import akshare as ak
import pandas as pd
import datetime
import time
import logging

# 配置日志
logging.basicConfig(
    filename='stock_signals.log',
    level=logging.INFO,
    format='%(asctime)s [%(levelname)s] %(message)s',
)

# 板块关键词
TARGET_SECTORS = ['互联网服务', '芯片', '消费', '房地产']

def fetch_sector_stocks(sector_name):
    """
    获取某个行业板块的股票列表
    """
    try:
        df_plates = ak.stock_board_industry_name_em()
        if not df_plates['板块名称'].isin([sector_name]).any():
            logging.warning(f"{sector_name} not exists!")
            return pd.DataFrame()

        df = ak.stock_board_industry_cons_em(symbol=sector_name)
        return df[['代码', '名称']]
    except Exception as e:
        logging.error(f"获取板块 {sector_name} 股票失败: {e}")
        return pd.DataFrame()

def compute_signals(df):
    """
    根据行情数据计算交易信号
    """
    signals = []
    for _, row in df.iterrows():
        try:
            code = row['代码']
            name = row['名称']
            pct_today = row['当日涨跌幅']
            pct_year = row['年内涨跌幅']
            pe = row['市盈率']
            pb = row['市净率']
            
            signal = ''
            if pct_today < -5:
                signal += '今日大跌; '
            if pct_year < -20:
                signal += '年内大跌; '
            if pe < 50:
                signal += '低市盈率; '
            
            if signal:
                signals.append({
                    '代码': code,
                    '名称': name,
                    '当日涨跌幅': pct_today,
                    '年内涨跌幅': pct_year,
                    '市盈率': pe,
                    '市净率': pb,
                    '信号': signal.strip()
                })
        except Exception as e:
            logging.warning(f"处理股票 {row} 时出错: {e}")
    return pd.DataFrame(signals)

def fetch_and_analyze():
    """
    获取行情并计算信号
    """
    logging.info("开始获取并分析行情数据")
    all_stocks = pd.DataFrame()
    
    for sector in TARGET_SECTORS:
        df = fetch_sector_stocks(sector)
        if df.empty:
            continue
        
        logging.info(f"获取到板块 [{sector}] {len(df)} 只股票")
        for code in df['代码']:
            try:
                # 获取日K线
                kline = ak.stock_zh_a_hist(symbol=code, period='daily', adjust='qfq')
                kline['日期'] = pd.to_datetime(kline['日期'])
                kline.set_index('日期', inplace=True)
                
                today = kline.iloc[-1]
                close_today = today['收盘']
                close_5d = kline.iloc[-5]['收盘'] if len(kline) >= 5 else today['收盘']
                close_month = kline.iloc[-21]['收盘'] if len(kline) >= 21 else today['收盘']
                close_year = kline.iloc[0]['收盘']
                
                pct_today = (close_today / today['开盘'] - 1) * 100
                pct_5d = (close_today / close_5d -1) * 100
                pct_month = (close_today / close_month -1) * 100
                pct_year = (close_today / close_year -1) *100
                
                # 获取市盈率、市净率
                fundamentals = ak.stock_a_lg_indicator(symbol=code)
                pe = fundamentals.iloc[-1]['市盈率(TTM)']
                pb = fundamentals.iloc[-1]['市净率']
                
                all_stocks = pd.concat([all_stocks, pd.DataFrame([{
                    '代码': code,
                    '名称': df.loc[df['代码']==code, '名称'].values[0],
                    '当日涨跌幅': pct_today,
                    '5日涨跌幅': pct_5d,
                    '本月涨跌幅': pct_month,
                    '年内涨跌幅': pct_year,
                    '市盈率': pe,
                    '市净率': pb
                }])])
            except Exception as e:
                logging.error(f"获取股票 {code} 数据失败: {e}")
                continue
    
    signals = compute_signals(all_stocks)
    if not signals.empty:
        signals.to_csv(f'stock_signals_{datetime.date.today()}.csv', index=False, encoding='utf-8-sig')
        logging.info(f"生成信号 {len(signals)} 条，已写入 CSV。")
    else:
        logging.info("未发现符合条件的交易信号")

if __name__ == "__main__":
    fetch_and_analyze()