Natural gas increased 0.21 USD/MMBtu or 5.84% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Natural gas - values, historical data, forecasts and news - updated on March of 2025.

This statistic shows the stock prices of selected oil and gas commodities from January 2, 2020 to February 4, 2025. After the Russian invasion of Ukraine in February 2022, energy prices climbed significantly. The highest increase can be observed for natural gas, whose price peaked in August and September 2022. By the beginning of 2023, natural gas price started to decline.

This data package contains all the information related to the economy of a country including price index, commodities values and info about NASDAQ members.

Natural Gas Services stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

TTF Gas decreased 8.92 EUR/MWh or 17.69% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. EU Natural Gas TTF - values, historical data, forecasts and news - updated on March of 2025.

In 2023, the price of natural gas in Europe reached 13.1 constant U.S. dollars per million British thermal units, compared with 2.5 U.S. dollars in the U.S. This was a notable decrease compared to the previous year, which had seen a steep increase in prices due to an energy supply shortage exacerbated by the Russia-Ukraine war. Since 1980, natural gas prices have typically been higher in Europe than in the United States and are expected to remain so for the coming two years. This is due to the U.S. being a significantly larger natural gas producer than Europe.

What is natural gas and why is it gaining ground in the energy market? Natural gas is commonly burned in power plants with combustion turbines that generate electricity or used as a heating fuel. Given the fact that the world’s energy demand continues to grow, natural gas was seen by some industry leaders as an acceptable "bridge-fuel" to overcome the use of more emission-intensive energy sources such as coal. Subsequently, natural gas has become the main fuel for electricity generation in the U.S., while the global gas power generation share has reached 22 percent.

How domestic production shapes U.S. natural gas prices The combination of hydraulic fracturing (“fracking”) and horizontal drilling can be regarded as one of the oil and gas industry’s biggest breakthroughs in decades, with the U.S. being the largest beneficiary. This technology has helped the industry release unprecedented quantities of gas from deposits, mainly shale and tar sands that were previously thought either inaccessible or uneconomic. It is forecast that U.S. shale gas production could reach 35 trillion cubic feet in 2050, up from 1.77 trillion cubic feet in 2000.

The US oil stock market is a key component of the global energy sector and offers individuals and institutions the opportunity to participate in the growth and profitability of the oil and gas industry. This article explores the composition of the market, the influence of factors such as oil prices and government policies, and the emergence of renewable energy companies. It also highlights the risks associated with investing in oil stocks and advises investors to carefully analyze market conditions before m

Gasoline increased 0.22 USD/GAL or 10.89% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gasoline - values, historical data, forecasts and news - updated on March of 2025.

This statistic shows the stock price development of selected petroleum companies from January 2, 2020 to April 15, 2024. After the Russian invasion of Ukraine in February 2022, oil prices increased sharply in the first quarter of 2022 since many countries depend on Russian oil. Petroleum companies highly benefited from inclined oil prices, and saw significant increases in their share prices.

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.

Global Gas (HGAS): On the Rise or Just Hot Air?

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 datasets for the Role of Financial Investors on Commodity Futures Risk Premium are weekly datasets for the period from 1995 to 2015 for three commodities in the energy market: crude oil (WTI), heating oil, and natural gas. These datasets contain futures prices for different maturities, open interest positions for each commodity (long and short open interest positions), and S&P 500 composite index. The selected commodities are traded on the New York Mercantile Exchange (NYMEX). The data comes from the Thomson Reuters Datastream and from the Commodity Futures Trading Commission (CFTC).

The relationship between oil prices and the stock market is complex and influenced by various factors. Rising oil prices can lead to increased costs for businesses and decreased consumer spending, impacting stock prices. Oil and gas companies' earnings directly depend on oil prices, which in turn affect the overall stock market. Geopolitical events and supply-demand dynamics also influence oil prices. Conversely, stock market fluctuations can indirectly affect oil prices by impacting investor sentiment

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

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 the TR/CC CRB Unleaded Gas Index a Reliable Fuel 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

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North America Rolling Stock Market Size 2025-2029

The North America rolling stock market size is forecast to increase by USD 1.93 billion, at a CAGR of 4.1% between 2024 and 2029.

