R6 Class Representing a Ticker

Base class for getting all data related to ticker from Yahoo Finance API.

Format

An R6 class object

Public fields

symbol: Symbol for which data is retrieved.

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)

Arguments

symbol: Symbol.

Returns

A new `Ticker` object

Examples

aapl <- Ticker$new('aapl')

Method `set_symbol()`

Set a new symbol.

Usage

Ticker$set_symbol(symbol)

Arguments

symbol: New symbol

Examples

aapl <- Ticker$new('aapl')
aapl$set_symbol('msft')

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
)

Arguments

frequency: Annual or quarter.
clean_names: Logical; if TRUE, converts column names to snake case.

Returns

A tibble.

Examples

\donttest{
aapl <- Ticker$new('aapl')
aapl$get_balance_sheet('annual')
aapl$get_balance_sheet('quarter')
}

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)

Arguments

frequency: Annual or quarter.
clean_names: Logical; if TRUE, converts column names to snake case.

Returns

A tibble.

Examples

\donttest{
aapl <- Ticker$new('aapl')
aapl$get_cash_flow('annual')
aapl$get_cash_flow('quarter')
}

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
)

Arguments

frequency: Annual or quarter.
clean_names: Logical; if TRUE, converts column names to snake case.

Returns

A tibble.

Examples

\donttest{
aapl <- Ticker$new('aapl')
aapl$get_income_statement('annual')
aapl$get_income_statement('quarter')
}

Method `get_history()`

Retrieves historical pricing data.

Usage

Ticker$get_history(period = "ytd", interval = "1d", start = NULL, end = NULL)

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 or date object in yyyy-mm-dd format.

end

Specific ending date. String or date object in yyyy-mm-dd format.

Returns

A data.frame.

Examples

\donttest{
aapl <- Ticker$new('aapl')
aapl$get_history(start = '2022-07-01', interval = '1d')
aapl$get_history(start = '2022-07-01', end = '2022-07-14', interval = '1d')
aapl$get_history(period = '1mo', interval = '1d')
}

Method `clone()`

The objects of this class are cloneable with this method.

Usage

Ticker$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

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
# }

Format

Public fields

Active bindings

Methods

Public methods

Method new()

Usage

Arguments

Returns

Examples

Method set_symbol()

Usage

Arguments

Examples

Method get_balance_sheet()

Usage

Arguments

Returns

Examples

Method get_cash_flow()

Usage

Arguments

Returns

Examples

Method get_income_statement()

Usage

Arguments

Returns

Examples

Method get_history()

Usage

Arguments

Returns

Examples

Method clone()

Usage

Arguments

Examples

Method `new()`

Method `set_symbol()`

Method `get_balance_sheet()`

Method `get_cash_flow()`

Method `get_income_statement()`

Method `get_history()`

Method `clone()`