg = _build_user_message(
+                rec=rec,
+                strategy_label=strategy_label,
+                market_temp=market_temp,
+                temp_level=temp_level,
+                sectors_text=sectors_text,
+                stock_data=stock_data,
+            )
+
+            messages = [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_msg},
+            ]
+
+            resp = await chat_completion(messages)
+            if resp and resp.content:
+                analysis = resp.content.strip()
+                rec.llm_analysis = analysis
+                rec.llm_score = _extract_score(analysis)
+                enhanced_count += 1
+            else:
+                rec.llm_analysis = "AI 分析暂时不可用"
+
+        except asyncio.CancelledError:
+            logger.warning(f"AI 分析 {rec.ts_code} 被取消")
+            break
+        except Exception as e:
+            logger.error(f"AI 分析 {rec.ts_code} 失败: {e}")
+            rec.llm_analysis = "AI 分析暂时不可用"
+
+    # 无论成功失败都保存并广播
+    await _save_llm_analysis_to_db(recommendations)
+    await _broadcast_llm_ready(recommendations)
+
+    logger.info(f"AI 深度分析完成: {enhanced_count}/{len(recommendations)} 条")
+
+
+async def _fetch_stock_data(ts_code: str, sector: str) -> str:
+    """预先获取个股详细数据，拼接为文本供 LLM 分析"""
+    from app.llm.tool_executor import (
+        _get_stock_kline,
+        _get_stock_capital_flow,
+        _get_stock_technical_signal,
+        _get_sector_performance,
+    )
+
+    parts = []
+
+    # K 线（最近 30 天摘要）
+    try:
+        kline_text = await _get_stock_kline(ts_code, 60)
+        kline_data = json.loads(kline_text)
+        if isinstance(kline_data, list) and kline_data:
+            # 只取最近 10 条以控制 token
+            recent = kline_data[-10:]
+            kline_summary = "\n".join(
+                f"  {d.get('trade_date', '')}: 收{d.get('close', '')} "
+                f"涨跌{d.get('pct_chg', '')}% 量{d.get('vol', '')} "
+                f"MA5={d.get('ma5', '')} MA10={d.get('ma10', '')} MA20={d.get('ma20', '')} "
+                f"DIF={d.get('dif', '')} DEA={d.get('dea', '')} RSI={d.get('rsi14', '')}"
+                for d in recent
+            )
+            parts.append(f"## K线数据（近10日）\n{kline_summary}")
+    except Exception as e:
+        logger.debug(f"获取K线数据失败 {ts_code}: {e}")
+
+    # 资金流向
+    try:
+        flow_text = await _get_stock_capital_flow(ts_code, 5)
+        flow_data = json.loads(flow_text)
+        if isinstance(flow_data, list) and flow_data:
+            flow_summary = "\n".join(
+                f"  {d.get('trade_date', '')}: 主力净流入{d.get('main_net_inflow', 0)}万"
+                for d in flow_data[-5:]
+            )
+            parts.append(f"## 资金流向（近5日）\n{flow_summary}")
+    except Exception as e:
+        logger.debug(f"获取资金流向失败 {ts_code}: {e}")
+
+    # 技术信号
+    try:
+        signal_text = await _get_stock_technical_signal(ts_code)
+        parts.append(f"## 技术信号\n{signal_text}")
+    except Exception as e:
+        logger.debug(f"获取技术信号失败 {ts_code}: {e}")
+
+    # 板块表现
+    if sector:
+        try:
+            sector_text = await _get_sector_performance(sector)
+            parts.append(f"## 板块数据\n{sector_text}")
+        except Exception as e:
+            logger.debug(f"获取板块数据失败 {sector}: {e}")
+
+    return "\n\n".join(parts) if parts else "暂无额外数据"
+
+
+def _build_user_message(
+    rec,
+    strategy_label: str,
+    market_temp,
+    temp_level: str,
+    sectors_text: str,
+    stock_data: str,
+) -> str:
+    """构建完整的用户消息（含预获取的数据）"""
+    return f"""## 量化系统数据
