View Refinitiv's New York Stock Exchange (NYSE) Market Data and benefit from full-depth market-by-price data, available as real-time and historical records.

Updated daily, this data feed offers end of day prices for major US publicly traded stocks with history more than 20 years. Prices are provided both adjusted and unadjusted.

Key Features:

Covers all stocks with primary listing on NASDAQ, AMEX, NYSE and ARCA. Includes unadjusted and adjusted open, high, low, close, volume. Includes dividend history and split history. Updated at or before 5:00pm ET on all trading days. Exchange corrections are applied by 9:30pm ET.

Access LSEG's London Stock Exchange (LSE) Market Data, and find benchmarks, indices, and real-time and historic market information.

TagX is your trusted partner for stock market and financial data solutions. We specialize in delivering real-time and end-of-day data feeds that power software, trading algorithms, and risk management systems globally. Whether you're a financial institution, hedge fund, or individual investor, our reliable datasets provide essential insights into market trends, historical pricing, and key financial metrics.

TagX is committed to precision and reliability in stock market data. Our comprehensive datasets include critical information such as date, open/close/high/low prices, trading volume, EPS, P/E ratio, dividend yield, and more. Tailor your dataset to match your specific requirements, choosing from a wide range of parameters and coverage options across primary listings on NASDAQ, AMEX, NYSE, and ARCA exchanges.

Key Features of TagX Stock Market Data:

Custom Dataset Requests: Customize your data feed to focus on specific metrics and parameters crucial to your trading strategy.

Extensive Coverage: Access data from reputable exchanges and market participants, ensuring accuracy and completeness in your analyses.

Flexible Pricing Models: Choose pricing structures based on your selected parameters, offering cost-effective solutions tailored to your needs.

Why Choose TagX? Partner with TagX for precise, dependable, and customizable stock market data solutions. Whether you require real-time updates or end-of-day valuations, our datasets are designed to support informed decision-making and enhance your competitive edge in the financial markets. Trust TagX to deliver the data integrity and accuracy essential for maximizing your trading potential.

With LSEG's Hong Kong Stock Exchange Issuer Information feed Service (IIS), gain real-time trading news and announcements from HKEX listed companies.

CE Transact is the premier alternative data set for consumer spend on credit and debit cards, available as an aggregated feed. Hedge fund investors trust CE transaction data to track quarterly performance, company-reported KPIs, and earnings predictions for stock market strategic decision-making.

Access the LSEG's Cboe US market data in various ways designed and tailored for your specific needs and workflows.

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.

View LSEG's ICE Data Pricing and Reference Data, and find real-time market data, time-sensitive pricing, and reference data for securities trading.

Explore LSEG's Euronext Market Data, including full access to benchmarks and indices, and corporate action and dividend data.

According to Cognitive Market Research, the global Financial Data Service market size will be USD 24152.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 8.50% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 9661.00 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 7245.75 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 5555.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.5% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 1207.63 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.9% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 483.05 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.2% from 2024 to 2031.
Datafeed/API solutions are the dominant segment, as they allow seamless data integration into existing systems and platforms, making them ideal for companies requiring real-time data across multiple applications

Market Dynamics of Financial Data Service Market

Key Drivers for Financial Data Service Market

Increased Data-Driven Decision-Making to Boost Market Growth

As digital transformation sweeps through financial services, data-driven decision-making has become essential for businesses to remain competitive. Institutions, both financial and non-financial, are increasingly leveraging financial data to guide strategic investments, manage risks, and streamline operations. By utilizing real-time data and predictive analytics, companies gain actionable insights to optimize their investment portfolios and financial planning. With the enhanced capability to analyze data trends and assess market scenarios, businesses can mitigate risks more effectively, making this driver critical to the growth of the financial data service market. For instance, in September 2022, Alibaba Cloud, the digital technology and intellectual backbone of Alibaba Group, launched a comprehensive suite of Alibaba Cloud for Financial Services solutions. Comprising over 70 products, these solutions are designed to help financial services institutions of all sizes across banking, FinTech, insurance, and securities, digitalize their operations

Advancements in Analytics Technology to Drive Market Growth

The integration of advanced analytics technologies like artificial intelligence (AI) and machine learning (ML) in financial data services has significantly enhanced the accuracy and scope of market insights. AI and ML enable companies to process vast amounts of financial data, identify patterns, and make predictions, thus facilitating strategic planning and investment optimization. These technologies also allow for real-time insights, giving firms a competitive advantage in rapidly evolving markets. With continuous improvements in AI and ML, the demand for advanced data services is expected to grow, positioning this as a key driver of market expansion.

