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

Gain data-driven insights for informed investment decisions. Access market sentiment data since 2013 and customize the API for seamless integration. Maximize your stock market understanding with comprehensive analytics on global stock indices, and public and private companies. Analyze sentiment trends and investor behavior with confidence.

Sample Dataset - Historical News Sentiment data for your reference.

Key Features:

1. Tick-by-Tick Sentiment: Access detailed market dynamics with tick-by-tick sentiment data.
2. Custom API: Request a customizable API covering over 70,000 tickers, including major FX, commodities, topics, and people.
3. Proven Expertise: Trust our track record since 2013 for historical data on long-term sentiment patterns.
4. Uncover Hidden Insights: Gauge investor sentiment and reveal market opportunities with the custom API.
5. Real-Time Benchmarks: Enhance your strategies with real-time sentiment indicators.
6. Customizable and Flexible Delivery: Tailor the dataset to your requirements and integrate seamlessly into your workflows.

Gain a competitive edge with InfoTrie's Historical Tick-by-Tick Stock Market Sentiment Data. Request access now to elevate your investment strategies and make data-driven decisions.

More information on : https://infotrie.com/sentiment-analysis/

The best choice for those looking for license-free US market data for commercial use is US Equities Basic, which includes data display, redistribution, professional trading, and more.

US Equities Basic is based upon a derived IEX feed. The volume coverage is 3-5% of the total trading volume in North America, which helps entities mitigate license expenses and start with real-time data.

US Equities Basic provides raw quotes, trades, aggregated time series (OHLCV), and snapshots. Both REST API and WebSocket API are available.

End-of-day price information disseminated after 12:00 AM EST does not require licensing in the United States by law. This applies to all exchanges, even those not included in the US Equities Basic. Finazon combines all price information after every trading day, meaning that while markets are open, real-time prices are available from a subset of exchanges, and when markets close, data is synced and contains 100% of US volume. All historical prices are adjusted for corporate actions and splits.

Tip: Individuals with non-professional usage are not required to get exchange licenses for real-time data and, hence, are better off with the US Equities Max dataset.

Throughout the 1920s, prices on the U.S. stock exchange rose exponentially, however, by the end of the decade, uncontrolled growth and a stock market propped up by speculation and borrowed money proved unsustainable, resulting in the Wall Street Crash of October 1929. This set a chain of events in motion that led to economic collapse - banks demanded repayment of debts, the property market crashed, and people stopped spending as unemployment rose. Within a year the country was in the midst of an economic depression, and the economy continued on a downward trend until late-1932.

It was during this time where Franklin D. Roosevelt (FDR) was elected president, and he assumed office in March 1933 - through a series of economic reforms and New Deal policies, the economy began to recover. Stock prices fluctuated at more sustainable levels over the next decades, and developments were in line with overall economic development, rather than the uncontrolled growth seen in the 1920s. Overall, it took over 25 years for the Dow Jones value to reach its pre-Crash peak.

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.

The main stock market index in Japan (JP225) decreased 2147 points or 5.38% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on March of 2025.

• Data from Dec 1989 to Dec 2020.
• Includes Income Statement, Balance Sheet, and Cashflow statement.
• Adjusted for restatements.
• Includes valuation metrics such as enterprise valuation and market capitalization.
• Over 30 ratios such as p/e ratio, EBITDA/sales, gross margin etc..
• Standardized categories for comparison between companies.

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.

Can we predict stock market using machine learning? (LON:PNL 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

The Dow Jones Industrial Average (DJIA) is a stock market index used to analyze trends in the stock market. While many economists prefer to use other, market-weighted indices (the DJIA is price-weighted) as they are perceived to be more representative of the overall market, the Dow Jones remains one of the most commonly-used indices today, and its longevity allows for historical events and long-term trends to be analyzed over extended periods of time. Average changes in yearly closing prices, for example, shows how markets developed year on year. Figures were more sporadic in early years, but the impact of major events can be observed throughout. For example, the occasions where a decrease of more than 25 percent was observed each coincided with a major recession; these include the Post-WWI Recession in 1920, the Great Depression in 1929, the Recession of 1937-38, the 1973-75 Recession, and the Great Recession in 2008.

Graph and download economic data for NASDAQ Composite Index (NASDAQCOM) from 1971-02-05 to 2025-03-24 about NASDAQ, composite, stock market, indexes, and USA.

The main stock market index in Sweden (Stockholm) increased 140 points or 5.65% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from Sweden. Sweden Stock Market Index - values, historical data, forecasts and news - updated on March of 2025.

Historical option data in 2014 to 2021, dataset files in CSV format.

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.

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.

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.

What are the most successful trading algorithms? (NTAP 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

The main stock market index in Saudi Arabia (TASI) decreased 95 points or 0.79% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from Saudi Arabia. Saudi Arabia Stock Market (TASI) - values, historical data, forecasts and news - updated on March of 2025.

Access real-time and historical US equity options data included as part of Databento's OPRA data feed. Nasdaq PHLX is a full service options trading platform offering both electronic and floor-based trading. PHLX runs a customer priority, pro rata allocation model focused on executions for Complex and Simple Orders. Features of PHLX include: price improvement on XL (PIXL), complex order systerm, flexible routing strategies, and 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.

S&P 500: A Bull or a Bear?

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

Tesla stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-03-27 to 2025-03-26 about stock market, average, industry, and USA.