diff --git a/backend/app/engine/__pycache__/recommender.cpython-313.pyc b/backend/app/engine/__pycache__/recommender.cpython-313.pyc
index c493ec5c..f0bd9d9c 100644
Binary files a/backend/app/engine/__pycache__/recommender.cpython-313.pyc and b/backend/app/engine/__pycache__/recommender.cpython-313.pyc differ
diff --git a/backend/app/engine/__pycache__/screener.cpython-313.pyc b/backend/app/engine/__pycache__/screener.cpython-313.pyc
index b2f800e7..a1a743b4 100644
Binary files a/backend/app/engine/__pycache__/screener.cpython-313.pyc and b/backend/app/engine/__pycache__/screener.cpython-313.pyc differ
diff --git a/backend/app/engine/recommender.py b/backend/app/engine/recommender.py
index e221cbe7..4d4dfebf 100644
--- a/backend/app/engine/recommender.py
+++ b/backend/app/engine/recommender.py
@@ -245,9 +245,9 @@ async def _load_today_from_db() -> dict:
             from sqlalchemy import text
             import json
 
-            # 加载市场温度
+            # 加载市场温度（按 trade_date 取最新交易日）
             result = await db.execute(
-                text("SELECT * FROM market_temperature ORDER BY created_at DESC LIMIT 1")
+                text("SELECT * FROM market_temperature ORDER BY trade_date DESC LIMIT 1")
             )
             mt_row = result.fetchone()
             market_temp = None
@@ -264,12 +264,14 @@ async def _load_today_from_db() -> dict:
                     temperature=m["temperature"],
                 )
 
-            # 加载推荐（按 ts_code 去重，取最新一条）
+            # 加载推荐（取最近一个有数据的日期，按 ts_code 去重）
             result = await db.execute(
-                text("SELECT * FROM recommendations WHERE date(created_at) = :today "
-                     "AND id IN (SELECT MAX(id) FROM recommendations WHERE date(created_at) = :today GROUP BY ts_code) "
-                     "ORDER BY score DESC"),
-                {"today": today}
+                text("SELECT * FROM recommendations "
+                     "WHERE date(created_at) = (SELECT date(created_at) FROM recommendations ORDER BY created_at DESC LIMIT 1) "
+                     "AND id IN (SELECT MAX(id) FROM recommendations "
+                     "  WHERE date(created_at) = (SELECT date(created_at) FROM recommendations ORDER BY created_at DESC LIMIT 1) "
+                     "  GROUP BY ts_code) "
+                     "ORDER BY score DESC")
             )
             rows = result.fetchall()
             recommendations = []
diff --git a/backend/app/engine/screener.py b/backend/app/engine/screener.py
index 7243106a..d7f80ecd 100644
--- a/backend/app/engine/screener.py
+++ b/backend/app/engine/screener.py
@@ -1,11 +1,16 @@
-"""趋势突破统一筛选器
+"""趋势突破统一筛选器（自上而下方案）
 
-三阶段管道：全市场批量预筛 → 资金流过滤 → 逐股深度分析
-评分公式：趋势&时机30% + 资金流25% + 供需20% + 板块共振15% + 市场温度10%
+三阶段管道：
+  Step 1: 板块定位 — 找到有资金流入的热门板块 (3-5个)
+  Step 2: 板块内选股 — 在热门板块成分股中筛出有资金流入的候选 (30-50只)
+  Step 3: 深度分析 — 供需 + 价格行为 + 趋势 (10-15只推荐)
 
-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
-  - 盘后模式：用当日 Tushare 完整数据筛选
+评分公式：供需关系 40% + 价格行为 35% + 趋势 25%
+板块和资金流作为前置过滤条件，不参与评分。
+
+数据源：
+  - 盘中模式：Tushare 日线 + 腾讯实时行情混合
+  - 盘后模式：Tushare 当日完整数据
 """
 
 import logging
@@ -47,23 +52,40 @@ async def run_screening(trade_date: str = None) -> dict:
 
     market_temp_score = market_temp.temperature
 
-    # ── 板块热度（用于板块共振评分） ──
-    logger.info("=== 板块热度扫描 ===")
+    # ── Step 1: 板块定位 ──
+    logger.info("=== Step 1: 板块定位 ===")
     all_sectors = scan_hot_sectors(trade_date)
-    hot_sectors = all_sectors[:settings.top_sector_count]
+
+    # 前置过滤：只保留有资金流入 + 非末期的板块
+    hot_sectors = [
+        s for s in all_sectors
+        if s.capital_inflow > 0 and s.stage not in ("end",)
+    ][:settings.top_sector_count]
+
+    if not hot_sectors:
+        logger.info("无合格热门板块（需要资金流入+非末期），回退到全部板块")
+        hot_sectors = all_sectors[:settings.top_sector_count]
+
+    for s in hot_sectors:
+        logger.info(f"  目标板块: {s.sector_name} 涨幅{s.pct_change}% 资金{s.capital_inflow:.0f}万 "
+                     f"涨停{s.limit_up_count} 阶段={s.stage}")
 
