565 lines
22 KiB
Python
565 lines
22 KiB
Python
"""
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行业估值分析模块
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提供行业历史PE、PB、PS分位数分析功能以及行业拥挤度指标，包括：
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1. 行业历史PE、PB、PS数据获取
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2. 分位数计算
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3. 行业交易拥挤度计算
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"""
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import pandas as pd
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import numpy as np
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from sqlalchemy import create_engine, text
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import datetime
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import logging
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import json
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import redis
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import time
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from typing import Tuple, Dict, List, Optional, Union
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from .config import DB_URL, OUTPUT_DIR, LOG_FILE
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler(LOG_FILE),
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logging.StreamHandler()
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]
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)
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logger = logging.getLogger("industry_analysis")
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# 添加Redis客户端
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redis_client = redis.Redis(
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host='192.168.18.208', # Redis服务器地址，根据实际情况调整
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port=6379,
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password='wlkj2018',
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db=13,
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socket_timeout=5,
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decode_responses=True
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)
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class IndustryAnalyzer:
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"""行业估值分析器类"""
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def __init__(self, db_url: str = DB_URL):
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"""
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初始化行业估值分析器
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Args:
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db_url: 数据库连接URL
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"""
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self.engine = create_engine(
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db_url,
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pool_size=5,
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max_overflow=10,
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pool_recycle=3600
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)
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logger.info("行业估值分析器初始化完成")
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def get_industry_list(self) -> List[Dict]:
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"""
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获取所有行业列表
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Returns:
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行业列表，每个行业为一个字典，包含code和name
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"""
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try:
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query = text("""
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SELECT DISTINCT bk_code, bk_name
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FROM gp_hybk
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ORDER BY bk_name
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""")
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with self.engine.connect() as conn:
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result = conn.execute(query).fetchall()
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if result:
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return [{"code": str(row[0]), "name": row[1]} for row in result]
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else:
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logger.warning("未找到行业数据")
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return []
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except Exception as e:
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logger.error(f"获取行业列表失败: {e}")
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return []
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def get_concept_list(self) -> List[Dict]:
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"""
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获取所有概念板块列表
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Returns:
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概念板块列表，每个概念板块为一个字典，包含code和name
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"""
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try:
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query = text("""
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SELECT DISTINCT bk_code, bk_name
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FROM gp_gnbk
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ORDER BY bk_name
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""")
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with self.engine.connect() as conn:
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result = conn.execute(query).fetchall()
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if result:
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return [{"code": str(row[0]), "name": row[1]} for row in result]
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else:
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logger.warning("未找到概念板块数据")
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return []
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except Exception as e:
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logger.error(f"获取概念板块列表失败: {e}")
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return []
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def get_industry_stocks(self, industry_name: str) -> List[str]:
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"""
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获取指定行业的所有股票代码
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Args:
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industry_name: 行业名称
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Returns:
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股票代码列表
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"""
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try:
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query = text("""
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SELECT DISTINCT gp_code
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FROM gp_hybk
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WHERE bk_name = :industry_name
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""")
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with self.engine.connect() as conn:
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result = conn.execute(query, {"industry_name": industry_name}).fetchall()
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if result:
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return [row[0] for row in result]
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else:
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logger.warning(f"未找到行业 {industry_name} 的股票")
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return []
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except Exception as e:
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logger.error(f"获取行业股票失败: {e}")
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return []
