東証の変化率ランキングをスクレイピング【python】【selinium】【beautifulsoup】
pythonの勉強にヤフーファイナンスから値上がり率上位・値下がり率下位を自動抽出してテーブルに並べるプログラムを作った。
指定の時間にいつでも実行できるので、タスクマネージャーやLINE notifyと組み合わせて銘柄監視の自動化に役立てたい。
前段：seliniumを使って上昇率上位を抽出
下段：beautifulsoupを使って下落率下位を抽出
※それぞれ作成したテーブルを指定ファイルに画像保存
from selenium import webdriver
from selenium.webdriver.common.by import By
from webdriver_manager.chrome import ChromeDriverManager
from time import sleep
import pandas as pd
import japanize_matplotlib
import matplotlib.pyplot as plt
import requests
from bs4 import BeautifulSoup
#selinium version
def jp_stock_raking_morning_ascending_selinium():
#get latest chrome driver
browser = webdriver.Chrome(ChromeDriverManager().install())
#go to url
url = 'https://info.finance.yahoo.co.jp/ranking/?kd=1&mk=2&tm=d&vl=a'
browser.get(url)
#wait until load
sleep(4)
#get all page element(s)
elems_td = browser.find_elements(by=By.TAG_NAME, value="td")
#prepare open frame to dump data
keys = []
#clense html data
for elem_td in elems_td:
#make raw data to text
key = elem_td.text
#eliminate nonuseful data
key = key.replace("(株)","")
key = key.replace("株","")
key = key.split("\n")
#make each data to array
keys.extend(key)
#reorganize array
code = keys[1:135:9]
name = keys[0:135:9]
price = keys[4:135:9]
time = keys[5:135:9]
change = keys[6:135:9]
percent = keys[7:135:9]
volume = keys[8:135:9]
#shorten name to fit table
shortedname = []
for shortname in name:
shortname = shortname[:7]
shortedname.append(shortname)
shortedname
#insert table column
df = pd.DataFrame()
df["証券コード"] = code
df["企業"] = shortedname
df["価格"] = price
df["時間"] = time
df["価格差"] = change
df["前日比"] = percent
df["出来高"] = volume
#create table
fig, ax = plt.subplots(figsize=(10,14))
ax.axis('off')
ax.axis('tight')
table = ax.table(cellText=df.values,
colLabels=df.columns,
loc='center',
bbox=[0,0,1,1])
table.auto_set_font_size(False)
plt.title("東証上昇銘柄", fontsize=30)
table.set_fontsize(10)
plt.savefig('JPEQRankUp.png')
#beautifulsoup version
def jp_stock_raking_morning_descending_beautifulsoup():
import requests
from bs4 import BeautifulSoup
#access to website
url = 'https://info.finance.yahoo.co.jp/ranking/?kd=2&mk=2&tm=d&vl=a'
res = requests.get(url)
#interpret to HTML
soup = BeautifulSoup(res.text, 'html.parser')
#get each element
codes_li = []
codes = soup.find_all('li', attrs={'class': '_2AbqlM50'})
for code in codes:
code_li = code.text
codes_li.append(code_li)
codes_li = codes_li[0:45:3]
names_a = []
names = soup.find_all('a', attrs={'data-ylk': 'slk:name;pos:0'})
for name in names:
name_a = name.text
name_a = name_a.replace("(株)","")
names_a_short = name_a[:7]
names_a.append(names_a_short)
names_a = names_a[:15]
prices_span = []
prices = soup.find_all('span', attrs={'class': '_3rXWJKZF'})
for price in prices:
price_span = price.text
prices_span.append(price_span)
prices_span = prices_span[:60:4]
times_span = []
times = soup.find_all('span', attrs={'class': '_3Kcv5jz2'})
for time in times:
time_span = time.text
times_span.append(time_span)
times_span = times_span[:15]
changes_span = []
changes = soup.find_all('span', attrs={'class': '_3rXWJKZF'})
for change in changes:
change_span = change.text
changes_span.append(change_span)
changes_span = changes_span[1:60:4]
percents_span = []
percents = soup.find_all('span', attrs={'class': '_1-yujUee Sc83af6i'})
for percent in percents:
percent_span = percent.text
#percent_span = percent.string + '%'
percents_span.append(percent_span)
percents_span = percents_span[1:30:2]
volumes_span = []
volumes = soup.find_all('span', attrs={'class': '_1-yujUee'})
for volume in volumes:
volume_span = volume.text
volumes_span.append(volume_span)
volumes_span = volumes_span[3:60:4]
#organize in table
df = pd.DataFrame()
df["証券コード"] = codes_li
df["企業"] = names_a
df["価格"] = prices_span
df["時間"] = times_span
df["価格差"] = changes_span
df["前日比"] = percents_span
df["出来高"] = volumes_span
df
#create table
fig, ax = plt.subplots(figsize=(10,14))
ax.axis('off')
ax.axis('tight')
table = ax.table(cellText=df.values,
colLabels=df.columns,
loc='center',
bbox=[0,0,1,1])
table.auto_set_font_size(False)
plt.title("東証下落銘柄", fontsize=30)
table.set_fontsize(10)
plt.savefig('JPEQRankDown.png')
jp_stock_raking_morning_ascending_selinium()
jp_stock_raking_morning_descending_beautifulsoup()