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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.
The date for every symbol is saved in CSV format with common fields:
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.
Get comprehensive coverage for 70+ trading venues with Databento's historical data APIs. Available in multiple data formats including MBO, MBP, and more.
Unfortunately, the API this dataset used to pull the stock data isn't free anymore. Instead of having this auto-updating, I dropped the last version of the data files in here, so at least the historic data is still usable.
This dataset provides free end of day data for all stocks currently in the Dow Jones Industrial Average. For each of the 30 components of the index, there is one CSV file named by the stock's symbol (e.g. AAPL for Apple). Each file provides historically adjusted market-wide data (daily, max. 5 years back). See here for description of the columns: https://iextrading.com/developer/docs/#chart
Since this dataset uses remote URLs as files, it is automatically updated daily by the Kaggle platform and automatically represents the latest data.
List of stocks and symbols as per https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average
Thanks to https://iextrading.com for providing this data for free!
Data provided for free by IEX. View IEX’s Terms of Use.
Get Nasdaq real-time and historical data with support for fast market replay at over 19 million book updates per second. Test our data for free with only 4 lines of code.
Nasdaq TotalView-ITCH is a proprietary data feed that disseminates full order book depth and last sale data from the Nasdaq stock market (XNAS). It delivers every quote and order at each price level, along with any event that updates the order book after an order is placed, such as trade executions, modifications, or cancellations. Nasdaq is the most active US equity exchange by volume and represented 13.03% of the average daily volume (ADV) as of January 2025.
With its L3 granularity, Nasdaq TotalView-ITCH captures information beyond the L1, top-of-book data available through SIP feeds and enables more accurate modeling of book imbalances, trade directionality, quote lifetimes, and more. This includes explicit trade aggressor side, odd lots, auction imbalance data, and the Net Order Imbalance Indicator (NOII) for the Nasdaq Opening and Closing Crosses and Nasdaq IPO/Halt Cross—the best predictor of Nasdaq opening and closing prices available. Other key advantages of Nasdaq TotalView-ITCH over SIP data include faster real-time dissemination and precise exchange-side timestamping directly from Nasdaq.
Real-time Nasdaq TotalView-ITCH data is included with a Plus or Unlimited subscription through our Databento US Equities service. Historical data is available for usage-based rates or with any subscription. Visit our pricing page for more details or to upgrade your plan.
Breadth of coverage: 20,329 products
Asset class(es): Equities
Origin: Directly captured at Equinix NY4 (Secaucus, NJ) with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP.
Supported data encodings: DBN, CSV, JSON Learn more
Supported market data schemas: MBO, MBP-1, MBP-10, BBO-1s, BBO-1m, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics, Status, Imbalance Learn more
Resolution: Immediate publication, nanosecond-resolution timestamps
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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.
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Context:- Amazon.com, Inc. is an American multinational technology company specializing in e-commerce, cloud computing, digital streaming, and artificial intelligence. Founded by Jeff Bezos in 1994, Amazon has grown into one of the world’s most valuable companies, revolutionizing online retail and cloud services through its Amazon Web Services (AWS) division.
As of March 2025 Amazon has a market cap of $2.249 Trillion USD. This makes Amazon the world's 4th most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.
Content:-
This dataset covers Amazon’s daily stock price data from 2000 to 2025. It includes information on:
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14466026%2F5453b54c1a5488a995b51a5f3b23fd84%2FStock%20dataset%20variables.jpg?generation=1740822549719886&alt=media" alt="">
Time-period: 2000–2025
Acknowlegements This dataset belongs to me.I'm sharing it here for free.You may do with it as you wish.
Download real-time and historical stock price data, including all buy and sell orders at every price level. Get each trade tick-by-tick and order queue composition at all prices. Access high-fidelity US equities stock market data using our Python, Rust, and C++ APIs. Providing full order book depth (MBO), OHLC aggregates, and more.
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License information was derived automatically
Analysis of ‘Time Series Forecasting with Yahoo Stock Price ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/time-series-forecasting-with-yahoo-stock-price on 28 January 2022.
--- Dataset description provided by original source is as follows ---
Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.
There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.
Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.
A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.
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https://www.visualcapitalist.com/wp-content/uploads/2020/03/mm3_black_swan_events_shareable.jpg">
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New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.
Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.
The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)
Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.
Mining and updating of this dateset will depend upon Yahoo Finance .
Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting
--- Original source retains full ownership of the source dataset ---
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License information was derived automatically
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.
