はじめに
株価や為替、主要な株式指などの情報を簡単に取得できる yfinance。スクリーニングに使えそうなので調べてみた。
目次
yfinanceでできること。
yfinanceは、Pythonで株価や為替、主要な株式指数などの情報を取得できるライブラリ。pip install yfinanceでライブラリをインストールする。株価を自分で指定した期間、間隔で取得できるのでグラフにしてチャートにすることもできる。また、企業の財務三表や配当金情報など、Yahoo! ファイナンスで取得できる情報はほぼ取得できるので、企業情報を調べたり、自分でスクリーニングカスタマイズしたりと、色々用途がありそう。ただ、注意点としては、個人使用のみを目的としているツールである点。
株情報
銘柄の情報を取得する。
yf.Tickerでシンボルを指定して情報を取得する。基本情報は.infoで取得できる。
シンボルはYahoo! ファイナスで確認することができる。例として、記事を書いたときの時価総額上位のシンボルを列挙する。東証のシンボルであれば。下記のように後ろに.Tつける。
| 銘柄 | シンボル |
|---|---|
| トヨタ自動車(株) | 7203.T |
| (株)三菱ＵＦＪフィナンシャル・グループ | 8306.T |
| (株)日立製作所 | 6501.T |
| ソニーグループ(株) | 6758.T |
| (株)キーエンス | 6861.T |
| (株)リクルートホールディングス | 6098.T |
| (株)ファーストリテイリング | 9983.T |
| 日本電信電話(株) | 9432.T |
| 東京エレクトロン(株) | 8035.T |
| (株)三井住友フィナンシャルグループ | 8316.T |
米国株であれば。
| 銘柄 | シンボル |
|---|---|
| アップル | AAPL |
| マイクロソフト | MSFT |
| エヌビディア | NVDA |
| アマゾン・ドット・コム | AMZN |
| メタ・プラットフォームズ | META |
| アルファベット | GOOGL |
| タイワン・セミコンダクター・マニュファクチャリング | TSM |
| イーライ・リリー | LLY |
| ブロードコム | AVGO |
| テスラ | TSLA |
yf_example
import yfinance as yf
#Tikerで一つの銘柄の情報を取得 例は アップル。2024/8/17時点
STOCK = yf.Ticker("AAPL")
# 情報取得(.info)
STOCK_info = STOCK.info
# シンボル
print(f"{STOCK_info['underlyingSymbol']=}")
# STOCK_info['underlyingSymbol']='AAPL'
# 会社名
print(f"{STOCK_info['shortName']=}")
# STOCK_info['shortName']='Apple Inc.'
print(f"{STOCK_info['longName']=}")
# STOCK_info['longName']='Apple Inc.'
# 現在値
print(f"{STOCK_info['currentPrice']=}ドル")
# STOCK_info['currentPrice']=226.05ドル
# 52週最高値
print(f"{STOCK_info['fiftyTwoWeekLow']=}ドル")
# STOCK_info['fiftyTwoWeekLow']=164.08ドル
# 52週最低値
print(f"{STOCK_info['fiftyTwoWeekHigh']=}ドル")
# STOCK_info['fiftyTwoWeekHigh']=237.23ドル
.infoではその他、企業の基本情報など様々な情報をとれる。
.infoでとれる情報はこちら！
# address1:One Apple Park Way
# city:Cupertino
# state:CA
# zip:95014
# country:United States
# phone:408 996 1010
# website:https://www.apple.com
# industry:Consumer Electronics
# industryKey:consumer-electronics
# industryDisp:Consumer Electronics
# sector:Technology
# sectorKey:technology
# sectorDisp:Technology
# longBusinessSummary:Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, and HomePod. It also provides AppleCare support and cloud 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. In addition, the company offers various services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness 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 founded in 1976 and is headquartered in Cupertino, California.
