Leverage Databento's real-time stock API to get tick data with full order book depth (MBO). Offering seamless intraday market replay in a single API call.

Update

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.

Content

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.

Acknowledgements

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!

Terms of Use

Data provided for free by IEX. View IEX’s Terms of Use.

When there is a vast variety of metrics and tools available to gain market insight, Insider trading offers valuable clues to investors related to future share performance. We at Smart Insider provide global insider trading data and analysis on share transactions made by directors & senior staff in the shares of their own companies.

Monitoring all the insider trading activity is a huge task, we identify 'Smart Insiders' through specialist desktop and quantitative feeds that enable our clients to generate alpha.

Our experienced analyst team uses quantitative and qualitative methods to identify the stocks most likely to outperform based on deep analysis of insider trades, and the insiders themselves. Using our easy-to-read derived data we help our clients better understand insider transactions activity to make informed investment decisions.

We provide full customization of reports delivered by desktop, through feeds, or alerts. Our quant clients can receive data in a variety of formats such as XML, XLSX or API via SFTP or Snowflake.

Sample dataset for Desktop Service has been provided with some proprietary fields concealed. Upon request, we can provide a detailed Quant sample.

Tags: Stock Market Data, Equity Market Data, Insider Transactions Data, Insider Trading Intelligence, Trading Data, Investment Management, Alternative Investment, Asset Management, Equity Research, Market Analysis, Africa

Get comprehensive coverage for 70+ trading venues with Databento's historical data APIs. Available in multiple data formats including MBO, MBP, and more.

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.

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.

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.

Twelve Data is a technology-driven company that provides financial market data, financial tools, and dedicated solutions. Large audiences - from individuals to financial institutions - use our products to stay ahead of the competition and success.

At Twelve Data we feel responsible for where the markets are going and how people are able to explore them. Coming from different technological backgrounds, we see how the world is lacking the unique and simple place where financial data can be accessed by anyone, at any time. This is what distinguishes us from others, we do not only supply the financial data but instead, we want you to benefit from it, by using the convenient format, tools, and special solutions.

We believe that the human factor is still a very important aspect of our work and therefore our ethics guides us on how to treat people, with convenient and understandable resources. This includes world-class documentation, human support, and dedicated solutions.

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

Introduction

Since Investing.com does not have an API, I decided to develop this Python package in order to retrieve historical data from the companies that integrate the Continuous Spanish Stock Market. So on, I decided to generate, via investpy, the datasets for every company so that any Data Scientist or Data Enthusiastic can handle it and abstract their own conclusions and research.

The main purpose of developing investpy, the package from which these datasets have been retrieved, was to use it as the Data Extraction tool for its namesake section, for my Final Degree Project at the University of Salamanca titled "*Machine Learning for stock investment recommendation systems*". The package end up being so consistent, reliable and usable that it is going to be used as the main Data Extraction tool by another students in their Final Degree Projects named "*Recommender system of banking products*" and "*Robo-Advisor Application*".

License

MIT License

Additional Information

investpy, the Python package from which datasets were generated is currently in a development beta version, so please, if needed open an issue to solve all the possible problems the package may be causing or any dataset error. Also, any new ideas or proposals are welcome, and will be gladly implemented in the package if the are positive and useful.

For further information or any question feel free to contact me via email at alvarob96@usal.es

You can also check my Medium Publication, where I upload weekly posts related to Data Science and mainly on Data Extraction techniques via Web Scraping. In this case, you can read "investpy — a Python package for historical data extraction from the Spanish stock market" where I explain the basics on investpy development and some insights on Web Scraping with Python.

Disclaimer

This Python Package has been made for research purposes in order to fit a needs that Investing.com does not cover, so this package works like an Application Programming Interface (API) of Investing.com developed in an altruistic way. Conclude that this package is not related in any way with Investing.com or any dependant company, the only requirement for developing this package was to mention the source where data is retrieved.

Global Shares Data Reference data on more than 80K stocks worldwide. Historical data from 2000 onwards. Pay only for the parameters you need. Flexible in customizing our product to the customer's needs. Free test access as long as you need for integration. Reliable sources: issues documents, disclosure website, global depositories data and other open sources. The cost depends on the amount of required parameters and re-distribution right.

API Crude Oil Stock Change in the United States increased to 0.68 BBL/1Million in June 27 from -4.28 BBL/1Million in the previous week. This dataset provides - United States API Crude Oil Stock Change- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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:API Target Price Prediction

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.

How do you determine buy or sell? (LON:API 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.

API Group Soaring: (APG) 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

Our Financial API provides access to a vast collection of historical financial statements for over 50,000+ companies listed on major exchanges. With this powerful tool, you can easily retrieve balance sheets, income statements, and cash flow statements for any company in our extensive database. Stay informed about the financial health of various organizations and make data-driven decisions with confidence. Our API is designed to deliver accurate and up-to-date financial information, enabling you to gain valuable insights and streamline your analysis process. Experience the convenience and reliability of our company financial API today.

Databento provides the industry’s fastest cloud-based solutions for intraday and real-time tick data. First to deliver full L3 (MBO) over internet.

Access L2 market data with Databento's market by price (MBP-10) schema, which aggregates book depth by price and includes every order across the top ten price levels.

Access L3 market data with Databento's market-by-order (MBO) schema, which provides full order book depth, including every order at every price level, tick-by-tick with accurate queue position.

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.

(API) Abrdn Property Income: Renting Out the Future?

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

It is not so often that one can find fundamental data of companies on which it would be possible to accurately assess the value of a company.

So I decided to use yahoo_fin api to collect some fundamentals of 48 companies from the S&P 500 index.

The content of indicators in each table: - total assets. - cash. - stockholder equity. - profit. - revenue. - return on equity, return on assets, profit margin. - trailing P/E, P/S, P/B, PEG, forward P/E.

In addition, the dataset has prices for all stocks for four years.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries