Backtrader从0到1——第一个回测策略

0. 前言

本人翻阅了大量资料，以及市面上的Backtrade教程，大多都晦涩难懂，或者模棱两可，也不系统。迫于无奈，只能通过Backtrader的官方文档慢慢学习。可官方文档是英文的，使用翻译软件翻译的结果，也差强人意，只能凭借着即将消失的英文记忆，慢慢啃原文。

于是，笔者决定，将啃到的内容做一下整理，分享给大家。也是做一个Backtrader的系统性教程，希望大家支持。本系列文章除了讲解文档中的内容外，还有一些我自己的理解。

笔者不生产文档，只是文档的搬运工！！！

1. lines && index

要学习Backtrader，首先需要了解其中两个核心概念，lines和index。

1. lines

• 股票等金融产品中，往往有如下几个基本数据：
  • Open（开盘价），High（最高价），Low（最低价），Close（收盘价）， Volume（成交量），OpenInterest（持仓量）。
• 这些基本数据，沿着时间看，就是一条一条的线。因此一个数据源Feed中，通常存在6个lines。如果算上时间，就有7个lines。

2. index

• 在Python中，可以用索引0访问数组的第一个元素；
• 在Backtrader中，索引0表示lines中当前的值，1表示下一个值，-1表示上一个值，当然还有2，-2，3…以此类推（这里不理解没关系，后面结合代码理解，有个印象即可，即Backtrader中lines的索引和传统意义上的索引不一样）。

2. 生成大脑

1. 要知道，所谓量化回测，是模拟人的思维方式，自动化的进行回测。在这个过程中，我们要再现人的思考过程。表现在代码中就是：

• a. 要思考，要先有一个脑子cerebro，通过bt.Cerebro()实例化出一个；
• b. 有了脑子之后，还要让脑子转起来，思考起来cerebro.run()。

import backtrader as bt

if __name__ == '__main__':
    cerebro = bt.Cerebro()	# 创建决策大脑

    print('初始投资组合总价值: %.2f' % cerebro.broker.getvalue())

    cerebro.run()	# 大脑开始思考

    print('最终投资组合总价值: %.2f' % cerebro.broker.getvalue())

• 可是这个大脑里面空空如也，什么也没有，所以此时cerebro.run()就相当于是人在发呆了，什么也没干。

• 上面代码中用到了cerebro.broker.getvalue()方法，作用是返回当前投资组合的总价值，计算公式为：总价值 = 现金 + ∑（持仓数量 × 最新价格）。

2. 输出结果

初始投资组合总价值: 10000.00
最终投资组合总价值: 10000.00

• 可以看到，这个实例化出的脑子中，有一个初始总价值，为1W。对一些经济商来说，1W通常是一个起始价值。

broker的意思是经纪人，在早些时候，我们要想知道自己在股市中有多少钱，是需要到证券公司，找经纪人确认的。这里就是模拟了你向经纪人询问自己有多少钱的行为。后面这个broker我们还会经常用到。

3. 设置起始资金和佣金

1W太少了，我要拿10W进股市！！！

• 通过setcash()方法，可以设置初始资金。通过setcommission()方法设置佣金，如果是1%就填0.01，1除以100。

import backtrader as bt

if __name__ == '__main__':
    cerebro = bt.Cerebro()	# 创建决策大脑
    cerebro.broker.setcash(100000.0)
    cerebro.broker.setcommission(0.0002) # 0.02%的佣金

    print('初始投资组合总价值: %.2f' % cerebro.broker.getvalue())

    cerebro.run()	# 大脑开始思考

    print('最终投资组合总价值: %.2f' % cerebro.broker.getvalue())

