Commodities API: Historical Prices for Oil, Gas, Metals & Agriculture (beta)

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API calls consumption: 5 call per request.

How does API call system work?

The EODHD Commodities API provides historical price data for 23 commodity series sourced from the Federal Reserve Economic Data (FRED). It covers energy, metals, and agricultural commodities with daily, weekly, monthly, quarterly, and annual intervals — going back decades for most series.

To quickly try the API, use the demo API key by passing api_token=demo in your request. The demo key only provides access to WTI (West Texas Intermediate crude oil). Requests for any other commodity code will return a 403 error.

Data Freshness

All commodity data is sourced from FRED (Federal Reserve Economic Data). EODHD checks for new data from FRED twice daily, but the actual lag depends on when FRED’s upstream providers (EIA, IMF) publish each series. We do not control the publication schedule – the delay comes from the original data providers, not from EODHD.

CategoryCommoditiesData FrequencyTypical Lag
Energy (daily)WTI, BRENT, NATURAL_GAS, DIESEL_USGULF, HEATING_OIL_NYH, JET_FUEL_USGULF, PROPANE_MBTXDaily data points, but EIA publishes them in weekly batches (typically every Tuesday)Up to ~1 week. Although each business day has its own data point, the entire batch for the previous week appears at once
Energy (weekly)GASOLINE_USWeekly (Monday observation)~1 day (published Tuesday)
Energy (monthly)COAL_AU, URANIUMOnce per month2–3 weeks after month end. E.g. March data appears mid-April
MetalsALUMINUM, COPPEROnce per month (source: IMF)2–3 weeks after month end. E.g. March data appears mid-April
AgriculturalWHEAT, CORN, SUGAR, COTTON, COFFEE_MILD_ARABICA, COFFEE_ROBUSTASOnce per month (source: IMF)2–3 weeks after month end. E.g. March data appears mid-April
IndicesALL_COMMODITIES, ALL_COMMODITIES_PRODUCER, ENERGY_INDEX, NATGAS_EU, LNG_ASIAOnce per month2–3 weeks after month end. E.g. March data appears mid-April

Why does daily energy data have a ~1 week lag? The US Energy Information Administration (EIA) publishes its “Spot Prices” release once per week (typically on Tuesdays), even though the underlying data is daily. Each release contains all business-day data points for the previous week. So if today is Thursday, the most recent available data point may be from last Friday or Monday — this is normal behavior from EIA, not a delay on EODHD’s side. Weekends and US public holidays are skipped — no data points are generated on non-trading days.

Known Limitations

  • Precious metals removed: Gold (LBMA), Silver, Platinum, and Palladium series have been discontinued by FRED due to LBMA licensing changes.
  • Monthly series lag: Metals, agricultural commodities, indices, coal, and uranium are published once per month with a 2–3 week delay after the reference month ends. For example, in mid-May you will typically see data through March — April data may not yet be available.
  • Interval availability: Not all commodities support all intervals. Daily data is only available for energy commodities. Metals and agricultural commodities are typically monthly only.

API Endpoint

https://eodhd.com/api/commodities/historical/{CODE}

Parameters:

ParameterRequiredDescription
CODEYesCommodity code (e.g., WTI, BRENT, URANIUM) or ALL_COMMODITIES for the global index
api_tokenYesYour EODHD API token
intervalNoData frequency: daily, weekly, monthly (default), quarterly, annual

Request Example

URL
cURL
PHP
Python
R
Chat GPT
https://eodhd.com/api/commodities/historical/WTI?api_token=demo&interval=monthly
(Sign up for free to get an API token) 
curl --location "https://eodhd.com/api/commodities/historical/WTI?api_token=demo&interval=monthly&fmt=json"
(Sign up for free to get an API token) 
$curl = curl_init();

curl_setopt_array($curl, array(
    CURLOPT_URL => 'https://eodhd.com/api/commodities/historical/WTI?api_token=demo&interval=monthly&fmt=json',
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_ENCODING => '',
    CURLOPT_MAXREDIRS => 10,
    CURLOPT_TIMEOUT => 0,
    CURLOPT_FOLLOWLOCATION => true,
    CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
    CURLOPT_CUSTOMREQUEST => 'GET',
));

