We offer three easy-to-understand packages to fit your business needs. Visit intrinio.com/pricing to compare packages.

Bronze

The Bronze package is ideal for developing your idea and prototyping your platform with high-quality EOD options prices sourced from OPRA.

When you’re ready for launch, it’s a seamless transition to our Silver package for delayed options prices, Greeks and implied volatility, and unusual options activity, plus delayed equity prices.

• Latest EOD OPRA options prices

Exchange Fees & Requirements:

This package requires no paperwork or exchange fees.

Bronze Benefits:

• Web API access
• 300 API calls/minute limit
• File downloads
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Silver

The Silver package is ideal for clients that want delayed options data for their platform, or for startups in the development and testing phase. You’ll get 15-minute delayed options data, Greeks, implied volatility, and unusual options activity, plus the latest EOD options prices and delayed equity prices.

You can easily move up to the Gold package for real-time options and equity prices, additional access methods, and premium support options.

• 15-minute delayed OPRA options prices, Greeks & IV
• 15-minute delayed OPRA unusual options activity
• Latest EOD OPRA options prices
• 15-minute delayed equity prices
• Underlying security reference data

Exchange Fees & Requirements:

If you subscribe to the Silver package and will not display the data outside of your firm, you’ll need to fill out a simplified exchange agreement and send it back to us. There are no exchange fees and we can provide immediate access to the data.

If you subscribe to the Silver package and will display the data outside of your firm, we’ll work with your team to submit the correct paperwork to OPRA for approval. Once approved, OPRA will bill exchange fees directly to your firm – typically $600-$2000/month depending on your use case. These fees are the same no matter what data provider you use. Per-user reporting is not required, so there are no variable per user fees.

Silver Benefits:

• Assistance with OPRA paperwork
• Web API access
• 2,000 API calls/minute limit
• File downloads
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support
• Concierge customer success team
• Comarketing & promotional initiatives

Gold

The Gold package is ideal for funded companies that are in the growth or scaling stage, as well as institutions that are innovating within the fintech space. This full-service solution offers real-time options prices, Greeks and implied volatility, and unusual options activity, as well as the latest EOD options prices and real-time equity prices.

You’ll also have access to our wide range of modern access methods, third-party data via Intrinio’s API with licensing assistance, support from our team of expert engineers, custom delivery architectures, and much more.

• Real-time OPRA options prices, Greeks & IV
• Real-time OPRA unusual options activity
• Latest EOD OPRA options prices
• Real-time equity prices
• Underlying security reference data

Exchange Fees & Requirements:

If you subscribe to the Gold package, we’ll work with your team to submit the correct paperwork to OPRA for approval. Once approved, OPRA will bill exchange fees directly to your firm – typically $600-$2000/month depending on your use case. These fees are the same no matter what data provider you use. Per-user reporting is required, with an associated variable per user fee.

Gold Benefits:

• Assistance with OPRA paperwork
• Web API access
• 2,000 API calls/minute limit
• WebSocket access (additional fee)
• Customizable access methods (Snowflake, FTP, etc.)
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support
• Concierge customer success team
• Comarketing & promotional initiatives
• Access to engineering team

Platinum

Don’t see a package that fits your needs? Our team can design a premium custom package for your business.

Financial data service providers offer financial market data and related services, primarily real-time feeds, portfolio analytics, research, pricing and valuation data, to financial institutions, traders and investors. Companies aggregate data and content from stock exchange feeds, broker and dealer desks and regulatory filings to distribute financial news and business information to the investment community. Recent globalization of the world capital market has benefited the financial sector and increased trading speed. Businesses rely on real-time data more than ever to help them make informed decisions. When considering a data service provider, an easy-to-use interface that shows customized, relevant information is vital for clients. During times of economic uncertainty, this information becomes more crucial than ever. Clients want information as soon and as frequently as possible, causing providers to prioritize efficiency and delivery. This was evident during the pandemic, the high interest rate environment in the latter part of the period and as the Fed cuts rates in 2024. Increased automation has helped industry players process large volumes of financial data, reducing analysis and reporting times. In addition, automation has reduced operational costs and reduced human data errors. These trends have resulted in growing revenue, which has risen at a CAGR of 3.2% to $21.9 billion over the past five years, including a 3.5% uptick in 2024 alone. Corporate profit will continue to expand as inflationary concerns begin to wane slowly. This will lead many companies to take on new clients as financial data helps them gain insight into operating their business amid ongoing trends and economic shakeups. With technology constantly advancing, service providers will continue investing in research and development to improve their products and services and best serve their clients. As technological advances continue, smaller players will be able to better compete with larger industry players. While this may lead to new companies joining the industry, larger providers will resume consolidation activity to expand their customer base. Overall, revenue is expected to swell at a CAGR of 2.7% to $25.0 billion by the end of 2029.

