feat: 新增历史数据存储和长时间跨度分析功能

- SQLite数据库存储每日板块数据
- 连续上涨/下跌板块分析
- 板块轮动分析
- 近5日资金流向趋势
- 查看历史数据命令 (history)

README.md

@@ -1,11 +1,13 @@
 # A股板块盘后分析系统
 
-自动获取东方财富板块数据，生成盘后分析报告并发送邮件。
+自动获取东方财富板块数据，生成盘后分析报告并发送邮件。支持历史数据存储和长时间跨度分析。
 
 ## 功能特点
 
 - 每个交易日17:00自动执行
 - 获取行业板块和概念板块完整数据
+- **历史数据存储**（SQLite数据库）
+- **长时间跨度分析**（连续涨跌、板块轮动、资金趋势）
 - 生成分析总结邮件正文
 - 详细数据CSV文件作为附件
 - 发送到指定邮箱
@@ -14,11 +16,34 @@
 
 ### 正文（分析总结）
 
-- 市场整体趋势判断
-- 热门概念板块 TOP5
-- 行业涨幅/跌幅 TOP5
-- 主力资金大幅流入 TOP10
-- 主力资金大幅流出 TOP10
+**一、市场情绪分析**
+- 市场评级（强势上涨/偏强/平稳/偏弱/弱势下跌）
+- 涨跌板块统计、平均涨跌幅、资金净流
+
+**二、资金流向分析**
+- TOP5板块资金合计、资金集中度
+
+**三、板块强弱分析**
+- 强势板块数量、弱势板块数量及示例
+
+**四、概念板块热度**
+- 热门概念TOP5、冷门概念TOP5
+
+**五、行业板块排行**
+- 涨幅TOP5、跌幅TOP5
+
+**六、主力资金排行**
+- 大幅流入TOP10、大幅流出TOP10
+
+**七、投资建议**
+- 根据市场情绪给出策略建议
+
+**八、历史趋势分析** ⭐新增
+- 近期市场趋势（连续上涨/下跌/震荡）
+- 近5日资金流向详情
+- 连续上涨板块（近3日累计涨幅）
+- 连续下跌板块（近3日累计跌幅）
+- 板块轮动分析（新进入涨幅TOP10）
 
 ### 附件（详细数据）
 
@@ -52,6 +77,16 @@ python3 board_monitor.py report -v
 python3 board_monitor.py report --to other@example.com
 ```
 
+### 查看历史数据
+
+```bash
+# 查看近5日市场统计
+python3 board_monitor.py history --days 5
+
+# 查看指定板块历史
+python3 board_monitor.py history --board "电力设备" --days 10
+```
+
 ## 定时任务配置
 
 每个交易日（周一至周五）17:00自动执行：
@@ -60,11 +95,16 @@ python3 board_monitor.py report --to other@example.com
 0 17 * * 1-5 python3 board_monitor.py report
 ```
 
-## 数据来源
+## 数据存储
 
-东方财富HTTP API (http://push2.eastmoney.com)
+历史数据保存在 `data/board_history.db` SQLite数据库中，包含：
+- 每日板块涨跌幅
+- 主力资金流向
+- 领涨股信息
 
 ## 版本历史
 
-- v1.1.0 (2026-04-10) - 改为盘后报告模式，正文分析+附件详细数据
+- v1.3.0 (2026-04-10) - 新增历史数据存储和长时间跨度分析
+- v1.2.0 (2026-04-10) - 增加专业分析内容
+- v1.1.0 (2026-04-10) - 改为盘后报告模式
 - v1.0.0 (2026-04-10) - 初始版本

board_monitor.py

@@ -2,6 +2,7 @@
 """
 A股板块盘后分析系统
 获取东方财富板块数据，生成分析报告，发送邮件通知
+支持历史数据存储和长时间跨度分析
 """
 
 import urllib.request
@@ -9,9 +10,11 @@ import json
 import os
 import sys
 import subprocess
-from datetime import datetime
-from typing import List, Dict, Optional
+import sqlite3
+from datetime import datetime, timedelta
+from typing import List, Dict, Optional, Tuple
 from pathlib import Path
+from collections import defaultdict
 
 # 清除代理环境变量（解决代理问题）
 for proxy_var in ['http_proxy', 'https_proxy', 'HTTP_PROXY', 'HTTPS_PROXY']:
@@ -22,6 +25,9 @@ SCRIPT_DIR = Path(__file__).parent
 DATA_DIR = SCRIPT_DIR / "data"
 DATA_DIR.mkdir(exist_ok=True)
 
