Nickel fell to 15,260 USD/T on July 4, 2025, down 0.62% from the previous day. Over the past month, Nickel's price has fallen 1.20%, and is down 12.00% 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 July of 2025.

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

The article provides an overview of the nickel stock market price, highlighting the factors that influence it, such as supply and demand dynamics, government policies, and global economic conditions. It also discusses the recent volatility in nickel prices due to the electric vehicle industry, the COVID-19 pandemic, and geopolitical events. Investors and traders interested in nickel stocks can use this information to make informed investment decisions.

Learn about the key factors that influence the stock price of nickel, including global economic conditions, industrial demand, supply constraints, currency exchange rates, investor sentiment, and speculations.

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

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

In May 2024, the price of one metric ton of nickel stood at some ********* U.S. dollars. In comparison, in December 2016, the price of nickel was just below ****** U.S. dollars per metric ton. Thus, the nickel price has increased considerably in recent years, though it continuously fluctuates. In the beginning of 2022, however, the price of nickel skyrocketed due to disruptions to supply chains and a wide scarcity of raw materials and metals. Overview of nickel Discovered in 1751, nickel is a base metal with a silvery-white lustrous appearance that has a slightly golden tinge. The metal is crucial for many global industries, especially, for example, for the production of stainless-steel. Nickel is highly corrosion-resistant and is used to plate other metals in order to protect them. Because of these useful traits, nickel is used in more than ******* products worldwide, spanning from architectural, industrial, military, transportation and aerospace, marine, currency, and consumer applications. Nickel price dynamics Though nickel is the fifth most abundant element found on Earth, as with any commodity, the price of nickel can vary widely depending on global market conditions. Following the collapse of the Soviet Union, exports of nickel increased dramatically, dropping the price of nickel in the mid-1990s to below production costs. Nickel production in the Western Hemisphere was reduced during that period. Prices then increased again, up to a high of ****** U.S. dollars per metric ton in May 2007. Since then, nickel prices have decreased, and have remained between a low of ***** U.S. dollars per metric ton and a high of ****** U.S. dollars per metric ton between 2016 and 2021. It is forecast that the price of nickel will amount to more than ****** U.S. dollars per metric ton in 2025.

The article explores the various factors that influence the nickel price in the stock market, including demand and supply dynamics, global economic conditions, and market speculation. It highlights the importance of nickel in various industries and how its price is closely tied to the growth and performance of these sectors. The article also discusses the impact of supply disruptions, changes in government policies, and demand from emerging economies on the nickel market. Additionally, it emphasizes the rol

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's Volatility May Impact TR/CC CRB Nickel index 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.

TR/CC CRB Nickel Index: A Reliable Gauge of Nickel Market Performance?

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.

Nickel Price Outlook: TR/CC CRB Nickel index Faces Volatility Amidst Shifting Supply Dynamics.

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

Norilsk Nickel reported $4.8B in Stock for its fiscal semester ending in December of 2022. Data for Norilsk Nickel | GMKN - Stock including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Investing in nickel mining stocks, ETFs, and futures contracts. Factors influencing nickel price, demand, and market dynamics. Research and industry knowledge essential for successful nickel investments.

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: A Leading Indicator?

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.

Nickel Commodity Price Outlook: DJ Commodity Nickel Index Faces Volatility

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.

NIC NICKEL INDUSTRIES LIMITED

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

Nickel Mines reported $139.82M in Stock for its fiscal semester ending in December of 2024. Data for Nickel Mines | NIC - Stock including historical, tables and charts were last updated by Trading Economics this last July in 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.

Nickel index faces volatile future, analysts predict.

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

Nickel Mines reported $182.04M in Trade Creditors for its fiscal semester ending in December of 2024. Data for Nickel Mines | NIC - Trade Creditors including historical, tables and charts were last updated by Trading Economics this last July in 2025.

The nickel market, valued at approximately $XX million in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) exceeding 4.80% from 2025 to 2033. This expansion is fueled by several key factors. The burgeoning electric vehicle (EV) sector is a primary driver, with nickel a crucial component in EV batteries. Increasing demand for stainless steel, particularly in construction and infrastructure projects globally, further contributes to market growth. Furthermore, advancements in nickel plating technologies for enhancing corrosion resistance and aesthetics across various industries are also bolstering demand. However, the market faces certain constraints, including price volatility influenced by geopolitical factors and supply chain disruptions, as well as environmental concerns related to nickel mining and processing. Diversification of supply sources and sustainable mining practices are crucial for mitigating these challenges. The market is segmented by application, with stainless steel, alloys, plating, casting, and batteries representing major segments. Key players such as Anglo American, BHP, and Glencore are actively shaping market dynamics through strategic investments and technological innovations. Geographical distribution shows strong growth potential in the Asia-Pacific region, driven primarily by China and India's expanding industrial sectors and burgeoning EV markets. North America and Europe also present significant market opportunities, although growth rates may vary depending on regional economic conditions and policy initiatives. The forecast period of 2025-2033 presents exciting possibilities for nickel market players. Companies are focusing on enhancing operational efficiencies, exploring new nickel sources, and developing innovative technologies to improve sustainability. Strategic partnerships and collaborations are also becoming increasingly prevalent, aiming to secure reliable supply chains and meet the rising global demand. The industry's ability to address sustainability concerns and adapt to fluctuating market conditions will significantly influence its long-term growth trajectory. Government regulations promoting clean energy and sustainable industrial practices will play a pivotal role in shaping the future of the nickel market. The ongoing development and adoption of high-nickel cathode materials in EV batteries are expected to be a major catalyst for market expansion throughout the forecast period. Recent developments include: August 2022: NMDC Ltd. announced its decision to explore opportunities overseas in a bid to mine lithium, nickel, and cobalt in order to meet the growing demand in India. The state-run iron-ore producer is planning to start mining in Australia, as it holds a 90.02% stake in the country's Legacy Iron Ore Ltd., December 2021: Mitsui & Co. Mineral Resources Development (Asia) Corp. (MMRDA) and Sojitz will sell all their shares in CBNC (36% in total to Sumitomo Metal Mining Co. Ltd (SMM). With the sales of the shares, SMM's shareholding ratio in CBNC will increase from the current 54% of the outstanding shares to 90%., October 2021: Renault Group announced the signing of a Memorandum of Understanding (MoU) with Terrafame, for a future supply of nickel sulphate. With this agreement, Renault Group will secure a significant annual supply of nickel sulphate from Terrafame, representing up to 15 GWh of annual capacity., In July 2021: BHP announced the signing of a nickel supply agreement from its Nickel West asset in Western Australia, with one of the world's leading sustainable energy company, Tesla Inc.. Key drivers for this market are: Rising Demand for Corrosion Resistant Alloys in the Oil and Gas Industry, Other Drivers. Potential restraints include: Rising Demand for Corrosion Resistant Alloys in the Oil and Gas Industry, Other Drivers. Notable trends are: Increasing Demand for Stainless Steel.