964 lines
38 KiB
Python
964 lines
38 KiB
Python
#!/usr/bin/env python3
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"""
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A股板块盘后分析系统
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获取东方财富板块数据，生成分析报告，发送邮件通知
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支持历史数据存储和长时间跨度分析
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"""
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import urllib.request
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import json
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import os
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import sys
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import subprocess
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import sqlite3
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from datetime import datetime, timedelta
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from typing import List, Dict, Optional, Tuple
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from pathlib import Path
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from collections import defaultdict
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# 清除代理环境变量（解决代理问题）
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for proxy_var in ['http_proxy', 'https_proxy', 'HTTP_PROXY', 'HTTPS_PROXY']:
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os.environ.pop(proxy_var, None)
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# 配置
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SCRIPT_DIR = Path(__file__).parent
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DATA_DIR = SCRIPT_DIR / "data"
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DATA_DIR.mkdir(exist_ok=True)
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# 数据库文件
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DB_FILE = DATA_DIR / "board_history.db"
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# 东方财富API配置
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EASTMONEY_BASE_URL = "http://push2.eastmoney.com/api/qt/clist/get"
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# 板块类型
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BOARD_TYPES = {
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"industry": "m:90+t:2", # 行业板块
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"concept": "m:90+t:3", # 概念板块
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}
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# 数据字段
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FIELDS = "f12,f14,f2,f3,f62,f66,f84,f104,f125,f126,f127,f128"
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# ==================== 数据库操作 ====================
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def init_db():
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"""初始化数据库"""
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conn = sqlite3.connect(DB_FILE)
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cursor = conn.cursor()
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# 创建板块数据表
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS board_data (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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date TEXT NOT NULL,
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board_type TEXT NOT NULL,
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board_code TEXT NOT NULL,
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board_name TEXT NOT NULL,
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pct_change REAL,
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main_flow REAL,
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leader_name TEXT,
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created_at TEXT DEFAULT CURRENT_TIMESTAMP,
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UNIQUE(date, board_type, board_code)
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)
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''')
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# 创建索引
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cursor.execute('CREATE INDEX IF NOT EXISTS idx_date ON board_data(date)')
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cursor.execute('CREATE INDEX IF NOT EXISTS idx_board ON board_data(board_type, board_code)')
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conn.commit()
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conn.close()
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def save_to_db(board_type: str, boards: List[Dict], date: str = None):
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"""保存板块数据到数据库"""
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if date is None:
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date = datetime.now().strftime("%Y-%m-%d")
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conn = sqlite3.connect(DB_FILE)
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cursor = conn.cursor()
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for board in boards:
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cursor.execute('''
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INSERT OR REPLACE INTO board_data
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(date, board_type, board_code, board_name, pct_change, main_flow, leader_name)
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VALUES (?, ?, ?, ?, ?, ?, ?)
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''', (
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date,
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board_type,
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board['code'],
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board['name'],
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board['pct_change'],
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board['main_flow'],
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board['leader_name']
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))
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conn.commit()
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conn.close()
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def get_history_data(board_type: str, days: int = 5) -> Dict[str, List[Dict]]:
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"""
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获取历史板块数据
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返回:
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Dict[date, List[Dict]]: 按日期分组的数据
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"""
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conn = sqlite3.connect(DB_FILE)
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cursor = conn.cursor()
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start_date = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
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cursor.execute('''
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SELECT date, board_code, board_name, pct_change, main_flow, leader_name
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FROM board_data
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WHERE board_type = ? AND date >= ?