+# 数据库文件
+DB_FILE = DATA_DIR / "board_history.db"
+
 # 东方财富API配置
 EASTMONEY_BASE_URL = "http://push2.eastmoney.com/api/qt/clist/get"
 
@@ -35,6 +41,134 @@ BOARD_TYPES = {
 FIELDS = "f12,f14,f2,f3,f62,f66,f84,f104,f125,f126,f127,f128"
 
 
+# ==================== 数据库操作 ====================
+
+def init_db():
+    """初始化数据库"""
+    conn = sqlite3.connect(DB_FILE)
+    cursor = conn.cursor()
+    
+    # 创建板块数据表
+    cursor.execute('''
+        CREATE TABLE IF NOT EXISTS board_data (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            date TEXT NOT NULL,
+            board_type TEXT NOT NULL,
+            board_code TEXT NOT NULL,
+            board_name TEXT NOT NULL,
+            pct_change REAL,
+            main_flow REAL,
+            leader_name TEXT,
+            created_at TEXT DEFAULT CURRENT_TIMESTAMP,
+            UNIQUE(date, board_type, board_code)
+        )
+    ''')
+    
+    # 创建索引
+    cursor.execute('CREATE INDEX IF NOT EXISTS idx_date ON board_data(date)')
+    cursor.execute('CREATE INDEX IF NOT EXISTS idx_board ON board_data(board_type, board_code)')
+    
+    conn.commit()
+    conn.close()
+
+
+def save_to_db(board_type: str, boards: List[Dict], date: str = None):
+    """保存板块数据到数据库"""
+    if date is None:
+        date = datetime.now().strftime("%Y-%m-%d")
+    
+    conn = sqlite3.connect(DB_FILE)
+    cursor = conn.cursor()
+    
+    for board in boards:
+        cursor.execute('''
+            INSERT OR REPLACE INTO board_data 
+            (date, board_type, board_code, board_name, pct_change, main_flow, leader_name)
+            VALUES (?, ?, ?, ?, ?, ?, ?)
+        ''', (
+            date,
+            board_type,
+            board['code'],
+            board['name'],
+            board['pct_change'],
+            board['main_flow'],
+            board['leader_name']
+        ))
+    
+    conn.commit()
+    conn.close()
+
+
+def get_history_data(board_type: str, days: int = 5) -> Dict[str, List[Dict]]:
+    """
+    获取历史板块数据
+    
+    返回:
+        Dict[date, List[Dict]]: 按日期分组的数据
+    """
+    conn = sqlite3.connect(DB_FILE)
+    cursor = conn.cursor()
+    
+    start_date = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
+    
+    cursor.execute('''
+        SELECT date, board_code, board_name, pct_change, main_flow, leader_name
+        FROM board_data
+        WHERE board_type = ? AND date >= ?
+        ORDER BY date DESC
+    ''', (board_type, start_date))
+    
+    rows = cursor.fetchall()
+    conn.close()
+    
+    # 按日期分组
+    result = defaultdict(list)
+    for row in rows:
+        result[row[0]].append({
+            'code': row[1],
+            'name': row[2],
+            'pct_change': row[3],
+            'main_flow': row[4],
+            'leader_name': row[5]
+        })
+    
+    return dict(result)
+
+
+def get_board_history(board_type: str, board_name: str, days: int = 20) -> List[Dict]:
+    """获取单个板块的历史数据"""
+    conn = sqlite3.connect(DB_FILE)
+    cursor = conn.cursor()
+    
+    start_date = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
+    
+    cursor.execute('''
+        SELECT date, pct_change, main_flow
+        FROM board_data
+        WHERE board_type = ? AND board_name = ? AND date >= ?
+        ORDER BY date ASC
+    ''', (board_type, board_name, start_date))
+    
+    rows = cursor.fetchall()
+    conn.close()
+    
+    return [{'date': r[0], 'pct_change': r[1], 'main_flow': r[2]} for r in rows]
+
+
+def get_available_dates() -> List[str]:
+    """获取有数据的日期列表"""
+    conn = sqlite3.connect(DB_FILE)
+    cursor = conn.cursor()
+    
+    cursor.execute('SELECT DISTINCT date FROM board_data ORDER BY date DESC LIMIT 30')
+    dates = [r[0] for r in cursor.fetchall()]
+    
+    conn.close()
+    return dates
+
+
+# ==================== 数据获取 ====================
+
 def get_board_data(board_type: str, sort_by: str = "f3", limit: int = 100) -> Optional[List[Dict]]:
     """
     获取板块数据
@@ -213,6 +347,142 @@ def analyze_market(all_industry: List, all_concept: List) -> Dict:
     return analysis
 