• 输出：

初始投资组合总价值: 100000.00
最终投资组合总价值: 100000.00

4. 添加数据（重点）

数据源来自akshare，大家可以自行去官方文档查看相关接口。https://akshare.akfamily.xyz/index.html

1. 整理数据格式

• Backtrader需要的数据要有6个基本属性，可以通过bt.feeds.PandasData(dataname=, fromdate=, todate=)构造：
  • Open（开盘价），High（最高价），Low（最低价），Close（收盘价）， Volume（成交量），OpenInterest（持仓量）。
• Backtrade需要的数据对索引十分敏感，必须是时间类型。

import akshare as ak
import pandas as pd

# 1. 获取股票日线数据（akshare）
def GetStockDataDailyAK(code, fromdate, todate):
    """
    code: str，股票代码
    formdate: date，开始日期
    todate: date，结束日期
    """
    fromdate = fromdate.strftime('%Y%m%d')
    todate = todate.strftime('%Y%m%d')
    # 获取某只股票的日线级别前复权K线数据
    df = ak.stock_zh_a_hist(symbol=code, period="daily", start_date=fromdate, end_date=todate, adjust="qfq")
    # 改列名，让之后的操作更符合backtrade一般的操作习惯
    df = df.rename(columns={'日期':'date', '开盘':'open', '收盘':'close', '最高':'high', '成交量':'volume', '最低':'low'})
    # 新增一列 openinterest
    df['openinterest'] = 0
    # 设置索引为日期格式
    df.index = pd.to_datetime(df.date)
    # 对dateframe的数据列进行整合，使其符合backtrader期望的数据格式
    df = df[['open', 'high', 'low', 'close', 'volume', 'openinterest']]

    return df

2. 添加数据

• 使用bt.feeds.PandasData()方法构造数据源后，使用cerebro.adddata()方法将数据塞进大脑。

if __name__=='__main__':
    fromdate = datetime(2017, 1, 1)
    todate = datetime(2020, 1, 1)
    # 获取茅台的数据
    stock_df = GetStockDataDailyAK('600519', fromdate, todate)

    # 加载并读取数据源，fromdate(日期格式)，todate(日期格式)
    data = bt.feeds.PandasData(dataname=stock_df, fromdate=fromdate, todate=todate)
    
    # 3. 策略设置
    cerebro = bt.Cerebro() # 创建大脑
    # 将数据加入回测系统
    cerebro.adddata(data)
    # 经纪人
    startcash = 100000 # 初始资金为10W
    cerebro.broker.setcash(startcash)

    # 4. 执行回测
    print('初始总价值: %.2f' % cerebro.broker.getvalue())
    cerebro.run()
    print('最终总价值: %.2f' % cerebro.broker.getvalue())

5. 第一个策略——双均线

5.1 策略类

Backtrader中，策略也是一个类，需要继承bt.Strategy。

1. 我们来实现一个简单的策略，打印每一天的收盘价：

class MyStrategy(bt.Strategy):
    def log(self, txt, dt=None):
        ''' Logging function for this strategy'''
        dt = dt or self.datas[0].datetime.date(0)
        print('%s, %s' % (dt.isoformat(), txt))

    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close

    def next(self):
        # Simply log the closing price of the series from the reference
        self.log('Close, %.2f' % self.dataclose[0])

2. 里面有几个核心功能：

• log：
  • 日志功能，通常在进行一些操作时进行日志记录，如买入和卖出。使用方法也很简单，直接调用self.log(txt)，传一个字符串参数txt即可。打印格式为“日期，txt”。
• init：
  • 初始化功能，通常用来定义一些成员变量。这里需要着重解释一下，cerebro.adddata()方法可以执行多次，就是说可以向大脑中加入多个类似于1中的表格，也叫多个lines。使用self.datas[0]就访问到了第一次加入的lines。再.close就拿出了close对应的这一列。所以self.dataclose就是一条line，之后可以通过索引访问。
• next：
  • 策略执行的核心方法，Backtrader会遍一行一行的遍历lines。通过self.dataclose[0]，就可以访问到遍历位置的当前收盘价。