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.

Tradition is a top interdealer broker for OTC derivatives & commodities, offering trusted market data used by leading global financial institutions.

Alternative Data Provider Market Outlook

The global alternative data provider market size was valued at approximately USD 2.5 billion in 2023 and is expected to reach around USD 11 billion by 2032, growing at a robust CAGR of 18% during the forecast period. The surge in market size is primarily driven by the increasing demand for unique insights that alternative data provides to investment firms, hedge funds, and other financial institutions.

One of the prominent growth factors fueling the alternative data provider market is the escalating number of data sources. With the digital footprint expanding across social media, web scraping, credit card transactions, and satellite data, firms are constantly seeking new ways to gain a competitive edge. Social media platforms alone generate an immense volume of data daily, enabling businesses to derive real-time insights into consumer behavior, market trends, and sentiment analysis. This vast pool of unstructured data, when properly processed and analyzed, provides a goldmine of information for investment strategies and risk management.

Another significant growth driver is the increasing adoption of advanced analytical tools and artificial intelligence (AI). These technologies enable the efficient processing and analysis of large datasets, thus enhancing the accuracy and reliability of the insights derived. AI algorithms, in particular, are adept at identifying patterns and trends that may not be immediately apparent to human analysts. Moreover, the integration of machine learning techniques allows for continuous improvement in data analysis capabilities, making alternative data an indispensable tool for financial institutions aiming to stay ahead of the market.

Furthermore, the growing regulatory emphasis on transparency and accountability in financial markets is driving the adoption of alternative data. Regulatory bodies across the globe are increasingly scrutinizing traditional data sources to ensure fair trading practices and risk mitigation. In response, financial institutions are turning to alternative data providers to gain a more comprehensive view of market dynamics and to comply with stringent regulatory requirements. This shift toward greater transparency is expected to further bolster market growth.

Regionally, North America dominates the alternative data provider market, owing to the early adoption of advanced technologies and the presence of major financial hubs. However, other regions such as Asia Pacific and Europe are rapidly catching up. In Asia Pacific, the burgeoning fintech sector and the increasing number of start-ups are contributing significantly to market growth. Europe, on the other hand, is witnessing a surge in demand due to stringent regulatory frameworks and a growing emphasis on sustainable investing practices.

Data Type Analysis

The alternative data provider market can be segmented by data type into social media data, web scraped data, credit card transactions, satellite data, and others. Social media data is a significant segment that impacts the market due to the sheer volume and variety of data generated through various platforms like Facebook, Twitter, and LinkedIn. This data includes user posts, comments, likes, shares, and other forms of engagement that can be analyzed to gauge market sentiment and predict consumer behavior. Social media data is invaluable for real-time analysis and immediate insights, making it a crucial component for investment and marketing strategies.

Web scraped data is another vital segment, offering an extensive array of information collected from various online sources like e-commerce websites, news sites, blogs, and forums. This data type provides insights into market trends, product popularity, pricing strategies, and consumer preferences. Web scraping tools extract relevant information efficiently, which can then be analyzed to provide actionable insights for businesses looking to optimize their operations and investment strategies.

Credit card transaction data is a high-value segment, offering precise insights into consumer spending patterns and financial behaviors. This data can be used to track economic trends, monitor the performance of specific sectors, and forecast future spending habits. Financial institutions and hedge funds rely heavily on this type of data to make informed investment decisions and to develop targeted marketing campaigns. The granularity and accuracy of credit card transaction data make it a powerful tool for financial analysis.

Satellite data is an e

Market Size statistics on the Financial Data Service Providers industry in the US

Browse LSEG's market-leading global Pricing and Market Data for the financial markets, providing the broadest range of cross-asset market and pricing data.

Comprehensive dataset containing 3 verified Stock Market locations in United States with complete contact information, ratings, reviews, and location data.

Industry-specific Data Providers Market Outlook

The global industry-specific data providers market size is projected to experience significant growth, with a forecasted CAGR of 15.2% from 2024 to 2032, growing from USD 15.8 billion in 2023 to USD 46.9 billion by 2032. This growth trajectory is primarily driven by the increasing need for data-driven decision-making across various industries, ongoing digital transformation, and advancements in data analytics technologies.

