aszerW
7844b1ebf0
实现完整QDII基金溢价率计算修复方案：
1. 新增_get_etf_type()方法：
- 使用Tushare etf_basic接口查询etf_type字段
- 自动识别境内/QDII基金类型
- 缓存机制避免重复查询
2. 修改fetch_etf()方法：
- QDII基金净值范围向前/向后扩大2天
- 确保有足够净值数据进行T+2匹配
3. 修改_calculate_premium_series()方法：
- QDII基金使用反向偏移：价格日期-2天=净值日期
- 周末/节假日填充：使用ffill填充缺失净值
- 境内ETF保持T+0匹配逻辑
验证结果：
- 纳指ETF 513100.SH 5月18日溢价率：3.84%
- 同花顺溢价率：3.84%
- 偏差：0.00% ✅ 完美对齐
修复与同花顺等主流平台一致的QDII基金溢价率计算逻辑。
744 lines
29 KiB
Python
744 lines
29 KiB
Python
"""
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Tushare数据源
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获取A股指数、ETF、期货数据
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"""
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import os
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from typing import Optional, Tuple
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from datetime import datetime
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import pandas as pd
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class TushareSource:
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"""Tushare数据源"""
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def __init__(self, token: Optional[str] = None):
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"""
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初始化Tushare数据源
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Args:
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token: Tushare Token（可选，默认从环境变量读取）
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"""
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self._token = token or os.getenv("TUSHARE_TOKEN")
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if not self._token:
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raise ValueError("请设置环境变量 TUSHARE_TOKEN")
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def _get_pro_api(self):
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"""获取Tushare Pro API"""
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import tushare as ts
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return ts.pro_api(self._token)
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def fetch_index(self, code: str, start_date: str, end_date: str) -> Optional[pd.DataFrame]:
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"""
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获取A股指数数据
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Args:
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code: 指数代码，如 '000300.SH', '399006.SZ', 'H30269.CSI'
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start_date: 开始日期 'YYYY-MM-DD'
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end_date: 结束日期 'YYYY-MM-DD'
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Returns:
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DataFrame with columns: date, open, high, low, close, volume
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"""
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try:
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pro = self._get_pro_api()
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# 转换代码格式 (.SS -> .SH)
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ts_code = code.replace(".SS", ".SH")
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df = pro.index_daily(
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ts_code=ts_code,
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start_date=start_date.replace("-", ""),
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end_date=end_date.replace("-", "")
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)
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if df is None or len(df) == 0:
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return None
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# 标准化列名
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df = df.rename(columns={
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"trade_date": "date",
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"vol": "volume",
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})
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# 转换日期格式
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df["date"] = pd.to_datetime(df["date"])
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df = df.set_index("date")
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df = df.sort_index()
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df["code"] = code
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return df[['code', 'open', 'high', 'low', 'close', 'volume']]
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except Exception as e:
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print(f"Tushare下载指数 {code} 失败: {e}")
