# 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("DELL", "NVDA", "AMZN", "AAPL", "TSLA")
prices <- tq_get(x    = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to   = "2024-12-31")

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" "AMZN" "DELL" "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 AMZN       0.25
## 3 DELL       0.2 
## 4 NVDA       0.2 
## 5 TSLA       0.1

4 Build a portfolio

# ?tq_portfolio

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

portfolio_returns_tbl
## # A tibble: 144 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31          -0.0145 
##  2 2013-02-28          -0.00726
##  3 2013-03-28           0.0145 
##  4 2013-04-30           0.0415 
##  5 2013-05-31           0.117  
##  6 2013-06-28          -0.00265
##  7 2013-07-31           0.0970 
##  8 2013-08-30           0.0600 
##  9 2013-09-30           0.0677 
## 10 2013-10-31          -0.0167 
## # ℹ 134 more rows

5 Plot: Portfolio Histogram and Density

portfolio_returns_tbl %>%
    
    ggplot(mapping = aes(x = portfolio.returns)) +
    geom_histogram(fill = "magenta", binwidth = 0.01) +
    geom_density() +
    
    # Formatting
    scale_x_continuous(labels = scales::percent_format()) +

labs(x = "returns", 
     y = "distribution",
     Title = "Portfolio Histogram and Density")

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

In a typical quarter, between 2012-2024, this portoflio yields between 0-10% increase.