python 金融量化盘后分析系统V0.48.5

前言：
添加了多股回测结果导出CSV功能，点击回测结果导出将自动在项目的文件夹下自动创建 ‘策略结果’ 文件夹，之后会根据你策略文件的名称在‘策略结果’文件夹下再次创建一个以策略文件名命名的文件夹，最后创建一个以策略名称+时间的csv文件，如下图所示：


只修改了stock_backtrader.py文件，其他文件代码不变，在股票量化盘后分析系统V0.47文章里

# coding=utf-8
from __future__ import (absolute_import, division, print_function,
                        unicode_literals)
import datetime
import time
import pandas as pd
import backtrader as bt
import tushare as ts
import tk_window
import tkinter as tk
import tkinter.messagebox
from tkinter import ttk
import matplotlib.pyplot as plt
import mplfinance as mpf
import os
import threading
import inspect
import ctypes
import function
from matplotlib.backends.backend_tkagg import (FigureCanvasTkAgg, NavigationToolbar2Tk)  # 使用后端TkAgg

# pd.set_option()就是pycharm输出控制显示的设置
pd.set_option('expand_frame_repr', False)  # True就是可以换行显示。设置成False的时候不允许换行
pd.set_option('display.max_columns', None)  # 显示所有列
# pd.set_option('display.max_rows', None)  # 显示所有行
pd.set_option('colheader_justify', 'centre')  # 显示居中
# 保存token到本地,不进行本地保存可能出现ts.pro_bar()通用接口无法使用
ts.set_token('数据用的是tushare,没权限自己去注册个吧')
#  初始化pro接口
pro = ts.pro_api()


# class my_strategy(bt.Strategy):
#     # 设置简单均线周期，以备后面调用
#     params = (
#         ('maperiod21', 21),
#         ('maperiod55', 55),)
#
#     def log(self, txt, dt=None):
#         # 日记记录输出
#         dt = dt or self.datas[0].datetime.date(0)
#         print('%s, %s' % (dt.isoformat(), txt))
#
#     def __init__(self):
#         # 初始化数据参数
#         # 设置当前收盘价为dataclose
#         self.dataclose = self.datas[0].close
#
#         self.order = None
#         self.buyprice = None
#         self.buycomm = None
#
#         # 添加简单均线, subplot=False是否单独子图显示
#         self.sma21 = bt.indicators.SimpleMovingAverage(self.datas[0], period=self.params.maperiod21, plotname='mysma')
#         self.sma55 = bt.indicators.SimpleMovingAverage(self.datas[0], period=self.params.maperiod55, subplot=False)
#
#     def next(self):
#         # self.log('Close, %.2f' % self.dataclose[0])  # 输出打印收盘价
#         # self.log('持仓 %.2f' % self.position.size)  # 输出持仓
#         # 检查是否有订单发送当中，如果有则不再发送第二个订单
#         if self.order:
#             return
#
#         # 检查是否已经有仓位
#         if not self.position:
#             # 如果没有则可以执行一下策略了
#             if self.sma21[0] > self.sma55[0] and self.sma21[-1] < self.sma55[-1]:
#                 # 记录输出买入价格
#                 # self.log('买入信号产生的价格： %.2f' % self.dataclose[0])
#                 # 跟踪已经创建好的订单避免重复第二次交易
#                 self.order = self.buy()
#
#         else:
#             if self.sma21[0] < self.sma55[0] and self.sma21[-1] > self.sma55[-1]:
#                 # self.log('卖入信号产生的价格： %.2f' % self.dataclose[0])
#                 self.order = self.sell()
#
#     # 记录交易执行情况，输出打印
#     def notify_order(self, order):
#         if order.status in [order.Submitted, order.Accepted]:
#             # 如果有订单提交或者已经接受的订单，返回退出
#             return
#         # 主要是检查有没有成交的订单，如果有则日志记录输出价格，金额，手续费。注意，如果资金不足是不会成交订单的
#         if order.status in [order.Completed]:
#             # if order.isbuy():
#             #     self.log(
#             #         '实际买入价格: %.2f, 市值: %.2f, 手续费 %.2f' %
#             #         (order.executed.price,
#             #          order.executed.value,
#             #          order.executed.comm))
#             #
#             #     self.buyprice = order.executed.price
#             #     self.buycomm = order.executed.comm
#             # else:  # Sell
#             # self.log('实际卖出价格: %.2f, 市值: %.2f, 手续费 %.2f' %
#             #          (order.executed.price,
#             #           order.executed.value,
#             #           order.executed.comm))
#             # len(self)是指获取截至当前数据一共有多少根bar
#             # 以下代码就是指当交易发生时立刻记录下了当天有多少根bar
#             # 如果要表示当成交后过了5天卖，则可以这样写 if len(self) >= (self.bar_executed + 5):
#             self.bar_executed = len(self)
#
#         elif order.status in [order.Canceled, order.Margin, order.Rejected]:
#             self.log('Order Canceled/Margin/Rejected')
#
#         self.order = None
#
#     # 记录交易收益情况
#     # def notify_trade(self, trade):
#     #     if not trade.isclosed:  # 如果交易还没有关闭，则退出不输出显示盈利跟手续费
#     #         return
#     #     self.log('策略收益 %.2f, 成本 %.2f' %
#     #              (trade.pnl, trade.pnlcomm))
#
#     def stop(self):
#         # 策略停止输出结果
#         total_funds = self.broker.getvalue()
#     # print('MA均线： %2d日，总资金: %.2f' % (self.params.maperiod21, total_funds))
#

def run_cerebro():  # 策略回测
    for widget_backtrader_window in tk_window.centre_frame.winfo_children():
        widget_backtrader_window.destroy()
    backtrader_window = tk.PanedWindow(tk_window.centre_frame, opaqueresize=False)
    backtrader_window.pack(fill=tk.BOTH, expand=1)
    # 创建左边frame框架，主要放回测策略文件代码（暂未开发，占位而已）
    backtrader_left_frame = tk.Frame(backtrader_window, bg='#353535', bd=5, borderwidth=4)
    backtrader_left_frame.pack(fill=tk.BOTH, expand=1)

    # ******************************************************************************************************************
    # 设置策略文本内容功能
    # path = sys.path[0]  # 启动的py文件所在的路径
    File_Path = os.getcwd() + '\\策略文件'  # 获取到当前项目文件的目录，并检查是否有‘策略文件’文件夹，如果不存在则自动新建‘策略文件’文件夹
    if not os.path.exists(File_Path):
        os.makedirs(File_Path)
    pathList = os.path.split(File_Path)  # 分别获取得到绝对路径跟文件夹名称，得到的是个列表
    pathlist_name = pathList[-1]  # 获取文件夹名称，如果是[0]则是获取绝对路径
    # 设置treeview背景
    # strategystyle = ttk.Style()
    # strategystyle.configure('TFrame', background='#353535', foreground='white')

    # 写入父节名称
    strategy_list_tree = ttk.Treeview(backtrader_left_frame, show='tree')
    father_treeview = strategy_list_tree.insert('', 'end', text=pathlist_name, open=True)

    for filepath in os.listdir(File_Path):
        strategy_list_tree.insert(father_treeview, "end", text=filepath)  # 写入子节名称
    strategy_list_tree.pack(fill=tk.BOTH, expand=1)

    def create_newfile():
        if not os.path.exists(File_Path + '\\' + '新建策略.txt'):
            txt_file = open(File_Path + '\\' + '新建策略.txt', 'ab+')
            txt_file.close()
        else:
            txt_file = open(File_Path + '\\' + '新建策略1.txt', 'ab+')
            txt_file.close()

    def rename_newfile():
        rename_input_frame = tk.Toplevel()
        rename_input_frame.title('重命名')
        rename_input_frame.geometry('{}x{}+{}+{}'.format(180, 35, int(tk_window.screenWidth / 4),
                                                         int(tk_window.screenHeight / 4)))
        rename_input_var = tk.StringVar()
        rename_input_widget = tk.Entry(rename_input_frame, textvariable=rename_input_var, justify=tk.CENTER)
        rename_input_widget.pack(side=tk.LEFT)

        def rename_now():
            for item in strategy_list_tree.selection():
                item_text = strategy_list_tree.item(item, "text")  # 获取选中树形条目的名称
                os.rename(File_Path + '\\' + item_text, File_Path + '\\' + rename_input_var.get())

        rename_button = tk.Button(rename_input_frame, text='rename', height=1, command=rename_now)
        rename_button.pack(side=tk.RIGHT)

    def edit_file():
        for item in strategy_list_tree.selection():
            item_text = strategy_list_tree.item(item, "text")  # 获取选中树形条目的名称
            select_filepath = File_Path + '\\' + item_text  # 得到选中项目的绝对路径
            os.startfile(select_filepath)  # 打开文件，如果不是txt格式的文件会弹出窗口让你选择打开方式

    # 设置获取策略文件txt内容的函数，首先通过read获取内容，然后通过exec(use_strategy())将本函数返回的str格式文本内容转换成可执行的代码
    # 在每个策略运行前的代码先写class my_strategy(bt.Strategy):，然后再写exec(use_strategy())
    # 在运行策略是先选中你建立的txt策略文件，然后右键鼠标在弹出的菜单中选中使用该策略选项，之后再运行回测
    def use_strategy():
        for item in strategy_list_tree.selection():
            item_text = strategy_list_tree.item(item, "text")  # 获取选中树形条目的名称
            select_filepath = File_Path + '\\' + item_text  # 得到选中项目的绝对路径
            txt_file = open(select_filepath, 'r', encoding='UTF-8')
            txt_data = txt_file.read()
            txt_file.close()
        return txt_data


    def delect_file():
        for item in strategy_list_tree.selection():
            item_text = strategy_list_tree.item(item, "text")  # 获取选中树形条目的名称
            select_filepath = File_Path + '\\' + item_text  # 得到选中项目的绝对路径
            delect_confirm = tk.messagebox.askokcancel('提示', '要执行此操作吗？文件直接删除不放回收站！')
            if delect_confirm:  # 如果返回True，则执行删除，
                os.remove(select_filepath)  # 打开文件，如果不是txt格式的文件会弹出窗口让你选择打开方式