3. 策略加入大脑并执行

• cerebro.addstrategy(MyStrategy)知识涌进大脑！

if __name__=='__main__':
    fromdate = datetime(2017, 1, 1)
    todate = datetime(2020, 1, 1)
    # 获取茅台的数据
    stock_df = GetStockDataDailyAK('600519', fromdate, todate)

    # 加载并读取数据源，fromdate(日期格式)，todate(日期格式)
    data = bt.feeds.PandasData(dataname=stock_df, fromdate=fromdate, todate=todate)
    
    # 3. 策略设置
    cerebro = bt.Cerebro() # 创建大脑
    # 将数据加入回测系统
    cerebro.adddata(data)
    # 加入自己的策略
    cerebro.addstrategy(MyStrategy)
    # 经纪人
    startcash = 100000 # 初始资金为10W
    cerebro.broker.setcash(startcash)
    # 设置手续费
    cerebro.broker.setcommission(0.0002)

    # 4. 执行回测
    s = fromdate.strftime("%Y-%m-%d")
    t = todate.strftime("%Y-%m-%d")
    print('初始总价值: %.2f' % cerebro.broker.getvalue())
    cerebro.run()
    print('最终总价值: %.2f' % cerebro.broker.getvalue())

• 输出：

初始总价值: 100000.00
2017-01-03, Close, 122.56
2017-01-04, Close, 139.91
2017-01-05, Close, 134.74
2017-01-06, Close, 138.76
2017-01-09, Close, 136.51
2017-01-10, Close, 137.00
...
2019-12-27, Close, 983.32
2019-12-30, Close, 1006.12
2019-12-31, Close, 1003.32
最终总价值: 100000.00

• 之前我们的大脑只能发呆，现在至少可以做一些简单的“思考”了，不错，再接再厉。

5.2 策略参数

将策略中的某些值硬编码，并且无法轻松更改，这有点不切实际。参数可以派上用场。

1. 参数的定义很简单，如下所示：

class MyStrategy(bt.Strategy):
    params = (
    	('myparam', 27),
    	('exitbars', 5),
	)

2. 添加策略时，可以指定参数的值

# Add a strategy
cerebro.addstrategy(TestStrategy, myparam=20, exitbars=7)

5.3 添加指标

指标相信大家都不陌生，就是买卖的信号嘛。今天我们就为策略添加一个最最常见的指标，20日均线。

添加20日均线self.ma

• Backtrader中，提供了很多方便的指标计算方法，其中就包括均线；
• bt.indicators.SimpleMovingAverage()可以计算简单移动平均线，第一个参数传入收盘价，line格式，第二个参数传周期，因为我们是20日均线，所以传20即可。

class MyStrategy(bt.Strategy):
    params = (
        ('maperiod', 20),
    )

    def __init__(self):
        self.ma = bt.indicators.SimpleMovingAverage(self.datas[0], period=self.params.maperiod)
        # 引用到输入数据的close价格
        self.dataclose = self.datas[0].close

• 该方法返回的也是一条line，可以通过Backtrader特殊的索引形式访问。
• 需要注意的是，该指标至少需要20天，才可以算出一个，也就意味着，next方法将从lines的20行开始遍历。

5.4 买卖与订单order

1. notify_order模块

• 这里要向大家普及一个基本常识，当我们想买入一支股票时，首先要将这个买入请求，报到场内，这个买入请求就是一个订单。
• 当一个订单报到场内后，会有如下几种情况：
  • a. 订单已提交，但尚未被经纪商接受；
  • b. 订单已被经纪商接受，但尚未执行；
  • c. 订单执行，并已完成；
  • d. 订单被取消、保证金不足或拒绝。
• 重点关心订单被取消的情况，多数情况下是因为资金不够了，买不起了。
• notify_order模块就模拟了订单在场内的各种情况。订单处理完后要及时置空。

class MyStrategy(bt.Strategy):
	
