etf/datasource/tushare_source.py

"""
Tushare数据源

获取A股指数、ETF、期货数据
"""

import os
from typing import Optional
from datetime import datetime
import pandas as pd


class TushareSource:
    """Tushare数据源"""

    def __init__(self, token: Optional[str] = None):
        """
        初始化Tushare数据源
        
        Args:
            token: Tushare Token（可选，默认从环境变量读取）
        """
        self._token = token or os.getenv("TUSHARE_TOKEN")
        if not self._token:
            raise ValueError("请设置环境变量 TUSHARE_TOKEN")

    def _get_pro_api(self):
        """获取Tushare Pro API"""
        import tushare as ts
        return ts.pro_api(self._token)

    def fetch_index(self, code: str, start_date: str, end_date: str) -> Optional[pd.DataFrame]:
        """
        获取A股指数数据
        
        Args:
            code: 指数代码，如 '000300.SH', '399006.SZ', 'H30269.CSI'
            start_date: 开始日期 'YYYY-MM-DD'
            end_date: 结束日期 'YYYY-MM-DD'
        
        Returns:
            DataFrame with columns: date, open, high, low, close, volume
        """
        try:
            pro = self._get_pro_api()
            
            # 转换代码格式 (.SS -> .SH)
            ts_code = code.replace(".SS", ".SH")
            
            df = pro.index_daily(
                ts_code=ts_code,
                start_date=start_date.replace("-", ""),
                end_date=end_date.replace("-", "")
            )
            
            if df is None or len(df) == 0:
                return None
            
            # 标准化列名
            df = df.rename(columns={
                "trade_date": "date",
                "vol": "volume",
            })
            
            # 转换日期格式
            df["date"] = pd.to_datetime(df["date"])
            df = df.set_index("date")
            df = df.sort_index()
            df["code"] = code
            
            return df[['code', 'open', 'high', 'low', 'close', 'volume']]
            
        except Exception as e:
            print(f"Tushare下载指数 {code} 失败: {e}")
            return None

    def fetch_futures(self, code: str, start_date: str, end_date: str) -> Optional[pd.DataFrame]:
        """
        获取期货数据
        
        Args:
            code: 期货代码，如 'AU.SHF', 'CU.SHF'
            start_date: 开始日期
            end_date: 结束日期
        """
        try:
            pro = self._get_pro_api()
            
            # 使用 fut_daily 接口
            df = pro.fut_daily(
                ts_code=code,
                start_date=start_date.replace("-", ""),
                end_date=end_date.replace("-", "")
            )
            
            if df is None or len(df) == 0:
                return None
            
            # 标准化列名
            df = df.rename(columns={
                "trade_date": "date",
                "vol": "volume",
            })
            
            df["date"] = pd.to_datetime(df["date"])
            df = df.set_index("date")
            df = df.sort_index()
            df["code"] = code
            
            return df[['code', 'open', 'high', 'low', 'close', 'volume']]
            
        except Exception as e:
            print(f"Tushare下载期货 {code} 失败: {e}")
            return None

    def fetch_etf(self, code: str, start_date: str, end_date: str) -> Optional[pd.DataFrame]:
        """
        获取ETF价格数据
        
        Args:
            code: ETF代码，如 '159915.SZ', '518880.SH'
        """
        try:
            pro = self._get_pro_api()
            
            ts_code = code.replace(".SS", ".SH")
            
            df = pro.fund_daily(
                ts_code=ts_code,
                start_date=start_date.replace("-", ""),
                end_date=end_date.replace("-", "")
            )
            
            if df is None or len(df) == 0:
                return None
            
            df = df.rename(columns={
                "trade_date": "date",
                "vol": "volume",
            })
            
            df["date"] = pd.to_datetime(df["date"])
            df = df.set_index("date")
            df = df.sort_index()
            df["code"] = code
            
            return df[['code', 'open', 'high', 'low', 'close', 'volume']]
            
        except Exception as e:
            print(f"Tushare下载ETF {code} 失败: {e}")
            return None

    def fetch_etf_nav(self, code: str, start_date: str, end_date: str) -> Optional[pd.DataFrame]:
        """
        获取ETF净值数据
        
