def search_stock():
try:
search = request.form['input']
search_word = web_engine().search('{}'.format(search)) # 검색 엔진
search_word_code = str(search_word[0]).zfill(6)
institution_foriegner = foreinger_institurion(
).show_seach_page_foreigner_instition(search_word_code)
news_data = naver_news().news_information(search_word_code)
financial_data = korea_detail_information().kr_financial_data(
search_word_code)
main_page_value = main_page_information().main_page_data()
stock_information = detail_serach_page_imformation().main_information(
search_word_code)
nonprice_data = korea_detail_information().kr_detail_data(
search_word_code)
price_data = korea_detail_information().kr_price_data(
int(search_word_code))
price_diff_data = kr_page_data().change_rate_calculate()[int(
search_word_code)]
return render_template('kr_search_detail.html',
search_word_code=search_word_code,
main_page_value=main_page_value,
news_data=news_data,
nonprice_data=nonprice_data,
price_data=price_data,
financial_data=financial_data,
institution_foriegner=institution_foriegner,
stock_information=stock_information,
price_diff=price_diff_data,
current_time=date_method())
except:
return render_template('error_search_detail.html')
def daily_kospi_data_update(self):
engine = create_engine("mysql+pymysql://root:" + "0000" + "@127.0.0.1/web_db?charset=utf8",
encoding='utf-8')
conn = engine.connect()
today_data = wb.DataReader("^KS11","yahoo",datetime.date.today())
today_data.columns = ['high','low','open','close','volume','adj_close']
today_data['type'] = 'kospi'
today_data['date'] = date_method().date_num()
today_data.to_sql(name='kr_index_data_daily', con=engine, if_exists='append', index=False)
conn.close()
def korea_stock():
main_page_value = main_page_information().main_page_data()
foreinger_institurion_data = foreinger_institurion(
).daily_data_db_to_python()
kospi_change_data = kr_page_data().volatilty('kospi')
kosdaq_change_data = kr_page_data().volatilty('kosdaq')
kospi_volume_data = kr_page_data().volume('kospi')
kosdaq_volume_data = kr_page_data().volume('kosdaq')
return render_template(
'korea_stock.html',
main_page_value=main_page_value,
current_time=date_method(),
foreinger_institurion_data=foreinger_institurion_data,
kospi_change_data=kospi_change_data,
kosdaq_change_data=kosdaq_change_data,
kospi_volume_data=kospi_volume_data,
kosdaq_volume_data=kosdaq_volume_data)
def main_page():
main_page_value = main_page_information().main_page_data()
kospi_kosdaq_sum = main_page_information().main_page_calculate()
kospi_change_data = kr_page_data().volatilty('kospi')
kosdaq_change_data = kr_page_data().volatilty('kosdaq')
kospi_low_value_data = low_value_stock().final_data_to_df('kospi')[1]
kosdaq_low_value_data = low_value_stock().final_data_to_df('kosdaq')[1]
return render_template('main_page.html',
main_page_value=main_page_value,
kospi_kosdaq_sum=kospi_kosdaq_sum,
kospi_change_data=kospi_change_data,
kosdaq_change_data=kosdaq_change_data,
current_time=date_method().time(),
kospi_low_value_data=kospi_low_value_data,
kosdaq_low_value_data=kosdaq_low_value_data)
def final_value(self):
main_value = self.main_page_value()
world_value = self.world_page_value()
final_list = [
main_value[0], main_value[1], main_value[2], main_value[6],
main_value[7], main_value[8], world_value[3], world_value[4],
world_value[5], world_value[0], world_value[1], world_value[2],
world_value[6], world_value[7], world_value[8], world_value[9],
