stock/stock-html/services/market_sentiment.py

"""
市场情绪指标模块（P1）

从 stock_realtime_price 表直接计算市场情绪指标，无需额外数据源。

指标包括：
1. 涨停/跌停家数比
2. 连板高度（最高连板数）
3. 换手率中位数
4. 两市成交额
"""
import logging

logger = logging.getLogger(__name__)


def calc_market_sentiment():
    """
    从数据库实时行情表计算市场情绪指标

    返回:
        dict: {
            'limit_up_count': int,      # 涨停家数
            'limit_down_count': int,    # 跌停家数
            'up_down_ratio': float,     # 涨跌停比
            'sentiment': str,           # 情绪标签
            'consecutive_board': int,   # 最高连板数
            'turnover_median': float,   # 换手率中位数
            'total_amount': float,      # 两市成交额（亿）
            'market_temp': str,         # 市场温度（偏热/偏冷/正常）
            'score': int,               # 情绪评分增减（-10 ~ +10）
            'reasons': list,            # 评分原因
        }
    """
    from db import get_db, put_db

    conn = get_db()
    if not conn:
        return _empty_sentiment()

    try:
        cur = conn.cursor()

        # 涨停跌停统计（涨停：涨幅>=9.8%，跌停：跌幅<=-9.8%）
        cur.execute("""
            SELECT
                COUNT(*) FILTER (WHERE change_pct >= 9.8) AS limit_up,
                COUNT(*) FILTER (WHERE change_pct <= -9.8) AS limit_down,
                COUNT(*) FILTER (WHERE change_pct > 0) AS up_count,
                COUNT(*) FILTER (WHERE change_pct < 0) AS down_count,
                COUNT(*) FILTER (WHERE change_pct = 0) AS flat_count,
                COUNT(*) AS total,
                COALESCE(SUM(amount), 0) AS total_amount,
                COALESCE(PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY turnover), 0) AS turnover_median
            FROM stock_realtime_price
            WHERE volume > 0 AND price > 0
        """)
        row = cur.fetchone()
        if not row:
            return _empty_sentiment()

        limit_up = int(row[0] or 0)
        limit_down = int(row[1] or 0)
        up_count = int(row[2] or 0)
        down_count = int(row[3] or 0)
        flat_count = int(row[4] or 0)
        total = int(row[5] or 1)
        total_amount = float(row[6] or 0) / 1e8  # 转为亿
        turnover_median = float(row[7] or 0)

        # 涨跌停比
        up_down_ratio = round(limit_up / limit_down, 1) if limit_down > 0 else float(limit_up)

        # 情绪标签
        if limit_down == 0 and limit_up > 10:
            sentiment = '极度乐观'
        elif up_down_ratio >= 5:
            sentiment = '乐观'
        elif up_down_ratio >= 2:
            sentiment = '偏多'
        elif up_down_ratio >= 1:
            sentiment = '中性'
        elif up_down_ratio >= 0.5:
            sentiment = '偏空'
        else:
            sentiment = '悲观'

        # 市场温度
        if total_amount > 1.2e4:
            market_temp = '偏热'
        elif total_amount < 6000:
            market_temp = '偏冷'
        else:
            market_temp = '正常'

        # 连板高度：查找连续涨停的股票
        consecutive_board = _calc_max_consecutive_board(cur)

        # 评分
        score = 0
        reasons = []

        if up_down_ratio >= 5:
            score += 5
            reasons.append(f'涨跌停比{up_down_ratio}:1，情绪极度乐观(+5)')
        elif up_down_ratio >= 2:
            score += 3
            reasons.append(f'涨跌停比{up_down_ratio}:1，情绪偏多(+3)')
        elif up_down_ratio < 0.5:
            score -= 5
            reasons.append(f'涨跌停比{up_down_ratio}:1，情绪悲观(-5)')
        elif up_down_ratio < 1:
            score -= 3
            reasons.append(f'涨跌停比{up_down_ratio}:1，情绪偏空(-3)')

        if consecutive_board >= 5:
            score += 3
            reasons.append(f'最高{consecutive_board}连板，市场热度高(+3)')

        if total_amount > 1.2e4:
            score += 2
            reasons.append(f'两市成交额{total_amount:.0f}亿，交投活跃(+2)')
        elif total_amount < 6000:
            score -= 2
            reasons.append(f'两市成交额仅{total_amount:.0f}亿，交投清淡(-2)')

        score = max(-10, min(10, score))

        return {
            'limit_up_count': limit_up,
            'limit_down_count': limit_down,
            'up_count': up_count,
            'down_count': down_count,
            'up_down_ratio': up_down_ratio,
            'sentiment': sentiment,
            'consecutive_board': consecutive_board,
            'turnover_median': round(turnover_median, 2),
            'total_amount': round(total_amount, 0),
            'market_temp': market_temp,
            'score': score,
            'reasons': reasons,
        }
    except Exception as e:
        logger.error(f"计算市场情绪指标失败: {e}")
        return _empty_sentiment()
    finally:
        put_db(conn)


def _calc_max_consecutive_board(cur):
    """
    计算最高连板数（需要历史数据辅助判断）
    简化版：通过查找连续涨幅>=9.8%的股票

    由于实时表只有当日数据，这里用近似方法：
    查找涨停股票数量作为市场热度参考
    """
    try:
        # 查找涨停股票（涨幅>=9.8%）
        cur.execute("""
            SELECT COUNT(*) FROM stock_realtime_price
            WHERE change_pct >= 9.8 AND volume > 0
        """)
        limit_up_count = int(cur.fetchone()[0] or 0)

        # 简化：涨停家数>50视为有高连板可能
        if limit_up_count > 50:
            return 5
        elif limit_up_count > 30:
            return 4
        elif limit_up_count > 15:
            return 3
        elif limit_up_count > 5:
            return 2
        elif limit_up_count > 0:
            return 1
        return 0
    except Exception:
        return 0


def _empty_sentiment():
    """返回空情绪数据"""
    return {
        'limit_up_count': 0,
        'limit_down_count': 0,
        'up_count': 0,
        'down_count': 0,
        'up_down_ratio': 0,
        'sentiment': '无数据',
        'consecutive_board': 0,
        'turnover_median': 0,
        'total_amount': 0,
        'market_temp': '无数据',
        'score': 0,
        'reasons': [],
    }