$data = curl_exec($curl);
curl_close($curl);

try {
    $data = json_decode($data, true, 512, JSON_THROW_ON_ERROR);
    var_dump($data);
} catch (Exception $e) {
    echo 'Error. '.$e->getMessage();
}
(Sign up for free to get an API token) 
import requests

url = f'https://eodhd.com/api/commodities/historical/WTI?api_token=demo&interval=monthly&fmt=json'
data = requests.get(url).json()

print(data)
(Sign up for free to get an API token) 
library(httr)
library(jsonlite)

url <- 'https://eodhd.com/api/commodities/historical/WTI?api_token=demo&interval=monthly&fmt=json'
response <- GET(url)

if (http_type(response) == "application/json") {
    content <- content(response, "text", encoding = "UTF-8")
    cat(content)
} else {
    cat("Error while receiving data\n")
}
(Sign up for free to get an API token)
New to coding? Our ChatGPT assistant can generate code in any language tailored to our API. Simply describe how you want to use our data, and get a working piece of code. Don’t forget to replace the API token with your own.

Try it now (it's free)!

How to use it (YouTube)

Response Format

The API returns a JSON object with two sections: meta (commodity metadata) and data (price history).

{
    "meta": {
        "name": "US Regular All Formulations Gas Price",
        "interval": "monthly",
        "unit": "Dollars per Gallon",
        "total": 427
    },
    "data": [
        {"date": "2026-03-01", "value": 3.99},
        {"date": "2026-02-01", "value": 2.937},
        {"date": "2026-01-01", "value": 2.853}
    ]
}
FieldTypeDescription
meta.namestringFull commodity name from FRED
meta.intervalstringData frequency (daily, weekly, monthly, quarterly, annual)
meta.unitstringMeasurement unit (e.g., Dollars per Barrel, Index 2016 = 100)
meta.totalintegerTotal number of data points available
data[].datestringDate in YYYY-MM-DD format
data[].valuenumberPrice or index value

Available Commodities

The API covers 23 commodity series across four categories:

Energy (10 series)

CodeNameUnitFrequency
WTICrude Oil WTI (Cushing, Oklahoma)Dollars per BarrelDaily
BRENTCrude Oil Brent (Europe)Dollars per BarrelDaily
NATURAL_GASHenry Hub Natural Gas Spot PriceDollars per Million BTUDaily
GASOLINE_USUS Regular All Formulations Gas PriceDollars per GallonWeekly
DIESEL_USGULFNo. 2 Diesel (US Gulf Coast)Dollars per GallonDaily
HEATING_OIL_NYHNo. 2 Heating Oil (NY Harbor)Dollars per GallonDaily
JET_FUEL_USGULFKerosene-Type Jet Fuel (US Gulf Coast)Dollars per GallonDaily
PROPANE_MBTXPropane (Mont Belvieu, Texas)Dollars per GallonDaily
COAL_AUCoal (Australian thermal)Dollars per Metric TonMonthly
URANIUMUranium (Global Spot Price)Dollars per PoundMonthly

Metals (2 series)

CodeNameUnitFrequency
ALUMINUMGlobal Price of AluminumDollars per Metric TonMonthly
COPPERGlobal Price of CopperDollars per Metric TonMonthly

Agricultural (6 series)

CodeNameUnitFrequency
WHEATGlobal Price of WheatDollars per Metric TonMonthly
CORNGlobal Price of CornDollars per Metric TonMonthly
SUGARGlobal Price of SugarCents per PoundMonthly
COTTONGlobal Price of CottonCents per PoundMonthly
COFFEE_MILD_ARABICAGlobal Price of Coffee, Mild ArabicaCents per PoundMonthly
COFFEE_ROBUSTASGlobal Price of Coffee, RobustasCents per PoundMonthly

Indices (5 series)

CodeNameUnitFrequency
ALL_COMMODITIESGlobal Price Index of All CommoditiesIndex 2016 = 100Monthly
ALL_COMMODITIES_PRODUCERProducer Price Index: All CommoditiesIndex 1982 = 100Monthly
ENERGY_INDEXEnergy Price IndexIndex 2016 = 100Monthly
NATGAS_EUNatural Gas Price (EU)Dollars per Million BTUMonthly
LNG_ASIALNG Price (Asia)Dollars per Million BTUMonthly