One of the primary growth factors for the industry-specific data providers market is the escalating demand for real-time data and insights across various sectors. Businesses today are increasingly relying on accurate, timely data to inform their strategies, optimize operations, and stay competitive. With the proliferation of IoT devices, social media, and other digital platforms, the volume and variety of data generated have increased exponentially, necessitating specialized data providers who can offer tailored data solutions to meet specific industry needs.

Furthermore, the growth of artificial intelligence (AI) and machine learning (ML) technologies has bolstered the capabilities of data providers, enabling them to offer more sophisticated and actionable insights. These technologies allow for advanced data processing, predictive analytics, and automation, which are particularly valuable in sectors like BFSI, healthcare, and retail. As organizations recognize the potential of AI and ML to drive innovation and efficiency, their reliance on industry-specific data providers is expected to grow.

The increasing regulatory and compliance requirements across industries also contribute to the market's growth. Companies must adhere to various regulations regarding data privacy, security, and reporting, which can be complex and challenging to manage. Industry-specific data providers can help businesses navigate these regulatory landscapes by offering compliant data solutions and services. This not only ensures adherence to laws and regulations but also mitigates the risks associated with data breaches and non-compliance.

From a regional perspective, North America is expected to dominate the industry-specific data providers market, owing to its robust technological infrastructure and high adoption rates of advanced data analytics solutions. However, significant growth is anticipated in the Asia Pacific region, driven by the rapid digitalization of economies, increasing investments in technology, and the rising importance of data in decision-making processes. Europe, Latin America, and the Middle East & Africa will also see considerable growth, albeit at varying paces, as organizations in these regions increasingly recognize the value of specialized data services.

Data Type Analysis

The data type segment in the industry-specific data providers market encompasses various forms of data, including financial data, market data, consumer data, demographic data, and others. Financial data is critical for sectors such as BFSI, where accurate and timely financial information can make or break investment decisions. Market data, on the other hand, is essential for understanding market trends, competitive landscapes, and consumer behavior, which is invaluable for strategic planning and marketing efforts.

Consumer data has become increasingly important in recent years, especially with the rise of personalized marketing and customer-centric business models. This type of data includes information about consumer preferences, purchasing habits, and feedback, which can help companies tailor their products and services to better meet customer needs. Demographic data, which includes details about age, gender, income, and geographic location, is crucial for market segmentation and targeting specific customer groups effectively.

Other types of data, such as operational data and environmental data, also play significant roles in certain industries. For instance, operational data is vital for manufacturing and logistics companies to optimize their processes and improve efficiency. Environmental data is becoming increasingly important for companies looking to adhere to sustainability practices and reduce their environmental impact. The ability to provide these diverse data types tailored to specific industry needs makes industry-specific data providers indispensable partners for businesses across various sectors.

The demand for comprehensive data solutions that integrate multiple data types is also on the rise. Companies are increasingly loo

Financial Data Services Market size was valued at USD 23.3 Billion in 2023 and is projected to reach USD 42.6 Billion by 2031, growing at a CAGR of 8.1% during the forecast period 2024-2031.Global Financial Data Services Market DriversThe market drivers for the Financial Data Services Market can be influenced by various factors. These may include:The need for real-time analytics is growing: Real-time analytics are becoming more and more necessary in the financial sector due to the acceleration of data consumption. To reduce risks, make wise decisions, and enhance customer service, organizations need quick insights. Stakeholders are giving priority to solutions that enable quick data processing and analysis due to the increase in market volatility and complexity. The need for sophisticated analytical skills is driving providers of financial data services to modernize their products. As companies come to realize that using real-time data is crucial for keeping a competitive edge in a fast-paced financial climate, the competition among them to provide timely insights also boosts market growth.Growing Machine Learning and AI Adoption: Data analysis has been profoundly changed by the incorporation of AI and machine learning technology into financial data services. By enabling predictive analytics, these technologies help financial organizations make better decisions and reduce risk. Businesses can find trends that were previously invisible by automating data processing operations. This leads to more precise forecasts and improved investment plans. Furthermore, sophisticated algorithms are flexible enough to adjust to shifting circumstances, keeping organizations flexible. The increasing intricacy of financial markets necessitates the use of AI and machine learning, which in turn drives demand for sophisticated financial data services and promotes innovation in the sector.Global Financial Data Services Market RestraintsSeveral factors can act as restraints or challenges for the Financial Data Services Market. These may include:Difficulties in Regulatory Compliance: Regulations controlling data management, privacy, and financial transactions place heavy restrictions on the financial data services market. Regulations like the GDPR, CCPA, and banking industry standards like Basel III and SOX must all be complied with by organizations. Complying with these requirements frequently necessitates a significant investment in staff and compliance systems, which can be taxing, especially for smaller businesses. Regulations are dynamic, and different locations have different needs, which adds to the complexity and expense. Noncompliance not only results in monetary fines but also has the potential to harm an entity's image, so impeding market expansion.Dangers to Data Security: Threats to data security are a major impediment to the financial data services market. Because they manage sensitive data, financial institutions are often the targets of cyberattacks. Breach can lead to significant monetary losses, legal repercussions, and long-term harm to one's image. Although they can greatly increase operating expenses, investments in strong security measures like encryption, safe access protocols, and continual monitoring are crucial. Moreover, the dynamic strategies employed by cybercriminals need continuous adjustment, placing a burden on resources and detracting from the main operations of businesses. The evolution of security threats poses a challenge to preserving consumer trust, hence impeding industry expansion.