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def get_industry_valuation_data(self, industry_name: str, start_date: str, metric: str = 'pe') -> pd.DataFrame:
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"""
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获取行业估值数据，返回每日行业平均PE/PB/PS
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说明：
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- 行业估值数据是指行业内所有股票的平均PE/PB/PS的历史数据
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- 在计算过程中会剔除负值和极端值(如PE>1000)
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Args:
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industry_name: 行业名称
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start_date: 开始日期
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metric: 估值指标（pe、pb或ps）
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Returns:
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包含行业估值数据的DataFrame，主要包含以下列：
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- timestamp: 日期
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- avg_{metric}: 行业平均值
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- stock_count: 参与计算的股票数量
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"""
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try:
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# 验证metric参数
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if metric not in ['pe', 'pb', 'ps']:
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logger.error(f"不支持的估值指标: {metric}")
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return pd.DataFrame()
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# 获取行业所有股票
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stock_codes = self.get_industry_stocks(industry_name)
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if not stock_codes:
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return pd.DataFrame()
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# 构建查询 - 只计算每天的行业平均值和参与计算的股票数量
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query = text(f"""
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WITH valid_data AS (
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SELECT
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`timestamp`,
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symbol,
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{metric}
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FROM
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gp_day_data
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WHERE
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symbol IN :stock_codes AND
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`timestamp` >= :start_date AND
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{metric} > 0 AND
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{metric} < 1000 -- 过滤掉极端异常值
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)
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SELECT
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`timestamp`,
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AVG({metric}) as avg_{metric},
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COUNT(*) as stock_count
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FROM
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valid_data
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GROUP BY
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`timestamp`
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ORDER BY
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`timestamp`
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""")
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with self.engine.connect() as conn:
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# 获取汇总数据
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df = pd.read_sql(
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query,
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conn,
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params={"stock_codes": tuple(stock_codes), "start_date": start_date}
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)
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if df.empty:
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logger.warning(f"未找到行业 {industry_name} 的估值数据")
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return pd.DataFrame()
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logger.info(f"成功获取行业 {industry_name} 的{metric.upper()}数据，共 {len(df)} 条记录")
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return df
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except Exception as e:
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logger.error(f"获取行业估值数据失败: {e}")
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return pd.DataFrame()
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def calculate_industry_percentiles(self, data: pd.DataFrame, metric: str = 'pe') -> Dict:
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"""
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计算行业估值指标的当前分位数及历史统计值
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计算的是行业平均PE/PB/PS在其历史分布中的百分位，以及历史最大值、最小值、四分位数等
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Args:
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data: 历史数据DataFrame
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metric: 估值指标，pe、pb或ps
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Returns:
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包含分位数信息的字典
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"""
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if data.empty:
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logger.warning(f"数据为空，无法计算行业{metric}分位数")
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return {}
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# 验证metric参数
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if metric not in ['pe', 'pb', 'ps']:
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logger.error(f"不支持的估值指标: {metric}")
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return {}
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# 列名
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avg_col = f'avg_{metric}'
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# 获取最新值
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latest_data = data.iloc[-1]
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# 计算当前均值在历史分布中的百分位
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# 使用 <= 是为了计算当前值在历史数据中的累积分布函数值
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# 这样可以得到，有多少比例的历史数据小于等于当前值，即当前值的百分位
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percentile = (data[avg_col] <= latest_data[avg_col]).mean() * 100
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# 计算行业平均PE的历史最小值、最大值、四分位数等
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min_value = float(data[avg_col].min())
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max_value = float(data[avg_col].max())
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mean_value = float(data[avg_col].mean())
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median_value = float(data[avg_col].median())
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q1_value = float(data[avg_col].quantile(0.25))
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q3_value = float(data[avg_col].quantile(0.75))
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# 计算各种分位数
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result = {
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'date': latest_data['timestamp'].strftime('%Y-%m-%d'),
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'current': float(latest_data[avg_col]),
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'min': min_value,
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'max': max_value,
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'mean': mean_value,
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'median': median_value,
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'q1': q1_value,
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'q3': q3_value,
