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Getting Started

Marcelo Perlin

2022-09-23

Source: vignettes/getting-started.Rmd
getting-started.Rmd

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:

  1. check cache files for existing data
  2. if not in cache, fetch stock prices from YF and clean up the raw data
  3. write cache file if not available
  4. calculate all returns
  5. build diagnostics
  6. 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     2022-08-24       38.6      39.7    38.3    39.2 1.27e7    39.2 NA      
## 2 GM     2022-08-25       39.4      40.8    39.3    40.4 1.52e7    40.3  0.0290 
## 3 GM     2022-08-26       40.8      41.1    39.2    39.2 1.62e7    39.1 -0.0287 
## 4 GM     2022-08-29       38.8      40.0    38.7    39.5 1.34e7    39.4  0.00714
## 5 GM     2022-08-30       39.8      39.9    38.6    39.1 1.51e7    39.1 -0.00736
## 6 GM     2022-08-31       39.2      39.2    38.1    38.2 1.54e7    38.2 -0.0235 
## # … with 2 more variables: ret_closing_prices <dbl>,
## #   cumret_adjusted_prices <dbl>, and abbreviated variable names ¹​price_high,
## #   ²​price_low, ³​price_close, ⁴​price_adjusted, ⁵​ret_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     2022-06-15       33.4     34.6    33.3    34.2 1.91e7    34.1 NA      
##  2 GM     2022-06-16       32.8     32.8    30.9    31.4 2.28e7    31.3 -0.0807 
##  3 GM     2022-06-17       31.5     32.4    30.6    31.9 2.36e7    31.8  0.0146 
##  4 GM     2022-06-21       32.8     33.1    31.9    32.6 1.66e7    32.6  0.0238 
##  5 GM     2022-06-22       32.2     33.5    32.0    32.8 1.50e7    32.8  0.00582
##  6 GM     2022-06-23       32.9     33.3    32.2    33.0 1.44e7    32.9  0.00487
##  7 GM     2022-06-24       33.1     35      33.1    34.8 1.84e7    34.7  0.0555 
##  8 GM     2022-06-27       35.0     35.3    34.3    34.6 1.04e7    34.6 -0.00546
##  9 GM     2022-06-28       35.2     36.1    34.0    34.0 1.52e7    33.9 -0.0185 
## 10 GM     2022-06-29       33.8     33.9    32.7    33.5 1.14e7    33.4 -0.0159 
## # … with 197 more rows, 2 more variables: ret_closing_prices <dbl>,
## #   cumret_adjusted_prices <dbl>, and abbreviated variable names ¹​price_high,
## #   ²​price_low, ³​price_close, ⁴​price_adjusted, ⁵​ret_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 2022-06-15  34.1  133.  233 
## 2 2022-06-16  31.3  130.  213.
## 3 2022-06-17  31.8  129.  217.
## 4 2022-06-21  32.6  129.  237.
## 5 2022-06-22  32.8  129.  236.
## 6 2022-06-23  32.9  129.  235.