diff --git a/backend/app/crypto_agent/llm_signal_analyzer.py b/backend/app/crypto_agent/llm_signal_analyzer.py
index d1e4b1e..c262af9 100644
--- a/backend/app/crypto_agent/llm_signal_analyzer.py
+++ b/backend/app/crypto_agent/llm_signal_analyzer.py
@@ -199,13 +199,35 @@ class LLMSignalAnalyzer:
 7. entry_type 必须明确：信号已触发用 market，等待更好价位用 limit
 8. **position_size 必须明确**：根据信号质量和持仓情况给出 heavy/medium/light"""
 
-    def __init__(self):
-        """初始化分析器"""
+    def __init__(self, agent_type: str = "crypto"):
+        """初始化分析器
+
+        Args:
+            agent_type: 智能体类型，支持 'crypto', 'stock', 'smart'
+        """
         from app.config import get_settings
         self.news_service = get_news_service()
         settings = get_settings()
-        self.model_override = getattr(settings, 'crypto_agent_model', None)
-        logger.info(f"LLM 信号分析器初始化完成（含新闻舆情，模型: {self.model_override or '默认'}）")
+
+        # 根据智能体类型选择模型配置
+        model_config_map = {
+            'crypto': 'crypto_agent_model',
+            'stock': 'stock_agent_model',
+            'smart': 'smart_agent_model'
+        }
+
+        config_key = model_config_map.get(agent_type, 'crypto_agent_model')
+        self.model_override = getattr(settings, config_key, None)
+        self.agent_type = agent_type
+
+        agent_name_map = {
+            'crypto': '加密货币',
+            'stock': '美股',
+            'smart': '智能助手'
+        }
+        agent_name = agent_name_map.get(agent_type, '未知')
+
+        logger.info(f"LLM 信号分析器初始化完成（{agent_name}，模型: {self.model_override or '默认'}）")
 
     async def analyze(self, symbol: str, data: Dict[str, pd.DataFrame],
                        symbols: List[str] = None,
diff --git a/backend/app/stock_agent/stock_agent.py b/backend/app/stock_agent/stock_agent.py
index 8236cfd..c13afc8 100644
--- a/backend/app/stock_agent/stock_agent.py
+++ b/backend/app/stock_agent/stock_agent.py
@@ -25,7 +25,7 @@ class StockAgent:
         self.yfinance = get_yfinance_service()
         self.feishu = get_feishu_service()
         self.telegram = get_telegram_service()
-        self.llm_analyzer = LLMSignalAnalyzer()
+        self.llm_analyzer = LLMSignalAnalyzer(agent_type="stock")  # 指定使用 stock 模型配置
         self.signal_db = get_signal_db_service()  # 信号数据库服务
 
         # 状态管理
@@ -88,12 +88,19 @@ class StockAgent:
                     # 继续等待下一个整点
                     continue
 
-                # 在交易时间内，分析所有股票
+                # 在交易时间内，分析所有股票并收集结果
                 logger.info(f"开始分析 {len(self.symbols)} 只股票")
+                analysis_results = []
+
                 for symbol in self.symbols:
                     if not self.running:
                         break
-                    await self.analyze_symbol(symbol)
+                    result = await self.analyze_symbol(symbol)
+                    if result:
+                        analysis_results.append(result)
+
+                # 生成并发送汇总报告
+                await self._send_summary_report(analysis_results)
 
                 logger.info("本次分析完成")
 
@@ -143,13 +150,24 @@ class StockAgent:
 
         return False
 
-    async def analyze_symbol(self, symbol: str):
+    async def analyze_symbol(self, symbol: str) -> Optional[Dict[str, Any]]:
         """
         分析单个股票
 
