python 马丁回测 broker 报错 由于是用别人的库，所以不需要数据爬取，还是没搞懂，

在学习一篇关于马丁回测的博客中，出现了以下的问题，
这是那篇博客的地址
以下是我的代码，报错信息在最后，
由于是用别人的库，所以不需要数据爬取，还是没搞懂，

```python
import broker
import pandas as pd
import numpy as np
import tushare as ts
import matplotlib.pyplot as plt
from tushare import data



def __init__(self, startcash, start, end):
    self.cash = startcash  # 初始化现金
    self.hold = 0  # 初始化持仓金额
    self.holdper = self.hold / startcash  # 初始化仓位
    self.log = []  # 初始化日志
    self.cost = 0  # 成本价
    self.stock_num = 0  # 股票数量
    self.starttime = start  # 起始时间
    self.endtime = end  # 终止时间
    self.quantlog = []  # 交易量记录
    self.earn = []  # 总资产记录
    self.num_log = []
    self.droplog = [0]

def buy(self, currentprice, count):
        self.cash -= currentprice * count
        self.log.append('buy')
        self.hold += currentprice * count
        self.holdper = self.hold / (self.cash + self.hold)
        self.stock_num += count
        self.cost = self.hold / self.stock_num
        self.quantlog.append(count // 100)
        print('买入价：%.2f，手数：%d，现在成本价：%.2f，现在持仓：%.2f，现在筹码：%d' % (
        currentprice, count // 100, self.cost, self.holdper, self.stock_num // 100))
        self.earn.append(self.cash + currentprice * self.stock_num)
        self.num_log.append(self.stock_num)
        self.droplog = [0]

def sell(self, currentprice, count):
        self.cash += currentprice * count
        self.stock_num -= count
        self.log.append('sell')
        self.hold = self.stock_num * self.cost
        self.holdper = self.hold / (self.cash + self.hold)
        # self.cost = self.hold / self.stock_num
        print('卖出价：%.2f，手数：%d，现在成本价：%.2f，现在持仓：%.2f，现在筹码：%d' % (
        currentprice, count // 100, self.cost, self.holdper, self.stock_num // 100))
        self.quantlog.append(count // 100)
        self.earn.append(self.cash + currentprice * self.stock_num)
        self.num_log.append(self.stock_num)

def holdstock(self, currentprice):
        self.log.append('hold')
        # print('持有，现在仓位为：%.2f。现在成本：%.2f' %(self.holdper,self.cost))
        self.quantlog.append(0)
        self.earn.append(self.cash + currentprice * self.stock_num)
        self.num_log.append(self.stock_num)

def get_stock(self, code):
        df = ts.get_k_data(code, autype='qfq', start=self.starttime, end=self.endtime)
        df.index = pd.to_datetime(df.date)
        df = df[['open', 'high', 'low', 'close', 'volume']]
        return df
token = '495dc9cb17fb0c7e93f5402255a4aacee14a18eb7a312913e0850cc6'

ts.set_token(token)
pro = ts.pro_api()
def get_stock_pro(self, code):
        code = code + '.SH'
        df = pro.daily(ts_code= code, start_date = self.starttime, end_date= self.endtime)
        return df


def startback(self, data, everyChange, accDropday, backtesting=None):
    """
    回测函数
    """
    for i in range(len(data)):
        if i < 1:
            continue
        if i < accDropday:
            drop = backtesting.accumulateVar(everyChange, i, i)
            # print('现在累计涨跌幅度为：%.2f'%(drop))
            self.martin(data[i], data[i - 1], drop, everyChange, i)
        elif i < len(data) - 2:
            drop = backtesting.accumulateVar(everyChange, i, accDropday)
            # print('现在累计涨跌幅度为：%.2f'%(drop))
            self.martin(data[i], data[i - 1], drop, everyChange, i)
        else:
            if self.stock_num > 0:
                self.sell(data[-1], self.stock_num)
            else:
                self.holdstock(data[i])

def enter(self, currentprice, ex_price, accuDrop):
        if accuDrop < -0.01:  # and ex_price > currentprice:
            count = (self.cash + self.hold) * 0.24 // currentprice // 100 * 100
            print('再次入场')
            self.buy(currentprice, count)
        else:
            self.holdstock(currentprice)

def martin(self, currentprice, ex_price, accuDrop, everyChange, i, backtesting=None):
        diff = (ex_price - currentprice) / ex_price
        self.droplog.append(diff)

        if sum(self.droplog) <= 0:
            self.droplog = [0]

        if self.stock_num // 100 > 1:
            if sum(self.droplog) >= 0.04:
                if self.holdper * 2 < 0.24:
                    count = (self.cash + self.hold) * (0.25 - self.holdper) // currentprice // 100 * 100
                    self.buy(currentprice, count)
                elif self.holdper * 2 < 1 and (self.hold / currentprice) // 100 * 100 > 0 and backtesting.computeCon(
                        self.log) < 5:
                    self.buy(currentprice, (self.hold / currentprice) // 100 * 100)

                else:
                    self.sell(currentprice, self.stock_num // 100 * 100);
                    print('及时止损')

            elif (everyChange[i - 2] < 0 and everyChange[
                i - 1] < 0 and self.cost < currentprice):  # or (everyChange[i-1] < -0.04 and self.cost < currentprice):

                if (self.stock_num > 0) and ((self.stock_num * (1 / 2) // 100 * 100) > 0):

                    self.sell(currentprice, self.stock_num * (1 / 2) // 100 * 100)

                    # print("现在累计涨跌幅为： %.3f" %(accuDrop))
                elif self.stock_num == 100:
                    self.sell(currentprice, 100)
                else:
                    self.holdstock(currentprice)
            else:
                self.holdstock(currentprice)
        else:
            self.enter(currentprice, ex_price, accuDrop)



buylog = pd.Series(broker.log)

close = data.copy()
buy = np.zeros(len(close))
sell = np.zeros(len(close))
for i in range(len(buylog)):
    if buylog[i] == 'buy':
        buy[i] = close[i]
    elif buylog[i] == 'sell':
        sell[i] = close[i]

buy = pd.Series(buy)
sell = pd.Series(sell)
buy.index = close.index
sell.index = close.index
quantlog = pd.Series(broker.quantlog)
quantlog.index = close.index
earn = pd.Series(broker.earn)
earn.index = close.index

buy = buy.loc[buy > 0]
sell = sell.loc[sell>0]
plt.plot(close)
plt.scatter(buy.index,buy,label = 'buy')
plt.scatter(sell.index,sell, label = 'sell')
plt.title('马丁策略')
plt.legend()



#画图
plt.rcParams['font.sans-serif'] = ['SimHei']

fig, (ax1, ax2, ax3) = plt.subplots(3,figsize=(15,8))

ax1.plot(close)
ax1.scatter(buy.index,buy,label = 'buy',color = 'red')
ax1.scatter(sell.index,sell, label = 'sell',color = 'green')
ax1.set_ylabel('Price')
ax1.grid(True)
ax1.legend()

ax1.xaxis_date()
ax2.bar(quantlog.index, quantlog, width = 5)
ax2.set_ylabel('Volume')

ax2.xaxis_date()
ax2.grid(True)
ax3.xaxis_date()
ax3.plot(earn)
ax3.set_ylabel('总资产包括浮盈')
plt.show()