# 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)*100)

portfolio_sd_tidyquant_builtin_percent
## # A tibble: 1 × 2
##    Stdev tq_sd
##    <dbl> <dbl>
## 1 0.0235  2.35
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- 
mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.005899134

6 Plot

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 * 100,
                 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 0.590  2.35
sd_mean_tbl %>%
  
  ggplot(aes(x = Stdev, y = Mean, color = asset)) +
  geom_point() + 
  ggrepel::geom_text_repel(aes(label = asset))

### 24 Monlths 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(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))