         Args:
             symbol: 股票代码
+
+        Returns:
+            分析结果字典，包含股票信息和信号
         """
+        result = {
+            'symbol': symbol,
+            'current_price': 0,
+            'signals': [],
+            'analysis_summary': '',
+            'notified': False
+        }
+
         try:
             # 1. 获取多时间周期数据
             data = self.yfinance.get_multi_timeframe_data(symbol)
@@ -157,14 +175,15 @@ class StockAgent:
             # 2. 验证数据完整性
             if not self._validate_data(data):
                 logger.warning(f"{symbol} 数据不完整，跳过本次分析")
-                return
+                return result
 
             # 3. 获取当前价格
             ticker = self.yfinance.get_ticker(symbol)
             if not ticker:
                 logger.warning(f"无法获取 {symbol} 当前价格")
-                return
+                return result
             current_price = ticker['lastPrice']
+            result['current_price'] = current_price
 
             logger.info(f"\n{'='*60}")
             logger.info(f"📊 分析 {symbol} @ ${current_price:,.2f}")
@@ -172,19 +191,20 @@ class StockAgent:
 
             # 4. LLM 分析
             logger.info(f"\n🤖 【LLM 分析中...】")
-            result = await self.llm_analyzer.analyze(
+            analysis = await self.llm_analyzer.analyze(
                 symbol, data,
                 symbols=self.symbols,
                 position_info=None  # 美股不跟踪持仓
             )
 
             # 输出分析摘要
-            summary = result.get('analysis_summary', '无')
+            summary = analysis.get('analysis_summary', '无')
+            result['analysis_summary'] = summary
             logger.info(f"   市场状态: {summary}")
 
             # 输出新闻情绪
-            news_sentiment = result.get('news_sentiment', '')
-            news_impact = result.get('news_impact', '')
+            news_sentiment = analysis.get('news_sentiment', '')
+            news_impact = analysis.get('news_impact', '')
             if news_sentiment:
                 sentiment_icon = {'positive': '📈', 'negative': '📉', 'neutral': '➖'}.get(news_sentiment, '')
                 logger.info(f"   新闻情绪: {sentiment_icon} {news_sentiment}")
@@ -192,7 +212,7 @@ class StockAgent:
                 logger.info(f"   消息影响: {news_impact}")
 
             # 输出关键价位
-            levels = result.get('key_levels', {})
+            levels = analysis.get('key_levels', {})
             if levels.get('support') or levels.get('resistance'):
                 support_str = ', '.join([f"${s:,.2f}" for s in levels.get('support', [])[:2]])
                 resistance_str = ', '.join([f"${r:,.2f}" for r in levels.get('resistance', [])[:2]])
@@ -200,11 +220,12 @@ class StockAgent:
                 logger.info(f"   阻力位: {resistance_str or '-'}")
 
             # 5. 处理信号
-            signals = result.get('signals', [])
+            signals = analysis.get('signals', [])
+            result['signals'] = signals
 
             if not signals:
                 logger.info(f"\n⏸️  结论: 无交易信号，继续观望")
-                return
+                return result
 
             # 输出所有信号
             logger.info(f"\n🎯 【发现 {len(signals)} 个信号】")
@@ -229,30 +250,35 @@ class StockAgent:
 
             if not best_signal:
                 logger.info(f"\n⏸️  信号质量不高，不发送通知")
-                return
+                return result
 
             # 检查置信度阈值
             threshold = self.settings.stock_llm_threshold * 100
             if best_signal.get('confidence', 0) < threshold:
                 logger.info(f"\n⏸️  置信度不足 ({best_signal.get('confidence', 0)}% < {threshold}%)")
-                return
+                return result
 
             # 检查冷却时间
             if not self._should_send_signal(symbol, best_signal):
                 logger.info(f"\n⏸️  信号冷却中，不发送通知")
-                return
+                return result
 
             # 发送通知
             await self._send_signal_notification(symbol, best_signal, current_price)
+            result['notified'] = True
+            result['best_signal'] = best_signal
 
