aupdate

README_DATABASE.md

@@ -0,0 +1,188 @@
+# 数据库存储和Web展示功能
+
+## 功能概述
+
+本系统现已支持将策略筛选结果按策略分组存储到SQLite数据库，并提供Web界面进行可视化展示。
+
+## 🗄️ 数据库设计
+
+### 主要表结构
+
+1. **strategies** - 策略表
+   - 存储不同的交易策略信息
+   - 支持策略配置参数的JSON存储
+
+2. **scan_sessions** - 扫描会话表
+   - 记录每次市场扫描的信息
+   - 关联策略ID，记录扫描统计数据
+
+3. **stock_signals** - 股票信号表
+   - 存储具体的股票筛选信号
+   - 包含完整的K线数据和技术指标
+
+4. **pullback_alerts** - 回踩监控表
+   - 存储回踩提醒信息
+   - 关联原始信号，记录回踩详情
+
+### 数据库文件位置
+- 数据库文件: `data/trading.db`
+- 建表脚本: `src/database/schema.sql`
+
+## 🌐 Web展示功能
+
+### 启动Web服务
+```bash
+# 方法1: 使用启动脚本
+python start_web.py
+
+# 方法2: 直接运行
+cd web
+python app.py
+```
+
+### 访问地址
+- 首页: http://localhost:8080
+- 交易信号: http://localhost:8080/signals
+- 回踩监控: http://localhost:8080/pullbacks
+
+### 页面功能
+
+#### 1. 首页 (/)
+- 策略统计概览
+- 最新交易信号列表
+- 最近回踩提醒
+
+#### 2. 交易信号页面 (/signals)
+- 详细的信号列表
+- 支持策略和时间范围筛选
+- 分页显示
+
+#### 3. 回踩监控页面 (/pullbacks)
+- 回踩提醒记录
+- 风险等级分类
+- 统计图表
+
+### API接口
+
+- `GET /api/signals` - 获取信号数据
+- `GET /api/stats` - 获取策略统计
+- `GET /api/pullbacks` - 获取回踩提醒
+
+## 🔧 使用方法
+
+### 1. 策略扫描自动存储
+
+当运行K线形态策略扫描时，结果会自动存储到数据库：
+
+```python
+from src.strategy.kline_pattern_strategy import KLinePatternStrategy
+
+# 初始化策略（会自动创建数据库连接）
+strategy = KLinePatternStrategy(data_fetcher, notification_manager, config)
+
+# 执行市场扫描（结果自动存储到数据库）
+results = strategy.scan_market()
+```
+
+### 2. 手动数据库操作
+
+```python
+from src.database.database_manager import DatabaseManager
+
+# 初始化数据库管理器
+db_manager = DatabaseManager()
+
+# 获取最新信号
+signals = db_manager.get_latest_signals(limit=50)
+
+# 获取策略统计
+stats = db_manager.get_strategy_stats()
+
+# 按日期范围查询
+from datetime import date, timedelta
+start_date = date.today() - timedelta(days=7)
+recent_signals = db_manager.get_signals_by_date_range(start_date)
+```
+
+### 3. 多策略支持
+
+系统支持多个策略的数据分别存储：
+
+```python
+# 创建新策略
+strategy_id = db_manager.create_or_update_strategy(
+    strategy_name="新策略名称",
+    strategy_type="strategy_type",
+    description="策略描述",
+    config={"param1": "value1"}
+)
+```
+
+## 📊 数据库维护
+
+### 清理旧数据
+```python
+# 清理90天前的数据
+db_manager.cleanup_old_data(days_to_keep=90)
+```
+
+### 备份数据库
+```bash
+# 复制数据库文件进行备份
+cp data/trading.db data/trading_backup_$(date +%Y%m%d).db
+```
+
+## 🎨 Web界面特性
+
+- **响应式设计**: 支持桌面和移动设备
+- **实时更新**: 数据自动刷新
+- **交互式表格**: 支持排序、筛选
+- **美观界面**: 使用Bootstrap框架
+- **数据导出**: 支持CSV格式导出
+
+## 🚀 性能优化
+
+- **缓存机制**: 股票名称缓存，避免重复请求
+- **分页显示**: 大数据量分页加载
+- **索引优化**: 数据库关键字段建立索引
+- **批量操作**: 信号批量保存，提高性能
+
+## 🔍 故障排除
+
+### 常见问题
+
+1. **数据库文件权限问题**
+   ```bash
+   # 检查data目录权限
+   ls -la data/
+   # 如果需要，修改权限
+   chmod 755 data/
+   chmod 644 data/trading.db
+   ```
+
+2. **Web界面无法访问**
+   - 检查Flask是否已安装: `pip install flask`
+   - 确认端口5000是否被占用
+   - 查看控制台错误信息
+
+3. **数据库连接失败**
+   - 确认data目录存在且可写
+   - 检查SQLite库是否正常工作
+
+### 日志查看
+```bash
+# 查看应用日志
+tail -f logs/trading.log
+
+# 查看Web服务日志
+# 直接在启动Web服务的终端查看
+```
+
+## 📈 未来扩展
+
+- [ ] 支持更多数据库后端（MySQL, PostgreSQL）
+- [ ] 添加用户认证和权限管理
+- [ ] 实现策略回测结果存储
+- [ ] 添加图表可视化功能
+- [ ] 支持策略参数在线调整
+- [ ] 实现数据导入导出功能

config/config.yaml

@@ -56,10 +56,14 @@ strategy:
     enabled: true                # 是否启用K线形态策略
     min_entity_ratio: 0.55       # 前两根阳线实体最小占振幅比例（55%）
     final_yang_min_ratio: 0.40   # 最后阳线实体最小占振幅比例（40%）
-    timeframes: ["daily", "weekly"]  # 支持的时间周期
+    timeframes: ["1h", "daily", "weekly"]  # 支持的时间周期
-    scan_stocks_count: 1000        # 扫描股票数量限制
+    scan_stocks_count: 5000        # 扫描股票数量限制
     analysis_days: 60            # 分析的历史天数
 
+    # 回踩监控配置
+    pullback_tolerance: 0.02     # 回踩容忍度（2%），价格接近阴线最高点的阈值
+    monitor_days: 30             # 监控回踩的天数（信号触发后30天内监控）
+
 # 监控配置
 monitor:
   # 实时监控

generate_test_data.py

@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+"""
+生成测试数据用于Web界面展示
+"""
+
+import sys
+from pathlib import Path
+from datetime import datetime, date, timedelta
+import random
+
+# 添加项目根目录到路径
+current_dir = Path(__file__).parent
+sys.path.insert(0, str(current_dir))
+
+from loguru import logger
+from src.database.database_manager import DatabaseManager
+from src.utils.config_loader import ConfigLoader
+from src.data.data_fetcher import ADataFetcher
+from src.utils.notification import NotificationManager
+from src.strategy.kline_pattern_strategy import KLinePatternStrategy
+
+
+def generate_test_data():
+    """生成测试数据"""
+    logger.remove()
+    logger.add(sys.stdout, level="INFO", format="{time:HH:mm:ss} | {level} | {message}")
+
+    print("🧪 生成测试数据")
+    print("=" * 40)
+
+    try:
+        # 初始化组件
+        logger.info("初始化组件...")
+        config_loader = ConfigLoader()
+        config = config_loader.load_config()
+
+        data_fetcher = ADataFetcher()
+        notification_manager = NotificationManager(config.get('notification', {}))
+        db_manager = DatabaseManager()
+
+        # 初始化策略
+        kline_config = config.get('strategy', {}).get('kline_pattern', {})
+        strategy = KLinePatternStrategy(
+            data_fetcher=data_fetcher,
+            notification_manager=notification_manager,
+            config=kline_config,
+            db_manager=db_manager
+        )
+
+        # 测试股票列表
+        test_stocks = ["000001.SZ", "000002.SZ", "600000.SH"]
+
+        logger.info(f"开始分析 {len(test_stocks)} 只股票...")
+
+        total_signals = 0
+        for i, stock_code in enumerate(test_stocks, 1):
+            logger.info(f"[{i}/{len(test_stocks)}] 分析 {stock_code}...")
+
+            try:
+                # 创建会话
+                session_id = db_manager.create_scan_session(
+                    strategy_id=strategy.strategy_id,
+                    data_source="测试数据生成"
+                )
+
+                # 分析股票
+                stock_results = strategy.analyze_stock(stock_code, session_id=session_id, days=60)
+
+                # 统计信号
+                stock_signals = sum(len(signals) for signals in stock_results.values())
+                total_signals += stock_signals
+
+                # 更新会话统计
+                db_manager.update_scan_session_stats(session_id, 1, stock_signals)
+
+                logger.info(f"   发现 {stock_signals} 个信号")
+
+            except Exception as e:
+                logger.error(f"   分析失败: {e}")
+
+        logger.info(f"✅ 数据生成完成！总共生成 {total_signals} 个信号")
+
+        # 验证数据
+        latest_signals = db_manager.get_latest_signals(limit=10)
+        logger.info(f"📊 数据库中共有 {len(latest_signals)} 条最新信号")
+
+        if not latest_signals.empty:
+            logger.info("📋 信号示例:")
+            for _, signal in latest_signals.head(3).iterrows():
+                logger.info(f"   {signal['stock_code']}({signal['stock_name']}) - {signal['breakout_price']:.2f}元")
+
+        print("\n" + "=" * 40)
+        print("🌐 现在可以访问Web界面查看数据:")
+        print("   http://localhost:8080")
+        print("=" * 40)
+
+    except Exception as e:
+        logger.error(f"❌ 生成测试数据失败: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    generate_test_data()

requirements.txt

@@ -42,3 +42,6 @@ flake8>=6.0.0
 # Jupyter notebook support
 jupyter>=1.0.0
 ipykernel>=6.25.0
+
+# Web framework
+flask>=2.3.0

src/data/data_fetcher.py

@@ -17,6 +17,15 @@ class ADataFetcher:
     def __init__(self):
         """初始化数据获取器"""
         self.client = adata
+
+        # 股票名称缓存机制
+        self._stock_name_cache = {}
+        self._stock_list_cache = None
+        self._hot_stocks_cache = None
+        self._east_stocks_cache = None
+        self._cache_timestamp = None
+        self._cache_duration = 3600  # 缓存1小时
+
         logger.info("AData客户端初始化完成")
 
     def get_stock_list(self, market: str = "A") -> pd.DataFrame:
@@ -37,6 +46,259 @@ class ADataFetcher:
             logger.error(f"获取股票列表失败: {e}")
             return pd.DataFrame()
 
+    def get_filtered_a_share_list(self, exclude_st: bool = True, exclude_bj: bool = True, min_market_cap: float = 2000000000) -> pd.DataFrame:
+        """
+        获取过滤后的A股股票列表
+
+        Args:
+            exclude_st: 是否排除ST股票
+            exclude_bj: 是否排除北交所股票
+            min_market_cap: 最小市值要求（元），默认20亿
+
+        Returns:
+            过滤后的股票列表DataFrame
+        """
+        try:
+            # 获取完整股票列表
+            all_stocks = self.get_stock_list()
+
+            if all_stocks.empty:
+                return pd.DataFrame()
+
+            filtered_stocks = all_stocks.copy()
+            original_count = len(filtered_stocks)
+
+            # 排除北交所股票
+            if exclude_bj:
+                before_count = len(filtered_stocks)
+                filtered_stocks = filtered_stocks[filtered_stocks['exchange'] != 'BJ']
+                bj_excluded = before_count - len(filtered_stocks)
+                logger.info(f"排除北交所股票: {bj_excluded}只")
+
+            # 排除ST股票（包含ST、*ST、PT、退市等）
+            if exclude_st:
+                before_count = len(filtered_stocks)
+                # 排除包含ST、*ST、PT、退等字符的股票
+                st_pattern = r'(\*?ST|PT|退|暂停)'
+                filtered_stocks = filtered_stocks[~filtered_stocks['short_name'].str.contains(st_pattern, na=False, case=False)]
+                st_excluded = before_count - len(filtered_stocks)
+                logger.info(f"排除ST等风险股票: {st_excluded}只")
+
+            # 基于实际市值的筛选
+            if min_market_cap > 0:
+                before_count = len(filtered_stocks)
+                filtered_stocks = self._filter_by_real_market_cap(filtered_stocks, min_market_cap)
+                cap_excluded = before_count - len(filtered_stocks)
+                logger.info(f"排除小市值股票(基于实际市值): {cap_excluded}只")
+
+            # 统计最终结果
+            final_count = len(filtered_stocks)
+            excluded_count = original_count - final_count
+
+            # 添加完整股票代码（带交易所后缀）
+            if not filtered_stocks.empty and 'exchange' in filtered_stocks.columns:
+                filtered_stocks['full_stock_code'] = filtered_stocks.apply(
+                    lambda row: f"{row['stock_code']}.{row['exchange']}", axis=1
+                )
+
+                exchange_counts = filtered_stocks['exchange'].value_counts().to_dict()
+                exchange_detail = " | ".join([f"{k}: {v}只" for k, v in exchange_counts.items()])
+                logger.info(f"✅ 获取过滤后A股列表成功")
+                logger.info(f"📊 原始股票: {original_count}只 | 过滤后: {final_count}只 | 排除: {excluded_count}只")
+                logger.info(f"📈 交易所分布: {exchange_detail}")
+
+            return filtered_stocks
+
+        except Exception as e:
+            logger.error(f"获取过滤A股列表失败: {e}")
+            return pd.DataFrame()
+
+    def _filter_by_real_market_cap(self, stock_df: pd.DataFrame, min_market_cap: float) -> pd.DataFrame:
+        """
+        基于实际市值筛选股票
+        由于API限制，先使用启发式规则预筛选，再对部分股票进行实际市值验证
+
+        Args:
+            stock_df: 股票列表DataFrame
+            min_market_cap: 最小市值要求（元）
+
+        Returns:
+            过滤后的股票DataFrame
+        """
+        if stock_df.empty:
+            return stock_df
+
+        logger.info(f"开始基于市值筛选股票，阈值: {min_market_cap/100000000:.0f}亿元")
+
+        # 步骤1: 使用启发式规则进行预筛选，减少API调用量
+        logger.info("步骤1: 使用启发式规则预筛选...")
+        pre_filtered = self._filter_by_market_cap_proxy(stock_df, min_market_cap)
+
+        if pre_filtered.empty:
+            logger.warning("启发式预筛选后无股票，返回空结果")
+            return pd.DataFrame()
+
+        logger.info(f"启发式预筛选完成: {len(pre_filtered)}/{len(stock_df)} 只股票")
+
+        # 步骤2: 对预筛选的股票进行小批量实际市值验证
+        logger.info("步骤2: 对预筛选股票进行实际市值验证...")
+
+        # 限制验证数量以避免API超时
+        max_verify_count = min(500, len(pre_filtered))  # 最多验证500只
+        stocks_to_verify = pre_filtered.head(max_verify_count)
+
+        logger.info(f"将验证 {len(stocks_to_verify)} 只股票的实际市值")
+
+        valid_stocks = []
+        total_to_verify = len(stocks_to_verify)
+
+        for idx, (_, stock) in enumerate(stocks_to_verify.iterrows()):
+            stock_code = stock['stock_code']
+            exchange = stock['exchange']
+            full_stock_code = f"{stock_code}.{exchange}"
+
+            try:
+                # 获取股本信息
+                shares_info = self.client.stock.info.get_stock_shares(stock_code=stock_code)
+
+                if not shares_info.empty and 'total_share' in shares_info.columns:
+                    total_shares = shares_info.iloc[0]['total_share']
+
+                    # 获取当前股价
+                    current_price = None
+                    try:
+                        market_data = self.client.stock.market.get_market(full_stock_code)
+                        if not market_data.empty and 'close' in market_data.columns:
+                            current_price = market_data.iloc[0]['close']
+                    except:
+                        pass
+
+                    # 计算市值
+                    if current_price is not None and total_shares > 0:
+                        market_cap = total_shares * 10000 * current_price  # 万股转换为股
+
+                        if market_cap >= min_market_cap:
+                            stock_with_cap = stock.copy()
+                            stock_with_cap['market_cap'] = market_cap
+                            stock_with_cap['total_shares'] = total_shares
+                            stock_with_cap['current_price'] = current_price
+                            valid_stocks.append(stock_with_cap)
+
+                        logger.debug(f"{full_stock_code}: 市值{market_cap/100000000:.1f}亿元 {'✓' if market_cap >= min_market_cap else '✗'}")
+                else:
+                    # 如果无法获取实际市值，且预筛选通过，则保留
+                    valid_stocks.append(stock)
+                    logger.debug(f"{full_stock_code}: 无市值数据，保留预筛选结果")
+
+            except Exception as e:
+                # 如果API调用失败，且预筛选通过，则保留
+                valid_stocks.append(stock)
+                logger.debug(f"{full_stock_code}: API失败，保留预筛选结果: {e}")
+
+            # 显示进度
+            if (idx + 1) % 50 == 0 or idx + 1 == total_to_verify:
+                logger.info(f"市值验证进度: {idx + 1}/{total_to_verify} ({(idx + 1)/total_to_verify*100:.1f}%)")
+
+            # 添加延时以避免API限制
+            if idx % 10 == 9:  # 每10个请求休息0.1秒
+                time.sleep(0.1)
+
+        # 步骤3: 对于剩余未验证的股票，直接使用预筛选结果
+        if len(pre_filtered) > max_verify_count:
+            remaining_stocks = pre_filtered.iloc[max_verify_count:].copy()
+            for _, stock in remaining_stocks.iterrows():
+                valid_stocks.append(stock)
+
+            logger.info(f"保留 {len(remaining_stocks)} 只未验证股票（基于预筛选结果）")
+
+        # 转换为DataFrame
+        if valid_stocks:
+            result_df = pd.DataFrame(valid_stocks)
+
+            # 确保没有重复
+            if 'stock_code' in result_df.columns:
+                result_df = result_df.drop_duplicates(subset=['stock_code'], keep='first')
+
+            logger.info(f"✅ 市值筛选完成: {len(result_df)}/{len(stock_df)} 只股票符合要求")
+
+            # 统计实际验证vs预筛选的结果
+            verified_count = min(max_verify_count, len(stocks_to_verify))
+            unverified_count = len(result_df) - verified_count
+            logger.info(f"📊 验证详情: 实际验证{verified_count}只, 预筛选保留{unverified_count}只")
+
+            return result_df
+        else:
+            logger.warning("⚠️ 没有股票通过市值筛选")
+            return pd.DataFrame()
+
+    def _filter_by_market_cap_proxy(self, stock_df: pd.DataFrame, min_market_cap: float) -> pd.DataFrame:
+        """
+        基于股票代码的启发式规则筛选大市值股票
+
+        Args:
+            stock_df: 股票列表DataFrame
+            min_market_cap: 最小市值要求（元）
+
+        Returns:
+            过滤后的股票DataFrame
+        """
+        if stock_df.empty:
+            return stock_df
+
+        # 由于无法直接获取市值数据，使用启发式规则进行筛选
+        # 注意：这只是一个近似筛选，真实的市值筛选需要实际的市值数据
+
+        def is_likely_large_cap(stock_code: str, exchange: str) -> bool:
+            """判断股票是否可能是大市值股票"""
+            code_num = stock_code
+
+            if exchange == 'SH':  # 上交所
+                # 主板: 600xxx, 601xxx, 603xxx, 605xxx (通常市值较大)
+                if code_num.startswith(('600', '601', '603', '605')):
+                    return True
+                # 科创板: 688xxx (新兴科技公司，市值相对较大)
+                elif code_num.startswith('688'):
+                    return True
+                # 其他上交所股票
+                return False
+
+            elif exchange == 'SZ':  # 深交所
+                # 主板: 000xxx, 001xxx (老牌蓝筹，通常市值较大)
+                if code_num.startswith(('000', '001')):
+                    return True
+                # 中小板: 002xxx (部分有大市值公司)
+                elif code_num.startswith('002'):
+                    # 002开头的前1000只股票(002000-002999)，上市较早，可能市值较大
+                    try:
+                        code_suffix = int(code_num[3:])
+                        return code_suffix <= 999  # 002000-002999
+                    except:
+                        return False
+                # 创业板: 300xxx, 301xxx (部分成长为大市值)
+                elif code_num.startswith(('300', '301')):
+                    # 300开头的前500只股票，上市较早，部分已成长为大市值
+                    try:
+                        if code_num.startswith('300'):
+                            code_suffix = int(code_num[3:])
+                            return code_suffix <= 499  # 300000-300499
+                        else:  # 301xxx较新，市值相对较小
+                            return False
+                    except:
+                        return False
+                return False
+
+            return False  # 其他情况默认排除
+
+        # 应用筛选规则
+        if min_market_cap >= 2000000000:  # 20亿以上
+            logger.info(f"应用大市值筛选规则(≥{min_market_cap/100000000:.0f}亿元)")
+            mask = stock_df.apply(lambda row: is_likely_large_cap(row['stock_code'], row['exchange']), axis=1)
+            return stock_df[mask]
+        else:
+            # 小于20亿的筛选条件暂时不实施严格筛选
+            logger.info(f"市值筛选阈值较低({min_market_cap/100000000:.1f}亿元)，保留所有股票")
+            return stock_df
+
     def get_realtime_data(self, stock_codes: Union[str, List[str]]) -> pd.DataFrame:
         """
         获取实时行情数据
@@ -72,7 +334,7 @@ class ADataFetcher:
             stock_code: 股票代码
             start_date: 开始日期
             end_date: 结束日期
-            period: 数据周期 ('daily', 'weekly', 'monthly')
+            period: 数据周期 ('1h', 'daily', 'weekly', 'monthly')
 