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ORDER BY date DESC
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''', (board_type, start_date))
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rows = cursor.fetchall()
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conn.close()
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# 按日期分组
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result = defaultdict(list)
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for row in rows:
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result[row[0]].append({
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'code': row[1],
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'name': row[2],
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'pct_change': row[3],
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'main_flow': row[4],
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'leader_name': row[5]
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})
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return dict(result)
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def get_board_history(board_type: str, board_name: str, days: int = 20) -> List[Dict]:
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"""获取单个板块的历史数据"""
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conn = sqlite3.connect(DB_FILE)
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cursor = conn.cursor()
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start_date = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
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cursor.execute('''
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SELECT date, pct_change, main_flow
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FROM board_data
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WHERE board_type = ? AND board_name = ? AND date >= ?
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ORDER BY date ASC
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''', (board_type, board_name, start_date))
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rows = cursor.fetchall()
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conn.close()
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return [{'date': r[0], 'pct_change': r[1], 'main_flow': r[2]} for r in rows]
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def get_available_dates() -> List[str]:
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"""获取有数据的日期列表"""
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conn = sqlite3.connect(DB_FILE)
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cursor = conn.cursor()
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cursor.execute('SELECT DISTINCT date FROM board_data ORDER BY date DESC LIMIT 30')
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dates = [r[0] for r in cursor.fetchall()]
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conn.close()
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return dates
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# ==================== 数据获取 ====================
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def get_board_data(board_type: str, sort_by: str = "f3", limit: int = 100) -> Optional[List[Dict]]:
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"""
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获取板块数据
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参数:
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board_type: 板块类型 (industry/concept)
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sort_by: 排序字段 (f3=涨跌幅, f62=主力资金)
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limit: 返回数量
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返回:
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List[Dict]: 板块数据列表
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"""
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fs = BOARD_TYPES.get(board_type)
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if not fs:
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print(f"❌ 未知的板块类型: {board_type}")
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return None
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url = f"{EASTMONEY_BASE_URL}?fid={sort_by}&po=1&pz={limit}&pn=1&np=1&fltt=2&invt=2&fs={fs}&fields={FIELDS}"
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try:
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req = urllib.request.Request(url, headers={
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'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