             # 更新状态
             self.last_signals[symbol] = best_signal
             self.signal_cooldown[symbol] = datetime.now()
 
+            return result
+
         except Exception as e:
             logger.error(f"❌ 分析 {symbol} 出错: {e}")
             import traceback
             logger.error(traceback.format_exc())
+            return result
 
     def _get_best_signal(self, signals: List[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
         """获取最佳信号"""
@@ -301,8 +327,11 @@ class StockAgent:
             title = card['title']
             content = card['content']
 
+            # 根据信号方向选择颜色
+            color = "green" if signal.get('action') == 'buy' else "red"
+
             # 发送到飞书
-            await self.feishu.send_markdown(title, content)
+            await self.feishu.send_card(title, content, color)
 
             # 发送到 Telegram
             await self.telegram.send_message(self.llm_analyzer.format_signal_message(signal, symbol))
@@ -360,6 +389,138 @@ class StockAgent:
             }
         }
 
+    async def _send_summary_report(self, results: List[Dict[str, Any]]):
+        """
+        生成并发送分析汇总报告
+
+        Args:
+            results: 所有股票的分析结果列表
+        """
+        try:
+            now = datetime.now()
+            total = len(results)
+            with_signals = [r for r in results if r.get('signals')]
+            notified = [r for r in results if r.get('notified')]
+
+            # 统计信号
+            buy_signals = []
+            sell_signals = []
+            high_quality_signals = []  # A/B级信号
+
+            for r in with_signals:
+                for sig in r.get('signals', []):
+                    sig['symbol'] = r['symbol']
+                    sig['current_price'] = r.get('current_price', 0)
+
+                    if sig.get('action') == 'buy':
+                        buy_signals.append(sig)
+                    elif sig.get('action') == 'sell':
+                        sell_signals.append(sig)
+
+                    if sig.get('grade') in ['A', 'B']:
+                        high_quality_signals.append(sig)
+
+            # 按置信度排序
+            high_quality_signals.sort(key=lambda x: x.get('confidence', 0), reverse=True)
+
+            # 构建汇总报告
+            logger.info(f"\n{'='*80}")
+            logger.info(f"📊 美股分析汇总报告")
+            logger.info(f"{'='*80}")
+            logger.info(f"时间: {now.strftime('%Y-%m-%d %H:%M:%S')}")
+            logger.info(f"分析数量: {total} 只股票")
+            logger.info(f"有信号: {len(with_signals)} 只")
+            logger.info(f"已通知: {len(notified)} 只")
+            logger.info(f"")
+
+            # 显示高等级信号
+            if high_quality_signals:
+                logger.info(f"⭐ 高等级信号 (A/B级): {len(high_quality_signals)} 个")
+                for sig in high_quality_signals[:10]:  # 最多显示10个
+                    symbol = sig['symbol']
+                    action = '🟢 做多' if sig.get('action') == 'buy' else '🔴 做空'
+                    grade = sig.get('grade', 'D')
+                    confidence = sig.get('confidence', 0)
+                    price = sig.get('current_price', 0)
+                    entry = sig.get('entry_price', 0)
+
+                    logger.info(f"   {symbol} {action} [{grade}级] {confidence}% @ ${price:,.2f}")
+                    if entry > 0:
+                        logger.info(f"      入场: ${entry:,.2f}")
+                logger.info(f"")
+
+            # 统计汇总
+            logger.info(f"📈 做多信号: {len(buy_signals)} 个")
+            logger.info(f"📉 做空信号: {len(sell_signals)} 个")
+            logger.info(f"{'='*80}\n")
+
+            # 发送飞书汇总
+            await self._send_feishu_summary(
+                now, total, with_signals, notified,
+                buy_signals, sell_signals, high_quality_signals
+            )
+
+        except Exception as e:
+            logger.error(f"生成汇总报告失败: {e}")
+            import traceback
+            logger.error(traceback.format_exc())
+
+    async def _send_feishu_summary(
+        self,
+        now: datetime,
+        total: int,
+        with_signals: List,
+        notified: List,
+        buy_signals: List,
+        sell_signals: List,
+        high_quality_signals: List
+    ):
+        """发送飞书汇总报告"""
+        try:
+            # 构建内容
+            content_parts = [
+                f"**美股分析汇总报告**",
+                f"",
+                f"⏰ 时间: {now.strftime('%Y-%m-%d %H:%M')}",
+                f"",
+                f"📊 **分析概况**",
+                f"• 分析总数: {total} 只",
+                f"• 发现信号: {len(with_signals)} 只",
+                f"• 已发通知: {len(notified)} 只",
+                f"",
+            ]
+
+            # 高等级信号
+            if high_quality_signals:
+                content_parts.append(f"⭐ **高等级信号 (A/B级)**")
+                for sig in high_quality_signals[:5]:
+                    symbol = sig['symbol']
+                    action = '🟢 做多' if sig.get('action') == 'buy' else '🔴 做空'
+                    grade = sig.get('grade', 'D')
+                    confidence = sig.get('confidence', 0)
+                    content_parts.append(f"• {symbol} {action} {grade}级 {confidence}%")
+                content_parts.append(f"")
+
+            # 信号统计
+            content_parts.extend([
+                f"📈 做多信号: {len(buy_signals)} 个",
+                f"📉 做空信号: {len(sell_signals)} 个",
+                f"",
+                f"*⚠️ 仅供参考，不构成投资建议*"
+            ])
+
+            content = "\n".join(content_parts)
+
+            # 发送飞书
+            title = f"📊 美股分析汇总 ({now.strftime('%H:%M')})"
+            color = "blue"
+
+            await self.feishu.send_card(title, content, color)
+            logger.info("✅ 汇总报告已发送到飞书")
+
+        except Exception as e:
+            logger.error(f"发送飞书汇总失败: {e}")
+
 
