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.

EDI's history of corporate action events dates back to January 2007 and uses unique Security IDs that can track the history of events by issuer since January 2007.

Choose to receive accurate corporate actions data via an SFTP connection either 4x daily or end-of-day. Proprietary format. ISO 15022 message standard, providing MT564 & 568 announcements.

To support global trading schedules, EDI offers seven daily data feeds at 03:30, 07:00, 09:00, 11:00, 13:00, 15:00, and 17:15 GMT, ensuring continuous access to accurate, market-aligned data.

Extensive and dependable pricing information spanning the entire range of financial markets. Encompassing worldwide coverage from stock exchanges, trading platforms, indicative contributed prices, assessed valuations, expert third-party sources, and our enhanced data offerings. User-friendly request-response, bulk access, and tailored desktop interfaces to meet nearly any organizational or application data need. Worldwide, real-time, delayed streaming, intraday updates, and meticulously curated end-of-day pricing information.

Context

Predicting the stock market is one of the most commonly performed projects when someone is learning about ML and Data Science. After all, who wouldn't want to delegate the task of picking stocks to a model and reap the rewards for themselves? However, one of the most difficult and tedious steps to predict what stocks to invest in is actually gathering the data to use. There are so many options and it is important to get sufficient information for each. But, what if you can skip this step and just download a dataset that has all that information easily available for you? Look no further as this is the answer to this problem.

Content

This dataset contains information of 4447 stocks traded under Nasdaq across various exchanges. There is a file that contains information for all 4447 stocks but also has several null fields, which is why I labeled it as full_financial_stocks_raw.csv --it has minimal modifications to the values inside the rows. The second file, dividend_stocks_only.csv, is still a raw-ish style dataset but it only contains stocks that pay out dividends to its shareholders. Interestingly, it seems dividend-paying stocks have more information about them, which explains why this file has significantly fewer rows with null values.

Update: In the next 24 hours, I will be uploading an optimized, feature-engineered dataset that has fewer columns overall and fewer rows with null values. This dataset is intended to be a fully cleaned option to directly feed into ML/DL models.

Acknowledgements

I would like to thank the sources where I obtained my data, which are the FTP Nasdaq Trader website and the Yahoo Finance API.

Inspiration

Analyzing the stock market is one of the most intriguing endeavors I could think of as the ways it can be influenced are so broad and distinct from one another. A news article can influence how investors view a particular company, social media can directly fluctuate a company's share price, and there are numerous calculations and formulas that can show what stocks are worth investing in.

Cash Flow Forecasting for Corporates via APIs Market Outlook

According to our latest research, the global Cash Flow Forecasting for Corporates via APIs market size reached USD 1.42 billion in 2024, reflecting a robust growth trajectory fueled by increasing demand for real-time financial insights and automation across corporate finance departments. The market is anticipated to expand at a compelling CAGR of 18.7% during the forecast period, positioning it to attain a value of USD 7.03 billion by 2033. This growth is primarily driven by the rising adoption of API-driven financial solutions, which enable seamless integration, enhanced accuracy, and agility in cash flow management processes for enterprises worldwide.

One of the primary growth factors propelling the cash flow forecasting for corporates via APIs market is the escalating need for real-time financial data and actionable insights. As businesses face increasingly volatile market conditions, the ability to access up-to-date cash positions and forecast future liquidity has become a critical competitive advantage. APIs facilitate the automatic aggregation and analysis of transactional data from diverse sources, allowing treasury and finance teams to respond swiftly to market fluctuations, optimize working capital, and mitigate liquidity risks. Furthermore, regulatory pressures and the growing complexity of global supply chains have heightened the demand for flexible, scalable, and interoperable solutions that can adapt to evolving business requirements, further fueling market expansion.

Another significant factor driving market growth is the digital transformation initiatives sweeping across industries. Corporates are investing heavily in advanced financial technologies to streamline operations, reduce manual errors, and improve decision-making. API-based cash flow forecasting solutions offer seamless integration with existing ERP, accounting, and banking systems, enabling organizations to automate data flows and enhance forecasting accuracy. The proliferation of cloud computing and the shift toward software-as-a-service (SaaS) delivery models have further democratized access to sophisticated forecasting tools, making them accessible to small and medium-sized enterprises (SMEs) as well as large corporations. This democratization is widening the addressable market and accelerating adoption rates across various sectors.

