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We are excited to announce the yahoofinancer package. The yahoofinancer R package allows the retrieval of nearly all data visible via the Yahoo Finance front-end and aims to reduce the pre-processing steps needed in analyzing such data.
yahoofinancer is inspired by and a port of the Python package yahooquery. yahoofinancer is not affiliated, endorsed, or vetted by Yahoo, Inc. It’s an open-source tool that uses Yahoo’s publicly available APIs, and is intended for research and educational purposes.
With yahoofinancer, you can fetch the following data related to:
- company
- quote
- summary
- statistics
- historical data
- profile
- financials
- analysis
- options
- index
- summary
- historical data
- currency
- market summary
Installation
# Install release version from CRAN
install.packages("yahoofinancer")
# Install development version from GitHub
# install.packages("devtools")
devtools::install_github("rsquaredacademy/yahoofinancer")
Usage
Ticker
The Ticker class is the main workhorse of the package and through it we can
obtain most of the data related to a company.
Instantiate Ticker Class
We can instantiate the Ticker class by passing the company’s ticker symbol.
For instance, to get data for Apple Inc.,
pass aapl as the first argument to the Ticker class.
aapl <- Ticker$new('aapl')
We can use the validate() function to validate a symbol.
Regular Market Price
Let us begin by getting the real time price of Apple Inc..
aapl$quote$regularMarketPrice ## [1] 138.88
Summary
To retrieve data from the Summary tab, use the summary_detail property on the Ticker class.
aapl$summary_detail ## $maxAge ## [1] 1 ## ## $priceHint ## [1] 2 ## ## $previousClose ## [1] 145.03 ## ## $open ## [1] 142.06 ## ## $dayLow ## [1] 138.76 ## ## $dayHigh ## [1] 142.795 ## ## $regularMarketPreviousClose ## [1] 145.03 ## ## $regularMarketOpen ## [1] 142.06 ## ## $regularMarketDayLow ## [1] 138.76 ## ## $regularMarketDayHigh ## [1] 142.795 ## ## $dividendRate ## [1] 0.92 ## ## $dividendYield ## [1] 0.0063 ## ## $exDividendDate ## [1] 1667520000 ## ## $payoutRatio ## [1] 0.1473 ## ## $fiveYearAvgDividendYield ## [1] 1 ## ## $beta ## [1] 1.246644 ## ## $trailingPE ## [1] 23.74017 ## ## $forwardPE ## [1] 20.36364 ## ## $volume ## [1] 97918516 ## ## $regularMarketVolume ## [1] 97918516 ## ## $averageVolume ## [1] 85440921 ## ## $averageVolume10days ## [1] 96881500 ## ## $averageDailyVolume10Day ## [1] 96881500 ## ## $bid ## [1] 0 ## ## $ask ## [1] 138.63 ## ## $bidSize ## [1] 800 ## ## $askSize ## [1] 1300 ## ## $marketCap ## [1] 2.307158e+12 ## ## $fiftyTwoWeekLow ## [1] 129.04 ## ## $fiftyTwoWeekHigh ## [1] 182.94 ## ## $priceToSalesTrailing12Months ## [1] 5.85086 ## ## $fiftyDayAverage ## [1] 150.0812 ## ## $twoHundredDayAverage ## [1] 156.2758 ## ## $trailingAnnualDividendRate ## [1] 0.9 ## ## $trailingAnnualDividendYield ## [1] 0.006205612 ## ## $currency ## [1] "USD" ## ## $tradeable ## [1] FALSE
Statistics
If you are looking to retrieve data from the Statistics tab, use the following properties:
Key Statistics
The key_stats property returns KPIs for a given symbol. It is available raw
head(map(aapl$key_stats, 'raw')) ## $maxAge ## NULL ## ## $priceHint ## [1] 2 ## ## $enterpriseValue ## [1] 2.293495e+12 ## ## $forwardPE ## [1] 20.36364 ## ## $profitMargins ## [1] 0.2531 ## ## $floatShares ## [1] 15891255395
or formatted
head(map(aapl$key_stats, 'fmt')) ## $maxAge ## NULL ## ## $priceHint ## [1] "2" ## ## $enterpriseValue ## [1] "2.29T" ## ## $forwardPE ## [1] "20.36" ## ## $profitMargins ## [1] "25.31%" ## ## $floatShares ## [1] "15.89B"
The valuation_measures property retrieves valuation measures for most recent
four quarters.
aapl$valuation_measures ## date enterprise_value enterprise_value_ebitda_ratio ## 1 2021-09-30 2.384485e+12 2.384485e+12 ## 2 2021-12-31 2.963725e+12 2.963725e+12 ## 3 2022-03-31 2.888888e+12 2.888888e+12 ## 4 2022-06-30 2.269030e+12 2.269030e+12 ## 5 2022-09-30 2.274841e+12 2.274841e+12 ## enterprise_value_revenue_ratio forward_pe_ratio market_cap pb_ratio ## 1 2.384485e+12 2.384485e+12 2.384485e+12 2.384485e+12 ## 2 2.963725e+12 2.963725e+12 2.963725e+12 2.963725e+12 ## 3 2.888888e+12 2.888888e+12 2.888888e+12 2.888888e+12 ## 4 2.269030e+12 2.269030e+12 2.269030e+12 2.269030e+12 ## 5 2.274841e+12 2.274841e+12 2.274841e+12 2.274841e+12 ## pe_ratio peg_ratio ps_ratio ## 1 2.384485e+12 2.384485e+12 2.384485e+12 ## 2 2.963725e+12 2.963725e+12 2.963725e+12 ## 3 2.888888e+12 2.888888e+12 2.888888e+12 ## 4 2.269030e+12 2.269030e+12 2.269030e+12 ## 5 2.274841e+12 2.274841e+12 2.274841e+12
Historical Data
Use the get_history() method to retrieve historical pricing data for a given
symbol. By default, it returns YTD data.
