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Goal

Take raw prices of five individual stocks and transform them into monthly returns five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG”

1 Import stock prices

#choose stocks 
symbols <- c("COST", "TSLA", "NFLX", "GOOG")

prices <- tq_get(x     = symbols, 
                 get   = "stock.prices", 
                 from  = "2012-12-31", 
                 to    = "2017-12-31")

2 Convert prices to returns

asset_returns_tbl <- prices %>% 
    
    group_by(symbol) %>% 
    tq_transmute(select = adjusted, 
                 mutate_fun = periodReturn, 
                 period = "monthly", 
                 type = "log") %>% 
    ungroup() %>% 
    
    set_names(c("asset", "date", "returns"))

asset_returns_tbl
## # A tibble: 244 × 3
##    asset date        returns
##    <chr> <date>        <dbl>
##  1 COST  2012-12-31  0      
##  2 COST  2013-01-31  0.0359 
##  3 COST  2013-02-28 -0.00765
##  4 COST  2013-03-28  0.0465 
##  5 COST  2013-04-30  0.0216 
##  6 COST  2013-05-31  0.0138 
##  7 COST  2013-06-28  0.00854
##  8 COST  2013-07-31  0.0601 
##  9 COST  2013-08-30 -0.0458 
## 10 COST  2013-09-30  0.0291 
## # ℹ 234 more rows

3 Make plot

asset_returns_tbl %>%

    ggplot(aes(x = returns)) + 
    geom_density(aes(color = asset), show.legend = FALSE, alpha = 1) + 
    geom_histogram(aes(fill = asset), show.legend = FALSE, alpha = 0.3, binwidth = 0.01) + 
    facet_wrap(~asset, ncol = 1) + 

   # labeling 
    labs(title = "Destribution fo Monthly Returns, 2012-2016", 
         y     = "Frequency", 
         x     = "Rate of Returns", 
         caption = "A tipical monthly return is higher for SPY and IJS than for AGG, EEM, and EFA.")