Project Status: Active – The project has reached a stable, usable state and is being actively developed. Codecov test coverage R build (rcmdcheck) Status at rOpenSci Software Peer Review R-CMD-check


yfR facilitates importing stock prices from Yahoo finance, organizing the data in the tidy format and speeding up the process using a cache system and parallel computing. yfR is the second and backwards-incompatible version of BatchGetSymbols, released in 2016 (see vignette yfR and BatchGetSymbols for details).

In a nutshell, Yahoo Finance (YF) provides a vast repository of stock price data around the globe. It covers a significant number of markets and assets, being used extensively in academic research and teaching. In order to import the financial data from YF, all you need is a ticker (id of a stock, e.g. “GM” for General Motors) and a time period – first and last date.

The Data

The main function of the package, yfR::yf_get, returns a dataframe with the financial data. All price data is measured at the unit of the financial exchange. For example, price data for GM (NASDAQ/US) is measured in dollars, while price data for PETR3.SA (B3/BR) is measured in Reais (Brazilian currency).

The returned data contains the following columns:

ticker: The requested tickers (ids of stocks);

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

price_open: The opening price of the day/period;

price_high: The highest price of the day/period;

price_close: The close/last price of the day/period;

volume: The financial volume of the day/period, in the unit of the exchange;

price_adjusted: 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 real financial performance of stockholders;

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

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

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

Finding tickers

The easiest way to find the tickers of a company stock is to search for it in Yahoo Finance’s website. At the top page you’ll find a search bar:

YF Search

A company can have many different stocks traded at different markets (see picture above). As the example shows, Petrobras is traded at NYQ (New York Exchange), SAO (Sao Paulo/Brazil - B3 exchange) and BUE (Buenos Aires/Argentina Exchange), all with different symbols (tickers). For market indices, a list of tickers is available here.

Features of yfR



# CRAN (stable)

# Github (dev version)

# ropensci
install.packages("yfR", repos = "")

A simple example of usage


# set options for algorithm
my_ticker <- 'META'
first_date <- Sys.Date() - 30
last_date <- Sys.Date()

# fetch data
df_yf <- yf_get(tickers = my_ticker, 
                     first_date = first_date,
                     last_date = last_date)
#> ── Running yfR for 1 stocks | 2023-01-17 --> 2023-02-16 (30 days) ──
#> ℹ Downloading data for benchmark ticker ^GSPC
#> ℹ (1/1) Fetching data for META
#> !    - not cached
#> ✔    - cache saved successfully
#> ✔    - got 22 valid rows (2023-01-17 --> 2023-02-15)
#> ✔    - got 100% of valid prices -- Time for some tea?
#> ℹ Binding price data
#> ── Diagnostics ─────────────────────────────────────────────────────────────────
#> ✔ Returned dataframe with 22 rows -- Youre doing good!
#> ℹ Using 6.3 kB at /tmp/RtmpvCnCwr/yf_cache for 2 cache files
#> ℹ Out of 1 requested tickers, you got 1 (100%)

# output is a tibble with data
#> # A tibble: 6 × 11
#>   ticker ref_date   price_open price_h…¹ price…² price…³ volume price…⁴ ret_ad…⁵
#>   <chr>  <date>          <dbl>     <dbl>   <dbl>   <dbl>  <dbl>   <dbl>    <dbl>
#> 1 META   2023-01-17       136.      137.    134.    135. 2.11e7    135. NA      
#> 2 META   2023-01-18       136.      137.    133.    133. 2.02e7    133. -1.73e-2
#> 3 META   2023-01-19       132.      137.    132.    136. 2.86e7    136.  2.35e-2
#> 4 META   2023-01-20       136.      140.    135.    139. 2.86e7    139.  2.37e-2
#> 5 META   2023-01-23       139.      144.    139.    143. 2.75e7    143.  2.80e-2
#> 6 META   2023-01-24       142.      145     141.    143. 2.20e7    143. -9.07e-4
#> # … with 2 more variables: ret_closing_prices <dbl>,
#> #   cumret_adjusted_prices <dbl>, and abbreviated variable names ¹​price_high,
#> #   ²​price_low, ³​price_close, ⁴​price_adjusted, ⁵​ret_adjusted_prices


Package yfR is based on quantmod (@joshuaulrich) and uses one of its functions (quantmod::getSymbols) for fetching raw data from Yahoo Finance. As with any API, there is significant work in maintaining the code. Joshua was always fast and openminded in implemented required changes, and I’m very grateful for it.