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usage-alphavantagepf

Source: vignettes/usage-alphvantagepf.Rmd
usage-alphvantagepf.Rmd

Alphavantagepf interfaces the Alphavantage API to R in a way most compatible with normalized data stores. It returns data in data.table format, which is really the best choice for financial time series analysis.

There is one main function to call to access all of the API functionality provided by Alphavantage.

Start with API keys 

av_api_key("YOUR_API_KEY")
print(av_api_key())
#> [1] "YOUR_API_KEY" NA

Finding Functions and their defaults

To find parameters and defaults provided by the alphavantagepf package, use av_funhelp()

av_funhelp("SERIES_INTRADAY")
#> Function: TIME_SERIES_INTRADAY
#> Category: equity
#> 
#> Parameters:
#> R> symbol
#> R> interval (default: 15min)
#> O> adjusted
#> O> extended_hours
#> O> month
#> O> outputsize (default: compact)
#> O> entitlement (default: {entitlement})
#> [1] "Function: TIME_SERIES_INTRADAY\nCategory: equity\n\nParameters:\nR> symbol\nR> interval (default: 15min)\nO> adjusted\nO> extended_hours\nO> month\nO> outputsize (default: compact)\nO> entitlement (default: {entitlement})\n"

Required parameters are listed with “R” and optional parameters (and any default provided by this package) are listed with “O”

Getting Data from Alpha Vantage

Once the API key has been set, use the function av_get_pf() which requires at minimum two arguments, a symbol (put first to facilitate usage in pipes) and an Alphavantage “function” av_fun.

The resulting output will be a data.table that depends on the type of data requested. (Note that data is returned in a data.table, which can be cast as tibbles as necessary.)

The output will always include the symbol requested or the name of the av_fun used if a symbol isn’t relevent. If that variable isn’t wanted (e.g. when called within a grouping function group_by(symbol) |> do({}) then set symbolvarnm="".

  • If the data is a time series, the columns will all have the same type, and will be passed along unchanged.
  • Most other data will be a long-form (melted) datatable with at least the following columns:

  1. symbol will either be the symbol requested or the value of av_fun if a symbol isn’t relevant.

  2. variable which is the name of the data item

  3. value_str, value_num and/or value_df which will contain strings (converted to numeric if possible) or (in the case of value_df) a nested data.frame.

  4. ltype is the inferred data-type, helpful for selecting the correct columns.

Examples

Time series data 

av_get_pf("IBM","TIME_SERIES_INTRADAY") |> head()

   symbol  timestamp  open  high   low close   volume
   <char>     <IDat> <num> <num> <num> <num>    <int>
1:    IBM 1999-11-01  98.5  98.8  96.4  96.8  9551800
2:    IBM 1999-11-02  96.8  96.8  93.7  94.8 11105400

Mixed use data 

av_get_pf("","TOP_GAINERS_LOSERS")

Key: <variable>
               symbol             variable     ltype           value_df                                value_str value_num
               <char>               <char>    <char>             <list>                                   <char>     <num>
1: TOP_GAINERS_LOSERS         last_updated   numeric             [NULL]           2026-01-05 16:15:59 US/Eastern      2026
2: TOP_GAINERS_LOSERS             metadata character             [NULL] Top gainers, losers, and most actively t        NA
3: TOP_GAINERS_LOSERS most_actively_traded      list <data.frame[20x5]>                                     NULL        NA
4: TOP_GAINERS_LOSERS          top_gainers      list <data.frame[20x5]>                                     NULL        NA
5: TOP_GAINERS_LOSERS           top_losers      list <data.frame[20x5]>                                     NULL        NA

Helpers: av_extract_df

Extracting the nested data.frames can be a tedious task, so the helper function av_extract_df() can be used to filter for the correct variable and extract the data.frame

av_get_pf("","TOP_GAINERS_LOSERS")  |> av_extract_df("top_losers")

Key: <variable>
               symbol             variable     ltype           value_df                                value_str value_num
               <char>               <char>    <char>             <list>                                   <char>     <num>
1: TOP_GAINERS_LOSERS         last_updated   numeric             [NULL]           2026-01-05 16:15:59 US/Eastern      2026
2: TOP_GAINERS_LOSERS             metadata character             [NULL] Top gainers, losers, and most actively t        NA
3: TOP_GAINERS_LOSERS most_actively_traded      list <data.frame[20x5]>                                     NULL        NA
4: TOP_GAINERS_LOSERS          top_gainers      list <data.frame[20x5]>                                     NULL        NA
5: TOP_GAINERS_LOSERS           top_losers      list <data.frame[20x5]>                                     NULL        NA