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Enhance trading workflow with new order flow management

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- Added dingdanliu_nb_mflow for improved order processing
- Updated related scripts and configurations to support new functionality
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Win_home
2025-03-15 22:45:08 +08:00
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2.数据下载与处理/数据转换最终版/merged_tickdata_20240510.py Normal file
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import pandas as pd
import os
from datetime import time as s_time
from datetime import datetime
import chardet
import numpy as np
# 日盘商品期货交易品种
commodity_day_dict = {'bb': s_time(15,00), 'jd': s_time(15,00), 'lh': s_time(15,00), 'l': s_time(15,00), 'fb': s_time(15,00), 'ec': s_time(15,00),
'AP': s_time(15,00), 'CJ': s_time(15,00), 'JR': s_time(15,00), 'LR': s_time(15,00), 'RS': s_time(15,00), 'PK': s_time(15,00),
'PM': s_time(15,00), 'PX': s_time(15,00), 'RI': s_time(15,00), 'SF': s_time(15,00), 'SM': s_time(15,00), 'UR': s_time(15,00),
'WH': s_time(15,00), 'ao': s_time(15,00), 'br': s_time(15,00), 'wr': s_time(15,00),}
# 夜盘商品期货交易品种
commodity_night_dict = {'sc': s_time(2,30), 'bc': s_time(1,0), 'lu': s_time(23,0), 'nr': s_time(23,0),'au': s_time(2,30), 'ag': s_time(2,30),
'ss': s_time(1,0), 'sn': s_time(1,0), 'ni': s_time(1,0), 'pb': s_time(1,0),'zn': s_time(1,0), 'al': s_time(1,0), 'cu': s_time(1,0),
'ru': s_time(23,0), 'rb': s_time(23,0), 'hc': s_time(23,0), 'fu': s_time(23,0), 'bu': s_time(23,0), 'sp': s_time(23,0),
'PF': s_time(23,0), 'SR': s_time(23,0), 'CF': s_time(23,0), 'CY': s_time(23,0), 'RM': s_time(23,0), 'MA': s_time(23,0),
'TA': s_time(23,0), 'ZC': s_time(23,0), 'FG': s_time(23,0), 'OI': s_time(23,0), 'SA': s_time(23,0),
'p': s_time(23,0), 'j': s_time(23,0), 'jm': s_time(23,0), 'i': s_time(23,0), 'l': s_time(23,0), 'v': s_time(23,0),
'pp': s_time(23,0), 'eg': s_time(23,0), 'c': s_time(23,0), 'cs': s_time(23,0), 'y': s_time(23,0), 'm': s_time(23,0),
'a': s_time(23,0), 'b': s_time(23,0), 'rr': s_time(23,0), 'eb': s_time(23,0), 'pg': s_time(23,0), 'SH': s_time(23,00)}
# 金融期货交易品种
financial_time_dict = {'IH': s_time(15,00), 'IF': s_time(15,00), 'IC': s_time(15,00), 'IM': s_time(15,00),'T': s_time(15,00), 'TS': s_time(15,00),
'TF': s_time(15,00), 'TL': s_time(15,00)}
# 所有已列入的筛选品种
all_dict = {k: v for d in [commodity_day_dict, commodity_night_dict, financial_time_dict] for k, v in d.items()}
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_tickdata(merged_up_df, sp_char, alpha_chars, code_value):
# merged_up_df = pd.DataFrame()
# merged_up_df,alpha_chars,code_value = merged_old_unprocessed_tickdata(all_csv_files, sp_char)
while alpha_chars not in all_dict.keys():
print("%s期货品种未列入所有筛选条件中!!!"%(code_value))
continue
merged_df = pd.DataFrame()
merged_df =pd.DataFrame({'main_contract':merged_up_df['统一代码'],'symbol':merged_up_df['合约代码'],'datetime':merged_up_df['时间'],'lastprice':merged_up_df['最新'],'volume':merged_up_df['成交量'],