Restraint Factor for the Financial Data Service Market

High Cost of Data Services Will Limit Market Growth

The high cost of premium financial data services is a significant restraint, particularly for small and medium-sized enterprises (SMEs). Many advanced platforms and data feeds come with substantial subscription fees, limiting their accessibility to larger organizations with more considerable budgets. This cost barrier restricts smaller firms from fully integrating advanced data insights into their operations. As a result, high subscription costs prevent widespread adoption among SMEs, hindering the financial data service market’s overall growth potential.

Trends for the Financial Data Service Market

Blockchain-based Data Services as an opportunity for the market

Blockchain-based data services offer a secure, transparent, and decentralized approach to financial data management. By leveraging blockchain technology, finance data services can provide tamper-proof and auditable data storage, ensuring the integrity and accuracy of financial data. This can help...

algoseek Trade and Quote (TAQ) data contain all trades and top-of-book intraday quotes for all listed stocks, ETNs, ETFs, ADRs, and funds from 15+ US exchanges and marketplaces. TAQ data files are organized into a single format feed where events are ordered by the time received with nanosecond timestamps starting from 2016, and millisecond timestamps before. The entire trading session includes early and late hours from 04:00 to 20:00 EST

Explore Options Price Reporting Authority (OPRA) through LSEG. OPRA collects, consolidates and disseminates information for US Options.

All historical per-share time series, including prices, earnings per share, dividends per share, assets per share, cash flow per share, etc. need to be adjusted before meaningful conclusions can be drawn about growth rates, trends, etc.

The feed has all necessary fields so that clients can identify the country, security, currency, event and adjustment factor. Further the feed has been designed for clients to database the records or apply directly to price series data. The file also covers all corporate actions spanning multiple currencies for the same event.

The main benefit of this feed over competitor feeds is that it can be fully automated allowing the handling of messy cancellations and corporate action changes to happen seamlessly in the background. EDI has recently launched a new version (2) of the Worldwide Adjustment Factor feed service. The major three additions are as follows:

• Inclusion of FIGI codes. FIGI codes have been added which will allow clients to crosscheck data sets. • Fields that were sub-fields in the Detail field of the previous version now have their own fields. For example, DivType was in the Detail field (event description) as DIVPERIOD= This will allow for easier integration of the data. • The Reason codes are different, expanded to 3 characters enabling higher level groupings of reasons. For example, all dividend reasons begin with 01, with the 3rd character depending on whether it is cash, script or both. This allows greater granularity when deciding to apply or not.

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.

Explore LSEG's Toronto Stock Exchange (TSX) Market Data, representing a broad range of businesses from Canada and abroad.

Graph and download economic data for Producer Price Index by Industry: Petroleum Refineries: Liquefied Refinery Gases, Including Other Aliphatics (Feed Stock and Other Uses) (PCU324110324110R) from Jun 1985 to Jun 2025 about refineries, petroleum, stocks, PPI, industry, inflation, price index, indexes, price, and USA.

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.

Disclaimer

All information presented here is for display purpose only, and may not be complete nor accurate. This information does not constitute a financial advice, and should not be used to make any investment decisions or financial transactions. This author rejects any claims for liabilities resulting from the use, misuse, or abuse of this information. Use at your own risk.

Motivation

Due to time zone differences between Australia and most of the rest of the world, Australians have the advantage of knowing what happened at markets elsewhere in the world, before the Australian market (ASX) is open in the morning, Sydney time.

This prior knowledge provides an excellent opportunity for arbitrage. In the hands of a savvy day-trader, or a shrewd long-term investor, this information gives you the advantage of predicting the ASX, and achieve potentially significant financial gains.

Method

For the ten years period from 1/7/2010 to 30/6/2020, the daily closing prices for 41 global market indicators are collected from various reliable public-domain sources. We checked the data for error or omissions and normalised all tabulated records in a format that facilitates further analysis and visulaisation.

Those 41 market indicators are what we consider significant measures of various external factors that may affect the performance of the Australian Stock Market, as represented by the ASX200. Those indicators are:

• Nine other major stock market indices from the USA, Europe, and Asia.

• The exchange rate of the $AU against 10 world currencies that are most relevant to Australia's international trade.

• Official interest rates by the RBA and the US Feds, as indicators of affinity of foreign funds to Australia.

• Yield rates for governments-issued bonds by 10 countries from Western and Asian economies, as measures of relative availability of credit and cross-border investment. Bonds are grouped into "Short-term" (one year maturity) and "Long-term" (10 to 30 years maturity).

• Since Australia's economy is mainly an exporter of raw materials, we include prices for commodities that are most traded by Australia, as indicators for potential profitability for various relevant sectors of the ASX.

We feed relevant data to a machine learning model, which uses this data to extract heuristic parameters that are used to predict the ASX200 on daily basis, before market opens, and validates predictions at market close, with favourable results.

For more information, please visit the Tableau viz at: https://public.tableau.com/app/profile/yasser.ali.phd/viz/PredictingAustralianStockMarket/Story