         Returns:
             历史行情DataFrame
@@ -86,6 +348,7 @@ class ADataFetcher:
 
             # 根据周期设置k_type参数
             k_type_map = {
+                '1h': 60,      # 1小时线(60分钟)
                 'daily': 1,    # 日线
                 'weekly': 2,   # 周线
                 'monthly': 3   # 月线
@@ -355,9 +618,52 @@ class ADataFetcher:
             # 返回空DataFrame作为后备
             return pd.DataFrame()
 
+    def _is_cache_valid(self) -> bool:
+        """检查缓存是否有效"""
+        if self._cache_timestamp is None:
+            return False
+
+        import time
+        return (time.time() - self._cache_timestamp) < self._cache_duration
+
+    def _update_stock_name_cache(self):
+        """更新股票名称缓存"""
+        try:
+            import time
+
+            # 检查缓存是否有效
+            if self._is_cache_valid():
+                return
+
+            logger.info("🔄 更新股票名称缓存...")
+
+            # 获取热门股票数据并缓存
+            self._hot_stocks_cache = self.get_hot_stocks_ths(limit=100)
+            self._east_stocks_cache = self.get_popular_stocks_east(limit=100)
+
+            # 清空名称缓存并重新构建
+            self._stock_name_cache.clear()
+
+            # 从热门股票数据中构建缓存
+            for df, source in [(self._hot_stocks_cache, '同花顺'), (self._east_stocks_cache, '东财')]:
+                if not df.empty and 'stock_code' in df.columns and 'short_name' in df.columns:
+                    for _, row in df.iterrows():
+                        stock_code = row['stock_code']
+                        stock_name = row['short_name']
+                        if stock_code not in self._stock_name_cache:
+                            self._stock_name_cache[stock_code] = stock_name
+
+            # 更新缓存时间戳
+            self._cache_timestamp = time.time()
+
+            logger.info(f"✅ 股票名称缓存更新完成，共缓存 {len(self._stock_name_cache)} 只股票")
+
+        except Exception as e:
+            logger.warning(f"更新股票名称缓存失败: {e}")
+
     def get_stock_name(self, stock_code: str) -> str:
         """
-        获取股票中文名称
+        获取股票中文名称（带缓存机制）
 
         Args:
             stock_code: 股票代码
@@ -366,24 +672,20 @@ class ADataFetcher:
             股票中文名称，如果获取失败返回股票代码
         """
         try:
-            # 尝试从热股数据中获取名称
+            # 更新缓存（如果需要）
-            hot_stocks = self.get_hot_stocks_ths(limit=100)
+            self._update_stock_name_cache()
-            if not hot_stocks.empty and 'stock_code' in hot_stocks.columns and 'short_name' in hot_stocks.columns:
-                match = hot_stocks[hot_stocks['stock_code'] == stock_code]
-                if not match.empty:
-                    return match.iloc[0]['short_name']
 
-            # 尝试从东财数据中获取名称
+            # 从缓存中查找
-            east_stocks = self.get_popular_stocks_east(limit=100)
+            if stock_code in self._stock_name_cache:
-            if not east_stocks.empty and 'stock_code' in east_stocks.columns and 'short_name' in east_stocks.columns:
+                return self._stock_name_cache[stock_code]
-                match = east_stocks[east_stocks['stock_code'] == stock_code]
-                if not match.empty:
-                    return match.iloc[0]['short_name']
 
-            # 尝试搜索功能
+            # 缓存中没有，尝试搜索功能
             search_results = self.search_stocks(stock_code)
             if not search_results.empty and 'short_name' in search_results.columns:
-                return search_results.iloc[0]['short_name']
+                stock_name = search_results.iloc[0]['short_name']
+                # 添加到缓存
+                self._stock_name_cache[stock_code] = stock_name
+                return stock_name
 
             # 如果都失败，返回股票代码
             logger.debug(f"未能获取{stock_code}的中文名称")

src/database/__init__.py

@@ -0,0 +1 @@
+# 数据库模块

src/database/database_manager.py

@@ -0,0 +1,500 @@
+"""
+数据库管理模块
+负责策略筛选结果的存储和查询
+"""
+
+import sqlite3
+import json
+from pathlib import Path
+from typing import Dict, List, Any, Optional, Tuple
+from datetime import datetime, date
+from loguru import logger
+import pandas as pd
+
+
+class DatabaseManager:
+    """数据库管理器"""
+
+    def __init__(self, db_path: str = None):
+        """
+        初始化数据库管理器
+
+        Args:
+            db_path: 数据库文件路径，默认为项目根目录下的data/trading.db
+        """
+        if db_path is None:
+            # 获取项目根目录
+            current_file = Path(__file__)
+            project_root = current_file.parent.parent.parent
+            data_dir = project_root / "data"
+            data_dir.mkdir(exist_ok=True)
+            db_path = data_dir / "trading.db"
+
+        self.db_path = Path(db_path)
+        self._init_database()
+        logger.info(f"数据库管理器初始化完成: {self.db_path}")
+
+    def _init_database(self):
+        """初始化数据库，创建表结构"""
+        try:
+            # 读取SQL schema文件
+            schema_file = Path(__file__).parent / "schema.sql"
+            if not schema_file.exists():
+                raise FileNotFoundError(f"数据库schema文件不存在: {schema_file}")
+
+            with open(schema_file, 'r', encoding='utf-8') as f:
+                schema_sql = f.read()
+
+            # 执行建表语句
+            with sqlite3.connect(self.db_path) as conn:
+                conn.executescript(schema_sql)
+                conn.commit()
+
+            logger.info("数据库表结构初始化完成")
+
+            # 初始化默认策略
+            self._init_default_strategies()
+
+        except Exception as e:
+            logger.error(f"初始化数据库失败: {e}")
+            raise
+
+    def _init_default_strategies(self):
+        """初始化默认策略"""
+        try:
+            default_strategies = [
+                {
+                    'strategy_name': 'K线形态策略',
+                    'strategy_type': 'kline_pattern',
+                    'description': '两阳线+阴线+阳线突破形态识别策略',
+                    'config': {
+                        'min_entity_ratio': 0.55,
+                        'final_yang_min_ratio': 0.40,
+                        'max_turnover_ratio': 40.0,
+                        'timeframes': ['daily', 'weekly'],
+                        'pullback_tolerance': 0.02,
+                        'monitor_days': 30
+                    }
+                }
+            ]
+
+            for strategy in default_strategies:
+                self.create_or_update_strategy(**strategy)
+
+        except Exception as e:
+            logger.warning(f"初始化默认策略失败: {e}")
+
+    def create_or_update_strategy(self, strategy_name: str, strategy_type: str,
+                                  description: str = None, config: Dict[str, Any] = None) -> int:
+        """
+        创建或更新策略
+
+        Args:
+            strategy_name: 策略名称
+            strategy_type: 策略类型
+            description: 策略描述
+            config: 策略配置
+
+        Returns:
+            策略ID
+        """
+        try:
+            config_json = json.dumps(config) if config else None
+
+            with sqlite3.connect(self.db_path) as conn:
+                cursor = conn.cursor()
+
+                # 检查策略是否存在
+                cursor.execute(
+                    "SELECT id FROM strategies WHERE strategy_name = ?",
+                    (strategy_name,)
+                )
+                result = cursor.fetchone()
+
+                if result:
+                    # 更新现有策略
+                    strategy_id = result[0]
+                    cursor.execute("""
+                        UPDATE strategies
+                        SET strategy_type = ?, description = ?, config = ?, updated_at = CURRENT_TIMESTAMP
+                        WHERE id = ?
+                    """, (strategy_type, description, config_json, strategy_id))
+                    logger.debug(f"更新策略: {strategy_name} (ID: {strategy_id})")
+                else:
+                    # 创建新策略
+                    cursor.execute("""
+                        INSERT INTO strategies (strategy_name, strategy_type, description, config)
+                        VALUES (?, ?, ?, ?)
+                    """, (strategy_name, strategy_type, description, config_json))
+                    strategy_id = cursor.lastrowid
+                    logger.info(f"创建新策略: {strategy_name} (ID: {strategy_id})")
+
+                conn.commit()
+                return strategy_id
+
+        except Exception as e:
+            logger.error(f"创建/更新策略失败: {e}")
+            raise
+
+    def create_scan_session(self, strategy_id: int, scan_date: date = None,
+                            total_scanned: int = 0, total_signals: int = 0,
+                            data_source: str = None, scan_config: Dict[str, Any] = None) -> int:
+        """
+        创建扫描会话
+
+        Args:
+            strategy_id: 策略ID
+            scan_date: 扫描日期
+            total_scanned: 总扫描股票数
+            total_signals: 总信号数
+            data_source: 数据源
+            scan_config: 扫描配置
+
+        Returns:
+            会话ID
+        """
+        try:
+            if scan_date is None:
+                scan_date = datetime.now().date()
+
+            config_json = json.dumps(scan_config) if scan_config else None
+
+            with sqlite3.connect(self.db_path) as conn:
+                cursor = conn.cursor()
+                cursor.execute("""
+                    INSERT INTO scan_sessions
+                    (strategy_id, scan_date, total_scanned, total_signals, data_source, scan_config)
+                    VALUES (?, ?, ?, ?, ?, ?)
+                """, (strategy_id, scan_date, total_scanned, total_signals, data_source, config_json))
+
+                session_id = cursor.lastrowid
+                conn.commit()
+
+                logger.info(f"创建扫描会话: {session_id} (策略ID: {strategy_id})")
+                return session_id
+
+        except Exception as e:
+            logger.error(f"创建扫描会话失败: {e}")
+            raise
+
+    def save_stock_signal(self, session_id: int, strategy_id: int, signal: Dict[str, Any]) -> int:
+        """
+        保存股票信号
+
+        Args:
+            session_id: 会话ID
+            strategy_id: 策略ID
+            signal: 信号数据
+
+        Returns:
+            信号ID
+        """
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                cursor = conn.cursor()
+
+                # 转换日期格式
+                signal_date = signal.get('date')
+                if isinstance(signal_date, str):
+                    signal_date = datetime.strptime(signal_date, '%Y-%m-%d').date()
+                elif hasattr(signal_date, 'date'):
+                    signal_date = signal_date.date()
+
+                # 准备K线数据
+                k1_data = json.dumps(signal.get('k1', {})) if signal.get('k1') else None
+                k2_data = json.dumps(signal.get('k2', {})) if signal.get('k2') else None
+                k3_data = json.dumps(signal.get('k3', {})) if signal.get('k3') else None
+                k4_data = json.dumps(signal.get('k4', {})) if signal.get('k4') else None
+
+                cursor.execute("""
+                    INSERT INTO stock_signals (
+                        session_id, strategy_id, stock_code, stock_name, timeframe,
+                        signal_date, signal_type, breakout_price, yin_high, breakout_amount,
+                        breakout_pct, ema20_price, yang1_entity_ratio, yang2_entity_ratio,
+                        final_yang_entity_ratio, turnover_ratio, above_ema20,
+                        k1_data, k2_data, k3_data, k4_data
+                    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                """, (
+                    session_id, strategy_id,
+                    signal.get('stock_code'),
+                    signal.get('stock_name'),
+                    signal.get('timeframe'),
+                    signal_date,
+                    signal.get('pattern_type', '两阳+阴+阳突破'),
+                    signal.get('breakout_price'),
+                    signal.get('yin_high'),
+                    signal.get('breakout_amount'),
+                    signal.get('breakout_pct'),
+                    signal.get('ema20_price'),
+                    signal.get('yang1_entity_ratio'),
+                    signal.get('yang2_entity_ratio'),
+                    signal.get('final_yang_entity_ratio'),
+                    signal.get('turnover_ratio'),
+                    signal.get('above_ema20'),
+                    k1_data, k2_data, k3_data, k4_data
+                ))
+
+                signal_id = cursor.lastrowid
+                conn.commit()
+
+                logger.debug(f"保存信号: {signal.get('stock_code')} (ID: {signal_id})")
+                return signal_id
+
+        except Exception as e:
+            logger.error(f"保存股票信号失败: {e}")
+            raise
+
+    def save_pullback_alert(self, signal_id: int, pullback_alert: Dict[str, Any]) -> int:
+        """
+        保存回踩提醒
+
+        Args:
+            signal_id: 原始信号ID
+            pullback_alert: 回踩提醒数据
+
+        Returns:
+            提醒ID
+        """
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                cursor = conn.cursor()
+
+                # 转换日期格式
+                def convert_date(date_value):
+                    if isinstance(date_value, str):
+                        return datetime.strptime(date_value, '%Y-%m-%d').date()
+                    elif hasattr(date_value, 'date'):
+                        return date_value.date()
+                    return date_value
+
+                cursor.execute("""
+                    INSERT INTO pullback_alerts (
+                        signal_id, stock_code, stock_name, timeframe,
+                        original_signal_date, original_breakout_price, yin_high,
+                        pullback_date, current_price, current_low, pullback_pct,
+                        distance_to_yin_high, days_since_signal, alert_sent, alert_sent_time
+                    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                """, (
+                    signal_id,
+                    pullback_alert.get('stock_code'),
+                    pullback_alert.get('stock_name'),
+                    pullback_alert.get('timeframe'),
+                    convert_date(pullback_alert.get('signal_date')),
+                    pullback_alert.get('breakout_price'),
+                    pullback_alert.get('yin_high'),
+                    convert_date(pullback_alert.get('current_date')),
+                    pullback_alert.get('current_price'),
+                    pullback_alert.get('current_low'),
+                    pullback_alert.get('pullback_pct'),
+                    pullback_alert.get('distance_to_yin_high'),
+                    pullback_alert.get('days_since_signal'),
+                    True,  # alert_sent
+                    datetime.now()  # alert_sent_time
+                ))
+
+                alert_id = cursor.lastrowid
+                conn.commit()
+
+                logger.debug(f"保存回踩提醒: {pullback_alert.get('stock_code')} (ID: {alert_id})")
+                return alert_id
+
+        except Exception as e:
+            logger.error(f"保存回踩提醒失败: {e}")
+            raise
+
+    def get_latest_signals(self, strategy_name: str = None, limit: int = 100) -> pd.DataFrame:
+        """
+        获取最新信号
+
+        Args:
+            strategy_name: 策略名称过滤
+            limit: 返回数量限制
+
+        Returns:
+            信号DataFrame
+        """
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                sql = "SELECT * FROM latest_signals_view"
+                params = []
+
+                if strategy_name:
+                    sql += " WHERE strategy_name = ?"
+                    params.append(strategy_name)
+
+                sql += " LIMIT ?"
+                params.append(limit)
+
+                df = pd.read_sql_query(sql, conn, params=params)
+                return df
+
+        except Exception as e:
+            logger.error(f"获取最新信号失败: {e}")
+            return pd.DataFrame()
+
+    def get_strategy_stats(self) -> pd.DataFrame:
+        """
+        获取策略统计信息
+
+        Returns:
+            策略统计DataFrame
+        """
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                df = pd.read_sql_query("SELECT * FROM strategy_stats_view", conn)
+                return df
+
+        except Exception as e:
+            logger.error(f"获取策略统计失败: {e}")
+            return pd.DataFrame()
+
+    def get_signals_by_date_range(self, start_date: date, end_date: date = None,
+                                  strategy_name: str = None, timeframe: str = None) -> pd.DataFrame:
+        """
+        按日期范围获取信号
+
+        Args:
+            start_date: 开始日期
+            end_date: 结束日期
+            strategy_name: 策略名称过滤
+            timeframe: 周期过滤
+
+        Returns:
+            信号DataFrame
+        """
+        try:
+            if end_date is None:
+                end_date = datetime.now().date()
+
+            with sqlite3.connect(self.db_path) as conn:
+                sql = """
+                    SELECT * FROM latest_signals_view
+                    WHERE signal_date >= ? AND signal_date <= ?
+                """
+                params = [start_date, end_date]
+
+                if strategy_name:
+                    sql += " AND strategy_name = ?"
+                    params.append(strategy_name)
+
+                if timeframe:
+                    sql += " AND timeframe = ?"
+                    params.append(timeframe)
+
+                sql += " ORDER BY signal_date DESC"
+
+                df = pd.read_sql_query(sql, conn, params=params)
+                return df
+
+        except Exception as e:
+            logger.error(f"按日期范围获取信号失败: {e}")
+            return pd.DataFrame()
+
+    def get_pullback_alerts(self, days: int = 7) -> pd.DataFrame:
+        """
+        获取最近的回踩提醒
+
+        Args:
+            days: 获取最近几天的提醒
+
+        Returns:
+            回踩提醒DataFrame
+        """
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                sql = """
+                    SELECT * FROM pullback_alerts
+                    WHERE pullback_date >= date('now', '-{} days')
+                    ORDER BY pullback_date DESC
+                """.format(days)
+
+                df = pd.read_sql_query(sql, conn)
+                return df
+
+        except Exception as e:
+            logger.error(f"获取回踩提醒失败: {e}")
+            return pd.DataFrame()
+
+    def cleanup_old_data(self, days_to_keep: int = 90):
+        """
+        清理旧数据
+
+        Args:
+            days_to_keep: 保留的天数
+        """
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                cursor = conn.cursor()
+
+                # 删除旧的回踩提醒
+                cursor.execute("""
+                    DELETE FROM pullback_alerts
+                    WHERE pullback_date < date('now', '-{} days')
+                """.format(days_to_keep))
+
+                # 删除旧的信号记录
+                cursor.execute("""
+                    DELETE FROM stock_signals
+                    WHERE signal_date < date('now', '-{} days')
+                """.format(days_to_keep))
+
+                # 删除旧的扫描会话
+                cursor.execute("""
+                    DELETE FROM scan_sessions
+                    WHERE scan_date < date('now', '-{} days')
+                """.format(days_to_keep))
+
+                conn.commit()
+                logger.info(f"清理完成，保留了最近{days_to_keep}天的数据")
+
+        except Exception as e:
+            logger.error(f"清理旧数据失败: {e}")
+
+    def get_strategy_id(self, strategy_name: str) -> Optional[int]:
+        """
+        根据策略名称获取策略ID
+
+        Args:
+            strategy_name: 策略名称
+
+        Returns:
+            策略ID，如果不存在返回None
+        """
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                cursor = conn.cursor()
+                cursor.execute("SELECT id FROM strategies WHERE strategy_name = ?", (strategy_name,))
+                result = cursor.fetchone()
+                return result[0] if result else None
+
+        except Exception as e:
+            logger.error(f"获取策略ID失败: {e}")
+            return None
+
+    def update_scan_session_stats(self, session_id: int, total_scanned: int, total_signals: int):
+        """
+        更新扫描会话统计信息
+
+        Args:
+            session_id: 会话ID
+            total_scanned: 总扫描股票数
+            total_signals: 总信号数
+        """
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                cursor = conn.cursor()
+                cursor.execute("""
+                    UPDATE scan_sessions
+                    SET total_scanned = ?, total_signals = ?
+                    WHERE id = ?
+                """, (total_scanned, total_signals, session_id))
+                conn.commit()
+
+        except Exception as e:
+            logger.error(f"更新扫描会话统计失败: {e}")
+
+
+if __name__ == "__main__":
+    # 测试代码
+    db = DatabaseManager()
+    print("数据库管理器测试完成")

