Base class for getting all data related to ticker from Yahoo Finance API.
Active bindings
asset_profile
Information related to the company's location, operations, and officers.
calendar_events
Earnings and Revenue expectations for upcoming earnings date.
company_officers
Retrieves top executives for given symbol and their total pay package.
earnings_history
Data related to historical earnings (actual vs. estimate)
earnings
Historical earnings data.
earnings_trend
Historical trend data for earnings and revenue estimations
esg_scores
Data related to environmental, social, and governance metrics
financial_data
Financial key performance indicators
fund_bond_holdings
Retrieves aggregated maturity and duration information for a given symbol
fund_bond_ratings
Retrieves aggregated maturity and duration information
fund_equity_holdings
Fund equity holdings
fund_holding_info
Contains information for a funds top holdings, bond ratings, bond holdings, equity holdings, sector weightings, and category breakdown
fund_ownership
Top 10 owners of a given symbol
fund_performance
Historical return data for a given symbol and its specific category
fund_profile
Summary level information for a given symbol
fund_section_weightings
Retrieves aggregated sector weightings for a given symbol
fund_top_holdings
Retrieves Top 10 holdings for a given symbol
fund_holdings
Holding info for the given fund
grading_history
Data related to upgrades / downgrades by companies
index_trend
Trend data related to given symbol's index, specificially PE and PEG ratios
inside_holders
Data related to stock holdings of a given symbol(s) insiders
insider_transactions
Transactions by insiders for a given symbol(s)
institution_ownership
Top 10 owners of a given symbol
key_stats
KPIs for given symbol
major_holders
Data showing breakdown of owners of given symbol(s), insiders, institutions, etc.
page_views
Short, Mid, and Long-term trend data regarding a symbol's page views
price
Detailed pricing data for given symbol, exchange, quote type, currency, market cap, pre / post market data, etc.
quote_type
Stock exchange specific data for given symbol
recommendation_trend
Data related to historical recommendations (buy, hold, sell) for a given symbol
security_filings
Historical SEC filings
share_purchase_activity
High-level buy / sell data
summary_detail
Contains information available via the Summary tab in Yahoo Finance
summary_profile
Return business summary of given symbol
valuation_measures
Retrieves valuation measures for most recent four quarters
option_chain
Option chain data for all expiration dates for a given symbol
option_expiration_dates
Option expiration dates
option_strikes
Option strikes
quote
Get real-time quote information for given symbol
recommendations
Recommended symbols
technical_insights
Technical indicators for given symbol
Methods
Method new()
Create a new Ticker object.
Usage
Ticker$new(symbol = NA)
Examples
aapl <- Ticker$new('aapl')
Method get_balance_sheet()
Retrieves balance sheet data for most recent four quarters or most recent four years.
Usage
Ticker$get_balance_sheet(
frequency = c("annual", "quarter"),
clean_names = TRUE
)
Method get_cash_flow()
Retrieves cash flow data for most recent four quarters or most recent four years.
Usage
Ticker$get_cash_flow(frequency = c("annual", "quarter"), clean_names = TRUE)
Method get_income_statement()
Retrieves income statement data for most recent four quarters or most recent four years.
Usage
Ticker$get_income_statement(
frequency = c("annual", "quarter"),
clean_names = TRUE
)
Method get_history()
Retrieves historical pricing data.
Arguments
period
Length of time. Defaults to
'ytd'
. Valid values are:'1d'
'5d'
'1mo'
'3mo'
'6mo'
'1y'
'2y'
'5y'
'10y'
'ytd'
'max'
interval
Time between data points. Defaults to
'1d'
. Valid values are:'1m'
'2m'
'5m'
'15m'
'30m'
'60m'
'90m'
'1h'
'1d'
'5d'
'1wk'
'1mo'
'3mo'
start
Specific starting date.
String
ordate
object inyyyy-mm-dd
format.end
Specific ending date.
String
ordate
object inyyyy-mm-dd
format.