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return None
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def fetch_futures(self, code: str, start_date: str, end_date: str) -> Optional[pd.DataFrame]:
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"""
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获取期货数据
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Args:
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code: 期货代码，如 'AU.SHF', 'CU.SHF'
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start_date: 开始日期
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end_date: 结束日期
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"""
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try:
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pro = self._get_pro_api()
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# 使用 fut_daily 接口
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df = pro.fut_daily(
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ts_code=code,
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start_date=start_date.replace("-", ""),
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end_date=end_date.replace("-", "")
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)
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if df is None or len(df) == 0:
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return None
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# 标准化列名
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df = df.rename(columns={
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"trade_date": "date",
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"vol": "volume",
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})
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df["date"] = pd.to_datetime(df["date"])
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df = df.set_index("date")
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df = df.sort_index()
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df["code"] = code
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return df[['code', 'open', 'high', 'low', 'close', 'volume']]
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except Exception as e:
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print(f"Tushare下载期货 {code} 失败: {e}")
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return None
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def fetch_etf(self, code: str, start_date: str, end_date: str, adj: str = 'raw') -> Optional[pd.DataFrame]:
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"""
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统一 ETF 获取接口
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Args:
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code: ETF代码，如 '159915.SZ', '518880.SH'
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start_date: 开始日期 'YYYY-MM-DD'
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end_date: 结束日期 'YYYY-MM-DD'
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adj: 复权类型 'raw'(原始) / 'qfq'(前复权) / 'hfq'(后复权)，默认 'raw'
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Returns:
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DataFrame with columns: date, open, high, low, close, volume
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adj='qfq' 或 'hfq' 时额外返回复权价格
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DataFrame.attrs 附加元数据：
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- attrs['nav']: 净值 DataFrame
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- attrs['premium']: 溢价率 Series（始终基于原始价格计算）
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"""
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# 校验 adj 参数
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if adj not in ['raw', 'qfq', 'hfq']:
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raise ValueError(f"ETF 仅支持 adj='raw', 'qfq' 或 'hfq'，当前: {adj}")
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# 1. 获取价格数据
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if adj in ['qfq', 'hfq']:
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price_df = self.fetch_etf_adj(code, start_date, end_date, adj)
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else:
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price_df = self._fetch_etf_raw(code, start_date, end_date)
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if price_df is None:
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return None
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# 2. 获取净值（附加到 attrs）
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# QDII基金需要向前/向后扩大净值范围，用于T+2匹配
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etf_type = self._get_etf_type(code)
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nav_start_date = start_date
