405 lines
16 KiB
Python
405 lines
16 KiB
Python
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import akshare as ak
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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import os
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import time
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import pymysql
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import warnings
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warnings.filterwarnings('ignore')
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class SWIndustryHeatTracker:
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"""
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申万一级行业热度追踪器
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使用 index_analysis_daily_sw 接口（收盘后官方数据）
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支持自定义热度计算周期和权重，数据自动存入MySQL
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"""
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def __init__(self, data_dir="./sw_data", lookback_days=5, db_config=None):
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self.data_dir = data_dir
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self.lookback_days = lookback_days
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self.db_config = db_config # MySQL配置
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os.makedirs(data_dir, exist_ok=True)
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# 初始化交易日历
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self.trading_dates = self._load_trading_calendar()
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# 申万一级行业代码映射（31个行业）
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self.industry_map = {
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'801010': '农林牧渔', '801030': '基础化工', '801040': '钢铁',
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'801050': '有色金属', '801080': '电子', '801110': '家用电器',
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'801120': '食品饮料', '801130': '纺织服饰', '801140': '轻工制造',
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'801150': '医药生物', '801160': '公用事业', '801170': '交通运输',
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'801180': '房地产', '801200': '商贸零售', '801210': '社会服务',
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'801230': '综合', '801710': '建筑材料', '801720': '建筑装饰',
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'801730': '电力设备', '801740': '国防军工', '801750': '计算机',
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'801760': '传媒', '801770': '通信', '801780': '银行',
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'801790': '非银金融', '801880': '汽车', '801890': '机械设备',
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'801950': '煤炭', '801960': '石油石化', '801970': '环保',
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'801980': '美容护理'
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}
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# 热度指标权重配置
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self.weights = {
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'return': 0.35,
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'turnover': 0.25,
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'volume_ratio': 0.25,
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'momentum': 0.15
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}
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# 初始化数据库表
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if self.db_config:
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self._init_database()
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def _init_database(self):
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"""初始化MySQL数据库和表"""
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conn = None
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try:
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conn = pymysql.connect(**self.db_config)
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with conn.cursor() as cursor:
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# 创建表（如果不存在）
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create_table_sql = """
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CREATE TABLE IF NOT EXISTS sw_heat_daily (
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id INT AUTO_INCREMENT PRIMARY KEY,
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calc_date VARCHAR(8) NOT NULL COMMENT '计算日期(YYYYMMDD)',
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code VARCHAR(10) NOT NULL COMMENT '行业代码',
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name VARCHAR(20) NOT NULL COMMENT '行业名称',
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total_return DECIMAL(10,4) COMMENT '累计涨跌幅(%)',
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avg_turnover DECIMAL(10,4) COMMENT '平均换手率(%)',
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volume_ratio DECIMAL(10,4) COMMENT '量比',
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momentum DECIMAL(5,4) COMMENT '动量(上涨天数占比)',
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total_return_score DECIMAL(5,4) COMMENT '收益因子得分(0-1)',
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avg_turnover_score DECIMAL(5,4) COMMENT '换手因子得分(0-1)',
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volume_ratio_score DECIMAL(5,4) COMMENT '量比因子得分(0-1)',
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momentum_score DECIMAL(5,4) COMMENT '动量因子得分(0-1)',
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heat_score DECIMAL(6,2) COMMENT '热度综合得分(0-100)',
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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UNIQUE KEY uk_date_code (calc_date, code),
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INDEX idx_date (calc_date),
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INDEX idx_heat_score (calc_date, heat_score)
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) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='申万一级行业热度日表';
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"""
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cursor.execute(create_table_sql)
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conn.commit()
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print("✅ 数据库表 sw_heat_daily 初始化成功")
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except Exception as e:
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print(f"❌ 数据库初始化失败: {e}")
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raise
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finally:
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if conn:
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conn.close()
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def _load_trading_calendar(self):
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"""加载交易日历"""
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cache_file = f"{self.data_dir}/trading_calendar.csv"
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if os.path.exists(cache_file):
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df = pd.read_csv(cache_file)
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dates = df['trade_date'].astype(str).tolist()
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cache_time = datetime.fromtimestamp(os.path.getmtime(cache_file))
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if (datetime.now() - cache_time).days > 7:
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self._update_calendar_cache(cache_file)
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else:
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dates = self._update_calendar_cache(cache_file)
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return set(dates)
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def _update_calendar_cache(self, cache_file):
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"""更新交易日历缓存"""
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print("正在更新交易日历...")
