Use our Stock Market 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.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Dataset Information

This dataset includes daily price data for various stocks.

  Instruments Included

7000+ US Stocks

  Dataset Columns

symbol: The symbol of the stock. date: The date of the data. open: The opening price of the stock. high: The highest price of the stock. low: The lowest price of the stock. close: The closing price of the stock. volume: The volume of the stock. adj_close: The adjusted closing price of the stock.

  Data Splits

The… See the full description on the dataset page: https://huggingface.co/datasets/paperswithbacktest/Stocks-Daily-Price.

Context

This dataset contains daily stock data for Meta Platforms, Inc. (META), formerly Facebook Inc., from May 19, 2012, to January 20, 2025. It offers a comprehensive view of Meta’s stock performance and market fluctuations during a period of significant growth, acquisitions, and technological advancements. This dataset is valuable for financial analysis, market prediction, machine learning projects, and evaluating the impact of Meta’s business decisions on its stock price.

Content

The dataset includes the following key features:

Open: Stock price at the start of the trading day. High: Highest stock price during the trading day. Low: Lowest stock price during the trading day. Close: Stock price at the end of the trading day. Adj Close: Adjusted closing price, accounting for corporate actions like stock splits, dividends, and other financial adjustments. Volume: Total number of shares traded during the trading day.

Variables

Date: The date of the trading day, formatted as YYYY-MM-DD. Open: The stock price at the start of the trading day. High: The highest price reached by the stock during the trading day. Low: The lowest price reached by the stock during the trading day. Close: The stock price at the end of the trading day. Adj Close: The adjusted closing price, which reflects corporate actions like stock splits and dividend payouts. Volume: The total number of shares traded on that specific day.

Acknowledgements

This dataset was sourced from reliable public APIs such as Yahoo Finance or Alpha Vantage. It is provided for educational and research purposes and is not affiliated with Meta Platforms, Inc. Users are encouraged to adhere to the terms of use of the original data provider.

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.

LON:STG Stock: The Stock Market Bubble Is About to Burst

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

Stock Analysis Software Market Size And Forecast

Stock Analysis Software Market size was valued at USD 145.6 Million in 2023 and is projected to reach USD 450.68 Million by 2031, growing at a CAGR of 15.17% during the forecast period 2024-2031.

Global Stock Analysis Software Market Drivers

The market drivers for the Stock Analysis Software Market can be influenced by various factors. These may include:

Growing Interest from Investors: As more people and organizations engage in the stock market, there is an increasing need for tools that help monitor and evaluate investments. Automation and Efficiency: Software adoption is fueled by traders' and investors' need for automated solutions that will expedite their analysis and decision-making. Data Accessibility: An abundance of financial data, such as current stock prices and corporate details, presents prospects for thorough analytical instruments. Advanced Technologies: Adding AI and machine learning to stock analysis software improves its capacity for prediction and provides more individualized insights, which draws in more users. Growth in Retail Trading: Individual investors' need for user-friendly stock analysis tools has been fueled by the growing acceptance of retail trading platforms. Regulatory Compliance: Software solutions that support compliance are in great demand as financial markets become more regulated. Cost-Effectiveness: By eliminating the need for human analysts, automated analysis systems can offer both individual and institutional investors a more affordable option. Cross-platform Integration: Users seeking coherent investing ecosystems will find stock research software more appealing if it interfaces with other financial tools and platforms. Global Market Expansion: Software that can assess equities across multiple locations and adhere to international regulations is needed as stock markets become increasingly global. User-Friendly Interfaces: The movement toward more user-friendly interfaces increases the accessibility of stock analysis software, which encourages non-professional investors to use it.

Overview

This dataset contains historical daily prices for all tickers currently trading on NASDAQ. The up to date list is available from nasdaqtrader.com. The historic data is retrieved from Yahoo finance via yfinance python package.

It contains prices for up to 01 of April 2020. If you need more up to date data, just fork and re-run data collection script also available from Kaggle.

Data Structure

The date for every symbol is saved in CSV format with common fields:

• Date - specifies trading date
• Open - opening price
• High - maximum price during the day
• Low - minimum price during the day
• Close - close price adjusted for splits
• Adj Close - adjusted close price adjusted for both dividends and splits.
• Volume - the number of shares that changed hands during a given day

All that ticker data is then stored in either ETFs or stocks folder, depending on a type. Moreover, each filename is the corresponding ticker symbol. At last, symbols_valid_meta.csv contains some additional metadata for each ticker such as full name.

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.

