# 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("TSLA", "NKE", "JPM", "NVDA", "AAPL")

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 = "monthly",
                 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" "JPM"  "NKE"  "NVDA" "TSLA"
# 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 AAPL       0.25
## 2 JPM        0.25
## 3 NKE        0.2 
## 4 NVDA       0.2 
## 5 TSLA       0.1

4 Build a 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         -0.000775
##  2 2013-02-28          0.00586 
##  3 2013-03-28          0.0203  
##  4 2013-04-30          0.0742  
##  5 2013-05-31          0.0973  
##  6 2013-06-28         -0.0301  
##  7 2013-07-31          0.0740  
##  8 2013-08-30          0.0242  
##  9 2013-09-30          0.0535  
## 10 2013-10-31          0.00873 
## # ℹ 50 more rows

5 Plot: Portfolio Histogram and Density

portfolio_returns_tbl %>%
    
    ggplot(mapping = aes(x = portfolio.returns)) +
    geom_histogram(fill = "cornflowerblue", 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?

The bulk of the monthly returns are centered around 0% to 3%, with the peak of the curve being just above 2% per month. This means that in a typical month, you can expect the portfolio to give you a small positive return, though there is still a chance of losses (as low as -10%) or higher gains (up to about +10%). The portfolio usually grows a little each month, but it’s not risk-free.