"""
板块异动分析
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
from .akshare_client import get_akshare_client


class SectorChangeAnalyzer:
    """板块异动分析器"""

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

        Args:
            change_threshold: 涨跌幅阈值（%）
        """
        self.change_threshold = change_threshold
        self.akshare = get_akshare_client()

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

        Returns:
            异动板块列表
        """
        try:
            # 获取概念板块行情
            df = self.akshare.get_concept_spot()
            if df.empty:
                logger.warning("概念板块行情数据为空")
                return []

            # 转换数据类型（概念板块返回的列名）
            df['涨跌幅'] = pd.to_numeric(df['涨跌幅'], errors='coerce')
            df['涨跌额'] = pd.to_numeric(df['涨跌额'], errors='coerce')
            df['最新价'] = pd.to_numeric(df['最新价'], errors='coerce')
            df['成交额'] = pd.to_numeric(df['成交额'], errors='coerce')

            # 筛选异动板块
            hot_sectors = df[df['涨跌幅'] >= self.change_threshold].copy()

            if hot_sectors.empty:
                return []

            # 排序：涨幅优先，然后成交额
            hot_sectors = hot_sectors.sort_values(
                by=['涨跌幅', '成交额'],
                ascending=[False, False]
            )

            # 转换为结果列表
            results = []
            for _, row in hot_sectors.iterrows():
                results.append({
                    'name': row['板块名称'],
                    'change_pct': float(row['涨跌幅']),
                    'change_amount': float(row.get('涨跌额', 0)),
                    'volume': float(row.get('成交量', 0)) if '成交量' in row else 0.0,
                    'amount': float(row.get('成交额', 0)),
                    'leading_stock': row.get('领涨股', ''),
                    'ups': int(row.get('上涨家数', 0)),
                    'downs': int(row.get('下跌家数', 0)),
                    'timestamp': datetime.now()
                })

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

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

            # 如果是连接错误，发送通知
            if 'Connection' in str(e) or 'RemoteDisconnected' in str(e):
                notify_error(
                    title="A股板块监控 - 数据源连接失败",
                    message=f"akshare 概念板块 API 连接失败\n\n错误: {e}\n\n可能原因：\n- eastmoney API 服务不稳定\n- 网络连接问题\n- 建议：稍后自动重试或考虑使用 tushare",
                    level="warning"
                )

            return []

    def analyze_sector_momentum(self, sector_name: str) -> Dict:
        """
        分析板块动能

        Args:
            sector_name: 板块名称

        Returns:
            板块动能分析结果
        """
        try:
            # 获取板块成分股
            stocks_df = self.akshare.get_concept_stocks(sector_name)
            if stocks_df.empty:
                return {}

            # 获取实时行情
            spot_df = self.akshare.get_stock_spot()
            if spot_df.empty:
                return {}

            # 合并数据
            merged = pd.merge(
                stocks_df,
                spot_df,
                on='代码',
                how='inner'
            )

            if merged.empty:
                return {}

            # 计算统计
            total_stocks = len(merged)
            up_stocks = len(merged[merged['涨跌幅'] > 0])
            down_stocks = len(merged[merged['涨跌幅'] < 0])
            avg_change = merged['涨跌幅'].mean()
            max_change = merged['涨跌幅'].max()

            # 计算总成交额
            total_amount = merged['成交额'].sum() if '成交额' in merged.columns else 0

            # 找出涨幅最大的股票
            if not merged.empty:
                top_stock = merged.loc[merged['涨跌幅'].idxmax()]
            else:
                top_stock = None

            return {
                'sector_name': sector_name,
                'total_stocks': total_stocks,
                'up_stocks': up_stocks,
                'down_stocks': down_stocks,
                'up_down_ratio': f"{up_stocks}:{down_stocks}",
                'avg_change': float(avg_change) if pd.notna(avg_change) else 0,
                'max_change': float(max_change) if pd.notna(max_change) else 0,
                'total_amount': float(total_amount),
                'top_stock': {
                    'code': top_stock['代码'] if top_stock is not None else '',
                    'name': top_stock['名称'] if top_stock is not None else '',
                    'change': float(top_stock['涨跌幅']) if top_stock is not None else 0,
                } if top_stock is not None else None
            }

        except Exception as e:
            logger.error(f"分析板块动能失败 {sector_name}: {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"

            # 简单的原因分析
            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"