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'Referer': 'http://quote.eastmoney.com/'
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})
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with urllib.request.urlopen(req, timeout=15) as resp:
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data = json.loads(resp.read().decode())
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if data.get('data') and data['data'].get('diff'):
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items = data['data']['diff']
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boards = []
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for item in items:
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board = {
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'code': item.get('f12', ''),
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'name': item.get('f14', ''),
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'price': item.get('f2', 0) / 100 if item.get('f2') else 0,
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'pct_change': item.get('f3', 0) / 100 if item.get('f3') else 0,
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'main_flow': item.get('f62', 0) / 1e8 if item.get('f62') else 0, # 亿元
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'leader_code': item.get('f84', ''),
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'leader_name': item.get('f104', ''),
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}
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boards.append(board)
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return boards
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else:
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print(f"⚠️ API返回数据为空")
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return None
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except urllib.error.URLError as e:
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print(f"❌ 网络请求失败: {e}")
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return None
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except Exception as e:
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print(f"❌ 获取数据异常: {e}")
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return None
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def analyze_market(all_industry: List, all_concept: List) -> Dict:
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"""
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专业市场分析
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返回各种分析指标
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"""
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analysis = {}
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# 1. 市场整体统计
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if all_industry:
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up_count = len([b for b in all_industry if b['pct_change'] > 0])
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down_count = len([b for b in all_industry if b['pct_change'] < 0])
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flat_count = len([b for b in all_industry if b['pct_change'] == 0])
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avg_pct = sum(b['pct_change'] for b in all_industry) / len(all_industry)
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max_pct = max(b['pct_change'] for b in all_industry)
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min_pct = min(b['pct_change'] for b in all_industry)
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total_flow = sum(b['main_flow'] for b in all_industry)
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analysis['market_stats'] = {
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'up_count': up_count,
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'down_count': down_count,
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'flat_count': flat_count,
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'up_ratio': up_count / len(all_industry) * 100,
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'avg_pct': avg_pct,
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'max_pct': max_pct,
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'min_pct': min_pct,
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'total_flow': total_flow,
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}
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# 2. 资金集中度分析
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if all_industry:
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sorted_by_flow = sorted(all_industry, key=lambda x: x['main_flow'], reverse=True)