	...

    def notify_order(self, order):
        if order.status in [order.Submitted]:
            # 订单已提交，但尚未被经纪商接受
            self.log('ORDER SUBMITTED')
            self.order = order
        elif order.status in [order.Accepted]:
            # 订单已被经纪商接受，但尚未执行
            self.log('ORDER Accepted')
            self.order = order
        elif order.status in [order.Completed]:
            # 订单执行，并已完成
            if order.isbuy():
                self.log('BUY EXECUTED, %.2f' % order.executed.price)
            else:
                self.log('SELL EXECUTED, %.2f' % order.executed.price)
            self.order = None
        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            # 订单被取消、保证金不足或拒绝
            self.log('ORDER CANCELED/MARGIN/REJECTED')
            self.order = None

1. 买卖

• 我们指定一个很经典的买卖策略，当天收盘价上穿20日均线就买入，下穿20日均线就卖出。
• 买卖用buy()/sell()方法即可，该方法会返回一个订单类型，用self.order接收，这是一个继承自父类的成员变量。

class MyStrategy(bt.Strategy):
    
    ...
    
    def next(self):
        # 判断是否有订单正在进行
        if(self.order):
            return
        
        # 空仓（当前没有持仓）
        if not self.position:
            if self.datas[0].close[0] > self.ma[0]:
                self.order = self.buy(size=200)  # 买入两百股
        else:
            if self.datas[0].close[0] < self.ma[0]:
                self.order = self.sell(size=200) # 卖出两百股

• 参数size可以指定买入多少股。

5.5 完整策略代码

import pandas as pd
from datetime import datetime
import backtrader as bt
import akshare as ak


# 1. 获取股票日线数据（akshare）
def GetStockDataDailyAK(code, fromdate, todate):
    """
    code: str，股票代码
    formdate: date，开始日期
    todate: date，结束日期
    """
    fromdate = fromdate.strftime('%Y%m%d')
    todate = todate.strftime('%Y%m%d')
    # 获取某只股票的日线级别前复权K线数据
    df = ak.stock_zh_a_hist(symbol=code, period="daily", start_date=fromdate, end_date=todate, adjust="qfq")
    # 改列名，让之后的操作更符合backtrade一般的操作习惯
    df = df.rename(columns={'日期':'date', '开盘':'open', '收盘':'close', '最高':'high', '成交量':'volume', '最低':'low'})
    # 新增一列 openinterest
    df['openinterest'] = 0
    # 设置索引为日期格式
    df.index = pd.to_datetime(df.date)
    # 对dateframe的数据列进行整合，使其符合backtrader期望的数据格式
    df = df[['open', 'high', 'low', 'close', 'volume', 'openinterest']]

    return df


# 2. 构建策略：上穿20日均线买入，跌穿20日均线卖出
class MyStrategy(bt.Strategy):
    params = (
        ('maperiod', 20),
    )

    def __init__(self):
        self.order = None
        self.ma = bt.indicators.SimpleMovingAverage(self.datas[0], period=self.params.maperiod)
        # 引用到输入数据的close价格
        self.dataclose = self.datas[0].close


    def log(self, txt, dt=None):
        ''' 提供记录功能'''
        dt = dt or self.datas[0].datetime.date(0)
        print('%s, %s' % (dt.isoformat(), txt))


    def notify_order(self, order):
        if order.status in [order.Submitted]:
            # 订单已提交，但尚未被经纪商接受
            self.log('ORDER SUBMITTED')
            self.order = order
        elif order.status in [order.Accepted]:
            # 订单已被经纪商接受，但尚未执行
            self.log('ORDER Accepted')
            self.order = order
        elif order.status in [order.Completed]:
            # 订单执行，并已完成
            if order.isbuy():
                self.log('BUY EXECUTED, %.2f' % order.executed.price)
            else:
                self.log('SELL EXECUTED, %.2f' % order.executed.price)
            self.order = None
        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            # 订单被取消、保证金不足或拒绝
            self.log('ORDER CANCELED/MARGIN/REJECTED')
            self.order = None


    def next(self):
        # 判断是否有交易指令正在进行
        if(self.order):
            return
        
