Nasdaq Basic with NLS Plus is our most cost-effective solution for real-time US equities, offering the broadest coverage and added granularity—such as trade aggressor side—for a fraction of the cost of consolidated feed alternatives like SIP data.

This proprietary, consolidated data feed disseminates top-of-book (L1) data from every Nasdaq-operated venue and covers all US stocks and ETFs, including those listed on the NYSE, NYSE Arca, NYSE American, and Cboe exchanges. As the premium tier of Nasdaq's Basic product, it combines Nasdaq Last Sale (NLS) Plus and Nasdaq BBO (QBBO) to provide: - Best bid and offer (BBO) quotes for the Nasdaq stock market (XNAS), which are within 1% of the NBBO 99.22% of the time. - Tick-by-tick price and size for orders executed on Nasdaq (XNAS), Nasdaq BX (XBOS), and Nasdaq PSX (XPSX). - All off-exchange trades reported to FINRA/Nasdaq's Carteret and Chicago Trade Reporting Facilities (TRFs), which aggregate data from most of the 30 ATSs and account for approximately 45% to 49% of the average daily volume (ADV) in all exchange-listed securities.

With the addition of TRF data, Nasdaq Basic with NLS Plus captures the majority of the trading activity and liquidity within US equity markets. As of January 2025, this dataset represented 62.9% ADV, including both on-exchange and off-exchange trades.

This dataset is an ideal choice for market participants who need an accurate BBO but don't directly execute trades or display quotes for FINRA broker-dealer obligations. It also features substantially lower exchange license fees for real-time data compared to Nasdaq TotalView-ITCH, with pricing designed for distribution use cases and per-user rates that are reduced by more than 65%.

Real-time Nasdaq Basic with NLS Plus 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.

Breadth of coverage: 11,595 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: MBP-1, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Nasdaq Basic provides Best Bid and Offer and Last Sale information for all U.S. exchange-listed securities based on liquidity within the Nasdaq market center, as well as trades reported to the FINRA Trade Reporting Facility (TRF) operated in partnership with FINRA/Nasdaq TRF.

NASDAQ reported $1.24B in Gross Profit on Sales for its fiscal quarter ending in March of 2025. Data for NASDAQ | NDAQ - Gross Profit On Sales including historical, tables and charts were last updated by Trading Economics this last July in 2025.

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

Need a cost-effective, real-time U.S. equity quote and trade solution? Nasdaq Basic is the leading exchange-provided alternative for real-time Best Bid and Offer and Last Sale information for all U.S. exchange-listed stocks. With Basic, investors access a proprietary data product that provides accuracy, liquidity, instrument coverage and accessibility with significant cost-savings.

NASDAQ reported $853M in Cost of Sales for its fiscal quarter ending in March of 2025. Data for NASDAQ | NDAQ - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last July in 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.

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

The Nasdaq Composite index fell by approximately 2,400 points in the four weeks from February 12 to March 11, 2020, but has since recovered, peaking at over 18,647 points on July 10, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the Nasdaq Composite index stood at a little over 9,700 points. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the Nasdaq Composite index stood at a little over 9,700 points. Coronavirus concerns escalate The World Health Organization declared the coronavirus outbreak as a global pandemic in March 2020, setting the Dow Jones Industrial Average, the S&P 500, and other market indexes up for significant losses. With the stock markets destabilized, traders opted to sell their shares and were prepared to wait before investing in stocks again. Investors would have felt more confident if there were signs that the virus was being contained, but the number of cases continued to rise.

The price of Tesla shares traded on the Nasdaq stock exchange remained rather stable between July 2010 and January 2020. With the beginning of 2020, the price of Tesla share increased dramatically and stood at ****** U.S. dollars per share in November 2021. Since then, the price of Tesla share fluctuated significantly and reached its peak at ****** U.S. dollars per share in December 2024, before falling dramatically in February 2025. Why did Tesla's stock value go up in 2020? Despite the effects of the pandemic, Tesla share prices experienced a massive increase in 2020. Tesla kept increasing its output levels throughout the year, except for the second quarter, and released its new vehicle Tesla Model Y. Additionally, when the company was added to the S&P 500 index in August 2020, it instilled further trust in investors. In 2020, Tesla was the top-performing stock on the S&P 500 index, and two years later, in 2024, it ranked among the ten largest companies on the index by market capitalization. Steady growth in the last decade Founded in 2003, Tesla primarily focuses on designing and producing electric vehicles, as well as energy generation and storage systems. Since then, Tesla's revenue has steadily increased, reaching nearly ** million U.S. dollars in 2024. Most of the revenue came from automotive sales in 2024. Tesla's first electric car, the Roadster, was sold between 2008 and 2012. Currently, the company offers four primary electric vehicles: Model 3, Model Y, Model S, and Model X.

Nasdaq BX TotalView-ITCH is a proprietary data feed that provides full order book depth and last sale information. It displays every quote and order at each price level on the BX market (formerly the Boston Stock Exchange) and captures all order book activity, including trade executions, order modifications, cancellations, and other events that update the book's state. This L3 granularity offers data not found in SIP feeds—such as explicit trade aggressor side and odd lots—and enable accurate modeling of quote lifetimes and queue dynamics.

