我有一个学校项目，我需要从Yahoo Finance获取历史数据，然后母鸡对此进行一些计算并编写报告。

import numpy as np
import csv
import pandas_datareader as pdr


def dataanalysis(stock1,comp1,comp2,comp3):  # Function to download data from Yahoo
    stk1 = pdr.get_data_yahoo(str(stock1),start="1999-11-01",end="2019-11-01")  # Downloading 20 years of data
    cmp1 = pdr.get_data_yahoo(str(comp1),end="2019-11-01")
    cmp2 = pdr.get_data_yahoo(str(comp2),end="2019-11-01")
    cmp3 = pdr.get_data_yahoo(str(comp3),end="2019-11-01")

    stk1.sort_index(ascending=False,inplace=True)  # Put dem badboys in descending order
    cmp1.sort_index(ascending=False,inplace=True)
    cmp2.sort_index(ascending=False,inplace=True)
    cmp3.sort_index(ascending=False,inplace=True)

    stk1['Returns'] = (np.log(stk1['Close'] / stk1['Close'].shift(-1)))
    cmp1['Returns'] = (np.log(cmp1['Close'] / cmp1['Close'].shift(-1)))
    cmp2['Returns'] = (np.log(cmp2['Close'] / cmp2['Close'].shift(-1)))
    cmp3['Returns'] = (np.log(cmp3['Close'] / cmp3['Close'].shift(-1)))

    stk1.to_csv(str(stock1) + '.csv')  # Out putting data to csv files
    cmp1.to_csv(str(comp1) + '.csv')
    cmp2.to_csv(str(comp2) + '.csv')
    cmp3.to_csv(str(comp3) + '.csv')

    stk1_returns = list(stk1['Returns'])  # Creating a list from the 'Returns' column
    cmp1_returns = list(cmp1['Returns'])
    cmp2_returns = list(cmp2['Returns'])
    cmp3_returns = list(cmp3['Returns'])

    del stk1_returns[-1],cmp1_returns[-1],cmp2_returns[-1],cmp3_returns[-1]

    stk1_ret_avg,cmp1_ret_avg,cmp2_ret_avg,cmp3_ret_avg = np.average(stk1_returns),np.average(cmp1_returns),np.average(cmp2_returns),np.average(cmp3_returns)
    stk1_volit,cmp1_volit,cmp2_volit,cmp3_volit = np.std(stk1_returns),np.std(cmp1_returns),np.std(cmp2_returns),np.std(cmp3_returns)

    tickers = ["",str(stock1),str(comp1),str(comp2),str(comp3)]
    averages = ["Averages = ",stk1_ret_avg,cmp3_ret_avg]
    volatility = ["Volatility = ",stk1_volit,cmp3_volit]
    correlations = np.corrcoef([stk1_returns,cmp1_returns,cmp2_returns,cmp3_returns])

    data_analyzed = [tickers,averages,volatility,correlations]
    # print(data_analyzed)
    return data_analyzed


def print_results(group):
    ticker_list = list(group[0])
    with open(str(ticker_list[1]) + " and Comp"".csv","w") as group_anal:
        groupCSV = csv.writer(group_anal)
        for i in range(3):
            groupCSV.writerow(group[i])
        for i in range(2):
            groupCSV.writerow([])
        groupCSV.writerow(["Correlation Matrix"])
        groupCSV.writerow(ticker_list[1:5])
        for r in group[3]:
            groupCSV.writerow(r)


group1 = dataanalysis("AAPL","AMZN","INTC","MSFT")  # Running the function
# group2 = dataanalysis("BARC.L","BK","GS","DB")
group3 = dataanalysis("BRK-B","ALL","PGR","MKL")
group4 = dataanalysis("MCD","SBUX","YUM","WEN")
# group5 = dataanalysis("TSCO.L","CA.PA","SBRY.L","WMT")
group6 = dataanalysis("WWE","DISH","DIS","CMCSA")
print_results(group1)
# print_results(group2)
print_results(group3)
print_results(group4)
# print_results(group5)
print_results(group6)

这没有问题，但是如果我将其他注释掉的group2和5包括在内，则会出现以下错误：

File "C:/Users/HHF/OneDrive/Programming/Python/Assignments/Final Assignment/Financial Records/calculations.py",line 62,in <module>
    group2 = dataanalysis("BARC.L","DB")
  File "C:/Users/HHF/OneDrive/Programming/Python/Assignments/Final Assignment/Financial Records/calculations.py",line 40,in dataanalysis
    correlations = np.corrcoef([stk1_returns,cmp3_returns])
 "TypeError: unsupported operand type(s) for /: 'list' and 'int'" error.

我已经尝试了所有我能想到的，但没有任何效果。如果我删除所有带有周期符号的股票行情指标，它会起作用，但是我不确定为什么会这样，因为如果我打印stk1_returns，它似乎是常规列表。

非常感谢您能给我的任何帮助。