        # 空仓（当前没有持仓）
        if not self.position:
            if self.datas[0].close[0] > self.ma[0]:
                self.order = self.buy(size=200)  # 买入两百股
        else:
            if self.datas[0].close[0] < self.ma[0]:
                self.order = self.sell(size=200) # 卖出两百股


if __name__=='__main__':
    fromdate = datetime(2017, 1, 1)
    todate = datetime(2020, 1, 1)
    # 获取茅台的数据
    stock_df = GetStockDataDailyAK('600519', fromdate, todate)

    # 加载并读取数据源，fromdate(日期格式)，todate(日期格式)
    data = bt.feeds.PandasData(dataname=stock_df, fromdate=fromdate, todate=todate)
    
    # 3. 策略设置
    cerebro = bt.Cerebro() # 创建大脑
    # 将数据加入回测系统
    cerebro.adddata(data)
    # 加入自己的策略
    cerebro.addstrategy(MyStrategy)
    # 经纪人
    startcash = 100000 # 初始资金为10W
    cerebro.broker.setcash(startcash)
    # 设置手续费
    cerebro.broker.setcommission(0.0002)

    # 4. 执行回测
    s = fromdate.strftime("%Y-%m-%d")
    t = todate.strftime("%Y-%m-%d")
    print(f'初始资金：{startcash}\n回测时间：{s}   {t}')
    cerebro.run()
    portval = cerebro.broker.getvalue()
    print(f'剩余总资金：{portval}\n回测时间：{s}   {t}')