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'percentile': float(percentile),
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'stock_count': int(latest_data['stock_count'])
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}
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logger.info(f"计算行业 {metric} 分位数完成: 当前{metric}={result['current']:.2f}, 百分位={result['percentile']:.2f}%")
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return result
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def get_industry_crowding_index(self, industry_name: str, start_date: str = None, end_date: str = None, use_cache: bool = True) -> pd.DataFrame:
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"""
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计算行业交易拥挤度指标，并使用Redis缓存结果
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对于拥挤度指标，固定使用3年数据，不受start_date影响
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缓存时间为1天
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Args:
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industry_name: 行业名称
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start_date: 不再使用此参数，保留是为了兼容性
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end_date: 结束日期（默认为当前日期）
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use_cache: 是否使用缓存，默认为True
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Returns:
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包含行业拥挤度指标的DataFrame
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"""
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try:
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# 始终使用3年前作为开始日期
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three_years_ago = (datetime.datetime.now() - datetime.timedelta(days=3*365)).strftime('%Y-%m-%d')
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if end_date is None:
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end_date = datetime.datetime.now().strftime('%Y-%m-%d')
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# 检查缓存
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if use_cache:
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cache_key = f"industry_crowding:{industry_name}"
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cached_data = redis_client.get(cache_key)
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if cached_data:
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try:
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# 尝试解析缓存的JSON数据
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cached_df_dict = json.loads(cached_data)
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logger.info(f"从缓存获取行业 {industry_name} 的拥挤度数据")
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# 将缓存的字典转换回DataFrame
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df = pd.DataFrame(cached_df_dict)
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# 确保trade_date列是日期类型
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df['trade_date'] = pd.to_datetime(df['trade_date'])
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return df
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except Exception as cache_error:
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logger.warning(f"解析缓存的拥挤度数据失败，将重新查询: {cache_error}")
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# 获取行业所有股票
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stock_codes = self.get_industry_stocks(industry_name)
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if not stock_codes:
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return pd.DataFrame()
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# 优化方案：分别查询市场总成交额和行业成交额，然后在Python中计算比率
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# 查询1：获取每日总成交额
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query_total = text("""
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SELECT
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`timestamp` AS trade_date,
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SUM(amount) AS total_market_amount
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FROM
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gp_day_data
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WHERE
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`timestamp` BETWEEN :start_date AND :end_date
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GROUP BY
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`timestamp`
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ORDER BY
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`timestamp`
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""")
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# 查询2：获取行业每日成交额
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query_industry = text("""
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SELECT
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`timestamp` AS trade_date,
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SUM(amount) AS industry_amount
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FROM
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gp_day_data
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WHERE
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symbol IN :stock_codes AND
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`timestamp` BETWEEN :start_date AND :end_date
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GROUP BY
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`timestamp`
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ORDER BY
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`timestamp`
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""")
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with self.engine.connect() as conn:
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# 执行两个独立的查询
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df_total = pd.read_sql(
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query_total,
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conn,
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params={"start_date": three_years_ago, "end_date": end_date}
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)
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df_industry = pd.read_sql(
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query_industry,
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conn,
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params={
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"stock_codes": tuple(stock_codes),
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"start_date": three_years_ago,
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"end_date": end_date
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}
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)
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# 检查查询结果
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if df_total.empty or df_industry.empty:
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logger.warning(f"未找到行业 {industry_name} 的交易数据")
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return pd.DataFrame()
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# 在Python中合并数据并计算比率
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df = pd.merge(df_total, df_industry, on='trade_date', how='inner')
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# 计算行业成交额占比
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df['industry_amount_ratio'] = (df['industry_amount'] / df['total_market_amount']) * 100
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# 在Python中计算百分位
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df['percentile'] = df['industry_amount_ratio'].rank(pct=True) * 100
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# 添加拥挤度评级
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df['crowding_level'] = pd.cut(
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df['percentile'],
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bins=[0, 20, 40, 60, 80, 100],
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labels=['不拥挤', '较不拥挤', '中性', '较为拥挤', '极度拥挤']
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)
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# 将DataFrame转换为字典，以便缓存