 # 全局单例
 _stock_agent: Optional[StockAgent] = None
diff --git a/scripts/stock.sh b/scripts/stock.sh
index 0fd31bc..04b0776 100755
--- a/scripts/stock.sh
+++ b/scripts/stock.sh
@@ -11,17 +11,29 @@ cd "$(dirname "$0")/.." || exit 1
 if [ $# -eq 0 ]; then
     # 无参数，分析配置的所有股票
     echo "📊 分析配置的所有股票（将发送通知）..."
-    # 使用更可靠的方式获取股票代码
-    python3 -c "
-import sys
-sys.path.insert(0, 'backend')
-from app.config import get_settings
-settings = get_settings()
-symbols = [s.strip() for s in settings.stock_symbols.split(',') if s.strip()]
-print(' '.join(symbols))
-" 2>/dev/null | while read -r symbols; do
-        python3 scripts/test_stock.py $symbols
-    done
+
+    # 直接从 .env 文件读取股票代码
+    if [ -f .env ]; then
+        # 使用 grep 提取 STOCK_SYMBOLS 行，然后提取值
+        STOCKS=$(grep "^STOCK_SYMBOLS=" .env | cut -d'=' -f2)
+
+        if [ -z "$STOCKS" ]; then
+            echo "❌ 无法从 .env 文件读取股票列表"
+            exit 1
+        fi
+
+        echo "📋 股票列表: $STOCKS"
+
+        # 使用 read array 来正确处理空格分隔的股票代码
+        # 将逗号分隔转换为空格分隔
+        STOCKS_SPACE=$(echo "$STOCKS" | tr ',' ' ')
+
+        # 直接传递给 test_stock.py（不要用 while read 循环）
+        python3 scripts/test_stock.py $STOCKS_SPACE
+    else
+        echo "❌ .env 文件不存在"
+        exit 1
+    fi
 else
     # 分析指定的股票 - 使用引号正确传递参数
     echo "📊 分析股票: $*（将发送通知）"
diff --git a/scripts/test_stock.py b/scripts/test_stock.py
index e6181b5..8de92ca 100755
--- a/scripts/test_stock.py
+++ b/scripts/test_stock.py
@@ -20,6 +20,7 @@ from app.services.yfinance_service import get_yfinance_service
 from app.services.feishu_service import get_feishu_service
 from app.services.telegram_service import get_telegram_service
 from app.crypto_agent.llm_signal_analyzer import LLMSignalAnalyzer
+from app.config import get_settings
 from app.utils.logger import logger
 
