"""
统一数据获取器

分层架构：对外统一接口，对内按资产类型独立实现
支持：A股指数/ETF、港股指数、美股指数、期货、加密货币

用法:
    from datasource import UniversalDataFetcher
    
    fetcher = UniversalDataFetcher()
    
    # 单标的获取（自动识别类型）
    df = fetcher.fetch("000300.SH", "2024-01-01", "2024-12-31")
    
    # ETF获取（含净值）
    price_df, nav_df = fetcher.fetch_etf_with_nav("513100.SH", "2024-01-01", "2024-12-31")
    
    # 批量获取
    results = fetcher.fetch_batch(["000300.SH", "NDX", "N225"], "2024-01-01", "2024-12-31")
"""

import os
import time
from typing import Optional, Dict, List, Tuple
from datetime import datetime
import pandas as pd

from .tushare_source import TushareSource
from .yfinance_source import YFinanceSource
from .ssh_tunnel import SSHTunnelManager
from .asset_type_detector import AssetTypeDetector, AssetType


class UniversalDataFetcher:
    """
    统一数据获取器
    
    分层架构：
    - 对外：统一 fetch() 接口，自动路由
    - 对内：各资产类型独立方法，职责单一
    """
    
    def __init__(
        self,
        ssh_config: Optional[Dict] = None,
        use_cache: bool = True,
        cache_dir: str = "data/etf_cache/daily"
    ):
        """
        初始化
        
        Args:
            ssh_config: SSH隧道配置（用于港美股）
            use_cache: 是否使用本地缓存
            cache_dir: 缓存目录
        """
        self.ssh_config = ssh_config or {}
        self.use_cache = use_cache
        self.cache_dir = cache_dir
        
        # 数据源实例
        self._tushare = TushareSource()
        self._yfinance = YFinanceSource()
        
        # SSH隧道（延迟初始化）
        self._tunnel: Optional[SSHTunnelManager] = None
        self._tunnel_started = False
    
    def __enter__(self):
        """上下文管理器入口"""
        self._start_tunnel()
        return self
    
    def __exit__(self, exc_type, exc_val, exc_tb):
        """上下文管理器退出"""
        self._stop_tunnel()
    
    # ============================================================
    # SSH隧道管理
    # ============================================================
    
    def _start_tunnel(self) -> bool:
        """启动SSH隧道"""
        if self._tunnel_started:
            return True
        
        if self.ssh_config.get('enabled'):
            self._tunnel = SSHTunnelManager(self.ssh_config)
            if self._tunnel.start():
                self._tunnel_started = True
                return True
            return False
        return True
    
    def _stop_tunnel(self):
        """停止SSH隧道"""
        if self._tunnel:
            self._tunnel.stop()
            self._tunnel = None
            self._tunnel_started = False
    
    # ============================================================
    # 统一入口（自动路由）
    # ============================================================
    
    def fetch(
        self,
        code: str,
        start_date: str,
        end_date: str,
        retry: int = 3
    ) -> Optional[pd.DataFrame]:
        """
        统一数据获取入口
        
        自动识别资产类型并路由到对应方法
        
        Args:
            code: 标的代码
            start_date: 开始日期 'YYYY-MM-DD'
            end_date: 结束日期 'YYYY-MM-DD'
            retry: 重试次数
        
        Returns:
            DataFrame with columns: date, open, high, low, close, volume
        """
        asset_type = AssetTypeDetector.detect(code)
        
        for attempt in range(retry):
            try:
                # 路由到具体方法
                if asset_type == AssetType.CHINA_INDEX:
                    return self._fetch_china_index(code, start_date, end_date)
                elif asset_type == AssetType.CHINA_ETF:
                    return self._fetch_china_etf(code, start_date, end_date)
                elif asset_type == AssetType.US_INDEX:
                    return self._fetch_us_index(code, start_date, end_date)
                elif asset_type == AssetType.HK_INDEX:
                    return self._fetch_hk_index(code, start_date, end_date)
                elif asset_type == AssetType.FUTURES:
                    return self._fetch_futures(code, start_date, end_date)
                elif asset_type == AssetType.CRYPTO:
                    return self._fetch_crypto(code, start_date, end_date)
                else:
                    print(f"⚠️ 未知资产类型: {code} -> {asset_type}")
                    return None
                    
            except Exception as e:
                if attempt < retry - 1:
                    time.sleep(2)
                else:
                    print(f"✗ 获取 {code} 失败 (尝试 {attempt+1}/{retry}): {e}")
                    return None
        
        return None
    
    # ============================================================
    # 分层实现：各资产类型独立方法
    # ============================================================
    
    def _fetch_china_index(
        self,
        code: str,
        start_date: str,
        end_date: str
    ) -> Optional[pd.DataFrame]:
        """
        获取A股指数
        
        特点：Tushare API，无需SSH隧道
        """
        return self._tushare.fetch_index(code, start_date, end_date)
    
    def _fetch_china_etf(
        self,
        code: str,
        start_date: str,
        end_date: str
    ) -> Optional[pd.DataFrame]:
        """
        获取A股ETF价格
        
