# 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

# Choose stocks
symbols <- c("TSLA", "AMZN", "AAPL", "NVDA", "PG")

prices <- tq_get(x    = symbols, 
                 get. = "stock.prices",
                 from = "2012-12-31", 
                 to   = "2017-12-31")

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] "AAPL" "AMZN" "NVDA" "PG"   "TSLA"
# 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 AAPL        0.2
## 2 AMZN        0.2
## 3 NVDA        0.2
## 4 PG          0.2
## 5 TSLA        0.2

4 Build a 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 2013-03-28           0.0361 
##  2 2013-06-28           0.216  
##  3 2013-09-30           0.199  
##  4 2013-12-31           0.0555 
##  5 2014-03-31           0.0463 
##  6 2014-06-30           0.0652 
##  7 2014-09-30           0.0321 
##  8 2014-12-31           0.0299 
##  9 2015-03-31           0.0178 
## 10 2015-06-30           0.0888 
## 11 2015-09-30           0.0192 
## 12 2015-12-31           0.121  
## 13 2016-03-31          -0.00141
## 14 2016-06-30           0.0598 
## 15 2016-09-30           0.147  
## 16 2016-12-30           0.0705 
## 17 2017-03-31           0.149  
## 18 2017-06-30           0.124  
## 19 2017-09-29           0.0541 
## 20 2017-12-29           0.0598

5 Plot: Portfolio Histogram and Density

portfolio_returns_tbl %>%
    
    ggplot(mapping = aes(x = portfolio.returns)) +
    geom_histogram(fill     = "cornflowerblue", 
                   binwidth = 0.02) + 
    
    geom_density(bw = 0.015) + # Adjusting bindwidth
    
    # 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?

expected_return <- mean(portfolio_returns_tbl$portfolio.returns)
expected_return
## [1] 0.07946315
expected_return <- median(portfolio_returns_tbl$portfolio.returns)
expected_return
## [1] 0.05978888

Answer: In a typical quarter, I should expect a 0.05978889 (ca. 0.06) return. Based on the median function as the mean function sometimes exaggerate the typical value when your data includes extreme outliers.