     # 盘中用实时行情更新板块涨幅和涨停数
     if intraday:
         hot_sectors = await intraday_sector_scan(hot_sectors)
 
-    # ── 趋势突破三阶段管道 ──
-    logger.info("=== 趋势突破扫描 ===")
-    candidates = await scan_trend_breakout(
-        trade_date=trade_date,
-        market_temp=market_temp,
-        hot_sectors=hot_sectors,
-        intraday=intraday,
-    )
+    # ── Step 2: 板块内选股 ──
+    logger.info("=== Step 2: 板块内选股 ===")
+    candidates = await _select_from_hot_sectors(hot_sectors, trade_date, intraday)
+
+    if not candidates:
+        logger.info("=== Step 2 无候选，回退到全市场扫描 ===")
+        candidates = await scan_trend_breakout(
+            trade_date=trade_date,
+            market_temp=market_temp,
+            hot_sectors=hot_sectors,
+            intraday=intraday,
+        )
 
     if not candidates:
         logger.info("=== 筛选完成: 0 只股票 ===")
@@ -74,8 +96,9 @@ async def run_screening(trade_date: str = None) -> dict:
             "scan_mode": scan_mode,
         }
 
-    # ── 构建推荐列表 ──
-    recommendations = _build_trend_recommendations(
+    # ── Step 3: 供需 + 价格行为 + 趋势评分 ──
+    logger.info("=== Step 3: 深度分析 ===")
+    recommendations = _build_recommendations(
         candidates, market_temp, hot_sectors, market_temp_score, intraday,
     )
 
@@ -96,147 +119,498 @@ async def run_screening(trade_date: str = None) -> dict:
     }
 
 
-def _build_trend_recommendations(
+async def _select_from_hot_sectors(
+    hot_sectors: list[SectorInfo],
+    trade_date: str,
+    intraday: bool,
+) -> list[dict]:
+    """Step 2: 从热门板块成分股中选出有资金流入的候选
+
+    流程:
+    1. 收集所有热门板块的成分股代码
+    2. 用 get_daily_all + get_daily_basic 过滤市值/换手率
+    3. 用 get_moneyflow_batch 过滤主力净流入 > 0
+    4. 对候选做入场信号初筛（只需满足任一信号类型）
+    """
+    from app.data.tushare_client import tushare_client
+    from datetime import datetime, timedelta
+    import pandas as pd
+
+    if not trade_date:
+        trade_date = tushare_client.get_latest_trade_date()
+
+    # 收集热门板块成分股代码
+    sector_member_codes: set[str] = set()
+    sector_code_map: dict[str, str] = {}  # ts_code -> sector_name
+    for s in hot_sectors:
+        try:
+            members_df = tushare_client.get_ths_members(s.sector_code)
+            if not members_df.empty and "con_code" in members_df.columns:
+                codes = members_df["con_code"].tolist()
+                sector_member_codes.update(codes)
+                for c in codes:
+                    sector_code_map[c] = s.sector_name
+        except Exception as e:
+            logger.warning(f"获取板块 {s.sector_name} 成分股失败: {e}")
+
+    if not sector_member_codes:
+        logger.info("Step 2: 无板块成分股数据")
+        return []
+
+    logger.info(f"Step 2: 热门板块共 {len(sector_member_codes)} 只成分股")
+
+    # 过滤市值/换手率/ST/次新
+    stock_basic = tushare_client.get_stock_basic()
+    exclude_codes = set()
+    if not stock_basic.empty:
+        st_codes = set(stock_basic[stock_basic["name"].str.contains("ST", na=False)]["ts_code"])
+        exclude_codes.update(st_codes)
+        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"])
+        exclude_codes.update(new_codes)
+
+    # 行业映射
+    industry_map = {}
+    if not stock_basic.empty:
+        for _, row in stock_basic.iterrows():
+            industry_map[row["ts_code"]] = row.get("industry", "")
+
+    # 用 daily_basic 过滤
+    basic = tushare_client.get_daily_basic(trade_date)
+    if basic.empty:
+        logger.info("Step 2: daily_basic 无数据")
+        return []
+
+    basic["circ_mv"] = basic["circ_mv"] / 10000  # 万元 → 亿元
+
+    filtered_basic = basic[
+        (basic["ts_code"].isin(sector_member_codes)) &
+        (~basic["ts_code"].isin(exclude_codes)) &
+        (basic["circ_mv"] >= settings.min_circ_mv) &
+        (basic["circ_mv"] <= settings.max_circ_mv) &
+        (basic["turnover_rate"] >= settings.min_turnover_rate) &
+        (basic["turnover_rate"] <= settings.max_turnover_rate)
+    ].copy()
+
+    logger.info(f"Step 2 基本面过滤: {len(sector_member_codes)} 只 → {len(filtered_basic)} 只")
+
+    if filtered_basic.empty:
+        return []
+
+    # 资金流过滤：主力净流入 > 0
+    mf = tushare_client.get_moneyflow_batch(trade_date)
+    if mf.empty:
+        logger.info("Step 2: 资金流数据为空，跳过资金过滤")
+        candidate_codes = set(filtered_basic["ts_code"].tolist())
+    else:
+        mf["main_net_inflow"] = (
+            (mf["buy_elg_amount"] - mf["sell_elg_amount"]) +
+            (mf["buy_lg_amount"] - mf["sell_lg_amount"])
+        )
+        total = (
+            mf["buy_elg_amount"] + mf["sell_elg_amount"] +
+            mf["buy_lg_amount"] + mf["sell_lg_amount"] +
+            mf["buy_md_amount"] + mf["sell_md_amount"] +
+            mf["buy_sm_amount"] + mf["sell_sm_amount"]
+        )
+        mf["inflow_ratio"] = (mf["main_net_inflow"] / total.replace(0, float("nan")) * 100).fillna(0)
+
+        mf_positive = mf[
+            (mf["ts_code"].isin(set(filtered_basic["ts_code"]))) &
+            (mf["main_net_inflow"] > 0)
+        ].sort_values("main_net_inflow", ascending=False)
+
+        candidate_codes = set(mf_positive["ts_code"].tolist())
+
+        # 构建资金流查找表
+        mf_lookup = {}
+        for _, row in mf_positive.iterrows():
+            mf_lookup[row["ts_code"]] = {
+                "main_net_inflow": float(row["main_net_inflow"]),
+                "inflow_ratio": float(row.get("inflow_ratio", 0)),
+            }
+
+        logger.info(f"Step 2 资金流过滤: → {len(candidate_codes)} 只主力净流入 > 0")
+
+    if not candidate_codes:
+        return []
+
+    # 构建候选列表
+    import numpy as np
+    candidates = []
+    for ts_code in candidate_codes:
+        name = ""
+        if not stock_basic.empty:
+            row = stock_basic[stock_basic["ts_code"] == ts_code]
+            if not row.empty:
+                name = row.iloc[0]["name"]
+
+        sector_name = sector_code_map.get(ts_code, industry_map.get(ts_code, ""))
+        b_row = filtered_basic[filtered_basic["ts_code"] == ts_code]
+        turnover_rate = float(b_row.iloc[0]["turnover_rate"]) if not b_row.empty else 0
+        circ_mv = float(b_row.iloc[0]["circ_mv"]) if not b_row.empty else 0
+        pe = float(b_row.iloc[0]["pe"]) if not b_row.empty and pd.notna(b_row.iloc[0].get("pe")) else None
+        pb = float(b_row.iloc[0]["pb"]) if not b_row.empty and pd.notna(b_row.iloc[0].get("pb")) else None
+        volume_ratio = float(b_row.iloc[0]["volume_ratio"]) if not b_row.empty and pd.notna(b_row.iloc[0].get("volume_ratio")) else None
+
+        try:
+            mf_info = mf_lookup.get(ts_code, {})
+        except NameError:
+            mf_info = {}
+
+        candidates.append({
+            "ts_code": ts_code,
+            "name": name,
+            "sector": sector_name,
+            "turnover_rate": turnover_rate,
+            "circ_mv": circ_mv,
+            "pe": pe,
+            "pb": pb,
+            "volume_ratio": volume_ratio,
+            "main_net_inflow": mf_info.get("main_net_inflow", 0),
+            "inflow_ratio": mf_info.get("inflow_ratio", 0),
+        })
+
+    logger.info(f"Step 2 候选: {len(candidates)} 只")
+    return candidates
+
+
+def _build_recommendations(
     candidates: list[dict],
     market_temp: MarketTemperature,
     hot_sectors: list[SectorInfo],
     market_temp_score: float = 0,
     intraday: bool = False,
 ) -> list[Recommendation]:
-    """从趋势突破扫描结果构建推荐列表
+    """Step 3: 对候选做供需 + 价格行为 + 趋势深度分析
 