Coinbase's Climb: Can it Maintain Momentum? (COIN)

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 data files contain seven low-dimensional financial research data (in .txt format) and two high-dimensional daily stock prices data (in .csv format). The low-dimensional data sets are provided by Lorenzo Garlappi on his website, while the high-dimensional data sets are downloaded from Yahoo!Finance by the contributor's own effort. The description of the low-dimensional data sets can be found in DeMiguel et al. (2009, RFS). The two high-dimensional data sets contain daily adjusted close prices (from Jan 1, 2013 to Dec 31, 2014) of the stocks, which are in the index components list (as of Jan 7, 2015) of S&P 500 and Russell 2000 indices, respectively.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

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.

What are the most successful trading algorithms? (NTAP 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

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 we predict stock market using machine learning? (FZO 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

This repository contains a financial-domain-focused dataset for financial sentiment/emotion classification and stock market time series prediction. It's based on our paper: StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series accepted by AAAI 2023 Bridge (AI for Financial Services).

Data collection period: Jan 2020 - Dec 2020 Number of Utterance: 10,000 (train 80%, val 10%, test 10%) Sentiment classes: 2 [bullish (~positive), bearish (~negative)]

Emotion classes: 12 [ambiguous, amusement, anger, anxiety, belief, confusion, depression, disgust, excitement, optimism, panic, surprise]

tweet/processed.csv: 50,281 samples with text-processed data for Topic Modelling

tweet/train, val, test.csv: 10,000 samples in total. Each file has id, date, ticker, emo_label, senti_lable, original, and processed content. For the data curation, processing (e.g. emoji, CTAG, HTAG), and annotation, we refer to our paper. The dataset is used for Financial Sentiment/Emotion Classification tasks. price/38 companies: historical price data in csv format. The tweet and price dataset together are used for Multivariate Time Series tasks.

Tracking United HealthCare Stock Performance Since IPO

Dataset Description

This dataset provides historical stock data for UnitedHealth Group (UHG), one of the largest healthcare and insurance companies in the world. It covers stock prices, market capitalization, and trading volumes from the company's IPO to the present. As a Fortune 500 company with a significant market presence, analyzing UHG's stock performance can provide valuable insights into healthcare market trends, investment opportunities, and economic indicators.

Dataset Features

• Date – The trading date for the stock data.
• Open Price – Stock price at market open.
• Close Price – Stock price at market close.
• High – Highest stock price during the trading day.
• Low – Lowest stock price during the trading day.
• Volume – The number of shares traded on that day.
• Market Cap – The total market capitalization of UnitedHealth Group.

Dataset Distribution

• Data Volume: Number of records depends on trading days from IPO to present.
• Format: CSV, Excel, or other structured data formats.
• Update Frequency: Weekly.

Usage

This dataset is useful for:

• Stock Market Analysis – Analyzing historical stock price trends.
• Financial Forecasting – Predicting future stock price movements using machine learning.
• Investment Research – Assessing UnitedHealth Group’s stock as part of a portfolio.
• Market Trends – Understanding broader trends in the healthcare insurance sector.

Coverage

• Geographic Coverage: United States (NYSE).
• Time Range: From IPO to present.
• Economic Indicators: Healthcare sector, insurance market trends.

License

CC0 (Public Domain) – This dataset is freely available for public and commercial use.

Who Can Use This Dataset?

• Investors & Traders – To analyze market trends and make informed decisions.
• Economists & Researchers – To study healthcare market impacts.
• Data Scientists – To develop predictive stock models.

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

Stock price volatility in United States was reported at 24.99 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Stock price volatility - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Stock price volatility in Jamaica was reported at 15.09 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Jamaica - Stock price volatility - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Coinbase stock may experience volatility due to regulatory concerns and competition. The company's strong user base and expansion into new products offer potential for growth, but regulatory headwinds and intense competition pose risks to its revenue and profitability. Investors should carefully consider these factors before making investment decisions.

The latest closing stock price for Microsoft as of June 18, 2025 is 480.24. An investor who bought $1,000 worth of Microsoft stock at the IPO in 1986 would have $8,056,718 today, roughly 8,057 times their original investment - a 25.94% compound annual growth rate over 39 years. The all-time high Microsoft stock closing price was 480.24 on June 18, 2025. The Microsoft 52-week high stock price is 481.00, which is 0.2% above the current share price. The Microsoft 52-week low stock price is 344.79, which is 28.2% below the current share price. The average Microsoft stock price for the last 52 weeks is 422.77. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.