134 lines
4.5 KiB
Python
134 lines
4.5 KiB
Python
import pandas as pd
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import numpy as np
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from sqlalchemy import create_engine, text
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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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"""
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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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# 数据库连接配置
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engine = create_engine(
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"mysql+pymysql://yangfan@8.159.129.156:10836/level?charset=utf8mb4",
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connect_args={
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"password": "aA%8023321088"
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},
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pool_pre_ping=True
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)
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init_db(engine)
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# 1. 获取行情数据
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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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AND d.pre_close IS NOT NULL
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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"⚠️ 区间 [{start_date} , {end_date}] 内未获取到数据。")
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return
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# 强制类型转换
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df['pre_close'] = pd.to_numeric(df['pre_close'], errors='coerce')
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df['high'] = pd.to_numeric(df['high'], errors='coerce')
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df['close'] = pd.to_numeric(df['close'], errors='coerce')
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# 2. 涨停价计算规则 (已取消 ST 判定)
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def calc_limit(row):
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m = str(row['market'])
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p = row['pre_close']
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if pd.isna(p) or p <= 0: return 0.0
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# 仅保留板块判定
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if '创业板' in m or '科创板' in m:
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ratio = 1.2
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elif '北交所' in m:
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ratio = 1.3
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else:
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ratio = 1.1 # 普通主板（含ST股现在也按10%计算）
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# 采用标准四舍五入
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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. 筛选逻辑：利用 abs 差值判断，解决浮点数精度问题
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# 只要最高价触及理论涨停价（误差0.01以内）即视为触板
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is_at_limit = (df['high'] - df['limit_price']).abs() < 0.011
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is_close_at_limit = (df['close'] - df['limit_price']).abs() < 0.011
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df['is_limit'] = is_at_limit & is_close_at_limit
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df['is_broken'] = is_at_limit & (~is_close_at_limit)
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# 分离结果
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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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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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# 4. 事务入库：先删后插
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with engine.begin() as conn:
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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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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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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"📊 记录详情: 总计 {len(final_df)} 条 (涨停: {len(limit_df)} / 炸板: {len(broken_df)})")
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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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except Exception as e:
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print(f"❌ 运行失败: {e}")
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if __name__ == "__main__":
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# 配置日期区间
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START_DATE = '2024-09-23'
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END_DATE = '2026-04-27'
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analyze_and_save_limits(START_DATE, END_DATE) |