Examples
Here you’ll find a series of example of calls to
yf_get()
. Most arguments are self-explanatory, but you can
find more details at the help files.
The steps of the algorithm are:
- check cache files for existing data
- if not in cache, fetch stock prices from YF and clean up the raw data
- write cache file if not available
- calculate all returns
- build diagnostics
- return the data to the user
Fetching a single stock price
library(yfR)
# set options for algorithm
my_ticker <- 'GM'
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)
# output is a tibble with data
head(df_yf)
## # A tibble: 6 × 11
## ticker ref_date price_open price_high price_low price_close volume
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 GM 2025-09-08 58.1 58.3 57.4 58.2 6399200
## 2 GM 2025-09-09 58.1 58.2 57.6 58.0 4616400
## 3 GM 2025-09-10 57.9 57.9 56.6 57.3 7207400
## 4 GM 2025-09-11 57.5 58.7 57.5 58.7 5808500
## 5 GM 2025-09-12 59.8 60 58.5 58.5 12020300
## 6 GM 2025-09-15 59.0 59.0 58.3 59.0 4773400
## # ℹ 4 more variables: price_adjusted <dbl>, ret_adjusted_prices <dbl>,
## # ret_closing_prices <dbl>, cumret_adjusted_prices <dbl>
Fetching many stock prices
library(yfR)
library(ggplot2)
my_ticker <- c('TSLA', 'GM', 'MMM')
first_date <- Sys.Date() - 100
last_date <- Sys.Date()
df_yf_multiple <- yf_get(tickers = my_ticker,
first_date = first_date,
last_date = last_date)
p <- ggplot(df_yf_multiple, aes(x = ref_date, y = price_adjusted,
color = ticker)) +
geom_line()
p
Fetching daily/weekly/monthly/yearly price data
library(yfR)
library(ggplot2)
library(dplyr)
my_ticker <- 'GE'
first_date <- '2005-01-01'
last_date <- Sys.Date()
df_dailly <- yf_get(tickers = my_ticker,
first_date, last_date,
freq_data = 'daily') %>%
mutate(freq = 'daily')
df_weekly <- yf_get(tickers = my_ticker,
first_date, last_date,
freq_data = 'weekly') %>%
mutate(freq = 'weekly')
df_monthly <- yf_get(tickers = my_ticker,
first_date, last_date,
freq_data = 'monthly') %>%
mutate(freq = 'monthly')
df_yearly <- yf_get(tickers = my_ticker,
first_date, last_date,
freq_data = 'yearly') %>%
mutate(freq = 'yearly')
# bind it all together for plotting
df_allfreq <- bind_rows(
list(df_dailly, df_weekly, df_monthly, df_yearly)
) %>%
mutate(freq = factor(freq,
levels = c('daily',
'weekly',
'monthly',
'yearly'))) # make sure the order in plot is right
p <- ggplot(df_allfreq, aes(x = ref_date, y = price_adjusted)) +
geom_line() +
facet_grid(freq ~ ticker) +
theme_minimal() +
labs(x = '', y = 'Adjusted Prices')
print(p)
Changing format to wide
library(yfR)
library(ggplot2)
my_ticker <- c('TSLA', 'GM', 'MMM')
first_date <- Sys.Date() - 100
last_date <- Sys.Date()
df_yf_multiple <- yf_get(tickers = my_ticker,
first_date = first_date,
last_date = last_date)
print(df_yf_multiple)
## # A tibble: 207 × 11
## ticker ref_date price_open price_high price_low price_close volume
## * <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 GM 2025-06-30 49.6 49.6 48.9 49.2 9634700
## 2 GM 2025-07-01 48.8 52.1 48.8 52.0 13748000
## 3 GM 2025-07-02 52.0 52.8 52.0 52.7 9461800
## 4 GM 2025-07-03 52.8 54.0 52.7 52.9 6612300
## 5 GM 2025-07-07 52.3 53.3 51.4 51.8 11028600
## 6 GM 2025-07-08 52.2 52.9 52 52.2 7523400
## 7 GM 2025-07-09 52.6 52.8 52.3 52.6 5322600
## 8 GM 2025-07-10 52.4 53.3 52.3 53.2 6397200
## 9 GM 2025-07-11 52.8 53.6 52.6 53.4 7698200
## 10 GM 2025-07-14 53.3 53.7 52.7 53.3 6934000
## # ℹ 197 more rows
## # ℹ 4 more variables: price_adjusted <dbl>, ret_adjusted_prices <dbl>,
## # ret_closing_prices <dbl>, cumret_adjusted_prices <dbl>
l_wide <- yf_convert_to_wide(df_yf_multiple)
names(l_wide)
## [1] "price_open" "price_high" "price_low"
## [4] "price_close" "volume" "price_adjusted"
## [7] "ret_adjusted_prices" "ret_closing_prices" "cumret_adjusted_prices"
prices_wide <- l_wide$price_adjusted
head(prices_wide)
## # A tibble: 6 × 4
## ref_date GM MMM TSLA
## <date> <dbl> <dbl> <dbl>
## 1 2025-06-30 49.1 152. 318.
## 2 2025-07-01 51.9 153. 301.
## 3 2025-07-02 52.5 153. 316.
## 4 2025-07-03 52.8 152. 315.
## 5 2025-07-07 51.7 151. 294.
## 6 2025-07-08 52.1 153. 298.