# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Collect individual returns into a portfolio by assigning a weight to each stock

five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG”

from 2012-12-31 to 2017-12-31

1 Import stock prices

# Choose stocks

symbols <- c("SPY", "EFA", "IJS", "EEM", "AGG")


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") %>%
    slice(-1) %>%
    ungroup() %>%
    
    set_names(c("asset", "date", "returns"))

3 Assign a weight to each asset

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()

weights <- c (.25, .25, .2, .2, .1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
weights_tbl <- tibble(symbols, weights)
weights_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AGG        0.25
## 2 EEM        0.25
## 3 EFA        0.2 
## 4 IJS        0.2 
## 5 SPY        0.1

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col   = asset,
                 returns_col  = returns,
                 weights      = weights_tbl,
                 col_rename   = "returns",
                 rebalance_on = "months")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0204 
##  2 2013-02-28 -0.00239
##  3 2013-03-28  0.0121 
##  4 2013-04-30  0.0174 
##  5 2013-05-31 -0.0128 
##  6 2013-06-28 -0.0247 
##  7 2013-07-31  0.0321 
##  8 2013-08-30 -0.0224 
##  9 2013-09-30  0.0511 
## 10 2013-10-31  0.0301 
## # ℹ 50 more rows

5 Calculate Kurtosis

portfolio_skew_tidyquant_building_percent <- portfolio_returns_tbl  %>%

    tq_performance(Ra = returns,
                   performance_fun = table.Stats) %>%
    select(Kurtosis)
portfolio_skew_tidyquant_building_percent
## # A tibble: 1 × 1
##   Kurtosis
##      <dbl>
## 1    0.488

6 plot

Distibution of portfolio returns

portfolio_returns_tbl %>%
    
    ggplot(aes(x = returns)) +
    geom_histogram()

excpected returns vs Downside risk

 #transform data
 mean_kurt_tbl<-asset_returns_tbl %>%
    
    
    #calculate Mean return
    group_by(asset) %>%
    summarise(mean = mean(returns),
              kurt = kurtosis(returns)) %>%
    ungroup()
 #add portfolio stats
add_row(portfolio_returns_tbl %>%
    summarise(mean = mean(returns),
              kurt = kurtosis(returns)) %>%
    mutate(asset = "portfolio"))
## # A tibble: 2 × 3
##       mean   kurt asset    
##      <dbl>  <dbl> <chr>    
## 1  0.00590  0.488 portfolio
## 2 NA       NA     <NA>
 #plot
mean_kurt_tbl %>%
    
    ggplot(aes(x = kurt, y = mean)) +
    geom_point() +
    ggrepel::geom_text_repel(aes(label = asset, color = asset)) +
    
    #formatting
    theme(legend.position = "none") +
    scale_y_continuous(labels = scales :: percent_format(accuracy = 0.1))+
    #labeling
    labs(x = "kurtosis",
         y = "Expected Returns")

### Rolling 24 month Kurtosis

 #assign a value for window
window = 12



 #transform data:: calc 24 month rolling kurt
rolling_kurt_tbl <- portfolio_returns_tbl %>%
    
    tq_mutate(select     = returns,
              mutate_fun = rollapply,
              width      = window,
              FUN        = kurtosis,
              col_rename = "kurt") %>%
    na.omit() %>%
    select(-returns)

 #plot
rolling_kurt_tbl %>%
    
    ggplot(aes(x = date, y = kurt)) +
    geom_line(color = "cornflowerblue") +
    
    #formating
    scale_y_continuous(breaks = seq(-1,4,0.5)) +
    scale_x_continuous(breaks = scales::pretty_breaks(n = 7)) +
    theme(plot.title = element_text(hjust = 0.5))

    #Labeling
    labs(x = NULL,
         y = "Kurtosis",
         title = paste0("Rolling", window, "Month Kurtosis")) +
        annotate(geom = "text",
                 x = as.Date("2016-07-01"),y = 3,
                 size = 5, color = "red" , 
                 label = str_glue("downside Risk skyrocketed 
                                  toward the end of 2017"))
## NULL