Getting Started

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     2022-12-07       37.7      38.3    37.6    38.0 1.16e7    38.0 NA      
## 2 GM     2022-12-08       38.1      38.3    37.6    38.2 8.62e6    38.2  0.00632
## 3 GM     2022-12-09       37.8      38.6    37.8    38.3 8.87e6    38.3  0.00209
## 4 GM     2022-12-12       38.3      38.7    38.0    38.5 1.39e7    38.5  0.00549
## 5 GM     2022-12-13       39.7      40.0    38.5    38.8 1.22e7    38.8  0.00727
## 6 GM     2022-12-14       38.7      39.1    38.0    38.4 1.20e7    38.4 -0.0106 
## # … 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: 207 × 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     2022-09-28       34.8     35.5    34.4    35.2 1.20e7    35.2 NA      
##  2 GM     2022-09-29       34.0     34.4    32.8    33.3 1.48e7    33.2 -0.0565 
##  3 GM     2022-09-30       32.9     33.2    32.0    32.1 1.62e7    32.0 -0.0352 
##  4 GM     2022-10-03       32.5     33.3    31.6    32.9 1.47e7    32.8  0.0243 
##  5 GM     2022-10-04       34.2     35.8    33.9    35.8 1.98e7    35.7  0.0891 
##  6 GM     2022-10-05       34.4     35.0    33.8    34.8 1.87e7    34.8 -0.0268 
##  7 GM     2022-10-06       34.6     35.2    34.4    34.6 1.14e7    34.6 -0.00603
##  8 GM     2022-10-07       34.1     34.3    33.4    33.6 1.12e7    33.5 -0.0292 
##  9 GM     2022-10-10       32.4     32.6    31.1    32.3 2.30e7    32.2 -0.0396 
## 10 GM     2022-10-11       32.0     32.9    31.3    32.1 1.68e7    32.0 -0.00619
## # … with 197 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 2022-09-28  35.2  113.  288.
## 2 2022-09-29  33.2  111.  268.
## 3 2022-09-30  32.0  109.  265.
## 4 2022-10-03  32.8  112.  242.
## 5 2022-10-04  35.7  114.  249.
## 6 2022-10-05  34.8  114.  241.

Getting Started

Marcelo Perlin

2023-01-06

Examples

Fetching a single stock price

Fetching many stock prices

Fetching daily/weekly/monthly/yearly price data

Changing format to wide