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import akshare as ak
import pandas as pd
class FundService:
def search_funds(self, keyword, market_type='ETF'):
"""
搜索基金代码
:param keyword: 搜索关键词
:return: 匹配的基金列表
"""
try:
# 获取ETF和LOF数据
if market_type == 'ETF':
df = ak.fund_etf_spot_em()
else:
df = ak.fund_lof_spot_em()
# 转换列名
df = df.rename(columns={
"代码": "symbol",
"名称": "name",
"最新价": "price",
"涨跌额": "price_change",
"涨跌幅": "price_change_percent",
"成交量": "volume",
"流通市值": "market_value",
"总市值": "total_value",
"基金折价率": "discount_rate",
})
# 模糊匹配搜索（同时匹配代码和名称）
mask = (df['name'].str.contains(keyword, case=False, na=False) |
df['symbol'].str.contains(keyword, case=False, na=False))
results = df[mask]
# 格式化返回结果并处理 NaN 值
formatted_results = []
for _, row in results.iterrows():
formatted_results.append({
'name': row['name'] if pd.notna(row['name']) else '',
'symbol': str(row['symbol']) if pd.notna(row['symbol']) else '',
'price': float(row['price']) if pd.notna(row['price']) else 0.0,
'volume': float(row['volume']) if pd.notna(row['volume']) else 0.0,
'market_value': float(row['market_value']) if pd.notna(row['market_value']) else 0.0,
'total_value': float(row['total_value']) if pd.notna(row['total_value']) else 0.0,
})
return formatted_results
except Exception as e:
raise Exception(f"搜索基金代码失败: {str(e)}") |