        特点：Tushare fund_daily接口
        """
        return self._tushare.fetch_etf(code, start_date, end_date)
    
    def fetch_etf_with_nav(
        self,
        code: str,
        start_date: str,
        end_date: str
    ) -> Tuple[Optional[pd.DataFrame], Optional[pd.DataFrame], Optional[pd.Series]]:
        """
        获取ETF价格 + 净值 + 溢价率序列
        
        计算每一天的溢价率，用于分析溢价率走势
        
        Args:
            code: ETF代码
            start_date: 开始日期
            end_date: 结束日期
        
        Returns:
            (price_df, nav_df, premium_series)
            - price_df: ETF价格数据 (OHLCV)
            - nav_df: ETF净值数据
            - premium_series: 溢价率序列 (每天计算)
        """
        price_df = self._tushare.fetch_etf(code, start_date, end_date)
        nav_df = self._tushare.fetch_etf_nav(code, start_date, end_date)
        
        # 计算历史溢价率序列
        premium_series = None
        if price_df is not None and nav_df is not None and len(nav_df) > 0:
            premium_series = self._calculate_premium_series(price_df, nav_df)
        
        return price_df, nav_df, premium_series
    
    def _calculate_premium_series(
        self,
        price_df: pd.DataFrame,
        nav_df: pd.DataFrame
    ) -> Optional[pd.Series]:
        """
        计算历史溢价率序列
        
        溢价率 = (ETF收盘价 - ETF净值) / ETF净值
        
        注意：净值数据通常T+1公布，需要处理日期对齐问题
        
        Args:
            price_df: ETF价格数据（索引为日期）
            nav_df: ETF净值数据（索引为日期）
        
        Returns:
            溢价率Series（索引为日期，值为溢价率）
        """
        # 对齐日期：净值用ffill填充（因为T+1公布）
        # 价格日期可能比净值日期多一天
        aligned_nav = nav_df['nav'].reindex(price_df.index, method='ffill')
        
        # 计算溢价率
        close_prices = price_df['close']
        premium = (close_prices - aligned_nav) / aligned_nav
        
        # 过滤掉无效值（净值缺失的日期）
        premium = premium.dropna()
        
        return premium
    
    def _fetch_us_index(
        self,
        code: str,
        start_date: str,
        end_date: str
    ) -> Optional[pd.DataFrame]:
        """
        获取美股指数
        
        特点：YFinance，需要SSH隧道，指数代码转换
        """
        self._start_tunnel()
        return self._yfinance.fetch(code, start_date, end_date)
    
    def _fetch_hk_index(
        self,
        code: str,
        start_date: str,
        end_date: str
    ) -> Optional[pd.DataFrame]:
        """
        获取港股指数
        
        特点：YFinance，需要SSH隧道
        """
        self._start_tunnel()
        return self._yfinance.fetch(code, start_date, end_date)
    
    def _fetch_futures(
        self,
        code: str,
        start_date: str,
        end_date: str
    ) -> Optional[pd.DataFrame]:
        """
        获取期货数据
        
        特点：
        - 中国期货(.SHF/.DCE/.CZC): Tushare
        - NYMEX(.NYM): YFinance
        """
        if code.endswith('.NYM'):
            # NYMEX期货走YFinance
            self._start_tunnel()
            return self._yfinance.fetch(code, start_date, end_date)
        else:
            # 中国期货走Tushare
            return self._tushare.fetch_futures(code, start_date, end_date)
    
    def _fetch_crypto(
        self,
        code: str,
        start_date: str,
        end_date: str
    ) -> Optional[pd.DataFrame]:
        """
        获取加密货币
        
        特点：CCXT，不支持SOCKS5代理
        
        TODO: 实现加密货币获取
        """
        print(f"⚠️ 加密货币数据获取尚未实现: {code}")
        return None
    
    # ============================================================
    # 批量获取
    # ============================================================
    
    def fetch_batch(
        self,
        codes: List[str],
        start_date: str,
        end_date: str,
        retry: int = 3
    ) -> Dict[str, Optional[pd.DataFrame]]:
        """
        批量获取多只标的数据
        
        Args:
            codes: 代码列表
            start_date: 开始日期
            end_date: 结束日期
        
        Returns:
            {code: DataFrame}
        """
        results = {}
        
        # 按资产类型分组
        grouped = AssetTypeDetector.group_by_type(codes)
        
        print(f"开始获取 {len(codes)} 只标的...")
        for asset_type, code_list in grouped.items():
            print(f"  {asset_type.value}: {len(code_list)} 只")
        
        # 启动隧道（港美股需要）
        self._start_tunnel()
        
        for code in codes:
            results[code] = self.fetch(code, start_date, end_date, retry)
        
        return results
    
    # ============================================================
    # 辅助方法
    # ============================================================
    
    def get_asset_type(self, code: str) -> AssetType:
        """获取资产类型"""
        return AssetTypeDetector.detect(code)
    
    def is_supported(self, code: str) -> bool:
        """判断是否支持该代码"""
        return AssetTypeDetector.detect(code) != AssetType.UNKNOWN