Copper fell to 5.54 USD/Lbs on July 11, 2025, down 0.94% from the previous day. Over the past month, Copper's price has risen 14.41%, and is up 20.55% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Copper - values, historical data, forecasts and news - updated on July of 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Copperindex: The Future of Copper Pricing?

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

The copper price on the stock market is highly influential and often considered a leading indicator of economic strength and activity. This article explores the factors influencing copper price, its implications, and its role as an investment option.

Of the five largest copper mining companies, U.S. mining company Freeport-McMoRan saw the largest growth in its share price over recent years. Between January 2018 and March 2024, the share price of the Phoenix-based company saw its share price increase by over 141 percent. Conversely, over this period, Canada-based mining and metals company First Quantum Minerals saw slight decreases in their share price.

Discover the top copper companies on the stock market, including Freeport-McMoRan, Southern Copper, Grupo Mexico, and KGHM Polska Miedz. Learn about their operations, financial performance, and investment opportunities in the thriving copper industry.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Is Copper's Index a Reliable Guide?

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

In November 2024, the average monthly price for copper stood at over ***** U.S. dollars per metric ton. This is down from a monthly high exceeding ****** U.S. dollars in March 2024, which was among the highest monthly values observed in the past decade.

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Copper in Focus as TR/CC CRB Copper Index Outlook Matures

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

Blast Furnace Tuyere Stock Market Outlook

The global blast furnace tuyere stock market size was valued at approximately USD 455 million in 2023 and is projected to reach about USD 722 million by 2032, registering a compound annual growth rate (CAGR) of 5.25% over the forecast period. The growth of this market is majorly driven by the increased demand for steel and iron production, particularly in emerging economies where infrastructure development and urbanization are at their peak. The advancements in blast furnace technology also play a significant role in driving the market forward.

One of the primary growth factors of the blast furnace tuyere stock market is the rising demand for high-quality steel products across various industries, including automotive, construction, and consumer goods. As nations continue to develop and industrialize, the need for robust infrastructure and durable consumer products creates a parallel demand for advanced steel production techniques. This necessitates the use of efficient and durable tuyere stocks, which serve as critical components in the blast furnace operations for optimizing the combustion process and improving overall productivity.

Another significant factor contributing to market growth is the ongoing technological advancements in blast furnace operations. Innovations such as automated control systems and enhanced material compositions for tuyere stocks have resulted in more efficient and cost-effective production processes. These advancements not only enhance the lifespan and performance of the tuyere stocks but also contribute to reduced operational costs, thereby boosting the market demand. Furthermore, the integration of advanced sensors and monitoring systems in modern blast furnaces ensures better maintenance and operational efficiency, driving the need for high-quality tuyere stocks.

The increasing focus on reducing carbon emissions and promoting sustainable steel production is also a crucial factor propelling the market. Governments and environmental agencies worldwide are imposing stringent regulations on industrial emissions, prompting steel manufacturers to adopt cleaner and more efficient production methods. The use of advanced tuyere stocks, which optimize the blast furnace combustion process and minimize fuel consumption, aligns with these sustainability goals and supports market growth. Additionally, the shift towards electric arc furnaces (EAF) in certain regions, although a potential challenge, also opens avenues for innovation and development in tuyere stock applications.

From a regional perspective, Asia Pacific dominates the blast furnace tuyere stock market due to the presence of major steel-producing countries such as China, India, and Japan. The region's robust industrial base, coupled with significant investments in infrastructure development, drives the demand for steel and, consequently, for blast furnace tuyere stocks. North America and Europe also represent substantial market shares, driven by technological advancements and the presence of established steel industries. Meanwhile, Latin America and the Middle East & Africa are emerging markets with significant growth potential, supported by ongoing industrialization and infrastructure projects.

Material Type Analysis

The blast furnace tuyere stock market is segmented by material type into copper, copper alloy, cast iron, and others. Each material type offers distinct advantages and is selected based on specific application requirements and operational conditions. Copper tuyere stocks are highly preferred due to their excellent thermal conductivity and durability, which are critical for efficient heat transfer and prolonged service life in blast furnace operations. The use of copper tuyere stocks significantly enhances the performance of the blast furnace by ensuring consistent airflow and optimal combustion.

Copper alloy tuyere stocks are another popular choice due to their enhanced mechanical properties and resistance to wear and corrosion. The addition of other metals such as nickel, chromium, and aluminum to copper improves its strength and longevity, making it suitable for harsh operational environments. These tuyere stocks are particularly favored in high-capacity blast furnaces that operate under extreme temperatures and pressures, where durability and performance are paramount.

Cast iron tuyere stocks, although less common than copper and copper alloy counterparts, still hold a significant market share. Cast iron is known for its excellent casting properties and cost-effectiveness, maki

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Ero Copper's Copper Future: Shining Bright or Tarnishing? (ERO)

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

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Copperindex: The Key to Understanding Copper's Future?

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

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Southern Copper (SCCO) - A Copper Bottom?

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

Australia's copper mining industry is experiencing significant shifts, with Sandfire Resources Limited emerging as the leader among ASX-listed copper miners. With a market capitalization of around **** billion Australian dollars in June 2025, Sandfire has taken the top spot following BHP's acquisition of former industry leader OZ Minerals in early 2023. Copper mining in Australia Australia has some of the largest reserves of copper in the world. The leading markets for copper exports from Australia can be found in Asia, with China being the largest Australian copper recipient by value. Since 2019, the production of copper from Australian mines has followed a downward trend, with production quantities only slightly increasing in 2023. Despite this, Australia could play an important role in meeting the rising global demand for this critical mineral. Australia's global mining presence Australia's significance in the global mining industry extends beyond copper. BHP Group Limited, a British-Australian mining giant, leads the metals and mining companies on the ASX by market capitalization. BHP's diverse portfolio, which includes coal, copper, and iron ore, contributed to its impressive revenue of around **** billion U.S. dollars in fiscal year 2024. The mining industry's importance to Australia's economy is highlighted by its substantial value addition, exceeding *** billion Australian dollars in fiscal year 2024, and its role as a major employer in the country.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Copper index forecast: Steady growth anticipated

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

Southern Copper Corporation's strong financial performance, robust copper demand, and ongoing expansion efforts suggest potential for continued share price appreciation. However, commodity price volatility, geopolitical uncertainties, and operational risks pose risks that could impact its performance and stock value.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

M.A.C. Copper Shares Could See Upswing, Analysts Say (MTAL)

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

BHP Group Limited led the metals and mining companies listed on the Australian Securities Exchange as of June 2025, with a market capitalization of over *** billion Australian dollars. BHP (formerly known as BHP Billiton) is a British-Australian mining company, with its major headquarters in London and Melbourne. It is one of the leading mining companies in the world. Mining company BHP BHP is a global mining, metals, and petroleum company with operations in Australia, North America, South America, and the UK. In the fiscal year 2024, BHP’s revenue reached **** billion U.S. dollars. The profit of BHP was reported at over *** billion U.S. dollars in the same year. The company primarily focuses on the extraction of coal, copper, iron ore, and petroleum. BHP's iron ore segment had the highest revenue at over ** billion U.S. dollars in the fiscal year 2024.  Mining in Australia  Mining is one of Australia’s largest industries, and the country plays a crucial role in the trade of mining commodities. The value added by the mining industry in Australia exceeded ****billion Australian dollars in 2024. Furthermore, the mining industry provides employment opportunities to over *** thousand people in Australia. Australia’s role in the mining industry is expected to continue to grow, particularly in Asia, due to its vast resources, proximity, and willingness to participate in the global marketplace.