+- 股票: {rec.name}({rec.ts_code})
+- 所属板块: {rec.sector}
+- 策略类型: {strategy_label}
+- 综合评分: {rec.score}分（{rec.level}）
+- 各维度: 市场{rec.market_temp_score} | 板块{rec.sector_score} | 资金{rec.capital_score} | 技术{rec.technical_score} | 位置{rec.position_score} | 估值{rec.valuation_score}
+- 信号: {rec.signal}
+- 参考价: 入场{rec.entry_price or 'N/A'} / 目标{rec.target_price or 'N/A'} / 止损{rec.stop_loss or 'N/A'}
+- 量化理由: {"；".join(rec.reasons) if rec.reasons else "无"}
+
+## 市场环境
+- 市场温度: {market_temp.temperature if market_temp else 'N/A'}/100（{temp_level}）
+- 涨跌比: {market_temp.up_count if market_temp else 0}涨 / {market_temp.down_count if market_temp else 0}跌
+- 涨停: {market_temp.limit_up_count if market_temp else 0}家
+
+## 热门板块
+{sectors_text}
+
+## 个股详细数据
+{stock_data}
+
+请根据以上所有数据，按照指定格式输出深度分析报告。"""
+
+
+def _extract_score(text: str) -> float | None:
+    """从 AI 分析报告中提取评分（1-10）"""
+    match = re.search(r"###\s*AI\s*评分[^\d]*(\d+(?:\.\d+)?)", text)
+    if match:
+        score = float(match.group(1))
+        return min(max(score, 1), 10)
+    return None
+
+
+async def _save_llm_analysis_to_db(recommendations: list) -> None:
+    """将 AI 分析结果更新到数据库"""
+    try:
+        from app.db.database import get_db
+        from sqlalchemy import text
+
+        async with get_db() as db:
+            for rec in recommendations:
+                if not rec.llm_analysis:
+                    continue
+                await db.execute(
+                    text(
+                        "UPDATE recommendations SET llm_analysis = :analysis, "
+                        "llm_score = :score "
+                        "WHERE ts_code = :code AND date(created_at) = date('now', 'localtime') "
+                        "AND scan_session = :session"
+                    ),
+                    {
+                        "analysis": rec.llm_analysis,
+                        "score": rec.llm_score,
+                        "code": rec.ts_code,
+                        "session": rec.scan_session,
+                    },
+                )
+            await db.commit()
+    except Exception as e:
+        logger.error(f"保存 AI 分析到数据库失败: {e}")
+
+
+async def _broadcast_llm_ready(recommendations: list) -> None:
+    """通过 WebSocket 广播 AI 分析完成事件"""
+    try:
+        from app.api.websocket import broadcast_update
+        await broadcast_update({
+            "type": "llm_analysis_ready",
+            "count": len([r for r in recommendations if r.llm_analysis]),
+        })
+    except Exception as e:
+        logger.error(f"广播 AI 分析完成失败: {e}")
--- a/backend/app/llm/prompts.py
+++ b/backend/app/llm/prompts.py
@ -51,3 +51,81 @@ CHAT_SYSTEM_PROMPT = """\

 免责声明：你的分析仅供参考，不构成投资建议。投资有风险，入市需谨慎。
 """
+
+# ── AI 分析 Agent Prompt ──
+
+MOMENTUM_ANALYSIS_PROMPT = """\
+你是一位专业的 A 股趋势交易分析师。你需要评估一只处于热门板块中的强势股是否值得追入。
+
+系统已为你提供了该股票的量化评分、K线数据、资金流向、技术信号、板块数据等详细信息，请基于这些数据进行深度分析。
+
+重点关注：
+1. 当前趋势的持续性：量价是否配合？资金流入是否持续？
+2. 追入的安全性：当前位置高低？短期是否过热？
+3. 入场时机：应该回调到支撑位买入，还是突破追入？
+4. 风险收益比：上行目标空间 vs 下行止损空间
+
+请严格按以下格式输出分析报告：
+
+### 核心逻辑
+（1-2句核心投资逻辑，说明为什么值得关注）
+
+### 趋势分析
+（均线排列、MACD状态、成交量变化趋势，用数据说话）
+
+### 入场策略
+（建议的入场价位和方式：回调买入/突破买入/分批建仓）
+
+### 风险提示
+（主要风险因素：板块衰退、大盘系统性风险、量能不济等）
+
+### AI 评分
+（给出 1-10 分，格式为纯数字，如：7）
+"""
+
+POTENTIAL_ANALYSIS_PROMPT = """\
+你是一位专业的 A 股底部反转交易分析师。你需要评估一只处于底部的股票是否具备启动条件。
+
+系统已为你提供了该股票的量化评分、K线数据、资金流向、技术信号、板块数据等详细信息，请基于这些数据进行深度分析。
+
+重点关注：
+1. 底部信号的可信度：量价配合如何？多个技术指标是否共振？
+2. 可能的催化剂：板块轮动机会？资金是否有流入迹象？技术面是否接近突破？
+3. 时间窗口：底部蓄势了多久？均线是否收敛？何时可能启动？
+4. 风险：继续下行的概率和幅度？是否有基本面隐患？
+
+请严格按以下格式输出分析报告：
+
+### 底部信号分析
+（底部形态特征、量价变化、技术指标状态）
+
+### 催化剂判断