-    评分公式：趋势&时机30% + 资金流25% + 供需20% + 板块共振15% + 市场温度10%
+    评分公式：供需关系 40% + 价格行为 35% + 趋势 25%
+    板块和资金流已在前置过滤中处理。
     """
+    from app.data.tushare_client import tushare_client
+    from app.analysis.technical import add_all_indicators
+    from app.analysis.breakout_signals import (
+        classify_entry_signal,
+        score_supply_demand,
+        analyze_volume_pattern,
+        EntrySignal,
+    )
+    from app.analysis.signals import generate_signals
+    from app.analysis.capital_flow import _score_valuation
+
+    # 名称和行业映射
+    stock_basic = tushare_client.get_stock_basic()
+    name_map = {}
+    industry_map = {}
+    if not stock_basic.empty:
+        for _, row in stock_basic.iterrows():
+            name_map[row["ts_code"]] = row["name"]
+            industry_map[row["ts_code"]] = row.get("industry", "")
+
     recommendations = []
+    total = len(candidates)
+    signal_counts = {"breakout": 0, "pullback": 0, "launch": 0, "none": 0}
 
-    for stock in candidates:
-        ts_code = stock["ts_code"]
-        name = stock["name"]
-        sector = stock["sector"]
-        entry_signal_type = stock.get("entry_signal_type", "none")
-        entry_signal_score = stock.get("entry_signal_score", 0)
-        tech_signal = stock.get("tech_signal")
+    for idx, stock in enumerate(candidates):
+        ts_code = stock.get("ts_code", "")
+        if not ts_code:
+            continue
 
-        # 各维度得分
-        trend_timing_score = stock.get("trend_timing_score", 50)
-        supply_demand_score = stock.get("supply_demand_score", 50)
-        capital_score = stock.get("capital_score", 50)
-        position_score = stock.get("position_score", 50)
-        valuation_score = stock.get("valuation_score", 50)
+        name = stock.get("name") or name_map.get(ts_code, ts_code)
+        sector = stock.get("sector") or industry_map.get(ts_code, "")
 
