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.

The main stock market index of United States, the US500, rose to 6211 points on July 1, 2025, gaining 0.10% from the previous session. Over the past month, the index has climbed 4.64% and is up 12.75% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

FinFeedAPI provides equity market data covering over 11,000 symbols, featuring historical T+1 data with an unlimited loopback period. We deliver everything from detailed trade records and multiple levels of order book depth (Level 1-3) to crucial regulatory and system messages.

Our data is engineered for performance, featuring nano-second precision timestamps. This ensures a competitive edge for high-frequency trading by enabling fair, accurate, and auditable transaction sequencing, critical for regulatory compliance. Access comprehensive equity market intelligence directly through our robust API offerings.

Why FinFeedAPI?

Market Coverage & Data Depth: - Historical Data: T+1 data on 11K+ symbols with unlimited historical lookback. - Trade Feeds: Detailed trade records including timestamps, sizes, prices, and conditions (e.g., odd lot, intermarket sweep, extended hours). - Level 1 Quotes: Best bid/ask prices, sizes, and timestamps. - Level 2 Price Book: Market depth with multiple bid/ask prices and aggregate order sizes. - Level 3 Order Book: The complete order book detailing individual orders.

Essential Messages: - Admin Messages: Trading status, official open/close prices, auction states, short sale restrictions, retail liquidity indicators, security directory. - System Events: Exchange-level notifications for key trading session phases.

Precision & Reliability: - Nano-second Timestamps: Ensuring fair, accurate, and auditable transaction sequencing for HFT and compliance. - Institutional Trust: Relied upon by financial institutions for dependable equity market information.

Financial institutions and trading firms rely on FinFeedAPI for mission-critical equity market intelligence. We are committed to delivering clean, precise, and comprehensive data when it matters most. If you require dependable and granular stock market data, FinFeedAPI provides the actionable insights you need.

This dataset contains the historical stock prices of Google (GOOGL) from January 2020 to March 2025. The data was fetched from Yahoo Finance using Python’s yfinance library.

📈 Key Features:

Timeframe: January 2020 - March 2025 Stock Exchange: NASDAQ Data Source: Yahoo Finance File Format: CSV 👨‍💻 Potential Uses:

Stock price prediction using Machine Learning Time-series analysis Stock market trend visualization Algorithmic trading research 📢 Note: This dataset is for educational and research purposes only. It should not be used for actual trading.

We offer three easy-to-understand equity data packages to fit your business needs. Visit intrinio.com/pricing to compare packages.

Bronze

The Bronze package is ideal for developing your idea and prototyping your platform with high-quality EOD equity pricing data, standardized financial statement data, and supplementary fundamental datasets.

When you’re ready for launch, it’s a seamless transition to our Silver package for additional data sets, 15-minute delayed equity pricing data, expanded history, and more.

• Historical EOD equity prices & technicals (10 years history)
• Security reference data
• Standardized & as-reported financial statements (5 years history)
• 7 supplementary fundamental data sets

Bronze Benefits:

• Web API access
• 300 API calls/minute limit
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Silver

The Silver package is ideal for startups that are in development, testing, or in the beta launch phase. Hit the ground running with 15-minute delayed and historical intraday and EOD equity prices, plus our standardized and as-reported financial statement data with nine supplementary data sets, including insider transactions and institutional ownership.

When you’re ready to scale, easily move up to the Gold package for our full range of data sets and full history, real-time equity pricing data, premium support options, and much more.

• 15-minute delayed & historical intraday equity prices
• Historical EOD equity prices & technicals (full history)
• Security reference data
• Standardized & as-reported financial statements (10 years history)
• 9 supplementary fundamental data sets

Silver Benefits:

• Web API access
• 2,000 API calls/minute limit
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Gold

The Gold package is ideal for funded companies that are in the growth or scaling stage, as well as institutions that are innovating within the fintech space. This full-service solution offers our complete collection of equity pricing data feeds, from real-time to historical EOD, plus standardized financial statement data and nine supplementary feeds.

You’ll also have access to our wide range of modern access methods, third-party data via Intrinio’s API with licensing assistance, support from our team of expert engineers, custom delivery architectures, and much more.

• Real-time equity prices
• Historical intraday equity prices
• Historical EOD equity prices & technicals (full history)
• Security reference data
• Standardized & as-reported financial statements (full history)
• 9 supplementary fundamental data sets

Gold Benefits:

• No exchange fees
• No user reporting or variable per-user exchange fees
• High liquidity (6%+)
• Web API & WebSocket access
• 2,000 API calls/minute limit
• Customizable access methods (Snowflake, FTP, etc.)
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support
• Access to engineering team
• Concierge customer success team
• Comarketing & promotional initiatives

Platinum

Don’t see a package that fits your needs? Our team can design premium custom packages for institutions.

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.

The dataset contains data from 8/18/2004 to 2/18/2025 for GOOGL. The csv has the following header: Date,Open,High,Low,Close,Volume * Date: the date of the information * Open: price at market open * High: high price of the day * Low: lowest price of the day * Close: price at market close * Volume: volume of stock

Description

Historical daily stock market data for 4500+ Nasdaq listed companies. Dataset to be updated quarterly.