# fullTimeEmployees:161000
# companyOfficers:[{'maxAge': 1, 'name': 'Mr. Timothy D. Cook', 'age': 62, 'title': 'CEO & Director', 'yearBorn': 1961, 'fiscalYear': 2023, 'totalPay': 16239562, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Mr. Luca Maestri', 'age': 60, 'title': 'CFO & Senior VP', 'yearBorn': 1963, 'fiscalYear': 2023, 'totalPay': 4612242, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Mr. Jeffrey E. Williams', 'age': 59, 'title': 'Chief Operating Officer', 'yearBorn': 1964, 'fiscalYear': 2023, 'totalPay': 4637585, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Ms. Katherine L. Adams', 'age': 59, 'title': 'Senior VP, General Counsel & Secretary', 'yearBorn': 1964, 'fiscalYear': 2023, 'totalPay': 4618064, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': "Ms. Deirdre O'Brien", 'age': 56, 'title': 'Senior Vice President of Retail', 'yearBorn': 1967, 'fiscalYear': 2023, 'totalPay': 4613369, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Mr. Chris Kondo', 'title': 'Senior Director of Corporate Accounting', 'fiscalYear': 2023, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Mr. James Wilson', 'title': 'Chief Technology Officer', 'fiscalYear': 2023, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Suhasini Chandramouli', 'title': 'Director of Investor Relations', 'fiscalYear': 2023, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Mr. Greg Joswiak', 'title': 'Senior Vice President of Worldwide Marketing', 'fiscalYear': 2023, 'exercisedValue': 0, 'unexercisedValue': 0}, {'maxAge': 1, 'name': 'Mr. Adrian Perica', 'age': 49, 'title': 'Head of Corporate Development', 'yearBorn': 1974, 'fiscalYear': 2023, 'exercisedValue': 0, 'unexercisedValue': 0}]
# auditRisk:6
# boardRisk:1
# compensationRisk:2
# shareHolderRightsRisk:1
# overallRisk:1
# governanceEpochDate:1722470400
# compensationAsOfEpochDate:1703980800
# irWebsite:http://investor.apple.com/
# maxAge:86400
# priceHint:2
# previousClose:224.72
# open:223.92
# dayLow:223.6501
# dayHigh:226.8271
# regularMarketPreviousClose:224.72
# regularMarketOpen:223.92
# regularMarketDayLow:223.6501
# regularMarketDayHigh:226.8271
# dividendRate:1.0
# dividendYield:0.0045
# exDividendDate:1723420800
# payoutRatio:0.1476
# fiveYearAvgDividendYield:0.68
# beta:1.244
# trailingPE:34.458843
# forwardPE:30.220589
# volume:42366146
# regularMarketVolume:42366146
# averageVolume:65130295
# averageVolume10days:61813610
# averageDailyVolume10Day:61813610
# bid:225.98
# ask:226.1
# bidSize:200
# askSize:200
# marketCap:3436886884352
# fiftyTwoWeekLow:164.08
# fiftyTwoWeekHigh:237.23
# priceToSalesTrailing12Months:8.913019
# fiftyDayAverage:217.0224
# twoHundredDayAverage:191.4572
# trailingAnnualDividendRate:0.97
# trailingAnnualDividendYield:0.0043164827
# currency:USD
# enterpriseValue:3456176750592
# profitMargins:0.26441
# floatShares:15179658339
# sharesOutstanding:15204100096
# sharesShort:117696224
# sharesShortPriorMonth:132235437
# sharesShortPreviousMonthDate:1719532800
# dateShortInterest:1722384000
# sharesPercentSharesOut:0.0077
# heldPercentInsiders:0.02703
# heldPercentInstitutions:0.60719
# shortRatio:2.25
# shortPercentOfFloat:0.0077
# impliedSharesOutstanding:15410899968
# bookValue:4.382
# priceToBook:51.586037
# lastFiscalYearEnd:1696032000
# nextFiscalYearEnd:1727654400
# mostRecentQuarter:1719619200
# earningsQuarterlyGrowth:0.079
# netIncomeToCommon:101956001792
# trailingEps:6.56
# forwardEps:7.48
# pegRatio:3.02
# lastSplitFactor:4:1
# lastSplitDate:1598832000
# enterpriseToRevenue:8.963
# enterpriseToEbitda:26.227
# 52WeekChange:0.28786755
# SandP52WeekChange:0.26855552
# lastDividendValue:0.25
# lastDividendDate:1723420800
# exchange:NMS
# quoteType:EQUITY
# symbol:AAPL
# underlyingSymbol:AAPL
# shortName:Apple Inc.
# longName:Apple Inc.
# firstTradeDateEpochUtc:345479400
# timeZoneFullName:America/New_York
# timeZoneShortName:EDT
# uuid:8b10e4ae-9eeb-3684-921a-9ab27e4d87aa
# messageBoardId:finmb_24937
# gmtOffSetMilliseconds:-14400000
# currentPrice:226.05
# targetHighPrice:300.0
# targetLowPrice:183.86
# targetMeanPrice:236.23
# targetMedianPrice:240.5
# recommendationMean:2.0
# recommendationKey:buy
# numberOfAnalystOpinions:38
# totalCash:61801000960
# totalCashPerShare:4.065
# ebitda:131781001216
# totalDebt:101304000512
# quickRatio:0.798
# currentRatio:0.953
# totalRevenue:385603010560
# debtToEquity:151.862
# revenuePerShare:24.957
# returnOnAssets:0.22612
# returnOnEquity:1.60583
# freeCashflow:86158123008
# operatingCashflow:113040998400
# earningsGrowth:0.111
# revenueGrowth:0.049
# grossMargins:0.45962003
# ebitdaMargins:0.34175
# operatingMargins:0.29556
# financialCurrency:USD
# trailingPegRatio:2.1854
企業の財務三表を取得する。
