With LSEG's CME (Chicago Mercantile Exchange) Group Data, you can benefit from real-time and delayed data, and a wide range of global benchmarks.

CBOT operates as part of the CME Group, offering a wide range of futures and options contracts across various asset classes. CBOT specializes in trading futures and options contracts for agricultural commodities, such as corn, soybeans, wheat, and oats, as well as financial instruments, including interest rates and stock indexes.

NYMEX is a commodity futures exchange operating as part of the CME Group and primarily trades energy and metal contracts. NYMEX is known for trading futures contracts for crude oil, natural gas, heating oil, gasoline, and other energy products, as well as contracts for metals such as gold, silver, copper, and aluminum.

COMEX is a division of the CME Group. It is one of the primary futures and options trading platforms for metals, including gold, silver, copper, and aluminum.

Access CME Group data sourced directly from raw feeds at colocation sites. Try out our market data APIs for Python, C++, and other applications for free.

The CME Group Market Data Platform (MDP) 3.0 disseminates event-based bid, ask, trade, and statistical data for CME Group markets and also provides recovery and support services for market data processing. MDP 3.0 includes the introduction of Simple Binary Encoding (SBE) and Event Driven Messaging to the CME Group Market Data Platform. Simple Binary Encoding (SBE) is based on simple primitive encoding, and is optimized for low bandwidth, low latency, and direct data access. Since March 2017, MDP 3.0 has changed from providing aggregated depth at every price level (like CME's legacy FAST feed) to providing full granularity of every order event for every instrument's direct book. MDP 3.0 is the sole data feed for all instruments traded on CME Globex, including futures, options, spreads and combinations. Note: We classify exchange-traded spreads between futures outrights as futures, and option combinations as options.

Breadth of coverage: 2,138 products

Asset class(es): Futures, Options

Origin: Directly captured at Aurora DC3 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, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

CME reported $99.61B in Market Capitalization this July of 2025, considering the latest stock price and the number of outstanding shares.Data for CME - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last July in 2025.

CME stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Adaptive E-Learning for CME Market Outlook

According to our latest research, the global adaptive e-learning for Continuing Medical Education (CME) market size reached USD 2.34 billion in 2024, driven by the urgent demand for flexible, personalized medical education solutions. The market is projected to grow at a robust CAGR of 14.2% from 2025 to 2033, reaching a forecasted value of USD 7.12 billion by 2033. This remarkable expansion is fueled by technological advancements, increasing regulatory mandates for ongoing medical education, and the growing adoption of digital learning in healthcare. As per our latest research, the market's upward trajectory is indicative of a paradigm shift in the way healthcare professionals access and engage with CME content worldwide.

One of the primary growth drivers for the adaptive e-learning for CME market is the accelerating need for healthcare professionals to stay abreast of the latest medical advancements, protocols, and compliance requirements. With medical knowledge doubling at an unprecedented rate, traditional classroom-based CME methods are increasingly seen as inadequate. Adaptive e-learning platforms leverage artificial intelligence and data analytics to personalize learning pathways, ensuring that individual learners receive content tailored to their knowledge gaps and learning pace. This not only enhances learning outcomes but also improves retention, making adaptive e-learning an indispensable tool for clinicians, nurses, and allied health professionals seeking to maintain certifications and deliver high-quality patient care. The rising prevalence of chronic diseases and the rapid evolution of treatment modalities further amplify the need for continuous, accessible, and effective CME solutions.

Another significant factor propelling the adaptive e-learning for CME market is the widespread digital transformation across the healthcare sector. The proliferation of high-speed internet, mobile devices, and cloud-based technologies has made online CME more accessible than ever before. Healthcare organizations, academic institutions, and medical associations are increasingly investing in adaptive e-learning platforms to overcome geographical barriers and reach a broader audience of medical professionals. Moreover, the COVID-19 pandemic has accelerated the adoption of remote learning solutions, highlighting the flexibility and scalability of adaptive e-learning for CME. As a result, both developed and developing regions are witnessing a surge in demand for digital CME offerings that can be accessed anytime and anywhere, fostering a culture of lifelong learning among healthcare professionals.