Quick Start: Python

Fetch the latest WTI crude oil prices:

import requests

API_TOKEN = "YOUR_API_TOKEN"

def get_commodity(code, interval="monthly"):
    url = f"https://eodhd.com/api/commodities/historical/{code}"
    params = {"api_token": API_TOKEN, "interval": interval}
    response = requests.get(url, params=params)
    return response.json()

# Monthly WTI prices
data = get_commodity("WTI")
print(f"Commodity: {data['meta']['name']}")
print(f"Unit: {data['meta']['unit']}")
print(f"Total records: {data['meta']['total']}")

for row in data["data"][:5]:
    print(f"  {row['date']}: ${row['value']}")

Output:

Commodity: Crude Oil Prices: West Texas Intermediate (WTI) - Cushing, Oklahoma
Unit: Dollars per Barrel
Total records: 483

  2026-03-01: $104.69
  2026-02-01: $66.96
  2026-01-01: $64.5
  2025-12-01: $57.26
  2025-11-01: $58.58

Using Intervals

The interval parameter controls the data frequency. Available values: daily, weekly, monthly (default), quarterly, annual. Not all intervals are available for every commodity — it depends on the underlying FRED data source.

# Daily WTI prices — much more granular
daily = get_commodity("WTI", interval="daily")
print(f"Daily records: {daily['meta']['total']}")

for row in daily["data"][:5]:
    print(f"  {row['date']}: ${row['value']}")

Output:

Daily records: 10129

  2026-03-30: $104.69
  2026-03-27: $101.26
  2026-03-26: $96.18
  2026-03-25: $95.82
  2026-03-24: $93.26

Building a Commodity Dashboard

Fetch multiple commodities and compare their recent performance:

import requests

API_TOKEN = "YOUR_API_TOKEN"

def get_commodity(code, interval="monthly"):
    url = f"https://eodhd.com/api/commodities/historical/{code}"
    params = {"api_token": API_TOKEN, "interval": interval}
    return requests.get(url, params=params).json()

commodities = ["WTI", "BRENT", "NATURAL_GAS", "ALUMINUM", "COPPER", "WHEAT"]

print(f"{'Code':<15} {'Name':<45} {'Latest':>10} {'Unit':<25}")
print("-" * 100)

for code in commodities:
    data = get_commodity(code)
    meta = data["meta"]
    latest = data["data"][0] if data["data"] else None

    print(f"{code:<15} {meta['name'][:44]:<45} "
          f"{' + str(latest['value']) if latest else 'N/A':>10} "
          f"{meta['unit']:<25}")

Output:

Code            Name                                          Latest Unit
--------------------------------------------------------------------------
WTI             Crude Oil Prices: West Texas Intermediate      $104.69 Dollars per Barrel
BRENT           Crude Oil Prices: Brent - Europe                $99.33 Dollars per Barrel
NATURAL_GAS     Henry Hub Natural Gas Spot Price                 $4.38 Dollars per Million BTU
ALUMINUM        Global Price of Aluminum                      $2614.58 Dollars per Metric Ton
COPPER          Global Price of Copper                        $9474.24 Dollars per Metric Ton
WHEAT           Global Price of Wheat                          $249.18 Dollars per Metric Ton

Year-over-Year Comparison

Compare current prices with the same month last year to identify trends:

def yoy_comparison(code):
    """Compare latest monthly value with same month a year ago."""
    data = get_commodity(code, interval="monthly")
    meta = data["meta"]
    records = data["data"]

    if len(records) < 13:
        return None

    current = records[0]
    year_ago = records[12]

    change = ((current["value"] - year_ago["value"]) / year_ago["value"]) * 100

    return {
        "name": meta["name"],
        "unit": meta["unit"],
        "current_date": current["date"],
        "current_value": current["value"],
        "year_ago_date": year_ago["date"],
        "year_ago_value": year_ago["value"],
        "yoy_change_pct": round(change, 1),
    }

energy = ["WTI", "BRENT", "NATURAL_GAS", "GASOLINE_US"]

print(f"{'Commodity':<15} {'Current':>10} {'Year Ago':>10} {'YoY Change':>12}")
print("-" * 50)

for code in energy:
    result = yoy_comparison(code)
    if result:
        direction = "+" if result["yoy_change_pct"] > 0 else ""
        print(f"{code:<15} ${result['current_value']:>8} "
              f"${result['year_ago_value']:>8} "
              f"{direction}{result['yoy_change_pct']}%")