 
@@ -29,7 +30,21 @@ async def analyze(symbol: str, send_notification: bool = True):
     Args:
         symbol: 股票代码
         send_notification: 是否发送通知（默认True）
+
+    Returns:
+        分析结果字典
     """
+    result = {
+        'symbol': symbol,
+        'price': 0,
+        'signals': [],
+        'notified': False
+    }
+
+    # 获取配置
+    settings = get_settings()
+    threshold = settings.stock_llm_threshold * 100  # 转换为百分比
+
     print(f"\n{'='*60}")
     print(f"📊 分析 {symbol}")
     print(f"{'='*60}")
@@ -37,7 +52,7 @@ async def analyze(symbol: str, send_notification: bool = True):
     try:
         # 获取服务
         yf_service = get_yfinance_service()
-        llm = LLMSignalAnalyzer()
+        llm = LLMSignalAnalyzer(agent_type="stock")  # 指定使用 stock 模型配置
         feishu = get_feishu_service()
         telegram = get_telegram_service()
 
@@ -46,10 +61,11 @@ async def analyze(symbol: str, send_notification: bool = True):
         ticker = yf_service.get_ticker(symbol)
         if not ticker:
             print(f"❌ 无法获取 {symbol} 行情")
-            return
+            return result
 
         price = ticker['lastPrice']
         change = ticker['priceChangePercent']
+        result['price'] = price
         print(f"价格: ${price:,.2f} ({change:+.2f}%)")
         print(f"成交量: {ticker['volume']:,}")
 
@@ -59,17 +75,18 @@ async def analyze(symbol: str, send_notification: bool = True):
 
         if not data:
             print(f"❌ 无法获取K线数据")
-            return
+            return result
 
         print(f"时间周期: {', '.join(data.keys())}")
 
         # LLM分析
         print(f"\n🤖 LLM分析中...\n")
-        result = await llm.analyze(symbol, data, symbols=[symbol], position_info=None)
+        analysis = await llm.analyze(symbol, data, symbols=[symbol], position_info=None)
 
         # 输出结果
-        summary = result.get('analysis_summary', '')
-        signals = result.get('signals', [])
+        summary = analysis.get('analysis_summary', '')
+        signals = analysis.get('signals', [])
+        result['signals'] = signals
 
         print(f"市场状态: {summary}")
 
@@ -100,11 +117,11 @@ async def analyze(symbol: str, send_notification: bool = True):
                     short_reason = reason[:80] + "..." if len(reason) > 80 else reason
                     print(f"  理由: {short_reason}")
 
-            # 发送通知（仅发送置信度 >= 60% 的信号）
+            # 发送通知（仅发送置信度 >= 阈值的信号）
             if send_notification:
                 best_signal = None
                 for sig in signals:
-                    if sig.get('confidence', 0) >= 60 and sig.get('grade', 'D') != 'D':
+                    if sig.get('confidence', 0) >= threshold and sig.get('grade', 'D') != 'D':
                         best_signal = sig
                         break
 
@@ -120,15 +137,211 @@ async def analyze(symbol: str, send_notification: bool = True):
                     await feishu.send_card(title, content, color)
                     await telegram.send_message(llm.format_signal_message(best_signal, symbol))
                     print(f"\n📬 通知已发送：{title}")
+                    result['notified'] = True
                 else:
-                    print(f"\n⏸️  置信度不足，不发送通知")
+                    print(f"\n⏸️  置信度不足，不发送通知（阈值: {threshold}%）")
         else:
             print(f"\n⏸️  无交易信号")
 