Regulatory and accreditation requirements are also playing a pivotal role in shaping the adaptive e-learning for CME market. Many countries have instituted mandatory CME credits for medical license renewal, driving the need for efficient, trackable, and compliant educational solutions. Adaptive e-learning platforms excel in providing audit trails, automated assessments, and real-time progress tracking, which are essential for meeting regulatory standards. Additionally, the integration of advanced analytics enables organizations to measure the effectiveness of CME programs and optimize content delivery. This regulatory impetus, coupled with the increasing emphasis on evidence-based practice and patient safety, is compelling hospitals, academic institutions, and professional associations to adopt adaptive e-learning as a core component of their CME strategies.

From a regional perspective, North America currently dominates the adaptive e-learning for CME market, accounting for the largest share in 2024. This is attributed to the region's advanced healthcare infrastructure, high internet penetration, and stringent CME requirements. Europe follows closely, driven by progressive educational policies and strong governmental support for digital health initiatives. The Asia Pacific region is emerging as a high-growth market, fueled by expanding healthcare systems, rising investments in medical education, and increasing adoption of technology-driven learning solutions. Latin America and the Middle East & Africa, while still nascent, are expected to witness steady growth as digital literacy improves and healthcare modernization initiatives gain momentum. Overall, the global market landscape is characterized by dynamic regional trends that reflect varying levels of technological maturity, regulatory frameworks, and healthcare priorities.

Browse Micro E-mini Nasdaq-100 Index Futures (MNQ) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

The CME Group Market Data Platform (MDP) 3.0 disseminates event-based bid, ask, trade, and statistical data for CME Group markets and also provides recovery and support services for market data processing. MDP 3.0 includes the introduction of Simple Binary Encoding (SBE) and Event Driven Messaging to the CME Group Market Data Platform. Simple Binary Encoding (SBE) is based on simple primitive encoding, and is optimized for low bandwidth, low latency, and direct data access. Since March 2017, MDP 3.0 has changed from providing aggregated depth at every price level (like CME's legacy FAST feed) to providing full granularity of every order event for every instrument's direct book. MDP 3.0 is the sole data feed for all instruments traded on CME Globex, including futures, options, spreads and combinations. Note: We classify exchange-traded spreads between futures outrights as futures, and option combinations as options.

Origin: Directly captured at Aurora DC3 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, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Sharp economic volatility, the continued effects of high interest rates and mixed sentiment among investors created an uneven landscape for stock and commodity exchanges. While trading volumes soared in 2020 due to the pandemic and favorable financial conditions, such as zero percent interest rates from the Federal Reserve, the continued effects of high inflation in 2022 and 2023 resulted in a hawkish pivot on interest rates, which curtailed ROIs across major equity markets. Geopolitical volatility amid the Ukraine-Russia and Israel-Hamas wars further exacerbated trade volatility, as many investors pivoted away from traditional equity markets into derivative markets, such as options and futures to better hedge on their investment. Nonetheless, the continued digitalization of trading markets bolstered exchanges, as they were able to facilitate improved client service and stronger market insights for interested investors. Revenue grew an annualized 0.1% to an estimated $20.9 billion over the past five years, including an estimated 1.9% boost in 2025. A core development for exchanges has been the growth of derivative trades, which has facilitated a significant market niche for investors. Heightened options trading and growing attraction to agricultural commodities strengthened service diversification among exchanges. Major companies, such as CME Group Inc., introduced new tradeable food commodities for investors in 2024, further diversifying how clients engage in trades. These trends, coupled with strengthened corporate profit growth, bolstered exchanges’ profit. Despite current uncertainty with interest rates and the pervasive fear over a future recession, the industry is expected to do well during the outlook period. Strong economic conditions will reduce investor uncertainty and increase corporate profit, uplifting investment into the stock market and boosting revenue. Greater levels of research and development will expand the scope of stocks offered because new companies will spring up via IPOs, benefiting exchange demand. Nonetheless, continued threat from substitutes such as electronic communication networks (ECNs) will curtail larger growth, as better technology will enable investors to start trading independently, but effective use of electronic platforms by incumbent exchange giants such as NASDAQ Inc. can help stem this decline by offering faster processing via electronic trade floors and prioritizing client support. Overall, revenue is expected to grow an annualized 3.5% to an estimated $24.8 billion through the end of 2031.

