def test_with_a_holiday_1(self):
# 20210104 monday / 20210109 saturday
df = stock.get_market_fundamental_by_date("20210104", "20210109", "005930")
self.assertIsInstance(df, pd.DataFrame)
temp = np.isclose(df.iloc[0], [37528, 26.218750, 2.210938, 3166, 1.709961, 1416])
self.assertEqual(temp.sum(), 6)
self.assertEqual(len(df), 5)
def test_with_holidays(self):
# 20210103 sunday / 20210110 sunday
df = stock.get_market_fundamental_by_date("20210103", "20210110",
"005930")
self.assertIsInstance(df, pd.DataFrame)
temp = np.isclose(df.iloc[0], [37528, 26.22, 2.21, 3166, 1.71, 1416])
self.assertEqual(temp.sum(), 6)
self.assertEqual(len(df), 5)
def test_io(self):
df = stock.get_market_fundamental_by_date("20180212", "20180216", "006800")
self.assertIsInstance(df.index[0], pandas._libs.tslibs.timestamps.Timestamp)
self.assertIsInstance(df['DIV'].iloc[0], np.float64)
self.assertIsInstance(df['BPS'].iloc[0], np.int32)
self.assertIsInstance(df['PER'].iloc[0], np.float64)
self.assertIsInstance(df['EPS'].iloc[0], np.int32)
self.assertIsInstance(df['PBR'].iloc[0], np.float64)
def test_market_fundamental_2_format(self):
df = stock.get_market_fundamental_by_date("20180212", "20180216",
"006800")
self.assertEqual(type(df.index[0]), str)
self.assertEqual(type(df['DIV'].iloc[0]), np.float64)
self.assertEqual(type(df['BPS'].iloc[0]), np.int32)
self.assertEqual(type(df['PER'].iloc[0]), np.float64)
self.assertEqual(type(df['EPS'].iloc[0]), np.int32)
self.assertEqual(type(df['PBR'].iloc[0]), np.float64)
def test_with_freq(self):
df = stock.get_market_fundamental_by_date("20200101",
"20200430",
"005930",
freq="m")
self.assertIsInstance(df, pd.DataFrame)
temp = np.isclose(df.iloc[0], [35342, 8.54, 1.56, 6461, 2.57, 1416])
self.assertEqual(temp.sum(), 6)
self.assertEqual(len(df), 4)
def Daily_Crawling(start_date: str, end_date: str, code: str):
# 위 순서대로
df_market_cap = stock.get_market_cap_by_date(start_date, end_date, code)
df_ohlcv = stock.get_market_ohlcv_by_date(start_date, end_date, code)
df_fundamental = stock.get_market_fundamental_by_date(
start_date, end_date, code)
df = pd.concat([df_market_cap.iloc[:, 0], df_ohlcv, df_fundamental],
axis=1)
return df
def test_in_kosdaq(self):
df = stock.get_market_fundamental_by_date("20200101", "20200430",
"263720")
# BPS PER PBR EPS DIV DPS
# 날짜
# 2020-01-02 3033 48.78 6.59 410 0.0 0
# 2020-01-03 3033 45.24 6.12 410 0.0 0
# 2020-01-06 3033 43.90 5.93 410 0.0 0
# 2020-01-07 3033 45.73 6.18 410 0.0 0
# 2020-01-08 3033 44.15 5.97 410 0.0 0
self.assertIsInstance(df, pd.DataFrame)
temp = np.isclose(df.iloc[0], [3033, 48.78, 6.59, 410, 0.0, 0])
self.assertEqual(temp.sum(), 6)
def test_with_valid_business_days(self):
df = stock.get_market_fundamental_by_date("20210104", "20210108", "005930")
# BPS PER PBR EPS DIV DPS
# 날짜
# 2021-01-04 37528 26.218750 2.210938 3166 1.709961 1416
# 2021-01-05 37528 26.500000 2.240234 3166 1.690430 1416
# 2021-01-06 37528 25.953125 2.189453 3166 1.719727 1416
# 2021-01-07 37528 26.187500 2.210938 3166 1.709961 1416
# 2021-01-08 37528 28.046875 2.369141 3166 1.589844 1416
self.assertIsInstance(df, pd.DataFrame)
temp = np.isclose(df.iloc[0], [37528, 26.218750, 2.210938, 3166, 1.709961, 1416])
self.assertEqual(temp.sum(), 6)
self.assertEqual(len(df), 5)
def test_not_empty_result(self):