head(aapl$get_history()) ## date volume high low open close adj_close ## 1 2022-01-03 14:30:00 104487900 182.88 177.71 177.83 182.01 181.2599 ## 2 2022-01-04 14:30:00 99310400 182.94 179.12 182.63 179.70 178.9594 ## 3 2022-01-05 14:30:00 94537600 180.17 174.64 179.61 174.92 174.1992 ## 4 2022-01-06 14:30:00 96904000 175.30 171.64 172.70 172.00 171.2912 ## 5 2022-01-07 14:30:00 86709100 174.14 171.03 172.89 172.17 171.4605 ## 6 2022-01-10 14:30:00 106765600 172.50 168.17 169.08 172.19 171.4804
To get data from a specific date upto the current date, use the start
argument to supply the initial date. The time between data points can be
specified using the interval argument.
aapl$get_history(start = '2022-10-20', interval = '1d') ## date volume high low open close adj_close ## 1 2022-10-20 13:30:00 64522000 145.89 142.65 143.02 143.39 143.39 ## 2 2022-10-21 13:30:00 86464700 147.85 142.65 142.87 147.27 147.27 ## 3 2022-10-24 13:30:00 75981900 150.23 146.00 147.19 149.45 149.45 ## 4 2022-10-25 13:30:00 74732300 152.49 149.36 150.09 152.34 152.34 ## 5 2022-10-26 13:30:00 88194300 151.99 148.04 150.96 149.35 149.35 ## 6 2022-10-27 13:30:00 109180200 149.05 144.13 148.07 144.80 144.80 ## 7 2022-10-28 13:30:00 164762400 157.50 147.82 148.20 155.74 155.74 ## 8 2022-10-31 13:30:00 97943200 154.24 151.92 153.16 153.34 153.34 ## 9 2022-11-01 13:30:00 80379300 155.45 149.13 155.08 150.65 150.65 ## 10 2022-11-02 13:30:00 93604600 152.17 145.00 148.95 145.03 145.03 ## 11 2022-11-03 13:30:00 97572100 142.80 138.75 142.06 138.88 138.88
To retrieve data between two specific dates, use both the start and end
argument. The dates should be either String or Date object in yyyy-mm-dd
format.
aapl$get_history(start = '2022-10-01', end = '2022-10-14', interval = '1d') ## date volume high low open close adj_close ## 1 2022-10-03 13:30:00 114311700 143.07 137.69 138.21 142.45 142.45 ## 2 2022-10-04 13:30:00 87830100 146.22 144.26 145.03 146.10 146.10 ## 3 2022-10-05 13:30:00 79471000 147.38 143.01 144.07 146.40 146.40 ## 4 2022-10-06 13:30:00 68402200 147.54 145.22 145.81 145.43 145.43 ## 5 2022-10-07 13:30:00 85859100 143.10 139.45 142.54 140.09 140.09 ## 6 2022-10-10 13:30:00 74899000 141.89 138.57 140.42 140.42 140.42 ## 7 2022-10-11 13:30:00 77033700 141.35 138.22 139.90 138.98 138.98 ## 8 2022-10-12 13:30:00 70433700 140.36 138.16 139.13 138.34 138.34 ## 9 2022-10-13 13:30:00 113224000 143.59 134.37 134.99 142.99 142.99
The period arguments allows the user to retrieve data for specific length of time.
aapl$get_history(period = '1mo', interval = '1d') ## date volume high low open close adj_close ## 1 2022-10-04 13:30:00 87830100 146.22 144.26 145.03 146.10 146.10 ## 2 2022-10-05 13:30:00 79471000 147.38 143.01 144.07 146.40 146.40 ## 3 2022-10-06 13:30:00 68402200 147.54 145.22 145.81 145.43 145.43 ## 4 2022-10-07 13:30:00 85859100 143.10 139.45 142.54 140.09 140.09 ## 5 2022-10-10 13:30:00 74899000 141.89 138.57 140.42 140.42 140.42 ## 6 2022-10-11 13:30:00 77033700 141.35 138.22 139.90 138.98 138.98 ## 7 2022-10-12 13:30:00 70433700 140.36 138.16 139.13 138.34 138.34 ## 8 2022-10-13 13:30:00 113224000 143.59 134.37 134.99 142.99 142.99 ## 9 2022-10-14 13:30:00 88512300 144.52 138.19 144.31 138.38 138.38 ## 10 2022-10-17 13:30:00 85250900 142.90 140.27 141.07 142.41 142.41 ## 11 2022-10-18 13:30:00 99136600 146.70 140.61 145.49 143.75 143.75 ## 12 2022-10-19 13:30:00 61758300 144.95 141.50 141.69 143.86 143.86 ## 13 2022-10-20 13:30:00 64522000 145.89 142.65 143.02 143.39 143.39 ## 14 2022-10-21 13:30:00 86464700 147.85 142.65 142.87 147.27 147.27 ## 15 2022-10-24 13:30:00 75981900 150.23 146.00 147.19 149.45 149.45 ## 16 2022-10-25 13:30:00 74732300 152.49 149.36 150.09 152.34 152.34 ## 17 2022-10-26 13:30:00 88194300 151.99 148.04 150.96 149.35 149.35 ## 18 2022-10-27 13:30:00 109180200 149.05 144.13 148.07 144.80 144.80 ## 19 2022-10-28 13:30:00 164762400 157.50 147.82 148.20 155.74 155.74 ## 20 2022-10-31 13:30:00 97943200 154.24 151.92 153.16 153.34 153.34 ## 21 2022-11-01 13:30:00 80379300 155.45 149.13 155.08 150.65 150.65 ## 22 2022-11-02 13:30:00 93604600 152.17 145.00 148.95 145.03 145.03 ## 23 2022-11-03 13:30:00 97572100 142.80 138.75 142.06 138.88 138.88
Please view the documentation to see the valid values for interval and period.