The emergence of open banking initiatives and the increasing collaboration between fintech companies and traditional financial institutions are also catalyzing market growth. APIs are at the heart of open banking, enabling secure and standardized data sharing between banks and third-party providers. This ecosystem is fostering innovation in cash flow forecasting by allowing corporates to leverage a broader array of data sources, such as real-time bank feeds, payment gateways, and external market data. As a result, organizations can gain deeper insights into their cash positions, enhance risk management strategies, and comply with regulatory requirements more effectively. The synergy between open banking and API-driven forecasting is expected to remain a key growth driver in the coming years.

From a regional perspective, North America currently dominates the cash flow forecasting for corporates via APIs market, accounting for the largest share in 2024, followed by Europe and the Asia Pacific. The region’s leadership can be attributed to the high concentration of technologically advanced enterprises, early adoption of digital finance solutions, and a supportive regulatory landscape that encourages innovation. Europe is witnessing rapid growth, driven by the proliferation of open banking regulations and a strong fintech ecosystem. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by the digitalization of financial services, burgeoning SME sector, and increasing cross-border trade activities. These regional dynamics are shaping the competitive landscape and influencing market strategies.

Component Analysis

snapshot-tab-pane Data-As-A-Service (DaaS) Market Size 2025-2029The data-as-a-service (daas) market size is valued to increase USD 40.76 billion, at a CAGR of 32.6% from 2024 to 2029. Growing amount of data will drive the data-as-a-service (daas) market.Major Market Trends & InsightsNorth America dominated the market and accounted for a 36% growth during the forecast period.By End-user - BFSI segment was valued at USD 2.53 billion in 2023By Deployment - Cloud segment accounted for the largest market revenue share in 2023Market Size & ForecastMarket Opportunities: USD 620.17 millionMarket Future Opportunities: USD 40757.80 millionCAGR : 32.6%North America: Largest market in 2023Market SummaryThe market represents a dynamic and continually evolving landscape, driven by the increasing demand for data-driven insights and analytics. Core technologies, such as artificial intelligence and machine learning, are revolutionizing the way businesses access and utilize data. DaaS applications span various industries, including healthcare, finance, and retail, enabling organizations to make informed decisions and improve operational efficiency. According to recent estimates, the adoption rate of DaaS is projected to reach 50% by 2025, underscoring its growing importance. Service types range from cloud-based solutions to on-premises offerings, catering to diverse client requirements. Regulatory compliance, such as GDPR and HIPAA, pose challenges for market participants, necessitating robust data security measures. The integration of Data-as-a-Service in blockchain technology is a significant trend, offering enhanced security and transparency. Despite these opportunities, data privacy concerns and the growing amount of data continue to present challenges. The DaaS Market's ongoing evolution reflects the ever-changing needs of businesses in the digital age.What will be the Size of the Data-As-A-Service (DaaS) Market during the forecast period?Get Key Insights on Market Forecast (PDF) Request Free SampleHow is the Data-As-A-Service (DaaS) Market Segmented and what are the key trends of market segmentation?The data-as-a-service (daas) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userBFSIRetailTelecomOthersDeploymentCloudOn-premisesSectorLarge enterprisesSMEsGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)By End-user InsightsThe bfsi segment is estimated to witness significant growth during the forecast period.In the Business Financial Services Industry (BFSI), Data-as-a-Service (DaaS) is increasingly adopted for creating predictive models in trading, fund management, and risk control. The DaaS market's expansion, in terms of volume, variety, and complexity, enables financial institutions to derive valuable insights, enhancing their operational efficiency. Digital transformation in financial services has led to the evaluation of technology's impact and customer response to streamline financial operations. The financial sector witnesses significant data growth due to increasing financial transaction volumes. Digital marketing advancements and search engine optimization practices have altered consumer behavior, generating a vast amount of actionable data. Real-time data feeds and data warehousing solutions facilitate the processing and storage of this data.Data anonymization methods ensure data privacy, while predictive analytics models and machine learning algorithms help extract valuable insights. Data processing pipelines and data validation techniques ensure data accuracy, and API key management and containerization technologies secure access to this data. Data visualization dashboards and automated data pipelines simplify data analysis, with kubernetes orchestration ensuring scalability. Data access controls, data aggregation services, and data encryption methods secure data, while data transformation tools facilitate data integration. Cloud-based data storage and data security protocols ensure data availability and protection. The DaaS market's growth is evident, with 30% of financial firms adopting DaaS solutions.Furthermore, 45% of financial institutions anticipate a 35% increase in their data usage within the next two years. These statistics underscore the market's potential and the importance of DaaS for financial institutions. Market trends include the adoption of microservices architecture, big data processing, and data modeling techniques. Data version control, data quality metrics, data lineage tracking, and data governance policies ensure data accuracy and consistency. Data lake architectu

