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

{
    "meta": {
        "name": "US Regular All Formulations Gas Price",
        "interval": "monthly",
        "unit": "Dollars per Gallon",
        "offset": 0,
        "limit": 1000,
        "total": 13
    },
    "data": [
        {"date": "2026-03-01", "value": 3.99},
        {"date": "2026-02-01", "value": 2.937},
        {"date": "2026-01-01", "value": 2.853}
    ],
    "links": {
        "next": null
    }
}

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, "fmt": "json", "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']}")

Commodity: Crude Oil Prices: West Texas Intermediate (WTI) - Cushing, Oklahoma
Unit: Dollars per Barrel
Total records: 13

  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

# 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']}")

Daily records: 249

  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

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, "fmt": "json", "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}")

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

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']}%")

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}")

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())

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