    # 创建弹出菜单，为后面功能开发做准备
    strategy_menu = tk.Menu(backtrader_left_frame, tearoff=False)  # tearoff=True显示分割线
    strategy_menu.add_command(label='新建策略', command=create_newfile)  # 弹出菜单内容
    strategy_menu.add_command(label='编辑', command=edit_file)  # 弹出菜单内容
    strategy_menu.add_command(label='运行该策略', command=use_strategy)  # 弹出菜单内容
    strategy_menu.add_command(label='重命名', command=rename_newfile)  # 弹出菜单内容
    strategy_menu.add_command(label='删除', command=delect_file)
    strategy_menu.add_separator()
    strategy_menu.add_command(label='刷新', command=run_cerebro)

    def pop(event):
        strategy_menu.post(event.x_root, event.y_root)  # #设置弹出的位置

    strategy_list_tree.bind('', pop)  # 设置右键弹出菜单

    # ******************************************************************************************************************
    # 设置多股回测股票筛选条件功能，所有的筛选指标的值是昨日的数据值，当然也可以指定某一日的数据，此功能目前还不是必须，Mark一下
    # PE 市盈率（总市值/净利润， 亏损的PE为空）
    backtrader_pe_frame = tk.Frame(backtrader_left_frame, bg='#353535', bd=5, borderwidth=4)
    backtrader_pe_frame.pack()
    input_pe_leftvar = tk.StringVar()
    pe_leftwidget = tk.Entry(backtrader_pe_frame, textvariable=input_pe_leftvar, borderwidth=1, justify=tk.CENTER,
                             width=10)
    pe_leftwidget.pack(side=tk.LEFT, padx=4)
    multi_pe_label = tk.Label(backtrader_pe_frame, text='< PE <', height=1, bg='#353535', fg='white')
    multi_pe_label.pack(side=tk.LEFT)
    input_pe_rightvar = tk.StringVar()
    pe_rightwidget = tk.Entry(backtrader_pe_frame, textvariable=input_pe_rightvar, borderwidth=1, justify=tk.CENTER,
                              width=10)
    pe_rightwidget.pack(side=tk.LEFT, padx=4)

    # PB 市净率（总市值/净资产）
    backtrader_pb_frame = tk.Frame(backtrader_left_frame, bg='#353535', bd=5, borderwidth=4)
    backtrader_pb_frame.pack()
    input_pb_leftvar = tk.StringVar()
    pb_leftwidget = tk.Entry(backtrader_pb_frame, textvariable=input_pb_leftvar, borderwidth=1, justify=tk.CENTER,
                             width=10)
    pb_leftwidget.pack(side=tk.LEFT, padx=4)
    multi_pb_label = tk.Label(backtrader_pb_frame, text='< PB <', height=1, bg='#353535', fg='white')
    multi_pb_label.pack(side=tk.LEFT)
    input_pb_rightvar = tk.StringVar()
    pb_rightwidget = tk.Entry(backtrader_pb_frame, textvariable=input_pb_rightvar, borderwidth=1, justify=tk.CENTER,
                              width=10)
    pb_rightwidget.pack(side=tk.LEFT, padx=4)

    # PS 市销率
    backtrader_ps_frame = tk.Frame(backtrader_left_frame, bg='#353535', bd=5, borderwidth=4)
    backtrader_ps_frame.pack()
    input_ps_leftvar = tk.StringVar()
    ps_leftwidget = tk.Entry(backtrader_ps_frame, textvariable=input_ps_leftvar, borderwidth=1, justify=tk.CENTER,
                             width=10)
    ps_leftwidget.pack(side=tk.LEFT, padx=4)
    multi_ps_label = tk.Label(backtrader_ps_frame, text='< PS <', height=1, bg='#353535', fg='white')
    multi_ps_label.pack(side=tk.LEFT)
    input_ps_rightvar = tk.StringVar()
    ps_rightwidget = tk.Entry(backtrader_ps_frame, textvariable=input_ps_rightvar, borderwidth=1, justify=tk.CENTER,
                              width=10)
    ps_rightwidget.pack(side=tk.LEFT, padx=4)

    # volume_ratio 量比
    backtrader_volume_ratio_frame = tk.Frame(backtrader_left_frame, bg='#353535', bd=5, borderwidth=4)
    backtrader_volume_ratio_frame.pack()
    input_volume_ratio_leftvar = tk.StringVar()
    volume_ratio_leftwidget = tk.Entry(backtrader_volume_ratio_frame, textvariable=input_volume_ratio_leftvar,
                                       borderwidth=1, justify=tk.CENTER, width=9)
    volume_ratio_leftwidget.pack(side=tk.LEFT, padx=4)
    multi_volume_ratio_label = tk.Label(backtrader_volume_ratio_frame, text='< 量比 <', height=1, bg='#353535',
                                        fg='white')
    multi_volume_ratio_label.pack(side=tk.LEFT)
    input_volume_ratio_rightvar = tk.StringVar()
    volume_ratio_rightwidget = tk.Entry(backtrader_volume_ratio_frame, textvariable=input_volume_ratio_rightvar,
                                        borderwidth=1, justify=tk.CENTER, width=9)
    volume_ratio_rightwidget.pack(side=tk.LEFT, padx=4)

    # turnover_rate 换手率（%）
    backtrader_turnover_rate_frame = tk.Frame(backtrader_left_frame, bg='#353535', bd=5, borderwidth=4)
    backtrader_turnover_rate_frame.pack()
    input_turnover_rate_leftvar = tk.StringVar()
    turnover_rate_leftwidget = tk.Entry(backtrader_turnover_rate_frame, textvariable=input_turnover_rate_leftvar,
                                        borderwidth=1, justify=tk.CENTER, width=8)
    turnover_rate_leftwidget.pack(side=tk.LEFT, padx=4)
    multi_turnover_rate_label = tk.Label(backtrader_turnover_rate_frame, text='< 换手率 <', height=1, bg='#353535',
                                         fg='white')
    multi_turnover_rate_label.pack(side=tk.LEFT)
    input_turnover_rate_rightvar = tk.StringVar()
    turnover_rate_rightwidget = tk.Entry(backtrader_turnover_rate_frame, textvariable=input_turnover_rate_rightvar,
                                         borderwidth=1, justify=tk.CENTER, width=8)
    turnover_rate_rightwidget.pack(side=tk.LEFT, padx=4)

    backtrader_window.add(backtrader_left_frame, width=tk_window.screenHeight / 5.2)

    # total_share 总股本（万股）
    backtrader_total_share_frame = tk.Frame(backtrader_left_frame, bg='#353535', bd=5, borderwidth=4)
    backtrader_total_share_frame.pack()
    input_total_share_leftvar = tk.StringVar()
    total_share_leftwidget = tk.Entry(backtrader_total_share_frame, textvariable=input_total_share_leftvar,
                                      borderwidth=1, justify=tk.CENTER, width=5)
    total_share_leftwidget.pack(side=tk.LEFT, padx=4)
    multi_total_share_label = tk.Label(backtrader_total_share_frame, text='< 总股本(亿股) <', height=1, bg='#353535',
                                       fg='white')
    multi_total_share_label.pack(side=tk.LEFT)
    input_total_share_rightvar = tk.StringVar()
    total_share_rightwidget = tk.Entry(backtrader_total_share_frame, textvariable=input_total_share_rightvar,
                                       borderwidth=1, justify=tk.CENTER, width=5)
    total_share_rightwidget.pack(side=tk.LEFT, padx=4)

    # total_mv 总市值（万元）
    backtrader_total_mv_frame = tk.Frame(backtrader_left_frame, bg='#353535', bd=5, borderwidth=4)
    backtrader_total_mv_frame.pack()
    input_total_mv_leftvar = tk.StringVar()
    total_mv_leftwidget = tk.Entry(backtrader_total_mv_frame, textvariable=input_total_mv_leftvar,
                                   borderwidth=1, justify=tk.CENTER, width=5)
    total_mv_leftwidget.pack(side=tk.LEFT, padx=4)
    multi_total_mv_label = tk.Label(backtrader_total_mv_frame, text='< 总市值(亿元) <', height=1, bg='#353535',
                                    fg='white')
    multi_total_mv_label.pack(side=tk.LEFT)
    input_total_mv_rightvar = tk.StringVar()
    total_mv_rightwidget = tk.Entry(backtrader_total_mv_frame, textvariable=input_total_mv_rightvar,
                                    borderwidth=1, justify=tk.CENTER, width=5)
    total_mv_rightwidget.pack(side=tk.LEFT, padx=4)

    # float_share 流通股本（万股）
    backtrader_float_share_frame = tk.Frame(backtrader_left_frame, bg='#353535', bd=5, borderwidth=4)
    backtrader_float_share_frame.pack()
    input_float_share_leftvar = tk.StringVar()
    float_share_leftwidget = tk.Entry(backtrader_float_share_frame, textvariable=input_float_share_leftvar,
                                      borderwidth=1, justify=tk.CENTER, width=5)
    float_share_leftwidget.pack(side=tk.LEFT, padx=4)
    multi_float_share_label = tk.Label(backtrader_float_share_frame, text='< 流通股本(亿股) <', height=1, bg='#353535',
                                       fg='white')
    multi_float_share_label.pack(side=tk.LEFT)
    input_float_share_rightvar = tk.StringVar()
    float_share_rightwidget = tk.Entry(backtrader_float_share_frame, textvariable=input_float_share_rightvar,
                                       borderwidth=1, justify=tk.CENTER, width=5)
    float_share_rightwidget.pack(side=tk.LEFT, padx=4)


