# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

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

Choose your stocks.

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

1 Import stock prices

symbols <- c("COST", "LOW", "HD", "KR")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",
                 from = "2019-01-01")

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"))

asset_returns_tbl
## # A tibble: 124 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 COST  2019-06-28  0.0901
##  2 COST  2019-09-30  0.0886
##  3 COST  2019-12-31  0.0221
##  4 COST  2020-03-31 -0.0283
##  5 COST  2020-06-30  0.0638
##  6 COST  2020-09-30  0.160 
##  7 COST  2020-12-31  0.0873
##  8 COST  2021-03-31 -0.0647
##  9 COST  2021-06-30  0.118 
## 10 COST  2021-09-30  0.129 
## # ℹ 114 more rows

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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "COST" "HD"   "KR"   "LOW"
# weights
weights <- c(0.25, 0.2, 0.2, 0.1)
weights
## [1] 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 COST       0.25
## 2 HD         0.2 
## 3 KR         0.2 
## 4 LOW        0.1

4 Build a portfolio

portfolio_retuns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col   = asset,
                 returns_col  = returns,
                 weights      = w_tbl,
                 rebalance_on = "quarters")

portfolio_retuns_tbl
## # A tibble: 31 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2019-06-28          0.00841 
##  2 2019-09-30          0.0901  
##  3 2019-12-31          0.0284  
##  4 2020-03-31         -0.0609  
##  5 2020-06-30          0.146   
##  6 2020-09-30          0.0839  
##  7 2020-12-31         -0.000895
##  8 2021-03-31          0.0563  
##  9 2021-06-30          0.0550  
## 10 2021-09-30          0.0557  
## # ℹ 21 more rows

5 Plot: Portfolio Histogram and Density

Histogram & Density Plot

portfolio_retuns_tbl %>%
    
    ggplot(mapping = aes(x = portfolio.returns)) +
    geom_histogram(color = "black", fill = "cornflowerblue", binwidth = 0.03) +
    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?

The most common return from this portfolio would be around 0-2% return. The portfolio leans more to a positive return than a loss.