def k10FromDate():
prices = dict()
top10 = topK(10)
date = NaverDate.create(startDate='2019-01-02', endDate='2019-05-04')
for code in top10:
crawler = NaverStockCrawler.create(code)
data = crawler.crawling(date)
prices[code] = {
NaverDate.formatDate(item.date): item.close
for item in data
}
df = pd.DataFrame(prices)
df = df.fillna(method='ffill')
if df.isnull().values.any(): #그래도 구멍이 남아 있으면
df = df.fillna(method='bfill')
return df
def loadDomesticIndex(self, name, beforeStr, endStr):
crawler = NaverCrawler.create(targetName=name)
date = NaverDate.create(startDate=beforeStr, endDate=endStr)
data = crawler.crawling(dateData=date)
df = pd.DataFrame(columns=['종가', '전일비', '등락률', '거래량', '거래대금'])
for v in data:
df.loc[v.index()] = v.value()
return df
def loadDomesticIndex(self, name, beforeStr, endStr):
if not os.path.isfile(name):
print(name, 'collect...')
crawler = NaverCrawler.create(targetName=name.split('_')[0])
date = NaverDate.create(startDate=beforeStr, endDate=endStr)
data = crawler.crawling(dateData=date)
df = pd.DataFrame(columns=['종가', '전일비', '등락률', '거래량', '거래대금'])
for v in data:
df.loc[v.index()] = v.value()
df.to_hdf(name, key='df', mode='w')
else:
print(name, 'read...')
df = pd.read_hdf(name, key='df')
return df
def showGraphK10KOSPI200():
k10_price = k10FromDate()
k10_info = makeK10()
k10_historical_mc = k10_price * k10_info['Outstanding'] * k10_info[
'Floating']
k10 = pd.DataFrame()
k10['k10 Market Cap'] = k10_historical_mc.sum(axis=1)
k10['k10'] = k10['k10 Market Cap'] / k10['k10 Market Cap'][0] * 100
crawler = NaverCrawler.create(targetName='KPI200')
date = NaverDate.create(startDate='2019-01-02', endDate='2019-05-04')
kospi200 = crawler.crawling(dateData=date)
kospi200Close = {
NaverDate.formatDate(item.date): item.close
for item in kospi200
}
k200 = pd.DataFrame({'K200': kospi200Close})
plt.figure(figsize=(10, 5))
plt.plot(k10['k10'])
plt.plot(k200['K200'] / k200['K200'][0] * 100)
plt.legend(loc=0)
plt.grid(True, color='0.7', linestyle=':', linewidth=1)
def loadStockFromArr(self, name, targets, beforeStr, endStr):
prices = dict()
if not os.path.isfile(name):
date = NaverDate.create(startDate=beforeStr, endDate=endStr)
for target in targets:
print(target['Name'], 'collect...')
crawler = NaverStockCrawler.create(target['Code'])
data = crawler.crawling(date)
prices[target['Name']] = { pd.to_datetime(item.date, format='%Y-%m-%d') : item.close for item in data }
bonddf = pd.DataFrame(prices)
bonddf.to_hdf(name, key='df', mode='w')
else:
print(name, 'read...')