Examples
## ------------------------------------------------
## Method `Ticker$new`
## ------------------------------------------------
aapl <- Ticker$new('aapl')
## ------------------------------------------------
## Method `Ticker$set_symbol`
## ------------------------------------------------
aapl <- Ticker$new('aapl')
aapl$set_symbol('msft')
## ------------------------------------------------
## Method `Ticker$get_balance_sheet`
## ------------------------------------------------
# \donttest{
aapl <- Ticker$new('aapl')
aapl$get_balance_sheet('annual')
#> # A tibble: 4 x 24
#> end_date cash short~1 net_r~2 inven~3 other~4 total~5 long_~6 prope~7
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2021-09-25 34940000000 2.77e10 5.15e10 6.58e9 1.41e10 1.35e11 1.28e11 4.95e10
#> 2 2020-09-26 38016000000 5.29e10 3.74e10 4.06e9 1.13e10 1.44e11 1.01e11 4.53e10
#> 3 2019-09-28 48844000000 5.17e10 4.58e10 4.11e9 1.24e10 1.63e11 1.05e11 3.74e10
#> 4 2018-09-29 25913000000 4.04e10 4.90e10 3.96e9 1.21e10 1.31e11 1.71e11 4.13e10
#> # ... with 15 more variables: other_assets <dbl>, total_assets <dbl>,
#> # accounts_payable <dbl>, short_long_term_debt <dbl>,
#> # other_current_liab <dbl>, long_term_debt <dbl>, other_liab <dbl>,
#> # total_current_liabilities <dbl>, total_liab <dbl>, common_stock <dbl>,
#> # retained_earnings <dbl>, treasury_stock <dbl>,
#> # other_stockholder_equity <dbl>, total_stockholder_equity <dbl>,
#> # net_tangible_assets <dbl>, and abbreviated variable names ...
aapl$get_balance_sheet('quarter')
#> # 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 2022-06-25 27502000000 2.07e10 4.22e10 5.43e9 1.64e10 1.12e11 1.31e11 4.03e10
#> 2 2022-03-26 28098000000 2.34e10 4.54e10 5.46e9 1.58e10 1.18e11 1.41e11 3.93e10
#> 3 2021-12-25 37119000000 2.68e10 6.53e10 5.88e9 1.81e10 1.53e11 1.39e11 3.92e10
#> 4 2021-09-25 34940000000 2.77e10 5.15e10 6.58e9 1.41e10 1.35e11 1.28e11 4.95e10
#> # ... 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 ...
# }
## ------------------------------------------------
## Method `Ticker$get_cash_flow`
## ------------------------------------------------
# \donttest{
aapl <- Ticker$new('aapl')
aapl$get_cash_flow('annual')
#> # A tibble: 4 x 20
#> end_date net_i~1 depre~2 change~3 change~4 change~5 chang~6 change~7 total~8
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2021-09-25 9.47e10 1.13e10 2.98e 9 -1.01e10 1.40e10 -2.64e9 -6.15e 9 1.04e11
#> 2 2020-09-26 5.74e10 1.11e10 6.52e 9 6.92e 9 -1.98e 9 -1.27e8 8.81e 8 8.07e10
#> 3 2019-09-28 5.53e10 1.25e10 5.08e 9 2.45e 8 -2.55e 9 -2.89e8 -8.96e 8 6.94e10
#> 4 2018-09-29 5.95e10 1.09e10 -2.77e10 -5.32e 9 9.17e 9 8.28e8 3.00e10 7.74e10
#> # ... with 11 more variables: capital_expenditures <dbl>, investments <dbl>,
#> # other_cashflows_from_investing_activities <int>,
#> # total_cashflows_from_investing_activities <dbl>, dividends_paid <dbl>,
#> # net_borrowings <dbl>, other_cashflows_from_financing_activities <int>,
#> # total_cash_from_financing_activities <dbl>, change_in_cash <dbl>,
#> # repurchase_of_stock <dbl>, issuance_of_stock <int>, and abbreviated
#> # variable names 1: net_income, 2: depreciation, 3: change_to_netincome, ...