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top5_flow = sum(b['main_flow'] for b in sorted_by_flow[:5])
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analysis['fund_concentration'] = {
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'top5_flow': top5_flow,
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'top5_ratio': abs(top5_flow) / abs(analysis['market_stats']['total_flow']) * 100 if analysis['market_stats']['total_flow'] != 0 else 0,
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}
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# 3. 板块强弱分析
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if all_industry:
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strong_boards = [b for b in all_industry if b['pct_change'] > 1 and b['main_flow'] > 5]
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weak_boards = [b for b in all_industry if b['pct_change'] < -1 and b['main_flow'] < -5]
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analysis['strength'] = {
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'strong_count': len(strong_boards),
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'weak_count': len(weak_boards),
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'strong_boards': strong_boards[:5],
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'weak_boards': weak_boards[:5],
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}
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# 4. 概念板块热度分析
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if all_concept:
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hot_concepts = sorted([b for b in all_concept if b['main_flow'] > 0],
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key=lambda x: x['main_flow'], reverse=True)[:5]
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cold_concepts = sorted([b for b in all_concept if b['main_flow'] < 0],
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key=lambda x: x['main_flow'])[:5]
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analysis['concept_heat'] = {
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'hot': hot_concepts,
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'cold': cold_concepts,
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}
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# 5. 市场情绪判断
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if analysis.get('market_stats'):
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stats = analysis['market_stats']
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# 综合判断
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sentiment_score = 0
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# 涨跌比例贡献
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if stats['up_ratio'] > 70:
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sentiment_score += 2
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elif stats['up_ratio'] > 50:
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sentiment_score += 1
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elif stats['up_ratio'] < 30:
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sentiment_score -= 2
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elif stats['up_ratio'] < 50:
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sentiment_score -= 1
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# 平均涨跌幅贡献
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if stats['avg_pct'] > 0.5:
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sentiment_score += 1
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elif stats['avg_pct'] < -0.5:
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sentiment_score -= 1
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# 资金流向贡献
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if stats['total_flow'] > 50:
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sentiment_score += 2
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elif stats['total_flow'] > 0:
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sentiment_score += 1
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elif stats['total_flow'] < -50:
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sentiment_score -= 2
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elif stats['total_flow'] < 0:
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sentiment_score -= 1
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# 情绪等级
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if sentiment_score >= 4:
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sentiment = '强势上涨'