world_value[10], world_value[11], main_value[15], main_value[16],
main_value[17], main_value[18], main_value[19], main_value[20],
main_value[21], main_value[22], main_value[23], main_value[27],
main_value[28], main_value[29], main_value[30], main_value[31],
main_value[23], main_value[24], main_value[25], main_value[26],
main_value[3], main_value[4], main_value[5], main_value[9],
main_value[10], main_value[11]
]
today_date = date_method().date_num()
data = [today_date] + final_list
return data
def code_list_update(self): # DB에서 실제 정보가 있는 데이터가 없을때 새롭게 DB에 추가해주기
code_list = self.code_list()
total_code_list = code_information().code_data()['kr_stock_code']
non_list_code = list(set([int(i) for i in total_code_list]) - set(code_list))
# DB 엔진
engine = create_engine("mysql+pymysql://root:" + "0000" + "@127.0.0.1/web_db?charset=utf8",
encoding='utf-8')
conn = engine.connect()
if len(non_list_code) != 0:
# 우선 없는 데이터 추가
df_krx = fdr.StockListing('KRX')
kospi_kosdaq = df_krx[(df_krx['Market'] == 'KOSDAQ') | (df_krx['Market'] == 'KOSPI')].dropna()
kospi_kosdaq = kospi_kosdaq[
['Symbol', 'Market', 'Name', 'Sector', 'Industry', 'ListingDate', 'SettleMonth', 'HomePage', 'Region']]
kospi_kosdaq.columns = ['kr_stock_code', 'market', 'kr_stock_name', 'sector', 'industry', 'listingdate',
'settledate', 'homepage', 'region']
kospi_kosdaq['kr_stock_code'] = [int(i) for i in kospi_kosdaq['kr_stock_code']]
kospi_kosdaq = kospi_kosdaq.set_index('kr_stock_code')
add_df = kospi_kosdaq.loc[non_list_code]
add_df = add_df.reset_index()
add_df.columns = ['kr_stock_code', 'market', 'kr_stock_name', 'sector', 'industry', 'listingdate',
'settledate', 'homepage', 'region']
# DB kr_stock_list 추가
add_df.to_sql(name='kr_stock_list', con=engine, if_exists='append', index=False)
# DB kr_stock_daily 추가
for i in non_list_code:
code = str(i).zfill(6)
df = fdr.DataReader(code, date_method().date_num())
df = df.reset_index()
df['code'] = code
df.columns = ['date','kr_stock_open','kr_stock_high','kr_stock_low','kr_stock_close',
'kr_stock_volume','kr_stock_close_diff','kr_stock_code']
df = df[['kr_stock_code','date','kr_stock_open','kr_stock_close_diff','kr_stock_high','kr_stock_low',
'kr_stock_close','kr_stock_volume']]
df.to_sql(name='kr_stock_daily', con=engine, if_exists='append', index=False)
# 존재하는 데이터 업데이트
for i in code_list:
code = str(i).zfill(6)
df = fdr.DataReader(code, '2020-09-14')
df = df.reset_index()
df['code'] = code
df.columns = ['date', 'kr_stock_open', 'kr_stock_high', 'kr_stock_low', 'kr_stock_close',
'kr_stock_volume', 'kr_stock_close_diff', 'kr_stock_code']
df = df[
['kr_stock_code', 'date', 'kr_stock_open', 'kr_stock_close_diff', 'kr_stock_high', 'kr_stock_low',
'kr_stock_close', 'kr_stock_volume']]
df.to_sql(name='kr_stock_daily', con=engine, if_exists='append', index=False)
conn.close()
else:
#차이가 없을 경우 존재하는 데이터만 업데이트
for i in code_list:
code = str(i).zfill(6)
df = fdr.DataReader(code, date_method().date_num())
df = df.reset_index()
df['code'] = code
df.columns = ['date', 'kr_stock_open', 'kr_stock_high', 'kr_stock_low', 'kr_stock_close',
'kr_stock_volume', 'kr_stock_close_diff', 'kr_stock_code']
df = df[
['kr_stock_code', 'date', 'kr_stock_open', 'kr_stock_close_diff', 'kr_stock_high', 'kr_stock_low',
'kr_stock_close', 'kr_stock_volume']]
df.to_sql(name='kr_stock_daily', con=engine, if_exists='append', index=False)
conn.close()
def weekly_data(self):
code_list = [
str(i[0]).zfill(6) for i in kr_stock_weekly().kr_stock_code_list()
]
final_set = []