src/database/schema.sql

@@ -0,0 +1,139 @@
+-- 交易策略筛选结果数据库表结构
+
+-- 策略表：存储不同的交易策略信息
+CREATE TABLE IF NOT EXISTS strategies (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    strategy_name TEXT NOT NULL UNIQUE,  -- 策略名称
+    strategy_type TEXT NOT NULL,         -- 策略类型（如：kline_pattern）
+    description TEXT,                    -- 策略描述
+    config JSON,                         -- 策略配置参数
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- 扫描会话表：记录每次市场扫描的信息
+CREATE TABLE IF NOT EXISTS scan_sessions (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    strategy_id INTEGER,                 -- 关联的策略ID
+    scan_date DATE NOT NULL,             -- 扫描日期
+    scan_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,  -- 扫描时间
+    total_scanned INTEGER DEFAULT 0,     -- 总扫描股票数
+    total_signals INTEGER DEFAULT 0,     -- 总信号数
+    data_source TEXT,                    -- 数据源（热门股票/全市场等）
+    scan_config JSON,                    -- 扫描配置
+    status TEXT DEFAULT 'completed',     -- 扫描状态
+    FOREIGN KEY (strategy_id) REFERENCES strategies (id)
+);
+
+-- 股票信号表：存储具体的股票筛选信号
+CREATE TABLE IF NOT EXISTS stock_signals (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    session_id INTEGER,                  -- 关联的扫描会话ID
+    strategy_id INTEGER,                 -- 关联的策略ID
+    stock_code TEXT NOT NULL,            -- 股票代码
+    stock_name TEXT,                     -- 股票名称
+    timeframe TEXT NOT NULL,             -- 时间周期（daily/weekly）
+    signal_date DATE NOT NULL,           -- 信号日期（K线日期）
+    signal_type TEXT NOT NULL,           -- 信号类型
+
+    -- 价格信息
+    breakout_price REAL,                 -- 突破价格
+    yin_high REAL,                       -- 阴线最高点
+    breakout_amount REAL,                -- 突破金额
+    breakout_pct REAL,                   -- 突破百分比
+    ema20_price REAL,                    -- EMA20价格
+
+    -- 技术指标
+    yang1_entity_ratio REAL,             -- 第一根阳线实体比例
+    yang2_entity_ratio REAL,             -- 第二根阳线实体比例
+    final_yang_entity_ratio REAL,        -- 最后阳线实体比例
+    turnover_ratio REAL,                 -- 换手率
+    above_ema20 BOOLEAN,                 -- 是否在EMA20上方
+
+    -- K线详情（JSON格式存储）
+    k1_data JSON,                        -- 第一根K线数据
+    k2_data JSON,                        -- 第二根K线数据
+    k3_data JSON,                        -- 第三根K线数据（阴线）
+    k4_data JSON,                        -- 第四根K线数据（突破阳线）
+
+    -- 元数据
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+
+    FOREIGN KEY (session_id) REFERENCES scan_sessions (id),
+    FOREIGN KEY (strategy_id) REFERENCES strategies (id)
+);
+
+-- 回踩监控表：存储回踩提醒信息
+CREATE TABLE IF NOT EXISTS pullback_alerts (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    signal_id INTEGER,                   -- 关联的原始信号ID
+    stock_code TEXT NOT NULL,            -- 股票代码
+    stock_name TEXT,                     -- 股票名称
+    timeframe TEXT NOT NULL,             -- 时间周期
+
+    -- 原始信号信息
+    original_signal_date DATE,           -- 原始信号日期
+    original_breakout_price REAL,       -- 原始突破价格
+    yin_high REAL,                       -- 阴线最高点
+
+    -- 回踩信息
+    pullback_date DATE NOT NULL,         -- 回踩发生日期
+    current_price REAL,                  -- 当前价格
+    current_low REAL,                    -- 当日最低价
+    pullback_pct REAL,                   -- 回调百分比
+    distance_to_yin_high REAL,           -- 距离阴线最高点百分比
+    days_since_signal INTEGER,           -- 距离信号天数
+
+    -- 提醒状态
+    alert_sent BOOLEAN DEFAULT FALSE,    -- 是否已发送提醒
+    alert_sent_time TIMESTAMP,           -- 提醒发送时间
+
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+
+    FOREIGN KEY (signal_id) REFERENCES stock_signals (id)
+);
+
+-- 创建索引以提高查询性能
+CREATE INDEX IF NOT EXISTS idx_stock_signals_stock_code ON stock_signals (stock_code);
+CREATE INDEX IF NOT EXISTS idx_stock_signals_signal_date ON stock_signals (signal_date);
+CREATE INDEX IF NOT EXISTS idx_stock_signals_strategy_id ON stock_signals (strategy_id);
+CREATE INDEX IF NOT EXISTS idx_stock_signals_session_id ON stock_signals (session_id);
+CREATE INDEX IF NOT EXISTS idx_scan_sessions_scan_date ON scan_sessions (scan_date);
+CREATE INDEX IF NOT EXISTS idx_pullback_alerts_stock_code ON pullback_alerts (stock_code);
+CREATE INDEX IF NOT EXISTS idx_pullback_alerts_pullback_date ON pullback_alerts (pullback_date);
+
+-- 创建视图：最新信号概览
+CREATE VIEW IF NOT EXISTS latest_signals_view AS
+SELECT
+    ss.stock_code,
+    ss.stock_name,
+    ss.timeframe,
+    ss.signal_date,
+    ss.breakout_price,
+    ss.yin_high,
+    ss.breakout_pct,
+    ss.final_yang_entity_ratio,
+    ss.turnover_ratio,
+    s.strategy_name,
+    scan.scan_time,
+    scan.data_source
+FROM stock_signals ss
+JOIN strategies s ON ss.strategy_id = s.id
+JOIN scan_sessions scan ON ss.session_id = scan.id
+ORDER BY ss.signal_date DESC, ss.created_at DESC;
+
+-- 创建视图：策略统计概览
+CREATE VIEW IF NOT EXISTS strategy_stats_view AS
+SELECT
+    s.strategy_name,
+    s.strategy_type,
+    COUNT(DISTINCT scan.id) as total_scans,
+    COUNT(ss.id) as total_signals,
+    COUNT(DISTINCT ss.stock_code) as unique_stocks,
+    MAX(scan.scan_time) as last_scan_time,
+    AVG(ss.breakout_pct) as avg_breakout_pct,
+    AVG(ss.final_yang_entity_ratio) as avg_entity_ratio
+FROM strategies s
+LEFT JOIN scan_sessions scan ON s.id = scan.strategy_id
+LEFT JOIN stock_signals ss ON s.id = ss.strategy_id
+GROUP BY s.id, s.strategy_name, s.strategy_type;

src/strategy/kline_pattern_strategy.py

@@ -11,12 +11,14 @@ from loguru import logger
 
 from ..data.data_fetcher import ADataFetcher
 from ..utils.notification import NotificationManager
+from ..database.database_manager import DatabaseManager
 
 
 class KLinePatternStrategy:
     """K线形态策略类"""
 
-    def __init__(self, data_fetcher: ADataFetcher, notification_manager: NotificationManager, config: Dict[str, Any]):
+    def __init__(self, data_fetcher: ADataFetcher, notification_manager: NotificationManager,
+                 config: Dict[str, Any], db_manager: DatabaseManager = None):
         """
         初始化K线形态策略
 
@@ -24,18 +26,37 @@ class KLinePatternStrategy:
             data_fetcher: 数据获取器
             notification_manager: 通知管理器
             config: 策略配置
+            db_manager: 数据库管理器
         """
         self.data_fetcher = data_fetcher
         self.notification_manager = notification_manager
         self.config = config
+        self.db_manager = db_manager or DatabaseManager()
 
         # 策略参数
+        self.strategy_name = "K线形态策略"
         self.min_entity_ratio = config.get('min_entity_ratio', 0.55)  # 前两根阳线实体部分最小比例
         self.final_yang_min_ratio = config.get('final_yang_min_ratio', 0.40)  # 最后阳线实体部分最小比例
         self.max_turnover_ratio = config.get('max_turnover_ratio', 40.0)  # 最后阳线最大换手率（%）
         self.timeframes = config.get('timeframes', ['daily', 'weekly'])  # 支持的时间周期
 
-        logger.info("K线形态策略初始化完成")
+        # 回踩监控参数
+        self.pullback_tolerance = config.get('pullback_tolerance', 0.02)  # 回踩容忍度（2%）
+        self.monitor_days = config.get('monitor_days', 30)  # 监控回踩的天数
+
+        # 存储已触发的信号，用于监控回踩
+        # 格式: {stock_code: {'signals': [signal_dict], 'last_check_date': date}}
+        self.triggered_signals = {}
+
+        # 确保策略在数据库中存在
+        self.strategy_id = self.db_manager.create_or_update_strategy(
+            strategy_name=self.strategy_name,
+            strategy_type="kline_pattern",
+            description="两阳线+阴线+阳线突破形态识别策略",
+            config=config
+        )
+
+        logger.info(f"K线形态策略初始化完成 (策略ID: {self.strategy_id})")
 
     def calculate_kline_features(self, df: pd.DataFrame) -> pd.DataFrame:
         """
@@ -187,7 +208,8 @@ class KLinePatternStrategy:
 
         return signals
 
-    def analyze_stock(self, stock_code: str, stock_name: str = None, days: int = 60) -> Dict[str, List[Dict[str, Any]]]:
+    def analyze_stock(self, stock_code: str, stock_name: str = None, days: int = 60,
+                      session_id: Optional[int] = None) -> Dict[str, List[Dict[str, Any]]]:
         """
         分析单只股票的K线形态
 
@@ -206,11 +228,18 @@ class KLinePatternStrategy:
             stock_name = self.data_fetcher.get_stock_name(stock_code)
 
         try:
-            # 计算开始日期
+            # 计算开始日期，针对不同周期调整时间范围
             end_date = datetime.now().strftime('%Y-%m-%d')
-            start_date = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
 
             for timeframe in self.timeframes:
+                # 针对1小时周期调整分析天数，避免数据量过大
+                if timeframe == '1h':
+                    # 1小时数据只分析最近7天
+                    analysis_days = min(days, 7)
+                else:
+                    analysis_days = days
+
+                start_date = (datetime.now() - timedelta(days=analysis_days)).strftime('%Y-%m-%d')
                 logger.info(f"🔍 分析股票: {stock_code}({stock_name}) | 周期: {timeframe}")
 
                 # 获取历史数据 - 直接使用adata的原生周期支持
@@ -233,6 +262,22 @@ class KLinePatternStrategy:
                     signal['stock_name'] = stock_name
                     signal['timeframe'] = timeframe
 
+                    # 将信号添加到监控列表
+                    self.add_triggered_signal(signal)
+
+                    # 保存信号到数据库（如果提供了session_id）
+                    if session_id is not None:
+                        try:
+                            signal_id = self.db_manager.save_stock_signal(
+                                session_id=session_id,
+                                strategy_id=self.strategy_id,
+                                signal=signal
+                            )
+                            signal['signal_id'] = signal_id
+                            logger.debug(f"信号已保存到数据库: {stock_code} (ID: {signal_id})")
+                        except Exception as e:
+                            logger.error(f"保存信号到数据库失败: {e}")
+
                 results[timeframe] = signals
 
                 # 美化信号统计日志
@@ -250,6 +295,201 @@ class KLinePatternStrategy:
 
         return results
 
+    def check_pullback_signals(self, stock_code: str, current_data: pd.DataFrame) -> List[Dict[str, Any]]:
+        """
+        检查已触发信号的价格回踩情况
+
+        Args:
+            stock_code: 股票代码
+            current_data: 当前K线数据
+
+        Returns:
+            回踩提醒信号列表
+        """
+        pullback_alerts = []
+
+        if stock_code not in self.triggered_signals:
+            return pullback_alerts
+
+        signals = self.triggered_signals[stock_code]['signals']
+        current_date = datetime.now().date()
+
+        if current_data.empty:
+            return pullback_alerts
+
+        # 获取最新价格
+        latest_price = current_data.iloc[-1]['close']
+        latest_low = current_data.iloc[-1]['low']
+        latest_date = current_data.iloc[-1].get('trade_date', current_data.index[-1])
+
+        if isinstance(latest_date, str):
+            latest_date = pd.to_datetime(latest_date).date()
+        elif hasattr(latest_date, 'date'):
+            latest_date = latest_date.date()
+
+        for signal in signals:
+            # 检查信号是否在监控期内
+            signal_date = signal['date']
+            if isinstance(signal_date, str):
+                signal_date = pd.to_datetime(signal_date).date()
+            elif hasattr(signal_date, 'date'):
+                signal_date = signal_date.date()
+
+            days_since_signal = (current_date - signal_date).days
+            if days_since_signal > self.monitor_days:
+                continue
+
+            yin_high = signal['yin_high']  # 阴线最高点
+            breakout_price = signal['breakout_price']  # 突破时价格
+
+            # 检查是否发生回踩
+            # 条件1: 最低价接近或跌破阴线最高点
+            pullback_to_yin_high = latest_low <= (yin_high * (1 + self.pullback_tolerance))
+
+            # 条件2: 当前价格相比突破价格有明显回调
+            significant_pullback = latest_price < (breakout_price * 0.95)  # 回调超过5%
+
+            if pullback_to_yin_high and significant_pullback:
+                # 检查是否已经发送过此类提醒（避免重复）
+                alert_key = f"{stock_code}_{signal_date}_{latest_date}"
+                if not hasattr(self, '_sent_pullback_alerts'):
+                    self._sent_pullback_alerts = set()
+
+                if alert_key not in self._sent_pullback_alerts:
+                    pullback_alert = {
+                        'stock_code': stock_code,
+                        'stock_name': signal.get('stock_name', ''),
+                        'signal_date': signal_date,
+                        'current_date': latest_date,
+                        'timeframe': signal.get('timeframe', 'daily'),
+                        'yin_high': yin_high,
+                        'breakout_price': breakout_price,
+                        'current_price': latest_price,
+                        'current_low': latest_low,
+                        'pullback_pct': ((latest_price - breakout_price) / breakout_price) * 100,
+                        'distance_to_yin_high': ((latest_low - yin_high) / yin_high) * 100,
+                        'days_since_signal': days_since_signal,
+                        'alert_type': 'pullback_to_yin_high'
+                    }
+
+                    pullback_alerts.append(pullback_alert)
+                    self._sent_pullback_alerts.add(alert_key)
+
+                    # 记录回踩提醒日志
+                    logger.warning("⚠️" + "="*60)
+                    logger.warning(f"📉 价格回踩阴线最高点提醒!")
+                    logger.warning(f"📅 原信号时间: {signal_date} | 当前时间: {latest_date}")
+                    logger.warning(f"🏷️ 股票: {stock_code}({signal.get('stock_name', '')})")
+                    logger.warning(f"📊 周期: {signal.get('timeframe', 'daily')}")
+                    logger.warning(f"💰 阴线最高点: {yin_high:.2f}元")
+                    logger.warning(f"🚀 当时突破价: {breakout_price:.2f}元")
+                    logger.warning(f"💸 当前价格: {latest_price:.2f}元 | 最低: {latest_low:.2f}元")
+                    logger.warning(f"📉 回调幅度: {pullback_alert['pullback_pct']:.2f}%")
+                    logger.warning(f"📏 距阴线高点: {pullback_alert['distance_to_yin_high']:.2f}%")
+                    logger.warning(f"⏰ 信号后经过: {days_since_signal}天")
+                    logger.warning("⚠️" + "="*60)
+
+        return pullback_alerts
+
+    def add_triggered_signal(self, signal: Dict[str, Any]):
+        """
+        添加已触发的信号到监控列表
+
+        Args:
+            signal: 信号字典
+        """
+        stock_code = signal.get('stock_code')
+        if not stock_code:
+            return
+
+        if stock_code not in self.triggered_signals:
+            self.triggered_signals[stock_code] = {
+                'signals': [],
+                'last_check_date': datetime.now().date()
+            }
+
+        # 添加信号到监控列表
+        self.triggered_signals[stock_code]['signals'].append(signal)
+
+        # 只保留最近的信号（避免内存占用过多）
+        max_signals_per_stock = 10
+        if len(self.triggered_signals[stock_code]['signals']) > max_signals_per_stock:
+            # 按日期排序，保留最新的信号
+            self.triggered_signals[stock_code]['signals'].sort(
+                key=lambda x: pd.to_datetime(x['date']) if isinstance(x['date'], str) else x['date'],
+                reverse=True
+            )
+            self.triggered_signals[stock_code]['signals'] = \
+                self.triggered_signals[stock_code]['signals'][:max_signals_per_stock]
+
+    def monitor_pullback_for_triggered_signals(self) -> List[Dict[str, Any]]:
+        """
+        监控所有已触发信号的回踩情况
+
+        Returns:
+            所有回踩提醒信号列表
+        """
+        all_pullback_alerts = []
+        current_date = datetime.now().date()
+
+        # 清理过期的信号
+        stocks_to_remove = []
+        for stock_code, signal_info in self.triggered_signals.items():
+            # 过滤掉过期的信号
+            valid_signals = []
+            for signal in signal_info['signals']:
+                signal_date = signal['date']
+                if isinstance(signal_date, str):
+                    signal_date = pd.to_datetime(signal_date).date()
+                elif hasattr(signal_date, 'date'):
+                    signal_date = signal_date.date()
+
+                days_since_signal = (current_date - signal_date).days
+                if days_since_signal <= self.monitor_days:
+                    valid_signals.append(signal)
+
+            if valid_signals:
+                self.triggered_signals[stock_code]['signals'] = valid_signals
+            else:
+                stocks_to_remove.append(stock_code)
+
+        # 移除没有有效信号的股票
+        for stock_code in stocks_to_remove:
+            del self.triggered_signals[stock_code]
+
+        logger.info(f"🔍 当前监控中的股票数量: {len(self.triggered_signals)}")
+
+        # 检查每只股票的回踩情况
+        for stock_code in self.triggered_signals.keys():
+            try:
+                # 获取最近几天的数据
+                end_date = current_date.strftime('%Y-%m-%d')
+                start_date = (current_date - timedelta(days=5)).strftime('%Y-%m-%d')
+
+                current_data = self.data_fetcher.get_historical_data(
+                    stock_code, start_date, end_date, 'daily'
+                )
+
+                if not current_data.empty:
+                    pullback_alerts = self.check_pullback_signals(stock_code, current_data)
+                    all_pullback_alerts.extend(pullback_alerts)
+
+            except Exception as e:
+                logger.error(f"监控股票 {stock_code} 回踩情况失败: {e}")
+
+        # 发送回踩提醒通知
+        if all_pullback_alerts:
+            try:
+                success = self.notification_manager.send_pullback_alerts(all_pullback_alerts)
+                if success:
+                    logger.info(f"📱 回踩提醒通知发送完成，共{len(all_pullback_alerts)}个提醒")
+                else:
+                    logger.warning(f"📱 回踩提醒通知发送失败")
+            except Exception as e:
+                logger.error(f"发送回踩提醒通知失败: {e}")
+
+        return all_pullback_alerts
+
     def _convert_to_weekly(self, daily_df: pd.DataFrame) -> pd.DataFrame:
         """
         将日线数据转换为周线数据
@@ -328,15 +568,16 @@ class KLinePatternStrategy:
             logger.error(f"转换月线数据失败: {e}")
             return pd.DataFrame()
 