aapl$get_cash_flow('quarter')
#> # A tibble: 4 x 20
#> end_date net_inc~1 depre~2 chang~3 chang~4 change~5 chang~6 change~7 total~8
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 2022-06-25 1.94e10 2.80e9 3.87e9 -9.81e8 -4.72e 9 -1.6 e7 2.50e 9 2.29e10
#> 2 2022-03-26 2.50e10 2.74e9 2.47e9 9.48e9 -2.14e10 3.84e8 9.49e 9 2.82e10
#> 3 2021-12-25 3.46e10 2.70e9 3.11e9 -3.93e9 2.03e10 6.81e8 -1.05e10 4.70e10
#> 4 2021-09-25 2.06e10 2.99e9 -1.55e9 -8.81e9 1.40e10 -1.43e9 -5.60e 9 2.02e10
#> # ... 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, ...
# }
## ------------------------------------------------
## Method `Ticker$get_income_statement`
## ------------------------------------------------
# \donttest{
aapl <- Ticker$new('aapl')
aapl$get_income_statement('annual')
#> # A tibble: 4 x 16
#> end_date total_rev~1 cost_~2 gross~3 resea~4 selli~5 total~6 opera~7 total~8
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 2021-09-25 3.66e11 2.13e11 1.53e11 2.19e10 2.20e10 2.57e11 1.09e11 2.58e8
#> 2 2020-09-26 2.75e11 1.70e11 1.05e11 1.88e10 1.99e10 2.08e11 6.63e10 8.03e8
#> 3 2019-09-28 2.60e11 1.62e11 9.84e10 1.62e10 1.82e10 1.96e11 6.39e10 1.81e9
#> 4 2018-09-29 2.66e11 1.64e11 1.02e11 1.42e10 1.67e10 1.95e11 7.09e10 2.00e9
#> # ... with 7 more variables: ebit <dbl>, interest_expense <dbl>,
#> # income_before_tax <dbl>, income_tax_expense <dbl>,
#> # net_income_from_continuing_ops <dbl>, net_income <dbl>,
#> # net_income_applicable_to_common_shares <dbl>, and abbreviated variable
#> # names 1: total_revenue, 2: cost_of_revenue, 3: gross_profit,
#> # 4: research_development, 5: selling_general_administrative,
#> # 6: total_operating_expenses, 7: operating_income, ...
aapl$get_income_statement('quarter')
#> # 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 2022-06-25 8.30e10 4.71e10 3.59e10 6.80e9 6.01e9 5.99e10 2.31e10 -1 e7
#> 2 2022-03-26 9.73e10 5.47e10 4.26e10 6.39e9 6.19e9 6.73e10 3.00e10 1.6 e8
#> 3 2021-12-25 1.24e11 6.97e10 5.42e10 6.31e9 6.45e9 8.25e10 4.15e10 -2.47e8
#> 4 2021-09-25 8.34e10 4.82e10 3.52e10 5.77e9 5.62e9 5.96e10 2.38e10 -5.38e8
#> # ... with 7 more variables: ebit <dbl>, interest_expense <int>,
#> # 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, ...