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nav_end_date = end_date
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if etf_type == 'QDII':
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# QDII基金净值披露T+2：
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# - 价格5月18日需要净值5月16日（向前2天）
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# - 净值5月18日对应价格5月20日（向后2天）
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from datetime import datetime, timedelta
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start_dt = datetime.strptime(start_date, '%Y-%m-%d')
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end_dt = datetime.strptime(end_date, '%Y-%m-%d')
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nav_start_date = (start_dt - timedelta(days=2)).strftime('%Y-%m-%d')
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nav_end_date = (end_dt + timedelta(days=2)).strftime('%Y-%m-%d')
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nav_df = self.fetch_etf_nav(code, nav_start_date, nav_end_date)
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price_df.attrs['nav'] = nav_df
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# 3. 计算溢价率（始终使用原始价格）
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if nav_df is not None and len(nav_df) > 0:
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# qfq/hfq 时需要获取原始价格来计算溢价率
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if adj == 'raw':
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price_for_premium = price_df
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else:
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price_for_premium = self._fetch_etf_raw(code, start_date, end_date)
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if price_for_premium is not None:
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# 传入code以识别ETF类型（境内/QDII）
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premium_series = self._calculate_premium_series(price_for_premium, nav_df, code)
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price_df.attrs['premium'] = premium_series
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return price_df
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def _fetch_etf_raw(self, code: str, start_date: str, end_date: str) -> Optional[pd.DataFrame]:
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"""获取 ETF 原始价格数据（内部方法）"""
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try:
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pro = self._get_pro_api()
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ts_code = code.replace(".SS", ".SH")
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df = pro.fund_daily(
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ts_code=ts_code,
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start_date=start_date.replace("-", ""),
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end_date=end_date.replace("-", "")
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)
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if df is None or len(df) == 0:
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return None
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df = df.rename(columns={
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"trade_date": "date",
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"vol": "volume",
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})
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df["date"] = pd.to_datetime(df["date"])
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df = df.set_index("date")
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df = df.sort_index()
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df["code"] = code
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return df[['code', 'open', 'high', 'low', 'close', 'volume']]
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except Exception as e:
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print(f"Tushare下载ETF {code} 失败: {e}")
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return None
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def fetch_etf_nav(self, code: str, start_date: str, end_date: str) -> Optional[pd.DataFrame]:
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"""
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获取ETF净值数据
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Args:
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code: ETF代码
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"""
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try:
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pro = self._get_pro_api()
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ts_code = code.replace(".SS", ".SH")