Profile
Retrieve data from the Profile
tab. The summary_profile property returns the business summary for a given
symbol.
aapl$summary_profile ## $address1 ## [1] "One Apple Park Way" ## ## $city ## [1] "Cupertino" ## ## $state ## [1] "CA" ## ## $zip ## [1] "95014" ## ## $country ## [1] "United States" ## ## $phone ## [1] "408 996 1010" ## ## $website ## [1] "https://www.apple.com" ## ## $industry ## [1] "Consumer Electronics" ## ## $sector ## [1] "Technology" ## ## $longBusinessSummary ## [1] "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; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, and HomePod. Further, it provides AppleCare support and 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 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 incorporated in 1977 and is headquartered in Cupertino, California." ## ## $fullTimeEmployees ## [1] 164000 ## ## $companyOfficers ## list() ## ## $maxAge ## [1] 86400
The company_officers property retrieves top executives for given symbol and
their total pay package.
aapl$company_officers ## name age title ## 1 Mr. Timothy D. Cook 60 CEO & Director ## 2 Mr. Luca Maestri 57 CFO & Sr. VP ## 3 Mr. Jeffrey E. Williams 57 Chief Operating Officer ## 4 Ms. Katherine L. Adams 57 Sr. VP, Gen. Counsel & Sec. ## 5 Ms. Deirdre O'Brien 54 Sr. VP of People & Retail ## 6 Mr. Chris Kondo NA Sr. Director of Corp. Accounting ## 7 Mr. James Wilson NA Chief Technology Officer ## 8 Ms. Mary Demby NA Chief Information Officer ## 9 Ms. Nancy Paxton NA Sr. Director of Investor Relations & Treasury ## 10 Mr. Greg Joswiak NA Sr. VP of Worldwide Marketing ## year_born fiscal_year total_pay exercised_value unexercised_value ## 1 1961 2021 16386559 0 0 ## 2 1964 2021 5018883 0 0 ## 3 1964 2021 5017437 0 0 ## 4 1964 2021 5014533 0 0 ## 5 1967 2021 5061191 0 0 ## 6 NA NA NA 0 0 ## 7 NA NA NA 0 0 ## 8 NA NA NA 0 0 ## 9 NA NA NA 0 0 ## 10 NA NA NA 0 0
Financials
The get_balance_sheet() method retrieves balance sheet data for most recent
four quarters or most recent four years.
aapl$get_balance_sheet('annual')
## # A tibble: 4 x 24
## end_date cash short~1 net_r~2 inven~3 other~4 total~5 long_~6 prope~7
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2022-09-24 23646000000 2.47e10 6.09e10 4.95e9 2.12e10 1.35e11 1.21e11 8.42e10
## 2 2021-09-25 34940000000 2.77e10 5.15e10 6.58e9 1.41e10 1.35e11 1.28e11 4.95e10
## 3 2020-09-26 38016000000 5.29e10 3.74e10 4.06e9 1.13e10 1.44e11 1.01e11 4.53e10
## 4 2019-09-28 48844000000 5.17e10 4.58e10 4.11e9 1.24e10 1.63e11 1.05e11 3.74e10
## # ... with 15 more variables: other_assets <dbl>, total_assets <dbl>,
## # accounts_payable <dbl>, short_long_term_debt <dbl>,
## # other_current_liab <dbl>, long_term_debt <dbl>, other_liab <dbl>,
## # total_current_liabilities <dbl>, total_liab <dbl>, common_stock <dbl>,
## # retained_earnings <dbl>, treasury_stock <dbl>,
## # other_stockholder_equity <dbl>, total_stockholder_equity <dbl>,
## # net_tangible_assets <dbl>, and abbreviated variable names ...
The get_income_statement() method retrieves income statement data for most
recent four quarters or most recent four years.
aapl$get_income_statement('annual')
## # A tibble: 4 x 16
## end_date total_rev~1 cost_~2 gross~3 resea~4 selli~5 total~6 opera~7 total~8
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 2022-09-24 3.94e11 2.24e11 1.71e11 2.63e10 2.51e10 2.75e11 1.19e11 -3.34e8
## 2 2021-09-25 3.66e11 2.13e11 1.53e11 2.19e10 2.20e10 2.57e11 1.09e11 2.58e8
## 3 2020-09-26 2.75e11 1.70e11 1.05e11 1.88e10 1.99e10 2.08e11 6.63e10 8.03e8
## 4 2019-09-28 2.60e11 1.62e11 9.84e10 1.62e10 1.82e10 1.96e11 6.39e10 1.81e9
## # ... with 7 more variables: ebit <dbl>, interest_expense <dbl>,
## # income_before_tax <dbl>, income_tax_expense <dbl>,
## # net_income_from_continuing_ops <dbl>, net_income <dbl>,
## # net_income_applicable_to_common_shares <dbl>, and abbreviated variable
## # names 1: total_revenue, 2: cost_of_revenue, 3: gross_profit,
## # 4: research_development, 5: selling_general_administrative,
## # 6: total_operating_expenses, 7: operating_income, ...
The get_cash_flow() method retrieves cash flow data for most recent four
quarters or most recent four years.
