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.

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.

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.

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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.

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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.

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

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.

Success.ai offers a cutting-edge solution for businesses and organizations seeking Company Financial Data on private and public companies. Our comprehensive database is meticulously crafted to provide verified profiles, including contact details for financial decision-makers such as CFOs, financial analysts, corporate treasurers, and other key stakeholders. This robust dataset is continuously updated and validated using AI technology to ensure accuracy and relevance, empowering businesses to make informed decisions and optimize their financial strategies.

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Open Banking Market Size 2024-2028

The open banking market size is forecast to increase by USD 57.66 billion at a CAGR of 27.2% between 2023 and 2028. The market is witnessing significant growth due to the increasing demand for advanced Financial Management Tools that offer real-time access to Financial Data from multiple Financial Institutions. Open Banking Solutions, which utilize Open Banking APIs, enable automated savings, real-time transactions, and enhanced security features. The integration of Artificial Intelligence (AI) into these services further streamlines financial management and enhances personalized customer experiences. However, the handling of sensitive personal financial data necessitates strict adherence to guidelines and regulations to ensure data security and privacy. Key market trends include the growing preference for faster and more seamless payment processing, increased focus on data security, and the potential for increased competition among Financial Institutions as they adapt to the Open Banking landscape.

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Open banking, a financial services model that enables third-party providers to access customers' financial data through APIs, is revolutionizing the payment ecosystem. This innovative approach allows for more customer-centric services, personalized financial offerings, and informed financial decisions. Broadband connectivity plays a crucial role in the open banking landscape, ensuring seamless access to real-time data for machine learning algorithms and AI applications. These technologies are integral to the open banking model, as they enable advanced data analytics and the development of innovative financial services. Security is a top priority in the market. Financial institutions are investing heavily in advanced security measures to protect sensitive customer data from online fraud. AI and machine learning algorithms are being employed to detect and prevent fraudulent activities in real-time. E-commerce and open banking are natural partners, with the former benefiting from the real-time financial data access provided by the latter.

Further, open banking APIs are the backbone of this new financial services model, allowing for seamless integration between financial institutions and third-party service providers. These APIs enable the sharing of financial data in a secure and standardized manner, facilitating the development of innovative financial services. Personalized financial services are a key benefit of open banking. By leveraging big data analytics and AI, financial institutions can offer customized offerings tailored to individual customers' financial needs and preferences. In conclusion, open banking is transforming the payment ecosystem by enabling real-time data access, advanced data analytics, and the development of innovative financial services. With a focus on security and customer-centricity, this model is poised to disrupt traditional financial services and reshape the industry landscape.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD Billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Service

  Banking and capital markets
  Payments
  Digital currencies


Deployment

  On premise
  Cloud


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  South America



  Middle East and Africa

By Service Insights

The banking and capital markets segment is estimated to witness significant growth during the forecast period. The market is revolutionizing the banking and financial services sector in the global payment ecosystem. Through strategic collaborations and innovative service offerings, Open Banking is enhancing payment processes, expanding investment accessibility, and promoting financial inclusion. In June 2024, Euronet, a leading financial technology and payments provider, partnered with Fintech Galaxy to introduce a new Banking as a Service (BaaS) offering. This collaboration aims to deliver faster, more secure, and cost-effective account-based transactions for banks, fintechs, and merchants. Key features of this service include card as a service, real-time payment processing, and advanced fraud detection. By integrating with consumer bank accounts, this solution reduces transaction costs and promotes financial inclusion, while also driving the adoption of digital transactions in the European region.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in Open Banking is further fueling the growth of the market. Big data analytics is enabling financial institutions to gain valuable insights into customer behavior and preferences, leading to personalized services and improved customer experience. The use of Open Banking is

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.

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

Global Fixed Income Pricing Data. More than 420 pricing sources, including Stock Exchanges and OTC market. Pay only for the stock exchanges, parameters or regions you need. Flexible in customizing our product to the customer's needs. Free test access as long as you need for integration. Reliable sources: stock exchanges and market participants. The cost depends on the amount of required parameters and re-distribution right.

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

Yahoo Finance dataset provides information on top traded companies. It contains financial information on each company including stock ticker and risk scores and general company information such as company location and industry. Each record in the dataset is a unique stock, where multiple stocks can be related to the same company. Yahoo Finance dataset attributes include: company name, company ID, entity type, summary, stock ticker, currency, earnings, exchange, closing price, previous close, open, bid, ask, day range, week range, volume, and much more.

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

NYSE Integrated is a proprietary data feed that disseminates full order book updates from the New York Stock Exchange (XNYS). 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.

NYSE is the leading venue for listing blue-chip companies and large-cap stocks. Powered by NYSE's Pillar platform, its hybrid market model of floor-based auction and electronic trading allows it to capture a significant portion of trading activity during the US equity market open and close. As of January 2025, the NYSE represented approximately 6.31% of the average daily volume (ADV) across all exchange-listed US securities, including those listed on Nasdaq, other NYSE venues, and Cboe exchanges.

