WTI Oil Price — History, Shocks & Macro Signals

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.

WTI crude oil is the benchmark for US oil prices.
Its history reflects cycles of geopolitical shocks, economic expansions, and recessions.

Dataset: WTI Crude Oil Price (1986–2026) · Updated 2026-05-04

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

Oil prices spike during supply shocks and collapse during recessions — making them one of the clearest real-time signals of the global economic cycle.

Macro Takeaway

Oil prices are one of the main drivers of inflation shocks.
Sharp increases (1990, 2008, 2022) tighten financial conditions,
while collapses (2009, 2020) reflect demand destruction and recession risk.

The key distinction is whether price moves are driven by supply constraints
or demand weakness — two regimes with opposite implications for growth and markets.

Dataset Overview

Dataset Variables

The CSV and Excel files contain the following columns.

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.

Major Oil Price Shocks

• 1990 Gulf War spike
• 2008 commodity supercycle peak ($147)
• 2020 COVID collapse (negative futures)
• 2022 energy shock

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. This bidirectional relationship is examined in the complete guide to inflation. 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

Dataset Reference

Eco3min Research. (2026). WTI Crude Oil Price (1986–2026).<br> https://eco3min.fr/en/wti-crude-oil-price-dataset/