286 lines
8.7 KiB
Python
286 lines
8.7 KiB
Python
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import ftplib
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import os
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from datetime import datetime
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from ftplib import FTP, error_perm
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from pathlib import Path
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import akshare as ak
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import efinance as ef
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import qstock as qs
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import tushare as ts
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import pandas as pd
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import time
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import random
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from tabulate import tabulate
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import platform
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import xcsc_tushare as xc
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token = '0718534658b9d91b3f03dc8b220e4062193ebf6f6414d036505165e1'
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xc.set_token('bd4f26f1eca8d660bd23e229260df46002d630d6e1fb9226380edec8')
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# 仿真环境用这个方式可以连上，生产环境用这个连不上
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xcsc_pro = xc.pro_api(env='prd', server='http://116.128.206.39:7172')
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# 显示所有列
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pd.set_option('display.max_columns', None)
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# 显示所有行
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pd.set_option('display.max_rows', None)
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# 输出不折行
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pd.set_option('expand_frame_repr', False)
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# 最大列宽度
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pd.set_option('display.max_colwidth', None)
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def sleep():
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print("开始休眠，防止ip被拉黑或者进小黑屋")
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# 生成随机的休眠时间
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sleep_time = random.uniform(0, 2)
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# 执行休眠
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time.sleep(sleep_time)
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print("休眠结束！")
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def print_markdown(df: pd):
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print(tabulate(df, headers='keys', tablefmt='github'))
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def get_file(size):
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"""
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获取不同系统下的文件路径
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:param size:
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:return:
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"""
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sys_platform = platform.platform().lower()
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if "macos" in sys_platform:
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return f'/Users/renmeng/work_space/python_work/qnloft-get-web-everything/股票金融/量化交易/股票数据/{size}'
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elif "windows" in sys_platform:
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return f'E:\\Python_Workplace\qnloft-get-web-everything\\股票金融\\量化交易\\股票数据\\{size}'
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else:
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print("其他系统")
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def write_file(content, file_name, mode_type='a'):
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file = ""
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sys_platform = platform.platform().lower()
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if "macos" in sys_platform:
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file = f'/Users/renmeng/work_space/python_work/qnloft-get-web-everything/股票金融/量化交易/股票数据/{file_name}'
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elif "windows" in sys_platform:
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file = f'D:\\文档\\数据测试\\{file_name}'
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else:
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print("其他系统")
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path = Path(file)
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# 创建目录或文件
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if not path.exists():
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if path.is_dir():
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path.mkdir(parents=True) # 创建目录及其父目录
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else:
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path.touch() # 创建文件
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with path.open(mode=mode_type) as file:
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file.write(content)
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def create_file(file_name):
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path = Path(file_name)
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# 创建目录或文件
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if not path.exists():
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path.mkdir(parents=True) # 创建目录及其父目录
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def get_random_stock(size, n=0):
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if size == 'M':
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s_list = Path(get_file(size)).read_text().splitlines()
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elif size == 'S':
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s_list = Path(get_file(size)).read_text().splitlines()
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elif size == 'L':
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s_list = Path(get_file(size)).read_text().splitlines()
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else:
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raise ValueError("size输入错误！")
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if n > 0:
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return random.sample(s_list, n)
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else:
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return s_list
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def del_file_lines(size, to_delete):
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content = Path(get_file(size)).read_text()
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# 删除匹配的数字
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for char in to_delete:
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content = content.replace(char, '')
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# 将更新后的内容写回文件
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with Path(get_file(size)).open(mode='w') as file:
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file.writelines(content)
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def if_run(current_date=datetime.now()):
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"""
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判断当前日期是否是交易日
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:param current_date:
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:return:
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"""
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df = ak.tool_trade_date_hist_sina()
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# 将日期列转换为 datetime 类型
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df['trade_date'] = pd.to_datetime(df['trade_date'])
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date_now_hour = current_date.hour
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# 将日期格式化为 "yyyy-mm-dd" 形式
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formatted_date = current_date.strftime('%Y-%m-%d')
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# 检查 DataFrame 是否包含特定日期
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contains_target_date = (df['trade_date'] == formatted_date).any()
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print(f'当日日期是：{formatted_date} ,今日是否开盘：{contains_target_date}')
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if contains_target_date:
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return True
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return False
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def return_trading_day(now_date, offset_date, format_type='%Y%m%d'):
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"""
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获取当前时间的 前n个交易日期，后n个交易日期
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:param format_type:
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:param offset_date:
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:param now_date:
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:return:
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"""
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df = ak.tool_trade_date_hist_sina()
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# 将日期列转换为 datetime 类型
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df['trade_date'] = pd.to_datetime(df['trade_date'])
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if isinstance(now_date, datetime):
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formatted_date = now_date.strftime('%Y-%m-%d')
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else:
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formatted_date = now_date
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selected_indexes = df[df['trade_date'] == formatted_date].index
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return df.loc[selected_indexes + offset_date, 'trade_date'].dt.strftime(format_type).values[0]
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def upload_files_to_ftp(local_directory, remote_directory, file_name=None):
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"""
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ftp上传
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:param local_directory:
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:param remote_directory:
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:param file_name:
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:return:
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"""
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try:
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ftp = FTP("qxu1142200198.my3w.com")
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ftp.login('qxu1142200198', '48XZ55MB')
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except error_perm as e:
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print(f"Network error: {e}")
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return False
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try:
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# 切换目录
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ftp.cwd(remote_directory)
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except ftplib.error_perm:
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print("目录不存在, 开始创建...")