-        # 板块共振评分
-        sector_score = _score_sector_resonance(sector, hot_sectors)
-        sector_stage = _get_sector_stage(sector, hot_sectors)
+        try:
+            # 获取 120 日 K 线
+            df = tushare_client.get_stock_daily(ts_code, 120)
+            if df.empty or len(df) < 30:
+                continue
 
-        # 综合评分（新权重）
-        final_score = (
-            trend_timing_score * 0.30 +
-            capital_score * 0.25 +
-            supply_demand_score * 0.20 +
-            sector_score * 0.15 +
-            market_temp_score * 0.10
-        )
+            # 添加技术指标
+            df = add_all_indicators(df)
 
-        # 风险乘数
-        if tech_signal:
-            if tech_signal.rally_pct_5d > 20:
-                final_score *= 0.65
-            elif tech_signal.rally_pct_5d > 15:
-                final_score *= 0.80
+            # ── 入场信号分类 ──
+            entry_signal = classify_entry_signal(df)
+            signal_type = entry_signal["signal_type"]
+            if signal_type == EntrySignal.NONE:
+                signal_counts["none"] += 1
+                continue
+            signal_counts[signal_type.value] += 1
 
-        if sector_stage == "end":
-            final_score *= 0.70
-        elif sector_stage == "late":
-            final_score *= 0.88
+            # ── 三维度评分 ──
 
-        if market_temp_score < 30:
-            final_score *= 0.75
+            # 1. 供需关系评分 (40%)
+            supply_demand_score = score_supply_demand(df)
 
-        # 入场信号高置信度奖励
-        if entry_signal_score >= 80:
-            final_score *= 1.10
+            # 2. 价格行为评分 (35%)
+            price_action_score = _score_price_action(df, entry_signal)
 
-        # 确定信号和等级
-        level = _score_to_level(final_score)
-        signal = "HOLD"
-        if entry_signal_type != "none" and entry_signal_score >= 50 and position_score >= 30 and final_score >= 60:
-            signal = "BUY"
+            # 3. 趋势评分 (25%)
+            trend_score = _score_trend(df)
 
-        # 价格参考
-        entry_price = None
-        target_price = None
-        stop_loss = None
-        if tech_signal:
-            entry_price = tech_signal.support_price
-            target_price = tech_signal.resist_price
-            stop_loss = tech_signal.stop_loss_price
+            # 综合评分
+            final_score = (
+                supply_demand_score * 0.40 +
+                price_action_score * 0.35 +
+                trend_score * 0.25
+            )
 
-            # 根据入场信号类型调整参考价
-            details = stock.get("entry_signal_details", {})
-            if entry_signal_type == "breakout" and details.get("resist_level"):
-                entry_price = details["resist_level"]
-                target_price = round(entry_price * 1.05, 2)
-            elif entry_signal_type == "pullback" and details.get("support_price"):
-                entry_price = details["support_price"]
-                target_price = round(entry_price * 1.05, 2)
-            elif entry_signal_type == "launch" and details.get("resist_level"):
-                entry_price = round(details["resist_level"] * 1.01, 2)
-                target_price = round(details["resist_level"] * 1.08, 2)
+            # 风险乘数
+            tech_signal = generate_signals(ts_code, name)
 
-        # 生成推荐理由
-        reasons = _generate_reasons(stock, tech_signal, market_temp, intraday)
+            if tech_signal:
+                if tech_signal.rally_pct_5d > 20:
+                    final_score *= 0.65
+                elif tech_signal.rally_pct_5d > 15:
+                    final_score *= 0.80
 
-        # 风险提示
-        risk_note = _generate_risk_note(market_temp, tech_signal, stock)
+            # 板块末期惩罚（板块信息来自 hot_sectors）
+            sector_stage = _get_sector_stage(sector, hot_sectors)
+            if sector_stage == "end":
+                final_score *= 0.70
+            elif sector_stage == "late":
+                final_score *= 0.88
 
