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Twitter"The 'KGHM Dataset' is a meticulously curated collection of financial and economic data specifically designed for the purpose of stock price prediction for KGHM, a leading copper mining company. This dataset encompasses a wide range of features including historical prices, macroeconomic indicators, industry-related metrics, company-specific financials, and technical indicators. The dataset comprises 59 carefully selected features that have the potential to influence the stock price of KGHM. The data has been sourced from reputable platforms such as Yahoo Finance, Wikipedia, and the official website of KGHM. The dataset has undergone rigorous pre-processing and feature engineering to ensure data quality and relevance for the machine learning models used in the stock price prediction analysis. It serves as a valuable resource for conducting in-depth analysis and developing accurate predictive models for KGHM's stock price movements."
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TwitterOf 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 *** percent. Conversely, over this period, Canada-based mining and metals company First Quantum Minerals saw slight decreases in their share price.
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Copper rose to 5.19 USD/Lbs on December 2, 2025, up 0.35% from the previous day. Over the past month, Copper's price has risen 3.22%, and is up 25.36% 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 December of 2025.
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Southern Copper stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.
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This dataset encapsulates a detailed examination of market dynamics over a five-year period, focusing on the fluctuation of prices and trading volumes across a diversified portfolio. It covers various sectors including energy commodities like natural gas and crude oil, metals such as copper, platinum, silver, and gold, cryptocurrencies including Bitcoin and Ethereum, and key stock indices and companies like the S&P 500, Nasdaq 100, Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta Platforms. This dataset serves as a valuable resource for analyzing trends and patterns in global markets.
Date: The date of the recorded data, formatted as DD-MM-YYYY. Natural_Gas_Price: Price of natural gas in USD per million British thermal units (MMBtu). Natural_Gas_Vol.: Trading volume of natural gas Crude_oil_Price: Price of crude oil in USD per barrel. Crude_oil_Vol.: Trading volume of crude oil Copper_Price: Price of copper in USD per pound. Copper_Vol.: Trading volume of copper Bitcoin_Price: Price of Bitcoin in USD. Bitcoin_Vol.: Trading volume of Bitcoin Platinum_Price: Price of platinum in USD per troy ounce. Platinum_Vol.: Trading volume of platinum Ethereum_Price: Price of Ethereum in USD. Ethereum_Vol.: Trading volume of Ethereum S&P_500_Price: Price index of the S&P 500. Nasdaq_100_Price: Price index of the Nasdaq 100. Nasdaq_100_Vol.: Trading volume for the Nasdaq 100 index Apple_Price: Stock price of Apple Inc. in USD. Apple_Vol.: Trading volume of Apple Inc. stock Tesla_Price: Stock price of Tesla Inc. in USD. Tesla_Vol.: Trading volume of Tesla Inc. stock Microsoft_Price: Stock price of Microsoft Corporation in USD. Microsoft_Vol.: Trading volume of Microsoft Corporation stock Silver_Price: Price of silver in USD per troy ounce. Silver_Vol.: Trading volume of silver Google_Price: Stock price of Alphabet Inc. (Google) in USD. Google_Vol.: Trading volume of Alphabet Inc. stock Nvidia_Price: Stock price of Nvidia Corporation in USD. Nvidia_Vol.: Trading volume of Nvidia Corporation stock Berkshire_Price: Stock price of Berkshire Hathaway Inc. in USD. Berkshire_Vol.: Trading volume of Berkshire Hathaway Inc. stock Netflix_Price: Stock price of Netflix Inc. in USD. Netflix_Vol.: Trading volume of Netflix Inc. stock Amazon_Price: Stock price of Amazon.com Inc. in USD. Amazon_Vol.: Trading volume of Amazon.com Inc. stock Meta_Price: Stock price of Meta Platforms, Inc. (formerly Facebook) in USD. Meta_Vol.: Trading volume of Meta Platforms, Inc. stock Gold_Price: Price of gold in USD per troy ounce. Gold_Vol.: Trading volume of gold
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Investing in TSX copper stocks allows investors to gain exposure to the copper industry and potentially benefit from the demand and price fluctuations of copper in the global market. Learn about the advantages, including potential capital appreciation, diversification opportunities, and potential dividend income. However, it's important to understand the risks and conduct thorough research before investing.
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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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View monthly updates and historical trends for Copper Price. Source: World Bank. Track economic data with YCharts analytics.
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This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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Understanding copper metal stocks is crucial for gauging supply-demand dynamics and potential price movements. This article explains the sources of copper metal stocks, their importance, and the monitoring process, providing valuable insights for investors, traders, and policymakers.
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Southern Copper reported $1.03B in Stock for its fiscal quarter ending in September of 2025. Data for Southern Copper | SCCO - Stock including historical, tables and charts were last updated by Trading Economics this last December in 2025.
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The copper price share market is a specialized segment of the stock market where investors can buy and sell shares of copper mining companies. This article explains how the copper price share market is influenced by factors such as global copper supply and demand, macroeconomic conditions, commodity market trends, and company-specific factors. It also highlights the opportunities and risks associated with investing in the copper price share market.
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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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Southern Copper reported $115.06B in Market Capitalization this December of 2025, considering the latest stock price and the number of outstanding shares.Data for Southern Copper | SCCO - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last December in 2025.
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Capstone Copper Corp reported $7.04B in Market Capitalization this June of 2025, considering the latest stock price and the number of outstanding shares.Data for Capstone Copper Corp | CSC - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last December in 2025.
