feat: 优化前端显示&修复若干bug
This commit is contained in:
CaasianVale
@@ -5,6 +5,7 @@ import asyncio
|
||||
import os
|
||||
from typing import Dict, List, Optional, Tuple, Any
|
||||
from logger import get_logger
|
||||
import re
|
||||
|
||||
# 获取日志器
|
||||
logger = get_logger()
|
||||
@@ -57,27 +58,16 @@ class StockDataProvider:
|
||||
if end_date is None:
|
||||
end_date = datetime.now().strftime('%Y%m%d')
|
||||
|
||||
# 确保日期格式统一（移除可能的'-'符号）
|
||||
if isinstance(start_date, str) and '-' in start_date:
|
||||
start_date = start_date.replace('-', '')
|
||||
if isinstance(end_date, str) and '-' in end_date:
|
||||
end_date = end_date.replace('-', '')
|
||||
|
||||
try:
|
||||
# 验证股票代码格式
|
||||
if market_type == 'A':
|
||||
# 上海证券交易所股票代码以6开头
|
||||
# 深圳证券交易所股票代码以0或3开头
|
||||
# 科创板股票代码以688开头
|
||||
# 北京证券交易所股票代码以8开头
|
||||
valid_prefixes = ['0', '3', '6', '688', '8']
|
||||
valid_format = False
|
||||
|
||||
for prefix in valid_prefixes:
|
||||
if stock_code.startswith(prefix):
|
||||
valid_format = True
|
||||
break
|
||||
|
||||
if not valid_format:
|
||||
error_msg = f"无效的A股股票代码格式: {stock_code}。A股代码应以0、3、6、688或8开头"
|
||||
logger.error(f"[股票代码格式错误] {error_msg}")
|
||||
raise ValueError(error_msg)
|
||||
|
||||
logger.debug(f"获取A股数据: {stock_code}")
|
||||
|
||||
df = ak.stock_zh_a_hist(
|
||||
symbol=stock_code,
|
||||
start_date=start_date,
|
||||
@@ -96,13 +86,72 @@ class StockDataProvider:
|
||||
|
||||
elif market_type in ['US']:
|
||||
logger.debug(f"获取美股数据: {stock_code}")
|
||||
df = ak.stock_us_daily(
|
||||
symbol=stock_code,
|
||||
adjust="qfq"
|
||||
)
|
||||
# 过滤日期
|
||||
df = df[(df.index >= start_date) & (df.index <= end_date)]
|
||||
try:
|
||||
df = ak.stock_us_daily(
|
||||
symbol=stock_code,
|
||||
adjust="qfq"
|
||||
)
|
||||
logger.debug(f"美股数据原始列: {df.columns.tolist()}")
|
||||
logger.debug(f"美股数据形状: {df.shape}")
|
||||
|
||||
# 确保索引是日期时间类型
|
||||
if not isinstance(df.index, pd.DatetimeIndex):
|
||||
# 如果存在命名为'date'的列，将其设为索引
|
||||
if 'date' in df.columns:
|
||||
df['date'] = pd.to_datetime(df['date'])
|
||||
df.set_index('date', inplace=True)
|
||||
logger.debug("已将'date'列设置为索引")
|
||||
else:
|
||||
# 否则将当前索引转换为日期类型
|
||||
df.index = pd.to_datetime(df.index)
|
||||
logger.debug("已将索引转换为DatetimeIndex")
|
||||
|
||||
# 计算美股的成交额（Amount）= 成交量（Volume）× 收盘价（Close）
|
||||
volume_col = next((col for col in df.columns if col.lower() == 'volume'), None)
|
||||
close_col = next((col for col in df.columns if col.lower() == 'close'), None)
|
||||
|
||||
if volume_col and close_col:
|
||||
df['amount'] = df[volume_col] * df[close_col]
|
||||
logger.debug("已为美股数据计算成交额(amount)字段")
|
||||
else:
|
||||
logger.warning(f"美股数据缺少volume或close列，无法计算amount。当前列: {df.columns.tolist()}")
|
||||
# 添加空的amount列，避免后续处理错误
|
||||
df['amount'] = 0.0
|
||||
|
||||
# 将所有列名转为小写以进行统一处理
|
||||
df.columns = [col.lower() for col in df.columns]
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取美股数据失败 {stock_code}: {str(e)}")
|
||||
raise ValueError(f"获取美股数据失败 {stock_code}: {str(e)}")
|
||||
|
||||
# 将字符串日期转换为日期时间对象进行比较
|