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

Use our Stock Market dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

Smart Insider’s Global Share Buyback Database offers invaluable insights to investors on public equity market data. We provide detailed, up-to-date share buyback data covering over 55,000 companies globally including Africa, that’s every company that reports Buybacks through regulatory processes.

Our Share buyback data includes detailed information on all major buyback transactions including source announcements and derived analysis fields. Our platform adds a visual representation of the data, allowing investors to quickly identify patterns and make decisions based on their findings.

Get detailed share buyback insights with Smart Insider and stay ahead of the curve with accurate, historical buyback insight that helps you make better 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 CSV, XML or XLSX via SFTP, API or Snowflake.

Sample dataset for Desktop Service has been provided with limited fields. Upon request, we can provide a detailed Quant sample.

Tags: Equity Market Data, Stock Market Data, Corporate Actions Data, Corporate Buyback Data, Company Financial Data, Insider Trading Data, Africa

Gain strategic OTC real time contributions from sell side desks across money market, foreign exchange, commodities and energy, and equity.

Database As A Service Providers Market size was valued at USD 195.06 Billion in 2024 and is projected to reach USD 471.36 Billion by 2031, growing at a CAGR of 11.66% during the forecast period 2024-2031.

Global Database As A Service Providers Market Drivers

Cloud Adoption: The increasing adoption of cloud computing has led to a growing demand for cloud-based database services, offering scalability, flexibility, and reduced infrastructure costs. Data-Driven Decision Making: Organizations are increasingly relying on data to make informed decisions, driving the need for efficient and scalable database solutions. Big Data Growth: The exponential growth of data, fueled by sources like social media, IoT devices, and customer interactions, necessitates robust database management capabilities.

Global Database As A Service Providers Market Restraints

Data Security and Privacy Concerns: Organizations may have concerns about data security and privacy when using cloud-based database services. Vendor Lock-in: Relying on a single DBaaS provider can create vendor lock-in, limiting flexibility and increasing costs.

Utilize chip analysis and institutional buy-sell daily data to track the movement of funds in the stock market.

The main stock market index of United States, the US500, rose to 6644 points on September 26, 2025, gaining 0.59% from the previous session. Over the past month, the index has climbed 2.50% and is up 15.78% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on September of 2025.

Comprehensive dataset containing 26 verified Stock Market locations in India with complete contact information, ratings, reviews, and location data.

📹Project Video available on YouTube - https://youtu.be/WmJYHz_qn5s

Realistic Synthetic - AI Financial & Market Data for Gemini(Google), ChatGPT(OpenAI), Llama(Meta)

This dataset provides a synthetic, daily record of financial market activities related to companies involved in Artificial Intelligence (AI). There are key financial metrics and events that could influence a company's stock performance like launch of Llama by Meta, launch of GPT by OpenAI, launch of Gemini by Google etc. Here, we have the data about how much amount the companies are spending on R & D of their AI's Products & Services, and how much revenue these companies are generating. The data is from January 1, 2015, to December 31, 2024, and includes information for various companies : OpenAI, Google and Meta.

This data is available as a CSV file. We are going to analyze this data set using the Pandas DataFrame.

This analyse will be helpful for those working in Finance or Share Market domain.

From this dataset, we extract various insights using Python in our Project.

1) How much amount the companies spent on R & D ?

