# 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")

# Using tq_get() ----
prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2020-12-31",
                 to = "2023-12-31")

2 Convert prices to returns

asset_returns_tbl <- prices %>%

    # Calculate monthly returns
    group_by(symbol) %>%
    tq_transmute(select = adjusted,
                 mutate_fun = periodReturn,
                 period = "monthly",
                 type = "log") %>%
    slice(-1) %>%
    ungroup() %>%

    # remane
    set_names(c("asset", "date", "returns"))

# period_returns = c("yearly", "quarterly", "monthly", "weekly")

3 Assign a weight to each asset

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

w <- c(0.25,
       0.25,
       0.20,
       0.20,
       0.10)

w_tbl <- tibble(symbols, w)

4 Build a portfolio

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

portfolio_returns_rebalanced_monthly_tbl
## # A tibble: 36 × 2
##    date        returns
##    <date>        <dbl>
##  1 2021-01-29  0.0155 
##  2 2021-02-26  0.0258 
##  3 2021-03-31  0.0156 
##  4 2021-04-30  0.0191 
##  5 2021-05-28  0.0197 
##  6 2021-06-30  0.00304
##  7 2021-07-30 -0.0188 
##  8 2021-08-31  0.0128 
##  9 2021-09-30 -0.0265 
## 10 2021-10-29  0.0213 
## # ℹ 26 more rows
# write_rds(portfolio_returns_rebalanced_monthly_tbl,
#           "00_data/Ch03_portfolio_returns_rebalanced_monthly_tbl.rds")

5 Compute Standard Deviation

portfolio_sd_tidyquant_builtin_percent <- portfolio_returns_rebalanced_monthly_tbl %>%
    
    tq_performance(Ra = returns,
                   Rb = NULL, 
                   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 0.0408 0.0408
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- 
mean(portfolio_returns_rebalanced_monthly_tbl)

portfolio_mean_tidyquant_builtin_percent
## [1] NA

6 Plot

#Expected Return vs Risk
sd_mean_tbl <- asset_returns_tbl %>%
    
    group_by(asset) %>%
    tq_performance(Ra = returns,
                   performance_fun = table.Stats) %>%
    select(Mean = ArithmeticMean, Stdev) %>%
    ungroup() %>%
    
    
    # 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       -0.0028 0.0211
## 2 EEM       -0.005  0.0509
## 3 EFA        0.0034 0.05  
## 4 IJS        0.0079 0.0649
## 5 SPY        0.0079 0.0508
## 6 Portfolio NA      0.0408
sd_mean_tbl %>%
    
    ggplot(aes(x = Stdev, y = Mean, color = asset)) +
    geom_point()

24 Months Rolling Volatility

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

rolling_sd_tbl
## # A tibble: 13 × 2
##    date       rolling_sd
##    <date>          <dbl>
##  1 2022-12-30     0.0387
##  2 2023-01-31     0.0419
##  3 2023-02-28     0.0421
##  4 2023-03-31     0.0421
##  5 2023-04-28     0.0418
##  6 2023-05-31     0.0417
##  7 2023-06-30     0.0427
##  8 2023-07-31     0.0434
##  9 2023-08-31     0.0439
## 10 2023-09-29     0.0442
## 11 2023-10-31     0.0442
## 12 2023-11-30     0.0469
## 13 2023-12-29     0.0482
rolling_sd_tbl %>%
    
    ggplot(aes(x = date, y = rolling_sd)) +
    geom_line(color = "cornflowerblue") +

# Formatting
    scale_y_continuous(labels = scales::percent_format()) +
        
# Labeling
    labs(x = NULL,
         y = NULL,
         title = "24-Month Rolling Volatility") +
    theme(plot.title = element_text(hjust = 0.5))