for num, code in enumerate(code_list):
if num in list(range(100, 10000, 100)):
print(round(num / len(code_list), 2) * 100, '% 완료')
# 시가총액, 코스피 랭킹
try:
URL = "https://finance.naver.com/item/main.nhn?code={}"
index = requests.get(URL.format(code))
html = index.text
soup = BeautifulSoup(html, 'html.parser')
price_info = list(
filter(('').__ne__,
soup.find('div', class_='rate_info').text.replace(
'\t', '').split('\n')))
info1 = soup.find('div', class_='aside_invest_info').find(
'div', class_='first').text.replace('\t',
'').replace('\n', '')
info2 = soup.find('div', class_='aside_invest_info').find(
'table', class_='rwidth').text.split('\n')
info3 = soup.find('div', class_='aside_invest_info').find(
'table', class_='per_table')
info4 = soup.find('div', class_='aside_invest_info').find(
'table', summary="동일업종 PER 정보")
# OHLCV 정보
today_close = price_info[1][5:-3].strip()
today_close_diff = [
price_info[2][-2:],
price_info[2][:price_info[2].find('포인트')].strip()
]
today_close_percent = [
price_info[3][price_info[3].find('%') + 1:].strip(),
price_info[3][:price_info[3].find('%')]
]
today_open = price_info[28][:int(len(price_info[28]) / 2)]
today_high = price_info[20][:int(len(price_info[20]) / 2)]
today_low = price_info[30][:int(len(price_info[30]) / 2)]
today_vol = price_info[25]
# 시가총액, 코스피 랭킹 (info1)
market_list = ['시가총액시가총액', '시가총액순위', '상장주식수', '액면가l매매단위']
market_list_position = []
length = []
for i in range(len(market_list)):
length.append(len(market_list[i]))
market_list_position.append(info1.find(market_list[i]))
position_start = [
x + y for x, y in zip(market_list_position, length)
]
position_finish = market_list_position + [len(info1)]
basic_info = []
for x, y in zip(position_start, position_finish[1:]):
basic_info.append(info1[x:y])
market_value = basic_info[0]
kospi_rank = basic_info[1]
# 투자의견 (info2)
comment = info2[4]
goal_price = info2[6]
high_52week = info2[-6]
low_52week = info2[-4]
# PER, EPS, 추정PER,EPS,
PER = info3.find_all('em')[0].text
EPS = info3.find_all('em')[1].text
est_PER = info3.find_all('em')[2].text
est_EPS = info3.find_all('em')[3].text
PBR = info3.find_all('em')[4].text
BPS = info3.find_all('em')[5].text
DIV = info3.find_all('em')[6].text
# 동일 업종 PER, 예상 등락률
same_ind_per = info4.text.replace('\t', '').split('\n')[5]
same_ind_per_percentage = info4.text.replace(
'\t', '').split('\n')[-4]
# close_diff 값 차이 수정
i = today_close_diff
try:
if i[0] == '하락':
close_diff = -1 * float(i[1].replace(",", ''))
today_close_diff = close_diff
elif i[0] == '상승':
close_diff = float(i[1].replace(",", ''))
today_close_diff = close_diff
else:
close_diff = float(i[1].replace(",", ''))
today_close_diff = close_diff
except:
today_close_diff = close_diff
# close_change 값 수정
j = today_close_percent
try:
if j[0] == '마이너스':
close_change = -1 * float(j[1].replace(",", ''))
today_close_percent = close_change
elif i[0] == '플러스':
close_change = float(j[1].replace(",", ''))
today_close_percent = close_change
else:
today_close_percent = j[1]
except:
today_close_percent = close_change
date = [date_method().date_num()] * len(code)
final_value = [
code, date, today_close, today_close_diff,
today_close_percent, today_open, today_high, today_low,
today_vol, market_value, kospi_rank, comment, goal_price,
high_52week, low_52week, PER, EPS, est_PER, est_EPS, PBR,
BPS, DIV, same_ind_per, same_ind_per_percentage
]
except:
final_value = [code, '2020-09-04', 'NAN']
final_set.append(final_value)
data = pd.DataFrame(columns=[
