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

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 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.

Historical option data in 2019 to 2021, dataset files in CSV format.

Browse Tesla Inc (TSLA) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Nasdaq TotalView-ITCH is the proprietary data feed that provides full order book depth for Nasdaq market participants.

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 CBOE Volatility Index: VIX (VIXCLS) from 1990-01-02 to 2025-03-24 about VIX, volatility, stock market, and USA.

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.

The value of the DJIA index amounted to 43,191.24 at the end of March 2025, up from 21,917.16 at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29th of 2008, for instance, the Dow had a loss of 106.85 points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by 81.66 percent in one year, and 1933, year when the index registered a growth of 63.74 percent.

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.

Historical dataset of the daily level of the federal funds rate back to 1954. The fed funds rate is the interest rate at which depository institutions (banks and credit unions) lend reserve balances to other depository institutions overnight, on an uncollateralized basis. The Federal Open Market Committee (FOMC) meets eight times a year to determine the federal funds target rate.

The main stock market index in the United Kingdom (GB100) increased 479 points or 5.86% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on March of 2025.

The main stock market index in Kenya (Nairobi 20) increased 244 points or 12.12% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from Kenya. Kenya Stock Market (NSE20) - values, historical data, forecasts and news - updated on March of 2025.

The main stock market index in Hong Kong (HK50) increased 3587 points or 17.88% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from Hong Kong. Hong Kong Stock Market Index (HK50) - values, historical data, forecasts and news - updated on March of 2025.

Long term historical dataset of the broad price-adjusted U.S. dollar index published by the Federal Reserve. The index is adjusted for the aggregated home inflation rates of all included currencies. The price adjustment is especially important with our Asian and South American trading partners due to their significant inflation episodes of the 80s and 90s.

The main stock market index in Nigeria (NSE-All Share) increased 2706 points or 2.63% since the beginning of 2025, 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 March of 2025.

This table contains 25 series, with data for years 1956 - present (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Toronto Stock Exchange Statistics (25 items: Standard and Poor's/Toronto Stock Exchange Composite Index; high; Standard and Poor's/Toronto Stock Exchange Composite Index; close; Toronto Stock Exchange; oil and gas; closing quotations; Standard and Poor's/Toronto Stock Exchange Composite Index; low ...).

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 know when a stock will go up or down? (BEX 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

Graph and download economic data for NASDAQ 100 Index (NASDAQ100) from 1986-01-02 to 2025-03-26 about NASDAQ, stock market, indexes, and USA.

Context

This dataset contains historical data of stocks listed on IHSG with time ranges per minutes, hourly, and daily. The source of the dataset is taken from Yahoo Finance's public data and the IDX website which is listed in the metadata tab. This dataset was created with the intention of academic research purposes and not to be commercialized. If you have questions about the dataset, please ask in the discussion tab. Code snippet: https://github.com/muamkh/IHSGstockscraper

Content

Stock minutes data is taken from 1 November 2021 until 6 January 2023. Stock hourly data is taken from 16 April 2020 until 6 January 2023. Stock daily data is taken from 16 April 2001 until 6 January 2023. All of the data is using CSV format. Stock data isnt adjusted with dividend, stock split, and other corporate action.

Stocklist Structure

• Code = Stock code
• Name = Company name
• ListingDate = Listing date of stock on Indonesia Stock Exchange
• Shares = Amount of shares
• ListingBoard = Board category (Main Board, Development Board or Acceleration). More info: https://www.idx.co.id/en-us/products/stocks/
• Sector = Sector Category based on IDX-IC. More info: https://www.idx.co.id/en-us/products/stocks/
• LastPrice = Last stock price
• MarketCap = Market Capitalization.
• MinutesFirstAdded = Date the data first retrieved in minute range
• MinutesLastAdded = Date the data last retrieved in minute range
• HourlyFirstAdded = Date the data first retrieved in hourly range
• HourlyLastAdded = Date the data last retrieved in hourly range
• DailyFirstAdded = Date the data first retrieved in daily range
• DailyLastAdded = Date the data last retrieved in daily range

Struktur Data Saham

• timestamp = Date and time of stock transaction
• open = opening price
• low = lowest price in the timespan
• high = highest price in the timespan
• close = closing price
• volume = Total volume traded in the timespan