Enhance trading workflow with new order flow management

- Added dingdanliu_nb_mflow for improved order processing
- Updated related scripts and configurations to support new functionality

2.数据下载与处理/数据转换最终版/merged_tickdata__BIT_20240522.py

@@ -0,0 +1,68 @@
+import pandas as pd
+import os
+from datetime import time as s_time
+from datetime import datetime
+import chardet
+import numpy as np  
+
+
+def split_alpha_numeric(string):
+    alpha_chars = ""
+    numeric_chars = ""
+    for char in string:
+        if char.isalpha():
+            alpha_chars += char
+        elif char.isdigit():
+            numeric_chars += char
+    return alpha_chars, numeric_chars
+
+
+
+def merged_old_unprocessed_tickdata(all_csv_files, sp_char):
+    csv_files = [sp_file for sp_file in all_csv_files if sp_char in sp_file]
+    print("csv_files:", csv_files)
+    merged_up_df = pd.DataFrame()
+    dir = os.getcwd()
+    fileNum_errors = 0
+
+    # 循环遍历每个csv文件
+    for file in csv_files:
+        try:
+            df = pd.read_csv(file, 
+                            header=0,
+                            encoding='gbk',
+                            low_memory= False,
+                            # skiprows=0,
+                            # parse_dates=['时间']  #   注意此处增加的排序，为了后面按时间排序
+                            )
+        except:
+            file_path = os.path.join(dir, file)
+            fileNum_errors += 1
+            with open(file_path, 'rb') as file:
+                data = file.read()
+
+            # 使用chardet检测编码
+            detected_encoding = chardet.detect(data)['encoding']
+            # print("%s当前文件不为gbk格式,其文件格式为%s,需要转换为gbk格式,错误总数为%s"%(file,detected_encoding,fileNum_errors))
+            print("%s:%s当前文件不为gbk格式,其文件格式为%s,需要转换为gbk格式,错误总数为%s"%(datetime.now().strftime('%Y-%m-%d %H:%M:%S'),file_path,detected_encoding,fileNum_errors))
+            
+            with open('output_error.txt', 'a') as f:
+                print("%s:%s当前文件不为gbk格式,其文件格式为%s,需要转换为gbk格式,错误总数为%s"%(datetime.now().strftime('%Y-%m-%d %H:%M:%S'),file_path,detected_encoding,fileNum_errors), file = f)
+                
+
+        # 删除重复行
+        df.drop_duplicates(inplace=True) 
+        # 将数据合并到新的DataFrame中
+        merged_up_df = pd.concat([merged_up_df, df], ignore_index=True)
+        
+    # 删除重复列
+    merged_up_df.drop_duplicates(subset=merged_up_df.columns.tolist(), inplace=True)
+    # 重置行索引
+    merged_up_df.reset_index(inplace=True, drop=True)
+
+    # merged_up_df,alpha_chars,code_value = insert_main_contract(merged_up_df)
+    # 打印提示信息
+    # print("按年份未处理的CSV文件合并成功！")
+
+    return merged_up_df #,alpha_chars,code_value
+