        Args:
            code: ETF代码
        """
        try:
            pro = self._get_pro_api()
            
            ts_code = code.replace(".SS", ".SH")
            
            df = pro.fund_nav(
                ts_code=ts_code,
                start_date=start_date.replace("-", ""),
                end_date=end_date.replace("-", "")
            )
            
            if df is None or len(df) == 0:
                return None
            
            df = df.rename(columns={
                "nav_date": "date",
                "unit_nav": "nav",
            })
            
            df["date"] = pd.to_datetime(df["date"])
            df = df.set_index("date")
            df = df.sort_index()
            df["code"] = code
            
            return df[['code', 'nav']]
            
        except Exception as e:
            print(f"Tushare下载ETF净值 {code} 失败: {e}")
            return None

    def is_china_index(self, code: str) -> bool:
        """判断是否为A股指数（排除 ETF）"""
        # 先排除 ETF
        if self._is_etf_code(code):
            return False
        return code.endswith(".SH") or code.endswith(".SZ") or code.endswith(".SS") or code.endswith(".CSI")

    def is_futures(self, code: str) -> bool:
        """判断是否为中国期货（仅支持上期所、大商所、郑商所）"""
        # 只支持中国交易所期货（.SHF上期所、.DCE大商所、.CZC郑商所）
        # NYMEX (.NYM) 和 ICE (.ICE) 走 YFinance
        return ".SHF" in code or ".DCE" in code or ".CZC" in code
    
    def is_china_stock(self, code: str) -> bool:
        """判断是否为A股股票（6位数字代码 + .SZ/.SH/.SS）"""
        # 股票代码：000001.SZ, 600000.SH 等
        # 区分指数：指数代码通常是 000xxx.SH, 399xxx.SZ, H30xxx.CSI
        # 股票代码通常是 00xxxx.SZ, 30xxxx.SZ, 60xxxx.SH, 000xxx.SH（部分）
        import re
        # 股票代码模式：6位数字 + .SZ/.SH/.SS
        # 排除指数：000xxx.SH (指数), 399xxx.SZ (指数), Hxxxxx.CSI (指数)
        if not re.match(r'^\d{6}\.(SZ|SH|SS)$', code):
            return False
        # 000xxx.SH 可能是指数也可能是股票，需要更细致判断
        # 简化处理：000/001/002/003 开头 + .SZ 是股票，600/601/603 开头 + .SH 是股票
        prefix = code[:3]
        suffix = code.split('.')[1]
        if suffix == 'SZ' and prefix in ['000', '001', '002', '003', '300']:
            return True
        if suffix == 'SH' and prefix in ['600', '601', '603', '605', '688']:
            return True
        return False

    def fetch(self, code: str, start_date: str, end_date: str, adj: str = 'raw') -> Optional[pd.DataFrame]:
        """
        通用数据获取（自动判断类型，支持 adj 参数）
        
        Args:
            code: 代码
            start_date: 开始日期
            end_date: 结束日期
            adj: 复权类型 'raw'(原始) / 'qfq'(前复权) / 'hfq'(后复权)，默认 'raw'
        
        Returns:
            DataFrame with columns: date, open, high, low, close, volume
            adj='hfq' 时 A股 ETF 会额外返回 adj_factor, close_hfq
        """
        # 校验 adj 参数
        if adj not in ['raw', 'qfq', 'hfq']:
            raise ValueError(f"adj 参数必须是 'raw', 'qfq' 或 'hfq'，当前: {adj}")
        
        # 原始数据
        if adj == 'raw':
            # 优先判断 ETF（修复：ETF 原始数据获取）
            if self._is_etf_code(code):
                return self.fetch_etf(code, start_date, end_date)
            elif self.is_china_index(code):
                return self.fetch_index(code, start_date, end_date)
            elif self.is_futures(code):
                return self.fetch_futures(code, start_date, end_date)
            elif self.is_china_stock(code):
                return self.fetch_stock_adj(code, start_date, end_date, adj='raw')
            else:
                return None
        
