Python

yfinance 모듈 사용해보기

J.H_DA 2022. 3. 21. 16:48

yfinances는 파이썬을 통해 주식의 데이터를 가져올 수 있는 모듈이다.

 

import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt
In [30]:
data=yf.download(['AAPL',"TSLA","CPNG"],start='2019-01-01') #뒤에 인자값들을 시작값으로함. 스타트는 1월1일인데 전날도 포함된

[*********************100%***********************]  3 of 3 completed
In [21]:
data['Close']["AAPL"]
Out[21]:
Date
2018-12-31     39.435001
2019-01-02     39.480000
2019-01-03     35.547501
2019-01-04     37.064999
2019-01-07     36.982498
                 ...    
2022-03-14    150.619995
2022-03-15    155.089996
2022-03-16    159.589996
2022-03-17    160.619995
2022-03-18    163.979996
Name: AAPL, Length: 811, dtype: float64
In [24]:
aapl = yf.Ticker("AAPL")
In [23]:
aapl.dividends # 애플 주식의 배당 내역
Out[23]:
Date
1987-05-11    0.000536
1987-08-10    0.000536
1987-11-17    0.000714
1988-02-12    0.000714
1988-05-16    0.000714
                ...   
2021-02-05    0.205000
2021-05-07    0.220000
2021-08-06    0.220000
2021-11-05    0.220000
2022-02-04    0.220000
Name: Dividends, Length: 74, dtype: float64
In [25]:
aapl.splits # 주당분할
Out[25]:
Date
1987-06-16    2.0
2000-06-21    2.0
2005-02-28    2.0
2014-06-09    7.0
2020-08-31    4.0
Name: Stock Splits, dtype: float64
In [27]:
aapl.recommendations # 애널리스트 정보
Out[27]:
FirmTo GradeFrom GradeActionDate2012-02-09 08:17:002012-03-08 07:33:002012-03-13 06:08:002012-03-14 06:13:002012-03-14 08:21:00...2022-01-28 14:19:012022-01-28 15:10:562022-01-31 15:44:192022-02-15 15:31:582022-03-11 11:57:08
Canaccord Genuity Buy   main
FBN Securities Outperform   main
Jefferies Buy   main
Morgan Stanley Overweight   main
Canaccord Genuity Buy   main
... ... ... ...
New Street Research Neutral Sell up
Oppenheimer Outperform   main
Credit Suisse Neutral   main
Tigress Financial Strong Buy   main
Barclays Equal-Weight   main