+（可能的上涨催化剂：板块轮动、资金流入、技术突破等）
+
+### 埋伏策略
+（建议的建仓方式和价位、仓位建议、等待确认信号）
+
+### 风险提示
+（主要风险因素：继续下行风险、底部无效风险、时间成本等）
+
+### AI 评分
+（给出 1-10 分，格式为纯数字，如：6）
+"""
+
+ANALYSIS_USER_TEMPLATE = """\
+## 量化系统数据
+- 股票: {name}({ts_code})
+- 所属板块: {sector}
+- 策略类型: {strategy_label}
+- 综合评分: {score}分（{level}）
+- 各维度: 市场{market_temp_score} | 板块{sector_score} | 资金{capital_score} | 技术{technical_score} | 位置{position_score} | 估值{valuation_score}
+- 信号: {signal}
+- 参考价: 入场{entry_price} / 目标{target_price} / 止损{stop_loss}
+- 量化理由: {reasons}
+
+## 市场环境
+- 市场温度: {temperature}/100（{temp_level}）
+- 涨跌比: {up_count}涨 / {down_count}跌
+- 涨停: {limit_up_count}家
+
+请使用工具获取该股票的K线、资金流向、技术信号等数据，然后按照指定格式输出深度分析报告。"""
--- a/backend/app/llm/tool_executor.py
+++ b/backend/app/llm/tool_executor.py
@ -29,6 +29,10 @@ async def execute_tool(name: str, arguments: dict) -> str:
            )
        elif name == "search_stock":
            return await _search_stock(arguments["keyword"])
+        elif name == "get_stock_technical_signal":
+            return await _get_stock_technical_signal(arguments["ts_code"])
+        elif name == "get_sector_performance":
+            return await _get_sector_performance(arguments["sector_name"])
        else:
            return json.dumps({"error": f"未知工具: {name}"}, ensure_ascii=False)
    except Exception as e:
@ -117,3 +121,28 @@ async def _search_stock(keyword: str) -> str:
    ].head(10)
    data = matches[["ts_code", "name", "industry"]].to_dict(orient="records")
    return json.dumps(data, ensure_ascii=False, default=str)
+
+
+async def _get_stock_technical_signal(ts_code: str) -> str:
+    """获取个股技术面信号详情"""
+    from app.analysis.signals import generate_signals
+    signal = generate_signals(ts_code)
+    data = _clean_for_json(signal.model_dump())
+    return json.dumps(data, 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
+    sectors = await get_latest_sectors()
+    # 模糊匹配板块名称
+    matched = [s for s in sectors if sector_name in s.sector_name or s.sector_name in sector_name]
+    if not matched:
+        # 返回所有热门板块概览
+        data = [{"sector_name": s.sector_name, "pct_change": s.pct_change,
+                 "capital_inflow": s.capital_inflow, "limit_up_count": s.limit_up_count,
+                 "heat_score": s.heat_score, "stage": s.stage}
+                for s in sectors[:10]]
+        return json.dumps({"matched": False, "available_sectors": data}, ensure_ascii=False, default=str)
+    data = _clean_for_json([s.model_dump() for s in matched])
+    return json.dumps({"matched": True, "sectors": data}, ensure_ascii=False, default=str)
--- a/backend/app/llm/tools.py
+++ b/backend/app/llm/tools.py
@ -104,4 +104,38 @@ CHAT_TOOLS = [
            },
        },
    },
+    {
+        "type": "function",
+        "function": {
+            "name": "get_stock_technical_signal",
+            "description": "获取个股技术面信号详情，包括均线排列、MACD状态、RSI、布林带位置、量价关系、支撑压力位、位置安全评估",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "ts_code": {
+                        "type": "string",
+                        "description": "股票代码，如 '000001.SZ'",
+                    },
+                },
+                "required": ["ts_code"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "get_sector_performance",
+            "description": "获取板块近期涨跌表现、资金流入、涨停数等综合数据，用于判断板块强弱和轮动方向",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "sector_name": {
+                        "type": "string",
+                        "description": "板块名称关键词，如 '半导体'、'新能源'",
+                    },
+                },
