#!/usr/bin/env python3
"""
LOF基金溢价检查 (使用 akshare)
"""

import akshare as ak
import pandas as pd
from datetime import datetime

def clean_number(x):
    try:
        if pd.isna(x) or x == '-':
            return None
        return float(str(x).replace('%', '').replace('+', ''))
    except:
        return None

def get_lof_report():
    """生成LOF基金溢价报告"""
    df = ak.qdii_e_index_jsl()
    
    df['premium'] = df['T-1溢价率'].apply(clean_number)
    df['price'] = df['现价'].apply(clean_number)
    df['change'] = df['涨幅'].apply(clean_number)
    df['volume'] = df['成交'].apply(clean_number)
    
    # 过滤有效数据
    valid_df = df.dropna(subset=['premium', 'volume'])
    
    # 高溢价TOP10
    premium_top = valid_df.sort_values('premium', ascending=False).head(10)
    
    # 高成交TOP10
    volume_top = valid_df.sort_values('volume', ascending=False).head(10)
    
    report = []
    report.append("=" * 70)
    report.append("📊 LOF/QDII基金溢价排行 TOP 10")
    report.append("=" * 70)
    report.append(f"\n数据时间: {datetime.now().strftime('%Y-%m-%d %H:%M')}\n")
    
    report.append(f"{'代码':<10} {'名称':<16} {'溢价率':>10} {'现价':>8} {'涨幅':>8}")
    report.append("-" * 55)
    
    for _, row in premium_top.iterrows():
        code = str(row['代码'])
        name = str(row['名称'])[:14]
        premium = row['premium']
        price = row['price']
        change = row['change']
        report.append(f"{code:<10} {name:<16} {premium:>+9.2f}% {price:>7.3f} {change:>+7.2f}%")
    
    report.append("\n" + "=" * 70)
    report.append("💡 套利提示:")
    report.append("   溢价 > 2%: 可考虑申购→卖出")
    report.append("   成交量 > 1000万/日 流动性较好")
    report.append("=" * 70)
    
    return "\n".join(report)

if __name__ == "__main__":
    print(get_lof_report())