import subprocess import schedule import time from datetime import datetime # jerome：增加akshare库 import akshare as ak # jerome:增加下列库用于爬虫获取主力连续代码 import pandas as pd import requests from bs4 import BeautifulSoup import csv import re import os # jerome:增加文件名修改工作 import shutil # Jerome:需要设置的参数 new_directory = r"C:/simnow_trader" #设置运行目录为程序所在目录new_directory # 定义要启动的文件 files_to_run = ['dingdanliu_nb.py'] # 定义生成的保证金、手续费csv文件 fees_filepath = r'./futures_fees_info.csv' contracts_filepath = r'./main_contacts.csv' contracts_yd_filepath = r'./main_contacts_yd.csv' exchange_filepath = r'./exchange_for_months.csv' # jerome:修改运行目录 # 获取当前工作目录 current_directory = os.getcwd() print("当前工作目录：", current_directory) # 设置新的工作目录 os.chdir(new_directory) # 验证新的工作目录 updated_directory = os.getcwd() print("已更改为新的工作目录：", updated_directory) def run_scripts(): print("启动程序...") for file in files_to_run: time.sleep(1) # 使用subprocess模块运行命令 subprocess.Popen(['start', 'cmd', '/k', 'python', file], shell=True) print(file) print(datetime.now(),'程序重新启动完成，等待明天关闭重启') def close_scripts(): print("关闭程序...") # 通过创建一个包含关闭指定窗口命令的批处理文件来关闭CMD窗口 def close_specific_cmd_window(cmd_window_title): with open("close_cmd_window.bat", "w") as batch_file: batch_file.write(f'@echo off\nfor /f "tokens=2 delims=," %%a in (\'tasklist /v /fo csv ^| findstr /i "{cmd_window_title}"\') do taskkill /pid %%~a') # 运行批处理文件 subprocess.run("close_cmd_window.bat", shell=True) # 循环关闭所有脚本对应的CMD窗口 for title in files_to_run: close_specific_cmd_window(title) print(datetime.now(),'已关闭程序，等待重新运行程序') # jerome:增加使用akshare获取期货的手续费等数据，并保存到对应目录下 def get_futures_fees_info(): futures_fees_info_df = ak.futures_fees_info() futures_fees_info_df[['品种代码', '交割月份']] = futures_fees_info_df['合约代码'].apply(lambda x: pd.Series(split_alpha_numeric(x))) futures_fees_info_df.to_csv(fees_filepath, index=False) print("期货保证金、手续费csv文件已经保存!") def get_main_contacts(): url = "https://www.9qihuo.com/hangqing" # 发送GET请求，禁用SSL验证 response = requests.get(url, verify=False) response.encoding = 'utf-8' # 确保编码正确 # 解析网页内容 soup = BeautifulSoup(response.text, 'lxml') # 找到目标表格 table = soup.find('table', {'id': 'tblhangqinglist'}) # 初始化CSV文件 with open(r'./tmp_main_contacts.csv', mode='w', newline='', encoding='utf-8') as file: writer = csv.writer(file) # 遍历表格的所有行 for row in table.find_all('tr'): # 获取每一行的所有单元格 cols = row.find_all(['th', 'td']) # 提取文本内容并写入CSV文件 writer.writerow([col.text.strip() for col in cols]) df = pd.read_csv(r'./tmp_main_contacts.csv',encoding='utf-8') df['交易品种'] = df['合约'].str.split(r'[()]', n=1, expand=True)[0] df['主连代码'] = df['合约'].str.split(r'[()]', n=2, expand=True)[1] df[['品种代码', '交割月份']] = df['主连代码'].apply(lambda x: pd.Series(split_alpha_numeric(x))) if os.path.exists(contracts_filepath): os.remove(contracts_filepath) print("原有今日期货主连csv文件已经删除!") df.to_csv(r'./main_contacts.csv') os.remove(r'./tmp_main_contacts.csv') print("今日期货主连csv文件已经保存!") # 拆分字母和数字的函数 def split_alpha_numeric(s): match = re.match(r"([a-zA-Z]+)([0-9]+)", s) if match: return match.groups() else: return (s, None) # 如果没有匹配，返回原始字符串和None def rename_file(): # source_file = r'./main_contacts.csv' # target_file = r'./main_contacts_yd.csv' # 检查是否存在 main_contacts_yd.csv，如果存在则删除 if os.path.exists(contracts_yd_filepath): os.remove(contracts_yd_filepath) print("原有昨日期货主连csv文件已经删除!") shutil.copy(contracts_filepath, contracts_yd_filepath) # print(f"{contracts_filepath} has been copied to {contracts_yd_filepath}") print("今日期货主连csv文件已经修改为昨日主连csv文件!") # # 重命名文件 # if os.path.exists(source_file): # os.rename(source_file, target_file) # print(f'Renamed {source_file} to {target_file}') # else: # print(f'{source_file} does not exist') def exchange_for_months(): td_df = pd.read_csv(contracts_filepath, header = 0, usecols= [16, 17],names=['主连代码', '品种代码']) if not os.path.exists(contracts_yd_filepath): shutil.copy(contracts_filepath, contracts_yd_filepath) print("昨日主连csv文件不存在,已经使用今日主连csv替代!") yd_df = pd.read_csv(contracts_yd_filepath, header = 0, usecols= [16, 17],names=['主连代码', '品种代码']) # 合并两个 DataFrame merged_df = pd.merge(td_df, yd_df, on='品种代码', suffixes=('_今日', '_昨日')) # 检查 '主连代码' 是否相等 merged_df['主连代码_相等'] = merged_df['主连代码_今日'] == merged_df['主连代码_昨日'] if os.path.exists(exchange_filepath): os.remove(exchange_filepath) print("原有换月对比csv文件已经删除!") merged_df.to_csv(exchange_filepath) print("换月对比csv文件已经生成!") # 程序时间建议(以每晚9点交易为最开始时间）：1生成费率表和今日主连文件;2生成换月对比文件；3、启动晚间交易程序并登录账户； # 4关闭晚间交易程序；5启动白天交易程序并登录账户；6生成昨日主连文件；7关闭白天交易程序。 # 设置定时任务，关闭程序 schedule.every().day.at("15:10").do(close_scripts) schedule.every().day.at("03:00").do(close_scripts) # 设置定时任务，启动程序 schedule.every().day.at("08:55").do(run_scripts) schedule.every().day.at("20:55").do(run_scripts) # schedule.every().day.at("22:02").do(run_scripts) # 设置定时任务，生成费率表文件和今日主连文件 schedule.every().day.at("20:53").do(get_futures_fees_info) schedule.every().day.at("20:53").do(get_main_contacts) # schedule.every().day.at("22:01").do(get_futures_fees_info) # schedule.every().day.at("22:01").do(get_main_contacts) # 设置定时任务，生成换月对比文件 schedule.every().day.at("20:55").do(exchange_for_months) # schedule.every().day.at("22:02").do(exchange_for_months) # 设置定时任务，安排任务每天15:30执行, 生成昨日主连文件 schedule.every().day.at("15:05").do(rename_file) # 保持脚本运行，等待定时任务触发 while True: schedule.run_pending() time.sleep(1)