This dataset covers all securities traded or routed through the BX exchange platform, including those listed on the Nasdaq, NYSE, NYSE American, NYSE Arca, and Cboe exchanges under Reg NMS. As of January 2025, the Nasdaq BX exchange represented 0.13% of the average daily volume (ADV).

Nasdaq BX attracts retail-focused brokers and traders with its inverted pricing model, offering rebates for liquidity removers and its Retail Price Improvement (RPI) program. It disseminates the Retail Price Improvement Indicator (RPII) to signal the availability of RPI orders, which are designed to improve upon the NBBO and promote better pricing opportunities for retail investors.

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

Breadth of coverage: 11,544 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, 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

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 3.3 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 21.77 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.

Sell High: Time to Cash in on 5 of Nasdaq's Best Stocks

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 Stocks, Value of Shares Sold on the New York Stock Exchange for United States (M11003USM144NNBR) from Jan 1885 to Dec 1920 about stock market and USA.

In 2025, stock markets in the United States accounted for roughly ** percent of world stocks. The next largest country by stock market share was China, followed by the European Union as a whole. The New York Stock Exchange (NYSE) and the NASDAQ are the largest stock exchange operators worldwide. What is a stock exchange? The first modern publicly traded company was the Dutch East Industry Company, which sold shares to the general public to fund expeditions to Asia. Since then, groups of companies have formed exchanges in which brokers and dealers can come together and make transactions in one space. Stock market indices group companies trading on a given exchange, giving an idea of how they evolve in real time. Appeal of stock ownership Over half of adults in the United States are investing money in the stock market. Stocks are an attractive investment because the possible return is higher than offered by other financial instruments.

The New York Stock Exchange (NYSE) is the largest stock exchange in the world, with an equity market capitalization of almost ** trillion U.S. dollars as of June 2025. The following three exchanges were the NASDAQ, PINK Exchange, and the Frankfurt Exchange. What is a stock exchange? A stock exchange is a marketplace where stockbrokers, traders, buyers, and sellers can trade in equities products. The largest exchanges have thousands of listed companies. These companies sell shares of their business, giving the general public the opportunity to invest in them. The oldest stock exchange worldwide is the Frankfurt Stock Exchange, founded in the late sixteenth century. Other functions of a stock exchange Since these are publicly traded companies, every firm listed on a stock exchange has had an initial public offering (IPO). The largest IPOs can raise billions of dollars in equity for the firm involved. Related to stock exchanges are derivatives exchanges, where stock options, futures contracts, and other derivatives can be traded.

Consolidated last sale, exchange BBO and national BBO across all US equity options exchanges. Includes single name stock options (e.g. TSLA), options on ETFs (e.g. SPY, QQQ), index options (e.g. VIX), and some indices (e.g. SPIKE and VSPKE). This dataset is based on the newer, binary OPRA feed after the migration to SIAC's OPRA Pillar SIP in 2021. OPRA is notable for the size of its data and we recommend users to anticipate several TBs of data per day for the full dataset in its highest granularity (MBP-1).

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's Stock: Is it Soaring to New Heights? (NDAQ)

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.

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

Browse NASDAQ 100 (NDX) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Consolidated last sale, exchange BBO and national BBO across all US equity options exchanges. Includes single name stock options (e.g. TSLA), options on ETFs (e.g. SPY, QQQ), index options (e.g. VIX), and some indices (e.g. SPIKE and VSPKE). This dataset is based on the newer, binary OPRA feed after the migration to SIAC's OPRA Pillar SIP in 2021. OPRA is notable for the size of its data and we recommend users to anticipate several TBs of data per day for the full dataset in its highest granularity (MBP-1).

Origin: Options Price Reporting Authority

Supported data encodings: DBN, JSON, CSV Learn more

Supported market data schemas: MBP-1, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, TBBO, Trades, Statistics, Definition Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

The price of Amazon shares traded on the Nasdaq stock exchange fluctuated significantly but increased for the most part during the period between 2010 and 2025, peaking at 237.68 U.S. dollar per share in January 2025. Expansion during the pandemic Due to the rise of online shopping worldwide during the Covid-19 pandemic, Amazon's share prices saw an increase as the company experienced dramatic growth. As a result, the company's net sales revenue increased by almost 400 billion U.S. dollars between 2019 to 2024, growing ever since. However, the surge in Amazon's operations significantly increased the company's fulfillment expenses and shipping costs after 2020. The shift towards offline shopping and cost increases after the pandemic resulted in significant layoffs in 2022. Amazon Web Services Amazon is not only the world's most valuable retailer but also the leader in the cloud computing industry through Amazon Web Services (AWS). AWS is a platform that offers storage, servers, and networking to individuals, businesses, and organizations. Amazon's success is driven by its excellence in diverse sectors, but AWS stands as the primary source of profit. The cloud service has consistently grown in profitability, generating nearly 40 billion U.S. dollars in profit in 2024.