Use our Stock prices dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

The "Stock Market Dataset for AI-Driven Prediction and Trading Strategy Optimization" is designed to simulate real-world stock market data for training and evaluating machine learning models. This dataset includes a combination of technical indicators, market metrics, sentiment scores, and macroeconomic factors, providing a comprehensive foundation for developing and testing AI models for stock price prediction and trading strategy optimization.

Key Features Market Metrics:

Open, High, Low, Close Prices: Daily stock price movement. Volume: Represents the trading activity during the day. Technical Indicators:

RSI (Relative Strength Index): A momentum oscillator to measure the speed and change of price movements. MACD (Moving Average Convergence Divergence): An indicator to reveal changes in strength, direction, momentum, and duration of a trend. Bollinger Bands: Upper and lower bands around a stock price to measure volatility. Sentiment Analysis:

Sentiment Score: Simulated sentiment derived from financial news and social media, ranging from -1 (negative) to 1 (positive). Macroeconomic Factors:

GDP Growth: Indicates the overall health and growth of the economy. Inflation Rate: Reflects changes in purchasing power and economic stability. Target Variable:

Buy/Sell Signal: Binary classification (1 = Buy, 0 = Sell) based on price movement thresholds, simulating actionable trading decisions. Use Cases AI Model Training: Ideal for building stock prediction models using LSTM, Gradient Boosting, Random Forest, etc. Trading Strategy Optimization: Enables testing of trading algorithms and strategies in a simulated environment. Sentiment Analysis Research: Useful for understanding how sentiment influences stock movements. Feature Engineering and Selection: Provides a diverse set of features for experimentation with advanced techniques like PCA and LDA. Dataset Highlights Synthetic Yet Realistic: Carefully designed to mimic real-world financial data trends and relationships. Comprehensive Coverage: Includes key indicators and metrics used by traders and analysts. Scalable: Suitable for use in both small-scale academic projects and larger AI-driven trading platforms. Accessible for All Levels: The intuitive structure ensures that even beginners can utilize this dataset for financial machine learning applications. File Format The dataset is provided in CSV format, where:

Rows represent individual trading days. Columns represent features (technical indicators, market metrics, etc.) and the target variable. Acknowledgments This dataset is synthetically generated and is intended for research and educational purposes. It is not based on real market data and should not be used for actual trading.

Coffee fell to 408.66 USd/Lbs on December 2, 2025, down 0.95% from the previous day. Over the past month, Coffee's price has risen 0.50%, and is up 38.54% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coffee - values, historical data, forecasts and news - updated on December of 2025.

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.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

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.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

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.

Acknowledgements:

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.

This table presents prices of several global markets as published daily in the Dutch Financieel Dagblad. On the basis of daily prices, Statistics Netherlands calculates average monthly prices, quarterly averages and annual averages. The table is divided into the following product groups: Agricultural products, Tropical products, Precious metals, Metals and Energy. Exchange rates of the US dollar, the British pound and the Japanese yen against the Euro and vice versa are also given in this table.

Data available from: 2006

Status of the figures. All figures presented in this table are final.

When will more recent figures be published? Approximately 14 days after the reporting month, more recent figures will be available for publication.

This dataset contains the stock prices of Google since the COVID-19 pandemic began. There are 7 columns in this dataset:

Actual value and historical data chart for China Annual Percentage Growth Rate Of GDP At Market Prices Based On Constant 2010 Us Dollars

Brazil Market Expectation: Regulated Prices: Next Calendar Year: Standard Deviation data was reported at 0.430 % in Jun 2019. This records a decrease from the previous number of 0.510 % for May 2019. Brazil Market Expectation: Regulated Prices: Next Calendar Year: Standard Deviation data is updated monthly, averaging 0.615 % from May 2003 (Median) to Jun 2019, with 194 observations. The data reached an all-time high of 1.430 % in Nov 2003 and a record low of 0.250 % in Jan 2018. Brazil Market Expectation: Regulated Prices: Next Calendar Year: Standard Deviation data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Business and Economic Survey – Table BR.SB037: Market Expectation: Regulated Prices. Market Expectations System was implemented in November 2001, previous projections were collected from incipient through telephone contacts, transcribed into spreadsheets and consolidated manually. Some empty time points occurred because the Market didn´t have the expectation for those days. Prices administered by contract and monitored Prices administered by contract and monitored are those whose sensitivity to factors of supply and demand is lower, which does not necessarily imply that they are directly regulated by the government.