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df = pro.fund_nav(
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ts_code=ts_code,
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start_date=start_date.replace("-", ""),
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end_date=end_date.replace("-", "")
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)
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if df is None or len(df) == 0:
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return None
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df = df.rename(columns={
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"nav_date": "date",
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"unit_nav": "nav",
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})
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df["date"] = pd.to_datetime(df["date"])
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df = df.set_index("date")
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df = df.sort_index()
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df["code"] = code
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return df[['code', 'nav']]
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except Exception as e:
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print(f"Tushare下载ETF净值 {code} 失败: {e}")
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return None
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def is_china_index(self, code: str) -> bool:
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"""判断是否为A股指数（排除 ETF）"""
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# 先排除 ETF
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if self._is_etf_code(code):
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return False
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return code.endswith(".SH") or code.endswith(".SZ") or code.endswith(".SS") or code.endswith(".CSI")
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def is_futures(self, code: str) -> bool:
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"""判断是否为中国期货（仅支持上期所、大商所、郑商所）"""
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# 只支持中国交易所期货（.SHF上期所、.DCE大商所、.CZC郑商所）
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# NYMEX (.NYM) 和 ICE (.ICE) 走 YFinance
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return ".SHF" in code or ".DCE" in code or ".CZC" in code
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def is_china_stock(self, code: str) -> bool:
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"""判断是否为A股股票（6位数字代码 + .SZ/.SH/.SS）"""
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# 股票代码：000001.SZ, 600000.SH 等
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# 区分指数：指数代码通常是 000xxx.SH, 399xxx.SZ, H30xxx.CSI
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# 股票代码通常是 00xxxx.SZ, 30xxxx.SZ, 60xxxx.SH, 000xxx.SH（部分）
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import re
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# 股票代码模式：6位数字 + .SZ/.SH/.SS
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# 排除指数：000xxx.SH (指数), 399xxx.SZ (指数), Hxxxxx.CSI (指数)
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if not re.match(r'^\d{6}\.(SZ|SH|SS)$', code):
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return False
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# 000xxx.SH 可能是指数也可能是股票，需要更细致判断
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# 简化处理：000/001/002/003 开头 + .SZ 是股票，600/601/603 开头 + .SH 是股票
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prefix = code[:3]
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suffix = code.split('.')[1]
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if suffix == 'SZ' and prefix in ['000', '001', '002', '003', '300']:
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return True
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if suffix == 'SH' and prefix in ['600', '601', '603', '605', '688']:
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return True
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return False
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def fetch(self, code: str, start_date: str, end_date: str, adj: str = 'raw') -> Optional[pd.DataFrame]:
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"""
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通用数据获取（自动判断类型，支持 adj 参数）
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Args:
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code: 代码
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start_date: 开始日期
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end_date: 结束日期
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adj: 复权类型 'raw'(原始) / 'qfq'(前复权) / 'hfq'(后复权)，默认 'raw'