aapl$get_cash_flow('annual')
## # A tibble: 4 x 20
## end_date net_inc~1 depre~2 chang~3 change~4 change~5 chang~6 chang~7 total~8
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2022-09-24 9.98e10 1.11e10 1.00e10 -1.82e 9 9.93e 9 1.48e9 -8.39e9 1.22e11
## 2 2021-09-25 9.47e10 1.13e10 2.98e 9 -1.01e10 1.40e10 -2.64e9 -6.15e9 1.04e11
## 3 2020-09-26 5.74e10 1.11e10 6.52e 9 6.92e 9 -1.98e 9 -1.27e8 8.81e8 8.07e10
## 4 2019-09-28 5.53e10 1.25e10 5.08e 9 2.45e 8 -2.55e 9 -2.89e8 -8.96e8 6.94e10
## # ... with 11 more variables: capital_expenditures <dbl>, investments <dbl>,
## # other_cashflows_from_investing_activities <int>,
## # total_cashflows_from_investing_activities <dbl>, dividends_paid <dbl>,
## # net_borrowings <dbl>, other_cashflows_from_financing_activities <int>,
## # total_cash_from_financing_activities <dbl>, change_in_cash <dbl>,
## # repurchase_of_stock <dbl>, issuance_of_stock <int>, and abbreviated
## # variable names 1: net_income, 2: depreciation, 3: change_to_netincome, ...
Analysis
Earnings Estimate
aapl$earnings_trend$earnings_estimate ## date period analyst avg_estimate low_estimate high_estimate ## 1 2022-12-31 0q 27 2.08 1.93 2.20 ## 2 2023-03-31 +1q 26 1.47 1.34 1.61 ## 3 2023-09-30 0y 33 6.26 5.40 6.87 ## 4 2024-09-30 +1y 28 6.82 6.01 7.36 ## 5 <NA> +5y NA NA NA NA ## 6 <NA> -5y NA NA NA NA ## year_ago_eps ## 1 2.10 ## 2 1.52 ## 3 6.11 ## 4 6.26 ## 5 NA ## 6 NA
Revenue Estimate
aapl$earnings_trend$revenue_estimate ## date period analyst avg_estimate low_estimate high_estimate ## 1 2022-12-31 0q 23 1.27082e+11 1.21204e+11 1.32365e+11 ## 2 2023-03-31 +1q 23 9.73035e+10 8.96080e+10 1.02950e+11 ## 3 2023-09-30 0y 32 4.07153e+11 3.86805e+11 4.19743e+11 ## 4 2024-09-30 +1y 26 4.28651e+11 4.05730e+11 4.48983e+11 ## 5 <NA> +5y NA NA NA NA ## 6 <NA> -5y NA NA NA NA ## year_ago_revenue ## 1 NA ## 2 NA ## 3 3.94328e+11 ## 4 4.07153e+11 ## 5 NA ## 6 NA
Earnings History
aapl$earnings_history ## quarter period eps_estimate eps_actual eps_difference surprise_percent ## 1 2021-12-31 -4q 1.89 2.10 0.21 0.111 ## 2 2022-03-31 -3q 1.43 1.52 0.09 0.063 ## 3 2022-06-30 -2q 1.16 1.20 0.04 0.034 ## 4 2022-09-30 -1q 1.27 1.29 0.02 0.016
EPS Trend
aapl$earnings_trend$eps_trend ## date period current seven_days_ago thirty_days_ago sixty_days_ago ## 1 2022-12-31 0q 2.08 2.13 2.13 2.12 ## 2 2023-03-31 +1q 1.47 1.52 1.53 1.52 ## 3 2023-09-30 0y 6.26 6.43 6.46 6.45 ## 4 2024-09-30 +1y 6.82 6.90 6.88 6.88 ## 5 <NA> +5y NA NA NA NA ## 6 <NA> -5y NA NA NA NA ## ninety_days_ago ## 1 2.11 ## 2 1.52 ## 3 6.43 ## 4 6.79 ## 5 NA ## 6 NA
EPS Revision
aapl$earnings_trend$eps_revision ## date period up_last_7_days up_last_30_days down_last_30_days ## 1 2022-12-31 0q 4 6 15 ## 2 2023-03-31 +1q 4 5 13 ## 3 2023-09-30 0y 3 5 24 ## 4 2024-09-30 +1y 2 4 9 ## 5 <NA> +5y NA NA NA ## 6 <NA> -5y NA NA NA ## down_last_90_days ## 1 NA ## 2 NA ## 3 NA ## 4 NA ## 5 NA ## 6 NA
Growth Estimates
aapl$earnings_trend$growth ## date period growth ## 1 2022-12-31 0q -0.01000000 ## 2 2023-03-31 +1q -0.03300000 ## 3 2023-09-30 0y 0.02500000 ## 4 2024-09-30 +1y 0.08899999 ## 5 <NA> +5y 0.08890000 ## 6 <NA> -5y 0.22590000
Options
Option Chain