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The Yahoo Stocks Dataset is an invaluable resource for analysts, traders, and developers looking to enhance their financial data models or trading strategies. Sourced from Yahoo Finance, this dataset includes historical stock prices, market trends, and financial indicators. With its accurate and comprehensive data, it empowers users to analyze patterns, forecast trends, and build robust machine learning models.
Whether you're a seasoned stock market analyst or a beginner in financial data science, this dataset is tailored to meet diverse needs. It features details like stock prices, trading volume, and market capitalization, enabling a deep dive into investment opportunities and market dynamics.
For machine learning and AI enthusiasts, the Yahoo Stocks Dataset is a goldmine. It’s perfect for developing predictive models, such as stock price forecasting and sentiment analysis. The dataset's structured format ensures seamless integration into Python, R, and other analytics platforms, making data visualization and reporting effortless.
Additionally, this dataset supports long-term trend analysis, helping investors make informed decisions. It’s also an essential resource for those conducting research in algorithmic trading and portfolio management.
Key benefits include:
Download the Yahoo Stocks Dataset today and harness the power of financial data for your projects. Whether for AI, financial reporting, or trend analysis, this dataset equips you with the tools to succeed in the dynamic world of stock markets.
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Full historical data for the S&P 500 (ticker ^GSPC), sourced from Yahoo Finance (https://finance.yahoo.com/).
Including Open, High, Low and Close prices in USD + daily volumes.
Info about S&P 500: https://en.wikipedia.org/wiki/S%26P_500
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Graph and download economic data for Financial Market: Share Prices for United Kingdom (SPASTT01GBM661N) from Dec 1957 to May 2025 about stock market and United Kingdom.
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Graph and download economic data for Index of Common Stock Prices, New York Stock Exchange for United States (M11007USM322NNBR) from Jan 1902 to May 1923 about New York, stock market, indexes, and USA.
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License information was derived automatically
Interactive chart of the NASDAQ Composite stock market index since 1971. Historical data is inflation-adjusted using the headline CPI and each data point represents the month-end closing value. The current month is updated on an hourly basis with today's latest value.
Explore Apple Stock Data 2025 with comprehensive historical trends from 1980 to 2025. Analyze market cap insights, financial performance.
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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.
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
👏**Upvote this dataset if you find it interesting!**
Starbucks Corporation, together with its subsidiaries, operates as a roaster, marketer, and retailer of coffee worldwide.
The dataset includes the daily Starbucks Corporation stock price.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The dataset contains historical technical data of Dhaka Stock Exchange (DSE). The data was collected from different sources found in the internet where the data was publicly available. The data available here are used for information and research purposes and though to the best of our knowledge, it does not contain any mistakes, there might still be some mistakes. It is not encourages to use this dataset for portfolio management purposes and use this dataset out of your own interest. The contributors do not hold any liability if it is used for any purposes.
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License information was derived automatically
This dataset provides monthly stock price data for the MAG7 over the past 20 years (2004–2024). The data includes key financial metrics such as opening price, closing price, highest and lowest prices, trading volume, and percentage change. The dataset is valuable for financial analysis, stock trend forecasting, and portfolio optimization.
MAG7 refers to the seven largest and most influential technology companies in the U.S. stock market : - Microsoft (MSFT) - Apple (AAPL) - Google (Alphabet, GOOGL) - Amazon (AMZN) - Nvidia (NVDA) - Meta (META) - Tesla (TSLA)
These companies are known for their market dominance, technological innovation, and significant impact on global stock indices such as the S&P 500 and Nasdaq-100.
The dataset consists of historical monthly stock prices of MAG7, retrieved from Investing.com. It provides an overview of how these stocks have performed over two decades, reflecting market trends, economic cycles, and technological shifts.
Date
The recorded month and year (DD-MM-YYYY)Price
The closing price of the stock at the end of the monthOpen
The price at which the stock opened at the beginning of the monthHigh
The highest stock price recorded in the monthLow
The lowest stock price recorded in the monthVol.
The total trading volume for the monthChange %
The percentage change in stock price compared to the previous month
# 5. Data Source & Format
The dataset was obtained from Investing.com and downloaded in CSV format.
The data has been processed to ensure consistency and accuracy, with date formats standardized for time-series analysis.
# 6. Potential Use Cases
This dataset can be used for :ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically
S&P 500 index data including level, dividend, earnings and P/E ratio on a monthly basis since 1870. The S&P 500 (Standard and Poor's 500) is a free-float, capitalization-weighted index of the top ...
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
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.
The date for every symbol is saved in CSV format with common fields:
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.