831 rows × 4 columns

In [28]:
aapl.info # 애플 회사의 정보
Out[28]:
{'zip': '95014',
 'sector': 'Technology',
 'fullTimeEmployees': 100000,
 'longBusinessSummary': 'Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. It also sells various related services. In addition, the company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; AirPods Max, an over-ear wireless headphone; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, HomePod, and iPod touch. Further, it provides AppleCare support services; cloud services store services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts. Additionally, the company offers various services, such as Apple Arcade, a game subscription service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was incorporated in 1977 and is headquartered in Cupertino, California.',
 'city': 'Cupertino',
 'phone': '408 996 1010',
 'state': 'CA',
 'country': 'United States',
 'companyOfficers': [],
 'website': 'https://www.apple.com',
 'maxAge': 1,
 'address1': 'One Apple Park Way',
 'industry': 'Consumer Electronics',
 'ebitdaMargins': 0.33890998,
 'profitMargins': 0.26579002,
 'grossMargins': 0.43019,
 'operatingCashflow': 112241000448,
 'revenueGrowth': 0.112,
 'operatingMargins': 0.309,
 'ebitda': 128217997312,
 'targetLowPrice': 160,
 'recommendationKey': 'buy',
 'grossProfits': 152836000000,
 'freeCashflow': 80153247744,
 'targetMedianPrice': 199.5,
 'currentPrice': 163.98,
 'earningsGrowth': 0.25,
 'currentRatio': 1.038,
 'returnOnAssets': 0.19875,
 'numberOfAnalystOpinions': 44,
 'targetMeanPrice': 193.53,
 'debtToEquity': 170.714,
 'returnOnEquity': 1.45567,
 'targetHighPrice': 215,
 'totalCash': 63913000960,
 'totalDebt': 122797998080,
 'totalRevenue': 378323009536,
 'totalCashPerShare': 3.916,
 'financialCurrency': 'USD',
 'revenuePerShare': 22.838,
 'quickRatio': 0.875,
 'recommendationMean': 1.8,
 'exchange': 'NMS',
 'shortName': 'Apple Inc.',
 'longName': 'Apple Inc.',
 'exchangeTimezoneName': 'America/New_York',
 'exchangeTimezoneShortName': 'EDT',
 'isEsgPopulated': False,
 'gmtOffSetMilliseconds': '-14400000',
 'quoteType': 'EQUITY',
 'symbol': 'AAPL',
 'messageBoardId': 'finmb_24937',
 'market': 'us_market',
 'annualHoldingsTurnover': None,
 'enterpriseToRevenue': 7.229,
 'beta3Year': None,
 'enterpriseToEbitda': 21.33,
 '52WeekChange': 0.32895696,
 'morningStarRiskRating': None,
 'forwardEps': 6.56,
 'revenueQuarterlyGrowth': None,
 'sharesOutstanding': 16319399936,
 'fundInceptionDate': None,
 'annualReportExpenseRatio': None,
 'totalAssets': None,
 'bookValue': 4.402,
 'sharesShort': 110322490,
 'sharesPercentSharesOut': 0.0068,
 'fundFamily': None,
 'lastFiscalYearEnd': 1632528000,
 'heldPercentInstitutions': 0.59438,
 'netIncomeToCommon': 100554997760,
 'trailingEps': 6.015,
 'lastDividendValue': 0.22,
 'SandP52WeekChange': 0.13260198,
 'priceToBook': 37.251247,
 'heldPercentInsiders': 0.0007,