    # circ_mv 流通市值（万元）
    backtrader_circ_mv_frame = tk.Frame(backtrader_left_frame, bg='#353535', bd=5, borderwidth=4)
    backtrader_circ_mv_frame.pack()
    input_circ_mv_leftvar = tk.StringVar()
    circ_mv_leftwidget = tk.Entry(backtrader_circ_mv_frame, textvariable=input_circ_mv_leftvar,
                                  borderwidth=1, justify=tk.CENTER, width=5)
    circ_mv_leftwidget.pack(side=tk.LEFT, padx=4)
    multi_circ_mv_label = tk.Label(backtrader_circ_mv_frame, text='< 流通市值(亿元) <', height=1, bg='#353535',
                                   fg='white')
    multi_circ_mv_label.pack(side=tk.LEFT)
    input_circ_mv_rightvar = tk.StringVar()
    circ_mv_rightwidget = tk.Entry(backtrader_circ_mv_frame, textvariable=input_circ_mv_rightvar,
                                   borderwidth=1, justify=tk.CENTER, width=5)
    circ_mv_rightwidget.pack(side=tk.LEFT, padx=4)

    # free_share 自由流通股本（万股）
    backtrader_free_share_frame = tk.Frame(backtrader_left_frame, bg='#353535', bd=5, borderwidth=4)
    backtrader_free_share_frame.pack()
    input_free_share_leftvar = tk.StringVar()
    free_share_leftwidget = tk.Entry(backtrader_free_share_frame, textvariable=input_free_share_leftvar,
                                  borderwidth=1, justify=tk.CENTER, width=5)
    free_share_leftwidget.pack(side=tk.LEFT, padx=4)
    multi_free_share_label = tk.Label(backtrader_free_share_frame, text='< 自由流通股本(亿) <', height=1, bg='#353535',
                                   fg='white')
    multi_free_share_label.pack(side=tk.LEFT)
    input_free_share_rightvar = tk.StringVar()
    free_share_rightwidget = tk.Entry(backtrader_free_share_frame, textvariable=input_free_share_rightvar,
                                   borderwidth=1, justify=tk.CENTER, width=5)
    free_share_rightwidget.pack(side=tk.LEFT, padx=4)

    backtrader_window.add(backtrader_left_frame, width=tk_window.screenHeight / 4)

    # ******************************************************************************************************************
    # 创建右边图形输出框架，主要放回测分析显示跟用户输入的股票代码跟日期
    backtrader_plot_window = tk.PanedWindow(orient='vertical', opaqueresize=False)
    backtrader_window.add(backtrader_plot_window)

    backtrader_plot_window_top = tk.PanedWindow(opaqueresize=False)
    backtrader_plot_window.add(backtrader_plot_window_top)
    # ******************************************************************************************************************
    backtrader_top_left_frame = tk.Frame(backtrader_plot_window_top, width=tk_window.screenWidth,
                                         height=tk_window.screenHeight, relief=tk.SUNKEN, bg='#353535', bd=5,
                                         borderwidth=4)
    backtrader_top_left_frame.pack(fill=tk.BOTH)
    # 在主框架下创建股票代码输入子框架
    code_frame = tk.Frame(backtrader_top_left_frame, borderwidth=1, bg='#353535')
    code_frame.pack()
    # 创建标签‘股票代码’
    stock_label = tk.Label(code_frame, text='单股回测股票代码', bd=1, bg='#353535', fg='red')
    stock_label.pack(side=tk.LEFT)
    # 创建股票代码输入框
    input_code_frame = tk.Frame(backtrader_top_left_frame, borderwidth=1, bg='#353535')
    input_code_frame.pack()
    input_code_var = tk.StringVar()
    code_widget = tk.Entry(input_code_frame, textvariable=input_code_var, borderwidth=1, justify=tk.CENTER)
    code_widget.pack(side=tk.LEFT, padx=4)

    # 在主框架下创建股票日期输入框子框架
    input_date_frame = tk.Frame(backtrader_top_left_frame, borderwidth=1, bg='#353535')
    input_date_frame.pack()
    # 创建标签‘开始日期’
    date_start_label = tk.Label(input_date_frame, text='开始日期', bd=1, bg='#353535', fg='red')
    date_start_label.pack(side=tk.LEFT)
    # 创建开始日期代码输入框
    input_startdate_var = tk.StringVar()
    startdate_widget = tk.Entry(input_date_frame, textvariable=input_startdate_var, borderwidth=1, justify=tk.CENTER)
    startdate_widget.pack(side=tk.LEFT, padx=4)
    # 创建标签‘结束日期’
    date_end_label = tk.Label(input_date_frame, text='结束日期', bd=1, bg='#353535', fg='red')
    date_end_label.pack(side=tk.LEFT)
    # 创建结束日期代码输入框
    input_enddate_var = tk.StringVar()
    enddate_widget = tk.Entry(input_date_frame, textvariable=input_enddate_var, borderwidth=1, justify=tk.CENTER)
    enddate_widget.pack(side=tk.LEFT, padx=4)

    # 先把部件布局好了再backtrader_top_frame用.add()添加到backtrader_plot_window
    backtrader_plot_window_top.add(backtrader_top_left_frame, height=tk_window.screenHeight / 8,
                                   width=tk_window.screenHeight / 1.4)
    # ******************************************************************************************************************
    backtrader_top_right_frame = tk.Frame(backtrader_plot_window_top, width=tk_window.screenWidth,
                                          height=tk_window.screenHeight, relief=tk.SUNKEN, bg='#353535', bd=5,
                                          borderwidth=4)
    backtrader_top_right_frame.pack(fill=tk.BOTH)
    # 设置选中多股回测或全市回测时的命令函数

    multi_var = tk.IntVar()
    # 设置默认选项为1，默认全市股票回测
    multi_var.set(1)

    # 在主框架右边创建股票代码输入子框架
    multi_code_frame = tk.Frame(backtrader_top_right_frame, borderwidth=1, bg='#353535')
    multi_code_frame.pack()
    # 创建全市回测选择项
    all_stock_radiobutton = tk.Radiobutton(multi_code_frame, text='全市股票回测', variable=multi_var, value=1,
                                           bg='#353535', fg='red')
    all_stock_radiobutton.pack(side=tk.LEFT)
    # 创建标签‘股票代码’
    multi_stock_radiobutton = tk.Radiobutton(multi_code_frame, text='多股回测股票代码', variable=multi_var, value=2,
                                             bg='#353535', fg='red')
    multi_stock_radiobutton.pack(side=tk.LEFT)
    # 创建股票代码输入框
    input_multi_code_frame = tk.Frame(backtrader_top_right_frame, borderwidth=1, bg='#353535')
    input_multi_code_frame.pack()
    input_multi_code_var = tk.StringVar()
    multi_code_widget = tk.Entry(input_multi_code_frame, textvariable=input_multi_code_var, borderwidth=1,
                                 justify=tk.CENTER)
    multi_code_widget.pack(side=tk.LEFT, padx=4)


    # 在主框架下创建股票日期输入框子框架
    multi_input_date_frame = tk.Frame(backtrader_top_right_frame, borderwidth=1, bg='#353535')
    multi_input_date_frame.pack()
    # 创建标签‘开始日期’
    multi_date_start_label = tk.Label(multi_input_date_frame, text='开始日期', bd=1, bg='#353535', fg='red')
    multi_date_start_label.pack(side=tk.LEFT)
    # 创建开始日期代码输入框
    multi_input_startdate_var = tk.StringVar()
    multi_startdate_widget = tk.Entry(multi_input_date_frame, textvariable=multi_input_startdate_var, borderwidth=1,
                                      justify=tk.CENTER)
    multi_startdate_widget.pack(side=tk.LEFT, padx=4)
    # 创建标签‘结束日期’
    multi_date_end_label = tk.Label(multi_input_date_frame, text='结束日期', bd=1, bg='#353535', fg='red')
    multi_date_end_label.pack(side=tk.LEFT)
    # 创建结束日期代码输入框
    multi_input_enddate_var = tk.StringVar()
    multi_enddate_widget = tk.Entry(multi_input_date_frame, textvariable=multi_input_enddate_var, borderwidth=1,
                                    justify=tk.CENTER)
    multi_enddate_widget.pack(side=tk.LEFT, padx=4)

    # 先把部件布局好了再backtrader_top_frame用.add()添加到backtrader_plot_window
    backtrader_plot_window_top.add(backtrader_top_right_frame, height=tk_window.screenHeight / 10,
                                   width=tk_window.screenWidth / 2)
    # ******************************************************************************************************************

    backtrader_plot_window_bottom = tk.PanedWindow(opaqueresize=False)
    backtrader_plot_window.add(backtrader_plot_window_bottom)
    # 创建底部窗口框架,用来放图形输出
    backtrader_bottom_frame = tk.Frame(backtrader_plot_window_bottom, width=tk_window.screenWidth,
                                       height=tk_window.screenHeight, relief=tk.SUNKEN, bg='#353535', bd=5,
                                       borderwidth=4)
    backtrader_bottom_frame.pack(fill=tk.BOTH)

    backtrader_plot_window_bottom.add(backtrader_bottom_frame)

    # ******************************************************************************************************************

    def mplfinance_go():  # 图形输出渲染
        # 在backtrader_bottom_frame的原有基础上再创建一个框架，目的方便在更新股票股票回测时防止图形重叠
        for widget_backtrader_bottom_frame in backtrader_bottom_frame.winfo_children():
            widget_backtrader_bottom_frame.destroy()
        # 创建左右两个frame框架方便管理布局大小跟刷新，framed大小跟控件的长高有关
        backtrader_bottomleft_frame = tk.Frame(backtrader_bottom_frame, bg='#353535', bd=5, borderwidth=4)
        backtrader_bottomleft_frame.pack(side=tk.LEFT, fill=tk.BOTH, expand=1)
        backtrader_bottomright_frame = tk.Frame(backtrader_bottom_frame, bg='#353535', bd=5, borderwidth=4)
        backtrader_bottomright_frame.pack(side=tk.RIGHT, fill=tk.BOTH, expand=0)