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sentiment_desc = '市场情绪高涨，多数板块上涨，资金大幅流入，建议关注强势板块机会。'
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elif sentiment_score >= 2:
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sentiment = '偏强'
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sentiment_desc = '市场整体偏强，资金流向积极，可适度参与热门板块。'
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elif sentiment_score >= 0:
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sentiment = '平稳'
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sentiment_desc = '市场情绪平稳，涨跌均衡，建议观望或轻仓布局。'
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elif sentiment_score >= -2:
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sentiment = '偏弱'
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sentiment_desc = '市场整体偏弱，资金流出明显，建议谨慎操作，关注防御性板块。'
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else:
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sentiment = '弱势下跌'
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sentiment_desc = '市场情绪低迷，多数板块下跌，资金大幅流出，建议规避风险，等待企稳信号。'
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analysis['sentiment'] = {
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'score': sentiment_score,
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'level': sentiment,
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'description': sentiment_desc,
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}
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return analysis
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def analyze_history(all_industry: List, all_concept: List, history_days: int = 5) -> Dict:
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"""
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历史数据分析
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参数:
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all_industry: 今日行业板块数据
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all_concept: 今日概念板块数据
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history_days: 分析天数
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返回:
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Dict: 历史分析结果
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"""
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result = {
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'available_dates': [],
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'trend_analysis': {},
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'continuous_up': [],
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'continuous_down': [],
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'strong_rotation': {},
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'fund_trend': {},
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}
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# 获取有数据的日期
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available_dates = get_available_dates()
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result['available_dates'] = available_dates[:history_days]
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if len(available_dates) < 2:
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return result
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# 获取历史数据
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industry_history = get_history_data('industry', history_days + 2)
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concept_history = get_history_data('concept', history_days + 2)
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# 1. 连续上涨/下跌板块分析
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if len(available_dates) >= 3:
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recent_dates = available_dates[:3] # 最近3天
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for board in all_industry:
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history = get_board_history('industry', board['name'], days=5)
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if len(history) >= 3:
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# 检查连续上涨
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if all(h['pct_change'] > 0 for h in history[-3:]):
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total_pct = sum(h['pct_change'] for h in history[-3:])
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result['continuous_up'].append({
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'name': board['name'],
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'days': len([h for h in history if h['pct_change'] > 0]),
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'total_pct': total_pct,