 
+def analyze_history(all_industry: List, all_concept: List, history_days: int = 5) -> Dict:
+    """
+    历史数据分析
+    
+    参数:
+        all_industry: 今日行业板块数据
+        all_concept: 今日概念板块数据
+        history_days: 分析天数
+    
+    返回:
+        Dict: 历史分析结果
+    """
+    result = {
+        'available_dates': [],
+        'trend_analysis': {},
+        'continuous_up': [],
+        'continuous_down': [],
+        'strong_rotation': {},
+        'fund_trend': {},
+    }
+    
+    # 获取有数据的日期
+    available_dates = get_available_dates()
+    result['available_dates'] = available_dates[:history_days]
+    
+    if len(available_dates) < 2:
+        return result
+    
+    # 获取历史数据
+    industry_history = get_history_data('industry', history_days + 2)
+    concept_history = get_history_data('concept', history_days + 2)
+    
+    # 1. 连续上涨/下跌板块分析
+    if len(available_dates) >= 3:
+        recent_dates = available_dates[:3]  # 最近3天
+        
+        for board in all_industry:
+            history = get_board_history('industry', board['name'], days=5)
+            
+            if len(history) >= 3:
+                # 检查连续上涨
+                if all(h['pct_change'] > 0 for h in history[-3:]):
+                    total_pct = sum(h['pct_change'] for h in history[-3:])
+                    result['continuous_up'].append({
+                        'name': board['name'],
+                        'days': len([h for h in history if h['pct_change'] > 0]),
+                        'total_pct': total_pct,
+                    })
+                
+                # 检查连续下跌
+                elif all(h['pct_change'] < 0 for h in history[-3:]):
+                    total_pct = sum(h['pct_change'] for h in history[-3:])
+                    result['continuous_down'].append({
+                        'name': board['name'],
+                        'days': len([h for h in history if h['pct_change'] < 0]),
+                        'total_pct': total_pct,
+                    })
+        
+        # 排序
+        result['continuous_up'] = sorted(result['continuous_up'], key=lambda x: x['total_pct'], reverse=True)[:10]
+        result['continuous_down'] = sorted(result['continuous_down'], key=lambda x: x['total_pct'])[:10]
+    
+    # 2. 板块轮动分析
+    if len(available_dates) >= 2:
+        today_date = available_dates[0]
+        yesterday_date = available_dates[1]
+        
+        today_industry = industry_history.get(today_date, [])
+        yesterday_industry = industry_history.get(yesterday_date, [])
+        
+        if today_industry and yesterday_industry:
+            # 今日涨幅TOP10
+            today_top = sorted(today_industry, key=lambda x: x['pct_change'], reverse=True)[:10]
+            today_top_names = {b['name'] for b in today_top}
+            
+            # 昨日涨幅TOP10
+            yesterday_top = sorted(yesterday_industry, key=lambda x: x['pct_change'], reverse=True)[:10]
+            yesterday_top_names = {b['name'] for b in yesterday_top}
+            
+            # 新进入TOP10的板块（轮动进入）
+            new_in = today_top_names - yesterday_top_names
+            # 跌出TOP10的板块（轮动离开）
+            out_of = yesterday_top_names - today_top_names
+            
+            result['strong_rotation'] = {
+                'new_in': [b for b in today_top if b['name'] in new_in],
+                'out_of': list(out_of),
+            }
+    
+    # 3. 资金流向趋势分析
+    if len(available_dates) >= 5:
+        dates_5 = available_dates[:5]
+        fund_trend = {}
+        
+        for date in dates_5:
+            if date in industry_history:
+                total_flow = sum(b['main_flow'] for b in industry_history[date])
+                fund_trend[date] = total_flow
+        
+        result['fund_trend'] = fund_trend
+        
+        # 计算资金趋势
+        flows = list(fund_trend.values())
+        if len(flows) >= 3:
+            if all(f > 0 for f in flows[-3:]):
+                result['trend_analysis']['fund'] = '连续流入'
+            elif all(f < 0 for f in flows[-3:]):
+                result['trend_analysis']['fund'] = '连续流出'
+            else:
+                result['trend_analysis']['fund'] = '波动'
+    
+    # 4. 市场趋势判断
+    if len(available_dates) >= 5:
+        dates_5 = available_dates[:5]
+        avg_pcts = {}
+        
+        for date in dates_5:
+            if date in industry_history:
+                avg_pct = sum(b['pct_change'] for b in industry_history[date]) / len(industry_history[date])
+                avg_pcts[date] = avg_pct
+        
+        result['avg_pcts'] = avg_pcts
+        
+        # 判断趋势
+        pcts = list(avg_pcts.values())
+        if len(pcts) >= 3:
+            if all(p > 0 for p in pcts[-3:]):
+                result['trend_analysis']['market'] = '连续上涨'
+            elif all(p < 0 for p in pcts[-3:]):
+                result['trend_analysis']['market'] = '连续下跌'
+            else:
+                result['trend_analysis']['market'] = '震荡'
+    
+    return result
+
+
 def generate_daily_report(boards_data: Dict, to_email: str = "wlq@tphai.com") -> bool:
     """
     生成盘后分析报告并发送邮件
@@ -243,6 +513,9 @@ def generate_daily_report(boards_data: Dict, to_email: str = "wlq@tphai.com") ->
     # 执行专业分析
     analysis = analyze_market(all_industry, all_concept)
     