-        rec = Recommendation(
-            ts_code=ts_code,
-            name=name,
-            sector=sector,
-            score=round(final_score, 1),
-            market_temp_score=round(market_temp_score, 1),
-            sector_score=round(sector_score, 1),
-            capital_score=round(capital_score, 1),
-            technical_score=round(stock.get("technical_score", 50), 1),
-            position_score=round(position_score, 1),
-            valuation_score=round(valuation_score, 1),
-            signal=signal,
-            entry_price=entry_price,
-            target_price=target_price,
-            stop_loss=stop_loss,
-            reasons=reasons,
-            risk_note=risk_note,
-            level=level,
-            strategy="trend_breakout",
-            entry_signal_type=entry_signal_type,
-        )
-        recommendations.append(rec)
+            # 市场温度风控
+            if market_temp_score < 30:
+                final_score *= 0.75
+            elif market_temp_score < 50:
+                final_score *= 0.88
+
+            # 高置信度入场信号奖励
+            if entry_signal.get("signal_score", 0) >= 80:
+                final_score *= 1.10
+
+            # 估值评分（辅助参考，不参与主评分）
+            pe = stock.get("pe")
+            pb = stock.get("pb")
+            valuation_score = _score_valuation(pe, pb)
+
+            # 确定信号和等级
+            level = _score_to_level(final_score)
+            signal = "HOLD"
+            position_score = tech_signal.position_score if tech_signal else 50
+            if (signal_type != EntrySignal.NONE
+                    and entry_signal.get("signal_score", 0) >= 50
+                    and position_score >= 30
+                    and final_score >= 60):
+                signal = "BUY"
+
+            # 价格参考
+            entry_price = None
+            target_price = None
+            stop_loss = None
+            if tech_signal:
+                entry_price = tech_signal.support_price
+                target_price = tech_signal.resist_price
+                stop_loss = tech_signal.stop_loss_price
+
+                details = entry_signal.get("details", {})
+                st = signal_type.value
+                if st == "breakout" and details.get("resist_level"):
+                    entry_price = details["resist_level"]
+                    target_price = round(entry_price * 1.05, 2)
+                elif st == "pullback" and details.get("support_price"):
+                    entry_price = details["support_price"]
+                    target_price = round(entry_price * 1.05, 2)
+                elif st == "launch" and details.get("resist_level"):
+                    entry_price = round(details["resist_level"] * 1.01, 2)
+                    target_price = round(details["resist_level"] * 1.08, 2)
+
+            # 生成推荐理由
+            reasons = _generate_reasons(stock, entry_signal, tech_signal, df, intraday)
+            risk_note = _generate_risk_note(market_temp, tech_signal, stock)
+
+            # 量价模式
+            vol_pattern = analyze_volume_pattern(df)
+
+            rec = Recommendation(
+                ts_code=ts_code,
+                name=name,
+                sector=sector,
+                score=round(final_score, 1),
+                market_temp_score=round(market_temp_score, 1),
+                sector_score=round(_get_sector_heat(sector, hot_sectors), 1),
+                capital_score=round(_score_capital_simple(stock), 1),
+                technical_score=round(trend_score, 1),
+                position_score=round(position_score, 1),
+                valuation_score=round(valuation_score, 1),
+                signal=signal,
+                entry_price=entry_price,
+                target_price=target_price,
+                stop_loss=stop_loss,
+                reasons=reasons,
+                risk_note=risk_note,
+                level=level,
+                strategy="trend_breakout",
+                entry_signal_type=signal_type.value,
+            )
+            recommendations.append(rec)
+
+            if len(recommendations) >= settings.top_stock_count:
+                break
+
+        except Exception as e:
+            logger.debug(f"深度分析 {ts_code} 失败: {e}")
+            continue
+
+        # 让出控制权（同步函数中无法 await，跳过）
+        # idx % 10 == 0 的让步在 _select_from_hot_sectors 的上层 async 函数中处理
+
+    logger.info(
+        f"Step 3 入场信号分布: "
+        f"突破={signal_counts['breakout']} 回踩={signal_counts['pullback']} "
+        f"启动={signal_counts['launch']} 无信号={signal_counts['none']} "
+        f"(共分析{total}只)"
+    )
 
     return recommendations
 
 
-def _score_sector_resonance(sector_name: str, hot_sectors: list[SectorInfo]) -> float:
-    """板块共振评分 (0-100)"""
-    for s in hot_sectors:
-        if s.sector_name == sector_name:
-            score = 40  # 在热门板块列表中
-            score += s.heat_score * 0.3  # 板块热度贡献
-            if s.stage == "early":
-                score += 30
-            elif s.stage == "mid":
-                score += 20
-            elif s.stage == "late":
-                score += 5
-            return min(score, 100)
-    return 10.0  # 不在热门板块
+# ── 价格行为评分 ──
 
