修正逻辑

cal_date.py

@@ -132,8 +132,8 @@ def run(start_date, end_date, token):
 # 5. 程序入口（只改这里）
 # =======================
 if __name__ == "__main__":
-    START_DATE = "20240424"
+    START_DATE = "20240427"
-    END_DATE   = "20260424"
+    END_DATE   = "20260427"
 
     TUSHARE_TOKEN = "0fad3cf498757089e2630028455d5bbe1637475788bcdaa1f2175e93"
 

daily.py

@@ -144,7 +144,7 @@ def run(start_date, end_date):
 
 # ===== 输入参数 =====
 if __name__ == "__main__":
-    START_DATE = "20260424"
+    START_DATE = "20260427"
-    END_DATE = "20260424"
+    END_DATE = "20260427"
 
     run(START_DATE, END_DATE)

limit_up.py

@@ -1,16 +1,11 @@
 import pandas as pd
+import numpy as np
 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` (
@@ -34,6 +29,7 @@ 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
     """
+    # 数据库连接配置
     engine = create_engine(
         "mysql+pymysql://yangfan@8.159.129.156:10836/level?charset=utf8mb4",
         connect_args={
@@ -42,40 +38,61 @@ def analyze_and_save_limits(start_date, end_date):
         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
+    # 1. 获取行情数据
     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}' 
+    WHERE d.trade_date >= '{start_date}' 
       AND d.trade_date <= '{end_date}'
+      AND d.pre_close IS NOT NULL
     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。")
+            print(f"⚠️ 区间 [{start_date} , {end_date}] 内未获取到数据。")
             return
 
-        # 2. 涨停价计算规则
+        # 强制类型转换
+        df['pre_close'] = pd.to_numeric(df['pre_close'], errors='coerce')
+        df['high'] = pd.to_numeric(df['high'], errors='coerce')
+        df['close'] = pd.to_numeric(df['close'], errors='coerce')
+
+        # 2. 涨停价计算规则 (已取消 ST 判定)
         def calc_limit(row):
-            m, p = str(row['market']), float(row['pre_close'])
+            m = str(row['market'])
-            # 主板10%，创业板/科创板20%，北交所30%
+            p = row['pre_close']
-            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)
+
+            if pd.isna(p) or p <= 0: return 0.0
+
+            # 仅保留板块判定
+            if '创业板' in m or '科创板' in m:
+                ratio = 1.2
+            elif '北交所' in m:
+                ratio = 1.3
+            else:
+                ratio = 1.1  # 普通主板（含ST股现在也按10%计算）
+
+            # 采用标准四舍五入
             return round(p * ratio, 2)
 
         df['limit_price'] = df.apply(calc_limit, axis=1)
 
-        # 3. 筛选涨停(1)和炸板(2)
+        # 3. 筛选逻辑：利用 abs 差值判断，解决浮点数精度问题
-        df['is_limit'] = (df['high'] == df['limit_price']) & (df['close'] == df['limit_price'])
+        # 只要最高价触及理论涨停价（误差0.01以内）即视为触板
-        df['is_broken'] = (df['high'] == df['limit_price']) & (df['close'] < df['limit_price'])
+        is_at_limit = (df['high'] - df['limit_price']).abs() < 0.011
+        is_close_at_limit = (df['close'] - df['limit_price']).abs() < 0.011
 
+        df['is_limit'] = is_at_limit & is_close_at_limit
+        df['is_broken'] = is_at_limit & (~is_close_at_limit)
+
+        # 分离结果
         limit_df = df[df['is_limit']].copy()
         limit_df['status'] = 1
 
@@ -85,27 +102,23 @@ def analyze_and_save_limits(start_date, end_date):
         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
+            # 4. 事务入库：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）。")
+            print(f"📊 记录详情: 总计 {len(final_df)} 条 (涨停: {len(limit_df)} / 炸板: {len(broken_df)})")
         else:
             print("ℹ️ 该时段内全市场无涨停或炸板记录。")
 
@@ -113,14 +126,9 @@ def analyze_and_save_limits(start_date, end_date):
         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
+    # 配置日期区间
-    START_DATE = '2026-04-23'
+    START_DATE = '2024-09-23'
-
+    END_DATE = '2026-04-27'
-    # 结束日期
-    END_DATE = '2026-04-24'
 
     analyze_and_save_limits(START_DATE, END_DATE)