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Indonesia Mining Production: Stock: Value: Copper data was reported at 67,015.072 IDR bn in 2015. This records an increase from the previous number of 38,510.430 IDR bn for 2014. Indonesia Mining Production: Stock: Value: Copper data is updated yearly, averaging 36,422.868 IDR bn from Dec 1999 (Median) to 2015, with 17 observations. The data reached an all-time high of 97,709.947 IDR bn in 2006 and a record low of 199.475 IDR bn in 2000. Indonesia Mining Production: Stock: Value: Copper data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Mining and Manufacturing Sector – Table ID.BAE003: Mining Production: Stock.
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Stock Price Time Series for Co-Tech Development. Co-Tech Development Corporation, together with its subsidiaries, engages in the production and sale of copper foil for printed circuit board industry in Taiwan and China. It is also involved in investment activities. The company was formerly known as Co-Tech Copper Foil Corporation and changed its name to Co-Tech Development Corporation in June 2014. Co-Tech Development Corporation was founded in 1998 and is headquartered in Taipei, Taiwan.
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Capstone Copper Corp reported $55.91 in PE Price to Earnings for its fiscal quarter ending in June of 2025. Data for Capstone Copper Corp | CSC - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last December in 2025.
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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.
The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:
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.
This dataset is highly versatile and can be utilized for various financial research purposes:
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.
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.
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This dataset provides **insights into copper prices**, including current rates, historical trends, and key factors affecting price fluctuations. Copper is essential in **construction**, **electronics**, and **transportation** industries. Investors, traders, and analysts use accurate copper price data to guide decisions related to **trading**, **futures**, and **commodity investments**.
### **Key Features of the Dataset**
#### **Live Market Data and Updates**
Stay updated with the latest **copper price per pound** in USD. This data is sourced from exchanges like the **London Metal Exchange (LME)** and **COMEX**. Price fluctuations result from **global supply-demand shifts**, currency changes, and geopolitical factors.
#### **Interactive Copper Price Charts**
Explore **dynamic charts** showcasing real-time and historical price movements. These compare copper with **gold**, **silver**, and **aluminium**, offering insights into **market trends** and inter-metal correlations.
### **Factors Driving Copper Prices**
#### **1. Supply and Demand Dynamics**
Global copper supply is driven by mining activities in regions like **Peru**, **China**, and the **United States**. Disruptions in production or policy changes can cause **supply shocks**. On the demand side, **industrial growth** in countries like **India** and **China** sustains demand for copper.
#### **2. Economic and Industry Trends**
Copper prices often reflect **economic trends**. The push for **renewable energy** and **electric vehicles** has boosted long-term demand. Conversely, economic downturns and **inflation** can reduce demand, lowering prices.
#### **3. Impact of Currency and Trade Policies**
As a globally traded commodity, copper prices are influenced by **currency fluctuations** and **tariff policies**. A strong **US dollar** typically suppresses copper prices by increasing costs for international buyers. Trade tensions can also disrupt **commodity markets**.
### **Applications and Benefits**
This dataset supports **commodity investors**, **traders**, and **industry professionals**:
- **Investors** forecast price trends and manage **investment risks**.
- **Analysts** perform **market research** using price data to assess **copper futures**.
- **Manufacturers** optimize supply chains and **cost forecasts**.
Explore more about copper investments on **Money Metals**:
- [**Buy Copper Products**](https://www.moneymetals.com/buy/copper)
- [**95% Copper Pennies (Pre-1983)**](https://www.moneymetals.com/pre-1983-95-percent-copper-pennies/4)
- [**Copper Buffalo Rounds**](https://www.moneymetals.com/copper-buffalo-round-1-avdp-oz-999-pure-copper/297)
### **Copper Price Comparisons with Other Metals**
Copper prices often correlate with those of **industrial** and **precious metals**:
- **Gold** and **silver** are sensitive to **inflation** and currency shifts.
- **Iron ore** and **aluminium** reflect changes in **global demand** within construction and manufacturing sectors.
These correlations help traders develop **hedging strategies** and **investment models**.
### **Data Variables and Availability**
Key metrics include:
- **Copper Price Per Pound:** The current market price in USD.
- **Copper Futures Price:** Data from **COMEX** futures contracts.
- **Historical Price Trends:** Long-term movements, updated regularly.
Data is available in **CSV** and **JSON** formats, enabling integration with analytical tools and platforms.
### **Conclusion**
Copper price data is crucial for **monitoring global commodity markets**. From **mining** to **investment strategies**, copper impacts industries worldwide. Reliable data supports **risk management**, **planning**, and **economic forecasting**.
For more tools and data, visit the **Money Metals** [Copper Prices Page](https://www.moneymetals.com/copper-prices).
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Twitter"The 'KGHM Dataset' is a meticulously curated collection of financial and economic data specifically designed for the purpose of stock price prediction for KGHM, a leading copper mining company. This dataset encompasses a wide range of features including historical prices, macroeconomic indicators, industry-related metrics, company-specific financials, and technical indicators. The dataset comprises 59 carefully selected features that have the potential to influence the stock price of KGHM. The data has been sourced from reputable platforms such as Yahoo Finance, Wikipedia, and the official website of KGHM. The dataset has undergone rigorous pre-processing and feature engineering to ensure data quality and relevance for the machine learning models used in the stock price prediction analysis. It serves as a valuable resource for conducting in-depth analysis and developing accurate predictive models for KGHM's stock price movements."