184 lines
7.1 KiB
Python
184 lines
7.1 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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# jerome:增加文件名修改工作
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import shutil
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# Jerome:需要设置的参数
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new_directory = r"C:/simnow_trader" #设置运行目录为程序所在目录new_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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# 定义生成的保证金、手续费csv文件
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fees_filepath = r'./futures_fees_info.csv'
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contracts_filepath = r'./main_contacts.csv'
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contracts_yd_filepath = r'./main_contacts_yd.csv'
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exchange_filepath = r'./exchange_for_months.csv'
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# jerome:修改运行目录
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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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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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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['合约代码'].apply(lambda x: pd.Series(split_alpha_numeric(x)))
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futures_fees_info_df.to_csv(fees_filepath, index=False)
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print("期货保证金、手续费csv文件已经保存!")
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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(r'./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(r'./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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if os.path.exists(contracts_filepath):
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os.remove(contracts_filepath)
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print("原有今日期货主连csv文件已经删除!")
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df.to_csv(r'./main_contacts.csv')
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os.remove(r'./tmp_main_contacts.csv')
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print("今日期货主连csv文件已经保存!")
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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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def rename_file():
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# source_file = r'./main_contacts.csv'
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# target_file = r'./main_contacts_yd.csv'
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# 检查是否存在 main_contacts_yd.csv，如果存在则删除
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if os.path.exists(contracts_yd_filepath):
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os.remove(contracts_yd_filepath)
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print("原有昨日期货主连csv文件已经删除!")
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shutil.copy(contracts_filepath, contracts_yd_filepath)
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# print(f"{contracts_filepath} has been copied to {contracts_yd_filepath}")
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print("今日期货主连csv文件已经修改为昨日主连csv文件!")
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# # 重命名文件
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# if os.path.exists(source_file):
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# os.rename(source_file, target_file)
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# print(f'Renamed {source_file} to {target_file}')
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# else:
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# print(f'{source_file} does not exist')
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def exchange_for_months():
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td_df = pd.read_csv(contracts_filepath, header = 0, usecols= [16, 17],names=['主连代码', '品种代码'])
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if not os.path.exists(contracts_yd_filepath):
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shutil.copy(contracts_filepath, contracts_yd_filepath)
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print("昨日主连csv文件不存在,已经使用今日主连csv替代!")
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yd_df = pd.read_csv(contracts_yd_filepath, header = 0, usecols= [16, 17],names=['主连代码', '品种代码'])
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# 合并两个 DataFrame
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merged_df = pd.merge(td_df, yd_df, on='品种代码', suffixes=('_今日', '_昨日'))
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# 检查 '主连代码' 是否相等
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merged_df['主连代码_相等'] = merged_df['主连代码_今日'] == merged_df['主连代码_昨日']
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if os.path.exists(exchange_filepath):
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os.remove(exchange_filepath)
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print("原有换月对比csv文件已经删除!")
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merged_df.to_csv(exchange_filepath)
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print("换月对比csv文件已经生成!")
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# 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；
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# 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。
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# 设置定时任务，关闭程序
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schedule.every().day.at("15:10").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("08:55").do(run_scripts)
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schedule.every().day.at("20:55").do(run_scripts)
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# schedule.every().day.at("22:02").do(run_scripts)
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# 设置定时任务，生成费率表文件和今日主连文件
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schedule.every().day.at("20:53").do(get_futures_fees_info)
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schedule.every().day.at("20:53").do(get_main_contacts)
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# schedule.every().day.at("22:01").do(get_futures_fees_info)
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# schedule.every().day.at("22:01").do(get_main_contacts)
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# 设置定时任务，生成换月对比文件
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schedule.every().day.at("20:55").do(exchange_for_months)
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# schedule.every().day.at("22:02").do(exchange_for_months)
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# 设置定时任务，安排任务每天15:30执行, 生成昨日主连文件
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schedule.every().day.at("15:05").do(rename_file)
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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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