||||
try:
|
||||
# 尝试多种格式解析日期
|
||||
# 如果日期是数字格式（20220101），使用适当的格式
|
||||
if start_date.isdigit() and len(start_date) == 8:
|
||||
start_date_dt = pd.to_datetime(start_date, format='%Y%m%d')
|
||||
else:
|
||||
# 否则让pandas自动推断格式
|
||||
start_date_dt = pd.to_datetime(start_date)
|
||||
|
||||
if end_date.isdigit() and len(end_date) == 8:
|
||||
end_date_dt = pd.to_datetime(end_date, format='%Y%m%d')
|
||||
else:
|
||||
end_date_dt = pd.to_datetime(end_date)
|
||||
except Exception as e:
|
||||
logger.warning(f"日期转换出错: {str(e)}，使用默认值")
|
||||
# 如果转换失败，使用合理的默认值
|
||||
start_date_dt = pd.to_datetime('20000101', format='%Y%m%d')
|
||||
end_date_dt = pd.to_datetime(datetime.now().strftime('%Y%m%d'), format='%Y%m%d')
|
||||
|
||||
# 过滤日期
|
||||
try:
|
||||
df = df[(df.index >= start_date_dt) & (df.index <= end_date_dt)]
|
||||
logger.debug(f"日期过滤后数据点数: {len(df)}")
|
||||
except Exception as e:
|
||||
logger.warning(f"日期过滤出错: {str(e)}，返回原始数据")
|
||||
|
||||
elif market_type in ['ETF', 'LOF']:
|
||||
logger.debug(f"获取{market_type}基金数据: {stock_code}")
|
||||
df = ak.fund_etf_hist_sina(
|
||||
@@ -122,8 +171,31 @@ class StockDataProvider:
|
||||
# 实际数据列：['日期', '股票代码', '开盘', '收盘', '最高', '最低', '成交量', '成交额', '振幅', '涨跌幅', '涨跌额', '换手率']
|
||||
df.columns = ['Date', 'Code', 'Open', 'Close', 'High', 'Low', 'Volume', 'Amount', 'Amplitude', 'Change_pct', 'Change', 'Turnover']
|
||||
elif market_type in ['HK', 'US']:
|
||||
# 根据实际情况调整
|
||||
df.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Amount']
|
||||
# 美股数据列可能不同，需要通过映射处理
|
||||
columns_mapping = {
|
||||
'open': 'Open',
|
||||
'high': 'High',
|
||||
'low': 'Low',
|
||||
'close': 'Close',
|
||||
'volume': 'Volume',
|
||||
'amount': 'Amount'
|
||||
}
|
||||
|
||||
# 创建新的DataFrame以确保列顺序和存在性
|
||||
new_df = pd.DataFrame(index=df.index)
|
||||
|
||||
# 遍历映射，填充新DataFrame
|
||||
for orig_col, new_col in columns_mapping.items():
|
||||
if orig_col in df.columns:
|
||||
new_df[new_col] = df[orig_col]
|
||||
else:
|
||||
# 如果原始列不存在，创建一个填充0的列
|
||||
logger.warning(f"数据中缺少{orig_col}列，使用0值填充")
|
||||
new_df[new_col] = 0.0
|
||||
|
||||
# 替换原始df
|
||||
df = new_df
|
||||
|
||||
elif market_type in ['ETF', 'LOF']:
|
||||
# 基金数据可能有不同的列
|
||||
df.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Amount']
|
||||
@@ -143,7 +215,11 @@ class StockDataProvider:
|
||||
error_msg = f"获取{market_type}数据失败 {stock_code}: {str(e)}"
|
||||
logger.error(error_msg)
|
||||
logger.exception(e)
|
||||
raise Exception(error_msg)
|
||||
# 使用空的DataFrame并添加错误信息，而不是抛出异常
|
||||
# 这样上层调用者可以检查是否有错误并适当处理
|
||||
df = pd.DataFrame()
|
||||
df.error = error_msg # 添加错误属性
|
||||
return df
|
||||
|
||||
async def get_multiple_stocks_data(self, stock_codes: List[str],
|
||||
market_type: str = 'A',
|
||||
@@ -181,4 +257,4 @@ class StockDataProvider:
|
||||
results = await asyncio.gather(*tasks)
|
||||
|
||||
# 构建结果字典，过滤掉失败的请求
|
||||
return {code: df for code, df in results if df is not None}
|
||||
return {code: df for code, df in results if df is not None}
|
||||
Reference in New Issue
Block a user