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 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
# print('현금', restMoney)
# print('total', restMoney + stockMoney)
# print('수익률', (stockMoney + restMoney)/money )
moneydf = pd.DataFrame([[stockMoney + restMoney, stockMoney, restMoney]],
index=[current],
columns=['total', 'stock', 'rest'])
moneySum = pd.concat([moneySum, moneydf])
money = stockMoney + restMoney
printPd('##수익률', stockValue / beforeValue)
printPd('##소유주식', stockWallet)
printPd('##주식가격', moneyWallet)
printPd('##Total', moneySum)
# In[14]: 코스피 200 가져오기
crawler = NaverCrawler.create(targetName='KPI200')
date = NaverDate.create(startDate=startDateStr, endDate=endDateStr)
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[15]: 날짜 normalize
moneySum.index = moneySum.index.map(lambda dt: pd.to_datetime(dt.date()))
# moneySum
# df.loc[moneySum.index, :]
# df.index
# In[16]: 연평균 수익률
portfolio = moneySum['total'] / moneySum['total'].iloc[0]
# 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')
date = NaverDate.create(startDate='2007-01-01', endDate='2019-12-31')
data = crawler.crawling(dateData=date)
print(data)
# In[5]: pbr test
crawler = NaverPbrCrawler()
data = crawler.crawling('005930')
print(data)
# In[6]:
#%%