 'nextFiscalYearEnd': 1695600000,
 'yield': None,
 'mostRecentQuarter': 1640390400,
 'shortRatio': 1.2,
 'sharesShortPreviousMonthDate': 1643587200,
 'floatShares': 16302795170,
 'beta': 1.185531,
 'enterpriseValue': 2734947041280,
 'priceHint': 2,
 'threeYearAverageReturn': None,
 'lastSplitDate': 1598832000,
 'lastSplitFactor': '4:1',
 'legalType': None,
 'lastDividendDate': 1643932800,
 'morningStarOverallRating': None,
 'earningsQuarterlyGrowth': 0.204,
 'priceToSalesTrailing12Months': 7.0734663,
 'dateShortInterest': 1646006400,
 'pegRatio': 1.76,
 'ytdReturn': None,
 'forwardPE': 24.99695,
 'lastCapGain': None,
 'shortPercentOfFloat': 0.0068,
 'sharesShortPriorMonth': 91388112,
 'impliedSharesOutstanding': 0,
 'category': None,
 'fiveYearAverageReturn': None,
 'previousClose': 160.62,
 'regularMarketOpen': 160.51,
 'twoHundredDayAverage': 154.44424,
 'trailingAnnualDividendYield': 0.005385382,
 'payoutRatio': 0.1434,
 'volume24Hr': None,
 'regularMarketDayHigh': 164.48,
 'navPrice': None,
 'averageDailyVolume10Day': 102416350,
 'regularMarketPreviousClose': 160.62,
 'fiftyDayAverage': 166.5874,
 'trailingAnnualDividendRate': 0.865,
 'open': 160.51,
 'toCurrency': None,
 'averageVolume10days': 102416350,
 'expireDate': None,
 'algorithm': None,
 'dividendRate': 0.88,
 'exDividendDate': 1643932800,
 'circulatingSupply': None,
 'startDate': None,
 'regularMarketDayLow': 159.76,
 'currency': 'USD',
 'trailingPE': 27.261845,
 'regularMarketVolume': 123511692,
 'lastMarket': None,
 'maxSupply': None,
 'openInterest': None,
 'marketCap': 2676055080960,
 'volumeAllCurrencies': None,
 'strikePrice': None,
 'averageVolume': 93071551,
 'dayLow': 159.76,
 'ask': 164.34,
 'askSize': 1400,
 'volume': 123511692,
 'fiftyTwoWeekHigh': 182.94,
 'fromCurrency': None,
 'fiveYearAvgDividendYield': 1.13,
 'fiftyTwoWeekLow': 118.86,
 'bid': 164.28,
 'tradeable': False,
 'dividendYield': 0.0054,
 'bidSize': 900,
 'dayHigh': 164.48,
 'regularMarketPrice': 163.98,
 'preMarketPrice': None,
 'logo_url': 'https://logo.clearbit.com/apple.com',
 'trailingPegRatio': 3.0052}
In [29]:
aapl.history(period="max") # 애플의 주가 역사
Out[29]:
OpenHighLowCloseVolumeDividendsStock SplitsDate1980-12-121980-12-151980-12-161980-12-171980-12-18...2022-03-142022-03-152022-03-162022-03-172022-03-18
0.100323 0.100759 0.100323 0.100323 469033600 0.0 0.0
0.095525 0.095525 0.095089 0.095089 175884800 0.0 0.0
0.088546 0.088546 0.088110 0.088110 105728000 0.0 0.0
0.090291 0.090727 0.090291 0.090291 86441600 0.0 0.0
0.092908 0.093345 0.092908 0.092908 73449600 0.0 0.0
... ... ... ... ... ... ...
151.449997 154.119995 150.100006 150.619995 108732100 0.0 0.0
150.899994 155.570007 150.380005 155.089996 92964300 0.0 0.0
157.050003 160.000000 154.460007 159.589996 102300200 0.0 0.0
158.610001 161.000000 157.630005 160.619995 75615400 0.0 0.0
160.509995 164.479996 159.759995 163.979996 123351200 0.0 0.0