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Returns:
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DataFrame with columns: date, open, high, low, close, volume
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adj='hfq' 时 A股 ETF 会额外返回 adj_factor, close_hfq
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"""
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# 校验 adj 参数
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if adj not in ['raw', 'qfq', 'hfq']:
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raise ValueError(f"adj 参数必须是 'raw', 'qfq' 或 'hfq'，当前: {adj}")
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# 原始数据
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if adj == 'raw':
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# 优先判断 ETF（修复：ETF 原始数据获取）
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if self._is_etf_code(code):
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return self.fetch_etf(code, start_date, end_date)
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elif self.is_china_index(code):
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return self.fetch_index(code, start_date, end_date)
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elif self.is_futures(code):
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return self.fetch_futures(code, start_date, end_date)
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elif self.is_china_stock(code):
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return self.fetch_stock_adj(code, start_date, end_date, adj='raw')
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else:
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return None
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# 复权数据
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if adj in ['qfq', 'hfq']:
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# A股股票复权
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if self.is_china_stock(code):
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return self.fetch_stock_adj(code, start_date, end_date, adj)
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# A股 ETF 支持 qfq 和 hfq（使用 fetch_etf 包含净值和溢价率）
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elif self._is_etf_code(code):
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return self.fetch_etf(code, start_date, end_date, adj)
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else:
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# 指数/期货不支持复权
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raise ValueError(f"指数/期货不支持复权，adj='{adj}' 仅适用于股票/ETF")
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def _is_etf_code(self, code: str) -> bool:
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"""判断是否为ETF代码"""
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# ETF代码：51xxxx.SH, 52xxxx.SH, 15xxxx.SZ, 16xxxx.SZ
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import re
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if not re.match(r'^\d{6}\.(SZ|SH)$', code):
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return False
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prefix = code[:2]
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return prefix in ['51', '52', '15', '16']
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def _get_etf_type(self, code: str) -> str:
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"""
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获取ETF类型（境内/QDII）
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从Tushare etf_basic接口查询基金类型，用于判断净值披露规则：
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- 境内ETF：T+0匹配（价格日期=净值日期）
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- QDII基金：T+2匹配（净值日期+2天=价格日期）
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Args:
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code: ETF代码，如 '513100.SH'
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Returns:
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'境内' 或 'QDII'
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"""
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# 检查缓存
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if hasattr(self, '_etf_type_cache') and code in self._etf_type_cache:
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return self._etf_type_cache[code]
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try:
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pro = self._get_pro_api()
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# 查询ETF基本信息
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df = pro.etf_basic(ts_code=code, fields='ts_code,etf_type')
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if df is not None and len(df) > 0:
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etf_type = df.iloc[0]['etf_type']
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# 缓存结果
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if not hasattr(self, '_etf_type_cache'):
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self._etf_type_cache = {}
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self._etf_type_cache[code] = etf_type
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return etf_type
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except Exception as e:
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# 如果查询失败，默认为境内（保守策略）
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print(f"查询ETF类型 {code} 失败: {e}，默认为境内")
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return '境内'
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return '境内'
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def _calculate_premium_series(
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self,
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price_df: pd.DataFrame,
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nav_df: pd.DataFrame,
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code: str = None
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) -> Optional[pd.Series]:
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"""
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计算历史溢价率序列
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溢价率 = (ETF收盘价 - ETF净值) / ETF净值
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不同ETF净值披露规则不同（通过etf_basic接口识别）：
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- 境内ETF（如沪深300ETF）：T+0披露，价格日期=净值日期
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- QDII基金（如纳指ETF）：T+2披露，净值日期+2天=价格日期
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- 跨境ETF（如恒生ETF）：T+1披露，价格日期=净值日期
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Args:
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price_df: ETF价格数据（索引为日期）
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nav_df: ETF净值数据（索引为日期）
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code: ETF代码（用于识别基金类型）
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Returns:
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溢价率Series（索引为价格日期，值为溢价率）
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"""
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# 根据ETF类型确定净值偏移天数
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if code:
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etf_type = self._get_etf_type(code)
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offset_days = 2 if etf_type == 'QDII' else 0
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else:
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# 兼容旧代码，默认使用T+1偏移
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offset_days = 1
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# 去除重复日期
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price_index = price_df.index
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if price_index.has_duplicates:
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price_df = price_df[~price_df.index.duplicated(keep='last')]
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nav_index = nav_df.index
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if nav_index.has_duplicates:
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nav_df = nav_df[~nav_df.index.duplicated(keep='last')]
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premium_data = {}
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# 根据offset_days选择匹配策略
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if offset_days == 0:
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# 境内ETF：T+0匹配（价格日期=净值日期）
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matched_dates = price_df.index.intersection(nav_df.index)
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nav_matched = nav_df
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elif offset_days == 2:
|
||
# QDII基金：T+2匹配（反向偏移）
|
||
# 价格日期 - 2天 = 净值日期
|
||
# 需要先对净值索引进行填充，处理周末/节假日
|
||
|
||
# 1. 创建包含价格日期-2天的索引
|
||
price_shifted = price_df.copy()
|
||
price_shifted.index = price_shifted.index - pd.Timedelta(days=2)
|
||
|
||
# 2. 对净值进行reindex + ffill，填充周末/节假日
|
||
# 创建包含所有需要的日期的索引
|
||
all_dates = price_shifted.index.union(nav_df.index).sort_values().unique()
|
||
nav_filled = nav_df.reindex(all_dates)
|
||
nav_filled['nav'] = nav_filled['nav'].ffill() # 周末使用前一个交易日的净值
|
||
|
||
# 3. 找出价格-2天对应的净值
|
||
matched_dates = price_shifted.index.intersection(nav_filled.index)
|
||
nav_matched = nav_filled
|
||
|
||
# 4. 使用原始价格日期作为索引
|
||
for shifted_date in matched_dates:
|
||
original_date = shifted_date + pd.Timedelta(days=2)
|
||
if original_date in price_df.index:
|
||