+        return result
+
     except Exception as e:
         print(f"❌ 错误: {e}")
         import traceback
         traceback.print_exc()
+        return result
+
+
+def print_summary_report(results: list, send_notification: bool = True):
+    """打印汇总报告并发送通知
+
+    Args:
+        results: 分析结果列表
+        send_notification: 是否发送通知（默认True）
+    """
+    from app.config import get_settings
+    settings = get_settings()
+    threshold = settings.stock_llm_threshold * 100  # 获取阈值
+
+    total = len(results)
+    with_signals = [r for r in results if r.get('signals')]
+    notified = [r for r in results if r.get('notified')]
+
+    # 统计信号
+    buy_count = 0
+    sell_count = 0
+    high_quality_signals = []  # A/B级信号且达到阈值
+    all_signals = []  # 所有信号
+
+    for r in with_signals:
+        for sig in r.get('signals', []):
+            sig['symbol'] = r['symbol']
+            sig['current_price'] = r.get('price', 0)
+            all_signals.append(sig)
+
+            if sig.get('action') == 'buy':
+                buy_count += 1
+            elif sig.get('action') == 'sell':
+                sell_count += 1
+
+            # 只统计达到阈值的A/B级信号
+            if sig.get('grade') in ['A', 'B'] and sig.get('confidence', 0) >= threshold:
+                high_quality_signals.append(sig)
+
+    # 按置信度排序
+    high_quality_signals.sort(key=lambda x: x.get('confidence', 0), reverse=True)
+    all_signals.sort(key=lambda x: x.get('confidence', 0), reverse=True)
+
+    # 打印汇总
+    print("\n" + "="*80)
+    print("📊 美股分析汇总报告")
+    print("="*80)
+    print(f"分析数量: {total} 只股票")
+    print(f"有信号: {len(with_signals)} 只")
+    print(f"已通知: {len(notified)} 只")
+    print(f"通知阈值: {threshold}%")
+    print("")
+
+    # 显示高等级信号（达到阈值的）
+    if high_quality_signals:
+        print(f"⭐ 高等级信号达到阈值 (A/B级 >= {threshold}%): {len(high_quality_signals)} 个")
+        for sig in high_quality_signals[:10]:
+            symbol = sig['symbol']
+            action = '🟢 做多' if sig.get('action') == 'buy' else '🔴 做空'
+            grade = sig.get('grade', 'D')
+            confidence = sig.get('confidence', 0)
+            price = sig.get('current_price', 0)
+            entry = sig.get('entry_price', 0)
+
+            print(f"   {symbol} {action} [{grade}级] {confidence}% @ ${price:,.2f}")
+            if entry > 0:
+                print(f"      入场: ${entry:,.2f}")
+        print("")
+
+    # 显示未达到阈值但质量不错的信号
+    below_threshold = [s for s in all_signals
+                       if s.get('grade') in ['A', 'B'] and s.get('confidence', 0) < threshold]
+    if below_threshold:
+        print(f"⚠️  以下信号未达到通知阈值 ({threshold}%):")
+        for sig in below_threshold[:10]:
+            symbol = sig['symbol']
+            action = '🟢 做多' if sig.get('action') == 'buy' else '🔴 做空'
+            grade = sig.get('grade', 'D')
+            confidence = sig.get('confidence', 0)
+            print(f"   {symbol} {action} {grade}级 {confidence}%")
+        print("")
+
+    # 统计汇总
+    print(f"📈 做多信号: {buy_count} 个")
+    print(f"📉 做空信号: {sell_count} 个")
+    print("="*80)
+
+    # 发送汇总通知
+    if send_notification:
+        asyncio.run(send_summary_notification(
+            results, total, with_signals, notified,
+            buy_count, sell_count, high_quality_signals, all_signals, threshold
+        ))
+
+
+async def send_summary_notification(
+    results: list,
+    total: int,
+    with_signals: list,
+    notified: list,
+    buy_count: int,
