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Python

Beautiful Soup : Crawling the Top Market Cap. Stocks from KOSPI (2021.12.22)

  • Crawling data from Naver
  • Use request and BeautifulSoup

0. Read byte stream from url

url = 'https://finance.naver.com/sise/sise_market_sum.naver'
params = {
    'sosok' : '0',                                              # 1 : KOSPI, 2 : KOSDAQ
    'page' : '1',
}

response = requests.get(url, params=params)
# print(response)                                               # <Response [200]>
# print(type(response))                                         # <class 'requests.models.Response'>
# print(response.content)                                       # byte stream (encoded) : b'\n\n\n\n\n\n\n<!--
# print(type(response.content))                                 # <class 'bytes'>

soup = BeautifulSoup(response.content, "html.parser")
# print(soup)
# print(type(soup))                                             # <class 'bs4.BeautifulSoup'>

1.0 Read one stock's data

section = soup.find('tbody')
items = section.find_all('tr', onmouseover="mouseOver(this)")[0]
# print(items)
basic_info = items.get_text()
sinfo = basic_info.split("\n")
for i in range(len(sinfo)) :
    print(i, sinfo[i])
0
1 1
2 삼성전자
3 79,400
……

1.1 Read one page's data

section = soup.find('tbody')
items = section.find_all('tr', onmouseover="mouseOver(this)")

for item in items :
    basic_info = item.get_text()
    sinfo = basic_info.split("\n")

    # print(sinfo[1] + '\t' + sinfo[2] + '\t' + sinfo[3] + '\t' + sinfo[15])

    if sinfo[5] != '0' :                                        # data locations are moved when the price change is 0
        list = [1, 2, 3, 15]
    else :
        list = [1, 2, 3, 11]
    length = [4, 20, 9, 9]

    for i in range(len(list)) :
        spaces = length[i]
        for char in sinfo[list[i]] :
            if char >= '가' :                                   # count 2 spaces when the letter is Korean
                spaces -= 2
            else :
                spaces -= 1
        sinfo[list[i]] += spaces * ' '

        print(sinfo[list[i]], end=' ')

    print()
1    삼성전자             79,400    4,740,007
2    SK하이닉스           127,000   924,563
3    NAVER                378,500   621,737
4    삼성바이오로직스     901,000   596,147
5    삼성전자우           71,700    590,010
……

1.2 Read plural pages' data

pages = 2                                                       # input the last page's number

for page in range(1, pages + 1) :

    # Read data in each page
    params['page'] = page                                       # change the page number in the url
    response = requests.get(url, params=params)
    soup = BeautifulSoup(response.content, "html.parser")
    section = soup.find('tbody')
    items = section.find_all('tr', onmouseover="mouseOver(this)")

    # Get specific elements
    for item in items :
        basic_info = item.get_text()
        sinfo = basic_info.split("\n")

        # Set the required elements' location and their maximum size
        if sinfo[5] != '0' :                                    # data locations are moved when the price change is 0
            list = [1, 2, 3, 15]
        else :
            list = [1, 2, 3, 11]
        length = [4, 22, 9, 9]                                  # len("한국타이어앤테크놀로지") = 22

        # Print data
        for i in range(len(list)) :

            spaces = length[i]

            # Find the exact length of the text
            for char in sinfo[list[i]] :
                if char >= '가' :                               # count 2 spaces when the letter is Korean
                    spaces -= 2
                else :
                    spaces -= 1

            # Determine left or right alignment
            if sinfo[list[i]][0] < 'A' :
                sinfo[list[i]] = spaces * ' ' + sinfo[list[i]]
            else :
                sinfo[list[i]] += spaces * ' '

            # Print
            print(sinfo[list[i]], end=' ')

        print()
   1 삼성전자                  79,400 4,740,007
   2 SK하이닉스               127,000   924,563
   3 NAVER                    378,500   621,737
……
  73 한국타이어앤테크놀로지    39,850    49,364
……
 100 한솔케미칼               293,500    33,269

Numpy Financial : TVM (2021.11.08)

import numpy_financial as npf

# pv(rate, nper, pmt, fv=0, when='end')
pv = npf.pv(0.1, 2, 0, 12100)
print(round(pv, 4))

# fv(rate, nper, pmt, pv, when='end')
fv = npf.fv(0.1, 2, 0, -10000)
print(round(fv, 4))

# pmt(rate, nper, pv, fv=0, when='end')
pmt = npf.pmt(0.1, 10, -10000)
print(round(pmt, 4))

# npv(rate, values)
npv = npf.npv(0.1, [-10000, +1000, +1000, +11000])
print(round(npv, 4))

# irr(values)
irr = npf.irr([-10000, 1000, 1000, 11000])
print(round(irr, 4))

-10000.0
12100.0
1627.4539
-0.0
0.1