+                "required": ["sector_name"],
+            },
+        },
+    },
 ]
--- a/backend/astock.db
+++ b/backend/astock.db
--- a/frontend/.next/app-build-manifest.json
+++ b/frontend/.next/app-build-manifest.json
@ -1,69 +1,20 @@
 {
  "pages": {
-    "/_not-found/page": [
-      "static/chunks/webpack-76aa9cbbdedb6a49.js",
-      "static/chunks/fd9d1056-f8a2d551cbb94c85.js",
-      "static/chunks/117-d0aa9486d6cf1a7a.js",
-      "static/chunks/main-app-7d7e5d1021afd90c.js",
-      "static/chunks/app/_not-found/page-9a1795a099256da4.js"
-    ],
    "/layout": [
-      "static/chunks/webpack-76aa9cbbdedb6a49.js",
-      "static/chunks/fd9d1056-f8a2d551cbb94c85.js",
-      "static/chunks/117-d0aa9486d6cf1a7a.js",
-      "static/chunks/main-app-7d7e5d1021afd90c.js",
-      "static/css/e86a9d7e539c28dc.css",
-      "static/chunks/448-92a7b932cf4502ac.js",
-      "static/chunks/app/layout-97bdd231e78ddf3f.js"
-    ],
-    "/chat/page": [
-      "static/chunks/webpack-76aa9cbbdedb6a49.js",
-      "static/chunks/fd9d1056-f8a2d551cbb94c85.js",
-      "static/chunks/117-d0aa9486d6cf1a7a.js",
-      "static/chunks/main-app-7d7e5d1021afd90c.js",
-      "static/chunks/app/chat/page-2dd3304322bd4036.js"
-    ],
-    "/recommendations/page": [
-      "static/chunks/webpack-76aa9cbbdedb6a49.js",
-      "static/chunks/fd9d1056-f8a2d551cbb94c85.js",
-      "static/chunks/117-d0aa9486d6cf1a7a.js",
-      "static/chunks/main-app-7d7e5d1021afd90c.js",
-      "static/chunks/app/recommendations/page-ea6294fe452e71ef.js"
+      "static/chunks/webpack.js",
+      "static/chunks/main-app.js",
+      "static/css/app/layout.css",
+      "static/chunks/app/layout.js"
    ],
    "/page": [
-      "static/chunks/webpack-76aa9cbbdedb6a49.js",
-      "static/chunks/fd9d1056-f8a2d551cbb94c85.js",
-      "static/chunks/117-d0aa9486d6cf1a7a.js",
-      "static/chunks/main-app-7d7e5d1021afd90c.js",
-      "static/chunks/app/page-0a611a415022fe45.js"
+      "static/chunks/webpack.js",
+      "static/chunks/main-app.js",
+      "static/chunks/app/page.js"
    ],
-    "/stock/[code]/page": [
-      "static/chunks/webpack-76aa9cbbdedb6a49.js",
-      "static/chunks/fd9d1056-f8a2d551cbb94c85.js",
-      "static/chunks/117-d0aa9486d6cf1a7a.js",
-      "static/chunks/main-app-7d7e5d1021afd90c.js",
-      "static/chunks/app/stock/[code]/page-aa4270127391b661.js"
-    ],
-    "/login/page": [
-      "static/chunks/webpack-76aa9cbbdedb6a49.js",
-      "static/chunks/fd9d1056-f8a2d551cbb94c85.js",
-      "static/chunks/117-d0aa9486d6cf1a7a.js",
-      "static/chunks/main-app-7d7e5d1021afd90c.js",
-      "static/chunks/app/login/page-778febf452923618.js"
-    ],
-    "/sectors/page": [
-      "static/chunks/webpack-76aa9cbbdedb6a49.js",
-      "static/chunks/fd9d1056-f8a2d551cbb94c85.js",
-      "static/chunks/117-d0aa9486d6cf1a7a.js",
-      "static/chunks/main-app-7d7e5d1021afd90c.js",
-      "static/chunks/app/sectors/page-41a82a8a8f795a23.js"
-    ],
-    "/users/page": [
-      "static/chunks/webpack-76aa9cbbdedb6a49.js",
-      "static/chunks/fd9d1056-f8a2d551cbb94c85.js",
-      "static/chunks/117-d0aa9486d6cf1a7a.js",
-      "static/chunks/main-app-7d7e5d1021afd90c.js",
-      "static/chunks/app/users/page-e66e56f48050576b.js"
+    "/_not-found/page": [
+      "static/chunks/webpack.js",
+      "static/chunks/main-app.js",
+      "static/chunks/app/_not-found/page.js"
    ]
  }
 }
--- a/frontend/.next/build-manifest.json
+++ b/frontend/.next/build-manifest.json
@ -1,31 +1,29 @@
 {
  "polyfillFiles": [
-    "static/chunks/polyfills-42372ed130431b0a.js"
