R tidyquant script for workshop

tidyquant-demo.R

# install tidyverse if you don't have it
# install.packages("tidyverse")
library(tidyverse)

## Read the csv from a URL
url <- "http://assets.datacamp.com/course/compfin/sbuxPrices.csv"
df <- read_csv(url)

## lubridate package to format the date
# if you get an error below, are you sure you have lubridate? 
# If not, remember how to install (hint: like line 2).
library(lubridate)
df$Date <- mdy(df$Date)

## ggplot2

ggplot(df, aes(x = Date, y = `Adj Close`)) +
  geom_line()

## ggplot is based on layers, for example, to add labels

ggplot(df, aes(x = Date, y = `Adj Close`)) +
  geom_line() +
  labs(title = "Starbucks Stock Prices per Month",
       x = "Month",
       y = "ME Adjusted Stock Price")

## https://www.rstudio.com/wp-content/uploads/2016/11/ggplot2-cheatsheet-2.1.pdf

## tidyquant
# see https://business-science.github.io/tidyquant/
library(tidyquant)

## getting data
# see https://business-science.github.io/tidyquant/articles/TQ01-core-functions-in-tidyquant.html

tq_index_options()

# let's get the SP 500
tq_index("SP500")

## get quantitative data
tq_get_options()

# stocks
aapl_prices  <- tq_get("AAPL", get = "stock.prices", from = " 1990-01-01")

ggplot(aapl_prices, aes(x = date, y = close)) +
  geom_line() +
  labs(title = "AAPL Stock Price", x = "Date", y = "Close Price")

# dividends
aapl_divs <- tq_get("AAPL", get = "dividends", from = "1990-01-01")
aapl_divs

# splits
aapl_splits <- tq_get("AAPL", get = "splits", from = "1990-01-01")
aapl_splits

# financials
aapl_financials <- tq_get("AAPL", get = "financials")
aapl_financials

# piping %>% combines functions
aapl_financials %>%
  filter(type == "IS") %>%
  select(annual) %>%
  unnest()

# economic data
wti_price_usd <- tq_get("DCOILWTICO", get = "economic.data")
wti_price_usd 

# fx
eur_usd <- tq_get("EUR/USD", 
                  get = "exchange.rates", 
                  from = Sys.Date() - lubridate::days(10))
eur_usd 

## manipulation
# https://business-science.github.io/tidyquant/articles/TQ02-quant-integrations-in-tidyquant.html
# tq_transmute: Returns a new tidy data frame typically in a different periodicity than the input.

# FANG = FB, AMZN, NFLX, and GOOG from the beginning of 2013 to the end of 2016.
FANG_annual_returns <- FANG %>%
  group_by(symbol) %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "yearly", 
               type       = "arithmetic")


# with piping and ggplot, we can build out a very sophisticated plot
FANG_annual_returns %>%
  ggplot(aes(x = date, y = yearly.returns, fill = symbol)) +
  geom_bar(stat = "identity") +
  facet_wrap(~ symbol, ncol = 2)

# or we can be much more advanced
FANG_annual_returns %>%
  ggplot(aes(x = date, y = yearly.returns, fill = symbol)) +
  geom_bar(stat = "identity") +
  geom_hline(yintercept = 0, color = palette_light()[[1]]) +
  scale_y_continuous(labels = scales::percent) +
  labs(title = "FANG: Annual Returns",
       subtitle = "Get annual returns quickly with tq_transmute!",
       y = "Annual Returns", x = "") + 
  facet_wrap(~ symbol, ncol = 2) +
  theme_tq() + 
  scale_fill_tq()

# see https://business-science.github.io/tidyquant/articles/TQ02-quant-integrations-in-tidyquant.html

# See the other parts of tidyquant for deeper analysis
# https://business-science.github.io/tidyquant/articles/TQ05-performance-analysis-with-tidyquant.html