输出

初始资金：100000
回测时间：2017-01-01   2020-01-01
2017-02-14, ORDER SUBMITTED
2017-02-14, ORDER Accepted
2017-02-14, BUY EXECUTED, 137.54
2017-02-17, ORDER SUBMITTED
2017-02-17, ORDER Accepted
2017-02-17, SELL EXECUTED, 136.90
2017-02-20, ORDER SUBMITTED
2017-02-20, ORDER Accepted
2017-02-20, BUY EXECUTED, 138.41
2017-05-09, ORDER SUBMITTED
2017-05-09, ORDER Accepted
2017-05-09, SELL EXECUTED, 192.44
2017-05-10, ORDER SUBMITTED
2017-05-10, ORDER Accepted
2017-05-10, BUY EXECUTED, 196.40
2017-07-04, ORDER SUBMITTED
2017-07-04, ORDER Accepted
2017-07-04, SELL EXECUTED, 246.96
2017-07-12, ORDER SUBMITTED
2017-07-12, ORDER Accepted
2017-07-12, BUY EXECUTED, 260.58
2017-07-13, ORDER SUBMITTED
2017-07-13, ORDER Accepted
2017-07-13, SELL EXECUTED, 250.13
2017-07-21, ORDER SUBMITTED
2017-07-21, ORDER Accepted
2017-07-21, BUY EXECUTED, 261.74
2017-09-06, ORDER SUBMITTED
2017-09-06, ORDER Accepted
2017-09-06, SELL EXECUTED, 284.78
2017-09-14, ORDER SUBMITTED
2017-09-14, ORDER Accepted
2017-09-14, BUY EXECUTED, 287.77
2017-11-23, ORDER SUBMITTED
2017-11-23, ORDER Accepted
2017-11-23, SELL EXECUTED, 436.78
2017-12-12, ORDER SUBMITTED
2017-12-12, ORDER Accepted
2017-12-12, BUY EXECUTED, 453.48
2018-01-30, ORDER SUBMITTED
2018-01-30, ORDER Accepted
2018-01-30, SELL EXECUTED, 530.78
2018-02-01, ORDER SUBMITTED
2018-02-01, ORDER Accepted
2018-02-01, BUY EXECUTED, 561.78
2018-02-02, ORDER SUBMITTED
2018-02-02, ORDER Accepted
2018-02-02, SELL EXECUTED, 547.00
2018-02-23, ORDER SUBMITTED
2018-02-23, ORDER Accepted
2018-02-23, BUY EXECUTED, 541.78
2018-03-01, ORDER SUBMITTED
2018-03-01, ORDER Accepted
2018-03-01, SELL EXECUTED, 522.78
2018-03-02, ORDER SUBMITTED
2018-03-02, ORDER Accepted
2018-03-02, BUY EXECUTED, 532.78
2018-03-06, ORDER SUBMITTED
2018-03-06, ORDER Accepted
2018-03-06, SELL EXECUTED, 519.78
2018-03-09, ORDER SUBMITTED
2018-03-09, ORDER Accepted
2018-03-09, BUY EXECUTED, 522.33
2018-03-14, ORDER SUBMITTED
2018-03-14, ORDER Accepted
2018-03-14, SELL EXECUTED, 504.45
2018-03-15, ORDER SUBMITTED
2018-03-15, ORDER Accepted
2018-03-15, BUY EXECUTED, 522.80
2018-03-23, ORDER SUBMITTED
2018-03-23, ORDER Accepted
2018-03-23, SELL EXECUTED, 495.78
2018-04-25, ORDER SUBMITTED
2018-04-25, ORDER Accepted
2018-04-25, BUY EXECUTED, 483.78
2018-04-27, ORDER SUBMITTED
2018-04-27, ORDER Accepted
2018-04-27, SELL EXECUTED, 480.03
2018-05-08, ORDER SUBMITTED
2018-05-08, ORDER Accepted
2018-05-08, BUY EXECUTED, 492.81
2018-06-20, ORDER SUBMITTED
2018-06-20, ORDER Accepted
2018-06-20, SELL EXECUTED, 546.78
2018-06-21, ORDER SUBMITTED
2018-06-21, ORDER Accepted
2018-06-21, BUY EXECUTED, 571.00
2018-06-27, ORDER SUBMITTED
2018-06-27, ORDER Accepted
2018-06-27, SELL EXECUTED, 557.78
2018-07-13, ORDER SUBMITTED
2018-07-13, ORDER Accepted