企業の財務三表は、年毎と四半期毎の2種類を取得できる。
| 内容 | パラメータ |
|---|---|
| 損益計算書P/L | .income_stmt：年毎 .quarterly_income_stmt：四半期毎 |
| 貸借対照表B/S | .balance_sheet：年毎 .quarterly_balance_sheet：四半期毎 |
| キャッシュフロー計算書C/F | .cashflow：年毎 .quarterly_cashflow：四半期毎 |
yf_example
import yfinance as yf
import pandas
#TikerでAPLL指定。
STOCK = yf.Ticker("AAPL")
# 損益計算書P/L (.income_stmt/.quarterly_income_stmt)------------------------
STOCK_income_stmt = STOCK.income_stmt
print(f'{type(STOCK_income_stmt)=}\n{STOCK_income_stmt}')
# 四半期ごとの場合は
STOCK_quarterly_income_stmt = STOCK.quarterly_income_stmt
print(f'{type(STOCK_quarterly_income_stmt)=}\n{STOCK_quarterly_income_stmt}')
# csvへ出力
STOCK_income_stmt.to_csv(STOCK_info['underlyingSymbol']+"_損益計算書.csv")
# 貸借対照表B/S (.balance_sheet/.quarterly_balance_sheet)--------------------
STOCK_balance_sheet = STOCK.balance_sheet
print(f'{type(STOCK_balance_sheet)=}\n{STOCK_balance_sheet}')
# 四半期ごとの場合は
STOCK_quarterly_balance_sheet = STOCK.quarterly_balance_sheet
print(f'{type(STOCK_quarterly_balance_sheet)=}\n{STOCK_quarterly_balance_sheet}')
# csvへ出力
STOCK_balance_sheet.to_csv(STOCK_info['underlyingSymbol']+"_貸借対照表.csv")
# キャッシュフロー計算書C/F (.cashflow /.quarterly_cashflow )----------------
STOCK_cashflow = STOCK.cashflow
print(f'{type(STOCK_cashflow)=}\n{STOCK_cashflow}')
# 四半期ごとの場合は
STOCK_quarterly_cashflow = STOCK.quarterly_cashflow
print(f'{type(STOCK_quarterly_cashflow)=}\n{STOCK_quarterly_cashflow}')
# csvへ出力
STOCK_cashflow.to_csv(STOCK_info['underlyingSymbol']+"_キャッシュフロー計算書.csv")
財務三表で取得できる情報はこちら！
type(STOCK_income_stmt)=<class 'pandas.core.frame.DataFrame'>
2023-09-30 2022-09-30 2021-09-30 2020-09-30
Tax Effect Of Unusual Items 0.0 0.0 0.0 0.0
Tax Rate For Calcs 0.147 0.162 0.133 0.144
Normalized EBITDA 129188000000.0 133138000000.0 123136000000.0 81020000000.0
Net Income From Continuing Operation Net Minori... 96995000000.0 99803000000.0 94680000000.0 57411000000.0
Reconciled Depreciation 11519000000.0 11104000000.0 11284000000.0 11056000000.0
Reconciled Cost Of Revenue 214137000000.0 223546000000.0 212981000000.0 169559000000.0
EBITDA 129188000000.0 133138000000.0 123136000000.0 81020000000.0
EBIT 117669000000.0 122034000000.0 111852000000.0 69964000000.0
Net Interest Income -183000000.0 -106000000.0 198000000.0 890000000.0
Interest Expense 3933000000.0 2931000000.0 2645000000.0 2873000000.0
Interest Income 3750000000.0 2825000000.0 2843000000.0 3763000000.0
Normalized Income 96995000000.0 99803000000.0 94680000000.0 57411000000.0
Net Income From Continuing And Discontinued Ope... 96995000000.0 99803000000.0 94680000000.0 57411000000.0
Total Expenses 268984000000.0 274891000000.0 256868000000.0 208227000000.0
Total Operating Income As Reported 114301000000.0 119437000000.0 108949000000.0 66288000000.0
Diluted Average Shares 15812547000.0 16325819000.0 16864919000.0 17528214000.0
Basic Average Shares 15744231000.0 16215963000.0 16701272000.0 17352119000.0
Diluted EPS 6.13 6.11 5.61 3.28
Basic EPS 6.16 6.15 5.67 3.31
Diluted NI Availto Com Stockholders 96995000000.0 99803000000.0 94680000000.0 57411000000.0
Net Income Common Stockholders 96995000000.0 99803000000.0 94680000000.0 57411000000.0
Net Income 96995000000.0 99803000000.0 94680000000.0 57411000000.0
Net Income Including Noncontrolling Interests 96995000000.0 99803000000.0 94680000000.0 57411000000.0
Net Income Continuous Operations 96995000000.0 99803000000.0 94680000000.0 57411000000.0
Tax Provision 16741000000.0 19300000000.0 14527000000.0 9680000000.0
Pretax Income 113736000000.0 119103000000.0 109207000000.0 67091000000.0
Other Income Expense -382000000.0 -228000000.0 60000000.0 -87000000.0
Other Non Operating Income Expenses -382000000.0 -228000000.0 60000000.0 -87000000.0
Net Non Operating Interest Income Expense -183000000.0 -106000000.0 198000000.0 890000000.0
Interest Expense Non Operating 3933000000.0 2931000000.0 2645000000.0 2873000000.0
Interest Income Non Operating 3750000000.0 2825000000.0 2843000000.0 3763000000.0
