eodhd is a private company that offers APIs to a set of comprehensive and high quality financial data for over 70+ exchanges across the world. This includes:
- Adjusted and unadjusted prices of financial contracts (equity, funds, ETF, cryptocurrencies, ..)
- Financial information of companies (Balance Sheet, Income/Cashflow statement)
- Valuation indicators
- And more..
Package eodhdR2 is the second and backwards incompatible version of eodhd, allowing fast and intelligent access to most of the API’s endpoints.
- A local caching system that saves all API queries to the disk, improving execution time and reducing api calls on repeated queries.
- A quota management system, informing the user of how much of the API daily quota was used and how much time is left to refresh it.
- Function for aggregating and organizing financial information into a single dataframe, allowing easier access to clean financial data in the wide or long format.
# available in CRAN
install.package("eodhdR2")
# development version
devtools::install_github("EodHistoricalData/R-Library-for-financial-data-2024")After registering in the eodhd website and choosing a subscription, all users will authenticate an R session using a token from the website. For that:
- Create an account at https://eodhd.com/
- Go in “Settings” and look for your API token
While using eodhdR2, all authentications are managed with function
eodhdR2::set_token():
eodhdR2::set_token("YOUR_TOKEN")Alternatively, while testing the API, you can use the “demo” token for demonstration.
token <- eodhdR2::get_demo_token()
eodhdR2::set_token(token)
#> ✔ eodhd API token set
#> ℹ Account name: API Documentation 2 ([email protected])
#> ℹ Quota: 63463 | 10000000
#> ℹ Subscription: demo
#> ✖ You are using a **DEMONSTRATION** token for testing pourposes, with
#> limited access to the data repositories. See <https://eodhd.com/>
#> for registration and, after finding your token, use it with
#> function eodhdR2::set_token("TOKEN").ticker <- "AAPL"
exchange <- "US"
df_prices <- eodhdR2::get_prices(ticker, exchange)
#>
#> ── retrieving price data for ticker AAPL|US ────────────────────────────────────
#> ! Quota status: 63463|10000000, refreshing in 5.8 hours
#> ℹ cache file AAPL_US_eodhd_prices.rds saved
#> ✔ got 11021 rows of prices
#> ℹ got daily data from 1980-12-12 to 2024-08-30
head(df_prices)
#> date open high low close adjusted_close volume ticker
#> 1 1980-12-12 28.7392 28.8736 28.7392 28.7392 0.0989 469033600 AAPL
#> 2 1980-12-15 27.3728 27.3728 27.2608 27.2608 0.0938 175884800 AAPL
#> 3 1980-12-16 25.3792 25.3792 25.2448 25.2448 0.0869 105728000 AAPL
#> 4 1980-12-17 25.8720 26.0064 25.8720 25.8720 0.0891 86441600 AAPL
#> 5 1980-12-18 26.6336 26.7456 26.6336 26.6336 0.0917 73449600 AAPL
#> 6 1980-12-19 28.2464 28.3808 28.2464 28.2464 0.0972 48630400 AAPL
#> exchange ret_adj_close
#> 1 US NA
#> 2 US -0.05156724
#> 3 US -0.07356077
#> 4 US 0.02531646
#> 5 US 0.02918070
#> 6 US 0.05997819library(ggplot2)
p <- ggplot(df_prices, aes(y = adjusted_close, x = date)) +
geom_line() +
theme_light() +
labs(title = "Adjusted Prices of AAPL",
subtitle = "Prices are adjusted to splits, dividends and other corporate events",
x = "Data",
y = "Adjusted Prices")
p
ticker <- "AAPL"
exchange <- "US"
df_div <- eodhdR2::get_dividends(ticker, exchange)
#>
#> ── retrieving dividends for ticker AAPL|US ─────────────────────────────────────
#> ! Quota status: 63467|10000000, refreshing in 5.8 hours
#> ℹ cache file AAPL_US_eodhd_dividends.rds saved
#> ✔ got 84 rows of dividend data
head(df_div)