10405 rows × 7 columns

In [32]:
cpng = yf.Ticker("CPNG")
aapl.info
aapl.history(period="max")
Out[32]:
OpenHighLowCloseVolumeDividendsStock SplitsDate1980-12-121980-12-151980-12-161980-12-171980-12-18...2022-03-142022-03-152022-03-162022-03-172022-03-18
0.100323 0.100759 0.100323 0.100323 469033600 0.0 0.0
0.095525 0.095525 0.095089 0.095089 175884800 0.0 0.0
0.088546 0.088546 0.088110 0.088110 105728000 0.0 0.0
0.090291 0.090727 0.090291 0.090291 86441600 0.0 0.0
0.092908 0.093345 0.092908 0.092908 73449600 0.0 0.0
... ... ... ... ... ... ...
151.449997 154.119995 150.100006 150.619995 108732100 0.0 0.0
150.899994 155.570007 150.380005 155.089996 92964300 0.0 0.0
157.050003 160.000000 154.460007 159.589996 102300200 0.0 0.0
158.610001 161.000000 157.630005 160.619995 75615400 0.0 0.0
160.509995 164.479996 159.759995 163.979996 123351200 0.0 0.0

10405 rows × 7 columns

In [34]:
import pyupbit as upbit
upbit.get_tickers(fiat="KRW")
Out[34]:
['KRW-BTC',
 'KRW-ETH',
 'KRW-NEO',
 'KRW-MTL',
 'KRW-LTC',
 'KRW-XRP',
 'KRW-ETC',
 'KRW-OMG',
 'KRW-SNT',
 'KRW-WAVES',
 'KRW-XEM',
 'KRW-QTUM',
 'KRW-LSK',
 'KRW-STEEM',
 'KRW-XLM',
 'KRW-ARDR',
 'KRW-ARK',
 'KRW-STORJ',
 'KRW-GRS',
 'KRW-REP',
 'KRW-ADA',
 'KRW-SBD',
 'KRW-POWR',
 'KRW-BTG',
 'KRW-ICX',
 'KRW-EOS',
 'KRW-TRX',
 'KRW-SC',
 'KRW-ONT',
 'KRW-ZIL',
 'KRW-POLY',
 'KRW-ZRX',
 'KRW-LOOM',
 'KRW-BCH',
 'KRW-BAT',
 'KRW-IOST',
 'KRW-RFR',
 'KRW-CVC',
 'KRW-IQ',
 'KRW-IOTA',
 'KRW-MFT',
 'KRW-ONG',
 'KRW-GAS',
 'KRW-UPP',
 'KRW-ELF',
 'KRW-KNC',
 'KRW-BSV',
 'KRW-THETA',
 'KRW-QKC',
 'KRW-BTT',
 'KRW-MOC',
 'KRW-ENJ',
 'KRW-TFUEL',
 'KRW-MANA',
 'KRW-ANKR',
 'KRW-AERGO',
 'KRW-ATOM',
 'KRW-TT',
 'KRW-CRE',
 'KRW-MBL',
 'KRW-WAXP',
 'KRW-HBAR',
 'KRW-MED',
 'KRW-MLK',
 'KRW-STPT',
 'KRW-ORBS',
 'KRW-VET',
 'KRW-CHZ',
 'KRW-STMX',
 'KRW-DKA',
 'KRW-HIVE',
 'KRW-KAVA',
 'KRW-AHT',
 'KRW-LINK',
 'KRW-XTZ',
 'KRW-BORA',
 'KRW-JST',
 'KRW-CRO',
 'KRW-TON',
 'KRW-SXP',
 'KRW-HUNT',
 'KRW-PLA',
 'KRW-DOT',
 'KRW-SRM',
 'KRW-MVL',
 'KRW-STRAX',
 'KRW-AQT',
 'KRW-GLM',
 'KRW-SSX',
 'KRW-META',
 'KRW-FCT2',
 'KRW-CBK',
 'KRW-SAND',
 'KRW-HUM',
 'KRW-DOGE',
 'KRW-STRK',
 'KRW-PUNDIX',
 'KRW-FLOW',
 'KRW-DAWN',
 'KRW-AXS',
 'KRW-STX',
 'KRW-XEC',
 'KRW-SOL',
 'KRW-MATIC',
 'KRW-NU',
 'KRW-AAVE',
 'KRW-1INCH',
 'KRW-ALGO',
 'KRW-NEAR',
 'KRW-WEMIX',
 'KRW-AVAX',
 'KRW-T',
 'KRW-CELO']
In [37]:
upbit.get_current_price(["KRW-BTC", "KRW-ETH"])
Out[37]:
{'KRW-BTC': 50028000.0, 'KRW-ETH': 3482000.0}
In [46]:
ticker = "KRW-BTC"
interval="minute1"
to ="2022-01-01 00:00"
count=100
upbit.get_ohlcv(ticker=ticker, interval=interval)
Out[46]:
openhighlowclosevolumevalue2022-03-21 10:32:002022-03-21 10:33:002022-03-21 10:34:002022-03-21 10:35:002022-03-21 10:36:00...2022-03-21 13:47:002022-03-21 13:48:002022-03-21 13:49:002022-03-21 13:50:002022-03-21 13:51:00
50578000.0 50597000.0 50557000.0 50560000.0 0.797741 4.034548e+07
50557000.0 50645000.0 50557000.0 50619000.0 2.079545 1.052432e+08
50639000.0 50660000.0 50618000.0 50639000.0 0.593488 3.005209e+07
50639000.0 50658000.0 50619000.0 50658000.0 2.127970 1.077808e+08
50658000.0 50658000.0 50620000.0 50645000.0 1.808827 9.161522e+07
... ... ... ... ... ...
50060000.0 50080000.0 50060000.0 50075000.0 1.031175 5.162954e+07
50060000.0 50075000.0 50025000.0 50060000.0 4.791390 2.398578e+08
50060000.0 50081000.0 50060000.0 50060000.0 1.840917 9.217908e+07
50064000.0 50075000.0 50060000.0 50061000.0 2.164085 1.083352e+08
50061000.0 50075000.0 50060000.0 50061000.0 2.758902 1.381130e+08