+    # 执行历史分析
+    history_analysis = analyze_history(all_industry, all_concept, history_days=5)
+    
     # ========== 生成HTML正文 ==========
     html_lines = [
         "<h2>📊 A股板块盘后分析报告</h2>",
@@ -404,6 +677,62 @@ def generate_daily_report(boards_data: Dict, to_email: str = "wlq@tphai.com") ->
             html_lines.append("<li>等待市场企稳后再介入</li>")
             html_lines.append("</ul>")
     
+    # ===== 八、历史趋势分析 =====
+    html_lines.append("")
+    html_lines.append("<hr>")
+    html_lines.append("<h3>八、历史趋势分析</h3>")
+    
+    if history_analysis.get('available_dates'):
+        dates = history_analysis['available_dates']
+        html_lines.append(f"<p>数据覆盖: 近 {len(dates)} 个交易日 ({', '.join(dates[:3])} 等)</p>")
+        
+        # 市场趋势
+        if history_analysis.get('trend_analysis', {}).get('market'):
+            trend = history_analysis['trend_analysis']['market']
+            html_lines.append(f"<p><strong>近期趋势: {trend}</strong></p>")
+        
+        # 资金趋势
+        if history_analysis.get('trend_analysis', {}).get('fund'):
+            fund_trend = history_analysis['trend_analysis']['fund']
+            html_lines.append(f"<p>资金流向: {fund_trend}</p>")
+        
+        # 资金流向详情
+        if history_analysis.get('fund_trend'):
+            html_lines.append("<p>近5日资金流向:</p>")
+            html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
+            html_lines.append("<tr style='background:#f0f0f0'><th>日期</th><th>净流入(亿)</th></tr>")
+            for date, flow in history_analysis['fund_trend'].items():
+                flow_str = f"+{flow:.2f}" if flow > 0 else f"{flow:.2f}"
+                color = "green" if flow > 0 else "red"
+                html_lines.append(f"<tr><td>{date}</td><td style='color:{color}'>{flow_str}</td></tr>")
+            html_lines.append("</table>")
+    
+    # 连续上涨板块
+    if history_analysis.get('continuous_up'):
+        html_lines.append("<p>🔥 连续上涨板块 (近3日):</p>")
+        html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
+        html_lines.append("<tr style='background:#fff0f0'><th>板块</th><th>连涨天数</th><th>累计涨幅</th></tr>")
+        for b in history_analysis['continuous_up'][:5]:
+            html_lines.append(f"<tr><td>{b['name']}</td><td>{b['days']}天</td><td>+{b['total_pct']:.2f}%</td></tr>")
+        html_lines.append("</table>")
+    
+    # 连续下跌板块
+    if history_analysis.get('continuous_down'):
+        html_lines.append("<p>❄️ 连续下跌板块 (近3日):</p>")
+        html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
+        html_lines.append("<tr style='background:#f0f0ff'><th>板块</th><th>连跌天数</th><th>累计跌幅</th></tr>")
+        for b in history_analysis['continuous_down'][:5]:
+            html_lines.append(f"<tr><td>{b['name']}</td><td>{b['days']}天</td><td>{b['total_pct']:.2f}%</td></tr>")
+        html_lines.append("</table>")
+    
+    # 板块轮动
+    if history_analysis.get('strong_rotation', {}).get('new_in'):
+        html_lines.append("<p>🔄 板块轮动 (新进入涨幅TOP10):</p>")
+        html_lines.append("<ul>")
+        for b in history_analysis['strong_rotation']['new_in'][:5]:
+            html_lines.append(f"<li>{b['name']}: +{b['pct_change']:.2f}%</li>")
+        html_lines.append("</ul>")
+    
     html_lines.append("")
     html_lines.append("<hr>")
     html_lines.append("<p><em>📊 详细数据请查看附件 CSV 文件</em></p>")
@@ -487,6 +816,9 @@ def run_daily_report(to_email: str = "wlq@tphai.com", verbose: bool = False) ->
     返回:
         bool: 是否成功
     """
+    # 初始化数据库
+    init_db()
+    
     if verbose:
         print(f"\n📊 A股板块盘后分析")
         print("=" * 50)
@@ -506,6 +838,11 @@ def run_daily_report(to_email: str = "wlq@tphai.com", verbose: bool = False) ->
             boards_data[board_type] = boards
             if verbose:
                 print(f"✅ 成功获取 {len(boards)} 条数据")
+            
+            # 保存到数据库
+            save_to_db(board_type, boards)
+            if verbose:
+                print(f"   已保存到数据库")
         else:
             print(f"❌ 获取 {board_type} 数据失败")
             boards_data[board_type] = []
@@ -538,6 +875,11 @@ def main():
     report_parser.add_argument("--to", default="wlq@tphai.com", help="收件人邮箱")
     report_parser.add_argument("-v", "--verbose", action="store_true", help="显示详细日志")
     
