# 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

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()
symbols
## [1] "AGG" "EEM" "EFA" "IJS" "SPY"
#weights
weights <- c(0.25,0.25,0.2,0.2,0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols,weights)
w_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

# ?tq_portfolio

portfolio_returns_tbl <- asset_returns_tbl %>% 
    
    tq_portfolio(assets_col = asset, 
                returns_col = returns,
                weights = w_tbl,
                replace_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 -0.00220
##  3 2013-03-28  0.0127 
##  4 2013-04-30  0.0173 
##  5 2013-05-31 -0.0113 
##  6 2013-06-28 -0.0233 
##  7 2013-07-31  0.0342 
##  8 2013-08-30 -0.0231 
##  9 2013-09-30  0.0513 
## 10 2013-10-31  0.0305 
## # ℹ 50 more rows

Calculate Standard Deviation

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

portfolio_skew_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1   -0.221
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <-
    mean(portfolio_returns_tbl$returns)

portfolio_skew_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1   -0.221

##6 Plot

## Calculate sd of portfolio returns
sd_portfolio <- sd(portfolio_returns_tbl$returns)

mean_portfolio <- mean(portfolio_returns_tbl$returns)

portfolio_returns_tbl %>%

#Add a new variable
mutate(extreme_neg = ifelse(returns < mean_portfolio - 2 * sd_portfolio, "ext_neg",
                            "not_ext_neg")) %>%
    
    
    ggplot(aes(x = returns, fill = extreme_neg)) +
    geom_histogram(binwidth = 0.003)

scale_x_continuous(breaks = seq(-0.06,0.06,0.02))
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1
labs(x = "monthly returns")
## $x
## [1] "monthly returns"
## 
## attr(,"class")
## [1] "labels"

Scatterplot of Skewness Comparison

# Data Transformation: Caluculate Skewness
    asset_skewness_tbl <- asset_returns_tbl %>%
    group_by(asset) %>%
    summarise(skew = skewness(returns)) %>%
    ungroup() 

#Add Portfolio Skewness
add_row(tibble(asset= "Portfolio",
               skew = skewness(portfolio_returns_tbl$returns)))
## # A tibble: 2 × 2
##   asset       skew
##   <chr>      <dbl>
## 1 Portfolio -0.221
## 2 <NA>      NA
#Plot Skewness
rolling_skew_tbl <- asset_skewness_tbl %>%
    
    ggplot(aes(x = asset, y = skew, color = asset)) +
    geom_point() +
        ggrepel::geom_text_repel(aes(label = asset),
                                data = asset_skewness_tbl %>%
                                filter(asset == "Portfolio")) +
        labs(y = "skewness")

###Rolling Skewness

# Transform Data : Calculate Rolling Skewness
rolling_skew_tbl <- portfolio_returns_tbl %>%
    
    tq_mutate(select = returns, 
                mutate_fun = rollapply,
                width      = 24,
                FUN        = skewness,
                col_rename = "Skew") %>%
                  
                select(-returns) %>%
                 na.omit() 

    #Plot
    rolling_skew_tbl %>%
    ggplot(aes(x = date, y = Skew)) +
    geom_line(color = "cornflowerblue") +
    
    geom_hline(yintercept = 0, linetype = "dotted", size = 2) +
    
    #Formatting
    scale_y_continuous(limits = c(-1,1), breaks = seq(-1,1,0.2)) +
        theme(plot.title = element_text(hjust = 0.5)) +
        
        annotate(geom = "text",
                 x = as.Date("2016-07-01"), y = 0.8,
                             color = "red", size = 5,
                             label = str_glue("The 24 month rolling skewness is positive for about half of the lifetime even though the overall skewness is negative"))

    #Labelling
    labs(y = "Skewness",
         x = NULL,
         title = "Rolling 24-Month Skewness")
## $y
## [1] "Skewness"
## 
## $x
## NULL
## 
## $title
## [1] "Rolling 24-Month Skewness"
## 
## attr(,"class")
## [1] "labels"