Description

When approaching modeling problems in modern financial markets, there are many reasons to believe that the problems you are trying to solve are impossible. Even if you put aside the beliefs that the prices of financial instruments rationally reflect all available information, you’ll have to grapple with time series and distributions that have properties you don’t encounter in other sorts of modeling problems. Distributions can be famously fat-tailed, time series can be non-stationary, and data can generally fail to satisfy a lot of the underlying assumptions on which very successful statistical approaches rely. Layer on all of this the fact that the financial markets are ultimately a human endeavor involving a large number of individuals and institutions that are constantly changing with advances in technology and shifts in society, and responding to economic and geopolitical issues as they arise - and you can start to get a sense of the difficulties involved!

The data originates from Jane Street, offering a realistic representation of key tasks involved in successful trading within modern financial markets. Jane Street provides a dataset featuring anonymized and slightly modified market-related features and responders, reflecting the nature of their automated trading strategies. These adjustments maintain the core problem's integrity while protecting proprietary information and competitive interests. This approach creates a challenging, relevant task that mirrors the type of work Jane Street handles, emphasizing the importance of developing effective underlying models for trading success.

Citation

Maanit Desai, Yirun Zhang, Ryan Holbrook, Kait O'Neil, and Maggie Demkin. Jane Street Real-Time Market Data Forecasting. https://kaggle.com/competitions/jane-street-real-time-market-data-forecasting, 2024. Kaggle.

The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, 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 December of 2025.

Dataset

This dataset was created by Anshuman Acharya

Contents

PowerMap can infer the subject of trading volume in real-time. This information allows users to predict the order flow by investor type of institutional, foreign, and retail traders. By implementing Direct Market Access (DMA) and High-Frequency Trading (HFT) technology, PowerMap processes and delivers large-scale transactions in real time for the Korean market. Processing high volumes of stock transactions instantly requires robust data processing capabilities. PowerMap receives direct trade data from KRX and analyzes buy and sell signals for approximately 1,000 stocks in real time, covering KOSPI stocks with a market cap over 200 billion KRW ($133.38 million) and KOSDAQ stocks over 150 billion KRW ($103.81 million).

Key Features: 💠 Real-time investor type classification (institutional, and foreign institutional) 💠 Low-latency data ingestion 💠 Coverage of over 1,200 liquid KOSPI and KOSDAQ stocks 💠 Instantaneous detection of large-block trades and directional flow 💠 Scalable architecture for high-volume transaction analysis

Primary Use Cases: 🔹 Institutional and proprietary traders monitoring market sentiment shifts 🔹 Quant desks identifying real-time trade triggers and flow-based signals 🔹 Algo developers incorporating investor-type flow into trading strategies 🔹 Broker-dealers and research teams analyzing intraday market dynamics 🔹 Portfolio managers assessing liquidity and participation trends

Contact us for a real time order flow data in different markets. Stay ahead with TradePulse's order flow insights.

Unlock the power of real-time data! Explore the booming real-time index database market, projected to reach $32 billion by 2033. Discover key trends, leading companies (Elastic, AWS, Splunk), and regional insights in this comprehensive market analysis.

This dataset contains 20+ years of all items in the American art category sold on auction by Christie’s, Sotheby’s, Bonhams and Phillips from 2000 to date.

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.

This dataset aggregates real-time sentiment scores and metadata for financial news headlines, enabling rapid detection of market-moving events and trends. It includes headline text, publication details, sentiment analysis, relevance to financial markets, and links to affected stocks and sectors. Ideal for quantitative trading, risk monitoring, and financial news analytics.

The Real-Time Payments Market size is expected to reach a valuation of USD 296.0 billion in 2033 growing at a CAGR of 36.00%. The Real-Time Payments Market research report classifies market by share, trend, demand, forecast and based on segmentation.

The Real-Time Location System (RTLS) Market Report is Segmented by End-User Vertical (Healthcare, Transportation and Logistics, and More), Component (Hardware, Software, Services, and More), Technology (RFID, Wi-Fi, and More), Application (Asset Tracking, Work-In-Process Tracking, and More), and Geography (North America, South America, and More). The Market Forecasts are Provided in Terms of Value (USD).