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})
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# 检查连续下跌
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elif all(h['pct_change'] < 0 for h in history[-3:]):
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total_pct = sum(h['pct_change'] for h in history[-3:])
|
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result['continuous_down'].append({
|
||
'name': board['name'],
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'days': len([h for h in history if h['pct_change'] < 0]),
|
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'total_pct': total_pct,
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})
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||
|
||
# 排序
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||
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
|
||
|
||
参数:
|
||
boards_data: 板块数据字典 {'industry': [], 'concept': []}
|
||
to_email: 收件人邮箱
|
||
|
||
返回:
|
||
bool: 是否发送成功
|
||
"""
|
||
all_industry = boards_data.get('industry', [])
|
||
all_concept = boards_data.get('concept', [])
|
||
|
||
if not all_industry and not all_concept:
|
||
print("❌ 无数据，无法生成报告")
|
||
return False
|
||
|
||
# 分析总结
|
||
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
|
||
# 排序数据
|
||
industry_by_pct = sorted(all_industry, key=lambda x: x['pct_change'], reverse=True)
|
||
industry_by_flow = sorted(all_industry, key=lambda x: x['main_flow'], reverse=True)
|
||
concept_by_pct = sorted(all_concept, key=lambda x: x['pct_change'], reverse=True)
|
||
concept_by_flow = sorted(all_concept, key=lambda x: x['main_flow'], reverse=True)
|
||
|
||
# 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>",
|
||
f"<p>报告时间: {timestamp}</p>",
|
||
"",
|
||
"<hr>",
|
||
]
|
||
|
||
# ===== 一、市场情绪分析 =====
|
||
if analysis.get('sentiment'):
|
||
sent = analysis['sentiment']
|
||
stats = analysis.get('market_stats', {})
|
||
|
||
html_lines.append("")
|
||
html_lines.append("<h3>一、市场情绪分析</h3>")
|
||
html_lines.append(f"<p><strong>市场评级: {sent['level']}</strong></p>")
|
||
html_lines.append(f"<p>{sent['description']}</p>")
|
||
|
||
if stats:
|
||
html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
|
||
html_lines.append("<tr><td>上涨板块</td><td>{}</td><td>占比 {:.1f}%</td></tr>".format(stats['up_count'], stats['up_ratio']))
|
||
html_lines.append("<tr><td>下跌板块</td><td>{}</td><td>占比 {:.1f}%</td></tr>".format(stats['down_count'], 100 - stats['up_ratio']))
|
||
html_lines.append("<tr><td>平均涨跌</td><td>{:.2f}%</td><td>最大涨幅 {:.2f}%</td></tr>".format(stats['avg_pct'], stats['max_pct']))
|
||
html_lines.append("<tr><td>资金净流</td><td>{:.2f}亿</td><td>最大跌幅 {:.2f}%</td></tr>".format(stats['total_flow'], stats['min_pct']))
|
||
html_lines.append("</table>")
|
||
|
||
# ===== 二、资金流向分析 =====
|
||
if analysis.get('fund_concentration'):
|
||
fund = analysis['fund_concentration']
|
||
|
||
html_lines.append("")
|
||
html_lines.append("<h3>二、资金流向分析</h3>")
|
||
html_lines.append(f"<p>TOP5板块资金合计: <strong>{fund['top5_flow']:.2f}亿</strong></p>")
|
||
html_lines.append(f"<p>资金集中度: <strong>{fund['top5_ratio']:.1f}%</strong>（流入资金集中在少数板块）</p>")
|
||
|
||
# ===== 三、板块强弱分析 =====
|
||
if analysis.get('strength'):
|
||
strength = analysis['strength']
|
||
|
||
html_lines.append("")
|
||
html_lines.append("<h3>三、板块强弱分析</h3>")
|
||
html_lines.append(f"<p>强势板块（涨幅>1%且资金流入>5亿）: <strong>{strength['strong_count']} 个</strong></p>")
|
||
html_lines.append(f"<p>弱势板块（跌幅>1%且资金流出>5亿）: <strong>{strength['weak_count']} 个</strong></p>")
|
||
|
||
if strength['strong_boards']:
|
||
html_lines.append("<p>强势板块示例:</p>")
|
||
html_lines.append("<ul>")
|
||
for b in strength['strong_boards'][:3]:
|
||
html_lines.append("<li>{name}: +{pct:.2f}%, 资金+{flow:.2f}亿</li>".format(
|
||
name=b['name'], pct=b['pct_change'], flow=b['main_flow']))
|
||
html_lines.append("</ul>")
|
||
|
||
if strength['weak_boards']:
|
||
html_lines.append("<p>弱势板块示例:</p>")
|
||
html_lines.append("<ul>")
|
||
for b in strength['weak_boards'][:3]:
|
||
html_lines.append("<li>{name}: {pct:.2f}%, 资金{flow:.2f}亿</li>".format(
|
||
name=b['name'], pct=b['pct_change'], flow=b['main_flow']))
|
||
html_lines.append("</ul>")
|
||
|
||
# ===== 四、热门概念分析 =====
|
||
if analysis.get('concept_heat'):
|
||
heat = analysis['concept_heat']
|
||
|
||
html_lines.append("")
|
||
html_lines.append("<h3>四、概念板块热度</h3>")
|
||
|
||
if heat['hot']:
|
||
html_lines.append("<p>🔥 热门概念（资金流入TOP）:</p>")
|
||
html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
|
||
html_lines.append("<tr style='background:#fff0f0'><th>概念</th><th>资金(亿)</th><th>涨跌</th></tr>")
|
||
