Currency Conversion and Forex Data

Overview

In this guide you will learn how to discover which currencies Yahoo Finance supports and how to fetch current and historical exchange rates between any two of them. You will then combine two currency pairs (GBP/USD and EUR/USD) into a single normalized comparison chart with ggplot2. These workflows are unique to yahoofinancer — no other R package wraps Yahoo Finance’s currency endpoints — and the entire guide runs in under five minutes.

Required Packages

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

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

1. Discovering Supported Currencies

Before converting anything, it helps to know what Yahoo Finance offers. The get_currencies() helper returns the full catalogue of supported currencies — no arguments required.

currencies <- get_currencies()

head(currencies)
#>   short_name         long_name symbol   local_long_name
#> 1        USD         US Dollar    USD         US Dollar
#> 2        EUR              Euro    EUR              Euro
#> 3        GBP    Pound Sterling    GBP    Pound Sterling
#> 4        JPY      Japanese Yen    JPY      Japanese Yen
#> 5        CHF       Swiss Franc    CHF       Swiss Franc
#> 6        AUD Australian Dollar    AUD Australian Dollar

Exchange rates on Yahoo Finance are quoted as pairs: to convert from one currency to another, Yahoo builds the symbol FROM + TO + =X. For example, British pounds to US dollars becomes GBPUSD=X, and euros to dollars becomes EURUSD=X. You rarely need to type these symbols yourself, though — the currency_converter() helper shown next assembles them for you.

Note on Robustness: Like all yahoofinancer downloaders, get_currencies() returns invisible(NULL) instead of erroring when the network or API is unavailable. Assign the result and check it isn’t NULL before continuing.

2. Fetching Historical Exchange Rates

currency_converter() retrieves current and historical rates between any two supported currencies. You can request a fixed window with start and end dates:

gbp_usd <- currency_converter(
  from  = "GBP",
  to    = "USD",
  start = "2024-01-01",
  end   = "2024-12-31"
)

head(gbp_usd)
#>                  date   high    low   open  close volume adj_close
#> 1 2024-01-02 00:00:00 1.2734 1.2577 1.2732 1.2602      0    1.2602
#> 2 2024-01-03 00:00:00 1.2716 1.2595 1.2678 1.2631      0    1.2631
#> 3 2024-01-04 00:00:00 1.2709 1.2602 1.2689 1.2677      0    1.2677
#> 4 2024-01-05 00:00:00 1.2721 1.2614 1.2705 1.2717      0    1.2717
#> 5 2024-01-08 00:00:00 1.2741 1.2698 1.2718 1.2720      0    1.2720
#> 6 2024-01-09 00:00:00 1.2743 1.2688 1.2694 1.2694      0    1.2694

The result provides the familiar OHLCV price series: a data frame with date (POSIXct), high, low, open, close, and volume columns, plus adj_close for daily or longer intervals (unlike equity price queries, currency pairs omit a symbol column). A few things worth knowing:

  • Instead of explicit dates, you can pass a rolling window via period (e.g., period = "1mo"); valid values range from "1d" to "max", with "ytd" as the default.
  • interval controls granularity ("1h", "1d", "5d", "1wk", "1mo", "3mo"; default "1d"). Intraday intervals omit adj_close.
  • Forex pairs trade no shares, so volume is reported as 0 rather than a meaningful quantity.
  • currency_converter() is standalone — unlike most of the package, no Ticker object is needed.
# Example: Rolling 1-month window with weekly candle intervals
weekly_rates <- currency_converter(
  from     = "GBP",
  to       = "USD",
  period   = "1mo",
  interval = "1wk"
)

Note: For intraday analysis, remember FX markets close on weekends — hourly data will show natural gaps.

3. Comparing Two Pairs

The real power comes from comparing pairs side by side. Let’s fetch the euro against the dollar over the same window, stack both pairs into one long data frame, and label each row with its pair.

eur_usd <- currency_converter(
  from  = "EUR",
  to    = "USD",
  start = "2024-01-01",
  end   = "2024-12-31"
)

fx <- bind_rows(
  gbp_usd |> mutate(pair = "GBP/USD"),
  eur_usd |> mutate(pair = "EUR/USD")
)

The pound trades near $1.27 while the euro trades near $1.08, so plotting raw rates would compare levels, not movement. As in the portfolio guide, we normalize each series to 100 at the start of the period so the lines show percentage change — a like-for-like view of which currency strengthened against the dollar.