+    "static/chunks/polyfills.js"
+  ],
+  "devFiles": [
+    "static/chunks/react-refresh.js"
  ],
-  "devFiles": [],
  "ampDevFiles": [],
  "lowPriorityFiles": [
-    "static/_K30GhWJ6_9AsiU6iKw4I/_buildManifest.js",
-    "static/_K30GhWJ6_9AsiU6iKw4I/_ssgManifest.js"
+    "static/development/_buildManifest.js",
+    "static/development/_ssgManifest.js"
  ],
  "rootMainFiles": [
-    "static/chunks/webpack-76aa9cbbdedb6a49.js",
-    "static/chunks/fd9d1056-f8a2d551cbb94c85.js",
-    "static/chunks/117-d0aa9486d6cf1a7a.js",
-    "static/chunks/main-app-7d7e5d1021afd90c.js"
+    "static/chunks/webpack.js",
+    "static/chunks/main-app.js"
  ],
  "pages": {
    "/_app": [
-      "static/chunks/webpack-76aa9cbbdedb6a49.js",
-      "static/chunks/framework-f66176bb897dc684.js",
-      "static/chunks/main-9c5f6b283127d940.js",
-      "static/chunks/pages/_app-72b849fbd24ac258.js"
+      "static/chunks/webpack.js",
+      "static/chunks/main.js",
+      "static/chunks/pages/_app.js"
    ],
    "/_error": [
-      "static/chunks/webpack-76aa9cbbdedb6a49.js",
-      "static/chunks/framework-f66176bb897dc684.js",
-      "static/chunks/main-9c5f6b283127d940.js",
-      "static/chunks/pages/_error-7ba65e1336b92748.js"
+      "static/chunks/webpack.js",
+      "static/chunks/main.js",
+      "static/chunks/pages/_error.js"
    ]
  },
  "ampFirstPages": []
--- a/frontend/.next/cache/.tsbuildinfo
+++ b/frontend/.next/cache/.tsbuildinfo
--- a/frontend/.next/react-loadable-manifest.json
+++ b/frontend/.next/react-loadable-manifest.json
@ -1,20 +1 @@
-{
-  "components/capital-flow.tsx -> echarts": {
-    "id": 9614,
-    "files": [
-      "static/chunks/614.2cf8795c6fba79f8.js"
-    ]
-  },
-  "components/kline-chart.tsx -> echarts": {
-    "id": 9614,
-    "files": [
-      "static/chunks/614.2cf8795c6fba79f8.js"
-    ]
-  },
-  "components/score-radar.tsx -> echarts": {
-    "id": 9614,
-    "files": [
-      "static/chunks/614.2cf8795c6fba79f8.js"
-    ]
-  }
-}
+{}
--- a/frontend/.next/server/app-paths-manifest.json
+++ b/frontend/.next/server/app-paths-manifest.json
@ -1,11 +1,4 @@
 {
  "/_not-found/page": "app/_not-found/page.js",
-  "/api/chat/stream/route": "app/api/chat/stream/route.js",
-  "/chat/page": "app/chat/page.js",
-  "/recommendations/page": "app/recommendations/page.js",
-  "/page": "app/page.js",
-  "/stock/[code]/page": "app/stock/[code]/page.js",
-  "/login/page": "app/login/page.js",
-  "/sectors/page": "app/sectors/page.js",
-  "/users/page": "app/users/page.js"
+  "/page": "app/page.js"
 }
--- a/frontend/.next/server/app/recommendations/page.js
+++ b/frontend/.next/server/app/recommendations/page.js
--- a/frontend/.next/server/app/recommendations/page_client-reference-manifest.js
+++ b/frontend/.next/server/app/recommendations/page_client-reference-manifest.js
--- a/frontend/.next/server/interception-route-rewrite-manifest.js
+++ b/frontend/.next/server/interception-route-rewrite-manifest.js
@ -1 +1 @@
-self.__INTERCEPTION_ROUTE_REWRITE_MANIFEST="[]";
+self.__INTERCEPTION_ROUTE_REWRITE_MANIFEST="[]"
--- a/frontend/.next/server/middleware-build-manifest.js
+++ b/frontend/.next/server/middleware-build-manifest.js
@ -1 +1,32 @@
