def test_with_dash_dates(self):
df = stock.get_market_price_change_by_ticker(fromdate="2021-01-04",
todate="20210111")
temp = df.iloc[0:5, 1] == np.array([4615, 25150, 4895, 135500, 5680])
self.assertEqual(temp.sum(), 5)
df = stock.get_market_price_change_by_ticker(fromdate="2021-01-04",
todate="2021-01-11")
temp = df.iloc[0:5, 1] == np.array([4615, 25150, 4895, 135500, 5680])
self.assertEqual(temp.sum(), 5)
def test_price_query(self):
# holiday - holiday
df = stock.get_market_price_change_by_ticker("20040418", "20040418")
self.assertEqual(df.empty, True)
# holiday - weekday
# - 상장 폐지 종목 079660 (20190625)
df = stock.get_market_price_change_by_ticker("20190624", "20190630")
print(df.loc['079660'])
self.assertEqual(df.loc['079660']['종가'], 0)
self.assertEqual(df.loc['079660']['등락률'], -100)
# weekday - weekday
df = stock.get_market_price_change_by_ticker("20040420", "20040422")
self.assertNotEqual(df.empty, True)
def getTop10(self, start, end):
# df인 이유는 데이터프레임 객체이기 때문
# 모든 종목에 대한 데이터 프레임이 반환됨
df = stock.get_market_price_change_by_ticker(start, end)
#sort_values는 데이터프레임 객체에 존재하는 함수 등락률을 기준으로 내림차순 정렬 할것임
#head함수를 통해 데이터 슬라이싱
return self.to_dict(df.sort_values("등락률", ascending=False).head(10))
def test_with_valid_business_days(self):
df = stock.get_market_price_change_by_ticker(fromdate="20210104",
todate="20210111")
# 종목명 시가 종가 변동폭 등락률 거래량 거래대금
# 티커
# 095570 AJ네트웍스 4615 4360 -255 -553.0 3445854 16332546190
# 006840 AK홀딩스 25150 24800 -350 -139.0 605351 15190840550
# 027410 BGF 4895 4690 -205 -419.0 3296226 16198124270
# 282330 BGF리테일 135500 135500 0 0.0 399330 54474898000
# 138930 BNK금융지주 5680 5720 40 70.0 2211455 125465685660
self.assertIsInstance(df, pd.DataFrame)
temp = df.iloc[0:5, 1] == np.array([4615, 25150, 4895, 135500, 5680])
self.assertEqual(temp.sum(), 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 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 run_monthly_low_per(year, month, portfolio_num=5):
# 거래일
business_day = stock.get_business_days(year, month)
start = business_day[0]
end = business_day[-1]
# PER 0인 종목 필터링
df = stock.get_market_fundamental_by_ticker(date=start, market="KOSPI")
cond = df["PER"] != 0
df = df[cond]
# PER이 낮은 n개 종목 선정
low_per = df["PER"].nsmallest(n=portfolio_num)
# 등락률
fluctuation = stock.get_market_price_change_by_ticker(start, end)
df = fluctuation.loc[low_per.index]
# 수익률
ror = 1 + (df['등락률'].mean() * 0.01)
return ror
def run_annual_pbr_per_combo(year, month=6, portfolio_num=30):
# 거래일
business_day = stock.get_business_days(year, month)
start = business_day[0]
business_day = stock.get_business_days(year + 1, month - 1)
end = business_day[-1]
# 2.5 <= PER <= 10
df = stock.get_market_fundamental_by_ticker(date=start, market="KOSPI")
cond = (df["PER"] >= 2.5) & (df["PER"] <= 10) # 괄호 주의
df = df[cond]
# PBR이 낮은 30 종목
low_per = df["PBR"].nsmallest(n=portfolio_num)
# 등락률
fluctuation = stock.get_market_price_change_by_ticker(start, end)
df = fluctuation.loc[low_per.index]
# 수익률
ror = 1 + (df['등락률'].mean() * 0.01)
return ror
def test_io_for_holiday(self):
df = stock.get_market_price_change_by_ticker("20040418", "20040418")
self.assertEqual(df.empty, True)
def test_with_holidays(self):
df = stock.get_market_price_change_by_ticker(fromdate="20210710",
todate="20210711")
self.assertIsInstance(df, pd.DataFrame)
self.assertTrue(df.empty)
