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Goal

Take raw prices of five individual stocks and transform them into monthly returns five stocks: “TSLA”, “DELL”, “AAPL”, “SOXL”, “SMCI”

1 Import stock prices

# Choose stocks
symbols <- c("TSLA", "DELL", "AAPL", "SOXL", "SMCI")

prices <- tq_get(x    = symbols, 
                 get  = "stock.prices", 
                 from = "2012-01-01", 
                 to   = "2017-01-01")

2 Convert prices to returns

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

asset_returns_tbl
## # A tibble: 1,064 × 3
##    asset date        returns
##    <chr> <date>        <dbl>
##  1 TSLA  2012-01-06 -0.0426 
##  2 TSLA  2012-01-13 -0.166  
##  3 TSLA  2012-01-20  0.155  
##  4 TSLA  2012-01-27  0.0977 
##  5 TSLA  2012-02-03  0.0602 
##  6 TSLA  2012-02-10 -0.00161
##  7 TSLA  2012-02-17  0.117  
##  8 TSLA  2012-02-24 -0.0355 
##  9 TSLA  2012-03-02  0.00856
## 10 TSLA  2012-03-09  0.0204 
## # ℹ 1,054 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 = 0.0035)  +
    facet_wrap(~asset, ncol = 1) +
    
    # Labeling
    labs(title = "Distribution of Monthly Returns, 2012-2016",
         y = "Frequency",
         x = "Rate of Returns")