2018-07-13, BUY EXECUTED, 548.08
2018-07-31, ORDER SUBMITTED
2018-07-31, ORDER Accepted
2018-07-31, SELL EXECUTED, 526.08
2018-08-28, ORDER SUBMITTED
2018-08-28, ORDER Accepted
2018-08-28, BUY EXECUTED, 498.78
2018-09-03, ORDER SUBMITTED
2018-09-03, ORDER Accepted
2018-09-03, SELL EXECUTED, 457.78
2018-09-05, ORDER SUBMITTED
2018-09-05, ORDER Accepted
2018-09-05, BUY EXECUTED, 480.78
2018-09-06, ORDER SUBMITTED
2018-09-06, ORDER Accepted
2018-09-06, SELL EXECUTED, 457.78
2018-09-19, ORDER SUBMITTED
2018-09-19, ORDER Accepted
2018-09-19, BUY EXECUTED, 472.36
2018-10-11, ORDER SUBMITTED
2018-10-11, ORDER Accepted
2018-10-11, SELL EXECUTED, 440.79
2018-10-23, ORDER SUBMITTED
2018-10-23, ORDER Accepted
2018-10-23, BUY EXECUTED, 494.78
2018-10-24, ORDER SUBMITTED
2018-10-24, ORDER Accepted
2018-10-24, SELL EXECUTED, 433.89
2018-12-03, ORDER SUBMITTED
2018-12-03, ORDER Accepted
2018-12-03, BUY EXECUTED, 394.78
2018-12-20, ORDER SUBMITTED
2018-12-20, ORDER Accepted
2018-12-20, SELL EXECUTED, 382.00
2019-01-02, ORDER SUBMITTED
2019-01-02, ORDER Accepted
2019-01-02, BUY EXECUTED, 415.76
2019-03-11, ORDER SUBMITTED
2019-03-11, ORDER Accepted
2019-03-11, SELL EXECUTED, 545.82
2019-03-12, ORDER SUBMITTED
2019-03-12, ORDER Accepted
2019-03-12, BUY EXECUTED, 569.60
2019-03-13, ORDER SUBMITTED
2019-03-13, ORDER Accepted
2019-03-13, SELL EXECUTED, 551.28
2019-03-14, ORDER SUBMITTED
2019-03-14, ORDER Accepted
2019-03-14, BUY EXECUTED, 562.78
2019-05-07, ORDER SUBMITTED
2019-05-07, ORDER Accepted
2019-05-07, SELL EXECUTED, 699.06
2019-05-30, ORDER SUBMITTED
2019-05-30, ORDER Accepted
2019-05-30, BUY EXECUTED, 715.78
2019-06-03, ORDER SUBMITTED
2019-06-03, ORDER Accepted
2019-06-03, SELL EXECUTED, 697.78
2019-06-12, ORDER SUBMITTED
2019-06-12, ORDER Accepted
2019-06-12, BUY EXECUTED, 712.00
2019-07-16, ORDER SUBMITTED
2019-07-16, ORDER Accepted
2019-07-16, SELL EXECUTED, 795.77
2019-07-30, ORDER SUBMITTED
2019-07-30, ORDER Accepted
2019-07-30, BUY EXECUTED, 796.82
2019-08-02, ORDER SUBMITTED
2019-08-02, ORDER Accepted
2019-08-02, SELL EXECUTED, 764.32
2019-08-09, ORDER SUBMITTED
2019-08-09, ORDER Accepted
2019-08-09, BUY EXECUTED, 798.82
2019-09-12, ORDER SUBMITTED
2019-09-12, ORDER Accepted
2019-09-12, SELL EXECUTED, 886.32
2019-09-19, ORDER SUBMITTED
2019-09-19, ORDER CANCELED/MARGIN/REJECTED
2019-09-20, ORDER SUBMITTED
2019-09-20, ORDER CANCELED/MARGIN/REJECTED
2019-09-23, ORDER SUBMITTED
2019-09-23, ORDER CANCELED/MARGIN/REJECTED
2019-09-24, ORDER SUBMITTED
2019-09-24, ORDER CANCELED/MARGIN/REJECTED
2019-09-25, ORDER SUBMITTED
2019-09-25, ORDER CANCELED/MARGIN/REJECTED
2019-09-26, ORDER SUBMITTED
2019-09-26, ORDER CANCELED/MARGIN/REJECTED
2019-09-27, ORDER SUBMITTED
2019-09-27, ORDER CANCELED/MARGIN/REJECTED
