Load packages

# Core
library(tidyverse)

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library(tidyquant)

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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("NVDA", "INTC", "GOOG", "AMD", "AAPL")
prices <- tq_get(x = symbols, 
                 get = "stock.prices",
                 from = "2012-12-31",
                 to   = "2027-12-31")

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 weighting scheme)

symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols

## [1] "AAPL" "AMD"  "GOOG" "INTC" "NVDA"

## [1] "AAPL" "AMD"  "GOOG" "INTC" "NVDA"

weights <- c(0.20, 0.15, 0.15, 0.30, 0.25)
weights

## [1] 0.20 0.15 0.15 0.30 0.25

## [1] 0.20 0.15 0.15 0.30 0.25

w_tbl <- tibble(symbols, weights)
w_tbl

## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.2 
## 2 AMD        0.15
## 3 GOOG       0.15
## 4 INTC       0.3 
## 5 NVDA       0.25

## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.2 
## 2 AMD        0.15
## 3 GOOG       0.15
## 4 INTC       0.3 
## 5 NVDA       0.25

4 Build a portfolio

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

## Warning in check_weights(weights, assets_col, map, x): Sum of weights does not
## equal 1.

## Warning in Return.portfolio.geometric(R = R, weights = weights, wealth.index =
## wealth.index, : The weights for one or more periods do not sum up to 1:
## assuming a return of 0 for the residual weights

portfolio_returns_tbl

## # A tibble: 52 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28          0.0241  
##  2 2013-06-28          0.123   
##  3 2013-09-30          0.0403  
##  4 2013-12-31          0.122   
##  5 2014-03-31          0.0270  
##  6 2014-06-30          0.117   
##  7 2014-09-30          0.0245  
##  8 2014-12-31          0.00478 
##  9 2015-03-31          0.000522
## 10 2015-06-30         -0.0364  
## # ℹ 42 more rows

## # A tibble: 52 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28          0.0241  
##  2 2013-06-28          0.123   
##  3 2013-09-30          0.0403  
##  4 2013-12-31          0.122   
##  5 2014-03-31          0.0270  
##  6 2014-06-30          0.117   
##  7 2014-09-30          0.0245  
##  8 2014-12-31          0.00478 
##  9 2015-03-31          0.000522
## 10 2015-06-30         -0.0364  
## # ℹ 42 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() +
    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?
With my returns ranging from -40% to 40% I should expect around -5% and 23% returns on my portfolio as that is the highest distribution area and has a spike of density in those markers as shown in the curve of the plot.
I can also expect to have a positive return more often than not as there is more distribution above the 0% on the graph showing a typical return of around 10 percent on average.

Apply it to your data 5

Joseph Frates

2025-09-19