Browse Soybean Oil Futures (ZL) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

The CME Group Market Data Platform (MDP) 3.0 disseminates event-based bid, ask, trade, and statistical data for CME Group markets and also provides recovery and support services for market data processing. MDP 3.0 includes the introduction of Simple Binary Encoding (SBE) and Event Driven Messaging to the CME Group Market Data Platform. Simple Binary Encoding (SBE) is based on simple primitive encoding, and is optimized for low bandwidth, low latency, and direct data access. Since March 2017, MDP 3.0 has changed from providing aggregated depth at every price level (like CME's legacy FAST feed) to providing full granularity of every order event for every instrument's direct book. MDP 3.0 is the sole data feed for all instruments traded on CME Globex, including futures, options, spreads and combinations. Note: We classify exchange-traded spreads between futures outrights as futures, and option combinations as options.

Origin: Directly captured at Aurora DC3 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, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

CME - Aktualne wartości, dane historyczne, prognozy, statystyki, wykresy i kalendarz ekonomiczny - Jun 2025.Data for CME including historical, tables and charts were last updated by Trading Economics this last June in 2025.

Digital CME Cryptocurrency Reward Programs Market Outlook

As per the latest research, the global Digital CME Cryptocurrency Reward Programs market size stood at USD 4.9 billion in 2024. The market is expected to grow at a robust CAGR of 17.2% from 2025 to 2033, reaching a forecasted value of USD 19.7 billion by 2033. This impressive expansion is driven by increasing consumer demand for digital incentives, the rapid adoption of blockchain technology, and the growing integration of cryptocurrency rewards across diverse industries. The market’s growth trajectory is underpinned by innovation in program types, expanding applications, and the rising preference for digital assets as a medium of value exchange.

Several key growth factors are propelling the Digital CME Cryptocurrency Reward Programs market. One of the primary drivers is the escalating adoption of cryptocurrencies among both consumers and enterprises. As digital currencies become more mainstream, businesses are leveraging cryptocurrency-based reward programs to attract, engage, and retain customers. These programs offer unique value propositions, such as instant redemption, global accessibility, and enhanced security, which traditional rewards often lack. The integration of blockchain technology ensures transparency and immutability, further bolstering consumer trust. Additionally, the proliferation of digital wallets and payment platforms is making it easier for users to participate in and benefit from these reward schemes, thereby fueling market growth.

Another significant growth driver is the evolution of loyalty and incentive models in response to shifting consumer expectations. Modern consumers, particularly millennials and Gen Z, are increasingly seeking personalized, flexible, and technology-driven reward experiences. Digital CME Cryptocurrency Reward Programs cater to these preferences by enabling seamless, on-demand access to rewards, often with the added benefit of asset appreciation. Businesses across sectors such as retail, e-commerce, and financial services are innovating their loyalty offerings by incorporating crypto-based rewards, which not only enhance customer engagement but also differentiate their brand in a competitive landscape. This trend is further amplified by the gamification of rewards and the integration of staking and referral mechanisms, which encourage active participation and long-term loyalty.

The regulatory environment and technological advancements are also playing a pivotal role in shaping the market. Governments and regulatory bodies worldwide are gradually establishing clearer guidelines for the use of cryptocurrencies, which is instilling greater confidence among businesses and consumers alike. Furthermore, advancements in blockchain infrastructure, smart contracts, and interoperability solutions are enabling more secure, scalable, and user-friendly reward platforms. These developments are reducing entry barriers for enterprises and fostering innovation in program design and delivery. As a result, the Digital CME Cryptocurrency Reward Programs market is witnessing increased investment and strategic partnerships, further accelerating its growth trajectory.

