# 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, 
                rebalance_on = "months")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.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 Standard Deviation

portfolio_sd_tidyquant_builtin_percent <- portfolio_returns_tbl %>%
    
    tq_performance(Ra = portfolio.returns,
                   performance_fun = table.Stats) %>%
    
    select(Stdev) %>%
    mutate(tq_sd = round(Stdev, 4))

portfolio_sd_tidyquant_builtin_percent
## # A tibble: 1 × 2
##    Stdev  tq_sd
##    <dbl>  <dbl>
## 1 0.0235 0.0235
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.005899136

6 Plot

Expected Returns vs Risk

# Expected Returns vs Risk
sd_mean_tbl <- asset_returns_tbl %>%
    
    group_by(asset) %>%
    tq_performance(Ra = returns, 
                   performance_fun = table.Stats) %>%
    select(Mean = ArithmeticMean, Stdev) %>%
    ungroup() %>%
    mutate(Stdev = Stdev * 100,
           Mean = Mean   * 100) %>%
    
    # Add portfolio sd
    add_row(tibble(asset = "Portfolio", 
                   Mean = portfolio_mean_tidyquant_builtin_percent, 
                   Stdev = portfolio_sd_tidyquant_builtin_percent$tq_sd))

sd_mean_tbl
## # A tibble: 6 × 3
##   asset        Mean  Stdev
##   <chr>       <dbl>  <dbl>
## 1 AGG       0.17    0.86  
## 2 EEM       0.28    4.19  
## 3 EFA       0.6     3.26  
## 4 IJS       1.19    3.96  
## 5 SPY       1.21    2.72  
## 6 Portfolio 0.00590 0.0235
sd_mean_tbl %>%
    ggplot(aes(x = Stdev, y = Mean, color = asset)) +
    geom_point() +
    # ggrepel::geom_text_repel(aes(label = asset))+ # This line no longer works!
    # Use the standard geom_text() and nudge the labels slightly instead:
    geom_text(aes(label = asset),
              vjust = 1.5,   # Nudges labels down
              hjust = 0.5,   # Centers labels horizontally
              size = 4)      # Sets text size

24 Months Rolling Volatility

rolling_sd_tbl <- portfolio_returns_tbl %>%
    
    tq_mutate(select     = portfolio.returns,
              mutate_fun = rollapply,
              width      = 24,
              FUN        = sd,
              col_rename = "rolling_sd") %>%
    
    na.omit() %>%
    select(date, rolling_sd)

rolling_sd_tbl
## # A tibble: 37 × 2
##    date       rolling_sd
##    <date>          <dbl>
##  1 2014-12-31     0.0237
##  2 2015-01-30     0.0236
##  3 2015-02-27     0.0245
##  4 2015-03-31     0.0246
##  5 2015-04-30     0.0247
##  6 2015-05-29     0.0245
##  7 2015-06-30     0.0242
##  8 2015-07-31     0.0238
##  9 2015-08-31     0.0262
## 10 2015-09-30     0.0247
## # ℹ 27 more rows
rolling_sd_tbl %>%
    
    ggplot(aes(date, rolling_sd)) +
    geom_line(color = "cornflowerblue") +
    
    scale_y_continuous(labels = scales::percent) +
    scale_x_date(breaks = scales::breaks_pretty(n=7))+
    
    labs(title = "24-Month ROlling Volatility",
         x = NULL,
         y = NULL,) +
    theme(plot.title = element_text(hjust = 0.5))