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df = ak.tool_trade_date_hist_sina()
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df.to_csv(cache_file, index=False)
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return df['trade_date'].astype(str).tolist()
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def get_recent_trading_dates(self, n_days, end_date=None):
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"""获取最近N个交易日"""
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if end_date is None:
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end_date = datetime.now().strftime('%Y-%m-%d')
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valid_dates = [d for d in self.trading_dates if d <= end_date]
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return sorted(valid_dates)[-n_days:]
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def fetch_daily_data(self, trade_date):
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"""获取指定交易日的申万行业日报数据"""
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date_str = trade_date.replace('-', '')
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try:
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df = ak.index_analysis_daily_sw(
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symbol="一级行业",
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start_date=date_str,
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end_date=date_str
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)
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if df.empty:
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print(f"⚠️ {trade_date} 无数据")
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return None
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df = df.rename(columns={
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'指数代码': 'code',
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'指数名称': 'name',
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'发布日期': 'date',
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'收盘指数': 'close',
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'涨跌幅': 'return',
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'换手率': 'turnover',
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'成交量': 'volume',
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'成交额': 'amount',
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'市盈率': 'pe',
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'市净率': 'pb'
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})
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df['trade_date'] = trade_date
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df['code'] = df['code'].astype(str)
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return df
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except Exception as e:
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print(f"❌ 获取 {trade_date} 数据失败: {e}")
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return None
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def fetch_multi_days_data(self, n_days=None):
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"""获取最近N天的数据"""
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if n_days is None:
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n_days = self.lookback_days + 5
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dates = self.get_recent_trading_dates(n_days)
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print(f"📅 获取数据区间: {dates[0]} 至 {dates[-1]}")
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all_data = []
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for date in dates:
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df = self.fetch_daily_data(date)
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if df is not None:
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all_data.append(df)
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time.sleep(0.5)
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if not all_data:
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return None
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combined = pd.concat(all_data, ignore_index=True)
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combined['trade_date'] = pd.to_datetime(combined['trade_date'])
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return combined.sort_values(['code', 'trade_date'])
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def calculate_heat_score(self, df, lookback_days=None):
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"""计算板块热度复合指标"""
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if lookback_days is None:
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lookback_days = self.lookback_days
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latest_dates = df['trade_date'].unique()[-lookback_days:]
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recent_df = df[df['trade_date'].isin(latest_dates)].copy()
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all_dates = df['trade_date'].unique()
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if len(all_dates) >= lookback_days * 2:
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hist_dates = all_dates[-lookback_days*2:-lookback_days]
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hist_df = df[df['trade_date'].isin(hist_dates)]
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else:
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hist_df = None
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heat_data = []
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for code in recent_df['code'].unique():
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industry_data = recent_df[recent_df['code'] == code]
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if len(industry_data) < lookback_days:
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continue
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name = industry_data['name'].iloc[0]
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total_return = industry_data['return'].sum()
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avg_turnover = industry_data['turnover'].mean()
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recent_volume = industry_data['volume'].mean()
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if hist_df is not None and code in hist_df['code'].values:
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hist_volume = hist_df[hist_df['code'] == code]['volume'].mean()
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volume_ratio = recent_volume / hist_volume if hist_volume > 0 else 1.0
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else:
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volume_ratio = 1.0
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up_days = (industry_data['return'] > 0).sum()
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momentum = up_days / lookback_days
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heat_data.append({
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'code': code,
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'name': name,
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'total_return': total_return,
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'avg_turnover': avg_turnover,
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'volume_ratio': volume_ratio,
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'momentum': momentum,
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'latest_close': industry_data['close'].iloc[-1],
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'latest_return': industry_data['return'].iloc[-1],
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'up_days': up_days
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})