bonddf = pd.read_hdf(name, key='df')
return bonddf
def makeDataFrame():
crawler = NaverCrawler.create(targetName='KPI200')
date = NaverDate.create(startDate='2019-01-02', endDate='2019-05-04')
kospi200 = crawler.crawling(dateData=date)
kospi200Close = {
NaverDate.formatDate(item.date): item.close
for item in kospi200
}
worldDate = NaverDate.create(startDate='2019-01-02', endDate='2019-05-04')
worldCrawler = NaverWorldCrawler.create(targetName='SPI@SPX')
sp500 = worldCrawler.crawling(dateData=worldDate)
sp500Close = {
NaverDate.formatDate(item.date): item.close
for item in sp500
}
data = {'S&P500': sp500Close, 'KOSPI200': kospi200Close}
df = pd.DataFrame(data)
df = df.fillna(method='ffill')
if df.isnull().values.any():
df = df.fillna(method='bfill')
return df
def loadStockFromDict(self, name, targets, beforeStr, endStr):
prices = dict()
if not os.path.isfile(name):
date = NaverDate.create(startDate=beforeStr, endDate=endStr)
progress = 0
compliteLen = len(targets.keys())
for key in targets:
print(targets[key],'collect...', str(progress),'/',str(compliteLen) ,str(progress/compliteLen)+'%')
crawler = NaverStockCrawler.create(key)
data = crawler.crawling(date)
prices[targets[key]] = { pd.to_datetime(item.date, format='%Y-%m-%d') : item.close for item in data }
progress+=1
topdf = pd.DataFrame(prices)
topdf.to_hdf(name, key='df', mode='w')
else:
print(name, 'read...')
topdf = pd.read_hdf(name, key='df')
return topdf
prices = dict()
# In[12]: read
topcap = pd.read_hdf('ZIPTOPCAP2007-01-01-2019-12-31.h5')
# In[12]
targets = {}
for index, row in topcap.iterrows():
targets[row['Code']] = row['Name']
# In[12]: 종목별 종가
import os.path
name = 'STOCKZIPTOPCAP2007-01-01-2019-12-31.h5'
if not os.path.isfile(name):
date = NaverDate.create(startDate=beforeStr, endDate=endStr)
progress = 0
compliteLen = len(targets.keys())
for key in targets:
print(targets[key], 'collect...', str(progress), '/', str(compliteLen),
str(progress / compliteLen) + '%')
crawler = NaverStockCrawler.create(key)
data = crawler.crawling(date)
prices[targets[key]] = {
pd.to_datetime(item.date, format='%Y-%m-%d'): item.close
for item in data
}
progress += 1
topdf = pd.DataFrame(prices)
topdf.to_hdf('STOCKZIPTOPCAP2007-01-01-2019-12-31.h5', key='df', mode='w')
def crawling(self, dateData=''):
driver = webdriver.PhantomJS(
'C:/Users/lsj/Downloads/phantomjs-2.1.1-windows/bin/phantomjs.exe')
driver.get(self.makeWorldUrl())
data = []
pageNo = '1'
isRunning = True
while (isRunning):
elePage = driver.find_element_by_link_text(pageNo)
if not elePage:
break
elePage.click()
pageNo = elePage.text
text = driver.page_source
soup = bs4.BeautifulSoup(text, 'lxml')
table = soup.find(class_='tb_status2 tb_status2_t2').find('tbody')
rows = filter(lambda val: type(val) == bs4.element.Tag,
table.children)
#자식들에서 td태그를 찾음
tds = map(lambda row: row.find_all('td'), list(rows))
#1차원으로 변경
flattenTds = list(itertools.chain(*tds))
#없는 자식들 제거
tdsf = filter(lambda td: type(td) == bs4.element.Tag, flattenTds)
#텍스트 추출
values = map(lambda value: value.stripped_strings, tdsf)
#1차원으로 변경
strings = list(itertools.chain(*values))
#6개씩 자름
splitData = [strings[i:i + 6] for i in range(0, len(strings), 6)]
for one in splitData:
date = NaverDate.formatDate(date=one[0])
# print(dateData.startDate)
print(date)
# print(dateData.endDate)
if dateData.startDate <= date and date <= dateData.endDate:
resultData = NaverWorldResultData.create(date=one[0],
close=one[1],
diff=one[2],
open=one[3],
high=one[4],
low=one[5])
data.append(resultData)
elif dateData.startDate > date:
isRunning = False
break
# print('pageNo:' + str(pageNo))
# for value in data:
# print(value)
# print(data)
eleNext = driver.find_elements_by_css_selector('#dayPaging .next')
nextPageNo = str(int(pageNo) + 1)
print(nextPageNo)
if len(eleNext) > 0 and int(pageNo) % 10 == 0:
eleNext[0].click()
wait = WebDriverWait(driver, 10)
wait.until(
EC.presence_of_element_located((By.LINK_TEXT, nextPageNo)))
driver.implicitly_wait(1)
pageNo = nextPageNo
if len(driver.find_elements_by_link_text(pageNo)) == 0:
break
driver.close()