fx_performance <- fx |>
  group_by(pair) |>
  arrange(date, .by_group = TRUE) |>
  mutate(
    date = as.Date(date),
    normalized_rate = (close / close[1]) * 100
  ) |>
  ungroup()

# Where did each pair end the year?
fx_performance |>
  group_by(pair) |>
  slice_tail(n = 1) |>
  select(pair, date, close, normalized_rate)
#> # A tibble: 2 × 4
#>   pair    date       close normalized_rate
#>   <chr>   <date>     <dbl>           <dbl>
#> 1 EUR/USD 2024-12-31 1.038            96.2
#> 2 GBP/USD 2024-12-31 1.252            98.6

(A normalized value below 100 means the currency weakened against the dollar over the window; above 100 means it strengthened.)

4. Plotting GBP/USD vs EUR/USD

Because the data is tidy — one row per date per pair — plotting both series takes a single ggplot() call. Map color to pair and ggplot2 handles the rest.

ggplot(fx_performance, aes(x = date, y = normalized_rate, color = pair)) +
  geom_line(linewidth = 0.8) +
  geom_hline(yintercept = 100, linetype = "dashed", color = "grey50") +
  theme_minimal() +
  labs(
    title = "Pound vs Euro: Performance Against the US Dollar",
    subtitle = "Daily closing rates in 2024, normalized to 100 at the start of the year",
    x = "Date",
    y = "Normalized Rate (Base = 100)",
    color = "Pair"
  ) +
  theme(legend.position = "bottom")

In 2024 both European currencies lost ground against the dollar, but the chart makes it easy to spot divergences — months where sterling held up better than the euro, or vice versa. Swap in any other supported currencies from step 1 (e.g., "JPY", "INR", "CHF") to rerun the exact same workflow.

You now have a reusable recipe for fetching and visualizing foreign-exchange data, from quick spot checks to multi-pair comparisons.

5. Minimal Reproducible Example

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

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

# 1. Fetch historical exchange rates for two currency pairs
gbp_usd <- currency_converter(
  from  = "GBP",
  to    = "USD",
  start = "2024-01-01",
  end   = "2024-12-31"
)

eur_usd <- currency_converter(
  from  = "EUR",
  to    = "USD",
  start = "2024-01-01",
  end   = "2024-12-31"
)

# 2. Combine and normalize to base = 100
fx_performance <- bind_rows(
  gbp_usd |> mutate(pair = "GBP/USD"),
  eur_usd |> mutate(pair = "EUR/USD")
) |>
  group_by(pair) |>
  arrange(date, .by_group = TRUE) |>
  mutate(
    date = as.Date(date),
    normalized_rate = (close / close[1]) * 100
  ) |>
  ungroup()

# 3. Plot normalized exchange rates side-by-side
ggplot(fx_performance, aes(x = date, y = normalized_rate, color = pair)) +
  geom_line(linewidth = 0.8) +
  geom_hline(yintercept = 100, linetype = "dashed", color = "grey50") +
  theme_minimal() +
  labs(
    title = "Pound vs Euro: Performance Against the US Dollar",
    subtitle = "Daily closing rates in 2024, normalized to 100 at the start of the year",
    x = "Date",
    y = "Normalized Rate (Base = 100)",
    color = "Pair"
  ) +
  theme(legend.position = "bottom")

6. Summary

In this guide, you learned how to:

  1. Discover supported currencies: Access Yahoo Finance’s FX catalog with get_currencies().
  2. Fetch exchange rates: Query historical FX pairs using currency_converter() with custom date windows or rolling periods.
  3. Normalize and visualize: Stack currency series with bind_rows() and compare multi-pair relative movements with ggplot2.

7. Going Further

Now that you can work with exchange rates, explore related features yahoofinancer offers:

  • Portfolio Comparisons: The normalization technique used here is identical to the one covered in vignette("comparing-portfolios", package = "yahoofinancer").
  • Multi-Currency Valuations: Convert foreign stock prices into your home currency—see Recipe 6 in vignette("cookbook", package = "yahoofinancer").
  • Check Quoting Currencies: For equities outside the US, check quoting currencies with the currency field on Ticker (e.g., Ticker$new("RELIANCE.NS")$currency returns "INR").
  • Reference Documentation: For complete argument details and valid parameter values, see the documentation for currency_converter() and get_currencies().