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heat_df = pd.DataFrame(heat_data)
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for col in ['total_return', 'avg_turnover', 'volume_ratio', 'momentum']:
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heat_df[f'{col}_score'] = heat_df[col].rank(pct=True)
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heat_df['heat_score'] = (
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heat_df['total_return_score'] * self.weights['return'] +
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heat_df['avg_turnover_score'] * self.weights['turnover'] +
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heat_df['volume_ratio_score'] * self.weights['volume_ratio'] +
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heat_df['momentum_score'] * self.weights['momentum']
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) * 100
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heat_df['heat_level'] = pd.cut(
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heat_df['heat_score'],
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bins=[0, 20, 40, 60, 80, 100],
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labels=['极冷', '偏冷', '温和', '偏热', '极热']
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)
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heat_df['rank'] = heat_df['heat_score'].rank(ascending=False, method='min').astype(int)
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return heat_df.sort_values('heat_score', ascending=False)
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def save_to_mysql(self, heat_df, calc_date):
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"""
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将热度数据存入MySQL
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:param heat_df: 热度数据DataFrame
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:param calc_date: 计算日期，格式'YYYYMMDD'
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"""
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if not self.db_config:
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print("⚠️ 未配置数据库，跳过MySQL存储")
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return False
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conn = None
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try:
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conn = pymysql.connect(**self.db_config)
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cursor = conn.cursor()
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# 准备插入数据
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insert_sql = """
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INSERT INTO sw_heat_daily
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(calc_date, code, name, total_return, avg_turnover, volume_ratio, momentum,
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total_return_score, avg_turnover_score, volume_ratio_score, momentum_score, heat_score)
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VALUES
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(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
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ON DUPLICATE KEY UPDATE
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name = VALUES(name),
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total_return = VALUES(total_return),
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avg_turnover = VALUES(avg_turnover),
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volume_ratio = VALUES(volume_ratio),
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momentum = VALUES(momentum),
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total_return_score = VALUES(total_return_score),
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avg_turnover_score = VALUES(avg_turnover_score),
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volume_ratio_score = VALUES(volume_ratio_score),
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momentum_score = VALUES(momentum_score),
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heat_score = VALUES(heat_score),
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created_at = CURRENT_TIMESTAMP;
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"""
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# 转换数据为列表元组
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data_tuples = []
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for _, row in heat_df.iterrows():
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data_tuples.append((
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calc_date,
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row['code'],
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row['name'],
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float(row['total_return']),
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float(row['avg_turnover']),
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float(row['volume_ratio']),
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float(row['momentum']),
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float(row['total_return_score']),
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float(row['avg_turnover_score']),
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float(row['volume_ratio_score']),
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float(row['momentum_score']),
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float(row['heat_score'])
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))
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# 批量插入
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cursor.executemany(insert_sql, data_tuples)
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conn.commit()
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print(f"✅ MySQL存储成功: {calc_date} 共 {len(data_tuples)} 条记录")
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return True
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except Exception as e:
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print(f"❌ MySQL存储失败: {e}")
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if conn:
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conn.rollback()
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return False
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finally:
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if conn:
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conn.close()
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def generate_report(self, save=True, to_mysql=True):
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"""生成热度分析报告并存储"""
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calc_date = datetime.now().strftime('%Y%m%d') # YYYYMMDD格式
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print("="*80)
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print(f"🔥 申万一级行业热度报告 ({self.lookback_days}日复合指标)")
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print(f"📅 计算日期: {calc_date}")
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print(f"⏰ 生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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print("="*80)
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# 获取数据
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raw_data = self.fetch_multi_days_data()
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if raw_data is None:
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print("❌ 数据获取失败")
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|
return None
|
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|
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|
# 计算热度
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|||
|
|
heat_df = self.calculate_heat_score(raw_data)
