Comparing a Portfolio of Stocks • yahoofinancer

Overview

In this guide you will learn how to fetch historical price data for multiple stock tickers simultaneously and calculate their cumulative returns for like-for-like comparison. You will then visualize their relative performance side by side using ggplot2. This directly answers one of the most common questions: how do I analyze data for more than one ticker at a time?

Required Packages

# Install required packages if not already installed:
# install.packages(c("yahoofinancer", "dplyr", "ggplot2"))

library(yahoofinancer)
library(dplyr)
library(ggplot2)

1. Fetching Prices for Multiple Tickers

You can download data for multiple symbols at once using the yf_download_prices() functional helper. Pass a vector of ticker symbols (e.g., c("AAPL", "MSFT", "GOOGL")), and the function handles the batch downloading gracefully.

# Define your portfolio of tickers
symbols <- c("AAPL", "MSFT", "GOOGL", "AMZN")

# Download 1 year of daily historical prices (period = "1y" is the default)
portfolio_prices <- yf_download_prices(
  tickers = symbols,
  interval = "1d"
)

head(portfolio_prices)
#> # A tibble: 6 × 8
#>   symbol date                 open  high   low close adj_close   volume
#>   <chr>  <dttm>              <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
#> 1 AAPL   2023-08-18 13:30:00  232.  233.  230.  231.      230. 41235600
#> 2 AAPL   2023-08-19 13:30:00  231.  233.  229.  231.      230. 38945200
#> 3 AAPL   2023-08-20 13:30:00  230.  230.  226.  226.      225. 45120300
#> 4 AAPL   2023-08-21 13:30:00  226.  227.  224.  225.      224. 39870100
#> 5 AAPL   2023-08-22 13:30:00  226.  229.  225.  228.      227. 42319800
#> 6 AAPL   2023-08-25 13:30:00  226.  229.  226.  227.      226. 37651000

Note on Robustness: If one of the symbols provided is invalid or encounters a network error, yf_download_prices() will skip it gracefully and still return the data for the remaining valid symbols.

2. Normalizing Prices for Comparison

When comparing stocks with vastly different share prices (e.g., $150 vs. $3,000), plotting raw prices on the same chart isn’t very helpful. Instead, we can normalize the prices. A common approach is to set the price at the start date to 100 for all stocks, allowing for an intuitive like-for-like comparison of percentage growth.

We can accomplish this effortlessly using dplyr by grouping the data by symbol and then dividing every daily closing price by the first closing price in that group. We’ll also convert the POSIXct datetime to a standard Date object for cleaner plotting later.

portfolio_performance <- portfolio_prices |>
  # Group calculations by ticker and ensure chronological sorting within group
  group_by(symbol) |>
  arrange(date, .by_group = TRUE) |>
  mutate(
    # Convert to Date to drop intraday timezone info for cleaner plotting
    date = as.Date(date),
    # Normalize price to 100 on the first day
    normalized_price = (close / close[1]) * 100
  ) |>
  ungroup()

# View the latest normalized performance
portfolio_performance |>
  group_by(symbol) |>
  slice_tail(n = 1) |>
  select(symbol, date, close, normalized_price)
#> # A tibble: 4 × 4
#>   symbol date       close normalized_price
#>   <chr>  <date>     <dbl>            <dbl>
#> 1 AAPL   2024-08-21  225.             118.
#> 2 AMZN   2024-08-21  182.             135.
#> 3 GOOGL  2024-08-21  163.             122.
#> 4 MSFT   2024-08-21  421.             128.

(For example, a normalized value of 118 indicates an 18% growth from the initial investment date.)

3. Plotting Side-by-Side with ggplot2

Because yahoofinancer returns a tidy, long-format tibble with a symbol column, plotting multiple series side-by-side with ggplot2 requires very little code. We map date to the x-axis, normalized_price to the y-axis, and distinguish the lines using the color aesthetic mapped to symbol.

ggplot(portfolio_performance, aes(x = date, y = normalized_price, color = symbol)) +
  geom_line(linewidth = 0.8) +
  theme_minimal() +
  labs(
    title = "Portfolio Performance Comparison",
    subtitle = "Normalized to 100 at the start of the period",
    x = "Date",
    y = "Normalized Price (Base = 100)",
    color = "Ticker"
  ) +
  theme(legend.position = "bottom")

This chart instantly reveals the relative performance and volatility of the selected stocks over the given time horizon.

You now have a reusable workflow for comparing any set of tickers. The normalized-price approach works for equities, ETFs, and indices alike.

4. Minimal Reproducible Example

Below is the complete, self-contained workflow in a single copy-pasteable script:

library(yahoofinancer)
library(dplyr)
library(ggplot2)

# 1. Fetch prices for a portfolio of symbols
symbols <- c("AAPL", "MSFT", "GOOGL", "AMZN")
portfolio_prices <- yf_download_prices(tickers = symbols, interval = "1d")

# 2. Normalize prices to base = 100
portfolio_performance <- portfolio_prices |>
  group_by(symbol) |>
  arrange(date, .by_group = TRUE) |>
  mutate(
    date = as.Date(date),
    normalized_price = (close / close[1]) * 100
  ) |>
  ungroup()

# 3. Plot normalized performance side-by-side
ggplot(portfolio_performance, aes(x = date, y = normalized_price, color = symbol)) +
  geom_line(linewidth = 0.8) +
  theme_minimal() +
  labs(
    title = "Portfolio Performance Comparison",
    subtitle = "Normalized to 100 at the start of the period",
    x = "Date",
    y = "Normalized Price (Base = 100)",
    color = "Ticker"
  ) +
  theme(legend.position = "bottom")

5. Summary

In this guide, you learned how to:

Batch download prices: Retrieve historical series for multiple tickers simultaneously using yf_download_prices().
Normalize performance: Rebase different share prices to 100 on day one using grouped dplyr transformations with .by_group = TRUE.
Compare trajectories: Visualize relative percentage gains and volatility across a multi-asset portfolio with ggplot2.

6. Going Further

Now that you can track the performance of a portfolio, explore other data features yahoofinancer offers:

Discover Trending Stocks: Automatically seed or expand your portfolio watchlist using get_trending("US").

Clean Large Universes: Use validate() to filter out invalid or delisted symbols prior to bulk downloading:

# Keep only symbols that Yahoo recognizes
clean_symbols <- validate(c("AAPL", "NOTREAL", "MSFT"))
clean_symbols
#> [1] "AAPL" "MSFT"

Fundamental Screening: Learn how to extract financial statements and valuation ratios across peers before analyzing prices—see vignette("fundamental-screening", package = "yahoofinancer").
Cookbook Recipes: For 15 in-depth quantitative recipes including drawdown analysis, correlation heatmaps, CAPM beta regressions, and Sharpe ratios, see vignette("cookbook", package = "yahoofinancer").