# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Visualize expected returns and risk to make it easier to compare the performance of multiple assets and portfolios.

Choose your stocks.

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

1 Import stock prices

symbols <- c("NKE", "TSLA", "NVDA", "GOOGL", "ADDYY")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",
                 from = "2025-12-31",
                 to   = "2026-12-31")

2 Convert prices to returns (monthly)

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", "return"))

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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "ADDYY" "GOOGL" "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)

4 Build a portfolio

?tg_portfolio

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

portfolio_returns_tbl
## # A tibble: 10 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2026-01-30          -0.0144 
##  2 2026-02-27          -0.0310 
##  3 2026-03-31          -0.100  
##  4 2026-04-30           0.0931 
##  5 2026-05-29           0.0575 
##  6 2026-06-30          -0.0358 
##  7 2026-07-31          -0.0551 
##  8 2026-08-31          -0.00268
##  9 2026-09-30          -0.0174 
## 10 2026-10-19           0.0129

5 Compute Standard Deviation

portfolio_sd_tidyquant_builtin_percent <- portfolio_returns_tbl %>%
    
    tq_performance(Ra = portfolio.returns, 
                   performance_fun = table.Stats) %>%
    
    select(Stdev) %>%
    mutate(tq_sd = round(Stdev, 4))

portfolio_sd_tidyquant_builtin_percent * 100
##   Stdev tq_sd
## 1  5.49  5.49
# Mean of portfolio returns
portfolio_sd_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_sd_tidyquant_builtin_percent
## [1] -0.009310733

6 Plot: Expected Returns versus Risk

# Expected Returns vs Risk
sd_mean_tbl <- asset_returns_tbl %>%
    
    group_by(asset) %>%
    tq_performance(Ra = return, 
                   performance_fun = table.Stats) %>%
    select(Mean = ArithmeticMean, Stdev) %>%
    ungroup() %>%
    
    # Add portfolio Sd
    add_row(tibble(asset = "Portfolio" ,
                   Mean  = portfolio_sd_tidyquant_builtin_percent,
                   Stdev = sd(portfolio_returns_tbl$portfolio.returns)))

sd_mean_tbl
## # A tibble: 6 × 3
##   asset         Mean  Stdev
##   <chr>        <dbl>  <dbl>
## 1 ADDYY     -0.0096  0.0985
## 2 GOOGL      0.0098  0.110 
## 3 NKE       -0.0516  0.0799
## 4 NVDA       0.0159  0.0672
## 5 TSLA      -0.02    0.129 
## 6 Portfolio -0.00931 0.0549

How should you expect your portfolio to perform relative to its assets in the portfolio? Would you invest all your money in any of the individual stocks instead of the portfolio? Discuss both in terms of expected return and risk.
My diversified portfolio balances the weighted average of individual returns with lower overall risk, whereas investing in a single stock exposes me to much higher volatility for potentially extreme outcomes. Therefore, the portfolio provides a safer, more balanced risk-return trade-off compared to the high risk of putting all my money into just one stock.