-self.__BUILD_MANIFEST={polyfillFiles:["static/chunks/polyfills-42372ed130431b0a.js"],devFiles:[],ampDevFiles:[],lowPriorityFiles:[],rootMainFiles:["static/chunks/webpack-76aa9cbbdedb6a49.js","static/chunks/fd9d1056-f8a2d551cbb94c85.js","static/chunks/117-d0aa9486d6cf1a7a.js","static/chunks/main-app-7d7e5d1021afd90c.js"],pages:{"/_app":["static/chunks/webpack-76aa9cbbdedb6a49.js","static/chunks/framework-f66176bb897dc684.js","static/chunks/main-9c5f6b283127d940.js","static/chunks/pages/_app-72b849fbd24ac258.js"],"/_error":["static/chunks/webpack-76aa9cbbdedb6a49.js","static/chunks/framework-f66176bb897dc684.js","static/chunks/main-9c5f6b283127d940.js","static/chunks/pages/_error-7ba65e1336b92748.js"]},ampFirstPages:[]},self.__BUILD_MANIFEST.lowPriorityFiles=["/static/"+process.env.__NEXT_BUILD_ID+"/_buildManifest.js",,"/static/"+process.env.__NEXT_BUILD_ID+"/_ssgManifest.js"];
+self.__BUILD_MANIFEST = {
+  "polyfillFiles": [
+    "static/chunks/polyfills.js"
+  ],
+  "devFiles": [
+    "static/chunks/react-refresh.js"
+  ],
+  "ampDevFiles": [],
+  "lowPriorityFiles": [],
+  "rootMainFiles": [
+    "static/chunks/webpack.js",
+    "static/chunks/main-app.js"
+  ],
+  "pages": {
+    "/_app": [
+      "static/chunks/webpack.js",
+      "static/chunks/main.js",
+      "static/chunks/pages/_app.js"
+    ],
+    "/_error": [
+      "static/chunks/webpack.js",
+      "static/chunks/main.js",
+      "static/chunks/pages/_error.js"
+    ]
+  },
+  "ampFirstPages": []
+};
+self.__BUILD_MANIFEST.lowPriorityFiles = [
+"/static/" + process.env.__NEXT_BUILD_ID + "/_buildManifest.js",
+,"/static/" + process.env.__NEXT_BUILD_ID + "/_ssgManifest.js",
+
+];
--- a/frontend/.next/server/middleware-react-loadable-manifest.js
+++ b/frontend/.next/server/middleware-react-loadable-manifest.js
@ -1 +1 @@
-self.__REACT_LOADABLE_MANIFEST='{"components/capital-flow.tsx -> echarts":{"id":9614,"files":["static/chunks/614.2cf8795c6fba79f8.js"]},"components/kline-chart.tsx -> echarts":{"id":9614,"files":["static/chunks/614.2cf8795c6fba79f8.js"]},"components/score-radar.tsx -> echarts":{"id":9614,"files":["static/chunks/614.2cf8795c6fba79f8.js"]}}';
+self.__REACT_LOADABLE_MANIFEST="{}"
--- a/frontend/.next/server/next-font-manifest.js
+++ b/frontend/.next/server/next-font-manifest.js
@ -1 +1 @@
-self.__NEXT_FONT_MANIFEST='{"pages":{},"app":{},"appUsingSizeAdjust":false,"pagesUsingSizeAdjust":false}';
+self.__NEXT_FONT_MANIFEST="{\"pages\":{},\"app\":{},\"appUsingSizeAdjust\":false,\"pagesUsingSizeAdjust\":false}"
--- a/frontend/.next/server/pages-manifest.json
+++ b/frontend/.next/server/pages-manifest.json
@ -1 +1,5 @@
-{"/_app":"pages/_app.js","/_error":"pages/_error.js","/_document":"pages/_document.js","/404":"pages/404.html"}
+{
+  "/_app": "pages/_app.js",
+  "/_error": "pages/_error.js",
+  "/_document": "pages/_document.js"
+}
--- a/frontend/.next/server/server-reference-manifest.js
+++ b/frontend/.next/server/server-reference-manifest.js
@ -1 +1 @@
-self.__RSC_SERVER_MANIFEST="{\"node\":{},\"edge\":{},\"encryptionKey\":\"process.env.NEXT_SERVER_ACTIONS_ENCRYPTION_KEY\"}"
+self.__RSC_SERVER_MANIFEST="{\n  \"node\": {},\n  \"edge\": {},\n  \"encryptionKey\": \"process.env.NEXT_SERVER_ACTIONS_ENCRYPTION_KEY\"\n}"
--- a/frontend/.next/server/server-reference-manifest.json
+++ b/frontend/.next/server/server-reference-manifest.json
@ -1 +1,5 @@
-{"node":{},"edge":{},"encryptionKey":"0i2smImWurCmhv0CX5NVAgoXCo5ZisN2pLRhtFiEWCU="}
+{
+  "node": {},
+  "edge": {},
+  "encryptionKey": "b90zeeYzMcSqNivimWn6T2PQQNxojiQrSeFlOxUaP4Q="
+}
--- a/frontend/.next/server/webpack-runtime.js
+++ b/frontend/.next/server/webpack-runtime.js
@ -1 +1,215 @@