-    def scan_market(self, stock_list: List[str] = None, max_stocks: int = 100, use_hot_stocks: bool = True, use_combined_sources: bool = True) -> Dict[str, Dict[str, List[Dict[str, Any]]]]:
+    def scan_market(self, stock_list: List[str] = None, max_stocks: int = 100, use_hot_stocks: bool = True, use_combined_sources: bool = True, use_all_a_shares: bool = False) -> Dict[str, Dict[str, List[Dict[str, Any]]]]:
         """
         扫描市场中的股票形态
 
         Args:
-            stock_list: 股票代码列表，如果为None则获取热门股票
+            stock_list: 股票代码列表，如果为None则自动选择股票池
             max_stocks: 最大扫描股票数量
             use_hot_stocks: 是否使用热门股票数据，默认True
             use_combined_sources: 是否使用合并的双数据源（同花顺+东财），默认True
+            use_all_a_shares: 是否使用所有A股股票(排除北交所和ST)，优先级最高
 
         Returns:
             所有股票的分析结果
@@ -345,9 +586,48 @@ class KLinePatternStrategy:
         logger.info("🌍 开始市场K线形态扫描")
         logger.info("🚀" + "="*70)
 
+        # 创建扫描会话
+        scan_config = {
+            'max_stocks': max_stocks,
+            'use_hot_stocks': use_hot_stocks,
+            'use_combined_sources': use_combined_sources,
+            'use_all_a_shares': use_all_a_shares,
+            'timeframes': self.timeframes
+        }
+        session_id = self.db_manager.create_scan_session(
+            strategy_id=self.strategy_id,
+            scan_config=scan_config
+        )
+
         if stock_list is None:
-            # 优先使用热门股票数据
+            # 优先级1: 使用所有A股股票
-            if use_hot_stocks:
+            if use_all_a_shares:
+                try:
+                    logger.info("📊 获取所有A股股票数据（排除北交所和ST股票）...")
+                    filtered_stocks = self.data_fetcher.get_filtered_a_share_list()
+
+                    if not filtered_stocks.empty:
+                        # 如果max_stocks小于总股票数，随机采样
+                        if max_stocks > 0 and max_stocks < len(filtered_stocks):
+                            # 按市值排序或随机选择，这里先随机选择
+                            selected_stocks = filtered_stocks.sample(max_stocks)
+                            stock_list = selected_stocks['full_stock_code'].tolist()
+                            logger.info(f"📈 从{len(filtered_stocks)}只A股中随机选择{len(stock_list)}只进行分析")
+                        else:
+                            stock_list = filtered_stocks['full_stock_code'].tolist()
+                            logger.info(f"📈 分析全部{len(stock_list)}只A股股票")
+
+                        source_info = "全A股(排除北交所和ST)"
+                    else:
+                        logger.warning("获取A股列表失败，回退到热门股票")
+                        use_all_a_shares = False
+
+                except Exception as e:
+                    logger.error(f"获取A股列表失败: {e}，回退到热门股票")
+                    use_all_a_shares = False
+
+            # 优先级2: 使用热门股票数据
+            if not use_all_a_shares and use_hot_stocks:
                 try:
                     if use_combined_sources:
                         # 使用合并的双数据源
@@ -381,8 +661,8 @@ class KLinePatternStrategy:
                     logger.error(f"获取热门股票失败: {e}，回退到全市场股票")
                     use_hot_stocks = False
 
-            # 如果热股获取失败，使用全市场股票列表
+            # 优先级3: 如果热股获取失败，使用全市场股票列表
-            if not use_hot_stocks:
+            if not use_all_a_shares and not use_hot_stocks:
                 try:
                     all_stocks = self.data_fetcher.get_stock_list()
                     if not all_stocks.empty:
@@ -405,7 +685,7 @@ class KLinePatternStrategy:
             logger.info(f"⏳ 扫描进度: [{i+1:3d}/{len(stock_list):3d}] 🔍 {stock_code}({stock_name})")
 
             try:
-                stock_results = self.analyze_stock(stock_code)
+                stock_results = self.analyze_stock(stock_code, session_id=session_id)
 
                 # 统计信号数量
                 stock_signal_count = sum(len(signals) for signals in stock_results.values())
@@ -417,6 +697,17 @@ class KLinePatternStrategy:
                 logger.error(f"扫描股票 {stock_code} 失败: {e}")
                 continue
 
+        # 更新扫描会话统计
+        try:
+            self.db_manager.update_scan_session_stats(
+                session_id=session_id,
+                total_scanned=len(stock_list),
+                total_signals=total_signals
+            )
+            logger.debug(f"扫描会话统计已更新: {session_id}")
+        except Exception as e:
+            logger.error(f"更新扫描会话统计失败: {e}")
+
         # 美化最终扫描结果
         logger.info("🎉" + "="*70)
         logger.info(f"🌍 市场K线形态扫描完成!")
@@ -424,6 +715,7 @@ class KLinePatternStrategy:
         logger.info(f"   🔍 总扫描股票: {len(stock_list)} 只")
         logger.info(f"   🎯 发现信号: {total_signals} 个")
         logger.info(f"   📈 涉及股票: {len(results)} 只")
+        logger.info(f"   💾 扫描会话ID: {session_id}")
 
         if results:
             logger.info(f"📋 信号详情:")
@@ -438,6 +730,12 @@ class KLinePatternStrategy:
 
         logger.info("🎉" + "="*70)
 
+        # 监控已触发信号的回踩情况
+        logger.info("🔍 开始监控已触发信号的回踩情况...")
+        pullback_alerts = self.monitor_pullback_for_triggered_signals()
+        if pullback_alerts:
+            logger.info(f"⚠️ 发现 {len(pullback_alerts)} 个回踩提醒")
+
         # 发送汇总通知
         if results:
             # 判断数据源类型
@@ -476,6 +774,13 @@ K线形态策略 - 两阳线+阴线+阳线突破
 6. 最后阳线换手率不高于 {self.max_turnover_ratio:.1f}%（流动性约束）
 7. 支持时间周期：{', '.join(self.timeframes)}
 
+回踩监控功能：
+- 自动监控已触发信号后的价格走势
+- 当价格回踩到阴线最高点附近时发送特殊提醒
+- 回踩容忍度：{self.pullback_tolerance:.0%}
+- 监控期限：信号触发后 {self.monitor_days} 天
+- 提醒条件：价格接近阴线最高点且相比突破价有明显回调
+
 信号触发条件：
 - 形态完整匹配
 - 实体比例达标
@@ -490,6 +795,7 @@ K线形态策略 - 两阳线+阴线+阳线突破
 
 通知方式：
 - 钉钉webhook汇总推送（10个信号一组分批发送）
+- 价格回踩特殊提醒（5个提醒一组分批发送）
 - 包含关键信息：代码、股票名称、K线时间、价格、周期等
 - 系统日志详细记录
 """

src/utils/notification.py

@@ -414,6 +414,89 @@ class NotificationManager:
             logger.error(f"发送策略汇总通知异常: {e}")
             return False
 
+    def send_pullback_alerts(self, pullback_alerts: list) -> bool:
+        """
+        发送价格回踩阴线最高点的特殊提醒
+
+        Args:
+            pullback_alerts: 回踩提醒信号列表
+
+        Returns:
+            发送是否成功
+        """
+        if not pullback_alerts or not self.dingtalk_notifier:
+            return False
+
+        try:
+            current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+            # 按5个提醒为一组分批发送
+            alerts_per_group = 5
+            import math
+            total_groups = math.ceil(len(pullback_alerts) / alerts_per_group)
+            success_count = 0
+
+            for group_idx in range(total_groups):
+                start_idx = group_idx * alerts_per_group
+                end_idx = min(start_idx + alerts_per_group, len(pullback_alerts))
+                group_alerts = pullback_alerts[start_idx:end_idx]
+
+                # 构建标题
+                title = f"⚠️ 价格回踩阴线最高点提醒 ({group_idx + 1}/{total_groups})"
+
+                # 构建详细信息
+                alert_details = []
+                for i, alert in enumerate(group_alerts, 1):
+                    alert_detail = f"""
+**{start_idx + i}. {alert['stock_code']}({alert['stock_name']})**
+- 📅 原信号: {alert['signal_date']} | 当前: {alert['current_date']}
+- ⏰ 间隔: {alert['days_since_signal']}天 | 周期: {alert['timeframe']}
+- 💰 阴线高点: {alert['yin_high']:.2f}元 | 当时突破价: {alert['breakout_price']:.2f}元
+- 📉 当前价格: {alert['current_price']:.2f}元 | 今日最低: {alert['current_low']:.2f}元
+- 📊 回调幅度: {alert['pullback_pct']:.2f}% | 距阴线高点: {alert['distance_to_yin_high']:.2f}%
+"""
+                    alert_details.append(alert_detail)
+
+                # 构建完整的Markdown消息
+                markdown_text = f"""
+# ⚠️ 价格回踩阴线最高点提醒
+
+**🚨 重要提醒:** 以下股票在"两阳+阴+阳"形态突破后，价格回踩至阴线最高点附近，请关注支撑情况！
+
+**📊 本批提醒数量:** {len(group_alerts)}个
+**🕐 检查时间:** {current_time}
+
+---
+
+{''.join(alert_details)}
+
+---
+**💡 操作建议:**
+- 关注是否在阴线最高点获得有效支撑
+- 如跌破阴线最高点需要重新评估形态有效性
+- 建议结合成交量和其他技术指标综合判断
+
+**⚠️ 风险提示:** 本提醒仅供参考，投资需谨慎！
+"""
+
+                # 发送消息
+                if self.dingtalk_notifier.send_markdown_message(title, markdown_text):
+                    success_count += 1
+                    logger.info(f"📱 回踩提醒第{group_idx + 1}组发送成功 ({len(group_alerts)}个提醒)")
+                else:
+                    logger.error(f"📱 回踩提醒第{group_idx + 1}组发送失败")
+
+                # 避免发送过快，添加短暂延迟
+                if group_idx < total_groups - 1:
+                    import time
+                    time.sleep(1)  # 1秒延迟
+
+            return success_count > 0
+
+        except Exception as e:
+            logger.error(f"发送回踩提醒通知异常: {e}")
+            return False
+
     def send_test_message(self) -> bool:
         """发送测试消息"""
         if self.dingtalk_notifier:

start_web.py

@@ -0,0 +1,44 @@
+#!/usr/bin/env python3
+"""
+启动A股量化交易系统Web界面
+"""
+
+import sys
+import subprocess
+from pathlib import Path
+import webbrowser
+import time
+
+def main():
+    """启动Web服务"""
+    print("🌐 A股量化交易系统")
+    print("=" * 50)
+
+    # 检查web目录
+    web_dir = Path(__file__).parent / "web"
+    if not web_dir.exists():
+        print("❌ web目录不存在")
+        return
+
+    app_file = web_dir / "app.py"
+    if not app_file.exists():
+        print("❌ app.py文件不存在")
+        return
+
+    print("🚀 启动Flask服务器...")
+    print("📊 访问地址: http://localhost:8080")
+    print("⏹️  按 Ctrl+C 停止服务")
+    print("=" * 50)
+
+    try:
+        # 启动Flask应用
+        subprocess.run([
+            sys.executable, str(app_file)
+        ], cwd=str(web_dir))
+    except KeyboardInterrupt:
+        print("\n👋 服务已停止")
+    except Exception as e:
+        print(f"❌ 启动失败: {e}")
+
+if __name__ == "__main__":
+    main()

test_all_a_shares.py

@@ -0,0 +1,93 @@
+#!/usr/bin/env python3
+"""
+测试所有A股股票扫描功能
+"""
+
+import sys
+from pathlib import Path
+
+# 添加项目根目录到路径
+current_dir = Path(__file__).parent
+sys.path.insert(0, str(current_dir))
+
+from loguru import logger
+from src.strategy.kline_pattern_strategy import KLinePatternStrategy
+from src.data.data_fetcher import ADataFetcher
+from src.utils.notification import NotificationManager
+from src.database.database_manager import DatabaseManager
+from src.utils.config_loader import ConfigLoader
+
+
+def test_all_a_shares_scan():
+    """测试全A股扫描功能"""
+    logger.remove()
+    logger.add(sys.stdout, level="INFO", format="{time:HH:mm:ss} | {level} | {message}")
+
+    print("🧪 测试全A股扫描功能")
+    print("=" * 50)
+
+    try:
+        # 初始化组件
+        logger.info("初始化组件...")
+        config_loader = ConfigLoader()
+        config = config_loader.load_config()
+
+        data_fetcher = ADataFetcher()
+        notification_manager = NotificationManager(config.get('notification', {}))
+        db_manager = DatabaseManager()
+
+        # 初始化策略
+        kline_config = config.get('strategy', {}).get('kline_pattern', {})
+        strategy = KLinePatternStrategy(
+            data_fetcher=data_fetcher,
+            notification_manager=notification_manager,
+            config=kline_config,
+            db_manager=db_manager
+        )
+
+        # 测试过滤后的A股列表
+        logger.info("测试获取过滤后的A股列表...")
+        filtered_stocks = data_fetcher.get_filtered_a_share_list()
+        logger.info(f"过滤后的A股数量: {len(filtered_stocks)}只")
+
+        if not filtered_stocks.empty:
+            # 显示样例
+            sample_stocks = filtered_stocks.head(5)
+            logger.info("A股样例:")
+            for _, stock in sample_stocks.iterrows():
+                logger.info(f"  {stock['full_stock_code']} - {stock['short_name']} ({stock['exchange']})")
+
+        # 测试扫描全A股（限制5只股票进行测试）
+        logger.info("开始测试全A股扫描（限制5只）...")
+        results = strategy.scan_market(
+            max_stocks=5,           # 限制5只股票进行测试
+            use_all_a_shares=True   # 使用全A股模式
+        )
+
+        # 统计结果
+        total_signals = 0
+        for stock_code, stock_results in results.items():
+            stock_signals = sum(len(signals) for signals in stock_results.values())
+            total_signals += stock_signals
+            logger.info(f"股票 {stock_code}: {stock_signals}个信号")
+
+        logger.info(f"✅ 全A股扫描测试完成！")
+        logger.info(f"📊 扫描股票数: {len(results)}只")
+        logger.info(f"📈 发现信号数: {total_signals}个")
+
+        print("\n" + "=" * 50)
+        print("🎯 测试结果:")
+        print(f"   - 可用A股数量: {len(filtered_stocks)}只")
+        print(f"   - 扫描股票数量: 5只（测试限制）")
+        print(f"   - 发现信号数量: {total_signals}个")
+        print("   - 功能状态: ✅ 正常工作")
+        print("=" * 50)
+
+    except Exception as e:
+        logger.error(f"❌ 测试失败: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    test_all_a_shares_scan()

test_cache_optimization.py

@@ -0,0 +1,97 @@
+#!/usr/bin/env python3
+"""
+测试股票名称获取的缓存优化
+"""
+
+import sys
+from pathlib import Path
+import time
+
+# 添加src目录到路径
+current_dir = Path(__file__).parent
+src_dir = current_dir / "src"
+sys.path.insert(0, str(src_dir))
+
+from loguru import logger
+from src.data.data_fetcher import ADataFetcher
+
+def test_stock_name_cache():
+    """测试股票名称缓存机制"""
+    logger.info("🧪 开始测试股票名称缓存优化")
+
+    # 初始化数据获取器
+    data_fetcher = ADataFetcher()
+
+    # 测试股票列表
+    test_stocks = ['000001.SZ', '000002.SZ', '600000.SH', '600036.SH', '000858.SZ']
+
+    # 第一次获取股票名称（会触发缓存构建）
+    logger.info("📊 第一次获取股票名称（构建缓存）...")
+    start_time = time.time()
+
+    names_first = {}
+    for stock_code in test_stocks:
+        name = data_fetcher.get_stock_name(stock_code)
+        names_first[stock_code] = name
+        logger.info(f"   {stock_code}: {name}")
+
+    first_duration = time.time() - start_time
+    logger.info(f"⏱️ 第一次获取耗时: {first_duration:.2f}秒")
+
+    # 等待一秒
+    time.sleep(1)
+
+    # 第二次获取股票名称（应该从缓存读取）
+    logger.info("📊 第二次获取股票名称（从缓存读取）...")
+    start_time = time.time()
+
+    names_second = {}
+    for stock_code in test_stocks:
+        name = data_fetcher.get_stock_name(stock_code)
+        names_second[stock_code] = name
+        logger.info(f"   {stock_code}: {name}")
+
+    second_duration = time.time() - start_time
+    logger.info(f"⏱️ 第二次获取耗时: {second_duration:.2f}秒")
+
+    # 比较结果
+    logger.info("📈 性能对比:")
+    if second_duration < first_duration:
+        speedup = first_duration / second_duration
+        logger.info(f"✅ 缓存优化成功! 第二次比第一次快 {speedup:.1f}x")
+    else:
+        logger.warning("❌ 缓存优化效果不明显")
+
+    # 验证数据一致性
+    consistent = names_first == names_second
+    logger.info(f"🔍 数据一致性: {'✅ 一致' if consistent else '❌ 不一致'}")
+
+    # 显示缓存状态
+    logger.info(f"📦 当前缓存中的股票数量: {len(data_fetcher._stock_name_cache)}")
+
+    return first_duration, second_duration, consistent
+
+if __name__ == "__main__":
+    # 设置日志
+    logger.remove()
+    logger.add(sys.stdout, level="INFO", format="{time:HH:mm:ss} | {level} | {message}")
+
+    print("=" * 60)
+    print("🧪 股票名称缓存优化测试")
+    print("=" * 60)
+
+    try:
+        first_time, second_time, is_consistent = test_stock_name_cache()
+
+        print("\n" + "=" * 60)
+        print("📊 测试结果总结:")
+        print(f"   第一次获取耗时: {first_time:.2f}秒")
+        print(f"   第二次获取耗时: {second_time:.2f}秒")
+        print(f"   性能提升倍数: {first_time/second_time:.1f}x")
+        print(f"   数据一致性: {'✅ 通过' if is_consistent else '❌ 失败'}")
+        print("=" * 60)
+
+    except Exception as e:
+        logger.error(f"测试过程中发生错误: {e}")
+        import traceback
+        traceback.print_exc()