# }
## ------------------------------------------------
## Method `Ticker$get_history`
## ------------------------------------------------
# \donttest{
aapl <- Ticker$new('aapl')
aapl$get_history(start = '2022-07-01', interval = '1d')
#> date volume high low open close adj_close
#> 1 2022-07-01 13:30:00 71051600 139.04 135.66 136.04 138.93 138.7373
#> 2 2022-07-05 13:30:00 73353800 141.61 136.93 137.77 141.56 141.3636
#> 3 2022-07-06 13:30:00 74064300 144.12 141.08 141.35 142.92 142.7218
#> 4 2022-07-07 13:30:00 66253700 146.55 143.28 143.29 146.35 146.1470
#> 5 2022-07-08 13:30:00 64547800 147.55 145.00 145.26 147.04 146.8360
#> 6 2022-07-11 13:30:00 63141600 146.64 143.78 145.67 144.87 144.6690
#> 7 2022-07-12 13:30:00 77588800 148.45 145.05 145.76 145.86 145.6577
#> 8 2022-07-13 13:30:00 71185600 146.45 142.12 142.99 145.49 145.2882
#> 9 2022-07-14 13:30:00 78140700 148.95 143.25 144.08 148.47 148.2641
#> 10 2022-07-15 13:30:00 76259900 150.86 148.20 149.78 150.17 149.9617
#> 11 2022-07-18 13:30:00 81420900 151.57 146.70 150.74 147.07 146.8660
#> 12 2022-07-19 13:30:00 82982400 151.23 146.91 147.92 151.00 150.7905
#> 13 2022-07-20 13:30:00 64823400 153.72 150.37 151.12 153.04 152.8277
#> 14 2022-07-21 13:30:00 65086600 155.57 151.94 154.50 155.35 155.1345
#> 15 2022-07-22 13:30:00 66675400 156.28 153.41 155.39 154.09 153.8763
#> 16 2022-07-25 13:30:00 53623900 155.04 152.28 154.01 152.95 152.7378
#> 17 2022-07-26 13:30:00 55138700 153.09 150.80 152.26 151.60 151.3897
#> 18 2022-07-27 13:30:00 78620700 157.33 152.16 152.58 156.79 156.5725
#> 19 2022-07-28 13:30:00 81378700 157.64 154.41 156.98 157.35 157.1317
#> 20 2022-07-29 13:30:00 101786900 163.63 159.50 161.24 162.51 162.2846
#> 21 2022-08-01 13:30:00 67829400 163.59 160.89 161.01 161.51 161.2860
#> 22 2022-08-02 13:30:00 59907000 162.41 159.63 160.10 160.01 159.7880
#> 23 2022-08-03 13:30:00 82507500 166.59 160.75 160.84 166.13 165.8996
#> 24 2022-08-04 13:30:00 55474100 167.19 164.43 166.01 165.81 165.5800
#> 25 2022-08-05 13:30:00 56697000 165.85 163.00 163.21 165.35 165.3500
#> 26 2022-08-08 13:30:00 60276900 167.81 164.20 166.37 164.87 164.8700
#> 27 2022-08-09 13:30:00 63135500 165.82 163.25 164.02 164.92 164.9200
#> 28 2022-08-10 13:30:00 70170500 169.34 166.90 167.68 169.24 169.2400
#> 29 2022-08-11 13:30:00 57149200 170.99 168.19 170.06 168.49 168.4900
#> 30 2022-08-12 13:30:00 68039400 172.17 169.40 169.82 172.10 172.1000
#> 31 2022-08-15 13:30:00 54091700 173.39 171.35 171.52 173.19 173.1900
#> 32 2022-08-16 13:30:00 56377100 173.71 171.66 172.78 173.03 173.0300
#> 33 2022-08-17 13:30:00 79542000 176.15 172.57 172.77 174.55 174.5500
#> 34 2022-08-18 13:30:00 62290100 174.90 173.12 173.75 174.15 174.1500
#> 35 2022-08-19 13:30:00 70346300 173.74 171.31 173.03 171.52 171.5200
#> 36 2022-08-22 13:30:00 69026800 169.86 167.14 169.69 167.57 167.5700
#> 37 2022-08-23 13:30:00 54147100 168.71 166.65 167.08 167.23 167.2300
#> 38 2022-08-24 13:30:00 53841500 168.11 166.25 167.32 167.53 167.5300
#> 39 2022-08-25 13:30:00 51218200 170.14 168.35 168.78 170.03 170.0300
#> 40 2022-08-26 13:30:00 78961000 171.05 163.56 170.57 163.62 163.6200
#> 41 2022-08-29 13:30:00 73314000 162.90 159.82 161.15 161.38 161.3800