        # 以下函数作用是省略输入代码后缀.sz .sh
        def code_name_transform(get_stockcode):  # 输入的数字股票代码转换成字符串股票代码
            str_stockcode = str(get_stockcode)
            str_stockcode = str_stockcode.strip()  # 删除前后空格字符
            if 6 > len(str_stockcode) > 0:
                str_stockcode = str_stockcode.zfill(6) + '.SZ'  # zfill()函数返回指定长度的字符串，原字符串右对齐，前面填充0
            if len(str_stockcode) == 6:
                if str_stockcode[0:1] == '0':
                    str_stockcode = str_stockcode + '.SZ'
                if str_stockcode[0:1] == '3':
                    str_stockcode = str_stockcode + '.SZ'
                if str_stockcode[0:1] == '6':
                    str_stockcode = str_stockcode + '.SH'
            return str_stockcode

        # 交互数据的获取跟处理
        stock_name = input_code_var.get()
        code_name = code_name_transform(stock_name)
        start_date = input_startdate_var.get()
        end_date = input_enddate_var.get()
        try:
            class my_strategy(bt.Strategy):
                exec(use_strategy())
        except Exception as e_class:
            tk.messagebox.showwarning(title='错误', message='请先选择运行策略再进行回测')
            print('请先选择运行策略再进行回测')

        try:
            # adj='qfq'向前复权，freq='D 数据频度：日K线
            df = ts.pro_bar(ts_code=code_name, start_date=start_date, end_date=end_date, adj='qfq', freq='D')
            df['trade_date'] = pd.to_datetime(df['trade_date'])
            # df = df.drop(['change', 'pre_close', 'pct_chg', 'amount'], axis=1)

            # 设置用于backtrader的数据
            df_back = df.rename(columns={
     'vol': 'volume'})
            df_back.set_index('trade_date', inplace=True)  # 设置索引覆盖原来的数据
            df_back = df_back.sort_index(ascending=True)  # 将时间顺序升序，符合时间序列
            df_back['openinterest'] = 0
            # 喂养数据到backtrader当中去
            back_start_time = datetime.datetime.strptime(start_date, "%Y%m%d")  # str转换成时间格式2015-01-01 00:00:00
            back_end_time = datetime.datetime.strptime(end_date, '%Y%m%d')
            # print(back_start_time)
            data_back = bt.feeds.PandasData(dataname=df_back,
                                            fromdate=back_start_time,
                                            todate=back_end_time)

            # 设置用于mpf图形的数据
            # ：取所有行数据，后面取date列，open列等数据
            data_mpf = df.loc[:, ['trade_date', 'open', 'close', 'high', 'low', 'vol']]
            data_mpf = data_mpf.rename(
                columns={
     'trade_date': 'Date', 'open': 'Open', 'close': 'Close', 'high': 'High', 'low': 'Low',
                         'vol': 'Volume'})  # 更换列名，为后面函数变量做准备
            data_mpf.set_index('Date', inplace=True)  # 设置date列为索引，覆盖原来索引,这个时候索引还是 object 类型，就是字符串类型
            # 将object类型转化成 DateIndex 类型，pd.DatetimeIndex 是把某一列进行转换，同时把该列的数据设置为索引 index。
            data_mpf.index = pd.DatetimeIndex(data_mpf.index)
            data_mpf = data_mpf.sort_index(ascending=True)  # 将时间顺序升序，符合时间序列
            # print(data_mpf)

            # 创建策略容器
            cerebro = bt.Cerebro()
            # 添加自定义的策略my_strategy
            cerebro.addstrategy(my_strategy)
            # 添加数据
            cerebro.adddata(data_back)
            # 设置资金
            startcash = 100000
            cerebro.broker.setcash(startcash)
            # 设置每笔交易交易的股票数量
            cerebro.addsizer(bt.sizers.FixedSize, stake=100)
            # 设置手续费
            cerebro.broker.setcommission(commission=0.0005)
            # 输出初始资金
            d1 = back_start_time.strftime('%Y%m%d')
            d2 = back_end_time.strftime('%Y%m%d')

            cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='SharpeRatio')
            cerebro.addanalyzer(bt.analyzers.DrawDown, _name='DW')
            cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='TradeAnalyzer')
            cerebro.addanalyzer(bt.analyzers.Transactions, _name='Transactions')

            # 运行策略
            # stdstats=False不显示回测的统计结果
            result = cerebro.run(stdstats=True, optreturn=False)
            backtrader_analysis = result[0]
            # print(backtrader_analysis.analyzers.SharpeRatio.get_analysis())
            # print(backtrader_analysis.analyzers.DW.get_analysis())
            # print(backtrader_analysis.analyzers.TradeAnalyzer.get_analysis())
            # 在下面的占位符后面不能有空格，否则空格后面的输入信息是输不进treeview的单元格
            startcash_value = '初始资金：%.2f' % startcash
            endcash_value = '期末资金：%.2f' % cerebro.broker.getvalue()
            try:
                completed_net = '已完成盈亏：%.2f' % \
                                backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['pnl']['net']['total']
            except Exception as e0:
                completed_net = '已完成盈亏：%s' % None
            try:
                float_profit = '浮动盈亏：%.2f' % \
                               (cerebro.broker.getvalue() - startcash -
                                backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['pnl']['net']['total'])
            except Exception as e1:
                float_profit = '浮动盈亏：%s' % None
            try:
                completed_commission = '手续费用：%.2f' % (
                        backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['pnl']['gross']['total'] -
                        backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['pnl']['net']['total'])
            except Exception as e2:
                completed_commission = '手续费用：%s' % None
            start_backtrade_date = '回测开始时间：%s' % d1
            end_backtrade_date = '回测结束时间：%s' % d2
            try:
                sharpeRatio_value = '夏普比例：%.2f' % \
                                    backtrader_analysis.analyzers.SharpeRatio.get_analysis()['sharperatio']
            except Exception as e3:
                sharpeRatio_value = '夏普比例：%s' % None
            try:
                drawdown_value = '最大回撤：%.2f' % backtrader_analysis.analyzers.DW.get_analysis()['max']['drawdown']
            except Exception as e4:
                drawdown_value = '最大回撤：%s' % None
            try:
                moneydown_value = '最大资金回撤：%.2f' % \
                                  backtrader_analysis.analyzers.DW.get_analysis()['max']['moneydown']
            except Exception as e5:
                moneydown_value = '最大资金回撤：%s' % None
            try:
                max_drawdown_lastday = '最大回撤持续天数：%d' % \
                                       backtrader_analysis.analyzers.DW.get_analysis()['max']['len']
            except Exception as e6:
                max_drawdown_lastday = '最大回撤持续天数：%s' % None
            try:
                total_value = '交易次数：%d' % \
                              backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['total']['total']
            except Exception as e7:
                total_value = '交易次数：%s' % None
            try:
                uncompleted_trade = '未完成交易数量：%d' % \
                                    backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['total']['open']
            except Exception as e8:
                uncompleted_trade = '未完成交易数量：%s' % None
            try:
                completed_trade = '已完成交易数量：%d' % \
                                  backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['total']['closed']
            except Exception as e9:
                completed_trade = '已完成交易数量：%s' % None
            try:
                win_value = '盈利次数：%d' % backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['won']['total']
            except Exception as e10:
                win_value = '盈利次数：%s' % None
            try:
                lost_value = '亏损次数：%d' % backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['lost']['total']
            except Exception as e11:
                lost_value = '亏损次数：%s' % None
            try:
                win_rate = '胜率：%.2f' % (backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['won']['total'] /
                                        backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['total']['total'])
            except Exception as e12:
                win_rate = '胜率：%s' % None
            try:
                lost_rate = '败率：%.2f' % (backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['lost']['total'] /
                                         backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['total']['total'])
            except Exception as e13:
                lost_rate = '败率：%s' % None

            analysis_log = []  # 设置空列表用来接收回测记录
            history_trade_buy_date_list = []  # 设置空列表用来接收买点标记的时间日期，下面的空列表都是为标记做准备
            history_trade_sell_date_list = []
            history_trade_buy_vol_list = []
            history_trade_sell_vol_list = []
            history_trade_buy_price_list = []
            history_trade_sell_price_list = []
            trade_signal_buy = pd.DataFrame(columns=['Date', 'buy_price', 'buy_vol'])  # 创建买点dataframe
            trade_signal_sell = pd.DataFrame(columns=['Date', 'sell_price', 'sell_vol'])
            analysis_log.extend([startcash_value, endcash_value, float_profit, completed_net, completed_commission,
                                 start_backtrade_date, end_backtrade_date, sharpeRatio_value, drawdown_value,
                                 moneydown_value, max_drawdown_lastday, total_value, uncompleted_trade, completed_trade,
                                 win_value, lost_value, win_rate, lost_rate])
            for key, value in backtrader_analysis.analyzers.Transactions.get_analysis().items():
                trade_log = '日期：%s，价格：%.2f，数量：%d，市值：%.2f' % (key.strftime('%Y-%m-%d'), value[0][1],
                                                             value[0][0], value[0][4])
                analysis_log.extend([trade_log])

                history_trade_date = key.strftime('%Y-%m-%d')
                history_trade_price = value[0][1]
                history_trade_vol = value[0][0]

                if history_trade_vol > 0:
                    history_trade_buy_date_list.append(history_trade_date)
                    history_trade_buy_price_list.append(history_trade_price)
                    history_trade_buy_vol_list.append(history_trade_vol)
                elif history_trade_vol < 0:
                    history_trade_sell_date_list.append(history_trade_date)
                    history_trade_sell_price_list.append(history_trade_price)
                    history_trade_sell_vol_list.append(history_trade_vol)

            trade_signal_buy['Date'] = history_trade_buy_date_list
            trade_signal_buy['buy_price'] = history_trade_buy_price_list
            trade_signal_buy['buy_vol'] = history_trade_buy_vol_list
            trade_signal_buy.set_index('Date', inplace=True)
            trade_signal_buy.index = pd.DatetimeIndex(trade_signal_buy.index)

            trade_signal_sell['Date'] = history_trade_sell_date_list
            trade_signal_sell['sell_price'] = history_trade_sell_price_list
            trade_signal_sell['sell_vol'] = history_trade_sell_vol_list
            trade_signal_sell.set_index('Date', inplace=True)
            trade_signal_sell.index = pd.DatetimeIndex(trade_signal_sell.index)

            backtrader_treeview = ttk.Treeview(backtrader_bottomright_frame, columns=['1'], show='headings')
            # 在treeview布局钱先布局横竖滚动条，不然会出现布局问题，你可以试着将滚动条代码放在最后试下
            VScroll1 = ttk.Scrollbar(backtrader_bottomright_frame, orient='vertical', command=backtrader_treeview.yview)
            backtrader_treeview.configure(yscrollcommand=VScroll1.set)
            VScroll1.pack(side=tk.RIGHT, fill=tk.Y)

            backtrader_treeview.column('1', width=int(tk_window.screenWidth / 4), anchor='w')
            backtrader_treeview.heading('1', text='回测记录')
            backtrader_treeview.pack(side=tk.LEFT, fill=tk.BOTH, expand=0)
            for i in range(len(analysis_log)):  # 写入回测记录
                backtrader_treeview.insert('', 'end', values=analysis_log[i])