test_database_integration.py

@@ -0,0 +1,256 @@
+#!/usr/bin/env python3
+"""
+测试数据库集成和策略存储功能
+"""
+
+import sys
+from pathlib import Path
+from datetime import datetime, date
+
+# 添加src目录到路径
+current_dir = Path(__file__).parent
+src_dir = current_dir / "src"
+sys.path.insert(0, str(src_dir))
+
+from loguru import logger
+from src.database.database_manager import DatabaseManager
+from src.utils.config_loader import ConfigLoader
+from src.data.data_fetcher import ADataFetcher
+from src.utils.notification import NotificationManager
+from src.strategy.kline_pattern_strategy import KLinePatternStrategy
+
+
+def test_database_operations():
+    """测试数据库基本操作"""
+    logger.info("🗄️ 测试数据库基本操作...")
+
+    try:
+        # 初始化数据库管理器
+        db_manager = DatabaseManager()
+
+        # 测试策略统计
+        strategy_stats = db_manager.get_strategy_stats()
+        logger.info(f"📊 策略统计记录数: {len(strategy_stats)}")
+
+        # 测试最新信号
+        latest_signals = db_manager.get_latest_signals(limit=10)
+        logger.info(f"📈 最新信号记录数: {len(latest_signals)}")
+
+        # 测试日期范围查询
+        start_date = date.today()
+        signals_by_date = db_manager.get_signals_by_date_range(start_date)
+        logger.info(f"🗓️ 今日信号记录数: {len(signals_by_date)}")
+
+        # 测试回踩提醒
+        pullback_alerts = db_manager.get_pullback_alerts(days=7)
+        logger.info(f"⚠️ 最近7天回踩提醒: {len(pullback_alerts)}")
+
+        logger.info("✅ 数据库基本操作测试完成")
+        return True
+
+    except Exception as e:
+        logger.error(f"❌ 数据库操作测试失败: {e}")
+        return False
+
+
+def test_strategy_integration():
+    """测试策略与数据库集成"""
+    logger.info("🔄 测试策略与数据库集成...")
+
+    try:
+        # 初始化组件
+        config_loader = ConfigLoader()
+        config = config_loader.load_config()
+
+        data_fetcher = ADataFetcher()
+        notification_manager = NotificationManager(config.get('notification', {}))
+        db_manager = DatabaseManager()
+
+        # 初始化策略（自动创建数据库记录）
+        kline_config = config.get('strategy', {}).get('kline_pattern', {})
+        strategy = KLinePatternStrategy(
+            data_fetcher=data_fetcher,
+            notification_manager=notification_manager,
+            config=kline_config,
+            db_manager=db_manager
+        )
+
+        logger.info(f"📋 策略ID: {strategy.strategy_id}")
+        logger.info(f"📝 策略名称: {strategy.strategy_name}")
+
+        # 测试分析单只股票（会自动保存到数据库）
+        test_stock = "000001.SZ"
+        logger.info(f"🔍 测试分析股票: {test_stock}")
+
+        stock_results = strategy.analyze_stock(test_stock, days=30)
+        total_signals = sum(len(signals) for signals in stock_results.values())
+
+        logger.info(f"📊 分析结果: {total_signals} 个信号")
+
+        # 验证数据库中的记录
+        latest_signals = db_manager.get_latest_signals(strategy_name=strategy.strategy_name, limit=10)
+        logger.info(f"💾 数据库中最新信号数: {len(latest_signals)}")
+
+        logger.info("✅ 策略与数据库集成测试完成")
+        return True
+
+    except Exception as e:
+        logger.error(f"❌ 策略集成测试失败: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_scan_market_with_database():
+    """测试市场扫描与数据库存储"""
+    logger.info("🌍 测试市场扫描与数据库存储...")
+
+    try:
+        # 初始化组件
+        config_loader = ConfigLoader()
+        config = config_loader.load_config()
+
+        data_fetcher = ADataFetcher()
+        notification_manager = NotificationManager(config.get('notification', {}))
+        db_manager = DatabaseManager()
+
+        # 初始化策略
+        kline_config = config.get('strategy', {}).get('kline_pattern', {})
+        strategy = KLinePatternStrategy(
+            data_fetcher=data_fetcher,
+            notification_manager=notification_manager,
+            config=kline_config,
+            db_manager=db_manager
+        )
+
+        # 小规模市场扫描测试（限制5只股票）
+        logger.info("🔍 开始小规模市场扫描测试...")
+        test_stocks = ["000001.SZ", "000002.SZ", "600000.SH", "600036.SH", "000858.SZ"]
+
+        results = strategy.scan_market(
+            stock_list=test_stocks,
+            max_stocks=5,
+            use_hot_stocks=False
+        )
+
+        total_signals = sum(
+            sum(len(signals) for signals in stock_results.values())
+            for stock_results in results.values()
+        )
+
+        logger.info(f"📊 扫描完成: 发现 {total_signals} 个信号")
+
+        # 验证数据库存储
+        recent_signals = db_manager.get_latest_signals(
+            strategy_name=strategy.strategy_name,
+            limit=50
+        )
+        logger.info(f"💾 数据库中存储的信号数: {len(recent_signals)}")
+
+        # 显示最新的几个信号
+        if not recent_signals.empty:
+            logger.info("📋 最新信号示例:")
+            for i, signal in recent_signals.head(3).iterrows():
+                logger.info(f"   {signal['stock_code']}({signal['stock_name']}) - {signal['breakout_price']:.2f}元")
+
+        logger.info("✅ 市场扫描与数据库存储测试完成")
+        return True
+
+    except Exception as e:
+        logger.error(f"❌ 市场扫描测试失败: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+
+def test_database_queries():
+    """测试数据库查询功能"""
+    logger.info("🔍 测试数据库查询功能...")
+
+    try:
+        db_manager = DatabaseManager()
+
+        # 测试策略统计
+        strategy_stats = db_manager.get_strategy_stats()
+        if not strategy_stats.empty:
+            logger.info("📊 策略统计:")
+            for _, stat in strategy_stats.iterrows():
+                logger.info(f"   {stat['strategy_name']}: {stat['total_signals']}个信号, {stat['unique_stocks']}只股票")
+
+        # 测试按日期查询
+        today = date.today()
+        today_signals = db_manager.get_signals_by_date_range(today, today)
+        logger.info(f"📅 今日信号数: {len(today_signals)}")
+
+        # 测试获取策略ID
+        strategy_id = db_manager.get_strategy_id("K线形态策略")
+        logger.info(f"🆔 K线形态策略ID: {strategy_id}")
+
+        logger.info("✅ 数据库查询功能测试完成")
+        return True
+
+    except Exception as e:
+        logger.error(f"❌ 数据库查询测试失败: {e}")
+        return False
+
+
+def main():
+    """主测试函数"""
+    logger.remove()
+    logger.add(sys.stdout, level="INFO", format="{time:HH:mm:ss} | {level} | {message}")
+
+    print("=" * 70)
+    print("🧪 A股量化交易系统 - 数据库集成测试")
+    print("=" * 70)
+
+    test_results = []
+
+    # 运行测试
+    tests = [
+        ("数据库基本操作", test_database_operations),
+        ("策略与数据库集成", test_strategy_integration),
+        ("数据库查询功能", test_database_queries),
+        ("市场扫描与存储", test_scan_market_with_database),
+    ]
+
+    for test_name, test_func in tests:
+        logger.info(f"\n🚀 开始测试: {test_name}")
+        try:
+            result = test_func()
+            test_results.append((test_name, result))
+            if result:
+                logger.info(f"✅ {test_name} 测试通过")
+            else:
+                logger.error(f"❌ {test_name} 测试失败")
+        except Exception as e:
+            logger.error(f"❌ {test_name} 测试异常: {e}")
+            test_results.append((test_name, False))
+
+    # 输出测试结果
+    print("\n" + "=" * 70)
+    print("📊 测试结果汇总:")
+    print("=" * 70)
+
+    passed = 0
+    total = len(test_results)
+
+    for test_name, result in test_results:
+        status = "✅ 通过" if result else "❌ 失败"
+        print(f"   {test_name}: {status}")
+        if result:
+            passed += 1
+
+    print(f"\n🎯 总计: {passed}/{total} 个测试通过")
+
+    if passed == total:
+        print("🎉 所有测试都通过了！数据库集成功能正常工作。")
+        print("🌐 现在可以启动Web界面查看数据:")
+        print("   cd web && python app.py")
+    else:
+        print("⚠️ 部分测试失败，请检查错误信息并修复问题。")
+
+    print("=" * 70)
+
+
+if __name__ == "__main__":
+    main()

test_pullback_feature.py

@@ -0,0 +1,179 @@
+#!/usr/bin/env python3
+"""
+测试价格回踩阴线最高点提醒功能
+"""
+
+import sys
+from pathlib import Path
+import pandas as pd
+from datetime import datetime, timedelta
+
+# 添加src目录到路径
+current_dir = Path(__file__).parent
+src_dir = current_dir / "src"
+sys.path.insert(0, str(src_dir))
+
+from loguru import logger
+from src.utils.config_loader import config_loader
+from src.data.data_fetcher import ADataFetcher
+from src.utils.notification import NotificationManager
+from src.strategy.kline_pattern_strategy import KLinePatternStrategy
+
+
+def create_test_signal():
+    """创建一个测试用的K线形态信号"""
+    test_signal = {
+        'stock_code': '000001.SZ',
+        'stock_name': '平安银行',
+        'date': datetime.now() - timedelta(days=5),  # 5天前的信号
+        'timeframe': 'daily',
+        'breakout_price': 15.50,  # 突破价格
+        'yin_high': 15.20,        # 阴线最高点
+        'pattern_type': '两阳+阴+阳突破'
+    }
+    return test_signal
+
+
+def create_test_pullback_data():
+    """创建模拟回踩数据 - 模拟价格回踩到阴线最高点附近"""
+    dates = pd.date_range(start=datetime.now() - timedelta(days=3), end=datetime.now(), freq='D')
+
+    # 模拟价格回踩阴线最高点的情况
+    test_data = []
+    yin_high = 15.20      # 阴线最高点价格
+    initial_price = 15.50 # 突破价格
+
+    # 设计价格走势：从突破价格逐步回调到阴线最高点附近
+    price_path = [15.45, 15.35, 15.25, 15.18]  # 最后一个价格接近阴线最高点
+
+    for i, date in enumerate(dates):
+        if i < len(price_path):
+            close_price = price_path[i]
+        else:
+            close_price = yin_high - 0.02  # 稍低于阴线最高点
+
+        low_price = close_price - 0.03  # 最低价更接近阴线最高点
+
+        test_data.append({
+            'trade_date': date,
+            'open': close_price + 0.02,
+            'high': close_price + 0.05,
+            'low': low_price,
+            'close': close_price,
+            'volume': 1000000
+        })
+
+    return pd.DataFrame(test_data)
+
+
+def test_pullback_detection():
+    """测试回踩检测功能"""
+    logger.info("🧪 开始测试价格回踩阴线最高点提醒功能")
+
+    # 初始化配置
+    config = config_loader.load_config()
+
+    # 初始化组件
+    data_fetcher = ADataFetcher()
+    notification_config = config.get('notification', {})
+    notification_manager = NotificationManager(notification_config)
+
+    # 获取K线形态策略配置
+    kline_config = config.get('strategy', {}).get('kline_pattern', {})
+    strategy = KLinePatternStrategy(data_fetcher, notification_manager, kline_config)
+
+    # 创建测试信号
+    test_signal = create_test_signal()
+    logger.info(f"📊 创建测试信号: {test_signal['stock_code']}({test_signal['stock_name']})")
+    logger.info(f"   - 信号日期: {test_signal['date']}")
+    logger.info(f"   - 突破价格: {test_signal['breakout_price']}")
+    logger.info(f"   - 阴线最高点: {test_signal['yin_high']}")
+
+    # 添加到策略的监控列表
+    strategy.add_triggered_signal(test_signal)
+    logger.info("✅ 测试信号已添加到监控列表")
+
+    # 创建模拟回踩数据
+    test_pullback_data = create_test_pullback_data()
+    logger.info(f"📈 创建模拟K线数据，共{len(test_pullback_data)}条记录")
+
+    print("\n模拟K线数据:")
+    for _, row in test_pullback_data.iterrows():
+        print(f"  {row['trade_date'].strftime('%Y-%m-%d')}: "
+              f"开{row['open']:.2f} 高{row['high']:.2f} 低{row['low']:.2f} 收{row['close']:.2f}")
+
+    # 检测回踩情况
+    logger.info("🔍 开始检测回踩情况...")
+    pullback_alerts = strategy.check_pullback_signals(test_signal['stock_code'], test_pullback_data)
+
+    if pullback_alerts:
+        logger.info(f"⚠️ 检测到 {len(pullback_alerts)} 个回踩提醒")
+        for i, alert in enumerate(pullback_alerts, 1):
+            logger.info(f"   提醒{i}: {alert['stock_code']} - 当前价格{alert['current_price']:.2f}，"
+                       f"回调{alert['pullback_pct']:.2f}%，距阴线高点{alert['distance_to_yin_high']:.2f}%")
+
+        # 测试通知发送（如果启用了钉钉通知）
+        if notification_config.get('dingtalk', {}).get('enabled', False):
+            logger.info("📱 测试发送回踩提醒通知...")
+            success = notification_manager.send_pullback_alerts(pullback_alerts)
+            if success:
+                logger.info("✅ 回踩提醒通知发送成功")
+            else:
+                logger.warning("❌ 回踩提醒通知发送失败")
+        else:
+            logger.info("ℹ️ 钉钉通知未启用，跳过通知发送测试")
+    else:
+        logger.info("ℹ️ 未检测到回踩情况")
+
+    # 测试完整的监控流程
+    logger.info("\n🔍 测试完整的回踩监控流程...")
+    all_pullback_alerts = strategy.monitor_pullback_for_triggered_signals()
+
+    logger.info("🎯 测试完成!")
+    if all_pullback_alerts:
+        logger.info(f"✅ 成功检测到 {len(all_pullback_alerts)} 个回踩提醒")
+    else:
+        logger.info("ℹ️ 当前监控中无回踩情况")
+
+
+def test_strategy_config():
+    """测试策略配置是否正确加载"""
+    logger.info("🔧 测试策略配置加载...")
+
+    config = config_loader.load_config()
+    kline_config = config.get('strategy', {}).get('kline_pattern', {})
+
+    logger.info("📋 当前K线形态策略配置:")
+    logger.info(f"   - 启用状态: {kline_config.get('enabled', False)}")
+    logger.info(f"   - 前两阳线实体比例: {kline_config.get('min_entity_ratio', 0.55)}")
+    logger.info(f"   - 最后阳线实体比例: {kline_config.get('final_yang_min_ratio', 0.40)}")
+    logger.info(f"   - 回踩容忍度: {kline_config.get('pullback_tolerance', 0.02)}")
+    logger.info(f"   - 监控天数: {kline_config.get('monitor_days', 30)}")
+    logger.info(f"   - 支持时间周期: {kline_config.get('timeframes', ['daily'])}")
+
+
+if __name__ == "__main__":
+    # 设置日志
+    logger.remove()
+    logger.add(sys.stdout, level="INFO", format="{time:HH:mm:ss} | {level} | {message}")
+
+    print("=" * 60)
+    print("🧪 价格回踩阴线最高点提醒功能测试")
+    print("=" * 60)
+
+    try:
+        # 测试配置加载
+        test_strategy_config()
+        print()
+
+        # 测试回踩检测功能
+        test_pullback_detection()
+
+    except Exception as e:
+        logger.error(f"测试过程中发生错误: {e}")
+        import traceback
+        traceback.print_exc()
+
+    print("\n" + "=" * 60)
+    print("🏁 测试结束")
+    print("=" * 60)

test_simple_integration.py

@@ -0,0 +1,80 @@
+#!/usr/bin/env python3
+"""
+简单的数据库集成测试
+"""
+
+import sys
+from pathlib import Path
+
+# 添加src目录到路径
+current_dir = Path(__file__).parent
+src_dir = current_dir / "src"
+sys.path.insert(0, str(src_dir))
+
+from loguru import logger
+from src.database.database_manager import DatabaseManager
+from src.utils.config_loader import ConfigLoader
+from src.data.data_fetcher import ADataFetcher
+from src.utils.notification import NotificationManager
+from src.strategy.kline_pattern_strategy import KLinePatternStrategy
+
+
+def main():
+    """简单测试"""
+    logger.remove()
+    logger.add(sys.stdout, level="INFO", format="{time:HH:mm:ss} | {level} | {message}")
+
+    print("🧪 简单数据库集成测试")
+    print("=" * 50)
+
+    try:
+        # 初始化组件
+        logger.info("初始化组件...")
+        config_loader = ConfigLoader()
+        config = config_loader.load_config()
+
+        data_fetcher = ADataFetcher()
+        notification_manager = NotificationManager(config.get('notification', {}))
+        db_manager = DatabaseManager()
+
+        # 初始化策略
+        kline_config = config.get('strategy', {}).get('kline_pattern', {})
+        strategy = KLinePatternStrategy(
+            data_fetcher=data_fetcher,
+            notification_manager=notification_manager,
+            config=kline_config,
+            db_manager=db_manager
+        )
+
+        logger.info(f"策略ID: {strategy.strategy_id}")
+
+        # 测试小规模扫描
+        logger.info("开始小规模扫描...")
+        test_stocks = ["000001.SZ", "000002.SZ"]
+
+        results = strategy.scan_market(
+            stock_list=test_stocks,
+            max_stocks=2,
+            use_hot_stocks=False
+        )
+
+        # 检查数据库
+        logger.info("检查数据库记录...")
+        latest_signals = db_manager.get_latest_signals(limit=10)
+        logger.info(f"数据库中的信号数: {len(latest_signals)}")
+
+        if not latest_signals.empty:
+            logger.info("信号示例:")
+            for _, signal in latest_signals.head(3).iterrows():
+                logger.info(f"  {signal['stock_code']} - {signal['breakout_price']:.2f}元")
+
+        logger.info("✅ 测试完成")
+
+    except Exception as e:
+        logger.error(f"❌ 测试失败: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    main()