head(aapl$option_chain) ## # A tibble: 6 x 16 ## expiration option_type contra~1 strike curre~2 last_~3 change perce~4 ## <dttm> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> ## 1 2022-11-04 00:00:00 call AAPL221~ 70 USD 69.8 0 0 ## 2 2022-11-04 00:00:00 call AAPL221~ 75 USD 64.6 0 0 ## 3 2022-11-04 00:00:00 call AAPL221~ 80 USD 59.7 0 0 ## 4 2022-11-04 00:00:00 call AAPL221~ 85 USD 59.6 0 0 ## 5 2022-11-04 00:00:00 call AAPL221~ 90 USD 50.0 0 0 ## 6 2022-11-04 00:00:00 call AAPL221~ 95 USD 44.9 0 0 ## # ... with 8 more variables: open_interest <int>, bid <dbl>, ask <dbl>, ## # contract_size <chr>, last_trade_date <dttm>, implied_volatility <dbl>, ## # in_the_money <lgl>, volume <int>, and abbreviated variable names ## # 1: contract_symbol, 2: currency, 3: last_price, 4: percent_change
Option Expiration Dates
aapl$option_expiration_dates ## [1] "2022-11-04" "2022-11-11" "2022-11-18" "2022-11-25" "2022-12-02" ## [6] "2022-12-09" "2022-12-16" "2023-01-20" "2023-02-17" "2023-03-17" ## [11] "2023-04-21" "2023-05-19" "2023-06-16" "2023-07-21" "2023-09-15" ## [16] "2024-01-19" "2024-03-15" "2024-06-21" "2025-01-17"
Option Strikes
aapl$option_strikes ## [1] 30.0 35.0 40.0 45.0 50.0 55.0 60.0 65.0 70.0 75.0 80.0 85.0 ## [13] 90.0 95.0 100.0 105.0 110.0 115.0 120.0 125.0 126.0 127.0 128.0 129.0 ## [25] 130.0 131.0 132.0 133.0 134.0 135.0 136.0 137.0 138.0 139.0 140.0 141.0 ## [37] 142.0 143.0 144.0 145.0 146.0 147.0 148.0 149.0 150.0 152.5 155.0 157.5 ## [49] 160.0 162.5 165.0 167.5 170.0 172.5 175.0 177.5 180.0 185.0 190.0 195.0 ## [61] 200.0 205.0 210.0 215.0 220.0 225.0 230.0 235.0 240.0 245.0 250.0 255.0 ## [73] 260.0 265.0 270.0 275.0 280.0 285.0 290.0 295.0 300.0 310.0 320.0
Holding Pattern
Data showing breakdown of owners of given symbol(s), insiders, institutions, etc.
aapl$major_holders ## $maxAge ## [1] 1 ## ## $insidersPercentHeld ## [1] 0.00072 ## ## $institutionsPercentHeld ## [1] 0.60186 ## ## $institutionsFloatPercentHeld ## [1] 0.60229 ## ## $institutionsCount ## [1] 5463
Top 10 owners of a given symbol.
aapl$institution_ownership ## date organization percent_held position ## 1 2022-06-30 Vanguard Group, Inc. (The) NA 1277319054 ## 2 2022-06-30 Blackrock Inc. NA 1028688317 ## 3 2022-06-30 Berkshire Hathaway, Inc NA 894802319 ## 4 2022-06-30 State Street Corporation NA 598178524 ## 5 2022-06-30 FMR, LLC NA 344317974 ## 6 2022-06-30 Geode Capital Management, LLC NA 278256192 ## 7 2022-06-30 Price (T.Rowe) Associates Inc NA 237910783 ## 8 2022-06-30 Morgan Stanley NA 182450565 ## 9 2022-06-30 Northern Trust Corporation NA 179828922 ## 10 2022-06-30 Bank of America Corporation NA 149133915 ## value percent_change ## 1 177394076456 0.0058 ## 2 142864238487 0.0010 ## 3 124270150431 0.0044 ## 4 83075036333 -0.0255 ## 5 47818881910 -0.0207 ## 6 38644221303 0.0227 ## 7 33041050704 0.0207 ## 8 25338735358 0.4585 ## 9 24974641565 -0.0333 ## 10 20711718843 0.0328
Others
Data related to historical recommendations (buy, hold, sell) for a given symbol.
aapl$recommendation_trend ## period strong_buy buy hold sell strong_sell ## 1 0m 11 21 6 0 0 ## 2 -1m 13 25 6 1 0 ## 3 -2m 14 25 7 1 0 ## 4 -3m 13 20 8 0 0
Technical indicators for given symbol.
aapl$technical_insights$secReports[[1]] ## $id ## [1] "0000320193-22-000108_320193" ## ## $type ## [1] "Periodic Financial Reports" ## ## $title ## [1] "10-K : Periodic Financial Reports" ## ## $description ## [1] "Annual report pursuant to Section 13 and 15(d)" ## ## $filingDate ## [1] 1.666915e+12 ## ## $snapshotUrl ## [1] "https://cdn.yahoofinance.com/prod/sec-reports-thumbnails/0000320193/000032019322000108/thumbnail.png" ## ## $formType ## [1] "10-K"
Symbols displayed in the People Also Watch sidebar.
aapl$recommendations ## symbol score ## 1 AMZN 0.323217 ## 2 TSLA 0.303238 ## 3 META 0.288718 ## 4 GOOG 0.286876 ## 5 NFLX 0.218178
Indices
Use the Index class for getting all data related to indices from Yahoo Finance API.