-(()=>{"use strict";var e={},r={};function t(o){var n=r[o];if(void 0!==n)return n.exports;var a=r[o]={exports:{}},u=!0;try{e[o](a,a.exports,t),u=!1}finally{u&&delete r[o]}return a.exports}t.m=e,t.n=e=>{var r=e&&e.__esModule?()=>e.default:()=>e;return t.d(r,{a:r}),r},(()=>{var e,r=Object.getPrototypeOf?e=>Object.getPrototypeOf(e):e=>e.__proto__;t.t=function(o,n){if(1&n&&(o=this(o)),8&n||"object"==typeof o&&o&&(4&n&&o.__esModule||16&n&&"function"==typeof o.then))return o;var a=Object.create(null);t.r(a);var u={};e=e||[null,r({}),r([]),r(r)];for(var f=2&n&&o;"object"==typeof f&&!~e.indexOf(f);f=r(f))Object.getOwnPropertyNames(f).forEach(e=>u[e]=()=>o[e]);return u.default=()=>o,t.d(a,u),a}})(),t.d=(e,r)=>{for(var o in r)t.o(r,o)&&!t.o(e,o)&&Object.defineProperty(e,o,{enumerable:!0,get:r[o]})},t.f={},t.e=e=>Promise.all(Object.keys(t.f).reduce((r,o)=>(t.f[o](e,r),r),[])),t.u=e=>""+e+".js",t.o=(e,r)=>Object.prototype.hasOwnProperty.call(e,r),t.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},t.X=(e,r,o)=>{var n=r;o||(r=e,o=()=>t(t.s=n)),r.map(t.e,t);var a=o();return void 0===a?e:a},(()=>{var e={658:1},r=r=>{var o=r.modules,n=r.ids,a=r.runtime;for(var u in o)t.o(o,u)&&(t.m[u]=o[u]);a&&a(t);for(var f=0;f<n.length;f++)e[n[f]]=1};t.f.require=(o,n)=>{e[o]||(658!=o?r(require("./chunks/"+t.u(o))):e[o]=1)},module.exports=t,t.C=r})()})();
+/*
+ * ATTENTION: An "eval-source-map" devtool has been used.
+ * This devtool is neither made for production nor for readable output files.
+ * It uses "eval()" calls to create a separate source file with attached SourceMaps in the browser devtools.
+ * If you are trying to read the output file, select a different devtool (https://webpack.js.org/configuration/devtool/)
+ * or disable the default devtool with "devtool: false".
+ * If you are looking for production-ready output files, see mode: "production" (https://webpack.js.org/configuration/mode/).
+ */
+/******/ (() => { // webpackBootstrap
+/******/ 	"use strict";
+/******/ 	var __webpack_modules__ = ({});
+/************************************************************************/
+/******/ 	// The module cache
+/******/ 	var __webpack_module_cache__ = {};
+/******/ 	
+/******/ 	// The require function
+/******/ 	function __webpack_require__(moduleId) {
+/******/ 		// Check if module is in cache
+/******/ 		var cachedModule = __webpack_module_cache__[moduleId];
+/******/ 		if (cachedModule !== undefined) {
+/******/ 			return cachedModule.exports;
+/******/ 		}
+/******/ 		// Create a new module (and put it into the cache)
+/******/ 		var module = __webpack_module_cache__[moduleId] = {
+/******/ 			id: moduleId,
+/******/ 			loaded: false,
+/******/ 			exports: {}
+/******/ 		};
+/******/ 	
+/******/ 		// Execute the module function
+/******/ 		var threw = true;
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+/******/ 			__webpack_modules__[moduleId](module, module.exports, __webpack_require__);
+/******/ 			threw = false;
+/******/ 		} finally {
+/******/ 			if(threw) delete __webpack_module_cache__[moduleId];
+/******/ 		}
+/******/ 	
+/******/ 		// Flag the module as loaded
+/******/ 		module.loaded = true;
+/******/ 	
+/******/ 		// Return the exports of the module
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+/******/ 	}
+/******/ 	
+/******/ 	// expose the modules object (__webpack_modules__)
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+/******/ 	
+/************************************************************************/
+/******/ 	/* webpack/runtime/compat get default export */
+/******/ 	(() => {
+/******/ 		// getDefaultExport function for compatibility with non-harmony modules
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+/******/ 			var getter = module && module.__esModule ?