            # 合并前面的买卖数据dataframe，为绘图做准备
            trade_all = pd.merge(left=data_mpf, right=trade_signal_buy, left_index=True, right_index=True, how='outer')
            trade_all = pd.merge(left=trade_all, right=trade_signal_sell, left_index=True, right_index=True,
                                 how='outer')
            # print(trade_all)
            # grid = False不显示分割线
            # cerebro.plot(style='candlestick', grid=False, iplot=False)
            colors_set = mpf.make_marketcolors(up='red', down='green', edge='i', wick='i', volume='in', inherit=True)
            # 设置图形风格,gridaxis:设置网格线位置,gridstyle:设置网格线线型,y_on_right:设置y轴位置是否在右
            mpf_style = mpf.make_mpf_style(gridaxis='horizontal', gridstyle='-.', y_on_right=False, facecolor='white',
                                           figcolor='white', marketcolors=colors_set)
            # 添加买卖点附图
            try:  # 设置try语句是预防当只有一个买信号没有卖信号发生报错的情况，比如002978 605388
                add_plot = [
                    mpf.make_addplot(trade_all['buy_price'], scatter=True, markersize=130, marker='^', color='r'),
                    mpf.make_addplot(trade_all['sell_price'], scatter=True, markersize=130, marker='v', color='g')]
                daily_fig, axlist = mpf.plot(data_mpf, type='candle', mav=(21, 55), volume=True, show_nontrading=False,
                                             style=mpf_style, addplot=add_plot, returnfig=True)
                canvas_stock_daily_basic = FigureCanvasTkAgg(daily_fig, master=backtrader_bottomleft_frame)
                canvas_stock_daily_basic.draw()
                toolbar_stock_daily_basic = NavigationToolbar2Tk(canvas_stock_daily_basic, backtrader_bottomleft_frame)
                toolbar_stock_daily_basic.update()  # 显示图形导航工具条
                canvas_stock_daily_basic._tkcanvas.pack(side=tk.LEFT, fill=tk.BOTH, expand=1)
                plt.cla()  # 清除axes，即当前 figure 中的活动的axes，但其他axes保持不变。
            except Exception as e_plot1:
                try:
                    add_plot = [
                        mpf.make_addplot(trade_all['buy_price'], scatter=True, markersize=130, marker='^', color='r')]
                    daily_fig, axlist = mpf.plot(data_mpf, type='candle', mav=(21, 55), volume=True,
                                                 show_nontrading=False,
                                                 style=mpf_style, addplot=add_plot, returnfig=True)
                    canvas_stock_daily_basic = FigureCanvasTkAgg(daily_fig, master=backtrader_bottomleft_frame)
                    canvas_stock_daily_basic.draw()
                    toolbar_stock_daily_basic = NavigationToolbar2Tk(canvas_stock_daily_basic,
                                                                     backtrader_bottomleft_frame)
                    toolbar_stock_daily_basic.update()  # 显示图形导航工具条
                    canvas_stock_daily_basic._tkcanvas.pack(side=tk.LEFT, fill=tk.BOTH, expand=1)
                    plt.cla()
                except Exception as e_plot2:
                    daily_fig, axlist = mpf.plot(data_mpf, type='candle', mav=(21, 55), volume=True,
                                                 show_nontrading=False,
                                                 style=mpf_style, returnfig=True)
                    canvas_stock_daily_basic = FigureCanvasTkAgg(daily_fig, master=backtrader_bottomleft_frame)
                    canvas_stock_daily_basic.draw()
                    toolbar_stock_daily_basic = NavigationToolbar2Tk(canvas_stock_daily_basic,
                                                                     backtrader_bottomleft_frame)
                    toolbar_stock_daily_basic.update()  # 显示图形导航工具条
                    canvas_stock_daily_basic._tkcanvas.pack(side=tk.LEFT, fill=tk.BOTH, expand=1)
                    plt.cla()
        except Exception as e_cerebro:
            tk.messagebox.showwarning(title='错误',
                                      message='%s 数据不足！请查看股票策略指标的参数跟回测日期的数据是否相符以支持回测' % code_name)
            print('%s 数据不足！请查看股票策略指标的参数跟回测日期的数据是否相符以支持回测' % code_name)

    # ******************************************************************************************************************

    def backtrader_go():
        plt.close('all')  # 先关闭下plt,不关闭的话会在你点完mpl回测后再点backtrader回测报错，可以试着去掉看下有什么BUG

        # 以下函数作用是省略输入代码后缀.sz .sh
        def code_name_transform(get_stockcode):  # 输入的数字股票代码转换成字符串股票代码
            str_stockcode = str(get_stockcode)
            str_stockcode = str_stockcode.strip()  # 删除前后空格字符
            if 6 > len(str_stockcode) > 0:
                str_stockcode = str_stockcode.zfill(6) + '.SZ'  # zfill()函数返回指定长度的字符串，原字符串右对齐，前面填充0
            if len(str_stockcode) == 6:
                if str_stockcode[0:1] == '0':
                    str_stockcode = str_stockcode + '.SZ'
                if str_stockcode[0:1] == '3':
                    str_stockcode = str_stockcode + '.SZ'
                if str_stockcode[0:1] == '6':
                    str_stockcode = str_stockcode + '.SH'
            return str_stockcode

        # 交互数据的获取跟处理
        stock_name = input_code_var.get()
        code_name = code_name_transform(stock_name)
        start_date = input_startdate_var.get()
        end_date = input_enddate_var.get()

        class my_strategy(bt.Strategy):
            exec(use_strategy())

        # noinspection PyBroadException
        try:
            # adj='qfq'向前复权，freq='D 数据频度：日K线
            df = ts.pro_bar(ts_code=code_name, start_date=start_date, end_date=end_date, adj='qfq', freq='D')
            df['trade_date'] = pd.to_datetime(df['trade_date'])
            # df = df.drop(['change', 'pre_close', 'pct_chg', 'amount'], axis=1)

            # 设置用于backtrader的数据
            df_back = df.rename(columns={
     'vol': 'volume'})
            df_back.set_index('trade_date', inplace=True)  # 设置索引覆盖原来的数据
            df_back = df_back.sort_index(ascending=True)  # 将时间顺序升序，符合时间序列
            df_back['openinterest'] = 0
            # 喂养数据到backtrader当中去
            back_start_time = datetime.datetime.strptime(start_date, "%Y%m%d")  # str转换成时间格式2015-01-01 00:00:00
            back_end_time = datetime.datetime.strptime(end_date, '%Y%m%d')
            # print(back_start_time)
            data_back = bt.feeds.PandasData(dataname=df_back,
                                            fromdate=back_start_time,
                                            todate=back_end_time)

            # 创建策略容器
            cerebro_single = bt.Cerebro()
            # 添加自定义的策略my_strategy
            cerebro_single.addstrategy(my_strategy)
            # 添加数据
            cerebro_single.adddata(data_back)
            # 设置资金
            startcash_single = 100000
            cerebro_single.broker.setcash(startcash_single)
            # 设置每笔交易交易的股票数量
            cerebro_single.addsizer(bt.sizers.FixedSize, stake=100)
            # 设置手续费
            cerebro_single.broker.setcommission(commission=0.0005)
            # 运行策略,stdstats=False不显示回测的统计结果
            cerebro_single.run(stdstats=True, optreturn=False)
            # grid = False不显示分割线
            cerebro_single.plot(style='candlestick', grid=False, iplot=False)
        except Exception as e:
            tk.messagebox.showwarning(title='错误',
                                      message='%s 数据不足！请查看股票策略指标的参数跟回测日期的数据是否相符以支持回测' % code_name)
            print('%s 数据不足！请查看股票策略指标的参数跟回测日期的数据是否相符以支持回测' % code_name)

    # ******************************************************************************************************************

    def multibacktrader_go():
        # 在backtrader_bottom_frame的原有基础上再创建一个框架，目的方便在更新股票股票回测时防止图形重叠
        for widget_backtrader_bottom_frame in backtrader_bottom_frame.winfo_children():
            widget_backtrader_bottom_frame.destroy()
        # 底部回测刷新状态栏专用
        for widget_state_label in tk_window.bottom_frame.winfo_children():
            widget_state_label.destroy()
        function.time_clock()
    # ******************************************************************************************************************
        multi_df = pd.DataFrame(columns=['股票代码', '股票名称', '初始资金', '期末资金', '浮动盈亏', '已完成盈亏', '手续费',
                                         '夏普', '最大回撤', '资金回撤', '已回撤天数', '交易次数', '未完成交易', '已完成交易',
                                         '盈亏次数', '亏损次数', '胜率', '败率'])
        # 先设置表的列名有哪些
        multi_area = ('股票代码', '股票名称', '初始资金', '期末资金', '浮动盈亏', '已完成盈亏', '手续费', '夏普', '最大回撤',
                      '资金回撤', '已回撤天数', '交易次数', '未完成交易', '已完成交易', '盈亏次数', '亏损次数', '胜率', '败率')

        multi_stock_treeview = ttk.Treeview(backtrader_bottom_frame, columns=multi_area, show='headings')
        # 在treeview布局钱先布局横竖滚动条，不然会出现布局问题，你可以试着将滚动条代码放在最后试下
        VScroll1 = ttk.Scrollbar(backtrader_bottom_frame, orient='vertical', command=multi_stock_treeview.yview)
        multi_stock_treeview.configure(yscrollcommand=VScroll1.set)
        VScroll1.pack(side=tk.RIGHT, fill=tk.Y)