 
-def _get_sector_score(sector_name: str, hot_sectors: list[SectorInfo]) -> float:
-    """获取板块在热门板块中的得分"""
-    for s in hot_sectors:
-        if s.sector_name == sector_name:
-            return s.heat_score
-    return 30.0
+def _score_price_action(df, entry_signal: dict) -> float:
+    """价格行为学评分 (0-100)
+
+    维度：
+    - 入场信号类型质量 (40): 突破型/回踩型/启动型各自的得分
+    - K线形态强度 (30): 突破日/回踩日的K线实体占比、下影线、收盘位置
+    - 支撑阻力位质量 (30): 关键价格位置的测试情况
+    """
+    score = 0
+    last = df.iloc[-1]
+    details = entry_signal.get("details", {})
+    signal_type = entry_signal.get("signal_type")
+
+    # 入场信号类型质量 (40)
+    signal_score = entry_signal.get("signal_score", 0)
+    score += signal_score * 0.40
+
+    # K线形态强度 (30)
+    day_range = last["high"] - last["low"]
+    if day_range > 0:
+        # 实体占比（实体/全振幅）
+        body = abs(last["close"] - last["open"])
+        body_ratio = body / day_range
+        if body_ratio > 0.7:
+            score += 20  # 大实体，方向明确
+        elif body_ratio > 0.4:
+            score += 12
+        elif body_ratio > 0.2:
+            score += 6
+
+        # 收盘位置（越接近高点越好）
+        close_position = (last["close"] - last["low"]) / day_range
+        if close_position > 0.8:
+            score += 10  # 收在上部 20%
+        elif close_position > 0.6:
+            score += 6
+        elif close_position > 0.4:
+            score += 3
+
+    # 支撑阻力位质量 (30)
+    if signal_type and signal_type.value == "breakout":
+        # 突破型：阻力位被突破的力度
+        breakout_pct = details.get("breakout_pct", 0)
+        vol_ratio = details.get("volume_ratio", 1)
+        if breakout_pct > 2 and vol_ratio > 2:
+            score += 30  # 强力突破
+        elif breakout_pct > 1 and vol_ratio > 1.5:
+            score += 20
+        elif breakout_pct > 0:
+            score += 10
+
+    elif signal_type and signal_type.value == "pullback":
+        # 回踩型：支撑位的精确度
+        support_ma = details.get("support_ma", "")
+        shrink = details.get("volume_shrink_ratio", 1)
+        if support_ma == "MA20" and shrink < 0.6:
+            score += 30  # 精确回踩 MA20 且大幅缩量
+        elif support_ma == "MA20":
+            score += 22
+        elif support_ma == "MA10" and shrink < 0.6:
+            score += 18
+        else:
+            score += 10
+
+    elif signal_type and signal_type.value == "launch":
+        # 启动型：整理的充分度
+        range_pct = details.get("price_range_pct", 10)
+        shrink = details.get("volume_shrink_ratio", 1)
+        if range_pct < 3 and shrink < 0.4:
+            score += 30  # 极度缩量窄幅整理
+        elif range_pct < 5 and shrink < 0.6:
+            score += 20
+        else:
+            score += 10
+    else:
+        score += 10
+
+    return min(score, 100)
+
+
+# ── 趋势评分 ──
+
+
+def _score_trend(df) -> float:
+    """趋势评分 (0-100)
+
+    维度：
+    - 均线排列 (40): MA5>MA10>MA20>MA60
+    - 更高高点/更高低点结构 (35): 近 20 日价格结构
+    - MA20 方向 (25): MA20 是否持续上行
+    """
+    import pandas as pd
+    score = 0
+    last = df.iloc[-1]
+
+    # 均线排列 (40)
+    ma_cols = [c for c in ["ma5", "ma10", "ma20", "ma60"] if c in df.columns]
+    if len(ma_cols) >= 4 and not any(pd.isna(last[c]) for c in ma_cols):
+        if last["ma5"] > last["ma10"] > last["ma20"] > last["ma60"]:
+            score += 40  # 完美多头
+        elif last["ma5"] > last["ma10"] > last["ma20"]:
+            score += 28
+        elif last["ma5"] > last["ma20"]:
+            score += 15
+    elif "ma5" in df.columns and "ma20" in df.columns:
+        if not pd.isna(last["ma5"]) and not pd.isna(last["ma20"]) and last["ma5"] > last["ma20"]:
+            score += 15
+
+    # 更高高点/更高低点结构 (35)
+    if len(df) >= 20:
+        recent = df.tail(20)
+        # 检查高点抬升
+        first_10_high = recent["high"].iloc[:10].max()
+        second_10_high = recent["high"].iloc[10:].max()
+        # 检查低点抬升
+        first_10_low = recent["low"].iloc[:10].min()
+        second_10_low = recent["low"].iloc[10:].min()
+
+        if second_10_high > first_10_high and second_10_low > first_10_low:
+            score += 35  # 既抬高点又抬低点，最健康
+        elif second_10_high > first_10_high:
+            score += 20  # 至少高点抬升
+        elif second_10_low > first_10_low:
+            score += 12  # 至少低点抬升
+
+    # MA20 方向 (25)
+    if "ma20" in df.columns and len(df) >= 5:
+        ma20_now = last["ma20"]
+        ma20_5d = df.iloc[-5]["ma20"]
+        if not pd.isna(ma20_now) and not pd.isna(ma20_5d) and ma20_5d > 0:
+            ma20_pct = (ma20_now - ma20_5d) / ma20_5d * 100
+            if ma20_pct > 2:
+                score += 25
+            elif ma20_pct > 1:
+                score += 18
+            elif ma20_pct > 0:
+                score += 10
+
+    return min(score, 100)
+
+
+# ── 辅助函数 ──
 
 
 def _get_sector_stage(sector_name: str, hot_sectors: list[SectorInfo]) -> str:
@@ -247,6 +621,41 @@ def _get_sector_stage(sector_name: str, hot_sectors: list[SectorInfo]) -> str:
     return "mid"
 
 
+def _get_sector_heat(sector_name: str, hot_sectors: list[SectorInfo]) -> float:
+    """获取板块热度得分"""
+    for s in hot_sectors:
+        if s.sector_name == sector_name:
+            return s.heat_score
+    return 30.0
+
+
+def _score_capital_simple(stock: dict) -> float:
+    """资金流简单评分（仅基于已有数据，不额外调 API）"""
+    main_net = stock.get("main_net_inflow", 0) or 0
+    inflow_ratio = stock.get("inflow_ratio", 0) or 0
+
+    score = 0
+    if main_net > 10000:
+        score += 60
+    elif main_net > 5000:
+        score += 45
+    elif main_net > 2000:
+        score += 30
+    elif main_net > 0:
+        score += 15
+
+    if inflow_ratio > 15:
+        score += 40
+    elif inflow_ratio > 10:
+        score += 30
+    elif inflow_ratio > 5:
+        score += 20
+    elif inflow_ratio > 0:
+        score += 10
+
+    return min(score, 100)
+
+
 def _score_to_level(score: float) -> str:
     if score >= 80:
         return "强烈推荐"
@@ -259,38 +668,45 @@ def _score_to_level(score: float) -> str:
 