'bid_p':merged_up_df['买一价'],'ask_p':merged_up_df['卖一价'],'bid_v':merged_up_df['买一量'],'ask_v':merged_up_df['卖一量']})
del merged_up_df
merged_df['datetime'] = pd.to_datetime(merged_df['datetime'])
merged_df['tmp_time'] = merged_df['datetime'].dt.strftime('%H:%M:%S.%f')
merged_df['time'] = merged_df['tmp_time'].apply(lambda x: datetime.strptime(x, '%H:%M:%S.%f')).dt.time
del merged_df['tmp_time']
merged_df = filter_tickdata_time(merged_df, alpha_chars)
del merged_df['time']
merged_df['datetime'] = sorted(merged_df['datetime'])
print("%s%s数据生成成功!"%(code_value,sp_char))
return merged_df
def merged_new_tickdata(merged_up_df, sp_char, alpha_chars, code_value):
# merged_up_df = pd.DataFrame()
# merged_up_df,alpha_chars,code_value = merged_new_unprocessed_tickdata(all_csv_files, sp_char)
while alpha_chars not in all_dict.keys():
print("%s期货品种未列入所有筛选条件中!!!"%(code_value))
continue
#日期修正
# merged_df['业务日期'] = pd.to_datetime(merged_df['业务日期'])
# merged_df['业务日期'] = merged_df['业务日期'].dt.strftime('%Y-%m-%d')
# merged_df['最后修改时间'] = pd.to_datetime(merged_df['最后修改时间'])
merged_up_df['datetime'] = merged_up_df['业务日期'].astype(str) + ' '+merged_up_df['最后修改时间'].astype(str) + '.' + merged_up_df['最后修改毫秒'].astype(str) # merged_df['最后修改时间'].dt.time.astype(str)
# 将'datetime' 列的数据类型更改为 datetime 格式，如果数据转换少8个小时，可以用timedelta处理
merged_up_df['datetime'] = pd.to_datetime(merged_up_df['datetime'], errors='coerce', format='%Y-%m-%d %H:%M:%S.%f')
#计算瞬时成交量
merged_up_df['volume'] = merged_up_df['数量'] - merged_up_df['数量'].shift(1)
merged_up_df['volume'] = merged_up_df['volume'].fillna(0)
merged_df = pd.DataFrame()
merged_df =pd.DataFrame({'main_contract':merged_up_df['统一代码'],'symbol':merged_up_df['合约代码'],'datetime':merged_up_df['datetime'],'lastprice':merged_up_df['最新价'],'volume':merged_up_df['volume'],
'bid_p':merged_up_df['申买价一'],'ask_p':merged_up_df['申卖价一'],'bid_v':merged_up_df['申买量一'],'ask_v':merged_up_df['申卖量一']})
del merged_up_df
# merged_df['datetime'] = pd.to_datetime(merged_df['datetime'])
merged_df['tmp_time'] = merged_df['datetime'].dt.strftime('%H:%M:%S.%f')
merged_df['time'] = merged_df['tmp_time'].apply(lambda x: datetime.strptime(x, '%H:%M:%S.%f')).dt.time
del merged_df['tmp_time']
merged_df = filter_tickdata_time(merged_df, alpha_chars)
del merged_df['time']
# merged_df['datetime'] = sorted(merged_df['datetime'])
sorted_merged_df = merged_df.sort_values(by = ['datetime'], inplace=True)
print("%s%s数据生成成功!"%(code_value,sp_char))
return merged_df
def filter_tickdata_time(filter_df, alpha_chars):
if alpha_chars in financial_time_dict.keys():
drop_index1 = pd.DataFrame().index
drop_index2 = filter_df.loc[(filter_df['time'] > s_time(11, 30, 0, 000000)) & (filter_df['time'] < s_time(13, 0, 0, 000000))].index
drop_index3 = filter_df.loc[(filter_df['time'] > s_time(15, 0, 0, 000000)) | (filter_df['time'] < s_time(9, 30, 0, 000000))].index
drop_index4 = pd.DataFrame().index
print("按照中金所交易时间筛选金融期货品种")
elif alpha_chars in commodity_night_dict.keys():
if commodity_night_dict[alpha_chars] == s_time(23,00):