2) Revenue Earned by the companies

3) Date-wise Impact on the Stock

4) Events when Maximum Stock Impact was observed

5) AI Revenue Growth of the companies

6) Correlation between the columns

7) Expenditure vs Revenue year-by-year

8) Event Impact Analysis

9) Change in the index wrt Year & Company

These are the main Features/Columns available in the dataset :

1) Date: This column indicates the specific calendar day for which the financial and AI-related data is recorded. It allows for time-series analysis of the trends and impacts.

2) Company: This column specifies the name of the company to which the data in that particular row belongs. Examples include "OpenAI" and "Meta".

3) R&D_Spending_USD_Mn: This column represents the Research and Development (R&D) spending of the company, measured in Millions of USD. It serves as an indicator of a company's investment in innovation and future growth, particularly in the AI sector.

4) AI_Revenue_USD_Mn: This column denotes the revenue generated specifically from AI-related products or services, also measured in Millions of USD. This metric highlights the direct financial success derived from AI initiatives.

5) AI_Revenue_Growth_%: This column shows the percentage growth of AI-related revenue for the company on a daily basis. It indicates the pace at which a company's AI business is expanding or contracting.

6) Event: This column captures any significant events or announcements made by the company that could potentially influence its financial performance or market perception. Examples include "Cloud AI launch," "AI partnership deal," "AI ethics policy update," and "AI speech recognition release." These events are crucial for understanding sudden shifts in stock impact.

7) Stock_Impact_%: This column quantifies the percentage change in the company's stock price on a given day, likely in response to the recorded financial metrics or events. It serves as a direct measure of market reaction.

AXISBANK OHLCV Data: Multi-Interval Historical Stock Prices (NSE)

Overview

This dataset provides comprehensive historical Open, High, Low, Close, and Volume (OHLCV) data for Axis Bank (AXISBANK), a prominent Indian stock listed on the National Stock Exchange (NSE). The data has been consolidated from various time intervals (1-minute, 5-minute, 15-minute, and 1-day), offering a granular yet unified view for diverse analytical needs, from high-frequency trading simulations to long-term trend analysis.

Data Source

The raw data was collected programmatically using the Groww API. The specific API endpoint used for fetching charting data is: https://groww.in/v1/api/charting_service/v4/chart/exchange/NSE/segment/CASH. While efforts have been made during data fetching and consolidation to ensure accuracy, please be aware that financial data can sometimes be subject to minor corrections or revisions by data providers.

Dataset Contents

This dataset is provided as a single, unified CSV file (e.g., unified_axisbank_ohlcv_data.csv), which has been consolidated from multiple JSON files representing different time intervals (1m, 5m, 15m, 1d).

The unified CSV file contains the following columns:

• Symbol: The stock ticker symbol (AXISBANK).
• Interval: The original time interval of the candle (1m, 5m, 15m, 1d).
• DateTime: The human-readable timestamp of the candle (derived from Unix Timestamp, e.g., YYYY-MM-DD HH:MM:SS).
• Open: The opening price of the stock during that interval.
• High: The highest price reached during that interval.
• Low: The lowest price reached during that interval.
• Close: The closing price of the stock during that interval.
• Volume: The trading volume (number of shares traded) during that interval.
• Timestamp: The raw Unix timestamp.

Overall Date Range and Record Count: The exact historical date range and total number of records for the complete dataset depend on the full content and consolidation process of the original JSON files. Your conversion script will provide the precise earliest and latest dates, as well as the total number of records in this unified CSV file. Based on the original file snippets, the data appears to span from at least mid-2022 into mid-2025.

Potential Use Cases

This dataset can be highly valuable for various applications in quantitative finance and data science, including: * Algorithmic Trading Strategy Development: Backtesting and optimizing trading strategies across different timeframes for AXISBANK. * Technical Analysis: Generating charts, calculating technical indicators (e.g., Moving Averages, RSI, MACD, Bollinger Bands) specific to AXISBANK. * Machine Learning for Price Prediction: Training models to forecast future stock prices, trends, or volatility for AXISBANK. * Market Trend Analysis: Studying short-term and long-term market behavior, liquidity, and price action of Axis Bank. * Educational Purposes: A clean, multi-interval dataset ideal for learning and practicing data analysis with financial time series.

Disclaimer

This dataset is provided for informational and educational purposes only. It should not be considered financial advice, investment recommendations, or a solicitation to buy or sell any securities. Trading and investing in financial markets involve significant risk, and past performance is not indicative of future results. Always conduct your own thorough research and consult with a qualified financial advisor before making any investment decisions. The creators of this dataset are not liable for any losses incurred from its use.