        HScroll1 = ttk.Scrollbar(backtrader_bottom_frame, orient='horizontal', command=multi_stock_treeview.xview)
        multi_stock_treeview.configure(xscrollcommand=HScroll1.set)
        HScroll1.pack(side=tk.BOTTOM, fill=tk.X)

        for i in range(len(multi_area)):  # 命名列表名
            multi_stock_treeview.column(multi_area[i], width=8, anchor='center')
            multi_stock_treeview.heading(multi_area[i], text=multi_area[i])
        multi_stock_treeview.pack(fill=tk.BOTH, expand=1)

    # ******************************************************************************************************************

        # 将获取的txt文字转换成pycharm可执行代码
        class my_strategy(bt.Strategy):
            exec(use_strategy())

        # 先设置一个用来接收回测股票代码的列表
        multi_stock_list = []

        # 以下函数作用是省略输入代码后缀.sz .sh
        def multi_code_name_transform(get_stockcode):  # 输入的数字股票代码转换成字符串股票代码
            str_stockcode = str(get_stockcode).split(',')  # 分隔符是小写,不是大写，逗号
            for s in str_stockcode:
                s = s.strip()  # 删除前后空格字符
                if 6 > len(s) > 0:
                    s = s.zfill(6) + '.SZ'  # zfill()函数返回指定长度的字符串，原字符串右对齐，前面填充0
                if len(s) == 6:
                    if s[0:1] == '0':
                        s = s + '.SZ'
                    if s[0:1] == '3':
                        s = s + '.SZ'
                    if s[0:1] == '6':
                        s = s + '.SH'
                multi_stock_list.append(s)
            return multi_stock_list

        # 交互数据的获取跟处理
        stock_name = input_multi_code_var.get()
        df_basic_all = pro.stock_basic(exchange='', list_status='L')  # 获取所有上市公司的信息为全部上市公司回测做准备

        if multi_var.get() == 1:  # 如果输入的股票代码为空值
            # 全市场回测股票筛选功能代码，不适用于自己输入的多个股票筛选
            # 首先获取今天时间
            now_time = datetime.datetime.now()
            # 转化成tushare的时间格式
            strf_time = now_time.strftime('%Y%m%d')
            # 获取上交所上一个交易日日期,PS:tushare指数的数据信息好像当天只能获取上一个交易日的数据
            pre_trade_date = pro.trade_cal(exchange='SSE', is_open='1', start_date=strf_time, fields='pretrade_date')
            pre_trade_d = pre_trade_date.at[0, 'pretrade_date']

            # 获取每日指标数据，单位是万股，万元
            df_screen = pro.daily_basic(ts_code='', trade_date=pre_trade_d,
                                        fields='ts_code, turnover_rate, volume_ratio, pe, pb, ps, total_share, '
                                               'float_share, free_share, total_mv, circ_mv ')
            # noinspection PyBroadException
            try:  # PE
                if input_pe_leftvar.get() and input_pe_rightvar.get():
                    df_screen = df_screen[(df_screen['pe'] > float(input_pe_leftvar.get())) &
                                          (df_screen['pe'] < float(input_pe_rightvar.get()))]

                elif input_pe_leftvar.get() and not input_pe_rightvar.get():
                    df_screen = df_screen[df_screen['pe'] > float(input_pe_leftvar.get())]

                elif input_pe_rightvar.get() and not input_pe_leftvar.get():
                    # 这里将PE的空值设置为0是因为tushare将PE为负的数值设置成NaN,只有设置成0，我们才好对输入的小于数值进行筛选
                    df_screen['pe'].fillna(0, inplace=True)
                    df_screen = df_screen[df_screen['pe'] < float(input_pe_rightvar.get())]

                elif not input_pe_rightvar.get() and not input_pe_leftvar.get():
                    df_screen = df_screen
            except Exception as pe_error:
                tk.messagebox.showwarning(title='pe_error', message='PE数据输入错误，该筛选功能不运行')

            try:  # PB
                if input_pb_leftvar.get() and input_pb_rightvar.get():
                    df_screen = df_screen[(df_screen['pb'] > float(input_pb_leftvar.get())) &
                                          (df_screen['pb'] < float(input_pb_rightvar.get()))]

                elif input_pb_leftvar.get() and not input_pb_rightvar.get():
                    df_screen = df_screen[df_screen['pb'] > float(input_pb_leftvar.get())]

                elif input_pb_rightvar.get() and not input_pb_leftvar.get():
                    # 这里将PE的空值设置为0是因为tushare将PE为负的数值设置成NaN,只有设置成0，我们才好对输入的小于数值进行筛选
                    df_screen['pb'].fillna(0, inplace=True)
                    df_screen = df_screen[df_screen['pb'] < float(input_pb_rightvar.get())]

                elif not input_pb_rightvar.get() and not input_pb_leftvar.get():
                    df_screen = df_screen
            except Exception as pb_error:
                tk.messagebox.showwarning(title='pb_error', message='PB数据输入错误，该筛选功能不运行')

            try:  # PS
                if input_ps_leftvar.get() and input_ps_rightvar.get():
                    df_screen = df_screen[(df_screen['ps'] > float(input_ps_leftvar.get())) &
                                          (df_screen['ps'] < float(input_ps_rightvar.get()))]

                elif input_ps_leftvar.get() and not input_ps_rightvar.get():
                    df_screen = df_screen[df_screen['ps'] > float(input_ps_leftvar.get())]

                elif input_ps_rightvar.get() and not input_ps_leftvar.get():
                    # 这里将PE的空值设置为0是因为tushare将PE为负的数值设置成NaN,只有设置成0，我们才好对输入的小于数值进行筛选
                    df_screen['ps'].fillna(0, inplace=True)
                    df_screen = df_screen[df_screen['ps'] < float(input_ps_rightvar.get())]

                elif not input_ps_rightvar.get() and not input_ps_leftvar.get():
                    df_screen = df_screen
            except Exception as ps_error:
                tk.messagebox.showwarning(title='ps_error', message='PS数据输入错误，该筛选功能不运行')

            try:  # 量比
                if input_volume_ratio_leftvar.get() and input_volume_ratio_rightvar.get():
                    df_screen = df_screen[(df_screen['volume_ratio'] > float(input_volume_ratio_leftvar.get())) &
                                          (df_screen['volume_ratio'] < float(input_volume_ratio_rightvar.get()))]

                elif input_volume_ratio_leftvar.get() and not input_volume_ratio_rightvar.get():
                    df_screen = df_screen[df_screen['volume_ratio'] > float(input_volume_ratio_leftvar.get())]

                elif input_volume_ratio_rightvar.get() and not input_volume_ratio_leftvar.get():
                    df_screen = df_screen[df_screen['volume_ratio'] < float(input_volume_ratio_rightvar.get())]

                elif not input_volume_ratio_rightvar.get() and not input_volume_ratio_leftvar.get():
                    df_screen = df_screen
            except Exception as volume_ratio_error:
                tk.messagebox.showwarning(title='volume_ratio_error', message='量比数据输入错误，该筛选功能不运行')

            try:  # 换手率%
                if input_turnover_rate_leftvar.get() and input_turnover_rate_rightvar.get():
                    df_screen = df_screen[(df_screen['turnover_rate'] > float(input_turnover_rate_leftvar.get())) &
                                          (df_screen['turnover_rate'] < float(input_turnover_rate_rightvar.get()))]

                elif input_turnover_rate_leftvar.get() and not input_turnover_rate_rightvar.get():
                    df_screen = df_screen[df_screen['turnover_rate'] > float(input_turnover_rate_leftvar.get())]

                elif input_turnover_rate_rightvar.get() and not input_turnover_rate_leftvar.get():
                    df_screen = df_screen[df_screen['volume_ratio'] < float(input_volume_ratio_rightvar.get())]

                elif not input_turnover_rate_rightvar.get() and not input_turnover_rate_leftvar.get():
                    df_screen = df_screen
            except Exception as turnove_error:
                tk.messagebox.showwarning(title='ERROR', message='换手率数据输入错误，该筛选功能不运行')

            try:  # 总股本（亿股）
                if input_total_share_leftvar.get() and input_total_share_rightvar.get():
                    df_screen = df_screen[(round(df_screen['total_share']/10000, 2) > float(input_total_share_leftvar.get())) &
                                          (round(df_screen['total_share']/10000, 2) < float(input_total_share_rightvar.get()))]

                elif input_total_share_leftvar.get() and not input_total_share_rightvar.get():
                    df_screen = df_screen[(round(df_screen['total_share']/10000, 2) > float(input_total_share_leftvar.get()))]

                elif input_total_share_rightvar.get() and not input_total_share_leftvar.get():
                    df_screen = df_screen[(round(df_screen['total_share']/10000, 2) < float(input_total_share_rightvar.get()))]

                elif not input_total_share_rightvar.get() and not input_total_share_leftvar.get():
                    df_screen = df_screen
            except Exception as total_share_error:
                tk.messagebox.showwarning(title='total_share_error', message='总股本数据输入错误，该筛选功能不运行')

