FRED PCOPPUSDM — Daily CSV Download (Copper Price History)
Copper — sometimes called “Dr. Copper” for its perceived ability to diagnose the health of the global economy — is the most watched industrial metal. Its price reflects global manufacturing activity, construction demand, and the infrastructure investment cycle. Copper’s correlation with global GDP growth makes it a real-time proxy for economic momentum. FRED series PCOPPUSDM since 1986.
Dataset: Copper Price History (1986–2026) · Updated —
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Source: FRED series PCOPPUSDM · IMF — International Financial Statistics via FRED
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 US industrial production and the ISM Manufacturing PMI helps situate this indicator within the broader macro regime.
Dataset Overview
| Indicator | Copper Price History (1986–2026) |
|---|---|
| Geography | Global |
| Frequency | Monthly |
| Period | 1986–2026 |
| Variables | date, copper_usd_lb |
| Format | CSV, Excel (XLSX) |
| Sources | IMF — International Financial Statistics via FRED |
| Last updated | — |
Dataset Variables
The CSV and Excel files contain the following columns.
| Column | Type | Description |
|---|---|---|
date | Date (YYYY-MM-DD) | Observation date |
copper_usd_lb | Float | Copper price in US dollars per pound |
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 PCOPPUSDM:
https://fred.stlouisfed.org/graph/fredgraph.csv?id=PCOPPUSDM
Direct CSV Access — Eco3min Structured Dataset
https://eco3min.fr/dataset/copper-price.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/copper-price.csv" df = pd.read_csv(url, parse_dates=["date"]) print(df.head()) print(df["copper_usd_lb"].describe())
Using the Dataset in R
library(readr) url <- "https://eco3min.fr/dataset/copper-price.csv" df <- read_csv(url) head(df) summary(df$copper_usd_lb)
Both examples load the dataset directly from the URL — no download or API key required.
Methodology
Primary source is the IMF’s International Financial Statistics, published monthly via FRED series PCOPPUSDM. Prices reflect the LME (London Metal Exchange) monthly average settlement.
This dataset is updated monthly 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
Copper sits at the intersection of global manufacturing demand and commodity supply constraints. Its price co-moves with industrial production and PMIs — but also reflects China’s construction cycle and the energy transition’s structural demand shift (EVs, renewables, grid infrastructure).
- WTI Crude Oil Price — Fellow cyclical commodity — oil and copper often diverge on supply vs demand dynamics
- Brent Crude Oil Price — Global energy benchmark — cross-reference with copper for demand-side confirmation
- US Industrial Production — Hard output data that copper price anticipates
- ISM Manufacturing PMI — Survey-based manufacturing activity — copper reacts to PMI moves
- US Dollar Index (DTWEXBGS) — Strong dollar typically suppresses USD-denominated commodity prices
Related Research
Copper’s macro significance extends beyond simple demand — its relationship with the dollar cycle and real interest rates shapes commodity regime transitions.
Macroeconomic Dataset Hub
This dataset is part of the Eco3min macro-financial data repository.
Explore the Eco3min Dataset Hub
Sources
- IMF — International Financial Statistics via FRED