for b in heat['hot'][:5]:
|
||
pct_str = f"+{b['pct_change']:.2f}%" if b['pct_change'] > 0 else f"{b['pct_change']:.2f}%"
|
||
html_lines.append(f"<tr><td>{b['name']}</td><td>+{b['main_flow']:.2f}</td><td>{pct_str}</td></tr>")
|
||
html_lines.append("</table>")
|
||
|
||
if heat['cold']:
|
||
html_lines.append("<p>❄️ 冷门概念（资金流出TOP）:</p>")
|
||
html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
|
||
html_lines.append("<tr style='background:#f0f0ff'><th>概念</th><th>资金(亿)</th><th>涨跌</th></tr>")
|
||
for b in heat['cold'][:5]:
|
||
pct_str = f"+{b['pct_change']:.2f}%" if b['pct_change'] > 0 else f"{b['pct_change']:.2f}%"
|
||
html_lines.append(f"<tr><td>{b['name']}</td><td>{b['main_flow']:.2f}</td><td>{pct_str}</td></tr>")
|
||
html_lines.append("</table>")
|
||
|
||
# ===== 五、行业板块排行 =====
|
||
html_lines.append("")
|
||
html_lines.append("<hr>")
|
||
html_lines.append("")
|
||
html_lines.append("<h3>五、行业板块涨跌排行</h3>")
|
||
|
||
html_lines.append("<p>📈 涨幅 TOP5:</p>")
|
||
html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
|
||
html_lines.append("<tr style='background:#f0f0f0'><th>板块</th><th>涨跌幅</th><th>主力资金</th><th>领涨股</th></tr>")
|
||
for board in industry_by_pct[:5]:
|
||
pct_str = f"+{board['pct_change']:.2f}%" if board['pct_change'] > 0 else f"{board['pct_change']:.2f}%"
|
||
flow_str = f"+{board['main_flow']:.2f}亿" if board['main_flow'] > 0 else f"{board['main_flow']:.2f}亿"
|
||
html_lines.append(f"<tr><td>{board['name']}</td><td>{pct_str}</td><td>{flow_str}</td><td>{board['leader_name'] or '-'}</td></tr>")
|
||
html_lines.append("</table>")
|
||
|
||
html_lines.append("<p>📉 跌幅 TOP5:</p>")
|
||
html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
|
||
html_lines.append("<tr style='background:#f0f0f0'><th>板块</th><th>涨跌幅</th><th>主力资金</th></tr>")
|
||
for board in industry_by_pct[-5:]:
|
||
pct_str = f"+{board['pct_change']:.2f}%" if board['pct_change'] > 0 else f"{board['pct_change']:.2f}%"
|
||
flow_str = f"+{board['main_flow']:.2f}亿" if board['main_flow'] > 0 else f"{board['main_flow']:.2f}亿"
|
||
html_lines.append(f"<tr><td>{board['name']}</td><td>{pct_str}</td><td>{flow_str}</td></tr>")
|
||
html_lines.append("</table>")
|
||
|
||
# ===== 六、主力资金排行 =====
|
||
html_lines.append("")
|
||
html_lines.append("<h3>六、主力资金流向排行</h3>")
|
||
|
||
html_lines.append("<p>💰 大幅流入 TOP10 (≥10亿):</p>")
|
||
html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
|
||
html_lines.append("<tr style='background:#f0f0f0'><th>板块</th><th>资金(亿)</th><th>涨跌</th></tr>")
|
||
inflow_boards = [b for b in industry_by_flow if b['main_flow'] > 10][:10]
|
||
for board in inflow_boards:
|
||
pct_str = f"+{board['pct_change']:.2f}%" if board['pct_change'] > 0 else f"{board['pct_change']:.2f}%"
|
||
html_lines.append(f"<tr><td>{board['name']}</td><td>+{board['main_flow']:.2f}</td><td>{pct_str}</td></tr>")
|
||
html_lines.append("</table>")
|
||
|
||
html_lines.append("<p>💸 大幅流出 TOP10 (≤-10亿):</p>")
|
||
html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
|
||
html_lines.append("<tr style='background:#f0f0f0'><th>板块</th><th>资金(亿)</th><th>涨跌</th></tr>")
|
||
outflow_boards = [b for b in industry_by_flow if b['main_flow'] < -10][:10]
|
||
for board in outflow_boards:
|
||
pct_str = f"+{board['pct_change']:.2f}%" if board['pct_change'] > 0 else f"{board['pct_change']:.2f}%"
|
||
html_lines.append(f"<tr><td>{board['name']}</td><td>{board['main_flow']:.2f}</td><td>{pct_str}</td></tr>")
|
||
html_lines.append("</table>")
|
||
|
||
# ===== 七、投资建议 =====
|
||
html_lines.append("")
|
||
html_lines.append("<hr>")
|
||
html_lines.append("<h3>七、投资建议</h3>")
|
||
|
||
if analysis.get('sentiment'):
|
||
sent = analysis['sentiment']
|
||
|
||
if sent['score'] >= 2:
|
||
html_lines.append("<p><strong>策略建议: 积极参与</strong></p>")
|
||
html_lines.append("<ul>")
|
||
html_lines.append("<li>关注资金大幅流入的热门板块，如新能源、科技类</li>")
|
||
html_lines.append("<li>可适当追涨强势板块龙头股</li>")
|
||
html_lines.append("<li>注意板块轮动节奏，避免追高</li>")
|
||
html_lines.append("</ul>")
|
||
elif sent['score'] >= 0:
|
||
html_lines.append("<p><strong>策略建议: 观望为主</strong></p>")
|
||
html_lines.append("<ul>")
|
||
html_lines.append("<li>等待明确的市场方向信号</li>")
|
||
html_lines.append("<li>可轻仓布局有资金流入的潜力板块</li>")
|
||
html_lines.append("<li>规避资金流出明显的板块</li>")
|
||
html_lines.append("</ul>")
|
||
else:
|
||
html_lines.append("<p><strong>策略建议: 谨慎防守</strong></p>")
|
||
html_lines.append("<ul>")
|
||
html_lines.append("<li>控制仓位，规避风险板块</li>")
|
||
html_lines.append("<li>关注防御性板块如银行、医药等</li>")
|
||
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>")
|
||
|
||
html_body = "\n".join(html_lines)
|
||
|
||
# 生成附件文件（CSV格式）
|
||