Operating Income 114301000000.0 119437000000.0 108949000000.0 66288000000.0
Operating Expense 54847000000.0 51345000000.0 43887000000.0 38668000000.0
Research And Development 29915000000.0 26251000000.0 21914000000.0 18752000000.0
Selling General And Administration 24932000000.0 25094000000.0 21973000000.0 19916000000.0
Gross Profit 169148000000.0 170782000000.0 152836000000.0 104956000000.0
Cost Of Revenue 214137000000.0 223546000000.0 212981000000.0 169559000000.0
Total Revenue 383285000000.0 394328000000.0 365817000000.0 274515000000.0
Operating Revenue 383285000000.0 394328000000.0 365817000000.0 274515000000.0
type(STOCK_quarterly_income_stmt)=<class 'pandas.core.frame.DataFrame'>
2024-06-30 2024-03-31 ... 2023-03-31 2022-12-31
Tax Effect Of Unusual Items 0.0 0.0 ... NaN NaN
Tax Rate For Calcs 0.159 0.158 ... NaN NaN
Normalized EBITDA 28202000000.0 30736000000.0 ... NaN NaN
Net Income From Continuing Operation Net Minori... 21448000000.0 23636000000.0 ... NaN NaN
Reconciled Depreciation 2850000000.0 2836000000.0 ... NaN NaN
Reconciled Cost Of Revenue 46099000000.0 48482000000.0 ... NaN NaN
EBITDA 28202000000.0 30736000000.0 ... NaN NaN
EBIT 25352000000.0 27900000000.0 ... NaN NaN
Net Interest Income NaN NaN ... -12000000.0 -135000000.0
Interest Expense NaN NaN ... 930000000.0 1003000000.0
Interest Income NaN NaN ... 918000000.0 868000000.0
Normalized Income 21448000000.0 23636000000.0 ... NaN NaN
Net Income From Continuing And Discontinued Ope... 21448000000.0 23636000000.0 ... NaN NaN
Total Expenses 60425000000.0 62853000000.0 ... NaN NaN
Total Operating Income As Reported 25352000000.0 27900000000.0 ... NaN NaN
Diluted Average Shares 15348175000.0 15464709000.0 ... NaN NaN
Basic Average Shares 15287521000.0 15405856000.0 ... NaN NaN
Diluted EPS 1.4 1.53 ... NaN NaN
Basic EPS 1.4 1.53 ... NaN NaN
Diluted NI Availto Com Stockholders 21448000000.0 23636000000.0 ... NaN NaN
Net Income Common Stockholders 21448000000.0 23636000000.0 ... NaN NaN
Net Income 21448000000.0 23636000000.0 ... NaN NaN
Net Income Including Noncontrolling Interests 21448000000.0 23636000000.0 ... NaN NaN
Net Income Continuous Operations 21448000000.0 23636000000.0 ... NaN NaN
Tax Provision 4046000000.0 4422000000.0 ... NaN NaN
Pretax Income 25494000000.0 28058000000.0 ... NaN NaN
Other Income Expense 142000000.0 158000000.0 ... NaN NaN
Other Non Operating Income Expenses 142000000.0 158000000.0 ... NaN NaN
Net Non Operating Interest Income Expense NaN NaN ... -12000000.0 -135000000.0
Interest Expense Non Operating NaN NaN ... 930000000.0 1003000000.0
Interest Income Non Operating NaN NaN ... 918000000.0 868000000.0
Operating Income 25352000000.0 27900000000.0 ... NaN NaN
Operating Expense 14326000000.0 14371000000.0 ... NaN NaN
Research And Development 8006000000.0 7903000000.0 ... NaN NaN
Selling General And Administration 6320000000.0 6468000000.0 ... NaN NaN
Gross Profit 39678000000.0 42271000000.0 ... NaN NaN
Cost Of Revenue 46099000000.0 48482000000.0 ... NaN NaN
Total Revenue 85777000000.0 90753000000.0 ... NaN NaN
Operating Revenue 85777000000.0 90753000000.0 ... NaN NaN
[39 rows x 7 columns]
type(STOCK_balance_sheet)=<class 'pandas.core.frame.DataFrame'>
2023-09-30 2022-09-30 ... 2020-09-30 2019-09-30
Treasury Shares Number 0.0 NaN ... NaN NaN
Ordinary Shares Number 15550061000.0 15943425000.0 ... 16976763000.0 NaN
Share Issued 15550061000.0 15943425000.0 ... 16976763000.0 NaN
Net Debt 81123000000.0 96423000000.0 ... 74420000000.0 NaN
Total Debt 123930000000.0 132480000000.0 ... 122278000000.0 NaN
... ... ... ... ... ...
Cash Cash Equivalents And Short Term Investments 61555000000.0 48304000000.0 ... 90943000000.0 NaN
Other Short Term Investments 31590000000.0 24658000000.0 ... 52927000000.0 NaN
Cash And Cash Equivalents 29965000000.0 23646000000.0 ... 38016000000.0 NaN
Cash Equivalents 1606000000.0 5100000000.0 ... 20243000000.0 NaN
Cash Financial 28359000000.0 18546000000.0 ... 17773000000.0 NaN
[68 rows x 5 columns]
type(STOCK_quarterly_balance_sheet)=<class 'pandas.core.frame.DataFrame'>
2024-06-30 2024-03-31 ... 2023-03-31 2022-12-31
Treasury Shares Number NaN NaN ... NaN NaN
Ordinary Shares Number 15222259000.0 15337686000.0 ... NaN NaN
Share Issued 15222259000.0 15337686000.0 ... NaN NaN
Net Debt 75739000000.0 71895000000.0 ... NaN NaN
Total Debt 101304000000.0 104590000000.0 ... NaN NaN
... ... ... ... ... ...