Instantiate Index Class
We can instantiate the Index class by passing the index symbol. For instance,
to get data for Nifty 50,
pass ^NSEI as the first argument to the Index class.
nifty <- Index$new('^NSEI')
Summary
Use the summary_detail property to retrieve data from the Summary tab.
nifty$summary_detail ## $language ## [1] "en-US" ## ## $region ## [1] "US" ## ## $quoteType ## [1] "INDEX" ## ## $typeDisp ## [1] "Index" ## ## $quoteSourceName ## [1] "Free Realtime Quote" ## ## $triggerable ## [1] TRUE ## ## $customPriceAlertConfidence ## [1] "HIGH" ## ## $currency ## [1] "INR" ## ## $tradeable ## [1] FALSE ## ## $cryptoTradeable ## [1] FALSE ## ## $messageBoardId ## [1] "finmb_INDEXNSEI" ## ## $exchangeTimezoneName ## [1] "Asia/Kolkata" ## ## $exchangeTimezoneShortName ## [1] "IST" ## ## $gmtOffSetMilliseconds ## [1] 19800000 ## ## $market ## [1] "in_market" ## ## $esgPopulated ## [1] FALSE ## ## $regularMarketChangePercent ## [1] 0.1238129 ## ## $regularMarketPrice ## [1] 18075.05 ## ## $marketState ## [1] "REGULAR" ## ## $exchange ## [1] "NSI" ## ## $shortName ## [1] "NIFTY 50" ## ## $firstTradeDateMilliseconds ## [1] 1.190001e+12 ## ## $priceHint ## [1] 2 ## ## $regularMarketChange ## [1] 22.35156 ## ## $regularMarketTime ## [1] 1667552435 ## ## $regularMarketDayHigh ## [1] 18108 ## ## $regularMarketDayRange ## [1] "18017.15 - 18108.0" ## ## $regularMarketDayLow ## [1] 18017.15 ## ## $regularMarketVolume ## [1] 0 ## ## $regularMarketPreviousClose ## [1] 18052.7 ## ## $bid ## [1] 0 ## ## $ask ## [1] 0 ## ## $bidSize ## [1] 0 ## ## $askSize ## [1] 0 ## ## $fullExchangeName ## [1] "NSE" ## ## $regularMarketOpen ## [1] 18053.4 ## ## $averageDailyVolume3Month ## [1] 275566 ## ## $averageDailyVolume10Day ## [1] 245930 ## ## $fiftyTwoWeekLowChange ## [1] 2891.65 ## ## $fiftyTwoWeekLowChangePercent ## [1] 0.1904481 ## ## $fiftyTwoWeekRange ## [1] "15183.4 - 18350.95" ## ## $fiftyTwoWeekHighChange ## [1] -275.8984 ## ## $fiftyTwoWeekHighChangePercent ## [1] -0.01503456 ## ## $fiftyTwoWeekLow ## [1] 15183.4 ## ## $fiftyTwoWeekHigh ## [1] 18350.95 ## ## $fiftyDayAverage ## [1] 17526.7 ## ## $fiftyDayAverageChange ## [1] 548.3535 ## ## $fiftyDayAverageChangePercent ## [1] 0.03128676 ## ## $twoHundredDayAverage ## [1] 16994.76 ## ## $twoHundredDayAverageChange ## [1] 1080.293 ## ## $twoHundredDayAverageChangePercent ## [1] 0.06356624 ## ## $sourceInterval ## [1] 15 ## ## $exchangeDataDelayedBy ## [1] 15 ## ## $symbol ## [1] "^NSEI"
Historical Data
Use the get_history() method to retrieve historical data for a given
index. By default, it returns YTD data.
nifty$get_history(start = '2022-10-20', interval = '1d') ## date volume high low open close adj_close ## 1 2022-10-20 03:45:00 249600 17584.15 17421.00 17423.10 17563.95 17563.95 ## 2 2022-10-21 03:45:00 277700 17670.15 17520.75 17622.85 17576.30 17576.30 ## 3 2022-10-24 03:45:00 45000 17777.55 17707.40 17736.35 17730.75 17730.75 ## 4 2022-10-25 03:45:00 251400 17811.50 17637.00 17808.30 17656.35 17656.35 ## 5 2022-10-27 03:45:00 324600 17783.90 17654.50 17771.40 17736.95 17736.95 ## 6 2022-10-28 03:45:00 250000 17838.90 17723.70 17756.40 17786.80 17786.80 ## 7 2022-10-31 03:45:00 227200 18022.80 17899.90 17910.20 18012.20 18012.20 ## 8 2022-11-01 03:45:00 349900 18175.80 18060.15 18130.70 18145.40 18145.40 ## 9 2022-11-02 03:45:00 270900 18178.75 18048.65 18177.90 18082.85 18082.85 ## 10 2022-11-03 03:45:00 213000 18106.30 17959.20 17968.35 18052.70 18052.70 ## 11 2022-11-04 09:00:35 0 18108.00 18017.15 18053.40 18075.05 18075.05
To get data from a specific date upto the current date, use the start
argument to supply the initial date. The time between data points can be
specified using the interval argument.