#> date ticker exchange declarationDate recordDate paymentDate period
#> 1 1987-05-11 AAPL US <NA> <NA> <NA> <NA>
#> 2 1987-08-10 AAPL US <NA> <NA> <NA> <NA>
#> 3 1987-11-17 AAPL US <NA> <NA> <NA> <NA>
#> 4 1988-02-12 AAPL US <NA> <NA> <NA> <NA>
#> 5 1988-05-16 AAPL US <NA> <NA> <NA> <NA>
#> 6 1988-08-15 AAPL US <NA> <NA> <NA> <NA>
#> value unadjustedValue currency
#> 1 0.00054 0.12096 USD
#> 2 0.00054 0.06048 USD
#> 3 0.00071 0.07952 USD
#> 4 0.00071 0.07952 USD
#> 5 0.00071 0.07952 USD
#> 6 0.00071 0.07952 USDlibrary(ggplot2)
p <- ggplot(df_div, aes(y = value, x = date)) +
geom_point(size = 1) +
theme_light() +
labs(title = "Adjusted Dividends of AAPL",
x = "Data",
y = "Adjusted Dividends")
p
ticker <- "AAPL"
exchange <- "US"
l_fun <- eodhdR2::get_fundamentals(ticker, exchange)
#>
#> ── retrieving fundamentals for ticker AAPL|US ──────────────────────────────────
#> ! Quota status: 63469|10000000, refreshing in 5.8 hours
#> ✔ querying API
#> ✔ got 13 elements in raw list
names(l_fun)
#> [1] "General" "Highlights" "Valuation"
#> [4] "SharesStats" "Technicals" "SplitsDividends"
#> [7] "AnalystRatings" "Holders" "InsiderTransactions"
#> [10] "ESGScores" "outstandingShares" "Earnings"
#> [13] "Financials"wide_financials <- eodhdR2::parse_financials(l_fun, "wide")
#>
#> ── Parsing financial data for Apple Inc | AAPL ──
#>
#> ℹ parsing Balance_Sheet data
#> ℹ quarterly
#> ℹ yearly
#> ℹ parsing Cash_Flow data
#> ℹ quarterly
#> ℹ yearly
#> ℹ parsing Income_Statement data
#> ℹ quarterly
#> ℹ yearly
#> ✔ got 564 rows of financial data (wide format)
head(wide_financials)
#> # A tibble: 6 × 127
#> date filing_date ticker company_name frequency type_financial
#> <date> <date> <chr> <chr> <chr> <chr>
#> 1 2024-06-30 2024-08-02 AAPL Apple Inc quarterly Balance_Sheet
#> 2 2024-03-31 2024-05-03 AAPL Apple Inc quarterly Balance_Sheet
#> 3 2023-12-31 2024-02-02 AAPL Apple Inc quarterly Balance_Sheet
#> 4 2023-09-30 2023-11-03 AAPL Apple Inc quarterly Balance_Sheet
#> 5 2023-06-30 2023-08-04 AAPL Apple Inc quarterly Balance_Sheet
#> 6 2023-03-31 2023-05-05 AAPL Apple Inc quarterly Balance_Sheet
#> # ℹ 121 more variables: currency_symbol <chr>, totalAssets <dbl>,
#> # intangibleAssets <dbl>, earningAssets <dbl>, otherCurrentAssets <dbl>,
#> # totalLiab <dbl>, totalStockholderEquity <dbl>, deferredLongTermLiab <dbl>,
#> # otherCurrentLiab <dbl>, commonStock <dbl>, capitalStock <dbl>,
#> # retainedEarnings <dbl>, otherLiab <dbl>, goodWill <dbl>, otherAssets <dbl>,
#> # cash <dbl>, cashAndEquivalents <dbl>, totalCurrentLiabilities <dbl>,
#> # currentDeferredRevenue <dbl>, netDebt <dbl>, shortTermDebt <dbl>, …long_financials <- eodhdR2::parse_financials(l_fun, "long")
#>
#> ── Parsing financial data for Apple Inc | AAPL ──
#>
#> ℹ parsing Balance_Sheet data
#> ℹ quarterly
#> ℹ yearly
#> ℹ parsing Cash_Flow data
#> ℹ quarterly
#> ℹ yearly
#> ℹ parsing Income_Statement data
#> ℹ quarterly
#> ℹ yearly
#> ✔ got 67680 rows of financial data (long format)
head(long_financials)
#> # A tibble: 6 × 9
#> date filing_date ticker company_name frequency type_financial
#> <date> <date> <chr> <chr> <chr> <chr>
#> 1 2024-06-30 2024-08-02 AAPL Apple Inc quarterly Balance_Sheet
#> 2 2024-06-30 2024-08-02 AAPL Apple Inc quarterly Balance_Sheet
#> 3 2024-06-30 2024-08-02 AAPL Apple Inc quarterly Balance_Sheet
#> 4 2024-06-30 2024-08-02 AAPL Apple Inc quarterly Balance_Sheet
#> 5 2024-06-30 2024-08-02 AAPL Apple Inc quarterly Balance_Sheet
#> 6 2024-06-30 2024-08-02 AAPL Apple Inc quarterly Balance_Sheet
#> # ℹ 3 more variables: currency_symbol <chr>, name <chr>, value <dbl>