def lstm_test3():
se = StartEndLogging()
modeling_target_qs = scw.gets_modeling_target()
for modeling_company in modeling_target_qs[:10]:
model = LstmTraining(modeling_company.com_code, LSTM_KWARGS)
model.modeling3()
se.end()
def code_init():
se = StartEndLogging()
try:
cdw.delete()
code_df = pd.read_csv(CODE_FILE_PATH, delimiter=',', encoding='utf-8')
code_df = code_df.fillna('')
cdw.insert(code_df)
except Exception as e:
log.error(e)
sys.exit()
se.end(f'{len(code_df)} codes insert!')
def start_crawler(self, is_disp_processing_step=False):
se_check = StartEndLogging()
about_period_months = int((self._e_date - self._s_date).days / 30) + 2
log.info(f'Input days: {self._s_date} ~ {self._e_date} '
f'({(self._e_date - self._s_date).days + 1} days)')
if is_disp_processing_step:
lists = tqdm(range(about_period_months))
else:
lists = range(about_period_months)
try:
for _ in lists:
cnt_work_days, cnt_skip_days = self._go_end_trading_day(not self._is_start) \
if self._is_start else \
self._change_calendar_month()
if cnt_work_days == 0: break
prev_work_date = None
for day_idx in reversed(range(cnt_work_days)):
curr_work_day = self._get_day_data(day_idx)
self._cnt_work_days += 1
if self._we_date is None:
self._we_date = parse(curr_work_day).date()
self._ws_date = parse(curr_work_day).date()
if self._ws_date == self._s_date:
self._is_stop = True
break
if self._ws_date < self._s_date:
self._is_stop = True
os.remove(
os.path.join(CRAWLING_TARGET_PATH,
f'{curr_work_day}.csv'))
self._ws_date = prev_work_date
self._cnt_work_days -= 1
break
prev_work_date = self._ws_date
if str(self._ws_date)[:7] == str(self._s_date)[:7]:
self._is_stop = True
if self._is_stop:
break
except Exception as e:
log.error(e)
log.info(
f'Working days: '
f'{self._ws_date} ~ {self._we_date} ({self._cnt_work_days} days)')
self._web_driver.quit()
se_check.end()
def insert_company_from_market():
se = StartEndLogging()
scw.delete()
market_qs = smdw.gets(date=smdw.get_date(is_add_one=False))
log.debug(f'market data size: {len(market_qs)}')
company_objects = []
for market_data in market_qs:
company_object = scw.make_object(market_data)
if company_object is not None:
company_objects.append(company_object)
scw.insert(company_objects)
se.end()
def update_modelingdata_from_market():
"""
daily 작업에서는 추가되는 회사 데이터의 건수에 대한 카운팅은 무시하고
업데이트 되지 않은 마켓 데이터를 일괄로 추가한다.
- 이 작업은 시스템 초기화나 주간 작업에서만 수행한다.
:return: 없음
"""
se = StartEndLogging()
try:
data_df = read_frame(smdw.gets('date', date__gt=smlw.get_date()))
smlw.insert(data_df[['date','com_code']+MODELING_COLUMNS])
except Exception as e:
log.error(e)
sys.exit()
se.end()
def insert_modelingdata_from_market():
se = StartEndLogging()
company_qs = scw.gets('id')
company_size = len(company_qs)
log.debug(f'company size: {company_size}')
smlw.delete()
def get_normal_marketdata(com_code):
company_market_qs = smdw.gets(com_code=com_code)
first_data = company_market_qs.first()
yesterday_data, first_normal_data = first_data, first_data
if first_data.t_volume != 0:
diff_ratio = company_market_qs.last(
).t_volume / first_data.t_volume
else:
# 첫번째 t_volume값이 0이기 때문에 전체 변동량 체크가 불가능함.
# 따라서 세부 체크가 수행될 수 있도록 diff_ratio를 설정함
diff_ratio = TOTAL_CHECK_MAX_RATIO + 1.
if diff_ratio > TOTAL_CHECK_MAX_RATIO or diff_ratio < TOTAL_CHECK_MIN_RATIO:
for market_data in company_market_qs[
1:]: # 첫번째 값은 위에서 first()을 이용해 이미 사용
if yesterday_data.t_volume != 0 and market_data.t_volume != 0:
diff_ratio = market_data.t_volume / yesterday_data.t_volume
if diff_ratio > DAY_CHECK_MAX_RATIO or diff_ratio < DAY_CHECK_MIN_RATIO:
first_normal_data = market_data
# 당일 거래량이 없는 종목은 감자/증자 대상으로 간주한다.
if market_data.volume == 0:
first_normal_data = market_data
yesterday_data = market_data
normal_qs = company_market_qs.filter(date__gte=first_normal_data.date)
log.debug(f'com_code: {com_code}, modeling data size: '
f'total({len(company_market_qs)}), normal({len(normal_qs)})')
return normal_qs
for company in tqdm(company_qs):
market_df = read_frame(get_normal_marketdata(company.com_code))
company.data_size = len(market_df)
company.save()
market_df = market_df[['date', 'com_code'] + MODELING_COLUMNS]
smlw.insert(market_df)
se.end()
def insert_marketdata_from_mergefile():
insert_se = StartEndLogging('insert marketdata')
col_names = ['com_code', 'com_name', 'm_type', 'm_dept', 'close',
'diff', 'ratio', 'open', 'high', 'low', 'volume',
'value', 't_value', 't_volume', 'date']
# merge_reader = pd.read_csv(MERGE_FILE_PATH, encoding='CP949', low_memory=False,
# names=col_names, chunksize=3000, skiprows=[0])
#
# loop_cnt = 1
# for merge_df in merge_reader:
# merge_df = merge_df.fillna(0)
# smdw.insert(merge_df)
# insert_se.mid(f'{loop_cnt * 3000}')
# loop_cnt += 1
smdw.delete()
insert_se.end()
def update_company_from_market():
se = StartEndLogging()
market_qs = smdw.gets(date=smdw.get_date(is_min=False, is_add_one=False))
company_df = read_frame(scw.gets('id'))
log.debug(f'market data size: {len(market_qs)}, company data size: {len(company_df)}')
company_objects = []
for market_data in market_qs:
company = company_df[company_df['com_code']==market_data.com_code]
if company.empty:
company_object = scw.make_object(market_data)
else:
company_object = scw.make_object(company, market_data)
if company_object is not None:
company_objects.append(company_object)
scw.insert(company_objects)
se.end()
def update_marketdata_from_crawler():
m_type_dict = scdw.get_type_dict('A', is_name_index=True)
def file_processing(csv_file_name):
trading_df = pd.read_csv(os.path.join(CRAWLING_TARGET_PATH, csv_file_name),
delimiter=',', encoding='CP949', names=COLUMN_NAMES,
skiprows=[0])
trading_df = trading_df.fillna(0)
trading_df['date'] = parse(str(re.findall('\d{8}', csv_file_name)[0])).date()
trading_df['m_type'] = trading_df['m_type']\
.apply(lambda m_type: m_type_dict[str(m_type)])
trading_df = trading_df.drop(['m_dept'], axis=1)
smdw.insert(trading_df)
shutil.move(os.path.join(CRAWLING_TARGET_PATH, csv_file_name),
os.path.join(CRAWLING_BACKUP_PATH, csv_file_name))
se = StartEndLogging()
try:
for file_name in tqdm(sorted(os.listdir(CRAWLING_TARGET_PATH))):
file_processing(file_name)
se.mid(file_name)
except Exception as e:
log.error(e)
sys.exit()
se.end()
def lstm_test():
se = StartEndLogging()
modeling_target_qs = scw.gets_modeling_target()
log.info(len(modeling_target_qs))
cnt_skip_trend, cnt_skip_accuracy = 0, 0
for modeling_company in modeling_target_qs[:15]:
model = LstmTraining(modeling_company.com_code, kwargs)
is_skip = model.modeling()
se.mid(f'{modeling_company.com_code}')
if is_skip['trend']:
cnt_skip_trend += 1
if is_skip['accuracy']:
cnt_skip_accuracy += 1
log.info(
f'modeling total count: {len(modeling_target_qs)}, '
f'trend skip: {cnt_skip_trend}, accuracy skip: {cnt_skip_accuracy}')
se.end()
def today_modeling():
se = StartEndLogging()
modeling_target_qs = scw.gets_modeling_target()
modeling_size = len(modeling_target_qs)
cnt_processing = 0
cnt_skip_trend, cnt_skip_accuracy = 0, 0
for modeling_company in modeling_target_qs:
model = LstmTraining(modeling_company.com_code, LSTM_KWARGS)
is_skip = model.modeling2()
cnt_processing += 1
se.mid(f'{modeling_company.com_code}, {cnt_processing}/{modeling_size}')
if is_skip['trend']:
cnt_skip_trend += 1
if is_skip['accuracy']:
cnt_skip_accuracy += 1
log.info(f'modeling total count: {len(modeling_target_qs)}, '
f'trend skip: {cnt_skip_trend}, accuracy skip: {cnt_skip_accuracy}')
se.end()
buy_money = y_trading.buy_price * y_trading.volume
diff_money = base_money_for_cnt - buy_money
sell_money = y_trading.sell_price * y_trading.volume
result_money = diff_money + sell_money
total_money += result_money
y_trading.save()
account.balance = total_money - TRADING_BASE_MONEY
account.ratio = total_money / TRADING_BASE_MONEY
account.base_money = total_money
account.save()
except Exception as e:
raise Exception(e)
if __name__ == '__main__':
tse = StartEndLogging('daily processing')
try:
start_krx_crawling()
update_marketdata_from_crawler()
update_company_from_market()
update_modelingdata_from_market()
yesterday_trading_result()
# today_modeling()
# today_trading()
except Exception as err:
log.error(err)
tse.end('daily processing')