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 2025-03-14 47.8 48.8 47.6 48.3 14143900
## 2 GM 2025-03-17 48.5 49.3 48.3 49 14102400
## 3 GM 2025-03-18 49.3 49.8 48.2 48.7 19265400
## 4 GM 2025-03-19 48.8 50.2 48.7 49.8 13291600
## 5 GM 2025-03-20 48.8 50.0 48.8 49.4 12906900
## 6 GM 2025-03-21 49.0 50.0 48.7 49.8 37624900
## # 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 2025-01-03 51.5 52.0 50.5 51.8 8949900
## 2 GM 2025-01-06 52.8 54.4 52.2 53.5 9728200
## 3 GM 2025-01-07 53.7 53.7 51.9 52.0 9093300
## 4 GM 2025-01-08 51.8 51.8 50.3 51 7243700
## 5 GM 2025-01-10 50.8 50.8 49.8 49.8 7847700
## 6 GM 2025-01-13 49.0 50.1 49.0 49.9 7950900
## 7 GM 2025-01-14 50.4 51.4 50.2 50.9 7170100
## 8 GM 2025-01-15 52.2 53.0 51.6 51.7 7518900
## 9 GM 2025-01-16 51.7 52.0 51.2 51.8 6415400
## 10 GM 2025-01-17 51.9 52.2 50.7 51.0 9620000
## # 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 2025-01-03 51.6 129. 410.
## 2 2025-01-06 53.4 130. 411.
## 3 2025-01-07 51.8 132. 394.
## 4 2025-01-08 50.9 134. 395.
## 5 2025-01-10 49.7 131. 395.
## 6 2025-01-13 49.8 134. 403.