"""
板块异动分析（Tushare 版本）
检测板块涨跌幅、量能、资金流向异动
"""
import pandas as pd
from typing import Dict, List, Optional, Tuple
from datetime import datetime
from app.utils.logger import logger
from app.utils.error_handler import notify_error


class TushareSectorAnalyzer:
    """板块异动分析器（使用 Tushare 同花顺接口）"""

    def __init__(self, tushare_client, change_threshold: float = 2.0):
        """
        初始化异动分析器

        Args:
            tushare_client: TushareClient 实例
            change_threshold: 涨跌幅阈值（%）
        """
        self.change_threshold = change_threshold
        self.ts_client = tushare_client

    def detect_sector_changes(self) -> List[Dict]:
        """
        检测异动板块

        Returns:
            异动板块列表
        """
        try:
            # 使用 tushare 获取异动板块（一次性获取）
            df = self.ts_client.get_hot_sectors(threshold=self.change_threshold)

            if df.empty:
                logger.info("未检测到异动板块")
                return []

            # 转换为结果列表
            results = []
            for _, row in df.iterrows():
                # 成交额转换为万元
                amount_wan = row['amount'] / 10000 if row['amount'] > 0 else 0

                # 格式化成交额显示
                if amount_wan >= 100000:
                    amount_str = f"{amount_wan/100000:.1f}亿"
                elif amount_wan >= 10000:
                    amount_str = f"{amount_wan/10000:.1f}万"
                else:
                    amount_str = f"{amount_wan:.0f}元"

                results.append({
                    'name': row['name'],
                    'ts_code': row['ts_code'],
                    'change_pct': float(row['change_pct']),
                    'change': float(row.get('change', 0)),  # 涨跌额
                    'close': float(row['close']),
                    'amount': float(row['amount']),
                    'amount_str': amount_str,
                    'volume': float(row['volume']),
                    'turnover_rate': float(row.get('turnover_rate', 0)),  # 换手率
                    'trade_date': row['trade_date'],
                    'timestamp': datetime.now()
                })

            logger.info(f"检测到 {len(results)} 个异动概念板块（Tushare）")
            return results

        except Exception as e:
            error_msg = f"Tushare 检测板块异动失败: {e}"
            logger.error(error_msg)

            # 发送通知
            notify_error(
                title="A股板块监控 - Tushare 数据获取失败",
                message=f"错误: {e}\n\n可能原因：\n- Tushare token 未配置或无效\n- API 频率限制\n- 网络连接问题",
                level="warning"
            )

            return []

    def get_sector_stocks(self, ts_code: str, sector_name: str) -> List[Dict]:
        """
        获取板块成分股

        Args:
            ts_code: 板块指数代码
            sector_name: 板块名称

        Returns:
            成分股列表
        """
        try:
            # 获取成分股
            members_df = self.ts_client.get_sector_members(ts_code)

            if members_df.empty:
                logger.warning(f"板块 {sector_name} 成分股数据为空")
                return []

            # 获取成分股的行情数据
            stock_codes = members_df['ts_code'].tolist()

            # 限制数量，避免请求过多
            if len(stock_codes) > 50:
                stock_codes = stock_codes[:50]

            # 获取实时行情
            realtime_df = self.ts_client.get_realtime_data(stock_codes)

            if realtime_df.empty:
                logger.warning(f"板块 {sector_name} 成分股行情为空")
                return []

            # 合并数据
            merged = pd.merge(
                members_df,
                realtime_df,
                on='ts_code',
                how='inner'
            )

            if merged.empty:
                return []

            # 转换结果
            results = []
            for _, row in merged.iterrows():
                results.append({
                    'code': row['ts_code'],
                    'name': row.get('name', row.get('member_name', '')),
                    'price': float(row.get('close', 0)),
                    'change_pct': float(row.get('pct_chg', 0)),
                    'change_amount': float(row.get('change', 0)),
                    'amount': float(row.get('amount', 0)),
                    'volume': float(row.get('vol', 0)),
                })

            return results

        except Exception as e:
            logger.error(f"获取板块 {sector_name} 成分股失败: {e}")
            return []

    def get_hot_reason(self, sector_name: str, top_stocks: List[Dict]) -> str:
        """
        推测异动原因（基于龙头股分析）

        Args:
            sector_name: 板块名称
            top_stocks: 龙头股列表

        Returns:
            异动原因描述
        """
        try:
            if not top_stocks:
                return "板块整体异动"

            reasons = []

            # 检查是否有涨停股
            limit_up_count = sum(1 for s in top_stocks if s.get('change_pct', 0) >= 9.9)
            if limit_up_count > 0:
                reasons.append(f"{limit_up_count}只个股涨停")

            # 检查平均涨幅
            avg_change = sum(s.get('change_pct', 0) for s in top_stocks) / len(top_stocks)
            if avg_change >= 7:
                reasons.append("板块全线爆发")

            # 检查是否集中在某个龙头
            if len(top_stocks) >= 2:
                top1_change = top_stocks[0].get('change_pct', 0)
                top2_change = top_stocks[1].get('change_pct', 0)
                if top1_change - top2_change > 3:
                    reasons.append(f"{top_stocks[0].get('name', '')}龙头领涨")

            if reasons:
                return "，".join(reasons)
            else:
                return "资金集中流入"

        except Exception as e:
            logger.error(f"推测异动原因失败: {e}")
            return "板块异动"