 
 def _generate_reasons(
-    stock: dict, tech: TechnicalSignal | None,
-    market: MarketTemperature, intraday: bool = False,
+    stock: dict, entry_signal: dict, tech: TechnicalSignal | None,
+    df, intraday: bool = False,
 ) -> list[str]:
     """生成推荐理由"""
+    import pandas as pd
     reasons = []
-    entry_type = stock.get("entry_signal_type", "none")
-    signal_map = {"breakout": "突破型", "pullback": "回踩型", "launch": "启动型"}
-    entry_label = signal_map.get(entry_type, "")
+    signal_type = entry_signal.get("signal_type")
+    details = entry_signal.get("details", {})
+    signal_map = {EntrySignal.BREAKOUT: "突破型", EntrySignal.PULLBACK: "回踩型", EntrySignal.LAUNCH: "启动型"}
+    entry_label = signal_map.get(signal_type, "")
 
     # 入场信号
-    if entry_label:
-        details = stock.get("entry_signal_details", {})
-        if entry_type == "breakout":
+    if entry_label and signal_type:
+        st = signal_type.value
+        if st == "breakout":
             breakout_pct = details.get("breakout_pct", 0)
             vol_ratio = details.get("volume_ratio", 0)
             reasons.append(f"放量突破20日阻力位（涨幅{breakout_pct:.1f}%，量比{vol_ratio:.1f}倍）")
-        elif entry_type == "pullback":
+        elif st == "pullback":
             support = details.get("support_ma", "")
             shrink = details.get("volume_shrink_ratio", 0)
             reasons.append(f"缩量回踩{support}支撑（量能收缩至{shrink:.0%}）")
-        elif entry_type == "launch":
+        elif st == "launch":
             range_pct = details.get("price_range_pct", 0)
             shrink = details.get("volume_shrink_ratio", 0)
             reasons.append(f"高位缩量整理{range_pct:.1f}%后即将变盘（量缩至{shrink:.0%}）")
 
     # 供需分析
-    vol_trend = stock.get("volume_trend", "")
-    ds_ratio = stock.get("demand_supply_ratio", 1)
-    if ds_ratio > 1.5:
-        reasons.append(f"需求主导（上涨均量/下跌均量={ds_ratio:.1f}）")
-    elif vol_trend == "expanding":
-        reasons.append("量能逐步放大，资金持续介入")
+    if len(df) >= 10:
+        recent = df.tail(10)
+        up_days = recent[recent["pct_chg"] > 0]
+        down_days = recent[recent["pct_chg"] <= 0]
+        if len(up_days) > 0 and len(down_days) > 0:
+            avg_up_vol = up_days["vol"].mean()
+            avg_down_vol = down_days["vol"].mean()
+            if avg_down_vol > 0:
+                ds_ratio = avg_up_vol / avg_down_vol
+                if ds_ratio > 1.5:
+                    reasons.append(f"需求主导（上涨均量/下跌均量={ds_ratio:.1f}）")
 
     # 资金流
     main_net = stock.get("main_net_inflow", 0)
@@ -302,19 +718,7 @@ def _generate_reasons(
     # 板块
     sector = stock.get("sector", "")
     if sector:
-        reasons.append(f"所属板块【{sector}】")
-
-    # 技术面
-    if tech:
-        tech_reasons = []
-        if tech.ma_bullish:
-            tech_reasons.append("均线多头排列")
-        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_reasons:
-            reasons.append("技术面: " + "、".join(tech_reasons))
+        reasons.append(f"所属热门板块【{sector}】")
 
     return reasons[:3]
 
diff --git a/backend/app/llm/__pycache__/prompts.cpython-313.pyc b/backend/app/llm/__pycache__/prompts.cpython-313.pyc
index bd78c2ea..986ccbc2 100644
Binary files a/backend/app/llm/__pycache__/prompts.cpython-313.pyc and b/backend/app/llm/__pycache__/prompts.cpython-313.pyc differ
diff --git a/backend/app/llm/__pycache__/tool_executor.cpython-313.pyc b/backend/app/llm/__pycache__/tool_executor.cpython-313.pyc
index 2234162e..38a1e683 100644
Binary files a/backend/app/llm/__pycache__/tool_executor.cpython-313.pyc and b/backend/app/llm/__pycache__/tool_executor.cpython-313.pyc differ
diff --git a/backend/app/llm/__pycache__/tools.cpython-313.pyc b/backend/app/llm/__pycache__/tools.cpython-313.pyc
index d64b94e2..642eefe9 100644
Binary files a/backend/app/llm/__pycache__/tools.cpython-313.pyc and b/backend/app/llm/__pycache__/tools.cpython-313.pyc differ
diff --git a/backend/app/llm/prompts.py b/backend/app/llm/prompts.py
index b1053d18..7df91ac6 100644
--- a/backend/app/llm/prompts.py
+++ b/backend/app/llm/prompts.py
@@ -42,6 +42,11 @@ CHAT_SYSTEM_PROMPT = """\
 3. 搜索股票代码
 4. 基于数据给出专业的市场分析和投资建议
 