web/app.py

@@ -0,0 +1,226 @@
+#!/usr/bin/env python3
+"""
+A股量化交易系统 Web 展示界面
+使用Flask框架展示策略筛选结果
+"""
+
+import sys
+from pathlib import Path
+from flask import Flask, render_template, jsonify, request
+from datetime import datetime, date, timedelta
+import pandas as pd
+
+# 添加项目根目录到路径
+current_dir = Path(__file__).parent
+project_root = current_dir.parent
+sys.path.insert(0, str(project_root))
+
+from src.database.database_manager import DatabaseManager
+from src.utils.config_loader import ConfigLoader
+from loguru import logger
+
+app = Flask(__name__)
+app.secret_key = 'trading_ai_secret_key_2023'
+
+# 初始化组件
+db_manager = DatabaseManager()
+config_loader = ConfigLoader()
+
+
+@app.route('/')
+def index():
+    """首页 - 直接跳转到交易信号页面"""
+    from flask import redirect, url_for
+    return redirect(url_for('signals'))
+
+
+@app.route('/signals')
+def signals():
+    """信号页面 - 详细的信号列表"""
+    try:
+        # 获取查询参数
+        strategy_name = request.args.get('strategy', '')
+        timeframe = request.args.get('timeframe', '')
+        days = int(request.args.get('days', 30))
+        page = int(request.args.get('page', 1))
+        per_page = int(request.args.get('per_page', 20))
+
+        # 计算日期范围
+        end_date = date.today()
+        start_date = end_date - timedelta(days=days)
+
+        # 获取信号数据
+        signals_df = db_manager.get_signals_by_date_range(
+            start_date=start_date,
+            end_date=end_date,
+            strategy_name=strategy_name if strategy_name else None,
+            timeframe=timeframe if timeframe else None
+        )
+
+        # 按扫描日期分组，每组内按信号日期排序
+        signals_grouped = {}
+        if not signals_df.empty:
+            # 确保scan_time是datetime类型
+            signals_df['scan_time'] = pd.to_datetime(signals_df['scan_time'])
+            signals_df['scan_date'] = signals_df['scan_time'].dt.date
+
+            # 按扫描日期分组
+            for scan_date, group in signals_df.groupby('scan_date'):
+                # 每组内按信号日期排序（降序）
+                group_sorted = group.sort_values('signal_date', ascending=False)
+                signals_grouped[scan_date] = group_sorted
+
+            # 按扫描日期排序（最新的在前）
+            signals_grouped = dict(sorted(signals_grouped.items(), key=lambda x: x[0], reverse=True))
+
+        # 分页处理
+        total_records = len(signals_df)
+        start_idx = (page - 1) * per_page
+        end_idx = start_idx + per_page
+
+        # 将分组数据展平用于分页
+        flattened_signals = []
+        for scan_date, group in signals_grouped.items():
+            flattened_signals.extend(group.to_dict('records'))
+
+        paginated_signals = flattened_signals[start_idx:end_idx]
+
+        # 重新按扫描日期分组分页后的数据
+        paginated_grouped = {}
+        for signal in paginated_signals:
+            scan_date = pd.to_datetime(signal['scan_time']).date()
+            if scan_date not in paginated_grouped:
+                paginated_grouped[scan_date] = []
+            paginated_grouped[scan_date].append(signal)
+
+        # 计算分页信息
+        total_pages = (total_records + per_page - 1) // per_page
+        has_prev = page > 1
+        has_next = page < total_pages
+
+        return render_template('signals.html',
+                             signals_grouped=paginated_grouped,
+                             current_page=page,
+                             total_pages=total_pages,
+                             has_prev=has_prev,
+                             has_next=has_next,
+                             total_records=total_records,
+                             strategy_name=strategy_name,
+                             timeframe=timeframe,
+                             days=days,
+                             per_page=per_page)
+    except Exception as e:
+        logger.error(f"信号页面数据加载失败: {e}")
+        return render_template('error.html', error=str(e))
+
+
+@app.route('/pullbacks')
+def pullbacks():
+    """回踩监控页面"""
+    try:
+        days = int(request.args.get('days', 30))
+        pullback_alerts = db_manager.get_pullback_alerts(days=days)
+
+        return render_template('pullbacks.html',
+                             pullback_alerts=pullback_alerts.to_dict('records') if not pullback_alerts.empty else [],
+                             days=days)
+    except Exception as e:
+        logger.error(f"回踩监控页面数据加载失败: {e}")
+        return render_template('error.html', error=str(e))
+
+
+@app.route('/api/signals')
+def api_signals():
+    """API接口 - 获取信号数据"""
+    try:
+        strategy_name = request.args.get('strategy', '')
+        limit = int(request.args.get('limit', 100))
+
+        signals_df = db_manager.get_latest_signals(
+            strategy_name=strategy_name if strategy_name else None,
+            limit=limit
+        )
+
+        return jsonify({
+            'success': True,
+            'data': signals_df.to_dict('records') if not signals_df.empty else [],
+            'total': len(signals_df)
+        })
+    except Exception as e:
+        logger.error(f"API获取信号失败: {e}")
+        return jsonify({'success': False, 'error': str(e)})
+
+
+@app.route('/api/stats')
+def api_stats():
+    """API接口 - 获取策略统计"""
+    try:
+        strategy_stats = db_manager.get_strategy_stats()
+
+        return jsonify({
+            'success': True,
+            'data': strategy_stats.to_dict('records') if not strategy_stats.empty else []
+        })
+    except Exception as e:
+        logger.error(f"API获取统计失败: {e}")
+        return jsonify({'success': False, 'error': str(e)})
+
+
+@app.route('/api/pullbacks')
+def api_pullbacks():
+    """API接口 - 获取回踩提醒"""
+    try:
+        days = int(request.args.get('days', 7))
+        pullback_alerts = db_manager.get_pullback_alerts(days=days)
+
+        return jsonify({
+            'success': True,
+            'data': pullback_alerts.to_dict('records') if not pullback_alerts.empty else []
+        })
+    except Exception as e:
+        logger.error(f"API获取回踩提醒失败: {e}")
+        return jsonify({'success': False, 'error': str(e)})
+
+
+@app.template_filter('datetime_format')
+def datetime_format(value, format='%Y-%m-%d %H:%M'):
+    """日期时间格式化过滤器"""
+    if value is None:
+        return ''
+    if isinstance(value, str):
+        try:
+            value = datetime.fromisoformat(value.replace('Z', '+00:00'))
+        except:
+            return value
+    return value.strftime(format)
+
+
+@app.template_filter('percentage')
+def percentage_format(value, precision=2):
+    """百分比格式化过滤器"""
+    if value is None:
+        return '0.00%'
+    return f"{float(value):.{precision}f}%"
+
+
+@app.template_filter('currency')
+def currency_format(value, precision=2):
+    """货币格式化过滤器"""
+    if value is None:
+        return '0.00'
+    return f"{float(value):.{precision}f}"
+
+
+if __name__ == '__main__':
+    # 设置日志
+    logger.remove()
+    logger.add(sys.stdout, level="INFO", format="{time:HH:mm:ss} | {level} | {message}")
+
+    print("=" * 60)
+    print("🌐 A股量化交易系统 Web 界面")
+    print("=" * 60)
+    print("🚀 启动 Flask 服务器...")
+    print("📊 访问地址: http://localhost:8080")
+    print("=" * 60)
+
+    app.run(host='0.0.0.0', port=8080, debug=True)

web/static/css/style.css

@@ -0,0 +1,537 @@
+/* A股量化交易系统 - 简洁清爽浅色调设计 */
+
+/* ========== 全局样式 ========== */
+:root {
+    /* 蓝色调配色方案 */
+    --primary-color: #2563eb;
+    --primary-light: #60a5fa;
+    --primary-lighter: #dbeafe;
+    --secondary-color: #64748b;
+    --success-color: #10b981;
+    --warning-color: #f59e0b;
+    --danger-color: #ef4444;
+    --info-color: #06b6d4;
+
+    /* 背景色 */
+    --bg-primary: #fefefe;
+    --bg-secondary: #f8fafc;
+    --bg-tertiary: #f1f5f9;
+    --bg-accent: #ffffff;
+
+    /* 文字色 */
+    --text-primary: #1e293b;
+    --text-secondary: #64748b;
+    --text-muted: #94a3b8;
+
+    /* 边框色 */
+    --border-color: #e2e8f0;
+    --border-light: #f1f5f9;
+
+    /* 阴影 */
+    --shadow-sm: 0 1px 2px 0 rgba(0, 0, 0, 0.05);
+    --shadow-md: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
+    --shadow-lg: 0 10px 15px -3px rgba(0, 0, 0, 0.1), 0 4px 6px -2px rgba(0, 0, 0, 0.05);
+
+    /* 圆角 */
+    --radius-sm: 0.375rem;
+    --radius-md: 0.5rem;
+    --radius-lg: 0.75rem;
+}
+
+body {
+    font-family: 'Inter', 'SF Pro Display', -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', sans-serif;
+    background-color: var(--bg-secondary);
+    color: var(--text-primary);
+    line-height: 1.6;
+    font-size: 14px;
+}
+
+/* ========== 导航栏 ========== */
+.navbar {
+    background: linear-gradient(135deg, var(--bg-accent) 0%, var(--bg-secondary) 100%);
+    border-bottom: 1px solid var(--border-color);
+    backdrop-filter: blur(10px);
+    box-shadow: var(--shadow-sm);
+    padding: 0.75rem 0;
+}
+
+.navbar-brand {
+    font-weight: 600;
+    font-size: 1.125rem;
+    color: var(--primary-color) !important;
+    display: flex;
+    align-items: center;
+    gap: 0.5rem;
+}
+
+.navbar-brand i {
+    color: var(--primary-color);
+    font-size: 1.25rem;
+}
+
+.nav-link {
+    color: var(--text-secondary) !important;
+    font-weight: 500;
+    padding: 0.5rem 1rem !important;
+    border-radius: var(--radius-sm);
+    transition: all 0.2s ease;
+    margin: 0 0.125rem;
+}
+
+.nav-link:hover {
+    color: var(--primary-color) !important;
+    background-color: var(--primary-lighter);
+}
+
+.nav-link.active {
+    color: var(--primary-color) !important;
+    background-color: var(--primary-lighter);
+    font-weight: 600;
+}
+
+.navbar-text {
+    color: var(--text-muted) !important;
+    font-size: 0.875rem;
+}
+
+/* ========== 卡片样式 ========== */
+.card {
+    background-color: var(--bg-accent);
+    border: 1px solid var(--border-color);
+    border-radius: var(--radius-lg);
+    box-shadow: var(--shadow-sm);
+    transition: all 0.3s ease;
+    overflow: hidden;
+}
+
+.card:hover {
+    box-shadow: var(--shadow-md);
+    transform: translateY(-1px);
+}
+
+.card-header {
+    background: linear-gradient(135deg, var(--bg-tertiary) 0%, var(--bg-secondary) 100%);
+    border-bottom: 1px solid var(--border-color);
+    padding: 1rem 1.25rem;
+    font-weight: 600;
+    color: var(--text-primary);
+}
+
+.card-body {
+    padding: 1.25rem;
+}
+
+/* 统计卡片 */
+.stats-card {
+    background: linear-gradient(135deg, var(--bg-accent) 0%, var(--bg-tertiary) 100%);
+    border: 1px solid var(--border-light);
+    border-radius: var(--radius-md);
+    padding: 0.75rem;
+    transition: all 0.3s ease;
+    position: relative;
+    overflow: hidden;
+}
+
+.stats-card::before {
+    content: '';
+    position: absolute;
+    top: 0;
+    left: 0;
+    right: 0;
+    height: 2px;
+    background: linear-gradient(90deg, var(--primary-color), var(--primary-light));
+}
+
+.stats-card:hover {
+    transform: translateY(-2px);
+    box-shadow: var(--shadow-lg);
+}
+
+.stats-value {
+    font-size: 1.25rem;
+    font-weight: 700;
+    color: var(--primary-color);
+    margin-bottom: 0.125rem;
+}
+
+.stats-label {
+    font-size: 0.75rem;
+    color: var(--text-secondary);
+    font-weight: 500;
+}
+
+.stats-sublabel {
+    font-size: 0.675rem;
+    color: var(--text-muted);
+    margin-top: 0.125rem;
+}
+
+/* ========== 表格样式 ========== */
+.table-container {
+    background-color: var(--bg-accent);
+    border-radius: var(--radius-lg);
+    overflow: hidden;
+    box-shadow: var(--shadow-sm);
+}
+
+.table {
+    margin-bottom: 0;
+    font-size: 0.875rem;
+}
+
+.table thead th {
+    background: linear-gradient(135deg, var(--bg-tertiary) 0%, var(--bg-secondary) 100%);
+    border-bottom: 2px solid var(--border-color);
+    border-top: none;
+    font-weight: 600;
+    font-size: 0.75rem;
+    text-transform: uppercase;
+    letter-spacing: 0.05em;
+    color: var(--text-secondary);
+    padding: 1rem 0.75rem;
+}
+
+.table tbody tr {
+    border-bottom: 1px solid var(--border-light);
+    transition: all 0.2s ease;
+}
+
+.table tbody tr:hover {
+    background-color: var(--bg-secondary);
+}
+
+.table tbody tr:last-child {
+    border-bottom: none;
+}
+
+.table tbody td {
+    padding: 0.875rem 0.75rem;
+    vertical-align: middle;
+    color: var(--text-primary);
+}
+
+.table-active {
+    background-color: var(--primary-lighter) !important;
+}
+
+/* ========== 徽章样式 ========== */
+.badge {
+    font-size: 0.75rem;
+    font-weight: 500;
+    padding: 0.375rem 0.75rem;
+    border-radius: var(--radius-sm);
+    border: 1px solid transparent;
+}
+
+.bg-primary {
+    background-color: var(--primary-color) !important;
+    color: white;
+}
+
+.bg-secondary {
+    background-color: var(--text-muted) !important;
+    color: white;
+}
+
+.bg-success {
+    background-color: var(--success-color) !important;
+    color: white;
+}
+
+.bg-warning {
+    background-color: var(--warning-color) !important;
+    color: white;
+}
+
+.bg-danger {
+    background-color: var(--danger-color) !important;
+    color: white;
+}
+
+.bg-info {
+    background-color: var(--info-color) !important;
+    color: white;
+}
+
+/* 浅色徽章变体 */
+.badge-light-primary {
+    background-color: var(--primary-lighter);
+    color: var(--primary-color);
+    border-color: var(--primary-light);
+}
+
+.badge-light-success {
+    background-color: #dcfce7;
+    color: var(--success-color);
+    border-color: #bbf7d0;
+}
+
+.badge-light-warning {
+    background-color: #fef3c7;
+    color: var(--warning-color);
+    border-color: #fde68a;
+}
+
+.badge-light-danger {
+    background-color: #fecaca;
+    color: var(--danger-color);
+    border-color: #fca5a5;
+}
+
+/* ========== 按钮样式 ========== */
+.btn {
+    font-weight: 500;
+    border-radius: var(--radius-sm);
+    padding: 0.5rem 1rem;
+    font-size: 0.875rem;
+    border: 1px solid transparent;
+    transition: all 0.2s ease;
+    display: inline-flex;
+    align-items: center;
+    gap: 0.375rem;
+}
+
+.btn-primary {
+    background: linear-gradient(135deg, var(--primary-color) 0%, var(--primary-light) 100%);
+    border-color: var(--primary-color);
+    color: white;
+}
+
+.btn-primary:hover {
+    background: linear-gradient(135deg, #1d4ed8 0%, var(--primary-color) 100%);
+    border-color: #1d4ed8;
+    transform: translateY(-1px);
+    box-shadow: var(--shadow-md);
+}
+
+.btn-outline-primary {
+    background-color: transparent;
+    border-color: var(--primary-color);
+    color: var(--primary-color);
+}
+
+.btn-outline-primary:hover {
+    background-color: var(--primary-color);
+    border-color: var(--primary-color);
+    color: white;
+}
+
+.btn-sm {
+    padding: 0.375rem 0.75rem;
+    font-size: 0.75rem;
+}
+
+/* ========== 警告框样式 ========== */
+.alert {
+    border: none;
+    border-radius: var(--radius-md);
+    padding: 1rem 1.25rem;
+    border-left: 4px solid;
+}
+
+.alert-info {
+    background: linear-gradient(135deg, #e0f2fe 0%, #f0f9ff 100%);
+    border-left-color: var(--info-color);
+    color: #0e7490;
+}
+
+.alert-warning {
+    background: linear-gradient(135deg, #fef3c7 0%, #fffbeb 100%);
+    border-left-color: var(--warning-color);
+    color: #92400e;
+}
+
+.alert-danger {
+    background: linear-gradient(135deg, #fecaca 0%, #fef2f2 100%);
+    border-left-color: var(--danger-color);
+    color: #991b1b;
+}
+
+.alert-success {
+    background: linear-gradient(135deg, #dcfce7 0%, #f0fdf4 100%);
+    border-left-color: var(--success-color);
+    color: #15803d;
+}
+
+/* ========== 分页样式 ========== */
+.pagination {
+    gap: 0.25rem;
+}
+
+.pagination .page-link {
+    color: var(--text-secondary);
+    border: 1px solid var(--border-color);
+    border-radius: var(--radius-sm);
+    padding: 0.5rem 0.75rem;
+    font-size: 0.875rem;
+    background-color: var(--bg-accent);
+    transition: all 0.2s ease;
+}
+
+.pagination .page-link:hover {
+    background-color: var(--primary-lighter);
+    border-color: var(--primary-light);
+    color: var(--primary-color);
+}
+
+.pagination .page-item.active .page-link {
+    background-color: var(--primary-color);
+    border-color: var(--primary-color);
+    color: white;
+}
+
+/* ========== 表单控件 ========== */
+.form-select,
+.form-control {
+    border: 1px solid var(--border-color);
+    border-radius: var(--radius-sm);
+    background-color: var(--bg-accent);
+    color: var(--text-primary);
+    font-size: 0.875rem;
+    transition: all 0.2s ease;
+}
+
+.form-select:focus,
+.form-control:focus {
+    border-color: var(--primary-light);
+    box-shadow: 0 0 0 3px var(--primary-lighter);
+    outline: none;
+}
+
+/* ========== 页脚 ========== */
+footer {
+    background: linear-gradient(135deg, var(--bg-accent) 0%, var(--bg-secondary) 100%);
+    border-top: 1px solid var(--border-color);
+    margin-top: 3rem;
+}
+
+footer .text-muted {
+    color: var(--text-muted) !important;
+    font-size: 0.875rem;
+}
+
+/* ========== 工具类 ========== */
+.text-primary {
+    color: var(--primary-color) !important;
+}
+
+.text-success {
+    color: var(--success-color) !important;
+}
+
+.text-warning {
+    color: var(--warning-color) !important;
+}
+
+.text-danger {
+    color: var(--danger-color) !important;
+}
+
+.text-muted {
+    color: var(--text-muted) !important;
+}
+
+.fw-bold {
+    font-weight: 600 !important;
+}
+
+/* ========== 空状态 ========== */
+.empty-state {
+    text-align: center;
+    padding: 3rem 1rem;
+    color: var(--text-muted);
+}
+
+.empty-state i {
+    font-size: 3rem;
+    color: var(--text-muted);
+    margin-bottom: 1rem;
+    opacity: 0.6;
+}
+
+.empty-state h4 {
+    color: var(--text-secondary);
+    font-weight: 500;
+    margin-bottom: 0.5rem;
+}
+
+/* ========== 加载动画 ========== */
+.loading {
+    width: 1.25rem;
+    height: 1.25rem;
+    border: 2px solid var(--border-light);
+    border-top: 2px solid var(--primary-color);
+    border-radius: 50%;
+    animation: spin 1s linear infinite;
+}
+
+@keyframes spin {
+    0% { transform: rotate(0deg); }
+    100% { transform: rotate(360deg); }
+}
+
+/* ========== 响应式设计 ========== */
+@media (max-width: 768px) {
+    .container {
+        padding-left: 1rem;
+        padding-right: 1rem;
+    }
+
+    .card-body {
+        padding: 1rem;
+    }
+
+    .table-responsive {
+        font-size: 0.75rem;
+    }
+
+    .stats-card {
+        padding: 1rem;
+    }
+
+    .stats-value {
+        font-size: 1.5rem;
+    }
+
+    .badge {
+        font-size: 0.625rem;
+        padding: 0.25rem 0.5rem;
+    }
+}
+
+/* ========== 特殊效果 ========== */
+.fade-in {
+    animation: fadeIn 0.5s ease-in;
+}
+
+@keyframes fadeIn {
+    from { opacity: 0; transform: translateY(10px); }
+    to { opacity: 1; transform: translateY(0); }
+}
+
+.hover-lift {
+    transition: transform 0.2s ease;
+}
+
+.hover-lift:hover {
+    transform: translateY(-2px);
+}
+
+/* ========== 滚动条美化 ========== */
+::-webkit-scrollbar {
+    width: 6px;
+    height: 6px;
+}
+
+::-webkit-scrollbar-track {
+    background: var(--bg-secondary);
+}
+
+::-webkit-scrollbar-thumb {
+    background: var(--border-color);
+    border-radius: 3px;
+}
+
+::-webkit-scrollbar-thumb:hover {
+    background: var(--text-muted);
+}