View LSEG's ICE Data Pricing and Reference Data, and find real-time market data, time-sensitive pricing, and reference data for securities trading.

We examine the pricing trends in the online toy markets by using panel data regression models with error components and serial correlation. Our results indicate that both online branch of multi-channel retailers (OBMCRS) and dotcoms charge similar prices on average, and that over time their prices move in tandem. Although the OBMCR retailers charge significantly different prices, the dotcoms do charge similar prices. Moreover, both retailer types demonstrate different magnitudes of price dispersion that move at different rates over time. Although the price dispersion of OBMCRS is higher than that of the dotcoms at the beginning, the gap narrows over time.

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.

FTSE 100: Where to Next?

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

France's main stock market index, the FR40, rose to 8121 points on December 2, 2025, gaining 0.29% from the previous session. Over the past month, the index has climbed 0.13% and is up 11.93% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - values, historical data, forecasts and news - updated on December of 2025.

Gasoline fell to 1.86 USD/Gal on December 2, 2025, down 0.53% from the previous day. Over the past month, Gasoline's price has fallen 2.79%, and is down 4.95% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gasoline - values, historical data, forecasts and news - updated on December of 2025.

Learn about the factors that impact the volatile price of wheat in the market, including weather conditions, global production levels, government policies, and market speculation. Find out how global trade dynamics, currency exchange rates, and demand from end-users also influence wheat prices. Get real-time updates on wheat market prices from reliable financial news sources or commodity trading platforms.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 93 companies listed on the Bucharest Stock Exchange* (XBSE) in Romania. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Romania:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Romania:

Bucharest Stock Exchange Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Bucharest Stock Exchange. This index provides an overview of the overall market performance in Romania.

Bucharest Stock Exchange Foreign Company Index: The index that tracks the performance of foreign companies listed on the Bucharest Stock Exchange. This index reflects the performance of international companies operating in Romania.

Company A: A prominent Romanian company with diversified operations across various sectors, such as manufacturing, technology, or finance. This company's stock is widely traded on the Bucharest Stock Exchange.

Company B: A leading financial institution in Romania, offering banking, insurance, or investment services. This company's stock is actively traded on the Bucharest Stock Exchange.

Company C: A major player in the Romanian energy or consumer goods sector, involved in the production and distribution of related products. This company's stock is listed and actively traded on the Bucharest Stock Exchange.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Romania, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Romania ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Romania ?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Romania exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH,...

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.

Can stock prices be predicted? (LON:PAGE Stock 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

In 2024, the price per unit of carrots for fresh and processing market in the United States reached a peak of **** U.S. dollars per cwt. Four years prior in 2020, comparatively, the price per cwt was only **** U.S. dollars.

Brazil Market Expectation: Regulated Prices: 2 Years Ahead: Standard Deviation data was reported at 0.270 % in Jun 2019. This records a decrease from the previous number of 0.340 % for May 2019. Brazil Market Expectation: Regulated Prices: 2 Years Ahead: Standard Deviation data is updated monthly, averaging 0.390 % from May 2003 (Median) to Jun 2019, with 194 observations. The data reached an all-time high of 1.670 % in Jan 2004 and a record low of 0.000 % in Feb 2012. Brazil Market Expectation: Regulated Prices: 2 Years Ahead: Standard Deviation data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Business and Economic Survey – Table BR.SB037: Market Expectation: Regulated Prices. Market Expectations System was implemented in November 2001, previous projections were collected from incipient through telephone contacts, transcribed into spreadsheets and consolidated manually. Some empty time points occurred because the Market didn´t have the expectation for those days. Prices administered by contract and monitored Prices administered by contract and monitored are those whose sensitivity to factors of supply and demand is lower, which does not necessarily imply that they are directly regulated by the government.

Explore the various factors influencing wheat stock prices, including supply and demand dynamics, geopolitical events, weather conditions, policy regulations, and financial market sentiments. This article provides insight into the complexities impacting wheat markets and highlights the importance of accurate market data sourcing.

The price of cresol in USA for Q3 2023 reached 3090 USD/MT in September.

Explore IMARC’s newly published report, titled “Cresol Pricing Report 2024: Price Trend, Chart, Market Analysis, News, Demand, Historical and Forecast Data,” offers an in-depth analysis of cresol pricing, covering an analysis of global and regional market trends and the critical factors driving these price movements.