+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
+- 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数据
+- 不要使用过时的数据，必须先调用工具获取最新数据再回答
+
 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
 2. 分析要结合 A 股市场特点（资金驱动、板块轮动、情绪周期）
diff --git a/backend/app/llm/tool_executor.py b/backend/app/llm/tool_executor.py
index 3d6b13fa..4444493c 100644
--- a/backend/app/llm/tool_executor.py
+++ b/backend/app/llm/tool_executor.py
@@ -33,6 +33,8 @@ async def execute_tool(name: str, arguments: dict) -> str:
             return await _get_stock_technical_signal(arguments["ts_code"])
         elif name == "get_sector_performance":
             return await _get_sector_performance(arguments["sector_name"])
+        elif name == "get_realtime_indices":
+            return await _get_realtime_indices()
         else:
             return json.dumps({"error": f"未知工具: {name}"}, ensure_ascii=False)
     except Exception as e:
@@ -146,3 +148,41 @@ async def _get_sector_performance(sector_name: str) -> str:
         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)
+
+
+async def _get_realtime_indices() -> str:
+    """获取指数实时行情数据（盘中用腾讯实时数据）"""
+    from app.data.tencent_client import get_index_realtime
+    from app.config import is_market_session
+
+    is_trading = is_market_session()
+
+    try:
+        index_data = await get_index_realtime()
+        if not index_data:
+            return json.dumps({"error": "获取指数数据失败"}, ensure_ascii=False)
+
+        # 格式化数据
+        results = []
+        for ts_code, data in index_data.items():
+            results.append({
+                "ts_code": ts_code,
+                "name": data.get("name", ts_code),
+                "price": round(data.get("price", 0), 2),
+                "pct_chg": round(data.get("pct_chg", 0), 2),
+                "volume": data.get("volume", 0),
+                "amount": data.get("amount", 0),
+                "high": round(data.get("high", 0), 2),
+                "low": round(data.get("low", 0), 2),
+                "open": round(data.get("open", 0), 2),
+                "pre_close": round(data.get("pre_close", 0), 2),
+            })
+
+        return json.dumps({
+            "is_realtime": is_trading,
+            "mode": "盘中实时" if is_trading else "盘后收盘",
+            "indices": results
+        }, ensure_ascii=False, default=str)
+    except Exception as e:
+        logger.error(f"获取实时指数失败: {e}")
+        return json.dumps({"error": f"获取指数数据失败: {e}"}, ensure_ascii=False)
diff --git a/backend/app/llm/tools.py b/backend/app/llm/tools.py
index bb240030..85247ca7 100644
--- a/backend/app/llm/tools.py
+++ b/backend/app/llm/tools.py
@@ -138,4 +138,16 @@ CHAT_TOOLS = [
             },
         },
     },
+    {
+        "type": "function",
+        "function": {
+            "name": "get_realtime_indices",
+            "description": "获取指数实时行情数据，包括上证指数、深证成指、创业板指的实时涨跌幅和成交数据。盘中时段返回实时数据，盘后返回当日收盘数据",
+            "parameters": {
+                "type": "object",
+                "properties": {},
+                "required": [],
+            },
+        },
+    },
 ]
diff --git a/frontend/.next/app-build-manifest.json b/frontend/.next/app-build-manifest.json
index 687ee4ea..1d48e9dc 100644
--- a/frontend/.next/app-build-manifest.json
+++ b/frontend/.next/app-build-manifest.json
@@ -20,6 +20,11 @@
       "static/chunks/webpack.js",
       "static/chunks/main-app.js",
       "static/chunks/app/sectors/page.js"
+    ],
+    "/_not-found/page": [
+      "static/chunks/webpack.js",
+      "static/chunks/main-app.js",
+      "static/chunks/app/_not-found/page.js"
     ]
   }
 }
\ No newline at end of file
diff --git a/frontend/.next/server/app-paths-manifest.json b/frontend/.next/server/app-paths-manifest.json
index 092d16ac..1c039125 100644
--- a/frontend/.next/server/app-paths-manifest.json
+++ b/frontend/.next/server/app-paths-manifest.json
@@ -1,5 +1,6 @@
 {
+  "/_not-found/page": "app/_not-found/page.js",
   "/page": "app/page.js",
-  "/sectors/page": "app/sectors/page.js",
-  "/recommendations/page": "app/recommendations/page.js"
+  "/recommendations/page": "app/recommendations/page.js",
+  "/sectors/page": "app/sectors/page.js"
 }
\ No newline at end of file
diff --git a/frontend/.next/server/webpack-runtime.js b/frontend/.next/server/webpack-runtime.js
index 0a157f59..2854777e 100644
--- a/frontend/.next/server/webpack-runtime.js
+++ b/frontend/.next/server/webpack-runtime.js
@@ -125,7 +125,7 @@
 /******/ 	
 /******/ 	/* webpack/runtime/getFullHash */
 /******/ 	(() => {
-/******/ 		__webpack_require__.h = () => ("b931683b67a20916")
+/******/ 		__webpack_require__.h = () => ("37ba5b2074c7bffb")
 /******/ 	})();
 /******/ 	
 /******/ 	/* webpack/runtime/hasOwnProperty shorthand */
diff --git a/frontend/.next/trace b/frontend/.next/trace
index 0b809950..3ba30e11 100644
--- a/frontend/.next/trace
+++ b/frontend/.next/trace
@@ -19,3 +19,4 @@
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