Aluminum rose to 3,567.50 USD/T on April 21, 2026, up 0.35% from the previous day. Over the past month, Aluminum's price has risen 10.53%, and is up 48.98% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Aluminum - values, historical data, forecasts and news - updated on April of 2026.

Monthly and long-term aluminium price data (US$/mt): historical series and analyst forecasts curated by FocusEconomics.

Aluminium Prices in Feb 2026: China $3569/MT and India $3661/MT. Track MoM, YoY trends, historical graph, and metals outlook.

This dataset is created to enable data-driven forecasting and explainability for short-term price movements of LME Aluminum Spot Prices by combining:

• Metal spot prices
• Global equity & commodity indices
• Mining & aluminum company stock prices
• Freight & shipping stress indicator
• Physical aluminum inventory levels

The objective is to support deep learning, time-series modeling, and feature attribution research in commodity markets.

This dataset is structured as a SQLite3 database so that users can directly plug it into ML / DL pipelines without CSV wrangling.

🎯 Primary Use Case

Forecast 1-week ahead LME Aluminum spot prices using multi-source market signals and perform model explainability to understand market drivers.

🗂️ Database Structure

The database contains four core tables:

Table Name	Description
lme-aluminum-spot-prices	Historical LME Aluminum spot prices
google-finance-index	Global indices, mining stocks, volatility, oil proxy
lme-aluminum-daily-inventory	Daily physical aluminum inventory levels
baltic-dry-index	Global shipping & freight demand indicator

🪙 Target Variable

LME Aluminum Spot Price

• Daily spot price of aluminum traded on the LME
• Acts as the prediction target

Source: MetalAPI (TopCable)

🌍 Market & Equity Indicators

These indices and stocks capture macro, mining, risk sentiment, and industrial demand signals.

Ticker	What it Represents
SPGSIA	S&P Global BMI Industrial Metals Index
UKX	FTSE 100
.INX	S&P 500
INDEXDB:DAX	DAX
HSI	Hang Seng Index
INDEXFTSE:XIN9	FTSE China A50
ASX:RIO	Rio Tinto stock price
ASX:BHP	BHP stock price
NYSE:AA	Alcoa stock price
NSE:HINDALCO	Hindalco Industries stock price
NYMEX:BZW00	Brent crude proxy (energy cost for smelting)
INDEXCBOE:VXS	CBOE S&P 500 3-Month Volatility Index

Source: Google Finance

🏗️ Physical Supply Indicator

London Metal Exchange Inventory Data

Daily LME aluminum inventory stock levels.

Why important:

Inventory acts as real supply pressure signal — often leading price movement.

Source: WestMetall

🚢 Global Trade & Demand Indicator

Baltic Dry Index

Captures:

• Global shipping demand
• Industrial & commodity trade flow
• Leading signal for metal demand

Source: Investing.com

🧪 Potential Research & ML Use Cases

Beyond forecasting, this dataset enables:

1. Time-Series Forecasting

• LSTM / GRU / Temporal CNN / Transformer
• Multi-variate sequence modeling
• 7-day ahead forecasting

2. Feature Explainability

• SHAP / Attention visualization
• Identify which market signal moved aluminum price
• Inventory vs equity vs freight vs volatility impact

3. Causality & Lead-Lag Analysis

• Granger causality
• Cross-correlation heatmaps
• Discover leading indicators

4. Regime Detection

• Detect bull / bear / supply shock regimes
• Clustering on macro signals

5. Synthetic Trading Strategy Backtests

• Build signal-based aluminum trading strategy
• Use indices as leading signals

6. Supply Chain Intelligence Modeling

• Map inventory + freight + energy → price

7. Representation Learning

• Learn latent embeddings of commodity market states

8. Transfer Learning

• Use learned embeddings for Copper, Zinc, Nickel forecasting

⏱️ Time Range

01 Jan 2015 → 05 Feb 2026 Daily frequency

🧹 Preprocessing Notes

• All dates are aligned to daily frequency
• Missing market holidays handled via forward fill
• Ready for direct modeling from SQLite

⚙️ How to Use

import sqlite3
import pandas as pd

conn = sqlite3.connect("autonomous-metal-db.db")

spot = pd.read_sql("SELECT * FROM `lme-aluminum-spot-prices`", conn)
indices = pd.read_sql("SELECT * FROM `google-finance-index`", conn)
inventory = pd.read_sql("SELECT * FROM `lme-aluminum-daily-inventory`", conn)
bdi = pd.read_sql("SELECT * FROM `baltic-dry-index`", conn)

👤 Who Is This Dataset For?

• Quant researchers
• Commodity ...

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Metric	Value
Market Size (2025E)	USD 183.1 Billion
Market Size (2035F)	USD 331.1 Billion
CAGR (2025 to 2035)	6.1%

Aluminium Market Analyzed by Top Investment Segments

Series	CAGR (2025 to 2035)
Series 5 Aluminium	7.2%

Processing	CAGR (2025 to 2035)
Extrusions	7.4%

Country Wise Analysis

Country	CAGR (2025 to 2035)
USA	4.5%

Country	CAGR (2025 to 2035)
UK	4.2%

Country	CAGR (2025 to 2035)
European Union (EU)	4.8%

Country	CAGR (2025 to 2035)
Japan	4.1%

Country	CAGR (2025 to 2035)
South Korea	4.3%

Competitive Outlook

Company Name	Estimated Market Share (%)
China Hongqiao Group	14-19%
UC Rusal	9-13%
Alcoa Corporation	7-11%
Rio Tinto Aluminium	5-9%
Emirates Global Aluminium (EGA)	4-8%
Other Companies (combined)	45-55%

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Aluminum Index: The Future of TR/CC CRB?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Aluminum prices in , March, 2026 For that commodity indicator, we provide data from January 1960 to March 2026. The average value during that period was 1464.5 USD per metric ton with a minimum of 496.04 USD per metric ton in March 1962 and a maximum of 3577.86 USD per metric ton in June 1988. | TheGlobalEconomy.com