            try:  # total_mv 总市值（亿元）
                if input_total_mv_leftvar.get() and input_total_mv_rightvar.get():
                    df_screen = df_screen[(round(df_screen['total_mv']/10000, 2) > float(input_total_mv_leftvar.get())) &
                                          (round(df_screen['total_mv']/10000, 2) < float(input_total_mv_rightvar.get()))]

                elif input_total_mv_leftvar.get() and not input_total_mv_rightvar.get():
                    df_screen = df_screen[round(df_screen['total_mv']/10000, 2) > float(input_total_mv_leftvar.get())]

                elif input_total_mv_rightvar.get() and not input_total_mv_leftvar.get():
                    df_screen = df_screen[round(df_screen['total_mv']/10000, 2) < float(input_total_mv_rightvar.get())]

                elif not input_total_mv_rightvar.get() and not input_total_mv_leftvar.get():
                    df_screen = df_screen
            except Exception as total_mv_error:
                tk.messagebox.showwarning(title='total_mv_error', message='总市值数据输入错误，该筛选功能不运行')

            try:  # float_share 流通股本（亿股）
                if input_float_share_leftvar.get() and input_float_share_rightvar.get():
                    df_screen = df_screen[(round(df_screen['float_share']/10000, 2) > float(input_float_share_leftvar.get())) &
                                          (round(df_screen['float_share']/10000, 2) < float(input_float_share_rightvar.get()))]

                elif input_float_share_leftvar.get() and not input_float_share_rightvar.get():
                    df_screen = df_screen[round(df_screen['float_share']/10000, 2) > float(input_float_share_leftvar.get())]

                elif input_float_share_rightvar.get() and not input_float_share_leftvar.get():
                    df_screen = df_screen[round(df_screen['float_share']/10000, 2) < float(input_float_share_rightvar.get())]

                elif not input_float_share_rightvar.get() and not input_float_share_leftvar.get():
                    df_screen = df_screen
            except Exception as float_share_error:
                tk.messagebox.showwarning(title='float_share_error', message='流通股本数据输入错误，该筛选功能不运行')

            try:  # circ_mv 流通市值（万元）
                if input_circ_mv_leftvar.get() and input_circ_mv_rightvar.get():
                    df_screen = df_screen[(round(df_screen['circ_mv']/10000, 2) > float(input_circ_mv_leftvar.get())) &
                                          (round(df_screen['circ_mv']/10000, 2) < float(input_circ_mv_rightvar.get()))]

                elif input_circ_mv_leftvar.get() and not input_circ_mv_rightvar.get():
                    df_screen = df_screen[round(df_screen['circ_mv']/10000, 2) > float(input_circ_mv_leftvar.get())]
                    print(df_screen)
                elif input_circ_mv_rightvar.get() and not input_circ_mv_leftvar.get():
                    df_screen = df_screen[round(df_screen['circ_mv']/10000, 2) < float(input_circ_mv_rightvar.get())]

                elif not input_circ_mv_rightvar.get() and not input_circ_mv_leftvar.get():
                    df_screen = df_screen
            except Exception as circ_mv_error:
                tk.messagebox.showwarning(title='circ_mv_error', message='流通市值数据输入错误，该筛选功能不运行')

            try:  # free_share 自由流通股本（万股）
                if input_free_share_leftvar.get() and input_free_share_rightvar.get():
                    df_screen = df_screen[(round(df_screen['free_share']/10000, 2) > float(input_free_share_leftvar.get())) &
                                          (round(df_screen['free_share']/10000, 2) < float(input_free_share_rightvar.get()))]

                elif input_free_share_leftvar.get() and not input_free_share_rightvar.get():
                    df_screen = df_screen[round(df_screen['free_share']/10000, 2) > float(input_free_share_leftvar.get())]

                elif input_free_share_rightvar.get() and not input_free_share_leftvar.get():
                    df_screen = df_screen[round(df_screen['free_share']/10000, 2) < float(input_free_share_rightvar.get())]

                elif not input_free_share_rightvar.get() and not input_free_share_leftvar.get():
                    df_screen = df_screen
            except Exception as free_share_mv_error:
                tk.messagebox.showwarning(title='free_share_mv_error', message='自由流通股本数据输入错误，该筛选功能不运行')

            multi_code_name = df_screen['ts_code']
        if multi_var.get() == 2:
            multi_code_name = multi_code_name_transform(stock_name)

        start_date = multi_input_startdate_var.get()
        end_date = multi_input_enddate_var.get()
        # *************************************************************************************************************
        # 设置间计数器j，方便统计运行次数跟导出CSV格式数据做准备
        j = 0
        multi_state_label = tk.Label(tk_window.bottom_frame,
                                     text='此次回测一共有%d个股票，目前已经回测到第%d个股票' % (len(multi_code_name), j),
                                               height=1, bg='#353535', fg='white')
        multi_state_label.pack()

        multi_df_csv = pd.DataFrame(columns=['股票代码', '股票名称', '初始资金', '期末资金', '浮动盈亏', '已完成盈亏', '手续费',
                                         '夏普', '最大回撤', '资金回撤', '已回撤天数', '交易次数', '未完成交易', '已完成交易',
                                         '盈亏次数', '亏损次数', '胜率', '败率'], index=range(len(multi_code_name)))

        for multi_c in multi_code_name:  # 循环获取输入的股票代码
            try:
                # adj='qfq'向前复权，freq='D 数据频度：日K线
                df = ts.pro_bar(ts_code=multi_c, start_date=start_date, end_date=end_date, adj='qfq', freq='D')
                multi_stock_basic = pro.stock_basic(ts_code=multi_c, list_status='L')
                df['trade_date'] = pd.to_datetime(df['trade_date'])
                # df = df.drop(['change', 'pre_close', 'pct_chg', 'amount'], axis=1)
                # 设置用于backtrader的数据
                df_back = df.rename(columns={
     'vol': 'volume'})
                df_back.set_index('trade_date', inplace=True)  # 设置索引覆盖原来的数据
                df_back = df_back.sort_index(ascending=True)  # 将时间顺序升序，符合时间序列
                df_back['openinterest'] = 0
                # 喂养数据到backtrader当中去
                back_start_time = datetime.datetime.strptime(start_date, '%Y%m%d')  # str转换成时间格式2015-01-01 00:00:00
                back_end_time = datetime.datetime.strptime(end_date, '%Y%m%d')
                # print(back_start_time)

                data_back = bt.feeds.PandasData(dataname=df_back,
                                                fromdate=back_start_time,
                                                todate=back_end_time)

                # 创建策略容器
                cerebro = bt.Cerebro()
                # 添加自定义的策略my_strategy
                cerebro.addstrategy(my_strategy)
                # 添加数据
                cerebro.adddata(data_back)
                # 设置资金
                startcash = 100000
                cerebro.broker.setcash(startcash)
                # 设置每笔交易交易的股票数量
                cerebro.addsizer(bt.sizers.FixedSize, stake=100)
                # 设置手续费
                cerebro.broker.setcommission(commission=0.0005)
                # 输出初始资金
                d1 = back_start_time.strftime('%Y%m%d')
                d2 = back_end_time.strftime('%Y%m%d')

                cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='SharpeRatio')
                cerebro.addanalyzer(bt.analyzers.DrawDown, _name='DW')
                cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='TradeAnalyzer')
                cerebro.addanalyzer(bt.analyzers.Transactions, _name='Transactions')
                # 运行策略
                # stdstats=False不显示回测的统计结果
                result = cerebro.run(stdstats=True, optreturn=False)
                backtrader_analysis = result[0]
                multi_df['股票代码'] = multi_stock_basic['symbol']
                multi_df['股票名称'] = multi_stock_basic['name']
                multi_df['初始资金'] = startcash
                # 回测得不到的数据统一设置成-9999，方便回测结束后排序功能正常运行，后面设置的排序函数没有针对含有空值的处理代码
                try:
                    multi_df['期末资金'] = round(cerebro.broker.getvalue(), 2)
                except Exception as e1:
                    multi_df['期末资金'] = -9999
                try:
                    multi_df['浮动盈亏'] = round((cerebro.broker.getvalue() - startcash -
                                              backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['pnl']['net']
                                              ['total']), 2)
                except Exception as e2:
                    multi_df['浮动盈亏'] = -9999
                try:
                    multi_df['已完成盈亏'] = round(backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()
                                              ['pnl']['net']['total'], 2)
                except Exception as e3:
                    multi_df['已完成盈亏'] = -9999
                try:
                    multi_df['手续费'] = round((backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()
                                             ['pnl']['gross']['total'] -
                                             backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()
                                             ['pnl']['net']['total']), 2)
                except Exception as e4:
                    multi_df['手续费'] = -9999
                try:
                    multi_df['夏普'] = round(backtrader_analysis.analyzers.SharpeRatio.get_analysis()['sharperatio'], 2)
                except Exception as e5:
                    multi_df['夏普'] = -9999
                try:
                    multi_df['最大回撤'] = round(backtrader_analysis.analyzers.DW.get_analysis()['max']['drawdown'], 2)
                except Exception as e6:
                    multi_df['最大回撤'] = -9999
                try:
                    multi_df['资金回撤'] = round(backtrader_analysis.analyzers.DW.get_analysis()['max']['moneydown'], 2)
                except Exception as e7:
                    multi_df['资金回撤'] = -9999
                try:
                    multi_df['已回撤天数'] = backtrader_analysis.analyzers.DW.get_analysis()['max']['len']
                except Exception as e8:
                    multi_df['已回撤天数'] = -9999
                try:
                    multi_df['交易次数'] = backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['total']['total']
                except Exception as e9:
                    multi_df['交易次数'] = -9999
                try:
                    multi_df['未完成交易'] = backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['total']['open']
                except Exception as e10:
                    multi_df['未完成交易'] = -9999
                try:
                    multi_df['已完成交易'] = backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['total']['closed']
                except Exception as e11:
                    multi_df['已完成交易'] = -9999
                try:
                    multi_df['盈亏次数'] = backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['won']['total']
                except Exception as e12:
                    multi_df['盈亏次数'] = -9999
                try:
                    multi_df['亏损次数'] = backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['lost']['total']
                except Exception as e13:
                    multi_df['亏损次数'] = -9999
                try:
                    multi_df['胜率'] = round((backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['won']['total']
                                            / backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()
                                            ['total']['total']), 2)
                except Exception as e14:
                    multi_df['胜率'] = -9999
                try:
                    multi_df['败率'] = round(
                        (backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['lost']['total'] /
                         backtrader_analysis.analyzers.TradeAnalyzer.get_analysis()['total']['total']), 2)
                except Exception as e15:
                    multi_df['败率'] = -9999

                for i in range(len(multi_df.index)):  # 导入插入股票数据
                    # 插入的值数组格式用.tolist()转化成list格式，否则显示会多出‘跟[这种字符串
                    multi_stock_treeview.insert('', 'end', values=multi_df.values[i].tolist())
                print(multi_df)

            except Exception as e_cerebro:
                print('%s 数据不足！请查看股票策略指标的参数跟回测日期的数据是否相符以支持回测' % multi_c)
                continue