To retrieve data between two specific dates, use both the start and end
argument. The dates should be either String or Date object in yyyy-mm-dd
format.
nifty$get_history(start = '2022-10-01', end = '2022-10-14', interval = '1d') ## date volume high low open close adj_close ## 1 2022-10-03 03:45:00 278400 17114.65 16855.55 17102.10 16887.35 16887.35 ## 2 2022-10-04 03:45:00 226000 17287.30 17117.30 17147.45 17274.30 17274.30 ## 3 2022-10-06 03:45:00 265500 17428.80 17315.65 17379.25 17331.80 17331.80 ## 4 2022-10-07 03:45:00 216300 17337.35 17216.95 17287.20 17314.65 17314.65 ## 5 2022-10-10 03:45:00 234000 17280.15 17064.70 17094.35 17241.00 17241.00 ## 6 2022-10-11 03:45:00 282600 17261.80 16950.30 17256.05 16983.55 16983.55 ## 7 2022-10-12 03:45:00 256000 17142.35 16960.05 17025.55 17123.60 17123.60 ## 8 2022-10-13 03:45:00 266400 17112.35 16956.95 17087.35 17014.35 17014.35
The period arguments allows the user to retrieve data for specific length of time.
nifty$get_history(period = '1mo', interval = '1d') ## date volume high low open close adj_close ## 1 2022-10-04 03:45:00 226000 17287.30 17117.30 17147.45 17274.30 17274.30 ## 2 2022-10-06 03:45:00 265500 17428.80 17315.65 17379.25 17331.80 17331.80 ## 3 2022-10-07 03:45:00 216300 17337.35 17216.95 17287.20 17314.65 17314.65 ## 4 2022-10-10 03:45:00 234000 17280.15 17064.70 17094.35 17241.00 17241.00 ## 5 2022-10-11 03:45:00 282600 17261.80 16950.30 17256.05 16983.55 16983.55 ## 6 2022-10-12 03:45:00 256000 17142.35 16960.05 17025.55 17123.60 17123.60 ## 7 2022-10-13 03:45:00 266400 17112.35 16956.95 17087.35 17014.35 17014.35 ## 8 2022-10-14 03:45:00 227000 17348.55 17169.75 17322.30 17185.70 17185.70 ## 9 2022-10-17 03:45:00 212200 17328.55 17098.55 17144.80 17311.80 17311.80 ## 10 2022-10-18 03:45:00 239500 17527.80 17434.05 17438.75 17486.95 17486.95 ## 11 2022-10-19 03:45:00 210500 17607.60 17472.85 17568.15 17512.25 17512.25 ## 12 2022-10-20 03:45:00 249600 17584.15 17421.00 17423.10 17563.95 17563.95 ## 13 2022-10-21 03:45:00 277700 17670.15 17520.75 17622.85 17576.30 17576.30 ## 14 2022-10-24 03:45:00 45000 17777.55 17707.40 17736.35 17730.75 17730.75 ## 15 2022-10-25 03:45:00 251400 17811.50 17637.00 17808.30 17656.35 17656.35 ## 16 2022-10-27 03:45:00 324600 17783.90 17654.50 17771.40 17736.95 17736.95 ## 17 2022-10-28 03:45:00 250000 17838.90 17723.70 17756.40 17786.80 17786.80 ## 18 2022-10-31 03:45:00 227200 18022.80 17899.90 17910.20 18012.20 18012.20 ## 19 2022-11-01 03:45:00 349900 18175.80 18060.15 18130.70 18145.40 18145.40 ## 20 2022-11-02 03:45:00 270900 18178.75 18048.65 18177.90 18082.85 18082.85 ## 21 2022-11-03 03:45:00 213000 18106.30 17959.20 17968.35 18052.70 18052.70 ## 22 2022-11-04 09:00:36 0 18108.00 18017.15 18053.40 18074.30 18074.30
Please view the documentation to see the valid values for interval and period.
Currency
Summary
currency_summary() retrieve information available via the Summary tab in
Yahoo Finance.. View the documentation to learn more.
