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 2025-07-18 53.4 53.7 52.9 53.2 6497300
## 2 GM 2025-07-21 53.3 54.3 53.2 53.2 13055500
## 3 GM 2025-07-22 51.7 52.1 48.9 48.9 33792400
## 4 GM 2025-07-23 49.9 53.3 49.9 53.1 25657000
## 5 GM 2025-07-24 53.0 53.4 52.2 52.3 10219800
## 6 GM 2025-07-25 52.9 53.5 52.1 53.4 6491300
## # 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: 204 × 11
## ticker ref_date price_open price_high price_low price_close volume
## * <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 GM 2025-05-09 47.6 48.0 47.2 47.5 8582900
## 2 GM 2025-05-12 49.7 50.1 48.9 49.6 13160600
## 3 GM 2025-05-13 49.6 50.6 49.4 50.5 10427600
## 4 GM 2025-05-14 50.4 50.8 50.1 50.3 8949300
## 5 GM 2025-05-15 49.8 50.2 49.6 50.1 7286600
## 6 GM 2025-05-16 50.1 50.4 49.8 50.4 6099800
## 7 GM 2025-05-19 49.7 50.3 49.2 50.0 9406500
## 8 GM 2025-05-20 50.3 50.5 49.9 50.2 6565400
## 9 GM 2025-05-21 49.9 50.5 49.3 49.4 7515600
## 10 GM 2025-05-22 49.3 49.7 48.9 49.3 6378200
## # ℹ 194 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 2025-05-09 47.3 142. 298.
## 2 2025-05-12 49.5 150. 318.
## 3 2025-05-13 50.3 150. 334.
## 4 2025-05-14 50.2 147. 348.
## 5 2025-05-15 50.0 148. 343.
## 6 2025-05-16 50.2 152. 350.