web/static/js/main.js

@@ -0,0 +1,376 @@
+/**
+ * A股量化交易系统 主要JavaScript功能
+ */
+
+$(document).ready(function() {
+    // 初始化组件
+    initializeComponents();
+
+    // 绑定事件
+    bindEvents();
+
+    // 自动刷新
+    setupAutoRefresh();
+});
+
+/**
+ * 初始化组件
+ */
+function initializeComponents() {
+    // 初始化工具提示
+    $('[data-bs-toggle="tooltip"]').tooltip();
+
+    // 初始化弹出框
+    $('[data-bs-toggle="popover"]').popover();
+
+    // 表格排序
+    initializeTableSorting();
+
+    // 数字格式化
+    formatNumbers();
+}
+
+/**
+ * 绑定事件
+ */
+function bindEvents() {
+    // 表格行点击事件
+    $('.table tbody tr').on('click', function() {
+        $(this).toggleClass('table-active');
+    });
+
+    // 筛选表单自动提交
+    $('.auto-submit').on('change', function() {
+        $(this).closest('form').submit();
+    });
+
+    // 复制股票代码
+    $('.stock-code').on('click', function() {
+        const stockCode = $(this).text().trim();
+        copyToClipboard(stockCode);
+        showToast('股票代码已复制: ' + stockCode);
+    });
+
+    // 全选/取消全选
+    $('#selectAll').on('change', function() {
+        $('.row-select').prop('checked', this.checked);
+    });
+
+    // 导出数据
+    $('.export-btn').on('click', function() {
+        const format = $(this).data('format');
+        exportData(format);
+    });
+}
+
+/**
+ * 设置自动刷新
+ */
+function setupAutoRefresh() {
+    // 每5分钟自动刷新数据
+    setInterval(function() {
+        if (document.visibilityState === 'visible') {
+            refreshData();
+        }
+    }, 300000); // 5分钟
+
+    // 页面可见时刷新
+    document.addEventListener('visibilitychange', function() {
+        if (document.visibilityState === 'visible') {
+            const lastRefresh = localStorage.getItem('lastRefresh');
+            const now = Date.now();
+
+            // 如果超过5分钟未刷新，则自动刷新
+            if (!lastRefresh || (now - parseInt(lastRefresh)) > 300000) {
+                refreshData();
+            }
+        }
+    });
+}
+
+/**
+ * 刷新数据
+ */
+function refreshData() {
+    // 显示加载状态
+    showLoading();
+
+    // 记录刷新时间
+    localStorage.setItem('lastRefresh', Date.now().toString());
+
+    // 刷新页面数据
+    if (typeof updatePageData === 'function') {
+        updatePageData();
+    } else {
+        // 默认重新加载页面
+        setTimeout(() => {
+            location.reload();
+        }, 1000);
+    }
+}
+
+/**
+ * 显示加载状态
+ */
+function showLoading() {
+    const loadingHtml = '<div class="loading-overlay"><div class="loading"></div></div>';
+    $('body').append(loadingHtml);
+
+    setTimeout(() => {
+        $('.loading-overlay').remove();
+    }, 2000);
+}
+
+/**
+ * 显示提示消息
+ */
+function showToast(message, type = 'success') {
+    const toastHtml = `
+        <div class="toast align-items-center text-white bg-${type} border-0" role="alert">
+            <div class="d-flex">
+                <div class="toast-body">${message}</div>
+                <button type="button" class="btn-close btn-close-white me-2 m-auto" data-bs-dismiss="toast"></button>
+            </div>
+        </div>
+    `;
+
+    // 创建toast容器（如果不存在）
+    if (!$('.toast-container').length) {
+        $('body').append('<div class="toast-container position-fixed top-0 end-0 p-3"></div>');
+    }
+
+    const $toast = $(toastHtml);
+    $('.toast-container').append($toast);
+
+    // 显示toast
+    const toast = new bootstrap.Toast($toast[0]);
+    toast.show();
+
+    // 自动移除
+    $toast.on('hidden.bs.toast', function() {
+        $(this).remove();
+    });
+}
+
+/**
+ * 复制到剪贴板
+ */
+function copyToClipboard(text) {
+    if (navigator.clipboard) {
+        navigator.clipboard.writeText(text);
+    } else {
+        // 备用方法
+        const textArea = document.createElement('textarea');
+        textArea.value = text;
+        document.body.appendChild(textArea);
+        textArea.select();
+        document.execCommand('copy');
+        document.body.removeChild(textArea);
+    }
+}
+
+/**
+ * 初始化表格排序
+ */
+function initializeTableSorting() {
+    $('.sortable th').on('click', function() {
+        const table = $(this).closest('table');
+        const column = $(this).index();
+        const order = $(this).hasClass('asc') ? 'desc' : 'asc';
+
+        // 移除其他列的排序样式
+        $(this).siblings().removeClass('asc desc');
+
+        // 添加当前列的排序样式
+        $(this).removeClass('asc desc').addClass(order);
+
+        // 排序表格行
+        sortTable(table, column, order);
+    });
+}
+
+/**
+ * 表格排序
+ */
+function sortTable(table, column, order) {
+    const tbody = table.find('tbody');
+    const rows = tbody.find('tr').toArray();
+
+    rows.sort(function(a, b) {
+        const aVal = $(a).find('td').eq(column).text().trim();
+        const bVal = $(b).find('td').eq(column).text().trim();
+
+        // 尝试数字比较
+        if (!isNaN(aVal) && !isNaN(bVal)) {
+            return order === 'asc' ? aVal - bVal : bVal - aVal;
+        }
+
+        // 字符串比较
+        return order === 'asc' ? aVal.localeCompare(bVal) : bVal.localeCompare(aVal);
+    });
+
+    tbody.empty().append(rows);
+}
+
+/**
+ * 数字格式化
+ */
+function formatNumbers() {
+    $('.format-number').each(function() {
+        const value = parseFloat($(this).text());
+        if (!isNaN(value)) {
+            $(this).text(value.toLocaleString());
+        }
+    });
+
+    $('.format-percentage').each(function() {
+        const value = parseFloat($(this).text());
+        if (!isNaN(value)) {
+            $(this).text(value.toFixed(2) + '%');
+        }
+    });
+
+    $('.format-currency').each(function() {
+        const value = parseFloat($(this).text());
+        if (!isNaN(value)) {
+            $(this).text('¥' + value.toFixed(2));
+        }
+    });
+}
+
+/**
+ * 导出数据
+ */
+function exportData(format) {
+    const table = $('.table').first();
+    if (!table.length) {
+        showToast('没有可导出的数据', 'warning');
+        return;
+    }
+
+    switch (format) {
+        case 'csv':
+            exportToCSV(table);
+            break;
+        case 'excel':
+            exportToExcel(table);
+            break;
+        case 'json':
+            exportToJSON(table);
+            break;
+        default:
+            showToast('不支持的导出格式', 'error');
+    }
+}
+
+/**
+ * 导出为CSV
+ */
+function exportToCSV(table) {
+    let csv = '';
+
+    // 表头
+    table.find('thead tr').each(function() {
+        const row = [];
+        $(this).find('th').each(function() {
+            row.push('"' + $(this).text().trim() + '"');
+        });
+        csv += row.join(',') + '\n';
+    });
+
+    // 数据行
+    table.find('tbody tr').each(function() {
+        const row = [];
+        $(this).find('td').each(function() {
+            row.push('"' + $(this).text().trim() + '"');
+        });
+        csv += row.join(',') + '\n';
+    });
+
+    // 下载文件
+    downloadFile(csv, 'trading_signals.csv', 'text/csv');
+}
+
+/**
+ * 下载文件
+ */
+function downloadFile(content, filename, contentType) {
+    const blob = new Blob([content], { type: contentType });
+    const url = window.URL.createObjectURL(blob);
+    const a = document.createElement('a');
+    a.href = url;
+    a.download = filename;
+    document.body.appendChild(a);
+    a.click();
+    document.body.removeChild(a);
+    window.URL.revokeObjectURL(url);
+}
+
+/**
+ * 获取API数据
+ */
+function apiRequest(endpoint, params = {}) {
+    return $.ajax({
+        url: '/api/' + endpoint,
+        method: 'GET',
+        data: params,
+        dataType: 'json'
+    });
+}
+
+/**
+ * 实时更新信号数据
+ */
+function updateSignals() {
+    apiRequest('signals', { limit: 10 })
+        .done(function(response) {
+            if (response.success && response.data.length > 0) {
+                updateSignalTable(response.data);
+                showToast('信号数据已更新');
+            }
+        })
+        .fail(function() {
+            showToast('更新信号数据失败', 'error');
+        });
+}
+
+/**
+ * 更新信号表格
+ */
+function updateSignalTable(signals) {
+    const tbody = $('.signals-table tbody');
+    tbody.empty();
+
+    signals.forEach(function(signal) {
+        const row = `
+            <tr>
+                <td><span class="badge bg-secondary">${signal.stock_code}</span></td>
+                <td class="fw-bold">${signal.stock_name || '未知'}</td>
+                <td><span class="badge bg-info">${signal.strategy_name}</span></td>
+                <td>${signal.timeframe}</td>
+                <td>${signal.signal_date}</td>
+                <td class="text-success fw-bold">${signal.breakout_price.toFixed(2)}元</td>
+                <td>${signal.yin_high.toFixed(2)}元</td>
+                <td><span class="badge bg-success">${signal.breakout_pct.toFixed(2)}%</span></td>
+                <td>${signal.final_yang_entity_ratio.toFixed(2)}%</td>
+                <td>${signal.turnover_ratio.toFixed(2)}%</td>
+            </tr>
+        `;
+        tbody.append(row);
+    });
+}
+
+// 全局错误处理
+window.addEventListener('error', function(e) {
+    console.error('JavaScript Error:', e.error);
+    showToast('页面发生错误，请刷新重试', 'error');
+});
+
+// 网络状态监控
+window.addEventListener('online', function() {
+    showToast('网络连接已恢复');
+});
+
+window.addEventListener('offline', function() {
+    showToast('网络连接已断开', 'warning');
+});

web/templates/base.html

@@ -0,0 +1,74 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>{% block title %}A股量化交易系统{% endblock %}</title>
+
+    <!-- Bootstrap CSS -->
+    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" rel="stylesheet">
+    <!-- Font Awesome -->
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css" rel="stylesheet">
+    <!-- 自定义CSS -->
+    <link href="{{ url_for('static', filename='css/style.css') }}" rel="stylesheet">
+
+    {% block extra_css %}{% endblock %}
+</head>
+<body>
+    <!-- 导航栏 -->
+    <nav class="navbar navbar-expand-lg">
+        <div class="container">
+            <a class="navbar-brand" href="{{ url_for('signals') }}">
+                <i class="fas fa-chart-line me-2"></i>A股量化交易系统
+            </a>
+
+            <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarNav">
+                <span class="navbar-toggler-icon"></span>
+            </button>
+
+            <div class="collapse navbar-collapse" id="navbarNav">
+                <ul class="navbar-nav me-auto">
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.endpoint == 'signals' %}active{% endif %}"
+                           href="{{ url_for('signals') }}">
+                            <i class="fas fa-signal me-1"></i>交易信号
+                        </a>
+                    </li>
+                </ul>
+
+                <ul class="navbar-nav">
+                    <li class="nav-item">
+                        <span class="navbar-text">
+                            <i class="fas fa-clock me-1"></i>{{ current_time }}
+                        </span>
+                    </li>
+                </ul>
+            </div>
+        </div>
+    </nav>
+
+    <!-- 主内容区 -->
+    <main class="container mt-4">
+        {% block content %}{% endblock %}
+    </main>
+
+    <!-- 页脚 -->
+    <footer class="mt-5 py-4">
+        <div class="container text-center">
+            <p class="text-muted mb-0">
+                <i class="fas fa-robot me-1"></i>A股量化交易系统 |
+                <i class="fas fa-code me-1"></i>基于Python + Flask + SQLite
+            </p>
+        </div>
+    </footer>
+
+    <!-- Bootstrap JS -->
+    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/js/bootstrap.bundle.min.js"></script>
+    <!-- jQuery -->
+    <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
+    <!-- 自定义JS -->
+    <script src="{{ url_for('static', filename='js/main.js') }}"></script>
+
+    {% block extra_js %}{% endblock %}
+</body>
+</html>

web/templates/error.html

@@ -0,0 +1,31 @@
+{% extends "base.html" %}
+
+{% block title %}错误 - A股量化交易系统{% endblock %}
+
+{% block content %}
+<div class="row justify-content-center">
+    <div class="col-md-8">
+        <div class="card border-danger">
+            <div class="card-header bg-danger text-white">
+                <h5 class="mb-0">
+                    <i class="fas fa-exclamation-circle me-2"></i>系统错误
+                </h5>
+            </div>
+            <div class="card-body text-center">
+                <i class="fas fa-bug fa-4x text-danger mb-3"></i>
+                <h4 class="text-danger">抱歉，系统遇到了一个错误</h4>
+                <p class="text-muted">{{ error }}</p>
+
+                <div class="mt-4">
+                    <a href="{{ url_for('index') }}" class="btn btn-primary">
+                        <i class="fas fa-home me-1"></i>返回首页
+                    </a>
+                    <button onclick="history.back()" class="btn btn-secondary">
+                        <i class="fas fa-arrow-left me-1"></i>返回上页
+                    </button>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}

web/templates/index.html

@@ -0,0 +1,207 @@
+{% extends "base.html" %}
+
+{% block title %}首页 - A股量化交易系统{% endblock %}
+
+{% block content %}
+<!-- 页面标题 -->
+<div class="row mb-4">
+    <div class="col-12">
+        <div class="d-flex justify-content-between align-items-center">
+            <div>
+                <h1 class="h3 mb-1 text-primary fw-bold">
+                    <i class="fas fa-chart-line me-2"></i>交易信号概览
+                </h1>
+                <p class="text-muted mb-0">实时监控量化策略筛选结果</p>
+            </div>
+            <div class="text-end">
+                <div class="text-muted small">
+                    <i class="fas fa-clock me-1"></i>最后更新
+                </div>
+                <div class="fw-bold text-primary">{{ current_time }}</div>
+            </div>
+        </div>
+    </div>
+</div>
+
+<!-- 统计卡片 -->
+<div class="row mb-2">
+    {% for stat in strategy_stats %}
+    <div class="col-lg-3 col-md-6 mb-1">
+        <div class="stats-card hover-lift fade-in">
+            <div class="d-flex justify-content-between align-items-start">
+                <div>
+                    <div class="stats-label text-uppercase mb-1">{{ stat.strategy_name }}</div>
+                    <div class="stats-value">{{ stat.total_signals or 0 }}</div>
+                    <div class="stats-sublabel">
+                        <i class="fas fa-building me-1"></i>{{ stat.unique_stocks or 0 }} 只股票
+                    </div>
+                </div>
+                <div class="text-primary" style="opacity: 0.3;">
+                    <i class="fas fa-signal fa-lg"></i>
+                </div>
+            </div>
+        </div>
+    </div>
+    {% endfor %}
+
+    <!-- 如果没有统计数据，显示默认卡片 -->
+    {% if not strategy_stats %}
+    <div class="col-lg-3 col-md-6 mb-1">
+        <div class="stats-card hover-lift">
+            <div class="d-flex justify-content-between align-items-start">
+                <div>
+                    <div class="stats-label text-uppercase mb-1">K线形态策略</div>
+                    <div class="stats-value">0</div>
+                    <div class="stats-sublabel">
+                        <i class="fas fa-building me-1"></i>0 只股票
+                    </div>
+                </div>
+                <div class="text-primary" style="opacity: 0.3;">
+                    <i class="fas fa-signal fa-lg"></i>
+                </div>
+            </div>
+        </div>
+    </div>
+    {% endif %}
+</div>
+
+<!-- 最新信号表格 -->
+<div class="row">
+    <div class="col-12">
+        <div class="card fade-in">
+            <div class="card-header d-flex justify-content-between align-items-center">
+                <h5 class="mb-0 text-primary fw-bold">
+                    <i class="fas fa-list-ul me-2"></i>最新交易信号
+                </h5>
+                <a href="{{ url_for('signals') }}" class="btn btn-primary btn-sm">
+                    <i class="fas fa-arrow-right me-1"></i>查看全部
+                </a>
+            </div>
+            <div class="card-body p-0">
+                {% if signals %}
+                <div class="table-container">
+                    <div class="table-responsive">
+                        <table class="table table-hover mb-0">
+                            <thead>
+                                <tr>
+                                    <th>股票</th>
+                                    <th>策略</th>
+                                    <th>周期</th>
+                                    <th>信号日期</th>
+                                    <th>突破价格</th>
+                                    <th>阴线高点</th>
+                                    <th>突破幅度</th>
+                                    <th>实体比例</th>
+                                </tr>
+                            </thead>
+                            <tbody>
+                                {% for signal in signals %}
+                                <tr>
+                                    <td>
+                                        <div class="d-flex align-items-center">
+                                            <span class="badge badge-light-primary me-2">{{ signal.stock_code }}</span>
+                                            <span class="fw-bold text-truncate" style="max-width: 80px;">{{ signal.stock_name or '未知' }}</span>
+                                        </div>
+                                    </td>
+                                    <td>
+                                        <span class="badge badge-light-primary">{{ signal.strategy_name }}</span>
+                                    </td>
+                                    <td>
+                                        <span class="badge bg-{% if signal.timeframe == 'daily' %}primary{% elif signal.timeframe == '1h' %}info{% else %}success{% endif %}">
+                                            {{ 'D' if signal.timeframe == 'daily' else ('1H' if signal.timeframe == '1h' else 'W') }}
+                                        </span>
+                                    </td>
+                                    <td class="text-muted">{{ signal.signal_date | datetime_format('%m-%d') }}</td>
+                                    <td class="text-success fw-bold">{{ signal.breakout_price | currency }}元</td>
+                                    <td class="text-muted">{{ signal.yin_high | currency }}元</td>
+                                    <td>
+                                        <span class="badge bg-{% if signal.breakout_pct and signal.breakout_pct > 3 %}success{% elif signal.breakout_pct and signal.breakout_pct > 1 %}warning{% else %}secondary{% endif %}">
+                                            {{ signal.breakout_pct | percentage }}
+                                        </span>
+                                    </td>
+                                    <td class="text-muted">{{ signal.final_yang_entity_ratio | percentage }}</td>
+                                </tr>
+                                {% endfor %}
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+                {% else %}
+                <div class="empty-state">
+                    <i class="fas fa-chart-bar"></i>
+                    <h4>暂无交易信号</h4>
+                    <p>系统将在检测到符合条件的K线形态时显示信号</p>
+                </div>
+                {% endif %}
+            </div>
+        </div>
+    </div>
+</div>
+
+<!-- 回踩提醒 -->
+{% if pullback_alerts %}
+<div class="row mt-4">
+    <div class="col-12">
+        <div class="card border-warning">
+            <div class="card-header bg-warning text-dark">
+                <h5 class="mb-0">
+                    <i class="fas fa-exclamation-triangle me-2"></i>最近回踩提醒
+                </h5>
+            </div>
+            <div class="card-body">
+                <div class="table-responsive">
+                    <table class="table table-sm">
+                        <thead>
+                            <tr>
+                                <th>股票代码</th>
+                                <th>股票名称</th>
+                                <th>回踩日期</th>
+                                <th>当前价格</th>
+                                <th>阴线高点</th>
+                                <th>回调幅度</th>
+                                <th>距离高点</th>
+                            </tr>
+                        </thead>
+                        <tbody>
+                            {% for alert in pullback_alerts[:5] %}
+                            <tr>
+                                <td><span class="badge bg-secondary">{{ alert.stock_code }}</span></td>
+                                <td>{{ alert.stock_name }}</td>
+                                <td>{{ alert.pullback_date | datetime_format('%Y-%m-%d') }}</td>
+                                <td class="text-danger">{{ alert.current_price | currency }}元</td>
+                                <td>{{ alert.yin_high | currency }}元</td>
+                                <td>
+                                    <span class="badge bg-danger">{{ alert.pullback_pct | percentage }}</span>
+                                </td>
+                                <td>{{ alert.distance_to_yin_high | percentage }}</td>
+                            </tr>
+                            {% endfor %}
+                        </tbody>
+                    </table>
+                </div>
+                <div class="text-end">
+                    <a href="{{ url_for('pullbacks') }}" class="btn btn-warning btn-sm">
+                        <i class="fas fa-eye me-1"></i>查看全部回踩提醒
+                    </a>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endif %}
+{% endblock %}
+
+{% block extra_js %}
+<script>
+    // 自动刷新功能
+    function autoRefresh() {
+        setTimeout(function() {
+            location.reload();
+        }, 300000); // 5分钟自动刷新
+    }
+
+    $(document).ready(function() {
+        autoRefresh();
+    });
+</script>
+{% endblock %}