Content

Date: dates vary depending on stock, all in DD/MM/YYYY format Open: open price (in USD) High: high price (in USD) Low: low price (in USD) Close: close price (in USD) Adj close: adjusted close price (in USD) Volume: traded volume (in USD)

Acknowledgements:

Banner photo: https://www.pexels.com/photo/numbers-on-monitor-534216/

📈 Daily Historical Stock Price Data for Dow Inc. (2019–2025)

A clean, ready-to-use dataset containing daily stock prices for Dow Inc. from 2019-03-20 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Dow Inc. Ticker Symbol: DOW Date Range: 2019-03-20 to 2025-05-28 Frequency: Daily Total Records: 1557 rows (one per trading day)

  🔢 Columns… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-dow-inc-20192025.

The latest closing stock price for Exxon as of June 27, 2025 is 109.38. An investor who bought $1,000 worth of Exxon stock at the IPO in 1984 would have $41,833 today, roughly 42 times their original investment - a 9.60% compound annual growth rate over 41 years. The all-time high Exxon stock closing price was 122.12 on October 07, 2024. The Exxon 52-week high stock price is 126.34, which is 15.5% above the current share price. The Exxon 52-week low stock price is 97.80, which is 10.6% below the current share price. The average Exxon stock price for the last 52 weeks is 112.58. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

Description: This dataset contains 24 years of historical stock data for ITC Limited, a leading Indian multinational conglomerate engaged in businesses such as FMCG (Fast-Moving Consumer Goods), hotels, paperboards, and agri-business. The data spans from [start year] to [end year] and includes daily stock metrics such as opening price, closing price, high, low, volume, and more, providing a comprehensive view of ITC's performance in the National Stock Exchange (NSE).

Context: The dataset offers valuable insights into the long-term trends, volatility, and trading patterns of ITC stocks, facilitating quantitative analysis and investment research. Researchers, analysts, and investors can leverage this dataset to conduct historical performance analysis, develop trading strategies, and make informed investment decisions related to ITC Limited.

Sources: The dataset is sourced from reliable financial data providers and publicly available stock market archives. The data undergoes rigorous validation and cleaning processes to ensure accuracy and consistency, providing users with reliable information for their analyses.

Inspiration: The creation of this dataset was inspired by the growing interest in quantitative finance, stock market analysis, and algorithmic trading within the data science community. By making this dataset available on platforms like Kaggle, we aim to empower researchers, data scientists, and enthusiasts to explore the dynamics of ITC's stock performance and contribute to the advancement of financial analytics and investment strategies.

Context

Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 years) for all companies currently found on the S&P 500 index.

The script I used to acquire all of these .csv files can be found in this GitHub repository In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Content

The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 5 years of stock data (in the all_stocks_5yr.csv and corresponding folder) and a smaller version of the dataset (all_stocks_1yr.csv) with only the past year's stock data for those wishing to use something more manageable in size.

The folder individual_stocks_5yr contains files of data for individual stocks, labelled by their stock ticker name. The all_stocks_5yr.csv and all_stocks_1yr.csv contain this same data, presented in merged .csv files. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd Open - price of the stock at market open (this is NYSE data so all in USD) High - Highest price reached in the day Low Close - Lowest price reached in the day Volume - Number of shares traded Name - the stock's ticker name

Acknowledgements

I scraped this data from Google finance using the python library 'pandas_datareader'. Special thanks to Kaggle, Github and The Market.

Inspiration

This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!

• Data from Dec 1989 to Dec 2020.
• Includes Income Statement, Balance Sheet, and Cashflow statement.
• Adjusted for restatements.
• Includes valuation metrics such as enterprise valuation and market capitalization.
• Over 30 ratios such as p/e ratio, EBITDA/sales, gross margin etc..
• Standardized categories for comparison between companies.

The latest closing stock price for Bank Of America as of May 27, 2025 is 44.22. An investor who bought $1,000 worth of Bank Of America stock at the IPO in 1984 would have $30,631 today, roughly 31 times their original investment - a 8.79% compound annual growth rate over 41 years. The all-time high Bank Of America stock closing price was 47.44 on February 06, 2025. The Bank Of America 52-week high stock price is 48.08, which is 8.7% above the current share price. The Bank Of America 52-week low stock price is 33.06, which is 25.2% below the current share price. The average Bank Of America stock price for the last 52 weeks is 42.15. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

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

Historical data of the Taiwan Stock Exchange Weighted Index

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

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

📈 Daily Historical Stock Price Data for Eastern Bankshares, Inc. (2020–2025)

A clean, ready-to-use dataset containing daily stock prices for Eastern Bankshares, Inc. from 2020-10-15 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Eastern Bankshares, Inc. Ticker Symbol: EBC Date Range: 2020-10-15 to 2025-05-28 Frequency: Daily Total Records: 1159 rows (one per… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-eastern-bankshares-inc-20202025.

Custommade Historical Financial Data For 230M Companies Worldwide: - Data from 2017, 2018, 2019, 2020 & 2021 - Includes turnover, employee size. - Custommade based on geographical location, turnover range, employee range and industry type - Standardized database for all countries

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