+    # 查看历史命令
+    history_parser = subparsers.add_parser("history", help="查看历史数据统计")
+    history_parser.add_argument("--days", type=int, default=5, help="查看天数")
+    history_parser.add_argument("--board", type=str, help="查看指定板块历史")
+    
     args = parser.parse_args()
     
     if args.command == "test":
@@ -568,6 +910,52 @@ def main():
     elif args.command == "report":
         run_daily_report(to_email=args.to, verbose=args.verbose)
     
+    elif args.command == "history":
+        init_db()
+        dates = get_available_dates()
+        
+        if not dates:
+            print("❌ 暂无历史数据，请先执行 report 命令收集数据")
+            return
+        
+        print(f"\n📅 可用数据日期: {len(dates)} 天")
+        print(f"   最新: {dates[0] if dates else '无'}")
+        print(f"   范围: {dates[-1] if dates else '无'} ~ {dates[0] if dates else '无'}")
+        
+        if args.board:
+            # 查看指定板块历史
+            print(f"\n📊 板块 '{args.board}' 近 {args.days} 日数据:")
+            history = get_board_history('industry', args.board, days=args.days)
+            
+            if history:
+                print("-" * 50)
+                for h in history:
+                    pct_str = f"+{h['pct_change']:.2f}%" if h['pct_change'] > 0 else f"{h['pct_change']:.2f}%"
+                    flow_str = f"+{h['main_flow']:.2f}亿" if h['main_flow'] > 0 else f"{h['main_flow']:.2f}亿"
+                    print(f"  {h['date']}: {pct_str}, 资金{flow_str}")
+            else:
+                print(f"❌ 未找到板块 '{args.board}' 的历史数据")
+        else:
+            # 显示整体统计
+            print(f"\n📊 近 {args.days} 日市场统计:")
+            
+            # 资金流向
+            fund_trend = {}
+            for date in dates[:args.days]:
+                industry_data = get_history_data('industry', args.days).get(date, [])
+                if industry_data:
+                    total_flow = sum(b['main_flow'] for b in industry_data)
+                    avg_pct = sum(b['pct_change'] for b in industry_data) / len(industry_data)
+                    fund_trend[date] = {'flow': total_flow, 'avg_pct': avg_pct}
+            
+            print("-" * 50)
+            print(f"{'日期':<12} {'资金净流(亿)':<15} {'平均涨跌':<10}")
+            print("-" * 50)
+            for date, data in sorted(fund_trend.items(), reverse=True):
+                flow_str = f"+{data['flow']:.2f}" if data['flow'] > 0 else f"{data['flow']:.2f}"
+                pct_str = f"+{data['avg_pct']:.2f}%" if data['avg_pct'] > 0 else f"{data['avg_pct']:.2f}%"
+                print(f"{date:<12} {flow_str:<15} {pct_str:<10}")
+    
     else:
         parser.print_help()
 

data/board_detail_20260410.csv

@@ -1,5 +1,5 @@
 # A股板块详细数据
-# 生成时间: 2026-04-10 17:42:51
+# 生成时间: 2026-04-10 17:51:40
 
 === 行业板块涨跌幅排行 ===
 板块名称,涨跌幅(%),主力资金(亿),领涨股