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_h…¹ price…² price…³ volume price…⁴ ret_ad…⁵
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 GM 2023-01-03 34.0 34.3 33.4 33.8 1.18e7 33.8 NA
## 2 GM 2023-01-04 34.3 35.0 34.1 34.7 1.13e7 34.7 2.57e-2
## 3 GM 2023-01-05 34.2 35.4 34.1 35 1.19e7 35 8.94e-3
## 4 GM 2023-01-06 34.7 36.0 34.5 35.9 9.78e6 35.9 2.60e-2
## 5 GM 2023-01-09 36.5 36.8 35.8 35.9 1.16e7 35.9 2.78e-4
## 6 GM 2023-01-10 36.1 37.1 35.9 37.1 1.03e7 37.1 3.31e-2
## # … with 2 more variables: ret_closing_prices <dbl>,
## # cumret_adjusted_prices <dbl>, and abbreviated variable names ¹price_high,
## # ²price_low, ³price_close, ⁴price_adjusted, ⁵ret_adjusted_prices
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: 198 × 11
## ticker ref_date price_open price_…¹ price…² price…³ volume price…⁴ ret_ad…⁵
## * <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 GM 2022-10-24 34.9 35.9 34.5 35.7 1.86e7 35.6 NA
## 2 GM 2022-10-25 36.5 37.5 35.8 37.0 2.55e7 36.9 3.61e-2
## 3 GM 2022-10-26 37.4 38.3 37.2 37.9 1.96e7 37.8 2.30e-2
## 4 GM 2022-10-27 38.0 38.6 37.4 38.2 1.38e7 38.1 7.92e-3
## 5 GM 2022-10-28 38.2 38.9 38.1 38.8 1.05e7 38.8 1.81e-2
## 6 GM 2022-10-31 38.5 39.7 38.4 39.2 1.40e7 39.2 1.03e-2
## 7 GM 2022-11-01 39.9 40.1 38.8 39.3 1.08e7 39.3 2.55e-3
## 8 GM 2022-11-02 39.2 40.1 38.5 38.5 1.33e7 38.4 -2.11e-2
## 9 GM 2022-11-03 37.8 38.7 37.7 38.5 1.21e7 38.4 -2.60e-4
## 10 GM 2022-11-04 39.4 39.7 38.5 39 1.51e7 38.9 1.27e-2
## # … with 188 more rows, 2 more variables: ret_closing_prices <dbl>,
## # cumret_adjusted_prices <dbl>, and abbreviated variable names ¹price_high,
## # ²price_low, ³price_close, ⁴price_adjusted, ⁵ret_adjusted_prices
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 2022-10-24 35.6 117. 211.
## 2 2022-10-25 36.9 117. 222.
## 3 2022-10-26 37.8 121. 225.
## 4 2022-10-27 38.1 121. 225.
## 5 2022-10-28 38.8 125. 229.
## 6 2022-10-31 39.2 124. 228.