Real-Time Bidding Market By Size, Share, Trends, Growth, Forecast 2026, Segmented By Auction, By AD Format , By Application, By Device, By Region, Competition Forecast and Opportunities

The Market Data Platform market is experiencing robust growth, driven by the increasing demand for real-time data analytics and the proliferation of sophisticated trading strategies across financial institutions. The market's expansion is fueled by several key factors: the rise of algorithmic trading, the need for faster and more accurate market information, the growing adoption of cloud-based solutions, and the increasing regulatory scrutiny demanding robust data management and compliance. The market is witnessing a shift towards integrated platforms offering a broader range of data sources, advanced analytics capabilities, and improved connectivity. This trend is being further accelerated by the increasing adoption of artificial intelligence (AI) and machine learning (ML) for enhanced data analysis and prediction. Companies like Bloomberg, Refinitiv, and TRDATA are major players, but the market is also witnessing increased competition from innovative technology providers offering specialized solutions and niche capabilities. The forecast period from 2025-2033 suggests substantial growth, driven by the continuous adoption of these solutions across various segments of the financial services industry. The regional distribution will likely favor North America and Europe initially, followed by a gradual increase in adoption rates across Asia-Pacific and other emerging markets. The competitive landscape is dynamic, with established players facing challenges from agile startups offering innovative solutions. The success of individual vendors depends on their ability to provide high-quality data, superior analytical capabilities, seamless integration with existing infrastructure, robust security features, and a commitment to regulatory compliance. While larger players dominate market share, smaller, specialized firms are capitalizing on the demand for specialized data sets and tailored analytical tools. The increasing focus on data security and privacy will impact vendors’ strategies, with enhanced security measures and data governance becoming crucial differentiating factors. Future growth will depend on the industry's continued embrace of technology and the further development of AI/ML-driven analytical applications within the Market Data Platform ecosystem. This growth will likely result in increased consolidation and strategic partnerships in the coming years, shaping the future competitive landscape significantly.

The Real Time Payments Market will grow from USD 25.39 Billion in 2024 to USD 141.61 Billion by 2030 at a 33.17% CAGR.

Low‑Latency Market Data Distribution Market Outlook

According to our latest research, the global Low-Latency Market Data Distribution market size is valued at USD 8.3 billion in 2024 and is expected to reach USD 21.7 billion by 2033, growing at a robust CAGR of 11.3% during the forecast period. The primary growth driver for this market is the surging demand for real-time data transmission and analytics in financial services, particularly for algorithmic trading and high-frequency trading environments. As per our comprehensive analysis, the market is witnessing a paradigm shift toward ultra-fast data processing and distribution, underpinned by technological advancements and the growing complexity of global financial systems.

One of the pivotal growth factors propelling the Low-Latency Market Data Distribution market is the exponential rise in electronic trading across global financial markets. The financial sector, especially investment banks, hedge funds, and trading platforms, have become increasingly reliant on low-latency data feeds to gain a competitive edge. Algorithmic and high-frequency trading strategies demand the fastest possible access to market information, as even microsecond delays can translate into significant financial losses or missed opportunities. This has led to substantial investments in cutting-edge hardware, software, and network infrastructure designed to minimize latency. Furthermore, the proliferation of new financial instruments and the expansion of global trading venues have amplified the need for scalable and reliable low-latency solutions.

Technological innovation is another major catalyst for market expansion. The integration of advanced networking technologies such as Field-Programmable Gate Arrays (FPGAs), 5G, and edge computing has revolutionized the way market data is distributed. These technologies enable faster data transmission, reduce bottlenecks, and ensure seamless communication between disparate trading systems. Additionally, the adoption of cloud-based architectures and hybrid deployment models is facilitating greater flexibility and scalability for organizations. This enables them to manage fluctuating data volumes efficiently while maintaining ultra-low latency. Such advancements are not only transforming the financial sector but are also finding applications in other data-intensive industries such as telecommunications and government.

Regulatory compliance and market transparency are also fueling the adoption of low-latency data distribution solutions. Financial regulators across various regions have imposed stringent requirements for real-time reporting, surveillance, and risk management. Institutions are compelled to implement robust systems that can deliver accurate, real-time data to comply with these mandates. The need for proactive risk management and market surveillance has further underscored the importance of low-latency infrastructures, driving continuous innovation and investment in this domain. As a result, the market is witnessing increased collaboration between technology providers, financial institutions, and regulatory bodies to develop solutions that meet both performance and compliance requirements.

From a regional perspective, North America continues to dominate the Low-Latency Market Data Distribution market, accounting for the largest share in 2024. This leadership is primarily attributed to the presence of major financial hubs such as New York, Chicago, and Toronto, where high-frequency trading and real-time market analytics are critical. Europe follows closely, with key financial centers like London, Frankfurt, and Paris driving demand. The Asia Pacific region is emerging as a significant growth engine, propelled by rapid digitalization, expanding financial markets, and regulatory reforms in countries like China, Japan, and Singapore. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as financial modernization efforts gain momentum.

Component Analysis

The Component segment of the

Prices for United States Stock Market Index (US500) including live quotes, historical charts and news. United States Stock Market Index (US500) was last updated by Trading Economics this December 1 of 2025.

The Japan Payment System and the Market is Segmented by Type (P2P, P2B). The Market Sizes and Forecasts are Provided in Terms of Value (USD ) for all the Above Segments.

Live Briefs Investor – US Covering thousands of listed securities and events across 80 news categories, Live Briefs Investor US is specifically designed to keep individual investors and active traders on top of breaking news that is likely to affect their portfolios.