Cash Cash Equivalents And Short Term Investments 61801000000.0 67150000000.0 ... NaN NaN
Other Short Term Investments 36236000000.0 34455000000.0 ... NaN NaN
Cash And Cash Equivalents 25565000000.0 32695000000.0 ... NaN NaN
Cash Equivalents 2699000000.0 4468000000.0 ... NaN NaN
Cash Financial 22866000000.0 28227000000.0 ... NaN NaN
[70 rows x 7 columns]
type(STOCK_cashflow)=<class 'pandas.core.frame.DataFrame'>
2023-09-30 2022-09-30 ... 2020-09-30 2019-09-30
Free Cash Flow 99584000000.0 111443000000.0 ... 73365000000.0 NaN
Repurchase Of Capital Stock -77550000000.0 -89402000000.0 ... -72358000000.0 NaN
Repayment Of Debt -11151000000.0 -9543000000.0 ... -12629000000.0 NaN
Issuance Of Debt 5228000000.0 5465000000.0 ... 16091000000.0 NaN
Issuance Of Capital Stock NaN NaN ... 880000000.0 781000000.0
Capital Expenditure -10959000000.0 -10708000000.0 ... -7309000000.0 NaN
Interest Paid Supplemental Data 3803000000.0 2865000000.0 ... 3002000000.0 NaN
Income Tax Paid Supplemental Data 18679000000.0 19573000000.0 ... 9501000000.0 NaN
End Cash Position 30737000000.0 24977000000.0 ... 39789000000.0 NaN
Beginning Cash Position 24977000000.0 35929000000.0 ... 50224000000.0 NaN
Changes In Cash 5760000000.0 -10952000000.0 ... -10435000000.0 NaN
Financing Cash Flow -108488000000.0 -110749000000.0 ... -86820000000.0 NaN
Cash Flow From Continuing Financing Activities -108488000000.0 -110749000000.0 ... -86820000000.0 NaN
Net Other Financing Charges -6012000000.0 -6383000000.0 ... -2880000000.0 NaN
Cash Dividends Paid -15025000000.0 -14841000000.0 ... -14081000000.0 NaN
Common Stock Dividend Paid -15025000000.0 -14841000000.0 ... -14081000000.0 NaN
Net Common Stock Issuance -77550000000.0 -89402000000.0 ... -72358000000.0 NaN
Common Stock Payments -77550000000.0 -89402000000.0 ... -72358000000.0 NaN
Common Stock Issuance NaN NaN ... 880000000.0 781000000.0
Net Issuance Payments Of Debt -9901000000.0 -123000000.0 ... 2499000000.0 NaN
Net Short Term Debt Issuance -3978000000.0 3955000000.0 ... -963000000.0 NaN
Net Long Term Debt Issuance -5923000000.0 -4078000000.0 ... 3462000000.0 NaN
Long Term Debt Payments -11151000000.0 -9543000000.0 ... -12629000000.0 NaN
Long Term Debt Issuance 5228000000.0 5465000000.0 ... 16091000000.0 NaN
Investing Cash Flow 3705000000.0 -22354000000.0 ... -4289000000.0 NaN
Cash Flow From Continuing Investing Activities 3705000000.0 -22354000000.0 ... -4289000000.0 NaN
Net Other Investing Changes -1337000000.0 -2086000000.0 ... -909000000.0 NaN
Net Investment Purchase And Sale 16001000000.0 -9560000000.0 ... 5453000000.0 NaN
Sale Of Investment 45514000000.0 67363000000.0 ... 120391000000.0 NaN
Purchase Of Investment -29513000000.0 -76923000000.0 ... -114938000000.0 NaN
Net Business Purchase And Sale NaN -306000000.0 ... -1524000000.0 -624000000.0
Purchase Of Business NaN -306000000.0 ... -1524000000.0 -624000000.0
Net PPE Purchase And Sale -10959000000.0 -10708000000.0 ... -7309000000.0 NaN
Purchase Of PPE -10959000000.0 -10708000000.0 ... -7309000000.0 NaN
Operating Cash Flow 110543000000.0 122151000000.0 ... 80674000000.0 NaN
Cash Flow From Continuing Operating Activities 110543000000.0 122151000000.0 ... 80674000000.0 NaN
Change In Working Capital -6577000000.0 1200000000.0 ... 5690000000.0 NaN
Change In Other Working Capital NaN 478000000.0 ... 2081000000.0 -625000000.0
Change In Other Current Liabilities 3031000000.0 6110000000.0 ... 8916000000.0 NaN
Change In Other Current Assets -5684000000.0 -6499000000.0 ... -9588000000.0 NaN
Change In Payables And Accrued Expense -1889000000.0 9448000000.0 ... -4062000000.0 NaN
Change In Payable -1889000000.0 9448000000.0 ... -4062000000.0 NaN
Change In Account Payable -1889000000.0 9448000000.0 ... -4062000000.0 NaN
Change In Inventory -1618000000.0 1484000000.0 ... -127000000.0 NaN
Change In Receivables -417000000.0 -9343000000.0 ... 8470000000.0 NaN
Changes In Account Receivables -1688000000.0 -1823000000.0 ... 6917000000.0 NaN
Other Non Cash Items -2227000000.0 1006000000.0 ... -97000000.0 NaN
