# Load packages

# Core
library(tidyverse)
library(tidyquant)
library(ggrepel)

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)

4 Build a portfolio

# ?tq_portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col = asset,
                 returns_col = returns,
                 weigts = w_tbl,
                 rebalance_on = "months")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31          0.0259  
##  2 2013-02-28         -0.000266
##  3 2013-03-28          0.0162  
##  4 2013-04-30          0.0182  
##  5 2013-05-31         -0.00701 
##  6 2013-06-28         -0.0225  
##  7 2013-07-31          0.0363  
##  8 2013-08-30         -0.0238  
##  9 2013-09-30          0.0502  
## 10 2013-10-31          0.0321  
## # 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 0.024   2.4
# Mean of Portfolio Returns 
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.006889301

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_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 0.00689 2.4
sd_mean_tbl %>%
    
    ggplot(aes(x = Stdev, y = Mean)) +
    geom_point() +
    ggrepel::geom_text_repel(aes(label = asset))

24 Month Rolling Volitility

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 %>%
    
    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 Volitility") +
    theme(plot.title = element_text(hjust = 0.5))