United States Steel stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

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.

The latest closing stock price for Worthington Steel as of May 09, 2025 is 26.01. An investor who bought $1,000 worth of Worthington Steel stock at the IPO in 2023 would have $67 today, roughly 0 times their original investment - a 6.72% compound annual growth rate over 1 years. The all-time high Worthington Steel stock closing price was 46.06 on November 11, 2024. The Worthington Steel 52-week high stock price is 47.19, which is 81.4% above the current share price. The Worthington Steel 52-week low stock price is 21.30, which is 18.1% below the current share price. The average Worthington Steel stock price for the last 52 weeks is 32.71. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

Steel Tube stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

The latest closing stock price for Steel Partners Holdings LP as of May 09, 2025 is 37.25. An investor who bought $1,000 worth of Steel Partners Holdings LP stock at the IPO in 2011 would have $-1,000 today, roughly -1 times their original investment - a -51.73% compound annual growth rate over 14 years. The all-time high Steel Partners Holdings LP stock closing price was 1000000.00 on April 19, 2011. The Steel Partners Holdings LP 52-week high stock price is 48.45, which is 30.1% above the current share price. The Steel Partners Holdings LP 52-week low stock price is 27.95, which is 25% below the current share price. The average Steel Partners Holdings LP stock price for the last 52 weeks is 39.53. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

📈 Daily Historical Stock Price Data for China Steel Corporation (2005–2025)

A clean, ready-to-use dataset containing daily stock prices for China Steel Corporation from 2005-09-29 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: China Steel Corporation Ticker Symbol: 2002A.TW Date Range: 2005-09-29 to 2025-05-28 Frequency: Daily Total Records: 4824 rows (one… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-china-steel-corporation-20052025.

Reliance Steel & Aluminum stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

The latest closing stock price for Olympic Steel as of May 28, 2025 is 30.52. An investor who bought $1,000 worth of Olympic Steel stock at the IPO in 1994 would have $1,252 today, roughly 1 times their original investment - a 2.65% compound annual growth rate over 31 years. The all-time high Olympic Steel stock closing price was 70.94 on April 05, 2024. The Olympic Steel 52-week high stock price is 52.55, which is 72.2% above the current share price. The Olympic Steel 52-week low stock price is 26.32, which is 13.8% below the current share price. The average Olympic Steel stock price for the last 52 weeks is 37.82. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

📈 Daily Historical Stock Price Data for Algoma Steel Group Inc. (2021–2025)

A clean, ready-to-use dataset containing daily stock prices for Algoma Steel Group Inc. from 2021-03-04 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Algoma Steel Group Inc. Ticker Symbol: ASTL Date Range: 2021-03-04 to 2025-05-28 Frequency: Daily Total Records: 1064 rows (one per… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-algoma-steel-group-inc-20212025.

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.

(X) United States Steel: A Blast Furnace of Growth or a Cold Rolling of Profits?

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.

Olympic Steel Inc. (ZEUS): Steel the Show with Stellar Returns?

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.

Algoma Steel Stock (ASTL) Forecast

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.

Algoma Steel Stock (ASTL) Forecast Upbeat

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

Algoma Steel Group Inc. is expected to maintain its strong performance in the coming years, driven by the growing demand for steel in the automotive and construction sectors. The company's focus on operational efficiency and cost control is likely to contribute to its profitability. However, the company faces risks related to fluctuations in the price of steel, changes in trade policies, and labor costs. The company's dependence on a limited number of customers and its exposure to competition from domestic and international producers could also impact its performance.

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.

ASTL.WT:TSX Algoma Steel Group Inc. Wt

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

📈 Daily Historical Stock Price Data for Aichi Steel Corporation (2001–2025)

A clean, ready-to-use dataset containing daily stock prices for Aichi Steel Corporation from 2001-01-01 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Aichi Steel Corporation Ticker Symbol: 5482.T Date Range: 2001-01-01 to 2025-05-28 Frequency: Daily Total Records: 6085 rows (one per… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-aichi-steel-corporation-20012025.

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.

ASTLW Algoma Steel Group Inc. Warrant

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

Steel Dynamics stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

📈 Daily Historical Stock Price Data for Chubu Steel Plate Co., Ltd. (2022–2025)

A clean, ready-to-use dataset containing daily stock prices for Chubu Steel Plate Co., Ltd. from 2022-12-28 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Chubu Steel Plate Co., Ltd. Ticker Symbol: 5461.T Date Range: 2022-12-28 to 2025-05-28 Frequency: Daily Total Records: 590… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-chubu-steel-plate-co-ltd-20222025.

Steel Dynamics current p/s ratio as of May 13, 2025 is 1.22. Steel Dynamics average p/s ratio for 2024 was 1.15, a 21.05% increase from 2023. Steel Dynamics average p/s ratio for 2023 was 0.95, a 39.71% increase from 2022. Steel Dynamics average p/s ratio for 2022 was 0.68, a 17.07% increase from 2021. P/s ratio can be defined as the price to sales or PS ratio is calculated by taking the latest closing price and dividing it by the most recent sales per share number. The PS ratio is an additional way to assess whether a stock is over or under valued and is used primarily in cases where earnings are negative and the PE ratio cannot be utilized.