FRED DCOILWTICO — Daily CSV Download (WTI Crude Oil Price)

West Texas Intermediate (WTI) crude oil is the US benchmark for petroleum pricing and one of the most actively traded commodities in the world. Oil prices are both a cause and consequence of macroeconomic cycles — supply shocks drive inflation, while demand destruction during recessions drives prices lower. Daily observations from FRED series DCOILWTICO.

Dataset: WTI Crude Oil Price (1986–2026) · Updated —


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Source: FRED series DCOILWTICO · Federal Reserve Bank of St. Louis

Macro Takeaway

This indicator is a key component of the macro-financial monitoring framework. Its current level relative to its historical distribution — captured in the percentile and z-score above — provides immediate context for whether conditions are historically normal, stretched, or compressed.

Cross-referencing with the 10-year Treasury yield and the yield curve spread helps situate this indicator within the broader macro regime.

Dataset Overview

IndicatorWTI Crude Oil Price (1986–2026)
GeographyUnited States
FrequencyDaily (business days)
Period1986–2026
Variablesdate, wti_price_usd
FormatCSV, Excel (XLSX)
SourcesFederal Reserve Bank of St. Louis — FRED
Last updated—

Dataset Variables

The CSV and Excel files contain the following columns.

ColumnTypeDescription
dateDate (YYYY-MM-DD)Observation date
wti_price_usdFloatwti_price_usd value

Column names match the CSV headers exactly.

Download the Complete Dataset

The full dataset is available in CSV and Excel formats.

FRED Direct CSV Access

The underlying data is available from FRED under series code DCOILWTICO:

https://fred.stlouisfed.org/graph/fredgraph.csv?id=DCOILWTICO

Direct CSV Access — Eco3min Structured Dataset

https://eco3min.fr/dataset/wti-crude-oil.csv

This URL returns the complete dataset in CSV format. It can be used directly in pandas, R, curl, or any data tool.

Using the Dataset in Python

import pandas as pd

url = "https://eco3min.fr/dataset/wti-crude-oil.csv"
df = pd.read_csv(url, parse_dates=["date"])

print(df.head())
print(df["dcoilwtico"].describe())

Using the Dataset in R

library(readr)

url <- "https://eco3min.fr/dataset/wti-crude-oil.csv"
df <- read_csv(url)

head(df)
summary(df$dcoilwtico)

Both examples load the dataset directly from the URL — no download or API key required.

Methodology

The primary data source is the Federal Reserve’s FRED database, series DCOILWTICO. The data is published by the relevant US government agency and made available through FRED with consistent formatting and metadata.

This dataset is updated weekly (Saturday 08:00 UTC) via automated pull from the FRED API.

Historical Regimes

Historical regime analysis for this dataset will be added in a future update. The key stats block above provides immediate context for the current reading relative to the full historical distribution.

Related Macroeconomic Datasets

Oil prices are both a cause and consequence of macro cycles. Supply shocks drive inflation, while demand destruction during recessions drives prices lower. Cross-referencing WTI with inflation measures and the dollar index helps isolate the transmission channel — whether an oil move is driven by supply disruption, demand shifts, or dollar dynamics.

Related Research

Oil prices interact with monetary conditions through multiple channels: inflation expectations, real interest rates, central bank reaction functions, and dollar dynamics. The studies below connect energy shocks to the broader macro-financial framework.

Macroeconomic Dataset Hub

This dataset is part of the Eco3min macro-financial data repository.

Explore the Eco3min Dataset Hub

Sources

  • Federal Reserve Bank of St. Louis — FRED database

Suggested Citation