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Based on a ticker (id of a stock) and time period, this function will download stock price data from Yahoo Finance and organizes it in the long format. Yahoo Finance <> provides a vast repository of stock price data around the globe. It cover a significant number of markets and assets, being used extensively in academic research and teaching. In the website you can lookup the ticker of a company.


  first_date = Sys.Date() - 30,
  last_date = Sys.Date(),
  thresh_bad_data = 0.75,
  bench_ticker = "^GSPC",
  type_return = "arit",
  freq_data = "daily",
  how_to_aggregate = "last",
  do_complete_data = FALSE,
  do_cache = TRUE,
  cache_folder = yf_cachefolder_get(),
  do_parallel = FALSE,
  be_quiet = FALSE



A single or vector of tickers. If not sure whether the ticker is available, search for it in YF <>.


The first date of query (Date or character as YYYY-MM-DD)


The last date of query (Date or character as YYYY-MM-DD)


A percentage threshold for defining bad data. The dates of the benchmark ticker are compared to each asset. If the percentage of non-missing dates with respect to the benchmark ticker is lower than thresh_bad_data, the function will ignore the asset (default = 0.75)


The ticker of the benchmark asset used to compare dates. My suggestion is to use the main stock index of the market from where the data is coming from (default = ^GSPC (SP500, US market))


Type of price return to calculate: 'arit' - arithmetic (default), 'log' - log returns.


Frequency of financial data: 'daily' (default), 'weekly', 'monthly', 'yearly'


Defines whether to aggregate the data using the first observations of the aggregating period or last ('first', 'last'). For example, if freq_data = 'yearly' and how_to_aggregate = 'last', the last available day of the year will be used for all aggregated values such as price_adjusted. (Default = "last")


Return a complete/balanced dataset? If TRUE, all missing pairs of ticker-date will be replaced by NA or closest price (see input do_fill_missing_prices). Default = FALSE.


Use cache system? (default = TRUE)


Where to save cache files? (default = yfR::yf_cachefolder_get() )


Flag for using parallel or not (default = FALSE). Before using parallel, make sure you call function future::plan() first. See <> for more details.


Flag for not printing statements (default = FALSE)


A dataframe with the financial data for working days, when markets are open. All price data is measured at the unit of the financial exchange. For example, price data for META (NYSE/US) is measures in dollars, while price data for PETR3.SA (B3/BR) is measured in Reais (Brazilian currency).

The return dataframe contains the following columns:


The requested tickers (ids of stocks)


The reference day (this can also be year/month/week when using argument freq_data)


The opening price of the day/period


The highest price of the day/period


The close/last price of the day/period


The financial volume of the day/period


The stock price adjusted for corporate events such as splits, dividends and others -- this is usually what you want/need for studying stocks as it represents the actual financial performance of stockholders


The arithmetic or log return (see input type_return) for the adjusted stock prices


The arithmetic or log return (see input type_return) for the closing stock prices


The accumulated arithmetic/log return for the period (starts at 100%)

The cache system

The yfR`s cache system is basically a bunch of rds files that are saved every time data is imported from YF. It indexes all data by ticker and time period. Whenever a user asks for a dataset, it first checks if the ticker/time period exists in cache and, if it does, loads the data from the rds file.

By default, a temporary folder is used (see function yf_cachefolder_get, which means that all cache files are session-persistent. In practice, whenever you restart your R/RStudio session, all cache files are lost. This is a choice I've made due to the fact that merging adjusted stock price data after corporate events (dividends/splits) is a mess and prone to errors. This only happens for stock price data, and not indices data.

If you really need a persistent cache folder, which is Ok for indices data, simply set a path with argument cache_folder (see warning section).


Be aware that when using cache system in a local folder (and not the default tempdir()), the aggregate prices series might not match if a split or dividends event happens in between cache files.


# \donttest{
tickers <- c("TSLA", "MMM")

first_date <- Sys.Date() - 30
last_date <- Sys.Date()

df_yf <- yf_get(
  tickers = tickers,
  first_date = first_date,
  last_date = last_date
#> ── Running yfR for 2 stocks | 2024-05-25 --> 2024-06-24 (30 days) ──
#>  Downloading data for benchmark ticker ^GSPC
#>  (1/2) Fetching data for MMM
#> ! 	- not cached
#>  	- cache saved successfully
#>  	- got 18 valid rows (2024-05-28 --> 2024-06-21)
#>  	- got 100% of valid prices -- Well done !
#>  (2/2) Fetching data for TSLA
#> ! 	- not cached
#>  	- cache saved successfully
#>  	- got 18 valid rows (2024-05-28 --> 2024-06-21)
#>  	- got 100% of valid prices -- Good job !
#>  Binding price data
#> ── Diagnostics ─────────────────────────────────────────────────────────────────
#>  Returned dataframe with 36 rows -- All OK!
#>  Using 255.1 kB at /tmp/RtmpmjMQge/yf_cache for 86 cache files
#>  Out of 2 requested tickers, you got 2 (100%)

#> # A tibble: 36 × 11
#>    ticker ref_date   price_open price_high price_low price_close   volume
#>  * <chr>  <date>          <dbl>      <dbl>     <dbl>       <dbl>    <dbl>
#>  1 MMM    2024-05-28       99.6      100.       97.8        98.7  4360900
#>  2 MMM    2024-05-29       97.8       97.9      96.8        97.6  4826600
#>  3 MMM    2024-05-30       97.7       98.2      96.9        98.2  4520300
#>  4 MMM    2024-05-31       98.1      100.       97.4       100.  21754400
#>  5 MMM    2024-06-03      100.       101.       99.0        99.8  4508100
#>  6 MMM    2024-06-04       99         99.4      97.7        98.6  4988100
#>  7 MMM    2024-06-05       98.8       99.7      97.9        99.1  3115400
#>  8 MMM    2024-06-06       98.8       99.6      98.2        98.2  2510000
#>  9 MMM    2024-06-07       99.5      102.       98.4       101.   5636700
#> 10 MMM    2024-06-10      100.       102.      100.        101.   3507600
#> # ℹ 26 more rows
#> # ℹ 4 more variables: price_adjusted <dbl>, ret_adjusted_prices <dbl>,
#> #   ret_closing_prices <dbl>, cumret_adjusted_prices <dbl>
# }