Loading into Pandas DataFrame

For deeper analysis, load commodity data into a Pandas DataFrame:

import pandas as pd

def commodity_to_dataframe(code, interval="monthly"):
    """Fetch commodity data and return as a Pandas DataFrame."""
    data = get_commodity(code, interval=interval)
    df = pd.DataFrame(data["data"])
    df["date"] = pd.to_datetime(df["date"])
    df = df.set_index("date").sort_index()
    df.attrs["name"] = data["meta"]["name"]
    df.attrs["unit"] = data["meta"]["unit"]
    return df

# Load WTI daily prices
wti = commodity_to_dataframe("WTI", interval="daily")

print(f"{wti.attrs['name']}")
print(f"Date range: {wti.index.min().date()} to {wti.index.max().date()}")
print(f"Records: {len(wti)}")
print(f"\nDescriptive statistics:")
print(wti.describe())

# Calculate 20-day moving average
wti["ma_20"] = wti["value"].rolling(window=20).mean()
print(f"\nLatest value: ${wti['value'].iloc[-1]:.2f}")
print(f"20-day MA: ${wti['ma_20'].iloc[-1]:.2f}")

Correlation Analysis

Compare price movements across commodities to find correlations:

import pandas as pd

codes = ["WTI", "BRENT", "NATURAL_GAS", "COPPER", "ALUMINUM"]

# Fetch all commodities and merge on date
frames = {}
for code in codes:
    df = commodity_to_dataframe(code)
    frames[code] = df["value"]

combined = pd.DataFrame(frames).dropna()

# Calculate monthly returns
returns = combined.pct_change().dropna()

# Correlation matrix
print("Correlation Matrix (Monthly Returns)")
print("=" * 60)
print(returns.corr().round(2).to_string())

Output:

Correlation Matrix (Monthly Returns)
============================================================
              WTI  BRENT  NATURAL_GAS  COPPER  ALUMINUM
WTI          1.00   0.97         0.38    0.52      0.45
BRENT        0.97   1.00         0.35    0.50      0.44
NATURAL_GAS  0.38   0.35         1.00    0.21      0.18
COPPER       0.52   0.50         0.21    1.00      0.72
ALUMINUM     0.45   0.44         0.18    0.72      1.00

WTI and Brent are highly correlated (0.97) as expected. Copper and Aluminum also show strong co-movement (0.72), both being industrial metals sensitive to global manufacturing demand.

Data Source and Update Frequency

All commodity data is sourced from FRED (Federal Reserve Economic Data), maintained by the Federal Reserve Bank of St. Louis. EODHD checks for new data from FRED twice daily, but availability depends on when FRED’s upstream providers publish each series:

  • Daily energy series (WTI, Brent, Natural Gas, Diesel, Heating Oil, Jet Fuel, Propane) — data points exist for each business day, but the EIA publishes them in weekly batches (typically every Tuesday). The latest available data can be up to ~1 week old.
  • Weekly series (Gasoline) — published by EIA every Monday, available on EODHD the next day.
  • Monthly series (Metals, Agricultural, Indices, Coal, Uranium) — published once per month with a 2–3 week delay after the reference month ends. For example, March data typically appears in mid-April.

Data goes back to 1986 for major energy commodities (WTI, Brent) and to the 1990s for most metals and agricultural commodities.

API Endpoints Summary

EndpointDescription
GET /api/commodities/historical/{CODE}Historical prices for a specific commodity
GET /api/commodities/historical/ALL_COMMODITIESGlobal commodity price index

All endpoints require authentication via api_token parameter. The Commodities API is available across all plans, including free plan.

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