NYSE is also the only exchange to offer Designated Market Maker (DMM) privileges, allowing the floor to send D-Quote Orders, short for Discretionary Orders, throughout the day. Most D-Quote Orders execute in the closing auction, where they're known as Closing D Orders and allow traders to access the NYSE closing auction after 3:50 PM. This creates significant price discovery during the NYSE Closing Auction, where interest represented via the floor contributes more than 40% of total volume.

NYSE is also unique for being the only exchange with a Parity/Priority Allocation model for matching. This resembles a mixed FIFO and pro-rata matching algorithm, where the participant who sets the best price is matched first, and then the remaining shares are allocated to other orders entered by floor brokers at that price (parity allocation). Floor brokers may utilize e-Quotes to to receive such parity allocation of incoming executions.

With L3 granularity, NYSE Integrated captures information beyond the L1, top-of-book data available through SIP feeds, enabling accurate modeling of the book imbalances, queue dynamics, and the auction process. This data includes explicit trade aggressor side, odd lots, and imbalances. Auction imbalances offer valuable insights into NYSE’s opening and closing auctions by providing details like imbalance quantity, paired quantity, imbalance reference price, and book clearing price.

Historical data is available for usage-based rates or with any Databento US Equities subscription. Visit our pricing page for more details or to upgrade your plan.

Asset class: 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, TBBO, Trades, BBO-1s, BBO-1m, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Imbalance, Statistics, Status (Learn more)

Resolution: Immediate publication, nanosecond-resolution timestamps

Graph and download economic data for CBOE Volatility Index: VIX (VIXCLS) from 1990-01-02 to 2025-06-27 about VIX, volatility, stock market, and USA.

API Crude Oil Stock Change in the United States increased to -4.28 BBL/1Million in June 20 from -10.13 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.

Snapshot img

Financial Planning Software Market Size 2024-2028

The financial planning software market size is forecast to increase by USD 8.67 billion at a CAGR of 23.55% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing complexity of financial management and the integration of artificial intelligence (AI) technology. As businesses continue to manage increasingly intricate financial operations, the demand for advanced planning tools is on the rise. Moreover, AI's ability to analyze vast amounts of data and provide actionable insights is revolutionizing financial planning, enabling more accurate forecasting and efficient resource allocation. However, market expansion is not without challenges. Regulatory hurdles impact adoption, as financial institutions grapple with compliance requirements and data privacy and security concerns. The sensitive nature of financial data necessitates robust security measures, and breaches can result in severe consequences.
Supply chain inconsistencies also temper growth potential, as businesses require reliable and continuous access to software solutions to remain competitive. Companies seeking to capitalize on market opportunities must navigate these challenges effectively, investing in robust security frameworks and maintaining strong supplier relationships to ensure uninterrupted access to cutting-edge financial planning software.

What will be the Size of the Financial Planning Software Market during the forecast period?

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In the dynamic market, asset allocation and retirement planning remain key focus areas for both personal and institutional investors. Financial technology has disrupted traditional financial services, giving rise to online financial planning, personal finance apps, and digital banking solutions. These tools offer real-time financial insights, goal setting capabilities, and API integrations for seamless data exchange. Financial strategies are increasingly data-driven, with financial analysis, forecasting, and modeling software enabling informed decision-making. Wealth management and portfolio management software cater to high net worth individuals, while financial consulting services provide expert advice on complex financial matters. Financial regulations continue to shape the market, with a growing emphasis on data security and open banking.
Digital financial services, including investment planning and tax planning software, offer accessible financial solutions for individuals and businesses. Financial education tools and coaching services promote financial literacy, empowering users to make informed financial decisions. Risk management software and budgeting apps help users manage their financial risks and expenses, while financial reporting and analytics tools provide valuable insights for effective financial planning and performance tracking. Overall, the market continues to evolve, offering innovative solutions to meet the diverse needs of businesses and individuals.

How is this Financial Planning Software Industry segmented?

The financial planning software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Software
  Services


Application

  Financial advice and management
  Portfolio/accounting/trading management
  Wealth management
  Personal banking


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Rest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth due to the increasing need for businesses and individuals to effectively manage and organize their financial data. This demand is driven by various entities, including financial education programs and financial literacy initiatives, wealth management firms, financial institutions, and personal finance applications. Risk management, financial coaching, and financial dashboards are also integral components of financial planning software, providing valuable insights into financial literacy, regulations, and cash flow analysis. Moreover, small businesses and individual investors are leveraging financial planning services, financial forecasting, and financial consulting to make informed decisions about their financial future.

Machine learning and data analytics are increasingly being integrated into financial planning software, enabling advanced portfolio management, asset allocation, and open banking solutions. Financial technology, financial independence, investment planning, financial security, and financial services are all areas where financial planning software plays a crucial role