"""
龙头股筛选
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
from .akshare_client import get_akshare_client


class StockSelector:
    """龙头股筛选器"""

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

        Args:
            top_n: 返回前 N 只龙头股
        """
        self.top_n = top_n
        self.akshare = get_akshare_client()

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

        Args:
            sector_name: 板块名称

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

            # 获取实时行情
            spot_df = self.akshare.get_stock_spot()
            if spot_df.empty:
                logger.warning("获取实时行情失败")
                return []

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

            if merged.empty:
                return []

            # 数据类型转换
            merged['最新价'] = pd.to_numeric(merged['最新价'], errors='coerce')
            merged['涨跌幅'] = pd.to_numeric(merged['涨跌幅'], errors='coerce')
            merged['涨跌额'] = pd.to_numeric(merged['涨跌额'], errors='coerce')
            merged['成交量'] = pd.to_numeric(merged['成交量'], errors='coerce')
            merged['成交额'] = pd.to_numeric(merged['成交额'], errors='coerce')
            merged['换手率'] = pd.to_numeric(merged['换手率'], errors='coerce')
            merged['振幅'] = pd.to_numeric(merged['振幅'], errors='coerce')
            merged['量比'] = pd.to_numeric(merged['量比'], errors='coerce')

            # 过滤：只保留有成交额的股票
            merged = merged[merged['成交额'] > 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['涨跌幅']
                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"

                results.append({
                    'code': row['代码'],
                    'name': row['名称'],
                    'price': float(row['最新价']),
                    'change_pct': float(row['涨跌幅']),
                    'change_amount': float(row['涨跌额']),
                    'amount': float(row['成交额']),
                    'turnover': float(row['换手率']),
                    'volume_ratio': float(row.get('量比', 1)),
                    'amplitude': float(row.get('振幅', 0)),
                    '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['涨跌幅']
        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
        amount = row['成交额']
        if amount >= 100000:  # 10亿以上
            score += 30
        elif amount >= 50000:  # 5亿以上
            score += 25
        elif amount >= 10000:  # 1亿以上
            score += 20
        elif amount >= 5000:   # 5000万以上
            score += 15
        elif amount >= 1000:   # 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) - 适中换手率加分
        turnover = row['换手率']
        if 5 <= turnover <= 15:
            score += 10  # 适中换手率
        elif 15 < turnover <= 25:
            score += 8   # 活跃但不过热
        elif turnover > 25:
            score += 5    # 过热可能回调
        elif turnover > 0:
            score += 3    # 有成交即可
        else:
            score += 0

        return score