131 lines
4.9 KiB
Python
131 lines
4.9 KiB
Python
import pandas as pd
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from sqlalchemy import text
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import argparse
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from stock_config import get_engine
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# --- 数据库配置 ---
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# 请根据实际情况修改数据库连接信息
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# DB_CONN_STR = 'mysql+pymysql://root:password@127.0.0.1:3306/stock_data'
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def init_db(engine):
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"""
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初始化数据库表结构
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1. 增加 symbol 列，位于 trade_date 之后
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2. 建立 UNIQUE KEY 防止物理重复写入
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"""
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create_table_sql = """
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CREATE TABLE IF NOT EXISTS `stock_limit_data` (
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`id` BIGINT AUTO_INCREMENT PRIMARY KEY,
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`trade_date` DATE NOT NULL COMMENT '交易日期',
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`symbol` VARCHAR(10) NOT NULL COMMENT '股票代码',
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`ts_code` VARCHAR(20) NOT NULL COMMENT 'TS股票代码',
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`name` VARCHAR(50) NOT NULL COMMENT '股票名称',
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`status` TINYINT NOT NULL COMMENT '状态：1-涨停，2-炸板',
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`limit_price` DECIMAL(16, 4) COMMENT '计算出的理论涨停价',
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`close` DECIMAL(16, 4) COMMENT '实际收盘价',
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`market` VARCHAR(20) COMMENT '所属板块',
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UNIQUE KEY `uniq_date_code` (`trade_date`, `ts_code`)
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) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
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"""
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with engine.begin() as conn:
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conn.execute(text(create_table_sql))
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def analyze_and_save_limits(start_date, end_date):
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"""
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计算并在数据库中落地涨停、炸板详情
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"""
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engine = get_engine()
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# 0. 初始化检查表结构
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init_db(engine)
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# 1. 获取基础数据和行情数据
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query = f"""
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SELECT
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d.trade_date, d.symbol, d.ts_code, b.name,
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d.high, d.close, d.pre_close, b.market
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FROM stock_daily d
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JOIN stock_basic b ON d.ts_code = b.ts_code
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WHERE d.trade_date > '{start_date}'
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AND d.trade_date <= '{end_date}'
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ORDER BY d.trade_date ASC
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"""
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try:
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df = pd.read_sql(query, engine)
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if df.empty:
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print(f"⚠️ 在指定区间 ({start_date} -> {end_date}] 内未获取到数据。")
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return True
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# 2. 涨停价计算规则
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def calc_limit(row):
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m, p = str(row['market']), float(row['pre_close'])
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# 主板10%，创业板/科创板20%，北交所30%
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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)
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return round(p * ratio, 2)
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df['limit_price'] = df.apply(calc_limit, axis=1)
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# 3. 筛选涨停(1)和炸板(2)
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df['is_limit'] = (df['high'] == df['limit_price']) & (df['close'] == df['limit_price'])
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df['is_broken'] = (df['high'] == df['limit_price']) & (df['close'] < df['limit_price'])
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limit_df = df[df['is_limit']].copy()
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limit_df['status'] = 1
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broken_df = df[df['is_broken']].copy()
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broken_df['status'] = 2
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save_df = pd.concat([limit_df, broken_df])
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if not save_df.empty:
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# 4. 字段清洗与对齐
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final_df = save_df[[
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'trade_date', 'symbol', 'ts_code', 'name',
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'status', 'limit_price', 'close', 'market'
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]]
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# 5. 【核心防重逻辑】：开启事务，先删后插
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with engine.begin() as conn:
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# 获取待写入的所有交易日期
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target_dates = final_df['trade_date'].astype(str).unique()
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date_list = ", ".join([f"'{d}'" for d in target_dates])
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# 第一层逻辑防重：删除数据库中已存在的相同日期数据
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delete_query = f"DELETE FROM stock_limit_data WHERE trade_date IN ({date_list})"
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conn.execute(text(delete_query))
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# 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
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final_df.to_sql('stock_limit_data', conn, if_exists='append', index=False)
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print(f"✅ 处理成功！涉及交易日: {len(target_dates)} 天")
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print(f"📊 总计入库: {len(final_df)} 条记录（已自动去重并覆盖历史数据）。")
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else:
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print("ℹ️ 该时段内全市场无涨停或炸板记录。")
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return True
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except Exception as e:
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print(f"❌ 运行失败: {e}")
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return False
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# ============================================================
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# 用户配置区
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# ============================================================
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="计算并保存涨停/炸板数据")
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parser.add_argument("--start-date", default="2026-04-29", help="开始日期，格式 YYYY-MM-DD，实际查询为大于该日期")
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parser.add_argument("--end-date", default="2026-04-29", help="结束日期，格式 YYYY-MM-DD")
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args = parser.parse_args()
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# 开始日期：计算从该日期的下一个交易日开始
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START_DATE = args.start_date
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# 结束日期
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END_DATE = args.end_date
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analyze_and_save_limits(START_DATE, END_DATE)
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