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.

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

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.

Why FinFeedAPI?

Market Coverage & Data Depth: - Historical Data: T+1 data on 11K+ symbols with unlimited historical lookback. - Trade Feeds: Detailed trade records including timestamps, sizes, prices, and conditions (e.g., odd lot, intermarket sweep, extended hours). - Level 1 Quotes: Best bid/ask prices, sizes, and timestamps. - Level 2 Price Book: Market depth with multiple bid/ask prices and aggregate order sizes. - Level 3 Order Book: The complete order book detailing individual orders.

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.

Precision & Reliability: - Nano-second Timestamps: Ensuring fair, accurate, and auditable transaction sequencing for HFT and compliance. - Institutional Trust: Relied upon by financial institutions for dependable equity market information.

Financial institutions and trading firms rely on FinFeedAPI for mission-critical equity market intelligence. We are committed to delivering clean, precise, and comprehensive data when it matters most. If you require dependable and granular stock market data, FinFeedAPI provides the actionable insights you need.

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.

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.

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

This dataset offers both live (delayed) prices and End Of Day time series on equity options

1/ Live (delayed) prices for options on European stocks and indices including: Reference spot price, bid/ask screen price, fair value price (based on surface calibration), implicit volatility, forward Greeks : delta, vega Canari.dev computes AI-generated forecast signals indicating which option is over/underpriced, based on the holders strategy (buy and hold until maturity, 1 hour to 2 days holding horizon...). From these signals is derived a "Canari price" which is also available in this live tables.
Visit our website (canari.dev ) for more details about our forecast signals.

The delay ranges from 15 to 40 minutes depending on underlyings.

2/ Historical time series: Implied vol Realized vol Smile Forward
See a full API presentation here : https://youtu.be/qitPO-SFmY4 .

These data are also readily accessible in Excel thanks the provided Add-in available on Github: https://github.com/canari-dev/Excel-macro-to-consume-Canari-API

If you need help, contact us at: contact@canari.dev

User Guide: You can get a preview of the API by typing "data.canari.dev" in your web browser. This will show you a free version of this API with limited data.

Here are examples of possible syntaxes:

For live options prices: data.canari.dev/OPT/DAI data.canari.dev/OPT/OESX/0923 The "csv" suffix to get a csv rather than html formating, for example: data.canari.dev/OPT/DB1/1223/csv For historical parameters: Implied vol : data.canari.dev/IV/BMW

data.canari.dev/IV/ALV/1224

data.canari.dev/IV/DTE/1224/csv

Realized vol (intraday, maturity expressed as EWM, span in business days): data.canari.dev/RV/IFX ... Implied dividend flow: data.canari.dev/DIV/IBE ... Smile (vol spread between ATM strike and 90% strike, normalized to 1Y with factor 1/√T): data.canari.dev/SMI/DTE ... Forward: data.canari.dev/FWD/BNP ...

List of available underlyings: Code Name OESX Eurostoxx50 ODAX DAX OSMI SMI (Swiss index) OESB Eurostoxx Banks OVS2 VSTOXX ITK AB Inbev ABBN ABB ASM ASML ADS Adidas AIR Air Liquide EAD Airbus ALV Allianz AXA Axa BAS BASF BBVD BBVA BMW BMW BNP BNP BAY Bayer DBK Deutsche Bank DB1 Deutsche Boerse DPW Deutsche Post DTE Deutsche Telekom EOA E.ON ENL5 Enel INN ING IBE Iberdrola IFX Infineon IES5 Intesa Sanpaolo PPX Kering LOR L Oreal MOH LVMH LIN Linde DAI Mercedes-Benz MUV2 Munich Re NESN Nestle NOVN Novartis PHI1 Philips REP Repsol ROG Roche SAP SAP SNW Sanofi BSD2 Santander SND Schneider SIE Siemens SGE Société Générale SREN Swiss Re TNE5 Telefonica TOTB TotalEnergies UBSN UBS CRI5 Unicredito SQU Vinci VO3 Volkswagen ANN Vonovia ZURN Zurich Insurance Group

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

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.

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.

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.

Databento provides the industry’s fastest cloud-based solutions for intraday and real-time tick data. First to deliver full L3 (MBO) over internet.

Access L2 market data with Databento's market by price (MBP-10) schema, which aggregates book depth by price and includes every order across the top ten price levels.

Access L3 market data with Databento's market-by-order (MBO) schema, which provides full order book depth, including every order at every price level, tick-by-tick with accurate queue position.

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:API Target Price Prediction

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.

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

US Tariff Impact on the API Security Market

The imposition of U.S. tariffs has introduced significant challenges to the API Security Market, particularly affecting the IT and Telecom sectors. Tariffs have led to increased costs for telecom equipment and components imported from affected countries, with some hardware pricing seeing noticeable jumps due to tariffs reaching up to 100%.

These additional expenses often translate into higher prices for consumers and businesses, potentially reducing demand for telecom services. Furthermore, tariffs on hardware components essential for software development and deployment. Such as servers and networking equipment, have raised concerns about increased costs in the tech sector.

➤➤➤ Discover how our research uncovers business opportunities @ https://market.us/report/api-security-market/free-sample/

These cost escalations can strain the financial viability of API security investments, particularly for large enterprises relying on imported hardware and cloud infrastructure. The uncertainty surrounding trade policies may also deter institutional investors. Who are pivotal in the API Security Market, from committing capital to projects susceptible to tariff-induced cost fluctuations?

Economic Impact

• Increased Operational Costs: Tariffs have led to higher prices for imported hardware, escalating operational expenses for businesses.
• Inflationary Pressures: The added costs contribute to inflation, affecting consumer spending and investment returns.
• Investment Uncertainty: Unpredictable trade policies may lead to deferred investment decisions, impacting the flow of capital into API security projects.

Geographical Impact

• North America: As the largest market, North America faces significant exposure to tariff-induced cost increases, particularly in the IT and Telecom sectors.
• Asia-Pacific: Tariffs may shift investment focus towards Asia-Pacific regions with favorable trade conditions, altering the geographical distribution of API security investments.
• Global Supply Chains: Disruptions in global supply chains due to tariffs can affect the implementation and success of API security projects worldwide.

Business Impact

• Strategic Reassessment: Businesses may need to reevaluate supply chains and sourcing strategies to mitigate tariff impacts.
• Cost Management: Increased costs necessitate enhanced financial planning and cost-control measures to maintain project viability.
• Investor Relations: Transparency regarding tariff impacts becomes crucial in maintaining investor confidence and securing funding.

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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 determine buy or sell? (LON:API 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

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.

(API) Abrdn Property Income: Renting Out the Future?

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