200 rows × 6 columns

In [68]:
#### 3시 30분꺼지 프리하게 데이터 튜닝
# 주식, 코인 데이터를 가지고 데이터의 갯수 1000개 선택
# 데이터를 csv로 변환
# csv를 DB에 삽입
# mysql을 가지고 쿼리문으로 데이터를 받아와서(모듈로 생성, class로 구현)
# 주식의 기준-> 시가, 종가, 고가, 저가 소수점 2번째 자리까지 유지 반올림
# 주식 기준 NAn값 존재 시작되는 지점의 시가로 변환
# 컬럼을 하나 추가 종가-시가의 값을 삽입
# (등락폭/시가*100) 퍼센트 삽입
# 데이터 시각화-> 시가/종가 등락폭/볼륨 그래프 4개를 한 plot 표시
In [53]:
data=yf.download("NVDA",start='2018-01-01')

[*********************100%***********************]  1 of 1 completed
In [54]:
a=pd.DataFrame(data)
In [55]:
a["Gap"]=(a["Close"] - a["Open"].shift()).fillna(0)
In [56]:
a["Ratio"]=(((a["Gap"]/a["Open"])*100).shift()).fillna(0)
In [57]:
a
Out[57]:
OpenHighLowCloseAdj CloseVolumeGapRatioDate2018-01-022018-01-032018-01-042018-01-052018-01-08...2022-03-142022-03-152022-03-162022-03-172022-03-18
48.945000 49.875000 48.625000 49.837502 49.372402 35561600 0.000000 0.000000
51.025002 53.424999 50.937500 53.117500 52.621784 91470400 4.172501 0.000000
53.939999 54.512501 53.172501 53.397499 52.899174 58326800 2.372498 8.177365
53.547501 54.227501 52.770000 53.849998 53.347446 58012400 -0.090000 4.398401
55.099998 56.250000 54.645000 55.500000 54.982040 88121600 1.952499 -0.168075
... ... ... ... ... ... ... ...
218.690002 222.619995 211.589996 213.300003 213.300003 38535400 -16.919998 -1.872121
215.179993 230.380005 213.220001 229.729996 229.729996 49199600 11.039993 -7.736978
235.000000 245.970001 231.720001 244.960007 244.960007 67142200 29.780014 5.130585
241.199997 248.419998 239.059998 247.660004 247.660004 47194100 12.660004 12.672346
247.990005 265.690002 246.240005 264.529999 264.529999 72977200 23.330002 5.248758

1061 rows × 8 columns

In [58]:
b=a.to_csv("./nvda_stock.csv")
In [59]:
import mod_sql
In [61]:
_db = mod_sql.Database()
sql = """SELECT * FROM nvda_stock"""