        # 复权数据
        if adj in ['qfq', 'hfq']:
            # A股股票复权
            if self.is_china_stock(code):
                return self.fetch_stock_adj(code, start_date, end_date, adj)
            # A股 ETF 仅支持 hfq
            elif self._is_etf_code(code):
                if adj == 'hfq':
                    return self.fetch_etf_adj(code, start_date, end_date)
                else:
                    raise ValueError(f"ETF 仅支持 adj='hfq'（后复权），当前: {adj}")
            else:
                # 指数/期货不支持复权
                raise ValueError(f"指数/期货不支持复权，adj='{adj}' 仅适用于股票/ETF")
    
    def _is_etf_code(self, code: str) -> bool:
        """判断是否为ETF代码"""
        # ETF代码：51xxxx.SH, 52xxxx.SH, 15xxxx.SZ, 16xxxx.SZ
        import re
        if not re.match(r'^\d{6}\.(SZ|SH)$', code):
            return False
        prefix = code[:2]
        return prefix in ['51', '52', '15', '16']

    def fetch_etf_adj(self, code: str, start_date: str, end_date: str) -> Optional[pd.DataFrame]:
        """
        获取 ETF 后复权价格数据
        
        通过 fund_daily + fund_adj 手动计算后复权价格，消除份额折算（拆分）对收益率的影响。
        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'
        
        Returns:
            DataFrame with columns: date, open, close, adj_factor, close_hfq
        """
        try:
            pro = self._get_pro_api()
            ts_code = code.replace('.SS', '.SH')
            
            # 获取 fund_daily 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
            
            # 获取 fund_adj 数据（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
                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['adj_factor'] = 1.0
                df['close_hfq'] = df['close']
                df['code'] = code
                return df[['code', 'open', 'close', 'adj_factor', 'close_hfq']]
            
            # 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()
            
            # 合并 daily 和 adj
            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()
            
            # 复权因子对齐（用最新值）
            df_adj_aligned = df_adj.reindex(df_daily.index, method='ffill')
            df_adj_aligned['adj_factor'] = df_adj_aligned['adj_factor'].fillna(1.0)
            
            # 计算后复权价格
            df = df_daily.copy()
            df['adj_factor'] = df_adj_aligned['adj_factor']
            df['close_hfq'] = df['close'] * df['adj_factor']
            df['code'] = code
            
            return df[['code', 'open', 'close', 'adj_factor', 'close_hfq']]
            
        except Exception as e:
            print(f"Tushare下载ETF复权数据 {code} 失败: {e}")
            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
        
        使用 pro_bar 接口获取前复权(qfq)或后复权(hfq)价格。
        
        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
        """
        import tushare as ts
        
        if adj not in ['qfq', 'hfq']:
            raise ValueError(f"adj 参数必须是 'qfq' 或 'hfq'，当前: {adj}")
        
        try:
            ts_code = code.replace('.SS', '.SH')
            
            # 使用 pro_bar 接口获取复权数据
            df = ts.pro_bar(
                ts_code=ts_code,
                adj=adj,
                start_date=start_date.replace('-', ''),
                end_date=end_date.replace('-', ''),
                adjfactor=True  # 返回复权因子
            )
            
            if df is None or len(df) == 0:
                return None
            
            # 标准化列名
            df = df.rename(columns={
                'ts_code': 'code',
                'trade_date': 'date',
                'vol': 'volume',
            })
            
            # 转换日期格式
            df['date'] = pd.to_datetime(df['date'])
            df = df.set_index('date')
            df = df.sort_index()
            
            # 恢复原始代码格式（.SS -> .SH 反转）
            df['code'] = code
            
            # 标准化返回字段
            columns = ['code', 'open', 'high', 'low', 'close', 'volume']
            if 'adj_factor' in df.columns:
                columns.append('adj_factor')
            
            return df[columns]
            
        except Exception as e:
            print(f"Tushare下载股票复权数据 {code} 失败: {e}")
            return None