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df_dict = df.to_dict(orient='records')
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# 缓存结果，有效期1天（86400秒）
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if use_cache:
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try:
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redis_client.set(
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cache_key,
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json.dumps(df_dict, default=str), # 使用default=str处理日期等特殊类型
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ex=86400 # 1天的秒数
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)
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logger.info(f"已缓存行业 {industry_name} 的拥挤度数据，有效期为1天")
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except Exception as cache_error:
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logger.warning(f"缓存行业拥挤度数据失败: {cache_error}")
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logger.info(f"成功计算行业 {industry_name} 的拥挤度指标，共 {len(df)} 条记录")
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return df
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except Exception as e:
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logger.error(f"计算行业拥挤度指标失败: {e}")
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return pd.DataFrame()
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def get_industry_analysis(self, industry_name: str, metric: str = 'pe', start_date: str = None) -> Dict:
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"""
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获取行业综合分析结果
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Args:
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industry_name: 行业名称
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metric: 估值指标（pe、pb或ps）
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start_date: 开始日期（默认为3年前）
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Returns:
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行业分析结果字典，包含以下内容：
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- success: 是否成功
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- industry_name: 行业名称
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- metric: 估值指标
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- analysis_date: 分析日期
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- valuation: 估值数据，包含：
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- dates: 日期列表
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- avg_values: 行业平均值列表
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- stock_counts: 参与计算的股票数量列表
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- percentiles: 分位数信息，包含行业平均值的历史最大值、最小值、四分位数等
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- crowding（如有）: 拥挤度数据，包含：
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- dates: 日期列表
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- ratios: 拥挤度比例列表
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- percentiles: 拥挤度百分位列表
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- current: 当前拥挤度信息
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"""
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try:
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# 默认查询近3年数据
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if start_date is None:
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start_date = (datetime.datetime.now() - datetime.timedelta(days=3*365)).strftime('%Y-%m-%d')
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# 获取估值数据
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valuation_data = self.get_industry_valuation_data(industry_name, start_date, metric)
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if valuation_data.empty:
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return {"success": False, "message": f"无法获取行业 {industry_name} 的估值数据"}
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# 计算估值分位数
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percentiles = self.calculate_industry_percentiles(valuation_data, metric)
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if not percentiles:
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return {"success": False, "message": f"无法计算行业 {industry_name} 的估值分位数"}
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# 获取拥挤度指标（始终使用3年数据，不受start_date影响）
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crowding_data = self.get_industry_crowding_index(industry_name)
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# 为了兼容前端，准备一些行业平均值的历史统计数据
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avg_values = valuation_data[f'avg_{metric}'].tolist()
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# 准备返回结果
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result = {
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"success": True,
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"industry_name": industry_name,
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"metric": metric.upper(),
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"analysis_date": datetime.datetime.now().strftime('%Y-%m-%d'),
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"valuation": {
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"dates": valuation_data['timestamp'].dt.strftime('%Y-%m-%d').tolist(),
|
||
"avg_values": avg_values,
|
||
# 填充行业平均值的历史统计线
|
||
"min_values": [percentiles['min']] * len(avg_values), # 行业平均PE历史最小值
|
||
"max_values": [percentiles['max']] * len(avg_values), # 行业平均PE历史最大值
|
||
"q1_values": [percentiles['q1']] * len(avg_values), # 行业平均PE历史第一四分位数
|
||
"q3_values": [percentiles['q3']] * len(avg_values), # 行业平均PE历史第三四分位数
|
||
"median_values": [percentiles['median']] * len(avg_values), # 行业平均PE历史中位数
|
||
"stock_counts": valuation_data['stock_count'].tolist(),
|
||
"percentiles": percentiles
|
||
}
|
||
}
|
||
|
||
# 添加拥挤度数据（如果有）
|
||
if not crowding_data.empty:
|
||
current_crowding = crowding_data.iloc[-1]
|
||
result["crowding"] = {
|
||
"dates": crowding_data['trade_date'].dt.strftime('%Y-%m-%d').tolist(),
|
||
"ratios": crowding_data['industry_amount_ratio'].tolist(),
|
||
"percentiles": crowding_data['percentile'].tolist(),
|
||
"current": {
|
||
"date": current_crowding['trade_date'].strftime('%Y-%m-%d'),
|
||
"ratio": float(current_crowding['industry_amount_ratio']),
|
||
"percentile": float(current_crowding['percentile']),
|
||
"level": current_crowding['crowding_level'],
|
||
# 添加行业成交额比例的历史分位信息
|
||
"ratio_stats": {
|
||
"min": float(crowding_data['industry_amount_ratio'].min()),
|
||
"max": float(crowding_data['industry_amount_ratio'].max()),
|
||
"mean": float(crowding_data['industry_amount_ratio'].mean()),
|
||
"median": float(crowding_data['industry_amount_ratio'].median()),
|
||
"q1": float(crowding_data['industry_amount_ratio'].quantile(0.25)),
|
||
"q3": float(crowding_data['industry_amount_ratio'].quantile(0.75)),
|
||
}
|
||
}
|
||
}
|
||
|
||
return result
|
||
|
||
except Exception as e:
|
||
logger.error(f"获取行业综合分析失败: {e}")
|
||
return {"success": False, "message": f"获取行业综合分析失败: {e}"}
|
||
|
||
def get_stock_concepts(self, stock_code: str) -> List[str]:
|
||
"""
|
||
获取指定股票所属的概念板块列表
|
||
|
||
Args:
|
||
stock_code: 股票代码
|
||
|
||
Returns:
|
||
概念板块名称列表
|
||
"""
|
||
try:
|
||
# 转换股票代码格式
|
||
formatted_code = self._convert_stock_code_format(stock_code)
|
||
|
||
query = text("""
|
||
SELECT DISTINCT bk_name
|
||
FROM gp_gnbk
|
||
WHERE gp_code = :stock_code
|
||
""")
|
||
|
||
with self.engine.connect() as conn:
|
||
result = conn.execute(query, {"stock_code": formatted_code}).fetchall()
|
||
|
||
if result:
|
||
return [row[0] for row in result]
|
||
else:
|
||
logger.warning(f"未找到股票 {stock_code} 的概念板块数据")
|
||
return []
|
||
|
||
except Exception as e:
|
||
logger.error(f"获取股票概念板块失败: {e}")
|
||
return []
|
||
|
||
def _convert_stock_code_format(self, stock_code: str) -> str:
|
||
"""
|
||
转换股票代码格式
|
||
|
||
Args:
|
||
stock_code: 原始股票代码，格式如 "600519.SH"
|
||
|
||
Returns:
|
||
转换后的股票代码，格式如 "SH600519"
|
||
"""
|
||
try:
|
||
code, market = stock_code.split('.')
|
||
return f"{market}{code}"
|
||
except Exception as e:
|
||
logger.error(f"转换股票代码格式失败: {str(e)}")
|
||
return stock_code |