mirror of
https://github.com/ZhuLinsen/daily_stock_analysis
synced 2026-09-20 02:43:35 +08:00
198 lines
5.8 KiB
Python
198 lines
5.8 KiB
Python
# -*- coding: utf-8 -*-
|
||
"""
|
||
===================================
|
||
YfinanceFetcher - 兜底数据源 (Priority 4)
|
||
===================================
|
||
|
||
数据来源：Yahoo Finance（通过 yfinance 库）
|
||
特点：国际数据源、可能有延迟或缺失
|
||
定位：当所有国内数据源都失败时的最后保障
|
||
|
||
关键策略：
|
||
1. 自动将 A 股代码转换为 yfinance 格式（.SS / .SZ）
|
||
2. 处理 Yahoo Finance 的数据格式差异
|
||
3. 失败后指数退避重试
|
||
"""
|
||
|
||
import logging
|
||
from datetime import datetime
|
||
from typing import Optional
|
||
|
||
import pandas as pd
|
||
from tenacity import (
|
||
retry,
|
||
stop_after_attempt,
|
||
wait_exponential,
|
||
retry_if_exception_type,
|
||
before_sleep_log,
|
||
)
|
||
|
||
from .base import BaseFetcher, DataFetchError, STANDARD_COLUMNS
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
|
||
class YfinanceFetcher(BaseFetcher):
|
||
"""
|
||
Yahoo Finance 数据源实现
|
||
|
||
优先级：4（最低，作为兜底）
|
||
数据来源：Yahoo Finance
|
||
|
||
关键策略：
|
||
- 自动转换股票代码格式
|
||
- 处理时区和数据格式差异
|
||
- 失败后指数退避重试
|
||
|
||
注意事项：
|
||
- A 股数据可能有延迟
|
||
- 某些股票可能无数据
|
||
- 数据精度可能与国内源略有差异
|
||
"""
|
||
|
||
name = "YfinanceFetcher"
|
||
priority = 4
|
||
|
||
def __init__(self):
|
||
"""初始化 YfinanceFetcher"""
|
||
pass
|
||
|
||
def _convert_stock_code(self, stock_code: str) -> str:
|
||
"""
|
||
转换股票代码为 Yahoo Finance 格式
|
||
|
||
Yahoo Finance A 股代码格式：
|
||
- 沪市：600519.SS (Shanghai Stock Exchange)
|
||
- 深市：000001.SZ (Shenzhen Stock Exchange)
|
||
|
||
Args:
|
||
stock_code: 原始代码，如 '600519', '000001'
|
||
|
||
Returns:
|
||
Yahoo Finance 格式代码，如 '600519.SS', '000001.SZ'
|
||
"""
|
||
code = stock_code.strip()
|
||
|
||
# 已经包含后缀的情况
|
||
if '.SS' in code.upper() or '.SZ' in code.upper():
|
||
return code.upper()
|
||
|
||
# 去除可能的后缀
|
||
code = code.replace('.SH', '').replace('.sh', '')
|
||
|
||
# 根据代码前缀判断市场
|
||
if code.startswith(('600', '601', '603', '688')):
|
||
return f"{code}.SS"
|
||
elif code.startswith(('000', '002', '300')):
|
||
return f"{code}.SZ"
|
||
else:
|
||
logger.warning(f"无法确定股票 {code} 的市场，默认使用深市")
|
||
return f"{code}.SZ"
|
||
|
||
@retry(
|
||
stop=stop_after_attempt(3),
|
||
wait=wait_exponential(multiplier=1, min=2, max=30),
|
||
retry=retry_if_exception_type((ConnectionError, TimeoutError)),
|
||
before_sleep=before_sleep_log(logger, logging.WARNING),
|
||
)
|
||
def _fetch_raw_data(self, stock_code: str, start_date: str, end_date: str) -> pd.DataFrame:
|
||
"""
|
||
从 Yahoo Finance 获取原始数据
|
||
|
||
使用 yfinance.download() 获取历史数据
|
||
|
||
流程：
|
||
1. 转换股票代码格式
|
||
2. 调用 yfinance API
|
||
3. 处理返回数据
|
||
"""
|
||
import yfinance as yf
|
||
|
||
# 转换代码格式
|
||
yf_code = self._convert_stock_code(stock_code)
|
||
|
||
logger.debug(f"调用 yfinance.download({yf_code}, {start_date}, {end_date})")
|
||
|
||
try:
|
||
# 使用 yfinance 下载数据
|
||
df = yf.download(
|
||
tickers=yf_code,
|
||
start=start_date,
|
||
end=end_date,
|
||
progress=False, # 禁止进度条
|
||
auto_adjust=True, # 自动调整价格（复权）
|
||
)
|
||
|
||
if df.empty:
|
||
raise DataFetchError(f"Yahoo Finance 未查询到 {stock_code} 的数据")
|
||
|
||
return df
|
||
|
||
except Exception as e:
|
||
if isinstance(e, DataFetchError):
|
||
raise
|
||
raise DataFetchError(f"Yahoo Finance 获取数据失败: {e}") from e
|
||
|
||
def _normalize_data(self, df: pd.DataFrame, stock_code: str) -> pd.DataFrame:
|
||
"""
|
||
标准化 Yahoo Finance 数据
|
||
|
||
yfinance 返回的列名：
|
||
Open, High, Low, Close, Volume（索引是日期）
|
||
|
||
需要映射到标准列名：
|
||
date, open, high, low, close, volume, amount, pct_chg
|
||
"""
|
||
df = df.copy()
|
||
|
||
# 重置索引，将日期从索引变为列
|
||
df = df.reset_index()
|
||
|
||
# 列名映射（yfinance 使用首字母大写）
|
||
column_mapping = {
|
||
'Date': 'date',
|
||
'Open': 'open',
|
||
'High': 'high',
|
||
'Low': 'low',
|
||
'Close': 'close',
|
||
'Volume': 'volume',
|
||
}
|
||
|
||
df = df.rename(columns=column_mapping)
|
||
|
||
# 计算涨跌幅（因为 yfinance 不直接提供）
|
||
if 'close' in df.columns:
|
||
df['pct_chg'] = df['close'].pct_change() * 100
|
||
df['pct_chg'] = df['pct_chg'].fillna(0).round(2)
|
||
|
||
# 计算成交额（yfinance 不提供，使用估算值）
|
||
# 成交额 ≈ 成交量 * 平均价格
|
||
if 'volume' in df.columns and 'close' in df.columns:
|
||
df['amount'] = df['volume'] * df['close']
|
||
else:
|
||
df['amount'] = 0
|
||
|
||
# 添加股票代码列
|
||
df['code'] = stock_code
|
||
|
||
# 只保留需要的列
|
||
keep_cols = ['code'] + STANDARD_COLUMNS
|
||
existing_cols = [col for col in keep_cols if col in df.columns]
|
||
df = df[existing_cols]
|
||
|
||
return df
|
||
|
||
|
||
if __name__ == "__main__":
|
||
# 测试代码
|
||
logging.basicConfig(level=logging.DEBUG)
|
||
|
||
fetcher = YfinanceFetcher()
|
||
|
||
try:
|
||
df = fetcher.get_daily_data('600519') # 茅台
|
||
print(f"获取成功，共 {len(df)} 条数据")
|
||
print(df.tail())
|
||
except Exception as e:
|
||
print(f"获取失败: {e}")
|