'code', 'date', 'close', 'close_diff', 'close_percent', 'open',
'high', 'low', 'vol', 'market_value', 'kospi_rank', 'comment',
'goal_price', 'high_52week', 'low_52week', 'PER', 'EPS', 'est_PER',
'est_EPS', 'PBR', 'BPS', 'DIV', 'same_ind_per',
'same_ind_per_percentage'
])
for i in range(len(final_set)):
if len(final_set[i]) == 24:
data.loc[i] = final_set[i]
else:
data.loc[i] = final_set[i] + [
'NAN', 'NAN', 'NAN', 'NAN', 'NAN', 'NAN', 'NAN', 'NAN',
'NAN', 'NAN', 'NAN', 'NAN', 'NAN', 'NAN', 'NAN', 'NAN',
'NAN', 'NAN', 'NAN', 'NAN', 'NAN'
]
data.columns = [
'kr_stock_code', 'date', 'kr_stock_close', 'kr_stock_close_diff',
'kr_stock_close_percent', 'kr_stock_open', 'kr_stock_high',
'kr_stock_low', 'kr_stock_vol', 'kr_stock_market_value',
'kr_stock_kospi_rank', 'kr_stock_comment', 'kr_stock_goal_price',
'kr_stock_high_52week', 'kr_stock_low_52week', 'kr_stock_PER',
'kr_stock_EPS', 'kr_stock_est_PER', 'kr_stock_est_EPS',
'kr_stock_PBR', 'kr_stock_BPS', 'kr_stock_DIV',
'kr_stock_same_ind_per', 'kr_stock_same_ind_per_percentage'
]
return data
def daily_foreinger_institurion_crolling(self):
URL = "https://finance.naver.com/sise/"
index1 = requests.get(URL)
html = index1.text
soup = BeautifulSoup(html, 'html.parser')
table = soup.find('div', id='contentarea_left')
total_df = pd.DataFrame()
foreigner_buy = []
for i in table.find_all('table')[2].text.replace('\t', '').split('\n'):
if len(i) > 0:
if i == '0':
foreigner_buy.append('-')
foreigner_buy.append(i)
else:
foreigner_buy.append(i)
foreigner_buy = foreigner_buy[1:]
foreigner_buy_df = pd.DataFrame(np.array(foreigner_buy).reshape(-1, 4))
total_df = pd.concat([total_df, foreigner_buy_df], axis=0)
foreigner_sell = []
for i in table.find_all('table')[3].text.replace('\t', '').split('\n'):
if len(i) > 0:
if i == '0':
foreigner_sell.append('-')
foreigner_sell.append(i)
else:
foreigner_sell.append(i)
foreigner_sell = foreigner_sell[1:]
foreigner_sell_df = pd.DataFrame(
np.array(foreigner_sell).reshape(-1, 4))
total_df = pd.concat([total_df, foreigner_sell_df], axis=0)
institution_buy = []
for i in table.find_all('table')[4].text.replace('\t', '').split('\n'):
if len(i) > 0:
if i == '0':
institution_buy.append('-')
institution_buy.append(i)
else:
institution_buy.append(i)
institution_buy = institution_buy[1:]
institution_buy_df = pd.DataFrame(
np.array(institution_buy).reshape(-1, 4))
total_df = pd.concat([total_df, institution_buy_df], axis=0)
institurion_sell = []
for i in table.find_all('table')[5].text.replace('\t', '').split('\n'):
if len(i) > 0:
if i == '0':
institurion_sell.append('-')
institurion_sell.append(i)
else:
institurion_sell.append(i)
institurion_sell = institurion_sell[1:]
institurion_sell_df = pd.DataFrame(
np.array(institurion_sell).reshape(-1, 4))
total_df = pd.concat([total_df, institurion_sell_df], axis=0)
total_df.columns = ['kr_stock_name', 'close', '상승or하락', '변동율']
total_df = total_df.reset_index()
total_df = total_df[['kr_stock_name', 'close', '상승or하락', '변동율']]
total_df['change'] = None
for i in range(len(total_df)):
if total_df.loc[i]['상승or하락'] == '상승':
total_df.loc[i]['change'] = int(total_df.loc[i]['변동율'].replace(
',', ''))
elif total_df.loc[i]['상승or하락'] == '하락':
total_df.loc[i]['change'] = -1 * int(
total_df.loc[i]['변동율'].replace(',', ''))
else:
total_df.loc[i]['change'] = 0
total_df['date'] = date_method().date_num()
total_df['close'] = [
int(i.replace(",", "")) for i in total_df['close']
]
total_df = total_df[['kr_stock_name', 'date', 'close', 'change']]
return total_df