From a regional perspective, North America currently leads the global market, driven by high digital adoption rates, a mature fintech ecosystem, and favorable regulatory frameworks. However, the Asia Pacific region is emerging as a significant growth engine, supported by rapid digitalization, rising cryptocurrency adoption, and a large, tech-savvy population. Europe is also experiencing steady growth, with increasing enterprise participation and supportive policy developments. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, leveraging mobile-first strategies and innovative reward models to tap into underserved markets. This diverse regional landscape presents ample opportunities for market expansion and innovation across the globe.

Program Type Analysis

The Program Type segment of the Digital C

Explore the dynamics of soybean futures offered by CME Group, an essential tool for hedging and speculation in the agricultural commodities market. Understand pricing, risk factors, market volatility, and trading specifics including contract size, delivery months, and margin requirements. Stay informed with real-time data for strategic trading.

Browse NYISO Zone F Off-Peak Calendar-Day 5 MW Day-Ahead LBMP Futures (NFO) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

The CME Group Market Data Platform (MDP) 3.0 disseminates event-based bid, ask, trade, and statistical data for CME Group markets and also provides recovery and support services for market data processing. MDP 3.0 includes the introduction of Simple Binary Encoding (SBE) and Event Driven Messaging to the CME Group Market Data Platform. Simple Binary Encoding (SBE) is based on simple primitive encoding, and is optimized for low bandwidth, low latency, and direct data access. Since March 2017, MDP 3.0 has changed from providing aggregated depth at every price level (like CME's legacy FAST feed) to providing full granularity of every order event for every instrument's direct book. MDP 3.0 is the sole data feed for all instruments traded on CME Globex, including futures, options, spreads and combinations. Note: We classify exchange-traded spreads between futures outrights as futures, and option combinations as options.

Origin: Directly captured at Aurora DC3 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, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

The global Cystoid Macular Edema (CME) treatment market is experiencing significant growth, driven by an aging population, increasing prevalence of diabetes and other associated eye diseases, and advancements in diagnostic and therapeutic technologies. While precise market size figures for 2025 are unavailable in the provided data, considering a typical CAGR of 5-7% for ophthalmic drug markets and a reasonable starting point for 2019 (based on similar markets), a plausible market size for 2025 could be estimated at $1.5 billion. Projected growth, based on a conservative CAGR of 6%, would indicate a market value exceeding $2.5 billion by 2033. This growth is fueled by the increasing adoption of advanced treatment modalities such as anti-VEGF agents and carbonic anhydrase inhibitors, offering improved treatment outcomes compared to traditional steroids and NSAIDs. The market is segmented by drug type (NSAIDs, anti-VEGF agents, carbonic anhydrase inhibitors, steroids) and application (hospitals, ambulatory surgical centers, others), reflecting the diverse therapeutic approaches and healthcare settings involved in CME management. Geographic variations in healthcare infrastructure and access to advanced treatments influence regional market shares, with North America and Europe currently dominating due to higher adoption rates and healthcare spending. However, growth in emerging economies like Asia-Pacific is expected to accelerate in the coming years due to rising awareness, improved healthcare infrastructure, and increasing disposable incomes. The market faces challenges, including the high cost of advanced therapies and the need for regular monitoring, potentially limiting access for a portion of the patient population. Nevertheless, ongoing research and development efforts focusing on novel therapeutic approaches, improved drug delivery systems, and personalized medicine are expected to further propel market expansion in the long term. The competitive landscape involves a mix of established pharmaceutical companies and specialized biotechnology firms actively involved in developing and commercializing CME treatments. This dynamic environment fosters innovation and ensures a continuous supply of improved treatment options for patients suffering from this debilitating condition. This analysis showcases a promising future for the CME market with significant opportunities for growth and improvement in patient care.

Explore the factors influencing soybean futures on the Chicago Mercantile Exchange, including supply and demand dynamics, geopolitical developments, currency exchange rates, and biofuel production, highlighting the complexities of commodities trading.

Learn about the CME Group's corn futures settlement process, including final settlement pricing, physical delivery, and its impact on the agricultural sector. Understand how this critical process influences commodity trading, risk management, and supply chain stability.

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.

What are buy sell or hold recommendations? (CME 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

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.

CME feeder cattle futures

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