# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Collect individual returns into a portfolio by assigning a weight to each stock

Choose your stocks.

I looked at the fast food chains McDonald’s(MCD), Wendy’s(WEN), Taco Bell(YUM), Domino’s Pizza(DPZ), and Starbucks(SBUX).

from 2012-12-31 to 2017-12-31

1 Import stock prices

symbols <- c("MCD", "WEN", "YUM", "DPZ", "SBUX")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",
                 from = "2015-12-31",
                 to   = "2020-12-31")

2 Convert prices to returns (quarterly)

asset_returns_tbl <- prices %>%
    
    group_by(symbol) %>%
    
    tq_transmute(select     = adjusted,
                 mutate_fun = periodReturn,
                 period     = "quarterly",
                 type       = "log") %>%
    
    slice(-1) %>%
    
    ungroup() %>%

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

3 Assign a weight to each asset (change the weigting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "DPZ"  "MCD"  "SBUX" "WEN"  "YUM"
# weights
weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
weights
## [1] 0.2 0.2 0.2 0.2 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 DPZ         0.2
## 2 MCD         0.2
## 3 SBUX        0.2
## 4 WEN         0.2
## 5 YUM         0.2

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 = "quarters")

portfolio_returns_tbl
## # A tibble: 20 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2016-03-31           0.0756 
##  2 2016-06-30          -0.0354 
##  3 2016-09-30           0.0561 
##  4 2016-12-30           0.0692 
##  5 2017-03-31           0.0598 
##  6 2017-06-30           0.120  
##  7 2017-09-29          -0.0204 
##  8 2017-12-29           0.0585 
##  9 2018-03-29           0.0514 
## 10 2018-06-29          -0.0123 
## 11 2018-09-28           0.0866 
## 12 2018-12-31          -0.00966
## 13 2019-03-29           0.0988 
## 14 2019-06-28           0.100  
## 15 2019-09-30           0.00446
## 16 2019-12-31           0.0210 
## 17 2020-03-31          -0.227  
## 18 2020-06-30           0.199  
## 19 2020-09-30           0.113  
## 20 2020-12-30           0.0519

5 Plot: Portfolio Histogram and Density

portfolio_returns_tbl %>%
    ggplot(mapping = aes(x = portfolio.returns)) +
    geom_histogram(fill = "violet", binwidth = 0.01) +
    geom_density() +
    
    # Formatting
    scale_x_continuous(labels = scales::percent_format()) +
    
    labs(x = "returns",
         y = "distribution",
         title = "Portfolio Histogram & Density")

What return should you expect from the portfolio in a typical quarter?