"""
龙头股筛选（Tushare 版本）
从异动板块中筛选出龙头股
"""
import pandas as pd
from typing import Dict, List
from datetime import datetime
from app.utils.logger import logger


class TushareStockSelector:
    """龙头股筛选器（使用 Tushare）"""

    def __init__(self, tushare_client, top_n: int = 3):
        """
        初始化筛选器

        Args:
            tushare_client: TushareClient 实例
            top_n: 返回前 N 只龙头股
        """
        self.top_n = top_n
        self.ts_client = tushare_client

    def select_leading_stocks(self, ts_code: str, sector_name: str) -> List[Dict]:
        """
        筛选板块龙头股

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

        Returns:
            龙头股列表（已排序）
        """
        try:
            # 获取成分股
            members_df = self.ts_client.get_sector_members(ts_code)
            if members_df.empty:
                logger.warning(f"获取板块 {sector_name} 成分股失败")
                return []

            # ths_member 返回的是 con_code（成分股代码），需要用这个来查行情
            stock_codes = members_df['con_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"获取板块 {sector_name} 成分股行情失败")
                return []

            # 获取每日指标（换手率、量比）
            from datetime import datetime
            trade_date = datetime.now().strftime('%Y%m%d')
            basic_df = self.ts_client.get_stock_daily_basic(stock_codes, trade_date)

            # 合并数据 - 注意：ths_member 的 con_code 对应 daily 的 ts_code
            members_df = members_df.rename(columns={'con_code': 'stock_code'})
            realtime_df = realtime_df.rename(columns={'ts_code': 'stock_code'})

            if not basic_df.empty:
                basic_df = basic_df.rename(columns={'ts_code': 'stock_code'})
                merged = pd.merge(
                    members_df[['stock_code', 'con_name']],
                    realtime_df,
                    on='stock_code',
                    how='inner'
                )
                merged = pd.merge(
                    merged,
                    basic_df[['stock_code', 'turnover_rate', 'volume_ratio']],
                    on='stock_code',
                    how='left'
                )
            else:
                merged = pd.merge(
                    members_df[['stock_code', 'con_name']],
                    realtime_df,
                    on='stock_code',
                    how='inner'
                )

            if merged.empty:
                return []

            # 数据类型转换 - daily 接口返回 pct_chg 不是 pct_change
            merged['close'] = pd.to_numeric(merged['close'], errors='coerce')
            merged['pct_chg'] = pd.to_numeric(merged['pct_chg'], errors='coerce')
            merged['change'] = pd.to_numeric(merged['change'], errors='coerce')
            merged['vol'] = pd.to_numeric(merged['vol'], errors='coerce')
            # 注意：daily 接口的 amount 单位是千元，需要转换为元
            merged['amount'] = pd.to_numeric(merged['amount'], errors='coerce') * 1000

            # 换手率和量比填充默认值
            if 'turnover_rate' in merged.columns:
                merged['turnover_rate'] = pd.to_numeric(merged['turnover_rate'], errors='coerce').fillna(0)
            else:
                merged['turnover_rate'] = 0.0

            if 'volume_ratio' in merged.columns:
                merged['volume_ratio'] = pd.to_numeric(merged['volume_ratio'], errors='coerce').fillna(1.0)
            else:
                merged['volume_ratio'] = 1.0

            # 过滤：只保留有成交额的股票
            merged = merged[merged['amount'] > 0].copy()

            if merged.empty:
                return []

            # 计算综合评分
            merged['score'] = merged.apply(self._calculate_score, axis=1)

            # 排序：按综合得分
            merged = merged.sort_values('score', ascending=False)

            # 取前 N 只
            top_stocks = merged.head(self.top_n)

            # 转换结果
            results = []
            for _, row in top_stocks.iterrows():
                # 计算涨速等级
                change_pct = row['pct_chg']
                if change_pct >= 5:
                    speed_level = "⚡⚡⚡ 极快"
                elif change_pct >= 3:
                    speed_level = "⚡⚡ 快速"
                elif change_pct >= 1:
                    speed_level = "⚡ 较快"
                else:
                    speed_level = "🐌 平稳"

                # 计算振幅
                amplitude = 0.0
                if 'high' in row and 'low' in row and row['low'] > 0:
                    amplitude = (row['high'] - row['low']) / row['low'] * 100

                results.append({
                    'code': row['stock_code'],
                    'name': row['con_name'],
                    'price': float(row['close']),
                    'change_pct': float(row['pct_chg']),
                    'change_amount': float(row['change']),
                    'amount': float(row['amount']),
                    'turnover': float(row.get('turnover_rate', 0)),
                    'volume_ratio': float(row.get('volume_ratio', 1.0)),
                    'amplitude': amplitude,
                    'score': float(row['score']),
                    'speed_level': speed_level,
                })

            logger.info(f"板块 {sector_name} 龙头股筛选完成，Top {len(results)}")
            return results

        except Exception as e:
            logger.error(f"筛选龙头股失败 {sector_name}: {e}")
            return []

    def _calculate_score(self, row: pd.Series) -> float:
        """
        计算综合得分

        评分维度：
        - 涨跌幅 (40%)
        - 成交额 (30%)
        - 涨速 (20%)
        - 换手率 (10%)

        Args:
            row: 股票数据行

        Returns:
            综合得分
        """
        score = 0.0

        # 1. 涨跌幅得分 (40分) - 涨幅越高得分越高
        change_pct = row['pct_chg']
        if change_pct >= 7:
            score += 40  # 涨停级别
        elif change_pct >= 5:
            score += 35
        elif change_pct >= 3:
            score += 30
        elif change_pct >= 2:
            score += 25
        elif change_pct >= 1:
            score += 20
        elif change_pct > 0:
            score += 15
        else:
            score += max(0, 10 + change_pct * 5)  # 下跌也有基础分

        # 2. 成交额得分 (30分) - 成交额越大得分越高
        # 注意：amount 已在 select_leading_stocks 中从千元转换为元
        amount = row['amount']  # 单位是元
        if amount >= 1000000000:  # 10亿以上
            score += 30
        elif amount >= 500000000:  # 5亿以上
            score += 25
        elif amount >= 100000000:  # 1亿以上
            score += 20
        elif amount >= 50000000:   # 5000万以上
            score += 15
        elif amount >= 10000000:   # 1000万以上
            score += 10
        else:
            score += 5

        # 3. 涨速得分 (20分) - 简化用涨幅代替
        if change_pct >= 5:
            score += 20
        elif change_pct >= 3:
            score += 15
        elif change_pct >= 1:
            score += 10
        else:
            score += 5

        # 4. 换手率得分 (10分) - 使用真实换手率数据
        turnover_rate = row.get('turnover_rate', 0)
        if turnover_rate >= 15:
            score += 10  # 换手率极高，资金活跃
        elif turnover_rate >= 10:
            score += 9
        elif turnover_rate >= 7:
            score += 8
        elif turnover_rate >= 5:
            score += 7
        elif turnover_rate >= 3:
            score += 6
        elif turnover_rate >= 1:
            score += 4
        elif turnover_rate >= 0.5:
            score += 2
        else:
            score += 1  # 换手率较低

        return score