Stock Based Compensation 10833000000.0 9038000000.0 ... 6829000000.0 NaN
Deferred Tax NaN 895000000.0 ... -215000000.0 -340000000.0
Deferred Income Tax NaN 895000000.0 ... -215000000.0 -340000000.0
Depreciation Amortization Depletion 11519000000.0 11104000000.0 ... 11056000000.0 NaN
Depreciation And Amortization 11519000000.0 11104000000.0 ... 11056000000.0 NaN
Net Income From Continuing Operations 96995000000.0 99803000000.0 ... 57411000000.0 NaN
[53 rows x 5 columns]
type(STOCK_quarterly_cashflow)=<class 'pandas.core.frame.DataFrame'>
2024-06-30 2024-03-31 ... 2023-03-31 2022-12-31
Free Cash Flow 26707000000.0 20694000000.0 ... NaN NaN
Repurchase Of Capital Stock -26522000000.0 -23205000000.0 ... NaN NaN
Repayment Of Debt -3253000000.0 -3148000000.0 ... NaN NaN
Issuance Of Debt NaN NaN ... NaN NaN
Capital Expenditure -2151000000.0 -1996000000.0 ... NaN NaN
Interest Paid Supplemental Data NaN NaN ... 1170000000.0 703000000.0
Income Tax Paid Supplemental Data 4699000000.0 7276000000.0 ... NaN NaN
End Cash Position 26635000000.0 33921000000.0 ... NaN NaN
Beginning Cash Position 33921000000.0 41974000000.0 ... NaN NaN
Changes In Cash -7286000000.0 -8053000000.0 ... NaN NaN
Financing Cash Flow -36017000000.0 -30433000000.0 ... NaN NaN
Cash Flow From Continuing Financing Activities -36017000000.0 -30433000000.0 ... NaN NaN
Net Other Financing Charges -2347000000.0 -370000000.0 ... NaN NaN
Cash Dividends Paid -3895000000.0 -3710000000.0 ... NaN NaN
Common Stock Dividend Paid -3895000000.0 -3710000000.0 ... NaN NaN
Net Common Stock Issuance -26522000000.0 -23205000000.0 ... NaN NaN
Common Stock Payments -26522000000.0 -23205000000.0 ... NaN NaN
Net Issuance Payments Of Debt -3253000000.0 -3148000000.0 ... NaN NaN
Net Short Term Debt Issuance 997000000.0 2000000.0 ... NaN NaN
Short Term Debt Payments 997000000.0 2000000.0 ... 254000000.0 NaN
Net Long Term Debt Issuance -4250000000.0 -3150000000.0 ... NaN NaN
Long Term Debt Payments -4250000000.0 -3150000000.0 ... NaN NaN
Long Term Debt Issuance NaN NaN ... NaN NaN
Investing Cash Flow -127000000.0 -310000000.0 ... NaN NaN
Cash Flow From Continuing Investing Activities -127000000.0 -310000000.0 ... NaN NaN
Net Other Investing Changes -388000000.0 -445000000.0 ... NaN NaN
Net Investment Purchase And Sale 2412000000.0 2131000000.0 ... NaN NaN
Sale Of Investment 15444000000.0 17393000000.0 ... NaN NaN
Purchase Of Investment -13032000000.0 -15262000000.0 ... NaN NaN
Net PPE Purchase And Sale -2151000000.0 -1996000000.0 ... NaN NaN
Purchase Of PPE -2151000000.0 -1996000000.0 ... NaN NaN
Operating Cash Flow 28858000000.0 22690000000.0 ... NaN NaN
Cash Flow From Continuing Operating Activities 28858000000.0 22690000000.0 ... NaN NaN
Change In Working Capital 1684000000.0 -5764000000.0 ... NaN NaN
Change In Other Working Capital NaN NaN ... NaN 131000000.0
Change In Other Current Liabilities 3439000000.0 428000000.0 ... NaN NaN
Change In Other Current Assets -1188000000.0 -2981000000.0 ... NaN NaN
Change In Payables And Accrued Expense 1539000000.0 -12168000000.0 ... NaN NaN
Change In Payable 1539000000.0 -12168000000.0 ... NaN NaN
Change In Account Payable 1539000000.0 -12168000000.0 ... NaN NaN
Change In Inventory -12000000.0 190000000.0 ... NaN NaN
Change In Receivables -2094000000.0 8767000000.0 ... NaN NaN
Changes In Account Receivables -1030000000.0 1172000000.0 ... NaN NaN
Other Non Cash Items 7000000.0 -982000000.0 ... NaN NaN
Stock Based Compensation 2869000000.0 2964000000.0 ... NaN NaN
Depreciation Amortization Depletion 2850000000.0 2836000000.0 ... NaN NaN
Depreciation And Amortization 2850000000.0 2836000000.0 ... NaN NaN
Net Income From Continuing Operations 21448000000.0 23636000000.0 ... NaN NaN
配当金の情報を取得する。
配当金の情報は.dividendsで取得できる。
yf_example
import yfinance as yf
import pandas
#TikerでAPLL指定。
STOCK = yf.Ticker("AAPL")
# 配当(.dividends)==========================
STOCK_dividends = STOCK.dividends
print(f'{type(STOCK_dividends)=}\n{STOCK_dividends}')