result = _db.executeAll(sql)
result1=pd.DataFrame(result)
result1.head()
Out[61]:
DateOpenHighLowCloseAdj CloseVolumeGapRatio01234
2018-01-02 48.945000 49.875000 48.625000 49.837502 49.372402 35561600 0.000000 0.000000
2018-01-03 51.025002 53.424999 50.937500 53.117500 52.621784 91470400 4.172501 0.000000
2018-01-04 53.939999 54.512501 53.172501 53.397499 52.899174 58326800 2.372498 8.177365
2018-01-05 53.547501 54.227501 52.770000 53.849998 53.347446 58012400 -0.090000 4.398401
2018-01-08 55.099998 56.250000 54.645000 55.500000 54.982040 88121600 1.952499 -0.168075
In [62]:
result1["Date"]=pd.to_datetime(result1["Date"], format="%Y-%m-%d")
In [64]:
result1["Open"]=round(result1["Open"],2)
In [65]:
result1["High"]=round(result1["High"],2)
result1["Low"]=round(result1["Low"],2)
result1["Close"]=round(result1["Close"],2)
In [67]:
result1.fillna(method="ffill")
Out[67]:
DateOpenHighLowCloseAdj CloseVolumeGapRatio01234...10561057105810591060
2018-01-02 48.94 49.88 48.62 49.84 49.372402 35561600 0.000000 0.000000
2018-01-03 51.03 53.42 50.94 53.12 52.621784 91470400 4.172501 0.000000
2018-01-04 53.94 54.51 53.17 53.40 52.899174 58326800 2.372498 8.177365
2018-01-05 53.55 54.23 52.77 53.85 53.347446 58012400 -0.090000 4.398401
2018-01-08 55.10 56.25 54.65 55.50 54.982040 88121600 1.952499 -0.168075
... ... ... ... ... ... ... ... ...
2022-03-14 218.69 222.62 211.59 213.30 213.300003 38535400 -16.919998 -1.872121
2022-03-15 215.18 230.38 213.22 229.73 229.729996 49199600 11.039993 -7.736978
2022-03-16 235.00 245.97 231.72 244.96 244.960007 67142200 29.780014 5.130585
2022-03-17 241.20 248.42 239.06 247.66 247.660004 47194100 12.660004 12.672346
2022-03-18 247.99 265.69 246.24 264.53 264.529999 72977200 23.330002 5.248758

1061 rows × 9 columns

In [69]:
_date=result1["Date"]
_open=result1["Open"]
_close = result1["Close"]
plt.plot(_date, _open)
plt.plot(_date, _close)
plt.xlabel("date")
plt.ylabel("price")
plt.legend(["open", "close"])
plt.title("nvidia stock")
plt.xticks(rotation=45)
plt.grid(True)
In [ ]:
In [86]:
f, axes = plt.subplots(2, 2)
f.set_size_inches((12, 12))
plt.subplots_adjust(wspace = 0.15, hspace = 0.15)

# figure 전체 제목
f.suptitle('Nvidia_info', fontsize = 15)


# 선 그래프 예시(축 제목 설정)
axes[0, 0].plot(result1["Date"], result1["Open"], color = 'blue')
axes[0, 0].set_xlabel('Date', fontsize = 11)
axes[0, 0].set_ylabel('Open', fontsize = 11)
axes[0, 0].tick_params(labelrotation=45)
axes[0, 0].grid(True)

axes[0, 1].plot(result1["Date"], result1["Close"], color = 'blue')
axes[0, 1].set_xlabel('Date', fontsize = 11)
axes[0, 1].set_ylabel('Close', fontsize = 11)
axes[0, 1].tick_params(labelrotation=45)
axes[0, 1].grid(True)

axes[1, 0].plot(result1["Date"], result1["Gap"], color = 'blue')
axes[1, 0].set_xlabel('Date', fontsize = 11)
axes[1, 0].set_ylabel('Gap', fontsize = 11)
axes[1, 0].tick_params(labelrotation=45)
axes[1, 0].grid(True)

axes[1, 1].plot(result1["Date"], result1["Ratio"], color = 'blue')
axes[1, 1].set_xlabel('Date', fontsize = 11)
axes[1, 1].set_ylabel('Ratio', fontsize = 11)
axes[1, 1].tick_params(labelrotation=45)
axes[1, 1].grid(True)




plt.show()
728x90