2019-09-30, ORDER SUBMITTED
2019-09-30, ORDER CANCELED/MARGIN/REJECTED
2019-10-08, ORDER SUBMITTED
2019-10-08, ORDER CANCELED/MARGIN/REJECTED
2019-10-09, ORDER SUBMITTED
2019-10-09, ORDER CANCELED/MARGIN/REJECTED
2019-10-10, ORDER SUBMITTED
2019-10-10, ORDER CANCELED/MARGIN/REJECTED
2019-10-11, ORDER SUBMITTED
2019-10-11, ORDER CANCELED/MARGIN/REJECTED
2019-10-14, ORDER SUBMITTED
2019-10-14, ORDER CANCELED/MARGIN/REJECTED
2019-10-15, ORDER SUBMITTED
2019-10-15, ORDER CANCELED/MARGIN/REJECTED
2019-10-16, ORDER SUBMITTED
2019-10-16, ORDER CANCELED/MARGIN/REJECTED
2019-10-17, ORDER SUBMITTED
2019-10-17, ORDER CANCELED/MARGIN/REJECTED
2019-10-18, ORDER SUBMITTED
2019-10-18, ORDER CANCELED/MARGIN/REJECTED
2019-10-21, ORDER SUBMITTED
2019-10-21, ORDER CANCELED/MARGIN/REJECTED
2019-10-22, ORDER SUBMITTED
2019-10-22, ORDER CANCELED/MARGIN/REJECTED
2019-10-23, ORDER SUBMITTED
2019-10-23, ORDER CANCELED/MARGIN/REJECTED
2019-10-28, ORDER SUBMITTED
2019-10-28, ORDER CANCELED/MARGIN/REJECTED
2019-10-29, ORDER SUBMITTED
2019-10-29, ORDER CANCELED/MARGIN/REJECTED
2019-10-30, ORDER SUBMITTED
2019-10-30, ORDER CANCELED/MARGIN/REJECTED
2019-10-31, ORDER SUBMITTED
2019-10-31, ORDER CANCELED/MARGIN/REJECTED
2019-11-01, ORDER SUBMITTED
2019-11-01, ORDER CANCELED/MARGIN/REJECTED
2019-11-04, ORDER SUBMITTED
2019-11-04, ORDER CANCELED/MARGIN/REJECTED
2019-11-05, ORDER SUBMITTED
2019-11-05, ORDER CANCELED/MARGIN/REJECTED
2019-11-06, ORDER SUBMITTED
2019-11-06, ORDER CANCELED/MARGIN/REJECTED
2019-11-07, ORDER SUBMITTED
2019-11-07, ORDER CANCELED/MARGIN/REJECTED
2019-11-08, ORDER SUBMITTED
2019-11-08, ORDER CANCELED/MARGIN/REJECTED
2019-11-11, ORDER SUBMITTED
2019-11-11, ORDER CANCELED/MARGIN/REJECTED
2019-11-12, ORDER SUBMITTED
2019-11-12, ORDER CANCELED/MARGIN/REJECTED
2019-11-13, ORDER SUBMITTED
2019-11-13, ORDER CANCELED/MARGIN/REJECTED
2019-11-14, ORDER SUBMITTED
2019-11-14, ORDER CANCELED/MARGIN/REJECTED
2019-11-15, ORDER SUBMITTED
2019-11-15, ORDER CANCELED/MARGIN/REJECTED
2019-11-18, ORDER SUBMITTED
2019-11-18, ORDER CANCELED/MARGIN/REJECTED
2019-11-19, ORDER SUBMITTED
2019-11-19, ORDER CANCELED/MARGIN/REJECTED
2019-11-20, ORDER SUBMITTED
2019-11-20, ORDER CANCELED/MARGIN/REJECTED
2019-11-21, ORDER SUBMITTED
2019-11-21, ORDER CANCELED/MARGIN/REJECTED
2019-11-22, ORDER SUBMITTED
2019-11-22, ORDER CANCELED/MARGIN/REJECTED
2019-12-18, ORDER SUBMITTED
2019-12-18, ORDER CANCELED/MARGIN/REJECTED
2019-12-19, ORDER SUBMITTED
2019-12-19, ORDER CANCELED/MARGIN/REJECTED
2019-12-30, ORDER SUBMITTED
2019-12-30, ORDER CANCELED/MARGIN/REJECTED
2019-12-31, ORDER SUBMITTED
2019-12-31, ORDER CANCELED/MARGIN/REJECTED
剩余总资金：192051.28319999998
回测时间：2017-01-01   2020-01-01

可以看到，钱将近翻了一倍，茅台还是猛啊！！！后来我们的订单请求一直被拒绝，是因为股票涨的太狠，我们买不起了！