涨停板、炸板

2026-04-23 23:04:11 +08:00
parent 6a33d49bc6
commit 8827ba8cd6
1 changed files with 126 additions and 0 deletions
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+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)