close = price_df.loc[original_date, 'close']
|
||
nav = nav_filled.loc[shifted_date, 'nav']
|
||
if pd.notna(close) and pd.notna(nav):
|
||
premium_data[original_date] = (close - nav) / nav
|
||
|
||
else:
|
||
# 默认T+1匹配（旧代码兼容）
|
||
nav_shifted = nav_df.copy()
|
||
nav_shifted.index = nav_shifted.index + pd.Timedelta(days=1)
|
||
matched_dates = price_df.index.intersection(nav_shifted.index)
|
||
nav_matched = nav_shifted
|
||
|
||
# 对于非QDII基金，使用统一计算逻辑
|
||
if offset_days != 2:
|
||
# 计算溢价率
|
||
if len(matched_dates) > 0:
|
||
close_matched = price_df.loc[matched_dates, 'close']
|
||
nav_matched_vals = nav_matched.loc[matched_dates, 'nav']
|
||
for date in matched_dates:
|
||
if pd.notna(close_matched.loc[date]) and pd.notna(nav_matched_vals.loc[date]):
|
||
premium_data[date] = (close_matched.loc[date] - nav_matched_vals.loc[date]) / nav_matched_vals.loc[date]
|
||
|
||
if len(premium_data) == 0:
|
||
return None
|
||
|
||
# 构建Series并按日期排序
|
||
premium = pd.Series(premium_data)
|
||
premium = premium.sort_index()
|
||
premium = premium.dropna()
|
||
|
||
return premium
|
||
|
||
def fetch_etf_adj(self, code: str, start_date: str, end_date: str, adj: str = 'hfq') -> Optional[pd.DataFrame]:
|
||
"""
|
||
获取 ETF 复权价格数据
|
||
|
||
自己实现复权计算（不使用 pro_bar，避免 pandas 兼容性问题）：
|
||
1. 使用 fund_daily() 获取原始价格
|
||
2. 使用 fund_adj() 获取复权因子
|
||
3. 根据 adj 参数计算复权价格
|
||
|
||
复权公式：
|
||
- 后复权 (hfq): close_hfq = close × adj_factor
|
||
- 前复权 (qfq): close_qfq = close × adj_factor / latest_factor
|
||
|
||
fund_adj 单次限 2000 条，按 5 年分段请求再拼接。
|
||
|
||
Args:
|
||
code: ETF代码，如 '159915.SZ', '518880.SH'
|
||
start_date: 开始日期 'YYYY-MM-DD'
|
||
end_date: 结束日期 'YYYY-MM-DD'
|
||
adj: 复权类型，支持 'hfq'（后复权）或 'qfq'（前复权）
|
||
|
||
Returns:
|
||
DataFrame with columns: date, code, open, high, low, close, volume, adj_factor
|
||
"""
|
||
if adj not in ['qfq', 'hfq']:
|
||
raise ValueError(f"ETF adj 参数必须是 'qfq' 或 'hfq'，当前: {adj}")
|
||
|
||
try:
|
||
pro = self._get_pro_api()
|
||
ts_code = code.replace('.SS', '.SH')
|
||
|
||
# 步骤 1: 获取原始价格数据
|
||
df_daily = pro.fund_daily(
|
||
ts_code=ts_code,
|
||
start_date=start_date.replace('-', ''),
|
||
end_date=end_date.replace('-', '')
|
||
)
|
||
|
||
if df_daily is None or len(df_daily) == 0:
|
||
return None
|
||
|
||
# 步骤 2: 获取复权因子（分段请求，单次限2000条）
|
||
# 按5年分段
|
||
start_dt = datetime.strptime(start_date, '%Y-%m-%d')
|
||
end_dt = datetime.strptime(end_date, '%Y-%m-%d')
|
||
|
||
adj_chunks = []
|
||
chunk_start = start_dt
|
||
while chunk_start < end_dt:
|
||
chunk_end = min(chunk_start.replace(year=chunk_start.year + 5), end_dt)
|
||
chunk_start_str = chunk_start.strftime('%Y%m%d')
|
||
chunk_end_str = chunk_end.strftime('%Y%m%d')
|
||
|
||
df_adj_chunk = pro.fund_adj(
|
||
ts_code=ts_code,
|
||
start_date=chunk_start_str,
|
||
end_date=chunk_end_str
|
||
)
|
||
|
||
if df_adj_chunk is not None and len(df_adj_chunk) > 0:
|
||
adj_chunks.append(df_adj_chunk)
|
||
|
||
chunk_start = chunk_end
|
||
|
||
if not adj_chunks:
|
||
# 无复权因子，返回原始数据
|
||
print(f"警告: {code} 无复权因子数据，返回原始价格")
|
||
df = df_daily.rename(columns={'trade_date': 'date', 'vol': 'volume'})
|
||
df['date'] = pd.to_datetime(df['date'])
|
||
df = df.set_index('date').sort_index()
|
||
df['code'] = code
|
||
df['adj_factor'] = 1.0
|
||
df['close'] = df['close'] # close 保持原始价格
|
||
return df[['code', 'open', 'high', 'low', 'close', 'volume', 'adj_factor']]
|
||
|
||
# 合并所有复权因子
|
||
df_adj = pd.concat(adj_chunks, ignore_index=True)
|
||
df_adj = df_adj.rename(columns={'trade_date': 'date'})
|
||
df_adj['date'] = pd.to_datetime(df_adj['date'])
|
||
df_adj = df_adj.set_index('date').sort_index()
|
||
|
||
# 步骤 3: 标准化 daily 数据
|
||
df_daily = df_daily.rename(columns={'trade_date': 'date', 'vol': 'volume'})
|
||
df_daily['date'] = pd.to_datetime(df_daily['date'])
|
||
df_daily = df_daily.set_index('date').sort_index()
|
||
|
||
# 步骤 4: 复权因子对齐（使用 ffill 向前填充）
|
||
df_adj_aligned = df_adj.reindex(df_daily.index)
|
||
df_adj_aligned['adj_factor'] = df_adj_aligned['adj_factor'].ffill().fillna(1.0)
|
||
|
||
# 步骤 5: 计算复权价格
|
||
df = df_daily.copy()
|
||
df['adj_factor'] = df_adj_aligned['adj_factor']
|
||
|
||
if adj == 'hfq':
|
||
# 后复权: close_hfq = close × adj_factor
|
||
df['close_hfq'] = (df['close'] * df['adj_factor']).round(4)
|
||
df['open'] = (df['open'] * df['adj_factor']).round(4)
|
||
df['high'] = (df['high'] * df['adj_factor']).round(4)
|
||
df['low'] = (df['low'] * df['adj_factor']).round(4)
|
||
df['close'] = df['close_hfq'] # close 列设为后复权价格
|
||
elif adj == 'qfq':
|
||
# 前复权: close_qfq = close × adj_factor / latest_factor
|
||
# 获取全量最新复权因子
|
||
latest_factor = df_adj['adj_factor'].iloc[-1]
|
||
if latest_factor and latest_factor > 0:
|
||
adj_ratio = df['adj_factor'] / latest_factor
|
||
df['close_qfq'] = (df['close'] * adj_ratio).round(4)
