106 lines
4.0 KiB
Python
106 lines
4.0 KiB
Python
import pandas as pd
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from sqlalchemy import create_engine
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def analyze_stock_performance(start_date, end_date):
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# --- 数据库配置 ---
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try:
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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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# 1. 获取涨停当日股票及价格
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limit_query = f"""
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SELECT l.ts_code, l.name, d.open AS limit_day_open, d.high AS limit_day_high
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FROM stock_limit_data l
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LEFT JOIN stock_daily d ON l.ts_code = d.ts_code AND l.trade_date = d.trade_date
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WHERE l.trade_date = '{start_date}' AND l.status = 1
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"""
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df_limit = pd.read_sql(limit_query, engine)
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if df_limit.empty:
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print(f"提示：{start_date} 当日没有涨停记录。")
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return
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# 2. 获取行情区间数据
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codes = df_limit['ts_code'].unique().tolist()
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codes_tuple = f"('{codes[0]}')" if len(codes) == 1 else str(tuple(codes))
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daily_query = f"""
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SELECT ts_code, trade_date, high, low, close
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FROM stock_daily
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WHERE ts_code IN {codes_tuple} AND trade_date BETWEEN '{start_date}' AND '{end_date}'
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ORDER BY trade_date ASC
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"""
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df_daily = pd.read_sql(daily_query, engine)
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df_daily['trade_date'] = pd.to_datetime(df_daily['trade_date'])
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# 3. 核心逻辑处理
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final_report = []
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for _, limit_row in df_limit.iterrows():
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code = limit_row['ts_code']
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stock_data = df_daily[df_daily['ts_code'] == code].copy()
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if stock_data.empty: continue
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# A. 找到区间内的最高价及其第一次出现的日期
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max_high = float(stock_data['high'].max())
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# 获取最高价所在的那一行（如果有多个相同最高价，取第一个）
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max_high_row = stock_data[stock_data['high'] == max_high].iloc[0]
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max_date = max_high_row['trade_date']
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# B. 寻找最高价日期之后的最低价
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post_max_data = stock_data[stock_data['trade_date'] > max_date]
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if not post_max_data.empty:
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min_low = float(post_max_data['low'].min())
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last_close = float(post_max_data.iloc[-1]['close'])
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price_diff = max_high - min_low
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drop_pct = (price_diff / max_high * 100)
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rebound_pct = ((last_close - min_low) / min_low * 100) if min_low > 0 else 0
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else:
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# 如果最高点出现在结束日当天，则没有“后期最低价”
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min_low = None
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price_diff = 0
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drop_pct = 0
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rebound_pct = 0
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final_report.append({
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"代码": code,
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"名称": limit_row['name'],
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"当日开盘": float(limit_row['limit_day_open'] or 0),
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"当日最高": float(limit_row['limit_day_high'] or 0),
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"区间最高": max_high,
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"最高日期": max_date.strftime('%Y-%m-%d'),
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"见顶后最低": min_low,
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"最大回落幅%": round(drop_pct, 2),
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"低位至期末波动%": round(rebound_pct, 2)
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})
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# 4. 排序与输出
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if final_report:
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result_df = pd.DataFrame(final_report)
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result_df = result_df.sort_values(by="最大回落幅%", ascending=False)
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print(f"\n分析区间: {start_date} 至 {end_date}")
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print("说明: '见顶后最低' 指最高价日期之后的最低点")
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print("=" * 120)
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print(result_df.to_string(index=False, na_rep='N/A'))
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print("=" * 120)
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except Exception as e:
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print(f"发生错误: {e}")
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# ==========================================
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# 设定日期
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# ==========================================
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if __name__ == "__main__":
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START_INPUT = '2024-09-24'
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END_INPUT = '2026-04-23'
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analyze_stock_performance(START_INPUT, END_INPUT) |