import pandas as pd
from sqlalchemy import create_engine, text

# --- 数据库配置 ---
# 请根据实际情况修改数据库连接信息
# DB_CONN_STR = 'mysql+pymysql://root:password@127.0.0.1:3306/stock_data'


def init_db(engine):
    """
    初始化数据库表结构
    1. 增加 symbol 列，位于 trade_date 之后
    2. 建立 UNIQUE KEY 防止物理重复写入
    """
    create_table_sql = """
    CREATE TABLE IF NOT EXISTS `stock_limit_data` (
        `id` BIGINT AUTO_INCREMENT PRIMARY KEY,
        `trade_date` DATE NOT NULL COMMENT '交易日期',
        `symbol` VARCHAR(10) NOT NULL COMMENT '股票代码',
        `ts_code` VARCHAR(20) NOT NULL COMMENT 'TS股票代码',
        `name` VARCHAR(50) NOT NULL COMMENT '股票名称',
        `status` TINYINT NOT NULL COMMENT '状态：1-涨停，2-炸板',
        `limit_price` DECIMAL(16, 4) COMMENT '计算出的理论涨停价',
        `close` DECIMAL(16, 4) COMMENT '实际收盘价',
        `market` VARCHAR(20) COMMENT '所属板块',
        UNIQUE KEY `uniq_date_code` (`trade_date`, `ts_code`)
    ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
    """
    with engine.begin() as conn:
        conn.execute(text(create_table_sql))


def analyze_and_save_limits(start_date, end_date):
    """
    计算并在数据库中落地涨停、炸板详情
    """
    engine = create_engine(
        "mysql+pymysql://yangfan@8.159.129.156:10836/level?charset=utf8mb4",
        connect_args={
            "password": "aA%8023321088"
        },
        pool_pre_ping=True
    )

    # 0. 初始化检查表结构
    init_db(engine)

    # 1. 获取基础数据和行情数据
    query = f"""
    SELECT 
        d.trade_date, d.symbol, d.ts_code, b.name, 
        d.high, d.close, d.pre_close, b.market
    FROM stock_daily d
    JOIN stock_basic b ON d.ts_code = b.ts_code
    WHERE d.trade_date > '{start_date}' 
      AND d.trade_date <= '{end_date}'
    ORDER BY d.trade_date ASC
    """

    try:
        df = pd.read_sql(query, engine)
        if df.empty:
            print(f"⚠️ 在指定区间 ({start_date} -> {end_date}] 内未获取到数据。")
            return

        # 2. 涨停价计算规则
        def calc_limit(row):
            m, p = str(row['market']), float(row['pre_close'])
            # 主板10%，创业板/科创板20%，北交所30%
            ratio = 1.2 if ('创业板' in m or '科创板' in m) else (1.3 if '北交所' in m else 1.1)
            return round(p * ratio, 2)

        df['limit_price'] = df.apply(calc_limit, axis=1)

        # 3. 筛选涨停(1)和炸板(2)
        df['is_limit'] = (df['high'] == df['limit_price']) & (df['close'] == df['limit_price'])
        df['is_broken'] = (df['high'] == df['limit_price']) & (df['close'] < df['limit_price'])

        limit_df = df[df['is_limit']].copy()
        limit_df['status'] = 1

        broken_df = df[df['is_broken']].copy()
        broken_df['status'] = 2

        save_df = pd.concat([limit_df, broken_df])

        if not save_df.empty:
            # 4. 字段清洗与对齐
            final_df = save_df[[
                'trade_date', 'symbol', 'ts_code', 'name',
                'status', 'limit_price', 'close', 'market'
            ]]

            # 5. 【核心防重逻辑】：开启事务，先删后插
            with engine.begin() as conn:
                # 获取待写入的所有交易日期
                target_dates = final_df['trade_date'].astype(str).unique()
                date_list = ", ".join([f"'{d}'" for d in target_dates])

                # 第一层逻辑防重：删除数据库中已存在的相同日期数据
                delete_query = f"DELETE FROM stock_limit_data WHERE trade_date IN ({date_list})"
                conn.execute(text(delete_query))

                # 第二层物理防重：通过 to_sql 写入，若触发 Unique Key 冲突会抛出异常回滚
                final_df.to_sql('stock_limit_data', conn, if_exists='append', index=False)

            print(f"✅ 处理成功！涉及交易日: {len(target_dates)} 天")
            print(f"📊 总计入库: {len(final_df)} 条记录（已自动去重并覆盖历史数据）。")
        else:
            print("ℹ️ 该时段内全市场无涨停或炸板记录。")

    except Exception as e:
        print(f"❌ 运行失败: {e}")


# ============================================================
# 用户配置区
# ============================================================
if __name__ == "__main__":
    # 开始日期：计算从该日期的下一个交易日开始
    START_DATE = '2026-04-01'

    # 结束日期
    END_DATE = '2026-04-22'

    analyze_and_save_limits(START_DATE, END_DATE)