            # 将获取得到的数据插入到multi_df_csv中，为导出CSV格式文件内容做准备
            multi_df_csv.iloc[j] = multi_df.values[0]
            # print(multi_df_csv)

            j += 1
            multi_state_label.config(text='此次回测一共有%d个股票，目前已经回测到第%d个股票' % (len(multi_code_name), j))
            print('此次回测一共有%d个股票，目前已经回测到第%d个股票了，请耐心等待' % (len(multi_code_name), j))

        def stock_treeview_sort(tv, col, reverse):  # Treeview、列名、排列方式
            # tv.set指定item，如果不设定column和value，则返回他们的字典，如果设定了column，则返回该column的value，
            # 如果value也设定了，则作相应更改。
            # get_children()函数，其返回的是treeview中的记录号
            # 参照网上的treeview排序方法函数，由于股票的价格排序数据类型是浮点数字，在排序钱将价格类型由str转换成float,否则排序会不正确
            try:
                stockdata_list = [(float(tv.set(k, col)), k) for k in tv.get_children('')]
            except Exception:
                stockdata_list = [(tv.set(k, col), k) for k in tv.get_children('')]

            stockdata_list.sort(reverse=reverse)  # 排序方式
            # rearrange items in sorted positions
            # 根据排序后索引移动，enumerate() 函数用于将一个可遍历的数据对象(如列表、元组或字符串)组合为一个索引序列，同时列出数据和数据下标
            for index, (val, k) in enumerate(stockdata_list):
                tv.move(k, '', index)
                # print(k)
            # 重写标题，使之成为再点倒序的标题
            tv.heading(col, command=lambda col=col: stock_treeview_sort(tv, col, not reverse))

        for col in multi_area:
            multi_stock_treeview.column(col, anchor='center')
            multi_stock_treeview.heading(col, text=col,
                                         command=lambda col=col: stock_treeview_sort(multi_stock_treeview, col, False))
        # *************************************************************************************************************

        def result_tocsv():
            for item in strategy_list_tree.selection():
                item_text = strategy_list_tree.item(item, "text")  # 获取选中树形条目的名称
            csv_strategy_name = item_text.split('.')[0]  # 获取策略名称，作用是命名新建的CSV文件变量
            # 获取到当前项目文件的目录，并检查是否有‘策略结果’文件夹，如果不存在则自动新建‘策略结果’文件夹
            result_csv_Path = os.getcwd() + '\\策略结果' + '\\' + csv_strategy_name
            if not os.path.exists(result_csv_Path):
                os.makedirs(result_csv_Path)
            # 创建回测策略结果csv,首先获取时间str格式，用来对结果进行命名，这样文件的名称就不会重复了，还能知道时间
            csv_get_time = time.strftime('%Y-%m-%d %H%M%S', time.localtime(time.time()))

            if not os.path.exists(result_csv_Path + '\\' + csv_strategy_name + csv_get_time + '.csv'):
                multi_df_csv.to_csv(result_csv_Path + '\\' + csv_strategy_name + csv_get_time + '.csv', index=False,
                                    encoding='utf_8_sig')
            else:
                multi_df_csv.to_csv(result_csv_Path + '\\' + csv_strategy_name + csv_get_time + 'A.csv', index=False,
                                    encoding='utf_8_sig')



        # *************************************************************************************************************
        # 多股回测弹出菜单输出回测图形函数
        def backtrader_show_plot():
            plt.close('all')
            def code_name_transform(get_stockcode):  # 输入的数字股票代码转换成字符串股票代码
                str_stockcode = str(get_stockcode)
                str_stockcode = str_stockcode.strip()  # 删除前后空格字符
                if 6 > len(str_stockcode) > 0:
                    str_stockcode = str_stockcode.zfill(6) + '.SZ'  # zfill()函数返回指定长度的字符串，原字符串右对齐，前面填充0
                if len(str_stockcode) == 6:
                    if str_stockcode[0:1] == '0':
                        str_stockcode = str_stockcode + '.SZ'
                    if str_stockcode[0:1] == '3':
                        str_stockcode = str_stockcode + '.SZ'
                    if str_stockcode[0:1] == '6':
                        str_stockcode = str_stockcode + '.SH'
                return str_stockcode
            for multi_item in multi_stock_treeview.selection():
                multi_item_text = multi_stock_treeview.item(multi_item, "values")
                # print(multi_item_text[0])  # 输出所选行的第一列的值
                plot_code_name = code_name_transform(multi_item_text[0])

                # adj='qfq'向前复权，freq='D 数据频度：日K线
                df = ts.pro_bar(ts_code=plot_code_name, start_date=start_date, end_date=end_date, adj='qfq',
                                freq='D')
                df['trade_date'] = pd.to_datetime(df['trade_date'])

                # 设置用于backtrader的数据
                df_back = df.rename(columns={
     'vol': 'volume'})
                df_back.set_index('trade_date', inplace=True)  # 设置索引覆盖原来的数据
                df_back = df_back.sort_index(ascending=True)  # 将时间顺序升序，符合时间序列
                df_back['openinterest'] = 0
                # 喂养数据到backtrader当中去
                # str转换成时间格式2015-01-01 00:00:00
                back_start_time = datetime.datetime.strptime(start_date, '%Y%m%d')
                back_end_time = datetime.datetime.strptime(end_date, '%Y%m%d')
                data_back = bt.feeds.PandasData(dataname=df_back,
                                                fromdate=back_start_time,
                                                todate=back_end_time)

                # 创建策略容器
                cerebro_single = bt.Cerebro()
                # 添加自定义的策略my_strategy
                cerebro_single.addstrategy(my_strategy)
                # 添加数据
                cerebro_single.adddata(data_back)
                # 设置资金
                startcash_single = 100000
                cerebro_single.broker.setcash(startcash_single)
                # 设置每笔交易交易的股票数量
                cerebro_single.addsizer(bt.sizers.FixedSize, stake=100)
                # 设置手续费
                cerebro_single.broker.setcommission(commission=0.0005)
                # 运行策略,stdstats=False不显示回测的统计结果
                cerebro_single.run(stdstats=True, optreturn=False)
                # grid = False不显示分割线
                cerebro_single.plot(style='candlestick', grid=False, iplot=False)

        strategy_plot_menu = tk.Menu(backtrader_bottom_frame, tearoff=False)  # tearoff=True显示分割线
        strategy_plot_menu.add_command(label='回测图形', command=backtrader_show_plot)  # 弹出菜单内容
        strategy_plot_menu.add_command(label='回测结果导出', command=result_tocsv)  # 弹出菜单内容

        def pop_cerebro(event):
            strategy_plot_menu.post(event.x_root, event.y_root)  # #设置弹出的位置

        multi_stock_treeview.bind('', pop_cerebro)  # 设置右键弹出菜单



    # ******************************************************************************************************************
    # 线程管理，对运行中的策略进行结束功能
    def thread_start(func, *args):
        global t
        t = threading.Thread(target=func, args=args)
        t.setDaemon(True)
        t.start()

    def _async_raise(tid, exctype):
        tid = ctypes.c_long(tid)
        if not inspect.isclass(exctype):
            exctype = type(exctype)
        res = ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, ctypes.py_object(exctype))
        if res == 0:
            raise ValueError("invalid thread id")
        elif res != 1:
            ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, None)
            raise SystemError("PyThreadState_SetAsyncExc failed")

    def stop_thread(thread):
        _async_raise(thread.ident, SystemExit)

    # ******************************************************************************************************************
    # 在主框架下创建回测按钮子框架
    backtrade_left_button_frame = tk.Frame(backtrader_top_left_frame, borderwidth=1, bg='#353535')
    backtrade_left_button_frame.pack()

    backtrade_right_button_frame = tk.Frame(backtrader_top_right_frame, borderwidth=1, bg='#353535')
    backtrade_right_button_frame.pack()

    # 创建查询按钮并设置功能
    mplfinance_button = tk.Button(backtrade_left_button_frame, text='Mplfinance', height=1, bg='#353535', fg='red',
                                  command=mplfinance_go)
    mplfinance_button.pack(side=tk.LEFT, padx=4, pady=4)
    backtrader_button = tk.Button(backtrade_left_button_frame, text='BackTrader', height=1, bg='#353535', fg='red',
                                  command=backtrader_go)
    backtrader_button.pack(side=tk.RIGHT, padx=4, pady=4)

    multi_backtrader_button = tk.Button(backtrade_right_button_frame, text='MultiBackTrade', height=1,
                                        bg='#353535', fg='red', command=lambda: thread_start(multibacktrader_go))
    multi_backtrader_button.pack(side=tk.LEFT, padx=4, pady=4)

    multi_pause_button = tk.Button(backtrade_right_button_frame, text='Stop', height=1,
                                   bg='#353535', fg='red', command=lambda: stop_thread(t))
    multi_pause_button.pack(side=tk.LEFT, padx=4, pady=4)