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# Core
library(tidyverse)
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library(tidyquant)
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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("NKE", "ADS", "UA", "AMZN", "MSFT")

prices <- tq_get(x = symbols, 
                 get = "stock.prices", 
                 from = "2012-01-01",
                 to = "2017-01-01")
## Warning: There was 1 warning in `dplyr::mutate()`.
## ℹ In argument: `data.. = purrr::map(...)`.
## Caused by warning:
## ! x = 'ADS', get = 'stock.prices': Error in getSymbols.yahoo(Symbols = "ADS", env = <environment>, verbose = FALSE, : Unable to import "ADS".
## HTTP error 404.
##  Removing ADS.

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", "data", "returns"))

asset_returns_tbl
## # A tibble: 190 × 3
##    asset data        returns
##    <chr> <date>        <dbl>
##  1 NKE   2012-01-31  0.0715 
##  2 NKE   2012-02-29  0.0371 
##  3 NKE   2012-03-30  0.00815
##  4 NKE   2012-04-30  0.0311 
##  5 NKE   2012-05-31 -0.0302 
##  6 NKE   2012-06-29 -0.209  
##  7 NKE   2012-07-31  0.0615 
##  8 NKE   2012-08-31  0.0457 
##  9 NKE   2012-09-28 -0.0255 
## 10 NKE   2012-10-31 -0.0379 
## # ℹ 180 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, binwith = 0.01)) +
    facet_wrap(~ asset, ncol = 1) +
    
    # labelig
    labs(title= "Distrubution of Monthly Returns, 2012-2016",
         y = "frequency",
         x = "rate of returns ",
         capitation = "A typic montly return is high for spy and IJS than for AGG, EEM, and EFA")
## Warning in geom_histogram(aes(fill = asset, show.legend = FALSE, alpha = 0.3, :
## Ignoring unknown aesthetics: show.legend and binwith
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.