# 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,
                 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,
               rebalence_on = "months")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31           0.0204 
##  2 2013-02-28          -0.00220
##  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

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_builitin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builitin_percent
## [1] 0.006173974

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() %>%
  
  # Add portfolio sd
  add_row(tibble(asset = "Portfolio",
                 Mean  = portfolio_mean_tidyquant_builitin_percent,
                 Stdev = portfolio_sd_tidyquant_builtin_percent$tq_sd))
sd_mean_tbl
## # A tibble: 6 × 3
##   asset        Mean  Stdev
##   <chr>       <dbl>  <dbl>
## 1 AGG       0.0017  0.0086
## 2 EEM       0.0028  0.0419
## 3 EFA       0.006   0.0326
## 4 IJS       0.0119  0.0396
## 5 SPY       0.0121  0.0272
## 6 Portfolio 0.00617 0.0235
sd_mean_tbl %>%
  
  ggplot(aes(x = Stdev, y = Mean, color = asset)) + 
  geom_point() +
  ggrepel::geom_text_repel(aes(label = asset))

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     0.0239
##  2 2015-01-30     0.0241
##  3 2015-02-27     0.0250
##  4 2015-03-31     0.0251
##  5 2015-04-30     0.0251
##  6 2015-05-29     0.0249
##  7 2015-06-30     0.0246
##  8 2015-07-31     0.0241
##  9 2015-08-31     0.0264
## 10 2015-09-30     0.0249
## # 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())
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1
  # Labeling
  labs(x = NULL,
       y = NULL,
       title = "24-Month Rolling Volatility") + 
  theme(plot.title = element_text(hjust = 0.5))
## NULL