attachment_file = DATA_DIR / f"board_detail_{datetime.now().strftime('%Y%m%d')}.csv"
|
||
|
||
csv_lines = [
|
||
"# A股板块详细数据",
|
||
f"# 生成时间: {timestamp}",
|
||
"",
|
||
"=== 行业板块涨跌幅排行 ===",
|
||
"板块名称,涨跌幅(%),主力资金(亿),领涨股",
|
||
]
|
||
|
||
for board in industry_by_pct:
|
||
csv_lines.append(f"{board['name']},{board['pct_change']:.2f},{board['main_flow']:.2f},{board['leader_name'] or ''}")
|
||
|
||
csv_lines.append("")
|
||
csv_lines.append("=== 行业板块资金流向排行 ===")
|
||
csv_lines.append("板块名称,主力资金(亿),涨跌幅(%),领涨股")
|
||
|
||
for board in industry_by_flow:
|
||
csv_lines.append(f"{board['name']},{board['main_flow']:.2f},{board['pct_change']:.2f},{board['leader_name'] or ''}")
|
||
|
||
csv_lines.append("")
|
||
csv_lines.append("=== 概念板块涨跌幅排行 ===")
|
||
csv_lines.append("板块名称,涨跌幅(%),主力资金(亿),领涨股")
|
||
|
||
for board in concept_by_pct:
|
||
csv_lines.append(f"{board['name']},{board['pct_change']:.2f},{board['main_flow']:.2f},{board['leader_name'] or ''}")
|
||
|
||
csv_lines.append("")
|
||
csv_lines.append("=== 概念板块资金流向排行 ===")
|
||
csv_lines.append("板块名称,主力资金(亿),涨跌幅(%),领涨股")
|
||
|
||
for board in concept_by_flow:
|
||
csv_lines.append(f"{board['name']},{board['main_flow']:.2f},{board['pct_change']:.2f},{board['leader_name'] or ''}")
|
||
|
||
attachment_file.write_text("\n".join(csv_lines), encoding='utf-8')
|
||
|
||
# 发送邮件
|
||
email_script = SCRIPT_DIR.parent.parent / "skills/email/scripts/send_email.py"
|
||
|
||
subject = f"【A股板块盘后分析】{datetime.now().strftime('%Y-%m-%d')}"
|
||
|
||
cmd = [
|
||
"python3", str(email_script),
|
||
"send",
|
||
"--to", to_email,
|
||
"--subject", subject,
|
||
"--body", html_body,
|
||
"--html",
|
||
"--attach", str(attachment_file)
|
||
]
|
||
|
||
try:
|
||
result = subprocess.run(cmd, capture_output=True, text=True, timeout=30)
|
||
if result.returncode == 0:
|
||
print(f"✅ 报告发送成功: {to_email}")
|
||
print(f" 附件: {attachment_file}")
|
||
return True
|
||
else:
|
||
print(f"❌ 发送失败: {result.stderr}")
|
||
return False
|
||
except Exception as e:
|
||
print(f"❌ 发送异常: {e}")
|
||
return False
|
||
|
||
|
||
def run_daily_report(to_email: str = "wlq@tphai.com", verbose: bool = False) -> bool:
|
||
"""
|
||
执行盘后报告生成和发送
|
||
|
||
参数:
|
||
to_email: 收件人邮箱
|
||
verbose: 是否显示详细日志
|
||
|
||
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)
|
||
print(f"收件人: {to_email}")
|
||
print(f"时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||
|
||
# 获取所有板块数据
|
||
boards_data = {}
|
||
|
||
for board_type in ["industry", "concept"]:
|
||
if verbose:
|
||
print(f"\n📡 获取 {board_type} 板块数据...")
|
||
|
||
boards = get_board_data(board_type, limit=100)
|
||
|
||
if boards:
|
||
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] = []
|
||
|
||
# 生成并发送报告
|
||
if boards_data.get('industry') or boards_data.get('concept'):
|
||
return generate_daily_report(boards_data, to_email)
|
||
else:
|
||
print("❌ 所有数据获取失败，无法生成报告")
|
||
return False
|
||
|
||
|
||
def main():
|
||
"""命令行入口"""
|
||
import argparse
|
||
|
||
parser = argparse.ArgumentParser(description="A股板块盘后分析系统")
|
||
subparsers = parser.add_subparsers(dest="command", help="可用命令")
|
||
|
||
# 测试命令
|
||
subparsers.add_parser("test", help="测试API连接")
|
||
|
||
# 获取数据命令
|
||
get_parser = subparsers.add_parser("get", help="获取板块数据")
|
||
get_parser.add_argument("type", choices=["industry", "concept"], help="板块类型")
|
||
get_parser.add_argument("--limit", type=int, default=20, help="返回数量")
|
||
|
||
# 发送报告命令
|
||
report_parser = subparsers.add_parser("report", help="生成并发送盘后报告")
|
||
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":
|
||
print("\n🧪 测试东方财富API连接...")
|
||
for board_type in ["industry", "concept"]:
|
||
print(f"\n测试 {board_type} 板块...")
|
||
boards = get_board_data(board_type, limit=5)
|
||
if boards:
|
||
print(f"✅ 成功获取 {len(boards)} 条数据")
|
||
for board in boards[:3]:
|
||
pct_str = f"+{board['pct_change']:.2f}%" if board['pct_change'] > 0 else f"{board['pct_change']:.2f}%"
|
||
print(f" - {board['name']}: {pct_str}")
|
||
else:
|
||
print(f"❌ {board_type} 测试失败")
|
||
|
||
elif args.command == "get":
|
||
boards = get_board_data(args.type, limit=args.limit)
|
||
if boards:
|
||
print(f"\n📊 {args.type} 板块数据 ({len(boards)} 条)")
|
||
print("=" * 50)
|
||
for board in boards:
|
||
pct_str = f"+{board['pct_change']:.2f}%" if board['pct_change'] > 0 else f"{board['pct_change']:.2f}%"
|
||
flow_str = f"+{board['main_flow']:.2f}亿" if board['main_flow'] > 0 else f"{board['main_flow']:.2f}亿"
|
||
print(f"{board['name']}: {pct_str}, 主力{flow_str}")
|
||
else:
|
||
print("❌ 获取数据失败")
|
||
|
||
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()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main() |