df = stock.get_market_ohlcv_by_date("20190225", "20190228", "000660")
self.assertNotEqual(df.empty, True)
df = stock.get_market_price_change_by_ticker("20180301", "20180320")
self.assertNotEqual(df.empty, True)
df = stock.get_market_fundamental_by_ticker("20180305", "KOSPI")
self.assertNotEqual(df.empty, True)
df = stock.get_market_fundamental_by_date("20180301", "20180320",
'005930')
self.assertNotEqual(df.empty, True)
def make_band_data(option):
# st.write(option)
#오늘날짜까지
now = datetime.now()
today = '%s%s%s' % (now.year, now.month, now.day)
fun_df = stock.get_market_fundamental_by_date("20080101", today, option,
"m")
#Price 추가 => 이것의 문제는 수정주가가 아님
# fun_df['Close'] = fun_df['PER'] * fun_df['EPS']
#Price 수정 주가로 변경
p_df = stock.get_market_ohlcv_by_date("20080101", today, option, "m")
fun_df['Close'] = p_df['종가']
#PER Max/Min/half/3/1
e_max = round(fun_df['PER'].max(), 1)
if (e_max >= 30.00):
e_max = 30.00
e_min = round(fun_df['PER'].min(), 1)
e_half = round((e_max + e_min) / 2, 1)
e_3 = round((e_max - e_half) / 2 + e_half, 1)
e_1 = round((e_half - e_min) / 2 + e_min, 1)
#가격 데이터 만들기
fun_df[str(e_max) + "X"] = (fun_df['EPS'] * e_max).round(2)
fun_df[str(e_3) + "X"] = (fun_df['EPS'] * e_3).round(2)
fun_df[str(e_half) + "X"] = (fun_df['EPS'] * e_half).round(2)
fun_df[str(e_1) + "X"] = (fun_df['EPS'] * e_1).round(2)
fun_df[str(e_min) + "X"] = (fun_df['EPS'] * e_min).round(2)
#PBR Max/Min/half/3/1
b_max = round(fun_df['PBR'].max(), 1)
if (b_max >= 20.00):
b_max = 20.00
b_min = round(fun_df['PBR'].min(), 1)
b_half = round((b_max + b_min) / 2, 1)
b_3 = round((b_max - b_half) / 2 + b_half, 1)
b_1 = round((b_half - b_min) / 2 + b_min, 1)
#가격 데이터 만들기
fun_df[str(b_max) + "X"] = fun_df['BPS'] * b_max
fun_df[str(b_3) + "X"] = (fun_df['BPS'] * b_3).round(2)
fun_df[str(b_half) + "X"] = (fun_df['BPS'] * b_half).round(2)
fun_df[str(b_1) + "X"] = (fun_df['BPS'] * b_1).round(2)
fun_df[str(b_min) + "bX"] = (fun_df['BPS'] * b_min).round(2)
fun_df.round(decimals=2)
return fun_df
def test_with_valid_business_days(self):
df = stock.get_market_fundamental_by_date("20210104", "20210108",
"005930")
# BPS PER PBR EPS DIV DPS
# 날짜
# 2021-01-04 37528 26.22 2.21 3166 1.71 1416
# 2021-01-05 37528 26.50 2.24 3166 1.69 1416
# 2021-01-06 37528 25.96 2.19 3166 1.72 1416
# 2021-01-07 37528 26.18 2.21 3166 1.71 1416
# 2021-01-08 37528 28.05 2.37 3166 1.59 1416
self.assertIsInstance(df, pd.DataFrame)
temp = np.isclose(df.iloc[0], [37528, 26.22, 2.21, 3166, 1.71, 1416])
self.assertEqual(temp.sum(), 6)
self.assertEqual(len(df), 5)
def get_market_data(nowDate, companies, codes):
DIVs, BPSs, PERs, EPSs, PBRs=[],[],[],[],[]
DIV,BPS,PER,EPS,PBR = "","","","",""
for i in range(len(companies)):
df = stock.get_market_fundamental_by_date(nowDate,nowDate,codes[i])
L = list(df.iloc[0])
DIV,BPS,PER,EPS,PBR=L[0],L[1],L[2],L[3],L[4]
print(nowDate+" || ",companies[i],DIV,BPS,PER,EPS,PBR)
DIVs.append(DIV)
BPSs.append(BPS)
PERs.append(PER)
EPSs.append(EPS)
PBRs.append(PBR)
return DIVs, BPSs, PERs, EPSs, PBRs
def test_not_empty_result(self):
df = stock.get_market_ohlcv_by_date("20190225", "20190228", "000660")
self.assertFalse(df.empty)
df = stock.get_market_cap_by_date("20190101", "20190131", "005930")
self.assertFalse(df.empty)
df = stock.get_market_cap_by_ticker("20200625")