+    sell_count: int,
+    high_quality_signals: list,
+    all_signals: list,
+    threshold: float
+):
+    """发送汇总报告到飞书和Telegram
+
+    Args:
+        results: 分析结果列表
+        total: 总数
+        with_signals: 有信号的股票列表
+        notified: 已通知的股票列表
+        buy_count: 做多信号数量
+        sell_count: 做空信号数量
+        high_quality_signals: 达到阈值的高等级信号列表
+        all_signals: 所有信号列表
+        threshold: 通知阈值
+    """
+    try:
+        from datetime import datetime
+        feishu = get_feishu_service()
+        telegram = get_telegram_service()
+
+        now = datetime.now()
+
+        # 构建飞书汇总内容
+        content_parts = [
+            f"**📊 美股分析汇总报告**",
+            f"",
+            f"⏰ 时间: {now.strftime('%Y-%m-%d %H:%M')}",
+            f"",
+            f"📊 **分析概况**",
+            f"• 分析总数: {total} 只",
+            f"• 发现信号: {len(with_signals)} 只",
+            f"• 已发通知: {len(notified)} 只",
+            f"• 通知阈值: {threshold:.0f}%",
+            f"",
+        ]
+
+        # 高等级信号（达到阈值的）
+        if high_quality_signals:
+            content_parts.append(f"⭐ **高等级信号 (A/B级 ≥ {threshold:.0f}%)**")
+            for sig in high_quality_signals[:5]:
+                symbol = sig['symbol']
+                action = '🟢 做多' if sig.get('action') == 'buy' else '🔴 做空'
+                grade = sig.get('grade', 'D')
+                confidence = sig.get('confidence', 0)
+                content_parts.append(f"• {symbol} {action} {grade}级 {confidence}%")
+            content_parts.append(f"")
+
+        # 信号统计
+        content_parts.extend([
+            f"📈 做多信号: {buy_count} 个",
+            f"📉 做空信号: {sell_count} 个",
+            f"",
+            f"*⚠️ 仅供参考，不构成投资建议*"
+        ])
+
+        content = "\n".join(content_parts)
+
+        # 发送飞书
+        title = f"📊 美股分析汇总 ({now.strftime('%H:%M')})"
+        color = "blue"
+
+        await feishu.send_card(title, content, color)
+
+        # 发送 Telegram
+        telegram_msg = f"📊 *美股分析汇总*\n\n"
+        telegram_msg += f"时间: {now.strftime('%H:%M')}\n"
+        telegram_msg += f"分析: {total}只 | 信号: {len(with_signals)}只 | 通知: {len(notified)}只\n"
+        telegram_msg += f"阈值: {threshold:.0f}%\n\n"
+
+        if high_quality_signals:
+            telegram_msg += f"⭐ *高等级信号 (≥{threshold:.0f}%)*\n"
+            for sig in high_quality_signals[:5]:
+                symbol = sig['symbol']
+                action = '🟢 做多' if sig.get('action') == 'buy' else '🔴 做空'
+                grade = sig.get('grade', 'D')
+                confidence = sig.get('confidence', 0)
+                telegram_msg += f"{symbol} {action} {grade}级 {confidence}%\n"
+            telegram_msg += "\n"
+
+        telegram_msg += f"做多: {buy_count} | 做空: {sell_count}"
+
+        await telegram.send_message(telegram_msg)
+
+        print(f"\n📬 汇总报告已发送到飞书和Telegram")
+
+    except Exception as e:
+        print(f"❌ 发送汇总通知失败: {e}")
+        import traceback
+        traceback.print_exc()
 
 
 async def main():
@@ -167,8 +380,14 @@ async def main():
     print(f"股票: {', '.join(symbols)}")
     print(f"时间: {__import__('datetime').datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
 
+    # 收集所有分析结果
+    results = []
     for symbol in symbols:
-        await analyze(symbol.upper())
+        result = await analyze(symbol.upper(), send_notification=True)
+        results.append(result)
+
+    # 生成汇总报告并发送通知
+    print_summary_report(results, send_notification=True)
 
     print("\n" + "="*60)
     print("✅ 分析完成")