#> 42 2022-08-30 13:30:00 77906200 162.56 157.72 162.13 158.91 158.9100
#> 43 2022-08-31 13:30:00 87991100 160.58 157.14 160.31 157.22 157.2200
#> 44 2022-09-01 13:30:00 74229900 158.42 154.67 156.64 157.96 157.9600
#> 45 2022-09-02 13:30:00 76905200 160.36 154.97 159.75 155.81 155.8100
#> 46 2022-09-06 13:30:00 73714800 157.09 153.69 156.47 154.53 154.5300
#> 47 2022-09-07 13:30:00 87449600 156.67 153.61 154.82 155.96 155.9600
#> 48 2022-09-08 13:30:00 84923800 156.36 152.68 154.64 154.46 154.4600
#> 49 2022-09-09 13:30:00 68028800 157.82 154.75 155.47 157.37 157.3700
#> 50 2022-09-12 13:30:00 104956000 164.26 159.30 159.59 163.43 163.4300
#> 51 2022-09-13 13:30:00 122656600 160.54 153.37 159.90 153.84 153.8400
#> 52 2022-09-14 13:30:00 87965400 157.10 153.61 154.79 155.31 155.3100
#> 53 2022-09-15 13:30:00 90481100 155.24 151.38 154.65 152.37 152.3700
#> 54 2022-09-16 13:30:00 162157000 151.35 148.37 151.21 150.70 150.7000
#> 55 2022-09-19 13:30:00 81328800 154.56 149.10 149.31 154.48 154.4800
aapl$get_history(start = '2022-07-01', end = '2022-07-14', interval = '1d')
#> date volume high low open close adj_close
#> 1 2022-07-01 13:30:00 71051600 139.04 135.66 136.04 138.93 138.7373
#> 2 2022-07-05 13:30:00 73353800 141.61 136.93 137.77 141.56 141.3636
#> 3 2022-07-06 13:30:00 74064300 144.12 141.08 141.35 142.92 142.7218
#> 4 2022-07-07 13:30:00 66253700 146.55 143.28 143.29 146.35 146.1470
#> 5 2022-07-08 13:30:00 64547800 147.55 145.00 145.26 147.04 146.8360
#> 6 2022-07-11 13:30:00 63141600 146.64 143.78 145.67 144.87 144.6690
#> 7 2022-07-12 13:30:00 77588800 148.45 145.05 145.76 145.86 145.6577
#> 8 2022-07-13 13:30:00 71185600 146.45 142.12 142.99 145.49 145.2882
aapl$get_history(period = '1mo', interval = '1d')
#> date volume high low open close adj_close
#> 1 2022-08-22 13:30:00 69026800 169.86 167.14 169.69 167.57 167.57
#> 2 2022-08-23 13:30:00 54147100 168.71 166.65 167.08 167.23 167.23
#> 3 2022-08-24 13:30:00 53841500 168.11 166.25 167.32 167.53 167.53
#> 4 2022-08-25 13:30:00 51218200 170.14 168.35 168.78 170.03 170.03
#> 5 2022-08-26 13:30:00 78961000 171.05 163.56 170.57 163.62 163.62
#> 6 2022-08-29 13:30:00 73314000 162.90 159.82 161.15 161.38 161.38
#> 7 2022-08-30 13:30:00 77906200 162.56 157.72 162.13 158.91 158.91
#> 8 2022-08-31 13:30:00 87991100 160.58 157.14 160.31 157.22 157.22
#> 9 2022-09-01 13:30:00 74229900 158.42 154.67 156.64 157.96 157.96
#> 10 2022-09-02 13:30:00 76905200 160.36 154.97 159.75 155.81 155.81
#> 11 2022-09-06 13:30:00 73714800 157.09 153.69 156.47 154.53 154.53
#> 12 2022-09-07 13:30:00 87449600 156.67 153.61 154.82 155.96 155.96
#> 13 2022-09-08 13:30:00 84923800 156.36 152.68 154.64 154.46 154.46
#> 14 2022-09-09 13:30:00 68028800 157.82 154.75 155.47 157.37 157.37
#> 15 2022-09-12 13:30:00 104956000 164.26 159.30 159.59 163.43 163.43
#> 16 2022-09-13 13:30:00 122656600 160.54 153.37 159.90 153.84 153.84
#> 17 2022-09-14 13:30:00 87965400 157.10 153.61 154.79 155.31 155.31
#> 18 2022-09-15 13:30:00 90481100 155.24 151.38 154.65 152.37 152.37
#> 19 2022-09-16 13:30:00 162157000 151.35 148.37 151.21 150.70 150.70
#> 20 2022-09-19 13:30:00 81328800 154.56 149.10 149.31 154.48 154.48
# }