# type(STOCK_dividends)=<class 'pandas.core.series.Series'>
# Date
# 1987-05-11 00:00:00-04:00 0.000536
# 1987-08-10 00:00:00-04:00 0.000536
# 1987-11-17 00:00:00-05:00 0.000714
# 1988-02-12 00:00:00-05:00 0.000714
# 1988-05-16 00:00:00-04:00 0.000714
# ...
# 2023-08-11 00:00:00-04:00 0.240000
# 2023-11-10 00:00:00-05:00 0.240000
# 2024-02-09 00:00:00-05:00 0.240000
# 2024-05-10 00:00:00-04:00 0.250000
# 2024-08-12 00:00:00-04:00 0.250000
# Name: Dividends, Length: 84, dtype: float64
# csvへ出力
STOCK_dividends.to_csv(STOCK_info['underlyingSymbol']+"_配当.csv")
株価を取得する。
株価は、yf.downloadで取得できる。各パラメータは下記の通り。
| 内容 | パラメータ | 指定する値 |
|---|---|---|
| 銘柄 | tickers | 銘柄のシンボルを指定。 |
| 取得期間 | period | 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max |
| 取得間隔 | interval | 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo |
| 開始時期 | start | YYYY-MM-DD |
| 終了時期 | end | YYYY-MM-DD |
yf_example
import yfinance as yf
# 最大期間取得
STOCK_download = yf.download(tickers="AAPL", period="max")
print(STOCK_download)
# Open High Low Close Adj Close Volume
# Date
# 1980-12-12 0.128348 0.128906 0.128348 0.128348 0.098943 469033600
# 1980-12-15 0.122210 0.122210 0.121652 0.121652 0.093781 175884800
# 1980-12-16 0.113281 0.113281 0.112723 0.112723 0.086898 105728000
# 1980-12-17 0.115513 0.116071 0.115513 0.115513 0.089049 86441600
# 1980-12-18 0.118862 0.119420 0.118862 0.118862 0.091630 73449600
# ... ... ... ... ... ... ...
# 2024-08-12 216.070007 219.509995 215.600006 217.529999 217.529999 38028100
# 2024-08-13 219.009995 221.889999 219.009995 221.270004 221.270004 44155300
# 2024-08-14 220.570007 223.029999 219.699997 221.720001 221.720001 41960600
# 2024-08-15 224.600006 225.350006 222.759995 224.720001 224.720001 46414000
# 2024-08-16 223.919998 226.830002 223.649994 226.050003 226.050003 44289900
# 1分足
STOCK_download = yf.download(tickers="AAPL", period="1d",interval="1m")
print(STOCK_download)
# Open High Low Close Adj Close Volume
# Datetime
# 2024-08-16 09:30:00-04:00 223.919998 224.500000 223.701996 223.701996 223.701996 4780458
# 2024-08-16 09:31:00-04:00 223.699997 224.339996 223.650101 224.189896 224.189896 191182
# 2024-08-16 09:32:00-04:00 224.119995 224.119995 223.790100 223.969894 223.969894 96866
# 2024-08-16 09:33:00-04:00 223.940002 224.000000 223.660294 223.964996 223.964996 103738
# 2024-08-16 09:34:00-04:00 223.929993 224.000000 223.789993 223.880005 223.880005 100297
# ... ... ... ... ... ... ...
# 2024-08-16 15:55:00-04:00 225.830002 226.139999 225.809998 226.110001 226.110001 203415
# 2024-08-16 15:56:00-04:00 226.110001 226.139999 225.899994 226.059998 226.059998 156953
# 2024-08-16 15:57:00-04:00 226.070007 226.086899 225.979996 226.009995 226.009995 156152
# 2024-08-16 15:58:00-04:00 226.009995 226.020004 225.809998 225.850006 225.850006 226269
# 2024-08-16 15:59:00-04:00 225.860001 226.100006 225.790497 226.100006 226.100006 411741
# 日付指定
STOCK_download = yf.download(tickers="AAPL", start="2020-08-01", end="2024-08-01")
print(STOCK_download)
# Open High Low Close Adj Close Volume
# Date
# 2020-08-03 108.199997 111.637497 107.892502 108.937500 106.267006 308151200
# 2020-08-04 109.132500 110.790001 108.387497 109.665001 106.976677 173071600
# 2020-08-05 109.377502 110.392502 108.897499 110.062500 107.364433 121776800
# 2020-08-06 110.404999 114.412498 109.797501 113.902496 111.110313 202428800
# 2020-08-07 113.205002 113.675003 110.292503 111.112503 108.584114 198045600
# ... ... ... ... ... ... ...
# 2024-07-25 218.929993 220.850006 214.619995 217.490005 217.238556 51391200
# 2024-07-26 218.699997 219.490005 216.009995 217.960007 217.708008 41601300
# 2024-07-29 216.960007 219.300003 215.750000 218.240005 217.987686 36311800
# 2024-07-30 219.190002 220.330002 216.119995 218.800003 218.547043 41643800
# 2024-07-31 221.440002 223.820007 220.630005 222.080002 221.823242 50036300
株価はyf.Tickerでシンボルを指定して.historyで取得することもできる。パラメータは、yf.downloadと同じものを使える(シンボルは指定済みなのでtickersは除く)。
yf_example
import yfinance as yf
# APPLの株価データ(history)
STOCK = yf.Ticker("AAPL")
STOCK_history = STOCK.history(period="max", interval="3mo")
print(STOCK_history)
