HRC Steel rose to 880 USD/T on June 9, 2025, up 0.23% from the previous day. Over the past month, HRC Steel's price has fallen 1.12%, but it is still 21.38% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for HRC Steel.

Steel rose to 2,949 CNY/T on June 6, 2025, up 0.31% from the previous day. Over the past month, Steel's price has fallen 4.69%, and is down 14.47% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Steel - values, historical data, forecasts and news - updated on June of 2025.

China Warehouse Stock: Shanghai Future Exchange: Steel Rebar data was reported at 148,997.000 Ton in 13 May 2025. This records a decrease from the previous number of 168,703.000 Ton for 12 May 2025. China Warehouse Stock: Shanghai Future Exchange: Steel Rebar data is updated daily, averaging 18,645.000 Ton from Jul 2009 (Median) to 13 May 2025, with 3851 observations. The data reached an all-time high of 239,688.000 Ton in 17 Apr 2025 and a record low of 0.000 Ton in 20 Jun 2023. China Warehouse Stock: Shanghai Future Exchange: Steel Rebar data remains active status in CEIC and is reported by Shanghai Futures Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZB: Shanghai Futures Exchange: Commodity Futures: Stock.

China Warehouse Stock: Shanghai Future Exchange: Steel Wire Rod data was reported at 0.000 Ton in 13 May 2025. This stayed constant from the previous number of 0.000 Ton for 12 May 2025. China Warehouse Stock: Shanghai Future Exchange: Steel Wire Rod data is updated daily, averaging 0.000 Ton from Jul 2009 (Median) to 13 May 2025, with 3851 observations. The data reached an all-time high of 45,834.000 Ton in 17 May 2010 and a record low of 0.000 Ton in 13 May 2025. China Warehouse Stock: Shanghai Future Exchange: Steel Wire Rod data remains active status in CEIC and is reported by Shanghai Futures Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZB: Shanghai Futures Exchange: Commodity Futures: Stock.

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.

Gerdau's (GGB) Future: A Steel Of A Deal?

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.

Ternium: A Steel Giant's Future (TX)

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

Iron Ore rose to 96.18 USD/T on June 6, 2025, up 0.50% from the previous day. Over the past month, Iron Ore's price has fallen 3.17%, and is down 11.34% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Iron Ore - values, historical data, forecasts and news - updated on June of 2025.

U.S. Steel shares rose over 14% after President Trump ordered a review of its acquisition by Nippon Steel, signaling potential changes in the deal's future.

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.

Nickel Index: The Future of the Metal?

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 2025, the price of platinum is forecast to hover around 1,150 U.S. dollars per troy ounce. Meanwhile, the cost of per troy ounce of gold is expected to amount to 1,700 U.S. dollars.

Precious metals

Precious metals are counted among the most valuable commodities worldwide. The most well known such metals are gold, silver and the platinum group metals. A precious metal can be used as an industrial commodity or as an investment. The major areas of application include the following sectors: technology, car-making, industrial manufacturing and jewelry making. Furthermore, gold and silver are used as coinage metals, and gold reserves are held by the central banks of many countries worldwide in order to store value or for use as a redemption medium. The idea behind this procedure is that gold reserves will help secure and stabilize the countries’ respective currencies. At 8,100 tons, the United States is the country with the most extensive stock of gold. It is kept in an underground vault at the New York Federal Reserve Bank.

Russia, the United States, Canada, South Africa and China are the main producers of precious metals. Silver is the most abundant of the metals, followed by gold and palladium. Barrick Gold is the world’s largest gold mining company. The Toronto-based firm produced some five million ounces of gold in 2020. The leading silver producers include Mexico-based Fresnillo, Poland’s KGHM Polska Miedž and the mining giant Glencore. Anglo Platinum and Impala are the key mining companies to produce platinum group metals.

In 2023, Silver prices are expected to settle at around 23.5 U.S. dollars per troy ounce. It is expected to remain the precious metal with the lowest value per ounce. The price of gold is forecast to drop to around 1,663 U.S. dollars per ounce, making it the most expensive precious metal in 2023.

Aluminum rose to 2,480.75 USD/T on June 9, 2025, up 1.11% from the previous day. Over the past month, Aluminum's price has risen 0.40%, but it is still 3.55% lower than a year ago, 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 June 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.

NeoMetal: A Metal of the Future? (NMT)

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 Future of the Red Metal?

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

Tin fell to 32,343 USD/T on June 6, 2025, down 0.67% from the previous day. Over the past month, Tin's price has risen 2.23%, and is up 2.83% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Tin - values, historical data, forecasts and news - updated on June of 2025.

库存:仓单:上海期货交易所:线材在04-24-2025达0.000吨，相较于04-23-2025的0.000吨保持不变。库存:仓单:上海期货交易所:线材数据按日更新，07-03-2009至04-24-2025期间平均值为0.000吨，共3841份观测结果。该数据的历史最高值出现于05-17-2010，达45,834.000吨，而历史最低值则出现于04-24-2025，为0.000吨。CEIC提供的库存:仓单:上海期货交易所:线材数据处于定期更新的状态，数据来源于上海期货交易所，数据归类于中国经济数据库的金融市场 – Table CN.ZB: Shanghai Futures Exchange: Commodity Futures: Stock。

库存:仓单:上海期货交易所:螺纹钢在04-24-2025达203,303.000吨，相较于04-23-2025的201,797.000吨有所增长。库存:仓单:上海期货交易所:螺纹钢数据按日更新，07-03-2009至04-24-2025期间平均值为18,645.000吨，共3841份观测结果。该数据的历史最高值出现于04-17-2025，达239,688.000吨，而历史最低值则出现于06-20-2023，为0.000吨。CEIC提供的库存:仓单:上海期货交易所:螺纹钢数据处于定期更新的状态，数据来源于上海期货交易所，数据归类于中国经济数据库的金融市场 – Table CN.ZB: Shanghai Futures Exchange: Commodity Futures: Stock。

仓单库存:上海期货交易所:不锈钢在05-08-2020达833.000吨，相较于05-07-2020的833.000吨保持不变。仓单库存:上海期货交易所:不锈钢数据按日更新，01-09-2020至05-08-2020期间平均值为833.000吨，共77份观测结果。该数据的历史最高值出现于02-24-2020，达2,137.000吨，而历史最低值则出现于01-17-2020，为124.000吨。CEIC提供的仓单库存:上海期货交易所:不锈钢数据处于定期更新的状态，数据来源于上海期货交易所，数据归类于中国经济数据库的金融市场 – Table CN.ZB : 上海期货交易所 : 仓单库存。

Nickel fell to 15,395 USD/T on June 9, 2025, down 0.61% from the previous day. Over the past month, Nickel's price has fallen 1.00%, and is down 13.95% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Nickel - values, historical data, forecasts and news - updated on June of 2025.

Gerdau stock is predicted to yield high returns, driven by strong fundamentals and a favorable macroeconomic environment. However, investors should be aware of risks associated with the industry, such as cyclical downturns and geopolitical uncertainty. Additionally, the stock's performance may be impacted by changes in government policies, foreign exchange fluctuations, and global economic conditions.

Copper rose to 4.89 USD/Lbs on June 9, 2025, up 1.65% from the previous day. Over the past month, Copper's price has risen 6.69%, and is up 7.68% 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 June of 2025.