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+/******/ 		var getProto = Object.getPrototypeOf ? (obj) => (Object.getPrototypeOf(obj)) : (obj) => (obj.__proto__);
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+/******/ 			if(mode & 8) return value;
+/******/ 			if(typeof value === 'object' && value) {
+/******/ 				if((mode & 4) && value.__esModule) return value;
+/******/ 				if((mode & 16) && typeof value.then === 'function') return value;
+/******/ 			}
+/******/ 			var ns = Object.create(null);
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+/******/ 			var def = {};
+/******/ 			leafPrototypes = leafPrototypes || [null, getProto({}), getProto([]), getProto(getProto)];
+/******/ 			for(var current = mode & 2 && value; typeof current == 'object' && !~leafPrototypes.indexOf(current); current = getProto(current)) {
+/******/ 				Object.getOwnPropertyNames(current).forEach((key) => (def[key] = () => (value[key])));
+/******/ 			}
+/******/ 			def['default'] = () => (value);
+/******/ 			__webpack_require__.d(ns, def);
+/******/ 			return ns;
+/******/ 		};
+/******/ 	})();
+/******/ 	
+/******/ 	/* webpack/runtime/define property getters */
+/******/ 	(() => {
+/******/ 		// define getter functions for harmony exports
+/******/ 		__webpack_require__.d = (exports, definition) => {
+/******/ 			for(var key in definition) {
+/******/ 				if(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {
+/******/ 					Object.defineProperty(exports, key, { enumerable: true, get: definition[key] });
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+/******/ 	})();
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+/******/ 		// This file contains only the entry chunk.
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+/******/ 			return Promise.all(Object.keys(__webpack_require__.f).reduce((promises, key) => {
+/******/ 				__webpack_require__.f[key](chunkId, promises);
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+/******/ 	})();
+/******/ 	
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+/******/ 	(() => {
+/******/ 		// This function allow to reference async chunks and sibling chunks for the entrypoint
+/******/ 		__webpack_require__.u = (chunkId) => {
+/******/ 			// return url for filenames based on template
+/******/ 			return "" + chunkId + ".js";
+/******/ 		};
+/******/ 	})();
+/******/ 	
+/******/ 	/* webpack/runtime/getFullHash */
+/******/ 	(() => {
+/******/ 		__webpack_require__.h = () => ("457ff384495915e8")
+/******/ 	})();
+/******/ 	
+/******/ 	/* webpack/runtime/hasOwnProperty shorthand */
+/******/ 	(() => {
+/******/ 		__webpack_require__.o = (obj, prop) => (Object.prototype.hasOwnProperty.call(obj, prop))
+/******/ 	})();
+/******/ 	
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+/******/ 		// define __esModule on exports
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+/******/ 		// no baseURI
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+/******/ 		// object to store loaded chunks
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+/******/ 			for(var moduleId in moreModules) {
+/******/ 				if(__webpack_require__.o(moreModules, moduleId)) {
+/******/ 					__webpack_require__.m[moduleId] = moreModules[moduleId];
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+/******/ 			}
+/******/ 			if(runtime) runtime(__webpack_require__);
+/******/ 			for(var i = 0; i < chunkIds.length; i++)
+/******/ 				installedChunks[chunkIds[i]] = 1;
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+/******/ 		};
+/******/ 		
+/******/ 		// require() chunk loading for javascript
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+/******/ 			if(!installedChunks[chunkId]) {
+/******/ 				if("webpack-runtime" != chunkId) {
+/******/ 					installChunk(require("./" + __webpack_require__.u(chunkId)));
+/******/ 				} else installedChunks[chunkId] = 1;
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+/******/ 		module.exports = __webpack_require__;
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+/******/ 		// no HMR
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+/******/ 		// no HMR manifest
+/******/ 	})();
+/******/ 	
+/************************************************************************/
+/******/ 	
+/******/ 	
+/******/ })()
+;
--- a/frontend/.next/standalone/.next/BUILD_ID
+++ b/frontend/.next/standalone/.next/BUILD_ID
@ -1 +0,0 @@
-_K30GhWJ6_9AsiU6iKw4I
--- a/frontend/.next/standalone/.next/app-build-manifest.json
+++ b/frontend/.next/standalone/.next/app-build-manifest.json
@ -1,69 +0,0 @@
-{
-  "pages": {
-    "/_not-found/page": [
-      "static/chunks/webpack-76aa9cbbdedb6a49.js",
-      "static/chunks/fd9d1056-f8a2d551cbb94c85.js",
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-      "static/chunks/main-app-7d7e5d1021afd90c.js",
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--- a/frontend/.next/standalone/.next/server/app/_not-found.rsc
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