# 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

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

prices <- tq_get(x = symbol,
                 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()

3 Assign a weight to each asset

symbols <- asset_returns_tbl %>% distinct(symbol) %>% pull()
symbols
## [1] "AGG" "EEM" "EFA" "IJS" "SPY"
weight <- c(0.25,0.25,0.2,0.2,0.1)
weight
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weight)

4 Build a portfolio

# ?tq_portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    tq_portfolio(assets_col = symbol,
                 returns_col = monthly.returns,
                 weights = w_tbl,
                 rebalance_on = "months", 
                 col_rename = "returns")

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_tq_builtin_percent <- portfolio_returns_tbl %>%
    
    tq_performance(Ra = returns,
                   performance_fun = table.Stats) %>%
    
    select(Skewness) 

portfolio_skew_tq_builtin_percent
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1   -0.168

6 Plot

Distrubution of Portfolio Returns

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

Expected Return vs Downside Risk

Rolling 24-Month Kurtosis

window = 24

# Transform Data: Calculate 24-Month Rolling Kurtosis 

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")+
    
    #formatting
    scale_y_continuous(breaks = seq(-1, 4, 0.5)) +
    scale_x_date(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("Risk greatly rose throughout 2016 toward the end of 2017"))