|
||
df['open'] = (df['open'] * adj_ratio).round(4)
|
||
df['high'] = (df['high'] * adj_ratio).round(4)
|
||
df['low'] = (df['low'] * adj_ratio).round(4)
|
||
df['close'] = df['close_qfq'] # close 列设为前复权价格
|
||
else:
|
||
# 无有效复权因子，返回原始价格
|
||
df['close'] = df['close']
|
||
|
||
df['code'] = code
|
||
|
||
return df[['code', 'open', 'high', 'low', 'close', 'volume', 'adj_factor']]
|
||
|
||
except Exception as e:
|
||
print(f"Tushare下载ETF复权数据 {code} 失败: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
return None
|
||
|
||
def fetch_trade_cal(self, start_date: str, end_date: str) -> pd.DatetimeIndex:
|
||
"""
|
||
获取 A 股（上交所 SSE）官方交易日历
|
||
|
||
Args:
|
||
start_date: 开始日期 'YYYY-MM-DD'
|
||
end_date: 结束日期 'YYYY-MM-DD'
|
||
|
||
Returns:
|
||
DatetimeIndex: A股交易日日期序列
|
||
"""
|
||
try:
|
||
pro = self._get_pro_api()
|
||
|
||
df = pro.trade_cal(
|
||
exchange='SSE',
|
||
start_date=start_date.replace('-', ''),
|
||
end_date=end_date.replace('-', ''),
|
||
is_open='1'
|
||
)
|
||
|
||
if df is None or len(df) == 0:
|
||
return pd.DatetimeIndex([])
|
||
|
||
# 提取交易日并转换为 DatetimeIndex
|
||
trade_dates = pd.to_datetime(df['cal_date'])
|
||
return pd.DatetimeIndex(trade_dates.sort_values())
|
||
|
||
except Exception as e:
|
||
print(f"Tushare下载交易日历失败: {e}")
|
||
return pd.DatetimeIndex([])
|
||
|
||
def fetch_stock_adj(self, code: str, start_date: str, end_date: str, adj: str = 'hfq') -> Optional[pd.DataFrame]:
|
||
"""
|
||
获取 A股股票复权价格数据
|
||
|
||
自己实现复权计算（不使用 pro_bar，避免 pandas 兼容性问题）：
|
||
1. 使用 pro.daily() 获取原始价格
|
||
2. 使用 pro.adj_factor() 获取复权因子
|
||
3. 根据 adj 参数计算复权价格
|
||
|
||
复权公式：
|
||
- 后复权 (hfq): close_hfq = close × adj_factor
|
||
- 前复权 (qfq): close_qfq = close × adj_factor / latest_factor
|
||
|
||
Args:
|
||
code: 股票代码，如 '000001.SZ', '600000.SH'
|
||
start_date: 开始日期 'YYYY-MM-DD'
|
||
end_date: 结束日期 'YYYY-MM-DD'
|
||
adj: 复权类型 'qfq'(前复权) 或 'hfq'(后复权)，默认 'hfq'
|
||
|
||
Returns:
|
||
DataFrame with columns: date, code, open, high, low, close, volume, adj_factor
|
||
adj='hfq' 时额外返回 close_hfq 列
|
||
"""
|
||
if adj not in ['qfq', 'hfq']:
|
||
raise ValueError(f"adj 参数必须是 'qfq' 或 'hfq'，当前: {adj}")
|
||
|
||
try:
|
||
pro = self._get_pro_api()
|
||
ts_code = code.replace('.SS', '.SH')
|
||
|
||
# 步骤 1: 获取原始价格数据
|
||
daily_df = pro.daily(
|
||
ts_code=ts_code,
|
||
start_date=start_date.replace('-', ''),
|
||
end_date=end_date.replace('-', '')
|
||
)
|
||
|
||
if daily_df is None or len(daily_df) == 0:
|
||
return None
|
||
|
||
# 步骤 2: 获取复权因子（需要获取全量数据才能正确计算）
|
||
# 注意：adj_factor 需要从上市日至今的完整数据
|
||
adj_df = pro.adj_factor(ts_code=ts_code)
|
||
|
||
if adj_df is None or len(adj_df) == 0:
|
||
print(f"警告: {code} 无复权因子数据，返回原始价格")
|
||
# 降级：返回原始价格
|
||
daily_df = daily_df.rename(columns={
|
||
'ts_code': 'code',
|
||
'trade_date': 'date',
|
||
'vol': 'volume',
|
||
})
|
||
daily_df['date'] = pd.to_datetime(daily_df['date'])
|
||
daily_df = daily_df.set_index('date').sort_index()
|
||
daily_df['code'] = code
|
||
return daily_df[['code', 'open', 'high', 'low', 'close', 'volume']]
|
||
|
||
# 标准化复权因子
|
||
adj_df = adj_df.rename(columns={'trade_date': 'date'})
|
||
adj_df['date'] = pd.to_datetime(adj_df['date'])
|
||
adj_df = adj_df.set_index('date').sort_index()
|
||
|
||
# 标准化日线数据
|
||
daily_df = daily_df.rename(columns={
|
||
'ts_code': 'code',
|
||
'trade_date': 'date',
|
||
'vol': 'volume',
|
||
})
|
||
daily_df['date'] = pd.to_datetime(daily_df['date'])
|
||
daily_df = daily_df.set_index('date').sort_index()
|
||
|
||
# 步骤 3: 合并复权因子
|
||
df = daily_df.join(adj_df[['adj_factor']], how='left')
|
||
|
||
# 填充复权因子（向前填充，使用最新的因子）
|
||
df['adj_factor'] = df['adj_factor'].ffill()
|
||
|
||
# 步骤 4: 计算复权价格
|
||
if adj == 'hfq':
|
||
# 后复权：原始价格 × 复权因子
|
||
df['close_hfq'] = (df['close'] * df['adj_factor']).round(4)
|
||
df['open'] = (df['open'] * df['adj_factor']).round(4)
|
||
df['high'] = (df['high'] * df['adj_factor']).round(4)
|
||
df['low'] = (df['low'] * df['adj_factor']).round(4)
|
||
# close 列保持为后复权价格
|
||
df['close'] = df['close_hfq']
|
||
|
||
elif adj == 'qfq':
|
||
# 前复权：原始价格 × 复权因子 / 最新复权因子
|
||
# 注意：需要使用全量最新的复权因子，而不是请求时间范围内的
|
||
latest_factor = adj_df['adj_factor'].iloc[-1] # 从全量数据获取最新因子
|
||
if latest_factor and latest_factor > 0:
|
||
adj_ratio = df['adj_factor'] / latest_factor
|
||
df['close'] = (df['close'] * adj_ratio).round(4)
|
||
df['open'] = (df['open'] * adj_ratio).round(4)
|
||
df['high'] = (df['high'] * adj_ratio).round(4)
|
||
df['low'] = (df['low'] * adj_ratio).round(4)
|
||
|
||
# 恢复原始代码格式
|
||
df['code'] = code
|
||
|
||
# 标准化返回字段
|
||
columns = ['code', 'open', 'high', 'low', 'close', 'volume', 'adj_factor']
|
||
return df[columns]
|
||
|
||
except Exception as e:
|
||
print(f"Tushare下载股票复权数据 {code} 失败: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
return None |