Win_home
123 lines
4.2 KiB
Python
123 lines
4.2 KiB
Python
import subprocess
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import schedule
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import time
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from datetime import datetime
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# jerome：增加akshare库
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import akshare as ak
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# jerome:增加下列库用于爬虫获取主力连续代码
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import pandas as pd
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import requests
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from bs4 import BeautifulSoup
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import csv
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import re
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import os
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# 获取当前工作目录
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current_directory = os.getcwd()
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print("当前工作目录：", current_directory)
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# 设置新的工作目录
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new_directory = "C:/simnow_trader"
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os.chdir(new_directory)
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# 验证新的工作目录
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updated_directory = os.getcwd()
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print("已更改为新的工作目录：", updated_directory)
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# 定义要启动的文件
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files_to_run = ['dingdanliu_nb.py']
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def run_scripts():
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print("启动程序...")
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for file in files_to_run:
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time.sleep(1)
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# 使用subprocess模块运行命令
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subprocess.Popen(['start', 'cmd', '/k', 'python', file], shell=True)
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print(file)
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print(datetime.now(),'程序重新启动完成，等待明天关闭重启')
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def close_scripts():
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print("关闭程序...")
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# 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
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def close_specific_cmd_window(cmd_window_title):
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with open("close_cmd_window.bat", "w") as batch_file:
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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')
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# 运行批处理文件
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subprocess.run("close_cmd_window.bat", shell=True)
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# 循环关闭所有脚本对应的CMD窗口
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for title in files_to_run:
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close_specific_cmd_window(title)
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print(datetime.now(),'已关闭程序，等待重新运行程序')
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# 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
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def get_futures_fees_info():
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futures_fees_info_df = ak.futures_fees_info()
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futures_fees_info_df[['品种代码', '交割月份']] = futures_fees_info_df['合约'].apply(lambda x: pd.Series(split_alpha_numeric(x)))
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futures_fees_info_df.to_csv(r'./futures_fees_info.csv', index=False)
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print("期货费率表已经保存为futures_fees_info.csv")
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def get_main_contacts():
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url = "https://www.9qihuo.com/hangqing"
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# 发送GET请求，禁用SSL验证
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response = requests.get(url, verify=False)
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response.encoding = 'utf-8' # 确保编码正确
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# 解析网页内容
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soup = BeautifulSoup(response.text, 'lxml')
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# 找到目标表格
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table = soup.find('table', {'id': 'tblhangqinglist'})
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# 初始化CSV文件
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with open('tmp_main_contacts.csv', mode='w', newline='', encoding='utf-8') as file:
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writer = csv.writer(file)
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# 遍历表格的所有行
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for row in table.find_all('tr'):
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# 获取每一行的所有单元格
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cols = row.find_all(['th', 'td'])
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# 提取文本内容并写入CSV文件
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writer.writerow([col.text.strip() for col in cols])
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df = pd.read_csv('./tmp_main_contacts.csv',encoding='utf-8')
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df['交易品种'] = df['合约'].str.split(r'[()]', n=1, expand=True)[0]
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df['主连代码'] = df['合约'].str.split(r'[()]', n=2, expand=True)[1]
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df[['品种代码', '交割月份']] = df['主连代码'].apply(lambda x: pd.Series(split_alpha_numeric(x)))
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df.to_csv('./main_contacts.csv')
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print("期货主力品种表已经保存为main_contacts.csv")
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os.remove("./tmp_main_contacts.csv")
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# 拆分字母和数字的函数
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def split_alpha_numeric(s):
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match = re.match(r"([a-zA-Z]+)([0-9]+)", s)
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if match:
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return match.groups()
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else:
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return (s, None) # 如果没有匹配，返回原始字符串和None
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# 设置定时任务，关闭程序
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schedule.every().day.at("15:30").do(close_scripts)
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schedule.every().day.at("03:00").do(close_scripts)
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# 设置定时任务，启动程序
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schedule.every().day.at("08:57").do(run_scripts)
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schedule.every().day.at("20:55").do(run_scripts)
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# # # 设置定时任务，生成futures_fees_info和main_contacts
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schedule.every().day.at("20:50").do(get_futures_fees_info)
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schedule.every().day.at("20:50").do(get_main_contacts)
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# 保持脚本运行，等待定时任务触发
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while True:
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schedule.run_pending()
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time.sleep(1)
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