过滤当天更新的指数并计算天和周cci

signal_calc.py

@@ -70,6 +70,10 @@ def main_calc_process():
             continue
         # 将 'date' 列转换为 datetime 类型，并设置为索引
         df["date"] = pd.to_datetime(df["date"])
+        # 判断最新日期是否为今天，如果不是则跳过
+        today_str = datetime.now().strftime("%Y-%m-%d")
+        if df["date"].max().strftime("%Y-%m-%d") != today_str:
+            continue
         df = df.sort_values("date")
         df.set_index("date", inplace=True)
 
@@ -93,23 +97,40 @@ def main_calc_process():
             timeperiod=14,
         )
         df_weekly = df_weekly.tail(1)
-        cci = df_weekly["cci"].values[0]
-        logger.info(f"{i}/{len(code_list)}: {code} cci: {cci}")
+        week_cci = df_weekly["cci"].values[0]
+
+        df["cci"] = ta.CCI(
+            high=df["high"],
+            low=df["low"],
+            close=df["close"],
+            timeperiod=14,
+        )
+        cci = df["cci"].tail(1).values[0]
+        logger.info(f"{i}/{len(code_list)}: {code} week_cci: {week_cci} day_cci: {cci}")
         if cci < -100:
             signal_list.append(
-                {"code": code, "name": code_info["指数名称"], "cci": cci}
+                {
+                    "code": code,
+                    "name": code_info["指数名称"],
+                    "天cci": cci,
+                    "周cci": week_cci,
+                }
             )
             # break
     signal_df = pd.DataFrame(signal_list)
     # dingtalk.send_markdown(
     #     f"CCI信号", signal_df.to_markdown(tablefmt="simple", index=False)
     # )
-    dingtalk.send_text(
-        tabulate(signal_df, tablefmt="plain", headers="keys", showindex=False)
-    )
+    if len(signal_list) > 0:
+        dingtalk.send_text(
+            tabulate(signal_df, tablefmt="plain", headers="keys", showindex=False)
+        )
+    else:
+        logger.info("无信号")
 
 
 if __name__ == "__main__":
+    # main_calc_process()
     ...
     # main()
     # logger.info(datetime.now())