The market is witnessing significant growth due to several key factors. In North America, the construction, agriculture, and logistics sectors are driving the demand for rolling stock, particularly in the form of freight wagons. The transportation cost of freight remains a catalyst for the market's expansion. Furthermore, stringent safety and environmental regulations are pushing railroad companies to invest in advanced technologies such as computer systems, artificial intelligence, and big data analytics. In the public transportation sector, the trend towards green and sustainable modes of travel is boosting the demand for light rail systems. Additionally, the rail freight sector is benefiting from the increasing demand for crude oil, natural gas, and steel. Sensors and data analytics are also playing a crucial role in optimizing rail operations and enhancing efficiency. Overall, the market is poised for growth, with numerous opportunities in various sectors, including home furniture and travel.

What will be the Size of the market During the Forecast Period?

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The market encompasses the production and deployment of railway vehicles, including locomotives, freight wagons, and rapid transit vehicles. This market exhibits strong growth, driven by expanding railway networks and increasing demand for efficient transportation solutions in urban areas due to population growth. Government agencies and private entities invest heavily in this sector to address inadequate infrastructure and improve air quality in major metropolitan cities. 
Advanced technologies, such as computer vision and artificial intelligence, are integrated into rolling stock to enhance safety, efficiency, and passenger experience. Rolling stock Original Equipment Manufacturers (OEMs) and rail operators collaborate to produce innovative solutions, including low-floor trains equipped with Wi-Fi and tank wagons designed for various cargo transport needs. The market's size and direction reflect the ongoing evolution of transportation infrastructure and the growing importance of sustainable and technologically advanced rail solutions.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Rapid transit vehicles
  Railroad cars
  Locomotives


Type

  Rail freight
  Rail passenger


Component

  Wheels and axles
  Suspension systems
  Braking systems
  Power and transmission systems
  Others


Geography

  North America

    Canada
    Mexico
    US

By Product Insights

The rapid transit vehicles segment is estimated to witness significant growth during the forecast period. The market encompasses the production, refurbishment, and expansion of rail vehicles, including locomotives, freight, wagons, and rapid transit vehicles. In North America, this sector is experiencing significant growth due to increasing investments in railway networks by both government agencies and private entities. Rapid transit vehicles, such as metro trains and light rail transit systems, are expected to witness the fastest growth, driven by the construction of urban transit systems to enhance local and regional connectivity and stimulate economic activity. Moreover, the shift towards sustainable transportation solutions and concerns over environmental pollution from fossil fuel-driven vehicles have led to a focus on improving public transportation networks.
Key technologies, such as computer vision, artificial intelligence, Wi-Fi, IoT, big data analytics, and sensor networks, are being integrated into rolling stock to enhance safety, reliability, and energy efficiency. Regulations regarding safety and environmental impact continue to evolve, driving the modernization of rolling stock and the expansion of rail networks. The rail transportation sector is increasingly competing with road and air transportation, and the need to reduce greenhouse gas emissions, improve air quality, and mitigate traffic congestion in major metropolitan cities is a major factor driving demand for rail transportation. Railroad companies are focusing on fleet expansion and rolling stock refurbishment to meet the growing passenger demand and address reliability issues.

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Market Dynamics

Our North America Rolling Stock Market researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies

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.

Jadestone Energy (JSE) : Navigating the choppy waters of oil and gas

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

Crude Oil decreased 2.12 USD/BBL or 2.95% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on March of 2025.

The US crude oil stocks chart provides a visual representation of the levels of crude oil inventories in the United States. It is closely monitored by global oil markets as fluctuations in oil stocks can have a significant impact on oil prices. The chart is published by the US Energy Information Administration (EIA) and displays a time series of crude oil inventories. Traders, investors, and analysts use the chart to identify trends and patterns that could influence the oil market. It is an essential tool f

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 the Dow Jones U.S. Oil & Gas Index Poised for Growth?

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

Graph and download economic data for CBOE Crude Oil ETF Volatility Index (OVXCLS) from 2007-05-10 to 2025-03-25 about ETF, VIX, volatility, crude, oil, stock market, and USA.