Portfolio Risk Analytics Market Outlook

According to our latest research, the global Portfolio Risk Analytics market size reached USD 5.42 billion in 2024, reflecting robust demand for advanced risk management solutions across financial sectors. The market is expected to witness significant expansion, with a projected CAGR of 13.7% from 2025 to 2033. By the end of the forecast period, the Portfolio Risk Analytics market is anticipated to reach USD 16.17 billion. This strong growth trajectory is fueled by the increasing complexity of investment portfolios, stringent regulatory requirements, and the rising adoption of digital technologies to enhance risk assessment and decision-making processes.

One of the primary growth drivers for the Portfolio Risk Analytics market is the escalating complexity and diversity of global investment portfolios. As institutional investors, asset managers, and hedge funds increasingly diversify their holdings across asset classes, geographies, and financial instruments, the need for sophisticated risk analytics platforms has become paramount. These solutions enable organizations to aggregate, analyze, and visualize portfolio risks in real time, allowing for more informed decision-making and proactive risk mitigation. The proliferation of alternative investments, such as private equity, real assets, and derivatives, has further intensified the demand for comprehensive risk analytics tools capable of handling multi-dimensional data and delivering actionable insights across various market scenarios.

Another significant factor propelling market growth is the tightening of regulatory frameworks across the global financial landscape. Regulatory bodies such as the Basel Committee on Banking Supervision, the European Banking Authority, and the U.S. Securities and Exchange Commission have imposed rigorous risk management and compliance standards on financial institutions. These mandates require organizations to demonstrate robust risk measurement, stress testing, and reporting capabilities. As a result, the adoption of advanced Portfolio Risk Analytics solutions has surged, as these platforms facilitate compliance with evolving regulations while minimizing operational risks. The integration of artificial intelligence, machine learning, and big data analytics has further enhanced the predictive power and accuracy of these solutions, enabling financial institutions to stay ahead of regulatory expectations and market volatility.

Technological advancements and the digital transformation of the financial services industry have also played a pivotal role in the market's expansion. The widespread adoption of cloud computing, API-driven architectures, and advanced data visualization tools has revolutionized the delivery and scalability of risk analytics platforms. Financial institutions are increasingly leveraging cloud-based portfolio risk analytics to reduce IT infrastructure costs, enhance flexibility, and accelerate time-to-value. Furthermore, the integration of real-time data feeds, scenario analysis, and automated reporting capabilities has empowered organizations to monitor portfolio risks dynamically and respond swiftly to market shifts. The convergence of risk analytics with other digital tools, such as portfolio optimization and performance measurement, is creating a holistic ecosystem that supports end-to-end investment management.

In the realm of financial risk management, Collateral Risk Analytics has emerged as a crucial component for institutions aiming to safeguard their portfolios against potential losses. As financial transactions become increasingly complex, the ability to accurately assess and manage collateral risk is paramount. Collateral Risk Analytics tools provide financial institutions with the capability to evaluate the quality, value, and volatility of collateral assets, ensuring that they are adequately protected against market fluctuations. By integrating these analytics into their risk management frameworks, organizations can enhance their decision-making processes, optimize capital allocation, and comply with regulatory requirements. The growing emphasis on collateral management, driven by regulatory mandates and market dynamics, underscores the importance of adopting advanced analytics solutions to mitigate risks and enhance financial stability.

From a regional perspective, North America continues to lead t