currency_summary(from = "USD", to = "INR") ## $language ## [1] "en-US" ## ## $region ## [1] "US" ## ## $quoteType ## [1] "CURRENCY" ## ## $typeDisp ## [1] "Currency" ## ## $quoteSourceName ## [1] "Delayed Quote" ## ## $triggerable ## [1] TRUE ## ## $customPriceAlertConfidence ## [1] "HIGH" ## ## $currency ## [1] "INR" ## ## $shortName ## [1] "USD/INR" ## ## $regularMarketChangePercent ## [1] -0.2913199 ## ## $regularMarketPrice ## [1] 82.485 ## ## $regularMarketChange ## [1] -0.2409973 ## ## $regularMarketDayHigh ## [1] 82.731 ## ## $regularMarketDayLow ## [1] 82.45 ## ## $regularMarketPreviousClose ## [1] 82.726 ## ## $bid ## [1] 82.48 ## ## $ask ## [1] 82.53 ## ## $regularMarketOpen ## [1] 82.731 ## ## $fiftyTwoWeekLow ## [1] 73.6975 ## ## $fiftyTwoWeekHigh ## [1] 83.386 ## ## $fiftyDayAverage ## [1] 81.12029 ## ## $twoHundredDayAverage ## [1] 78.23625 ## ## $exchange ## [1] "CCY" ## ## $messageBoardId ## [1] "finmb_INR_X" ## ## $exchangeTimezoneName ## [1] "Europe/London" ## ## $exchangeTimezoneShortName ## [1] "GMT" ## ## $gmtOffSetMilliseconds ## [1] 0 ## ## $market ## [1] "ccy_market" ## ## $esgPopulated ## [1] FALSE ## ## $marketState ## [1] "REGULAR" ## ## $regularMarketTime ## [1] 1667552433 ## ## $regularMarketDayRange ## [1] "82.45 - 82.731" ## ## $regularMarketVolume ## [1] 0 ## ## $bidSize ## [1] 0 ## ## $askSize ## [1] 0 ## ## $fullExchangeName ## [1] "CCY" ## ## $averageDailyVolume3Month ## [1] 0 ## ## $averageDailyVolume10Day ## [1] 0 ## ## $fiftyTwoWeekLowChange ## [1] 8.787498 ## ## $fiftyTwoWeekLowChangePercent ## [1] 0.1192374 ## ## $fiftyTwoWeekRange ## [1] "73.6975 - 83.386" ## ## $fiftyTwoWeekHighChange ## [1] -0.901001 ## ## $tradeable ## [1] FALSE ## ## $cryptoTradeable ## [1] FALSE ## ## $fiftyTwoWeekHighChangePercent ## [1] -0.01080518 ## ## $firstTradeDateMilliseconds ## [1] 1.070237e+12 ## ## $priceHint ## [1] 4 ## ## $fiftyDayAverageChange ## [1] 1.364708 ## ## $fiftyDayAverageChangePercent ## [1] 0.01682326 ## ## $twoHundredDayAverageChange ## [1] 4.248749 ## ## $twoHundredDayAverageChangePercent ## [1] 0.05430665 ## ## $sourceInterval ## [1] 15 ## ## $exchangeDataDelayedBy ## [1] 0 ## ## $symbol ## [1] "USDINR=X"
Converter
currency_converter() retrieve current conversion rate between two currencies as
well as historical rates.
currency_converter('GBP', 'USD', '2022-07-01', '2022-07-10')
## date high low open close volume adj_close
## 1 2022-06-30 23:00:00 1.216205 1.198064 1.215998 1.216086 0 1.216086
## 2 2022-07-03 23:00:00 1.216515 1.208634 1.210580 1.210273 0 1.210273
## 3 2022-07-04 23:00:00 1.212606 1.190051 1.211402 1.211446 0 1.211446
## 4 2022-07-05 23:00:00 1.198638 1.187761 1.194957 1.194914 0 1.194914
## 5 2022-07-06 23:00:00 1.202183 1.191001 1.191895 1.192321 0 1.192321
## 6 2022-07-07 23:00:00 1.205531 1.192194 1.203196 1.202805 0 1.202805
The time between data points can be specified using the interval argument.
The period arguments allows the user to retrieve data for specific length of time.
currency_converter('GBP', 'USD', period = '1mo', interval = '1d')
## date high low open close volume adj_close
## 1 2022-10-03 23:00:00 1.144597 1.128222 1.133967 1.133414 0 1.133414
## 2 2022-10-04 23:00:00 1.149491 1.122776 1.145056 1.145134 0 1.145134
## 3 2022-10-05 23:00:00 1.138343 1.112038 1.135589 1.135538 0 1.135538
## 4 2022-10-06 23:00:00 1.122372 1.109201 1.116520 1.116208 0 1.116208
## 5 2022-10-09 23:00:00 1.111086 1.102354 1.107444 1.107273 0 1.107273
## 6 2022-10-10 23:00:00 1.117131 1.100183 1.107849 1.107641 0 1.107641
## 7 2022-10-11 23:00:00 1.109866 1.092645 1.096780 1.096780 0 1.096780
## 8 2022-10-12 23:00:00 1.136196 1.105828 1.109927 1.110556 0 1.110556
## 9 2022-10-13 23:00:00 1.135873 1.116196 1.130148 1.130391 0 1.130391
## 10 2022-10-16 23:00:00 1.143864 1.121705 1.123873 1.124417 0 1.124417
## 11 2022-10-17 23:00:00 1.141201 1.125809 1.135525 1.135396 0 1.135396
## 12 2022-10-18 23:00:00 1.134984 1.122246 1.133954 1.134430 0 1.134430
## 13 2022-10-19 23:00:00 1.133324 1.117368 1.120951 1.121101 0 1.121101
## 14 2022-10-20 23:00:00 1.129318 1.106378 1.122536 1.122146 0 1.122146
## 15 2022-10-23 23:00:00 1.137980 1.127485 1.133645 1.134469 0 1.134469
## 16 2022-10-24 23:00:00 1.149954 1.127370 1.130544 1.130774 0 1.130774
## 17 2022-10-25 23:00:00 1.162358 1.143419 1.146066 1.146224 0 1.146224
## 18 2022-10-26 23:00:00 1.164415 1.155095 1.163345 1.163819 0 1.163819
## 19 2022-10-27 23:00:00 1.160012 1.150616 1.156738 1.156604 0 1.156604
## 20 2022-10-31 00:00:00 1.161332 1.147987 1.160079 1.159595 0 1.159595
## 21 2022-11-01 00:00:00 1.156524 1.144675 1.147184 1.146815 0 1.146815
## 22 2022-11-02 00:00:00 1.152738 1.145856 1.149214 1.148950 0 1.148950
## 23 2022-11-03 00:00:00 1.142178 1.116258 1.138226 1.138433 0 1.138433
## 24 2022-11-04 08:59:42 1.124088 1.115101 1.115922 1.122322 0 1.122322
View the documentation to learn more.
Learning More
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