return data
# In[1]: 상장기업...
import pandas as pd
kospiCompanyDf = pd.read_excel('fin/시가총액_2019_01_02.xlsx',sheet_name='시가총액', skiprows=3, converters={'종목코드':str})
kospiCompanyDf = kospiCompanyDf.iloc[1:]
codeName = {}
for index, row in kospiCompanyDf.iterrows():
codeName[row['종목코드']] = row['종목명']
codeName
# In[2]: crawling 현재가...
from crawler.NaverStockCrawler import NaverStockCrawler
from crawler.data.NaverDate import NaverDate
prices = dict()
date = NaverDate.create(startDate=before, endDate=now)
progress = 0
compliteLen = len(codeName.keys())
for key in codeName:
print(codeName[key],'collect...', str(progress),'/',str(compliteLen) ,str(progress/compliteLen*100)+'%')
crawler = NaverStockCrawler.create(key)
data = crawler.crawling(date)
prices[key] = { pd.to_datetime(item.date, format='%Y-%m-%d') : item.close for item in data }
progress+=1
topDf = pd.DataFrame(prices)
topDf
# In[3]: 재무제표
upCodes = ['제조업', '은행업', '증권업', '보험업', '종합금융업', '여신전문금융업', '신용금고']
factors = ['per', 'pcr', 'pbr', 'roe', '당기순이익', '영업활동으로인한현금흐름', '투자활동으로인한현금흐름', '재무활동으로인한현금흐름']
factorDf = {}
import numpy as np
from sklearn.linear_model import LinearRegression
# In[2]: font 설정
import matplotlib.font_manager as fm
import matplotlib as mpl
import matplotlib.pyplot as plt
path = '/Library/Fonts/NanumBarunGothicLight.otf'
font_name = fm.FontProperties(fname=path, size=18).get_name()
print(font_name)
mpl.rc('font', family=font_name)
# In[3]: test
prices = dict()
date = NaverDate.create(startDate='1997-06-01')
crawler = NaverStockCrawler.create('035720', logging=True)
data = crawler.crawling(date)
prices['카카오'] = {
pd.to_datetime(item.date, format='%Y-%m-%d'): item.close
for item in data
}
topdf = pd.DataFrame(prices)
topdf
# crawler = NavarSearchCodeCrawler.create('KODEX')
# data = crawler.crawling()
# data
# In[4]: test2
print('collect...')
crawler = NaverCrawler.create(targetName='KOSPI')
kospi200Close = {
NaverDate.formatDate(item.date): item.close
for item in kospi200
}
k200 = pd.DataFrame({'K200': kospi200Close})
plt.figure(figsize=(10, 5))
plt.plot(k10['k10'])
plt.plot(k200['K200'] / k200['K200'][0] * 100)
plt.legend(loc=0)
plt.grid(True, color='0.7', linestyle=':', linewidth=1)
# In[3]: 코스피 가져오기
crawler = NaverCrawler.create(targetName='KPI200')
date = NaverDate.create(startDate='2018-06-01')
kospi200 = crawler.crawling(dateData=date)
df = pd.DataFrame(columns=['종가', '전일비', '등락률', '거래량', '거래대금'])
for v in kospi200:
df.loc[v.index()] = v.value()
df
# In[4]: 월 평균 값
monthly_df = df.resample('M', how={'종가': np.mean})
monthly_df
# In[5]: 모멘텀 구하기
monthly_df['종가'][-1] - monthly_df['종가']
# In[6]: 30종목 종가 버그 있음
prices = dict()
targets = topK(30) + [{'code': "114800", 'name': 'KODEX 인버스'}]