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ftp.mkd(remote_directory)
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try:
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path = Path(local_directory)
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# 判断是文件夹还是文件
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if path.is_dir() and file_name is None:
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files = os.listdir(local_directory) # get list of files in local directory
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for file in files:
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if file.endswith(".html"): # upload only .txt files
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local_file = f"{local_directory}/{file}"
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remote_file = f"{remote_directory}/{file}"
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print(local_file, "----->>", remote_file)
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with open(local_file, 'rb') as f:
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try:
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ftp.delete(remote_file)
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except ftplib.error_perm:
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print("目录不存在, 或已经删除...")
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ftp.storbinary(f"STOR {remote_file}", f) # upload file to FTP server
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if file_name is not None and len(file_name) > 0:
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local_file = f"{local_directory}/{file_name}"
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remote_file = f"{remote_directory}/{file_name}"
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print(local_file, "----->>", remote_file)
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with open(local_file, 'rb') as f:
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try:
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ftp.delete(remote_file)
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except ftplib.error_perm:
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print("目录不存在, 或已经删除...")
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ftp.storbinary(f"STOR {remote_file}", f) # upload file to FTP server
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finally:
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ftp.quit()
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return True
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def back_testing(df, cond: str, n=3):
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"""
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回撤测试
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:param df:
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:param cond:
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:param n:
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:return:
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"""
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filtered_df = df.eval(cond)
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total, tomorrow_rise, tomorrow_fall = 0, 0, 0
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res_df = pd.DataFrame(
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columns=['code', '日期', '明天涨跌幅', 'n日最大涨幅', 'n日平均涨跌幅', 'n日最大回撤'])
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for index, row in df.iterrows():
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if filtered_df[index] and index > n:
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# print(f'{row["trade_date"]} --> {row["KDJ_D"]} --> {row["KDJ_J"]}')
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total += 1
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# 明天涨跌幅情况
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tomorrow_pre = df.iloc[-index:-index + 1]['pct_chg'].values[0].round(2)
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# n日 最大涨幅
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max_h = df.iloc[-index:-index + n]['pct_chg'].max().round(2)
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# n日 平均涨跌幅
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mean_h = df.iloc[-index:-index + n]['pct_chg'].mean().round(2)
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# n日 最大回撤
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h_max = df.iloc[-index:-index + n]['high'].max()
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l_min = df.iloc[-index:-index + n]['low'].min()
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max_ret = f"{(h_max - l_min) / l_min:.2f}"
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res_df.loc[total, 'code'], res_df.loc[total, '日期'] = row['ts_code'], row["trade_date"]
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res_df.loc[total, '明天涨跌幅'] = tomorrow_pre
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res_df.loc[total, 'n日最大涨幅'] = max_h
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res_df.loc[total, 'n日平均涨跌幅'] = mean_h
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res_df.loc[total, 'n日最大回撤'] = max_ret
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return res_df
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def buying_and_selling_decisions(df, cond_buy: str, cond_sell: str, cond_buy_read=None, cond_sell_read=None):
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"""
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买卖测试
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:param df:
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:param cond_buy:
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:param cond_sell:
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:param cond_buy_read:
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:param cond_sell_read:
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:return:
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"""
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total, buy, sell, total_chg, buy_date, flag_ready_b, flag_ready_s = 0, 0, 0, 0, 0, False, False
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res_df = pd.DataFrame(
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columns=['code', '买入日期', '卖出日期', '盈利', '持仓天数'])
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for index, row in df.iterrows():
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trade_date, c = row["trade_date"], row['close']
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if (eval(cond_buy_read) or cond_buy_read is None) and buy == 0:
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# 准备买入
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print(f"{trade_date} {cond_buy_read}，准备买入！")
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flag_ready_b = True
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if eval(cond_buy) and buy == 0 and flag_ready_b:
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# 买入
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print(f"{trade_date} {cond_buy}，买入操作！")
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buy, buy_date = c, trade_date
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flag_ready_b = False
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if (eval(cond_sell_read) or cond_sell_read is None) and buy > 0:
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# 准备卖出
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print(f"{trade_date} {cond_sell_read}，准备卖出！")
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flag_ready_s = True
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if eval(cond_sell) and buy > 0 and flag_ready_s:
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print(f"{trade_date} {cond_sell}，卖出操作！")
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total += 1
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# 卖出
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sell = c
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# 计算盈利
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chg = round(((sell - buy) / buy) * 100, 2)
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# 计算持仓天数
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start_date = datetime.strptime(buy_date, "%Y%m%d")
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end_date = datetime.strptime(trade_date, "%Y%m%d")
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# 计算间隔天数
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interval_days = (end_date - start_date).days
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buy, flag_ready_s = 0, False
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res_df.loc[total, 'code'], res_df.loc[total, '买入日期'] = row['ts_code'], buy_date
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res_df.loc[total, '卖出日期'] = trade_date
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res_df.loc[total, '盈利'] = chg
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res_df.loc[total, '持仓天数'] = interval_days
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return res_df
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