Most of the largest and most respected retail and self-directed brokerage firms in the North America rely on MT Newswires to provide their clients with complete coverage of the financial markets. The Investor service includes timely and insightful commentary on equities, commodities, ETFs, economics, forex, options and fixed income assets throughout the day (6:30 am to 6:30 pm EST).

Every story is ticker-tagged and category-coded to allow for seamless platform integration. US Equities – significant events affecting individual public companies in the US: After-hours and pre-market news, trading activity and technical price level indications; Earnings estimate change alerts; Analyst Rating Changes- the most comprehensive view and coverage of rating changes available anywhere; ETF Power Play – daily trends in ETF trading activity; Mini and detailed sector summaries – pre-market, mid-day, and closing; Market Chatter – real-time coverage of trading desk rumors and breaking news; Zero noise: Only premium, original news and event analysis. Never any fillers (press releases, non-market related news, etc.).

Cryptocurrency Price & Market Data

Real-Time Market Trends & Volatility

By [source]

About this dataset

This dataset provides valuable insights into the volatile cryptocurrency markets by tracking real-time market data of various cryptocurrencies. It collects important data points such as the name of the cryptocurrency, symbol, price, hourly and daily change trends, 24 hour volume traded and market capitalization that can be used for a variety of purposes. Research professionals in fields such as blockchain technology, finance and economics can leverage this dataset to gain a better understanding of the digital currency market to move forward with informed decisions and effective recommendations. This includes gaining deeper insight into market movements of cryptocurrencies therefore set up indicators for potential pricing trends forecasting financial developments or analyze changes in prices over time

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How to use the dataset

This dataset provides real-time market data of various cryptocurrencies. Using this dataset, you can gain valuable insights into the markets and trends related to different digital currencies. Whether you are a research data scientist, financial analyst, investor or other professional, this dataset can help inform your decisions and recommendations.

The following is a guide on how to use this dataset: - Look up the names of the coins and their symbols included in the dataset using the 'coin' and 'symbol' columns respectively. This will enable you to distinguish between coins with similar names (such as Bitcoin vs Bitcoin Cash). - Use the 'price' column to find out what price each coin is trading for at any given time. - Use 1h, 24h and 7d columns to compare prices over time periods longer than one hour (for example from 1 hour ago to now or 7 days ago until today). This will give you an indication of price volatility over time periods longer than one hour which can be used for forecasting market movement trends or as input in deep learning/neural networks models for predicting future price movements of various cryptocurrencies
- 24h_volume gives volume traded in last 24 hours which helps users understand market momentum vis-a-vis volume traded during that period for cryptocurrency trading & understanding liquidity situation in any given period . 5 . The ‘mkt_cap’ column gives information about market capitalisation that shows how big/valuable a coin is & tells if its worth buying or not via comparison of current value & all available units combined together based on supply & demand forces at work in CryptoCurrency eco system.

Research Ideas

• Analyzing correlations between cryptocurrency market capitalization, prices, and 24 hour volumes to identify potential investment opportunities.
• Comparing the performance of distinct cryptocurrencies against one another to determine the potential winners in today’s digital currency market.
• Utilizing this dataset to build machine learning models that predict future cryptocurrency market trends based on historical data points such as 1h, 24h, 7d price changes and market capitalization levels over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: coin_gecko_2022-03-16.csv | Column name | Description | |:---------------|:-------------------------------------------------------------------------------| | coin | Name of the cryptocurrency. (String) | | symbol | Abbreviation of the cryptocurrency. (String) | | price | Latest price of the cryptocurrency in USD. (Float) | | 1h | Percentage change in price of the cryptocurrency in the last 1 hour. (Float) | | 24h | Percentage change in price of the cryptocurrency in the last 24 hours. (Float) | | 7d | Percentage change in price of the cryptocurrency in the last 7 days. (Float) | | 24h_volume | Total 24 hour ...

About this dataset

India's National Stock Exchange (NSE) has a total market capitalization of more than US$3.4 trillion, making it the world's 10th-largest stock exchange as of August 2021, with a trading volume of ₹8,998,811 crore (US$1.2 trillion) and more 2000 total listings.

NSE's flagship index, the NIFTY 50, is a 50 stock index is used extensively by investors in India and around the world as a barometer of the Indian capital market.

This dataset contains data of all company stocks listed in the NSE, allowing anyone to analyze and make educated choices about their investments, while also contributing to their countries economy.

How to use this dataset

• Create a time series regression model to predict NIFTY-50 value and/or stock prices.
• Explore the most the returns, components and volatility of the stocks.
• Identify high and low performance stocks among the list.

Highlighted Notebooks

• Your kernel can be featured here!
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Acknowledgements

License

CC0: Public Domain

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The Real-Time Index Database market is undergoing significant transformation, driven by the increasing demand for instant data processing and analytics across various industries. As organizations strive for agility and responsiveness, real-time indexing systems enable businesses to capture and retrieve data instantl