# 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
symbol <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "SPY" "EFA" "IJS" "EEM" "AGG"
# 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 SPY        0.25
## 2 EFA        0.25
## 3 IJS        0.2 
## 4 EEM        0.2 
## 5 AGG        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          0.0308  
##  2 2013-02-28         -0.000870
##  3 2013-03-28          0.0187  
##  4 2013-04-30          0.0206  
##  5 2013-05-31         -0.00535 
##  6 2013-06-28         -0.0229  
##  7 2013-07-31          0.0412  
##  8 2013-08-30         -0.0255  
##  9 2013-09-30          0.0544  
## 10 2013-10-31          0.0352  
## # … with 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 0.0266 0.0266
# Mean of Portfolio Returns 
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.007624594

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() %>% 
    
    # 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       0.0017  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 0.00762 0.0266
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     0.0262
##  2 2015-01-30     0.0262
##  3 2015-02-27     0.0275
##  4 2015-03-31     0.0276
##  5 2015-04-30     0.0276
##  6 2015-05-29     0.0276
##  7 2015-06-30     0.0274
##  8 2015-07-31     0.0266
##  9 2015-08-31     0.0295
## 10 2015-09-30     0.0283
## # … with 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))