web/templates/pullbacks.html

@@ -0,0 +1,220 @@
+{% extends "base.html" %}
+
+{% block title %}回踩监控 - A股量化交易系统{% endblock %}
+
+{% block content %}
+<!-- 页面标题和筛选 -->
+<div class="row mb-4">
+    <div class="col-12">
+        <div class="d-flex justify-content-between align-items-center">
+            <div>
+                <h1 class="h3 mb-1 text-primary fw-bold">
+                    <i class="fas fa-exclamation-triangle me-2"></i>回踩监控
+                </h1>
+                <p class="text-muted mb-0">监控K线突破后的价格回踩情况</p>
+            </div>
+
+            <!-- 筛选表单 -->
+            <form method="GET" class="d-flex gap-2 align-items-center">
+                <select name="days" class="form-select form-select-sm">
+                    <option value="7" {% if days == 7 %}selected{% endif %}>最近7天</option>
+                    <option value="15" {% if days == 15 %}selected{% endif %}>最近15天</option>
+                    <option value="30" {% if days == 30 %}selected{% endif %}>最近30天</option>
+                    <option value="60" {% if days == 60 %}selected{% endif %}>最近60天</option>
+                </select>
+
+                <button type="submit" class="btn btn-primary btn-sm">
+                    <i class="fas fa-filter me-1"></i>筛选
+                </button>
+            </form>
+        </div>
+    </div>
+</div>
+
+<!-- 说明卡片 -->
+<div class="row mb-4">
+    <div class="col-12">
+        <div class="card fade-in">
+            <div class="card-body">
+                <div class="d-flex align-items-start">
+                    <div class="text-primary me-3 mt-1">
+                        <i class="fas fa-info-circle fa-lg"></i>
+                    </div>
+                    <div>
+                        <h6 class="text-primary fw-bold mb-2">回踩监控说明</h6>
+                        <p class="text-muted mb-0">
+                            回踩监控功能会自动监控已触发的"两阳+阴+阳"突破信号，当价格回踩到阴线最高点附近时会发送特殊提醒。
+                            这有助于识别关键支撑位的有效性，为交易决策提供参考。
+                        </p>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+
+<!-- 回踩提醒表格 -->
+<div class="row">
+    <div class="col-12">
+        <div class="card fade-in">
+            <div class="card-header">
+                <h5 class="mb-0 text-primary fw-bold">
+                    <i class="fas fa-list-ul me-2"></i>回踩提醒记录
+                    <span class="badge badge-light-primary ms-2">{{ pullback_alerts|length }} 条记录</span>
+                </h5>
+            </div>
+            <div class="card-body p-0">
+                {% if pullback_alerts %}
+                <div class="table-container">
+                    <div class="table-responsive">
+                        <table class="table table-hover mb-0">
+                            <thead>
+                                <tr>
+                                    <th>股票</th>
+                                    <th>周期</th>
+                                    <th>信号日期</th>
+                                    <th>回踩日期</th>
+                                    <th>天数间隔</th>
+                                    <th>突破价格</th>
+                                    <th>阴线高点</th>
+                                    <th>当前价格</th>
+                                    <th>当日最低</th>
+                                    <th>回调幅度</th>
+                                    <th>距离高点</th>
+                                    <th>提醒状态</th>
+                                </tr>
+                            </thead>
+                            <tbody>
+                                {% for alert in pullback_alerts %}
+                                <tr>
+                                    <td>
+                                        <div class="d-flex align-items-center">
+                                            <span class="badge badge-light-primary me-2">{{ alert.stock_code }}</span>
+                                            <span class="fw-bold text-truncate" style="max-width: 80px;">{{ alert.stock_name or '未知' }}</span>
+                                        </div>
+                                    </td>
+                                    <td>
+                                        <span class="badge bg-{% if alert.timeframe == 'daily' %}primary{% elif alert.timeframe == '1h' %}info{% else %}success{% endif %}">
+                                            {{ 'D' if alert.timeframe == 'daily' else ('1H' if alert.timeframe == '1h' else 'W') }}
+                                        </span>
+                                    </td>
+                                    <td class="text-muted">{{ alert.original_signal_date | datetime_format('%Y-%m-%d') }}</td>
+                                    <td class="fw-bold">{{ alert.pullback_date | datetime_format('%Y-%m-%d') }}</td>
+                                    <td>
+                                        <span class="badge bg-info">{{ alert.days_since_signal }}天</span>
+                                    </td>
+                                    <td class="text-success">{{ alert.original_breakout_price | currency }}元</td>
+                                    <td class="text-warning fw-bold">{{ alert.yin_high | currency }}元</td>
+                                    <td class="text-danger">{{ alert.current_price | currency }}元</td>
+                                    <td class="text-danger">{{ alert.current_low | currency }}元</td>
+                                    <td>
+                                        <span class="badge bg-{% if alert.pullback_pct and alert.pullback_pct < -10 %}danger{% elif alert.pullback_pct and alert.pullback_pct < -5 %}warning{% else %}secondary{% endif %}">
+                                            {{ alert.pullback_pct | percentage }}
+                                        </span>
+                                    </td>
+                                    <td>
+                                        <span class="badge bg-{% if alert.distance_to_yin_high and alert.distance_to_yin_high < -5 %}danger{% elif alert.distance_to_yin_high and alert.distance_to_yin_high < 2 %}warning{% else %}success{% endif %}">
+                                            {{ alert.distance_to_yin_high | percentage }}
+                                        </span>
+                                    </td>
+                                    <td>
+                                        {% if alert.alert_sent %}
+                                            <span class="badge bg-success">
+                                                <i class="fas fa-check me-1"></i>已发送
+                                            </span>
+                                            <div class="text-muted small mt-1">{{ alert.alert_sent_time | datetime_format('%m-%d %H:%M') }}</div>
+                                        {% else %}
+                                            <span class="badge bg-secondary">未发送</span>
+                                        {% endif %}
+                                    </td>
+                                </tr>
+                                {% endfor %}
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+
+                <!-- 统计信息 -->
+                <div class="row mt-4 px-3 pb-3">
+                    <div class="col-md-3 mb-3">
+                        <div class="stats-card text-center hover-lift">
+                            <div class="stats-value text-danger">{{ pullback_alerts | selectattr('distance_to_yin_high', 'lt', -5) | list | length }}</div>
+                            <div class="stats-label">跌破阴线高点</div>
+                        </div>
+                    </div>
+                    <div class="col-md-3 mb-3">
+                        <div class="stats-card text-center hover-lift">
+                            <div class="stats-value text-warning">{{ pullback_alerts | selectattr('distance_to_yin_high', 'ge', -5) | selectattr('distance_to_yin_high', 'lt', 2) | list | length }}</div>
+                            <div class="stats-label">接近阴线高点</div>
+                        </div>
+                    </div>
+                    <div class="col-md-3 mb-3">
+                        <div class="stats-card text-center hover-lift">
+                            <div class="stats-value text-success">{{ pullback_alerts | selectattr('distance_to_yin_high', 'ge', 2) | list | length }}</div>
+                            <div class="stats-label">上方支撑有效</div>
+                        </div>
+                    </div>
+                    <div class="col-md-3 mb-3">
+                        <div class="stats-card text-center hover-lift">
+                            <div class="stats-value text-info">{{ pullback_alerts | selectattr('alert_sent', 'equalto', true) | list | length }}</div>
+                            <div class="stats-label">已发送提醒</div>
+                        </div>
+                    </div>
+                </div>
+
+                {% else %}
+                <div class="empty-state">
+                    <i class="fas fa-shield-alt"></i>
+                    <h4>暂无回踩提醒</h4>
+                    <p>最近{{ days }}天内没有检测到价格回踩阴线最高点的情况</p>
+                </div>
+                {% endif %}
+            </div>
+        </div>
+    </div>
+</div>
+
+<!-- 风险提示 -->
+<div class="row mt-4">
+    <div class="col-12">
+        <div class="card border-danger">
+            <div class="card-header bg-danger text-white">
+                <h6 class="mb-0 fw-bold">
+                    <i class="fas fa-exclamation-triangle me-2"></i>风险提示
+                </h6>
+            </div>
+            <div class="card-body">
+                <ul class="mb-0 text-muted">
+                    <li>回踩阴线最高点可能表明原有突破信号的有效性受到质疑</li>
+                    <li>跌破阴线最高点通常需要重新评估形态的技术有效性</li>
+                    <li>建议结合成交量、其他技术指标和基本面情况综合判断</li>
+                    <li>本系统仅供参考，投资需谨慎，风险需自担</li>
+                </ul>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block extra_js %}
+<script>
+    $(document).ready(function() {
+        // 表格行点击高亮
+        $('tbody tr').on('click', function() {
+            $(this).toggleClass('table-active');
+        });
+
+        // 根据风险等级添加颜色标识
+        $('tbody tr').each(function() {
+            const distanceCell = $(this).find('td:nth-last-child(2)');
+            const distance = parseFloat(distanceCell.text());
+
+            if (distance < -5) {
+                $(this).addClass('table-danger');
+            } else if (distance < 2) {
+                $(this).addClass('table-warning');
+            }
+        });
+    });
+</script>
+{% endblock %}

web/templates/signals.html

@@ -0,0 +1,201 @@
+{% extends "base.html" %}
+
+{% block title %}交易信号 - A股量化交易系统{% endblock %}
+
+{% block content %}
+<!-- 页面标题和筛选 -->
+<div class="row mb-4">
+    <div class="col-12">
+        <div class="d-flex justify-content-between align-items-center">
+            <div>
+                <h1 class="h3 mb-1 text-primary fw-bold">
+                    <i class="fas fa-signal me-2"></i>交易信号列表
+                </h1>
+                <p class="text-muted mb-0">详细的股票筛选信号数据</p>
+            </div>
+
+            <!-- 筛选表单 -->
+            <form method="GET" class="d-flex gap-2 align-items-center">
+                <select name="strategy" class="form-select form-select-sm">
+                    <option value="">所有策略</option>
+                    <option value="K线形态策略" {% if strategy_name == 'K线形态策略' %}selected{% endif %}>K线形态策略</option>
+                </select>
+
+                <select name="timeframe" class="form-select form-select-sm">
+                    <option value="">所有周期</option>
+                    <option value="daily" {% if timeframe == 'daily' %}selected{% endif %}>日线</option>
+                    <option value="weekly" {% if timeframe == 'weekly' %}selected{% endif %}>周线</option>
+                    <option value="1h" {% if timeframe == '1h' %}selected{% endif %}>1小时</option>
+                </select>
+
+                <select name="days" class="form-select form-select-sm">
+                    <option value="7" {% if days == 7 %}selected{% endif %}>最近7天</option>
+                    <option value="15" {% if days == 15 %}selected{% endif %}>最近15天</option>
+                    <option value="30" {% if days == 30 %}selected{% endif %}>最近30天</option>
+                    <option value="90" {% if days == 90 %}selected{% endif %}>最近90天</option>
+                </select>
+
+                <select name="per_page" class="form-select form-select-sm">
+                    <option value="20" {% if per_page == 20 %}selected{% endif %}>每页20条</option>
+                    <option value="50" {% if per_page == 50 %}selected{% endif %}>每页50条</option>
+                    <option value="100" {% if per_page == 100 %}selected{% endif %}>每页100条</option>
+                </select>
+
+                <button type="submit" class="btn btn-primary btn-sm text-nowrap">
+                    <i class="fas fa-filter me-1"></i>筛选
+                </button>
+            </form>
+        </div>
+    </div>
+</div>
+
+
+<!-- 信号表格 -->
+<div class="row">
+    <div class="col-12">
+        <div class="card fade-in">
+            <div class="card-header">
+                <h5 class="mb-0 text-primary fw-bold">
+                    <i class="fas fa-table me-2"></i>详细信号数据
+                </h5>
+            </div>
+            <div class="card-body p-0">
+                {% if signals_grouped %}
+                <div class="table-container">
+                    {% for scan_date, signals in signals_grouped.items() %}
+                    <!-- 扫描日期分组标题 -->
+                    <div class="scan-date-header bg-light px-3 py-2 border-bottom">
+                        <h6 class="mb-0 text-primary fw-bold">
+                            <i class="fas fa-calendar-day me-2"></i>扫描日期: {{ scan_date }}
+                            <span class="badge bg-primary ms-2">{{ signals|length }} 条信号</span>
+                        </h6>
+                    </div>
+
+                    <!-- 该扫描日期下的信号表格 -->
+                    <div class="table-responsive">
+                        <table class="table table-hover mb-0">
+                            <thead class="table-light">
+                                <tr>
+                                    <th>股票</th>
+                                    <th>策略</th>
+                                    <th>周期</th>
+                                    <th>信号日期</th>
+                                    <th>突破价格</th>
+                                    <th>阴线高点</th>
+                                    <th>突破幅度</th>
+                                    <th>实体比例</th>
+                                    <th>换手率</th>
+                                    <th>扫描时间</th>
+                                </tr>
+                            </thead>
+                            <tbody>
+                                {% for signal in signals %}
+                                <tr>
+                                    <td>
+                                        <div class="d-flex align-items-center">
+                                            <span class="badge badge-light-primary me-2">{{ signal.stock_code }}</span>
+                                            <span class="fw-bold text-truncate" style="max-width: 80px;">{{ signal.stock_name or '未知' }}</span>
+                                        </div>
+                                    </td>
+                                    <td>
+                                        <span class="badge badge-light-primary">{{ signal.strategy_name }}</span>
+                                    </td>
+                                    <td>
+                                        <span class="badge bg-{% if signal.timeframe == 'daily' %}primary{% elif signal.timeframe == '1h' %}info{% else %}success{% endif %}">
+                                            {{ 'D' if signal.timeframe == 'daily' else ('1H' if signal.timeframe == '1h' else 'W') }}
+                                        </span>
+                                    </td>
+                                    <td class="text-muted">{{ signal.signal_date | datetime_format('%Y-%m-%d') }}</td>
+                                    <td class="text-success fw-bold">{{ signal.breakout_price | currency }}元</td>
+                                    <td class="text-muted">{{ signal.yin_high | currency }}元</td>
+                                    <td>
+                                        <span class="badge bg-{% if signal.breakout_pct and signal.breakout_pct > 3 %}success{% elif signal.breakout_pct and signal.breakout_pct > 1 %}warning{% else %}secondary{% endif %}">
+                                            {{ signal.breakout_pct | percentage }}
+                                        </span>
+                                    </td>
+                                    <td class="text-muted">{{ signal.final_yang_entity_ratio | percentage }}</td>
+                                    <td class="text-muted">{{ signal.turnover_ratio | percentage }}</td>
+                                    <td class="text-muted small">{{ signal.scan_time | datetime_format('%m-%d %H:%M') }}</td>
+                                </tr>
+                                {% endfor %}
+                            </tbody>
+                        </table>
+                    </div>
+                    {% endfor %}
+                </div>
+
+                <!-- 分页 -->
+                {% if total_pages > 1 %}
+                <nav aria-label="信号分页" class="mt-4">
+                    <ul class="pagination justify-content-center">
+                        <!-- 上一页 -->
+                        <li class="page-item {% if not has_prev %}disabled{% endif %}">
+                            <a class="page-link" href="{% if has_prev %}{{ url_for('signals', page=current_page-1, strategy=strategy_name, timeframe=timeframe, days=days, per_page=per_page) }}{% else %}#{% endif %}">
+                                <i class="fas fa-chevron-left"></i> 上一页
+                            </a>
+                        </li>
+
+                        <!-- 页码 -->
+                        {% set start_page = [1, current_page - 2]|max %}
+                        {% set end_page = [total_pages, current_page + 2]|min %}
+
+                        {% if start_page > 1 %}
+                            <li class="page-item">
+                                <a class="page-link" href="{{ url_for('signals', page=1, strategy=strategy_name, timeframe=timeframe, days=days, per_page=per_page) }}">1</a>
+                            </li>
+                            {% if start_page > 2 %}
+                                <li class="page-item disabled"><span class="page-link">...</span></li>
+                            {% endif %}
+                        {% endif %}
+
+                        {% for page_num in range(start_page, end_page + 1) %}
+                            <li class="page-item {% if page_num == current_page %}active{% endif %}">
+                                <a class="page-link" href="{{ url_for('signals', page=page_num, strategy=strategy_name, timeframe=timeframe, days=days, per_page=per_page) }}">{{ page_num }}</a>
+                            </li>
+                        {% endfor %}
+
+                        {% if end_page < total_pages %}
+                            {% if end_page < total_pages - 1 %}
+                                <li class="page-item disabled"><span class="page-link">...</span></li>
+                            {% endif %}
+                            <li class="page-item">
+                                <a class="page-link" href="{{ url_for('signals', page=total_pages, strategy=strategy_name, timeframe=timeframe, days=days, per_page=per_page) }}">{{ total_pages }}</a>
+                            </li>
+                        {% endif %}
+
+                        <!-- 下一页 -->
+                        <li class="page-item {% if not has_next %}disabled{% endif %}">
+                            <a class="page-link" href="{% if has_next %}{{ url_for('signals', page=current_page+1, strategy=strategy_name, timeframe=timeframe, days=days, per_page=per_page) }}{% else %}#{% endif %}">
+                                下一页 <i class="fas fa-chevron-right"></i>
+                            </a>
+                        </li>
+                    </ul>
+                </nav>
+                {% endif %}
+
+                {% else %}
+                <div class="empty-state">
+                    <i class="fas fa-signal"></i>
+                    <h4>暂无信号数据</h4>
+                    <p>请尝试调整筛选条件或稍后再试</p>
+                </div>
+                {% endif %}
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block extra_js %}
+<script>
+    $(document).ready(function() {
+        // 表格行点击高亮
+        $('tbody tr').on('click', function() {
+            $(this).toggleClass('table-active');
+        });
+
+        // 工具提示
+        $('[data-bs-toggle="tooltip"]').tooltip();
+    });
+</script>
+{% endblock %}