Open High Low Close Volume Dividends Stock Splits
# Date
# 1984-12-01 00:00:00-05:00 0.100234 0.107117 0.085177 0.085177 11099804800 0.00 0.0
# 1985-03-01 00:00:00-05:00 0.085177 0.089479 0.058076 0.059796 12122745600 0.00 0.0
# 1985-06-01 00:00:00-04:00 0.058506 0.063668 0.049902 0.051622 10230729600 0.00 0.0
# 1985-09-01 00:00:00-04:00 0.051622 0.071411 0.050762 0.069261 8467782400 0.00 0.0
# 1985-12-01 00:00:00-05:00 0.069261 0.092061 0.066249 0.086038 13283872000 0.00 0.0
# ... ... ... ... ... ... ... ...
# 2023-09-01 00:00:00-04:00 188.525291 191.947765 164.826554 188.982941 3609892300 0.24 0.0
# 2023-12-01 00:00:00-05:00 189.610498 198.865372 178.572382 180.066711 3411621100 0.24 0.0
# 2024-03-01 00:00:00-05:00 179.099493 192.515742 163.668307 191.767624 4015037500 0.25 0.0
# 2024-06-01 00:00:00-04:00 192.676971 236.955721 191.927839 224.460190 3557721400 0.25 0.0
# 2024-08-16 00:00:00-04:00 223.919998 226.827103 223.650101 226.050003 42366146 0.00 0.0
為替情報
通貨を指定すれば、為替情報も取得できる。株価と同じくyf.downloadで取得する。
為替のシンボルは Yahoo! ファイナスで確認できる。
| 通貨 | シンボル |
|---|---|
| 米(ドル) / 日本(円) | USDJPY=X |
| 欧州(ユーロ) / 日本(円) | EURJPY=X |
| 中国(元) / 日本(円) | CNYJPY=X |
| 欧州(ユーロ) / 米(ドル) | EURUSD=X |
yf_example
import yfinance as yf
# 米(ドル) / 日本(円)
STOCK_download = yf.download(tickers="USDJPY=X", period="max", interval="1mo")
print(STOCK_download)
# Open High Low Close Adj Close Volume
# Date
# 1996-10-30 114.370003 114.480003 113.610001 114.180000 114.180000 0
# 1996-11-01 113.500000 113.500000 113.500000 113.500000 113.500000 0
# 1996-11-04 113.279999 113.980003 112.949997 113.879997 113.879997 0
# 1996-11-05 113.709999 114.330002 113.449997 114.250000 114.250000 0
# 1996-11-06 114.230003 114.680000 113.650002 113.949997 113.949997 0
# ... ... ... ... ... ... ...
# 2024-08-13 146.981995 147.929993 146.910995 146.981995 146.981995 0
# 2024-08-14 146.970993 147.552002 146.080994 146.970993 146.970993 0
# 2024-08-15 147.253006 149.263000 147.063004 147.253006 147.253006 0
# 2024-08-16 149.222000 149.229996 147.639008 149.222000 149.222000 0
# 2024-08-17 149.296997 149.319000 147.561005 147.578995 147.578995 0
株価と同じくyf.Tickerでシンボルを指定して取得することもできる。例えば、現在の買気配、売気配を取得したい場合は
yf_example
import yfinance as yf
# 欧州(ユーロ) / 日本(円)
STOCK = yf.Ticker("EURJPY=X")
# .info取得
STOCK_info = STOCK.info
# シンボル
print(f"{STOCK_info['underlyingSymbol']=}")
# ask/bid
print(f"買気配{STOCK_info['bid']=}円 売気配{STOCK_info['ask']=}円 ")
# STOCK_info['underlyingSymbol']='EURJPY=X'
# 買気配STOCK_info['bid']=162.776円 売気配STOCK_info['ask']=162.781円
各種指標情報
株価と同じくyf.downloadで取得できる。
| 指標 | シンボル |
|---|---|
| 日経225 | ^N225 |
| Dow 30 | ^DJI |
| S&P 500 | ^GSPC |
| NASDAQ | ^IXIC |
yf_example
import yfinance as yf
# 日経225
STOCK_download = yf.download(tickers="^N225", period="max", interval="1mo")
print(STOCK_download)
# Open High Low Close Adj Close Volume
# Date
# 1985-01-01 11543.000000 11992.309570 11543.000000 11992.309570 11992.309570 0
# 1985-02-01 11946.959961 12321.919922 11823.429688 12321.919922 12321.919922 0
# 1985-03-01 12412.139648 12604.019531 12263.849609 12580.759766 12580.759766 0
# 1985-04-01 12677.150391 12683.259766 12052.820312 12426.290039 12426.290039 0
# 1985-05-01 12456.650391 12790.269531 12358.030273 12758.459961 12758.459961 0
# ... ... ... ... ... ... ...
# 2024-04-01 40646.699219 40697.218750 36733.058594 38405.660156 38405.660156 2522300000
# 2024-05-01 38107.378906 39437.160156 37617.000000 38487.898438 38487.898438 2632400000
# 2024-06-01 38734.949219 39788.628906 37950.199219 39583.078125 39583.078125 2209000000
# 2024-07-01 39839.820312 42426.769531 37611.191406 39101.820312 39101.820312 2570500000
# 2024-08-01 38781.558594 38781.558594 31156.119141 38062.671875 38062.671875 2030400000