一家公司的股票数据挖掘

Question

python 新手，有一个问题需要解决，我遇到了障碍。 希望计算 Google 的月度和年度平均价格，并告诉我们从 2004 年到 2019 年 10 月 Google 最好和最差的六个月以及最好和最差的六年 平均价格定义为 ((v1*c1)+(v2*c2)+(v3*c3)+(v4*c4)...+(vn*cn)) / (v1+v2+v3+v4。 ..+vn) 其中 vi 是第 i 天的交易量，ci 是第 i 天的调整收盘价。

我已经能够从网络上导入数据，但现在我试图至少将一年变成一个元组，然后从中解决，但不知道如何将我拥有的数据转换为一个元组。 我尝试 function = tuple 但得到错误。我知道如何创建一个元组，但不知道如何从我拥有的数据中创建一个元组，或者我是否缺少将数据放入一个元组的步骤，或者我是否偏离了这种方法

这是我到目前为止的代码，如果有人能指出我正确的方向，将不胜感激

# url = "http://193.1.33.31:88/pa1/GOOGL.csv"


import csv

import begin
from read_from_file_or_net import get_stuff_from_net as gn

def main(csv_file: 'URL of CSV file'):
# def main(csv_file):

    try:
        print(f"{csv_file}")
        my_file = gn(csv_file)
        # with open(f".cache/{my_file}", "w") as output:
        #     output.write(my_file)

        my_file = my_file.split("\n")

        for row in my_file:
            row = row.strip().split(",")
            for cell in row:
                if cell.isalpha():
                    print(f"{cell}"+"", end="")
                elif "-" in cell:
                    print(f"{cell}", end="")
                elif "." in cell:
                    print(f"{float(cell):>10.2f}", end="")
                elif cell.isnumeric():
                    print(f"{int(cell):>15d}", end="")
                elif not cell.isspace():
                    print(f"{cell}", end="  ")
                #elif cell.istitle():
                   # print(f"{cell}", end="")
                #else:
                    #print("?", end="")
            print()

    except Exception as e:
        print(f"{e}")


if __name__ == "__main__":
    main("http://193.1.33.31:88/pa1/GOOGL.csv")

Answer 1

您应该为此使用pandas。它有很多强大的功能，不需要for-loop。

您可以直接从网页阅读csv

import pandas as pd

df = pd.read_csv('http://193.1.33.31:88/pa1/GOOGL.csv')

print(df.columns)
print(df.head())

您可以选择一年 - 即。 2018

year2018 = df[ (df['Date'] >= '2018-01-01') & (df['Date'] < '2019-01-01') ]

然后计算你的价值

result = (year2018['Volume'] * year2018['Adj Close']).sum() / year2018['Volume'].sum()

print(result)

---

编辑：其他年份的 Silimar

for year in range(2004, 2019):
    year = str(year)
    data = df[ df['Date'].str.startswith(year) ]
    result = (data['Volume'] * data['Adj Close']).sum() / data['Volume'].sum()

    print(year, result)

结果：

2004 80.44437157567273
2005 137.4076040074354
2006 203.03824165240846
2007 273.04059204266287
2008 227.86912213843564
2009 206.71221450434697
2010 268.65533171697064
2011 283.70689930771306
2012 322.70466840310667
2013 437.32701278816154
2014 567.9540540371448
2015 623.3613056057101
2016 757.9295821975054
2017 940.267270383813
2018 1115.287148437416

---

编辑：如果您将结果保留为元组(result, year)，那么您可以对它们进行排序以获得最好和最差的年份

import pandas as pd

df = pd.read_csv('http://193.1.33.31:88/pa1/GOOGL.csv')
#df['Date'] = pandas.to_datetime(df['Date'])

#print(df.columns)

year2018 = df[ (df['Date'] >= '2018-01-01') & (df['Date'] < '2019-01-01') ]

result = (year2018['Volume'] * year2018['Adj Close']).sum() / year2018['Volume'].sum()

#print(result)

all_results = []
for year in range(2004, 2019):
    year = str(year)
    data = df[ df['Date'].str.startswith(year) ]
    result = (data['Volume'] * data['Adj Close']).sum() / data['Volume'].sum()

    all_results.append( (result, year) )
    #print(year, result)

print('--- sorted by result ---')

sorted_results = sorted(all_results)

for result, year in sorted_results:
    print(year, result)

结果：

--- sorted by result ---
2004 80.44437157567273
2005 137.4076040074354
2006 203.03824165240846
2009 206.71221450434697
2008 227.86912213843564
2010 268.65533171697064
2007 273.04059204266287
2011 283.70689930771306
2012 322.70466840310667
2013 437.32701278816154
2014 567.9540540371448
2015 623.3613056057101
2016 757.9295821975054
2017 940.267270383813
2018 1115.287148437416

使用切片sorted_results[:6] 可以获得最差的六年，使用sorted_results[-6:] 可以获得最好的六年。如果你想要不同的顺序，你也可以使用reversed()。

---

编辑：几乎没有pandas

import requests
import csv

def main(url):
    r = requests.get(url)

    lines = r.text.split('\n')

    headers = lines[0].split(',')

    data = []

    for line in lines[1:]:
        line = line.strip()
        if line: # skip empty lines
            row = line.strip().split(',')

            # convert string to float/int
            row[1] = float(row[1])
            row[2] = float(row[2])
            row[3] = float(row[3])
            row[4] = float(row[4])
            row[5] = float(row[5])
            row[6] = int(row[6])

            data.append(row)

    return headers, data


if __name__ == "__main__":    
    headers, data = main('http://193.1.33.31:88/pa1/GOOGL.csv')

    print(headers)

    print('--- data ---')
    print(data[0])
    print(data[-1])

    # get only year 2018

    year2018 = []
    for row in data:
        if '2018-01-01' <= row[0] < '2019-01-01':
           year2018.append(row)

    print('--- year 2018 ---')
    print(year2018[0])
    print(year2018[-1])

    # your calculation

    a = 0
    b = 0
    for row in year2018:
        a += row[5] * row[6]
        b += row[6]

    result = a/b

    print(result)