def printstock():
now = time.strftime('%Y%m%d')
df = stock.get_market_price_change_by_ticker(now, now)
return jsonify({df.iloc[i].name:df.iloc[i,:].to_dict() for i in range(len(df))})
from pykrx import stock
import json
# 회사 이름에 대응되는 ticker라는 것이 있는데 그걸 받아온다
tickers = stock.get_market_ticker_list()
# print(tickers)
myJSON = {}
for ticker in tickers:
myJSON[ticker] = stock.get_market_ticker_name(ticker)
# name = stock.get_market_ticker_name("000540")
with open("tickerlist.json", "w") as JSON:
json.dump(myJSON, JSON, ensure_ascii=False)
# with open("tickerlist.json", "r") as JSON:
# myJSON = json.load(JSON)
print(myJSON)
ohlcv = stock.get_market_ohlcv_by_date("20200501", "20200510", "000660")
print(ohlcv)
df = stock.get_market_price_change_by_ticker("20200501", "20200510")
print(df)
from pykrx import stock
df = stock.get_market_price_change_by_ticker("20200319", "20200503")
print(df.loc["019170", '시가'])
print(df.loc["019170", '종가'])
df = stock.get_market_ohlcv_by_date("20200319", "20200503", "019170")
print(df.iloc[0]['시가'])
print(df.iloc[-1]['종가'])
def test_io_for_weekday(self):
df = stock.get_market_price_change_by_ticker("20040420", "20040422")
self.assertIsNotNone(df)
self.assertNotEqual(df.empty, True)
from pykrx import stock
df = stock.get_market_price_change_by_ticker("20100104", "20100129")
print(df)
#print(df.loc["005880"]["등락률"])
#print(df.loc["009280"]["등락률"])
def test_io_for_delisting(self):
# - 상장 폐지 종목 079660 (20190625)
df = stock.get_market_price_change_by_ticker("20190624", "20190630")
self.assertEqual(df.loc['079660']['종가'], 0)
self.assertEqual(df.loc['079660']['등락률'], -100)
font_family = "AppleGothic"
elif system_name == "Windows":
font_family = "Malgun Gothic"
else:
# Linux
# colab에서는 runtime을 <꼭> 재시작 해야합니다.
# 런타임을 재시작 하지 않고 폰트 설치를 하면 기본 설정 폰트가 로드되어 한글이 깨집니다.
import matplotlib.font_manager as fm
fontpath = '/usr/share/fonts/truetype/nanum/NanumBarunGothic.ttf'
font = fm.FontProperties(fname=fontpath, size=9)
fm._rebuild()
font_family = "NanumBarunGothic"
return font_family
get_font_family()
import matplotlib.pyplot as plt
font_family = get_font_family()
plt.rc("font", family=font_family)
plt.rc("axes", unicode_minus=False)
plt.style.use("ggplot")
df1 = stock.get_market_price_change_by_ticker("20200102", "20201230")
df2 = df1["변동폭"]
df1.to_excel('d:/finance/2020주가.xlsx')
df2.to_excel('d:/finance/2020변동폭.xlsx')
print(df2)
df2.plot()
plt.show()
from pykrx import stock
import pandas as pd
import itertools
nameList = list()
#tickers = stock.get_market_ticker_list()
#for each in map(lambda x : stock.get_market_ticker_name(x),tickers):
# nameList.append(each)
# 랭킹 매길때
df = stock.get_market_price_change_by_ticker("20200401", "20200427")
#print(df.sort_values('등락률',ascending=False))
#{df.iloc[i].name:df.iloc[i,:].to_dict() for i in range(len(df))}
#for i in itertools.islice(df.items(),2):
# print(i)
#print({df.iloc[i].name:df.iloc[i,:].to_dict() for i in range(3)})
df = df.sort_values("등락률",ascending=False).head(10)
print({df.iloc[i].name:df.iloc[i,:].to_dict() for i in range(len(df))})
#종목별 그래프 그릴 때
#df = stock.get_market_ohlcv_by_date("20200401", "20200402", "950140", "d")
#print(df.head(3))
def getTop10(self, start, end):
df = stock.get_market_price_change_by_ticker(start, end)
return self.to_dict(df.sort_values("등락률", ascending=False).head(10))