drop_index1 = filter_df.loc[(filter_df['time'] > s_time(10, 15, 0, 000000)) & (filter_df['time'] < s_time(10, 30, 0, 000000))].index
drop_index2 = filter_df.loc[(filter_df['time'] > s_time(11, 30, 0, 000000)) & (filter_df['time'] < s_time(13, 30, 0, 000000))].index
drop_index3 = filter_df.loc[(filter_df['time'] > s_time(15, 0, 0, 000000)) & (filter_df['time'] < s_time(21, 0, 0, 000000))].index
drop_index4 = filter_df.loc[(filter_df['time'] > s_time(23, 0, 0, 000000)) | (filter_df['time'] < s_time(9, 0, 0, 000000))].index
print("按照夜盘截止交易时间为23:00筛选商品期货品种")
elif commodity_night_dict[alpha_chars] == s_time(1,00):
drop_index1 = filter_df.loc[(filter_df['time'] > s_time(10, 15, 0, 000000)) & (filter_df['time'] < s_time(10, 30, 0, 000000))].index
drop_index2 = filter_df.loc[(filter_df['time'] > s_time(11, 30, 0, 000000)) & (filter_df['time'] < s_time(13, 30, 0, 000000))].index
drop_index3 = filter_df.loc[(filter_df['time'] > s_time(15, 0, 0, 000000)) & (filter_df['time'] < s_time(21, 0, 0, 000000))].index
drop_index4 = filter_df.loc[(filter_df['time'] > s_time(1, 0, 0, 000000)) & (filter_df['time'] < s_time(9, 0, 0, 000000))].index
print("按照夜盘截止交易时间为1:00筛选商品期货品种")
elif commodity_night_dict[alpha_chars] == s_time(2,30):
drop_index1 = filter_df.loc[(filter_df['time'] > s_time(10, 15, 0, 000000)) & (filter_df['time'] < s_time(10, 30, 0, 000000))].index
drop_index2 = filter_df.loc[(filter_df['time'] > s_time(11, 30, 0, 000000)) & (filter_df['time'] < s_time(13, 30, 0, 000000))].index
drop_index3 = filter_df.loc[(filter_df['time'] > s_time(15, 0, 0, 000000)) & (filter_df['time'] < s_time(21, 0, 0, 000000))].index
drop_index4 = filter_df.loc[(filter_df['time'] > s_time(2, 30, 0, 000000)) & (filter_df['time'] < s_time(9, 0, 0, 000000))].index
print("按照夜盘截止交易时间为2:30筛选商品期货品种")
else:
print("夜盘截止交易时间未设置或者设置错误！！！")
elif alpha_chars in commodity_day_dict.keys():
drop_index1 = filter_df.loc[(filter_df['time'] > s_time(10, 15, 0, 000000)) & (filter_df['time'] < s_time(10, 30, 0, 000000))].index
drop_index2 = filter_df.loc[(filter_df['time'] > s_time(11, 30, 0, 000000)) & (filter_df['time'] < s_time(13, 30, 0, 000000))].index
drop_index3 = filter_df.loc[(filter_df['time'] > s_time(15, 0, 0, 000000)) | (filter_df['time'] < s_time(9, 0, 0, 000000))].index
drop_index4 = pd.DataFrame().index
print("按照无夜盘筛选商品期货品种")
else:
print("%s期货品种未执行时间筛选中!!!"%(alpha_chars))
# 清理不在交易时间段的数据
# 数据清理
filter_df.drop(labels=drop_index1, axis=0, inplace=True)
filter_df.drop(drop_index2, axis=0, inplace=True)
filter_df.drop(drop_index3, axis=0, inplace=True)
filter_df.drop(drop_index4, axis=0, inplace=True)
return filter_df
def insert_main_contract(df):
# 添加主力连续的合约代码，主力连续为888，指数连续可以用999，次主力连续可以使用889，表头用“统一代码”
alpha_chars, numeric_chars = split_alpha_numeric(df.loc[0,'合约代码'])
code_value = alpha_chars + "889"
print("code_value characters:", code_value)
df.insert(loc=0,column="统一代码", value=code_value)
return df, alpha_chars, code_value
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:
