FredApi.jl offers the possibility to access the FRED Developer API in Julia.
This package is available in R as FredApi.
add FredApi
or
add https://github.com/markushhh/FredApi.jl/
The package contains following functions:
set_api_keyget_symbolssearch_symbolget_releaseget_metadataget_categoryget_sourcesget_sourcesave_api_keyload_api_key
You can either set up an environment variable for the FRED API key called "API_FRED" or use a local environment variable when entering a new julia session with
set_api_key("YOUR_KEY")
where you manually replace YOUR_KEY with your private API key.
To set an environment variable under windows, open command prompt and type in setx API_FRED "YOUR_KEY" where YOUR_KEY must be replaced with your API key.
To open the command prompt press Windows+R, then type in cmd and press Enter.
To set an environment variable on an other OS follow this link and add your OS.
You can request a FRED API key at https://research.stlouisfed.org/useraccount/login/secure/.
Another option is to write the API key to your home directory with save_api_key("YOUR_KEY"). It saves a .fred_api_key file into homedir() which can later be loaded into the current julia process with load_api_key(). This method requires you to load the key always if you start a new session.
using FredApi
set_api_key("API_FRED")
Following is an applied tutorial about how to use the package. Simply replace the symbol with your prefered symbol and run the function.
Download a full dataset with
get_symbols("GDPC1")
output
291×1 TimeArray{Float64,1,Date,Array{Float64,1}} 1947-01-01 to 2019-07-01
│ │ Value │
├────────────┼───────────┤
│ 1947-01-01 │ 2033.061 │
│ 1947-04-01 │ 2027.639 │
│ 1947-07-01 │ 2023.452 │
│ 1947-10-01 │ 2055.103 │
│ 1948-01-01 │ 2086.017 │
│ 1948-04-01 │ 2120.45 │
│ 1948-07-01 │ 2132.598 │
│ 1948-10-01 │ 2134.981 │
│ 1949-01-01 │ 2105.562 │
⋮
│ 2017-10-01 │ 18322.464 │
│ 2018-01-01 │ 18438.254 │
│ 2018-04-01 │ 18598.135 │
│ 2018-07-01 │ 18732.72 │
│ 2018-10-01 │ 18783.548 │
│ 2019-01-01 │ 18927.281 │
│ 2019-04-01 │ 19021.86 │
│ 2019-07-01 │ 19121.112 │
Download a subsample
get_symbols("GDPC1", "2010-01-01", "2019-01-01")
output
37×1 TimeArray{Float64,1,Date,Array{Float64,1}} 2010-01-01 to 2019-01-01
│ │ Value │
├────────────┼───────────┤
│ 2010-01-01 │ 15415.145 │
│ 2010-04-01 │ 15557.277 │
│ 2010-07-01 │ 15671.967 │
│ 2010-10-01 │ 15750.625 │
│ 2011-01-01 │ 15712.754 │
│ 2011-04-01 │ 15825.096 │
│ 2011-07-01 │ 15820.7 │
│ 2011-10-01 │ 16004.107 │
│ 2012-01-01 │ 16129.418 │
⋮
│ 2017-04-01 │ 18021.048 │
│ 2017-07-01 │ 18163.558 │
│ 2017-10-01 │ 18322.464 │
│ 2018-01-01 │ 18438.254 │
│ 2018-04-01 │ 18598.135 │
│ 2018-07-01 │ 18732.72 │
│ 2018-10-01 │ 18783.548 │
│ 2019-01-01 │ 18927.281 │
To change the time frame of the dataset, the collapse() function in TimeSeries.jl comes in really handy.
using TimeSeries
x = get_symbols("FEDFUNDS", "2000-01-01", "2019-01-01")
collapse(x, year, first)
output
20×1 TimeArray{Float64,1,Date,Array{Float64,1}} 2000-01-01 to 2019-01-01
│ │ Value │
├────────────┼───────┤
│ 2000-01-01 │ 5.45 │
│ 2001-01-01 │ 5.98 │
│ 2002-01-01 │ 1.73 │
│ 2003-01-01 │ 1.24 │
│ 2004-01-01 │ 1.0 │
│ 2005-01-01 │ 2.28 │
│ 2006-01-01 │ 4.29 │
│ 2007-01-01 │ 5.25 │
│ 2008-01-01 │ 3.94 │
⋮
│ 2012-01-01 │ 0.08 │
│ 2013-01-01 │ 0.14 │
│ 2014-01-01 │ 0.07 │
│ 2015-01-01 │ 0.11 │
│ 2016-01-01 │ 0.34 │
│ 2017-01-01 │ 0.65 │
│ 2018-01-01 │ 1.41 │
│ 2019-01-01 │ 2.4 │
To explore more options, go to TimeSeries.jl
using Plots
plot(x, legend = false)
In case you do not know which ID a time series is associated with, you can search for it using the API. Let's have a look over an example. We would like to download the French Gross Domestic Product. We would use search_symbol and explore the results.
x = search_symbol("GDP", "France")
println(x)
The resulting DataFrame is sorted by popularity, you can always sort by any column you'd like by applying sort!(x, j) to sort by the j-th column.