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|
|||
|
|
# 打印报告（省略，与之前相同）...
|
|||
|
|
print(f"\n🏆 热度 TOP 10 (近{self.lookback_days}日):")
|
|||
|
|
print("-"*80)
|
|||
|
|
top10 = heat_df.head(10)[['rank', 'name', 'heat_score', 'heat_level',
|
|||
|
|
'total_return', 'avg_turnover', 'volume_ratio', 'momentum']]
|
|||
|
|
|
|||
|
|
for _, row in top10.iterrows():
|
|||
|
|
print(f"{row['rank']:2d}. {row['name']:8s} | "
|
|||
|
|
f"热度:{row['heat_score']:5.1f}({row['heat_level']}) | "
|
|||
|
|
f"收益:{row['total_return']:+6.2f}% | "
|
|||
|
|
f"换手:{row['avg_turnover']:5.2f}% | "
|
|||
|
|
f"量比:{row['volume_ratio']:4.2f} | "
|
|||
|
|
f"动量:{row['momentum']:.0%}")
|
|||
|
|
|
|||
|
|
# 保存数据部分
|
|||
|
|
if save:
|
|||
|
|
today = datetime.now().strftime('%Y-%m-%d')
|
|||
|
|
|
|||
|
|
# # 1. 保存JSON（可选，用于调试）
|
|||
|
|
# result_file = f"{self.data_dir}/heat_report_{today}_{self.lookback_days}d.json"
|
|||
|
|
# heat_df.to_json(result_file, orient='records', force_ascii=False, indent=2)
|
|||
|
|
# print(f"\n💾 JSON报告已保存: {result_file}")
|
|||
|
|
|
|||
|
|
# 2. 保存CSV（可选，便于Excel查看）
|
|||
|
|
csv_file = f"{self.data_dir}/heat_ranking_{today}_{self.lookback_days}d.csv"
|
|||
|
|
heat_df.to_csv(csv_file, index=False, encoding='utf-8-sig')
|
|||
|
|
csv_file = f"{self.data_dir}/heat_ranking_newest_{self.lookback_days}d.csv"
|
|||
|
|
heat_df.to_csv(csv_file, index=False, encoding='utf-8-sig')
|
|||
|
|
print(f"💾 CSV排名已保存: {csv_file}")
|
|||
|
|
|
|||
|
|
# 3. 存入MySQL（新增）
|
|||
|
|
if to_mysql and self.db_config:
|
|||
|
|
self.save_to_mysql(heat_df, calc_date)
|
|||
|
|
|
|||
|
|
return heat_df
|
|||
|
|
|
|||
|
|
|
|||
|
|
# 使用示例
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
# 数据库配置
|
|||
|
|
# config = {
|
|||
|
|
# 'host': 'localhost',
|
|||
|
|
# 'port': 3306,
|
|||
|
|
# 'user': 'your_username',
|
|||
|
|
# 'password': 'your_password',
|
|||
|
|
# 'database': 'your_database',
|
|||
|
|
# 'charset': 'utf8mb4'
|
|||
|
|
# }
|
|||
|
|
config = {
|
|||
|
|
'host': '10.127.2.207',
|
|||
|
|
'user': 'financial_prod',
|
|||
|
|
'password': 'mmTFncqmDal5HLRGY0BV',
|
|||
|
|
'database': 'reference',
|
|||
|
|
'port': 3306,
|
|||
|
|
'charset': 'utf8mb4'
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
# 创建追踪器，传入数据库配置
|
|||
|
|
tracker = SWIndustryHeatTracker(
|
|||
|
|
lookback_days=5,
|
|||
|
|
db_config=config # 传入数据库配置
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 生成报告并自动存入MySQL
|
|||
|
|
result = tracker.generate_report(save=True, to_mysql=True)
|