# 读取csv文件，并使用第一行为列标题，编译不通过可以改为gbk
df = pd.read_csv(file,
header=0,
# usecols=[ 1, 2, 3, 7, 12, 13, 14, 15],
# names=[
# "合约代码",
# "时间",
# "最新",
# "成交量",
# "买一价",
# "卖一价",
# "买一量",
# "卖一量",
# ],
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
def merged_new_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:
# 读取csv文件，并使用第一行为列标题，编译不通过可以改为gbk
df = pd.read_csv(
file,
header=0,
# usecols=[0, 1, 4, 11, 20, 21, 22, 23, 24, 25, 43],
# names=[
# "交易日",
# "合约代码",
# "最新价",
# "数量",
# "最后修改时间",
# "最后修改毫秒",
# "申买价一",
# "申买量一",
# "申卖价一",
# "申卖量一",
# "业务日期",
# ],
encoding='gbk',
low_memory= False,
# skiprows=0,
# parse_dates=['业务日期','最后修改时间','最后修改毫秒'] # 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_path,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
def reinstatement_tickdata(merged_rs_df):
merged_rs_df['main_contract'] = merged_rs_df['main_contract'].astype(str)
merged_rs_df['symbol'] = merged_rs_df['symbol'].astype(str)
merged_rs_df['datetime'] = pd.to_datetime(merged_rs_df['datetime'], errors='coerce', format='%Y-%m-%d %H:%M:%S.%f')
# merged_rs_df['lastprice'] = merged_rs_df['lastprice'].astype(float)
merged_rs_df['volume'] = merged_rs_df['volume'].astype(int)
# merged_rs_df['bid_p'] = merged_rs_df['bid_p'].astype(float)
# merged_rs_df['ask_p'] = merged_rs_df['ask_p'].astype(float)
merged_rs_df['bid_v'] = merged_rs_df['bid_v'].astype(int)
merged_rs_df['ask_v'] = merged_rs_df['ask_v'].astype(int)
# 等比复权,先不考虑
# df['复权因子'] = df['卖一价'].shift() / df['买一价']
# df['复权因子'] = np.where(df['合约代码'] != df['合约代码'].shift(), df['卖一价'].shift() / df['买一价'], 1)
# df['复权因子'] = df['复权因子'].fillna(1)
# df['买一价_adj'] = df['买一价'] * df['复权因子'].cumprod()
# df['卖一价_adj'] = df['卖一价'] * df['复权因子'].cumprod()
# df['最新_adj'] = df['最新'] * df['复权因子'].cumprod()
# 等差复权
merged_rs_df['复权因子'] = np.where(merged_rs_df['symbol'] != merged_rs_df['symbol'].shift(), merged_rs_df['ask_p'].shift() - merged_rs_df['bid_p'], 0)
merged_rs_df['复权因子'] = merged_rs_df['复权因子'].fillna(0)
merged_rs_df['bid_p_adj'] = merged_rs_df['bid_p'] + merged_rs_df['复权因子'].cumsum()
merged_rs_df['ask_p_adj'] = merged_rs_df['ask_p'] + merged_rs_df['复权因子'].cumsum()
merged_rs_df['lastprice_adj'] = merged_rs_df['lastprice'] + merged_rs_df['复权因子'].cumsum()
# 将调整后的数值替换原来的值
merged_rs_df['bid_p'] = merged_rs_df['bid_p_adj'].round(4)
merged_rs_df['ask_p'] = merged_rs_df['ask_p_adj'].round(4)
merged_rs_df['lastprice'] = merged_rs_df['lastprice_adj'].round(4)
# 删除多余的值
del merged_rs_df['复权因子']
del merged_rs_df['bid_p_adj']
del merged_rs_df['ask_p_adj']
del merged_rs_df['lastprice_adj']
return merged_rs_df
# def find_files(all_csv_files):
# all_csv_files = sorted(all_csv_files)
# sp_old_chars = ['_2019','_2020','_2021']
# sp_old_chars = sorted(sp_old_chars)
# sp_new_chars = ['_2022','_2023']
# sp_new_chars = sorted(sp_new_chars)
# csv_old_files = [file for file in all_csv_files if any(sp_char in file for sp_char in sp_old_chars)]
# csv_new_files = [file for file in all_csv_files if any(sp_char in file for sp_char in sp_new_chars)]
# return csv_old_files, csv_new_files