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

Graph and download economic data for NASDAQ Composite Index (NASDAQCOM) from 1971-02-05 to 2025-07-15 about NASDAQ, composite, stock market, indexes, and USA.

NASDAQ Listed Companies Dataset

Description:

This dataset provides comprehensive information on companies listed on the NASDAQ stock exchange. It includes essential details about each company, making it a valuable resource for financial analysis, stock market research, and investment strategies.

Features:

• symbol: The unique ticker symbol used to identify the company's stock on the NASDAQ exchange.
• name: The full name of the company.
• currency: The currency in which the company's stock is traded.
• exchange: The stock exchange where the company is listed (in this case, NASDAQ).
• mic_code: The Market Identifier Code (MIC) for the NASDAQ exchange.
• country: The country where the company is headquartered.
• type: The type of company, such as common stock or preferred stock.
• Usage: This dataset can be used for various purposes including:

Stock Market Analysis:

Analyze stock symbols, company names, and market data.

Financial Modeling:

Incorporate company details into financial models and investment strategies.

Market Research:

Understand the distribution of companies by country and currency.

Data Visualization:

Create visualizations of the NASDAQ market landscape.

Data Source:

The data is sourced from the Twelve Data API, which provides up-to-date financial and stock market information.

Notes: The dataset includes only NASDAQ-listed companies and does not cover other exchanges. Ensure to comply with any data usage policies or licensing agreements associated with the data source. Feel free to adapt the description based on the specific details and attributes of your dataset.

Browse LSEG's US NASDAQ market data and find real-time trade and quote information in NASDAQ listed instruments from all regulated US exchanges and venues.

Global Stock Market Data. More than 150 pricing sources, including biggest world stock exchanges. 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.

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.

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.

List of companies in the NASDAQ exchanges.

Data and documentation are available on NASDAQ's official webpage. Data is updated regularly on the FTP site.

The file used in this repository:

Notes:

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This statistic shows the largest global stock exchanges globally as of March 2025, ranked by the value of electronic order book share trading. In that time, the NYSE Stock Market was the largest stock exchange worldwide, with the value of EOB shares traded amounting to *** trillion U.S. dollars. Stock exchanges — additional information Stock exchanges are an important part of the free market economic system and are the most important component of the stock market. A stock exchange provides the setting in which stockbrokers, sellers, buyers, and traders can be brought together to take part in the sale of shares, bonds, derivatives and other securities. The core function of a stock exchange is to enable the fair and orderly trading, as well as the provision of price information, of any securities being traded on that exchange. Originally the exchanges were physical places (in some world locations the goods are still traded over-the-counter) but with time, they took the shape of an electronic platform. In order that company shares may be bought, traded and sold on a stock exchange, the company is required to have undergone an initial public offering process (IPO) on that particular exchange. The initial public offering of Alibaba Group Holding, a Chinese company operating in the e-commerce sector, on the New York Stock Exchange in September 2014, was the largest listing in the United States since 1996. The IPO of Alibaba Group Holding raised approximately ***** billion U.S. dollars.

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.

NASDAQ Composite Index NASDAQ Composite Index

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

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-07-20 to 2025-07-17 about stock market, average, industry, and USA.

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.

Market Analysis: Stock Market Simulator The global stock market simulator market is projected to witness robust growth, with its market size expected to reach $X million by 2033, registering a substantial CAGR of XX% during the forecast period (2025-2033). This upsurge can be attributed to the increasing popularity of online trading, advancements in technology, and the growing awareness of financial literacy among individuals. Key market drivers include the rising demand for virtual trading platforms, the need for risk-free investment simulations, and the surge in smartphone and internet penetration. Key trends shaping the market are the integration of artificial intelligence (AI) and machine learning, which enhances the accuracy of simulations and provides personalized trading experiences. The market also benefits from the growing adoption of mobile trading terminals, cloud-based solutions, and gamified trading experiences, which make it more accessible and engaging for a wider user base. However, factors such as data security concerns, regulatory complexities, and competition from traditional investment platforms may pose restraints. The market is segmented by type (PC terminal, mobile terminal) and application (personal, enterprise, others). Major players in the industry include Warrior Trading, MarketWatch, TD Ameritrade, and Investopedia. North America and Europe are expected to remain dominant regions in the market due to their advanced financial markets and high adoption of technology. A stock market simulator is a software program that mimics the trading of stocks in a real-world stock market. It allows users to buy and sell stocks, track their performance, and learn about the stock market without risking any real money. Stock market simulators are used by a variety of people, including individual investors, students, and financial professionals. There are many different types of stock market simulators available, ranging from simple, web-based simulators to more complex, professional-grade simulators. Some simulators are free to use, while others require a subscription fee. Stock market simulators can be a valuable tool for learning about the stock market and developing trading skills. However, it is important to remember that simulators are not a perfect substitute for real-world trading. There are a number of factors that can affect the performance of a stock in the real world that are not simulated in a simulator.

Nigeria's main stock market index, the NSE-All Share, rose to 130284 points on July 17, 2025, gaining 1.02% from the previous session. Over the past month, the index has climbed 11.21% and is up 29.63% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Nigeria. Nigeria Stock Market NSE - values, historical data, forecasts and news - updated on July of 2025.

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

The number of domestic companies listed on the Nasdaq and on the New York Stock Exchange (NYSE) has seen some fluctuations since 2018. As of the end of 2023, the NYSE had a total of ***** listed domestic and international companies, while the figure for the Nasdaq was much higher, standing at *****. Despite this, the NYSE has a higher market capitalization than the Nasdaq. What are the top listed companies? The NYSE has been a home for stable and long-lasting firms, also known as “blue-chip” companies. For example, Berkshire Hathaway, established in 1839, is the largest company traded on the NYSE. On the other hand, the Nasdaq has been known for listing major tech companies. For instance, Apple is the largest company listed on the Nasdaq. As of 2024, both companies were among the biggest companies in the world in terms of market capitalization. Which stock exchange has the most companies worldwide? Although the NYSE and the Nasdaq are the world’s largest two stock market operators by market capitalization, the Japan Exchange Group (JPX) is the biggest stock exchange in the world based on the number of companies. The JPX was created in 2013 through the merger of the Tokyo Stock Exchange and the Osaka Securities Exchange and is also one of the largest stock exchanges in the world based on total market capitalization.

Graph and download economic data for CBOE NASDAQ 100 Volatility Index (VXNCLS) from 2001-02-02 to 2025-07-15 about VIX, volatility, stock market, and USA.

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

We offer historical price data for equity indexes, ETFs and individual stocks in a Open/High/Low/Close (OHLC) format and can add almost any other required metric. We cover all major markets and many minor markets. Available for one-time purchase or with regular updates. Real-time/near-time (usually anything quicker than a 15min delay) requires an additional licence from the respective exchange, anything slower does not.

Access real-time and historical US equity options data included as part of Databento's OPRA data feed. The NASDAQ Options Market offers immediate and automatic price improvement to orders. Orders designated to use the options routing feature are routed to other markets to ensure orders get the best price available. The NASDAQ Options Market also links to and complies with the obligations of the Options InterMarket Linkage.