self.assertFalse(df.empty)
df = stock.get_market_price_change_by_ticker("20180301", "20180320")
self.assertFalse(df.empty)
df = stock.get_market_fundamental_by_ticker("20180305", "KOSPI")
self.assertFalse(df.empty)
df = stock.get_market_fundamental_by_date("20180301", "20180320", '005930')
self.assertFalse(df.empty)
def _update_fundamentals(code: str, fromdate: date, todate: date):
df = pykrx_stock.get_market_fundamental_by_date(
fromdate=_date_to_str(fromdate),
todate=_date_to_str(todate),
ticker=code
)
funds = []
for idx, row in df.iterrows():
all_nan = True
for v in row:
if v:
all_nan = False
break
if not all_nan:
# noinspection PyUnresolvedReferences
funds.append(
Fundamental(
date=idx.date(),
bps=row.get('BPS'),
per=row.get('PER'),
pbr=row.get('PBR'),
eps=row.get('EPS'),
div=row.get('DIV'),
dps=row.get('DPS'),
)
)
with FundamentalTable(code=code, create_if_not_exists=True) as fund_table:
whitelist = []
exists_rows = fund_table.all()
for fund in funds:
if fund.date not in [row.date for row in exists_rows]:
whitelist.append(fund)
print(f'Inserting {len(whitelist)} records...')
fund_table.insert_all(whitelist)
def get_stock_by_day(code, start_date):
start_date_no_bar = collect_data.bar_remover(start_date)
start_date = start_date
# end_date는 오늘 날짜로 고정
today = datetime.datetime.today().strftime(
'%Y%m%d') # krx는 YYYYMMDD 형식, 야후는 YYYY-MM-DD 형식
stock_df = stock.get_market_ohlcv_by_date(
start_date_no_bar, today, code, adjusted=True) # 시가 고가 저가 종가 거래량
stock_df2 = stock.get_market_fundamental_by_date(
start_date_no_bar, today, code) # DIV BPS PER EPS PBR
today_pdr = datetime.datetime.today().strftime('%Y-%m-%d')
gold = pdr.get_data_yahoo('GC=F', start=start_date,
end=today_pdr)[['Adj Close', 'Volume']] # 금값
gold.columns = ['Adj Close_gold', 'Volume_gold']
nikkei = pdr.DataReader('^N225',
'yahoo',
start=start_date,
end=today_pdr)[['Adj Close',
'Volume']] # 일본 지수
nikkei.columns = ['Adj Close_nikkei', 'Volume_nikkei']
crude_oil = pdr.get_data_yahoo('CL=F', start=start_date,
end=today_pdr)[['Adj Close',
'Volume']] # 유가
crude_oil.columns = ['Adj Close_oil', 'Volume_oil']
change = pd.Series(
pdr.get_data_yahoo('KRW=X', start=start_date,
end=today_pdr)['Adj Close']) # 환율
# 데이터 leftjoin
data = pd.concat([stock_df, stock_df2], axis=1)
data = pd.merge(data,
gold,
how='left',
left_index=True,
right_index=True)
data = pd.merge(data,
nikkei,
how='left',
left_index=True,
right_index=True)
data = pd.merge(data,
crude_oil,
how='left',
left_index=True,
right_index=True)
data = pd.merge(data,
change,
how='left',
left_index=True,
right_index=True)
data.columns = [
'시가', '고가', '저가', '종가', '거래량', 'DIV', 'BPS', 'PER', 'EPS', 'PBR',
'Adj Close_gold', 'Volume_gold', 'Adj Close_nikkei',
'Volume_nikkei', 'Adj Close_oil', 'Volume_oil', 'Adj Close_change'
]
# 기존에 사용했던 가장 최근의 값으로 채우는 방법 이거 쓰면 거의 다날라감
data = data.fillna(method="pad")
# 주가 자체의 값이 아니라 등락율을 구하기로 해서 다음날과 비교해서 올라가면 1 아니면 0 마지막날은 그대로
for i in range(len(data['종가'])):
if i == 0:
continue
elif i > 0 and i < (len(data['종가']) - 1):
if data['종가'][i] < data['종가'][(i + 1)]:
data['종가'][i] = 1
else:
data['종가'][i] = 0
else:
pass
return data
def test_io_with_frequency(self):
df = stock.get_market_fundamental_by_date("20180101", "20180331",
"006800", "m")
self.assertIsNotNone(df)
self.assertEqual(len(df), 3)