13×3 DataFrame
│ Row │ popularity │ title │ id │
│ │ Int64 │ String │ String │
├─────┼────────────┼──────────────────┼───────────────────────────────────────────────────────────────────────────────────────────────────┤
│ 1 │ 47 │ CLVMNACSCAB1GQFR │ Real Gross Domestic Product for France │
│ 2 │ 24 │ CPMNACSCAB1GQFR │ Gross Domestic Product for France │
│ 3 │ 20 │ NYGDPPCAPKDFRA │ Constant GDP per capita for France │
│ 4 │ 16 │ RGDPNAFRA666NRUG │ Real GDP at Constant National Prices for France │
│ 5 │ 12 │ CLVMNACNSAB1GQFR │ Real Gross Domestic Product for France │
│ 6 │ 11 │ MKTGDPFRA646NWDB │ Gross Domestic Product for France │
│ 7 │ 7 │ NAEXKP01FRQ661S │ Gross Domestic Product by Expenditure in Constant Prices: Total Gross Domestic Product for France │
│ 8 │ 7 │ CPMNACNSAB1GQFR │ Gross Domestic Product for France │
│ 9 │ 5 │ NAEXKP01FRQ657S │ Gross Domestic Product by Expenditure in Constant Prices: Total Gross Domestic Product for France │
│ 10 │ 5 │ NAEXKP01FRQ189S │ Gross Domestic Product by Expenditure in Constant Prices: Total Gross Domestic Product for France │
│ 11 │ 3 │ NAEXKP01FRA189S │ Gross Domestic Product by Expenditure in Constant Prices: Total Gross Domestic Product for France │
│ 12 │ 2 │ NAEXKP01FRA657S │ Gross Domestic Product by Expenditure in Constant Prices: Total Gross Domestic Product for France │
│ 13 │ 1 │ NAEXKP01FRA661S │ Gross Domestic Product by Expenditure in Constant Prices: Total Gross Domestic Product for France │
Let's take the most popular series Real Gross Domestic Product for France and get more information about it.
get_metadata("CLVMNACSCAB1GQFR")
output
Metadata for: CLVMNACSCAB1GQFR
Title: Real Gross Domestic Product for France
Units: Millions of Chained 2010 Euros
Adjustment: Seasonally Adjusted
Frequency: Quarterly
Notes: Eurostat unit ID: CLV10_MNAC
Eurostat item ID = B1GQ
Eurostat country ID: FR
Seasonally and calendar adjusted data.
For euro area member states, the national currency series are converted into euros using the irrevocably fixed exchange rate. This preserves the same growth rates than for the previous national currency series. Both series coincide for years after accession to the euro area but differ for earlier years due to market exchange rate movements.
Copyright, European Union, http://ec.europa.eu, 1995-2016.Complete terms of use are available at http://ec.europa.eu/geninfo/legal_notices_en.htm#copyright
, to get release information about the series, run get_release("CLVMNACSCAB1GQFR")
output:
Dict{String,Any} with 6 entries:
"realtime_start" => "2020-01-07"
"name" => "National Accounts - GDP (Eurostat)"
"id" => 267
"realtime_end" => "2020-01-07"
"link" => "http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=…
"press_release" => true
, to get the category run: get_category("CLVMNACSCAB1GQFR"). In this example we get GDP.
To get all sources from FRED, run get_sources() which results in
89×3 DataFrame. Omitted printing of 1 columns
│ Row │ id │ name │
│ │ Int64 │ String │
├─────┼───────┼─────────────────────────────────────────────────────────┤
│ 1 │ 1 │ Board of Governors of the Federal Reserve System (US) │
│ 2 │ 3 │ Federal Reserve Bank of Philadelphia │
│ 3 │ 4 │ Federal Reserve Bank of St. Louis │
│ 4 │ 6 │ Federal Financial Institutions Examination Council (US) │
│ 5 │ 11 │ Dow Jones & Company │
│ 6 │ 14 │ University of Michigan │
│ 7 │ 15 │ Council of Economic Advisers (US) │
⋮
│ 82 │ 129 │ Moody’s │
│ 83 │ 133 │ DHI Group, Inc. │
│ 84 │ 135 │ Centers for Disease Control and Prevention …
For more information about a source from FRED, run get_source(1) with an Integer specifying the source, e.g.
Dict{String,Any} with 5 entries:
"realtime_start" => "2020-01-07"
"name" => "Board of Governors of the Federal Reserve System (US)"
"id" => 1
"realtime_end" => "2020-01-07"
"link" => "http://www.federalreserve.gov/"