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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...

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Sports Data Low-Latency Feed Market Outlook

According to our latest research for 2024, the global sports data low-latency feed market size stands at USD 1.47 billion. The market is experiencing robust growth, propelled by the increasing demand for real-time data delivery across various sports-related applications. The market is set to expand at a CAGR of 18.2% during the forecast period, reaching an estimated USD 6.45 billion by 2033. This growth is primarily driven by the rapid adoption of digital transformation in the sports industry, the surge in sports betting activities, and the proliferation of live streaming and fantasy sports platforms. As per our latest research, the sports data low-latency feed market is positioned for significant evolution, with technological advancements and increasing integration of artificial intelligence playing pivotal roles in shaping the industry landscape.

A major growth factor for the sports data low-latency feed market is the escalating demand for instant access to accurate sports information. With the rise of live sports broadcasting and the surge in in-play betting, stakeholders across the sports ecosystem require ultra-fast data feeds to ensure timely and precise delivery of critical match events, player statistics, and video content. This demand is further amplified by the expectations of modern sports fans, who seek real-time engagement and interactive experiences through various digital platforms. The integration of advanced data analytics and machine learning algorithms has enabled providers to deliver highly reliable and actionable insights, thereby enhancing the value proposition for end-users such as broadcasters, betting companies, and sports leagues. Consequently, the ability to provide low-latency data feeds has become a key differentiator in the competitive sports data market.

The proliferation of sports betting and fantasy sports platforms globally has significantly contributed to the growth of the sports data low-latency feed market. These platforms rely heavily on real-time data to facilitate seamless user experiences, minimize the risk of arbitrage, and ensure regulatory compliance. The legalization of sports betting in several jurisdictions, particularly in North America and Europe, has led to a surge in demand for high-speed, accurate data feeds that support dynamic odds calculation and in-play wagering. Furthermore, the increasing popularity of fantasy sports leagues has created new avenues for data feed providers to deliver comprehensive player statistics, match updates, and video highlights, all in real time. As the competitive landscape intensifies, companies are investing in advanced infrastructure and partnerships to enhance their data delivery capabilities and expand their market presence.

Technological advancements in network infrastructure, such as the deployment of 5G and edge computing, have played a crucial role in driving the sports data low-latency feed market forward. These technologies enable faster data transmission, reduced latency, and improved reliability, which are essential for delivering real-time sports content to a global audience. The adoption of cloud-based solutions has further facilitated scalability and flexibility, allowing stakeholders to manage large volumes of data efficiently and cost-effectively. Moreover, the integration of video analytics and artificial intelligence has opened new possibilities for automated content generation, personalized recommendations, and enhanced fan engagement. As the industry continues to evolve, the convergence of these technologies is expected to unlock new growth opportunities and redefine the standards for real-time sports data delivery.

Regionally, North America dominates the sports data low-latency feed market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The strong presence of major sports leagues, advanced technological infrastructure, and the rapid adoption of sports betting and fantasy sports platforms have positioned North America as the leading market. Europe is also experiencing significant growth, driven by the increasing popularity of football and the expansion of regulated betting markets. Asia Pacific is emerging as a high-growth region, fueled by rising sports viewership, digital transformation initiatives, and the growing adoption of cloud-based solutions. Latin America and the Middle East & Africa are witnessing steady growth, supported by investments in sports infrastructure and increasing internet penetration. The regional outlook for the mar

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This repository includes python scripts and input/output data associated with the following publication:

[1] Brown, P.R.; O'Sullivan, F. "Shaping photovoltaic array output to align with changing wholesale electricity price profiles." Applied Energy 2019. https://doi.org/10.1016/j.apenergy.2019.113734

Please cite reference [1] for full documentation if the contents of this repository are used for subsequent work.

Some of the scripts and data are also used in the following working paper:

[2] Brown, P.R.; O'Sullivan, F. "Spatial and temporal variation in the value of solar power across United States electricity markets". Working Paper, MIT Center for Energy and Environmental Policy Research. 2019. http://ceepr.mit.edu/publications/working-papers/705

All code is in python 3 and relies on a number of dependencies that can be installed using pip or conda.

Contents

pvvm.zip : Python module with functions for modeling PV generation, calculating PV revenues and capacity factors, and optimizing PV orientation.

notebooks.zip : Jupyter notebooks, including:

pvvm-pvtos-data.ipynb: Example scripts used to download and clean input LMP data, determine LMP node locations, and reproduce some figures in reference [1]

pvvm-pvtos-analysis.ipynb: Example scripts used to perform the calculations and reproduce some figures in reference [1]

pvvm-pvtos-plots.ipynb: Scripts used to produce additional figures in reference [1]

pvvm-example-generation.ipynb: Example scripts demonstrating the usage of the PV generation model and orientation optimization

html.zip : Static images of the above Jupyter notebooks for viewing without a python kernel

data.zip : Day-ahead and real-time nodal locational marginal prices (LMPs) for CAISO, ERCOT, MISO, NYISO, and ISONE.

At the time of publication of this repository, permission had not been received from PJM to republish their LMP data. If permission is received in the future, a new version of this repository will linked here with the complete dataset.

results.zip : Simulation results associated with reference [1] above, including modeled revenue, capacity factor, and optimized orientations for PV systems at all LMP nodes

Data terms and usage notes

ISO LMP data are used with permission from the different ISOs. Adapting the MIT License (https://opensource.org/licenses/MIT), "The data are provided 'as is', without warranty of any kind, express or implied, including but not limited to the warranties of merchantibility, fitness for a particular purpose and noninfringement. In no event shall the authors or sources be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the data or other dealings with the data." Copyright and usage permissions for the LMP data are available on the ISO websites, linked below.

ISO-specific notes:

CAISO data from http://oasis.caiso.com/mrioasis/logon.do are used pursuant to the terms at http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse.

ERCOT data are from http://www.ercot.com/mktinfo/prices.

MISO data are from https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/ and https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/.

PJM data were originally downloaded from https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx and https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx. At the time of this writing these data are currently hosted at https://dataminer2.pjm.com/feed/da_hrl_lmps and https://dataminer2.pjm.com/feed/rt_hrl_lmps.

NYISO data from http://mis.nyiso.com/public/ are used subject to the disclaimer at https://www.nyiso.com/legal-notice.

ISONE data are from https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly and https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final. The Material is provided on an "as is" basis. ISO New England Inc., to the fullest extent permitted by law, disclaims all warranties, either express or implied, statutory or otherwise, including but not limited to the implied warranties of merchantability, non-infringement of third parties' rights, and fitness for particular purpose. Without limiting the foregoing, ISO New England Inc. makes no representations or warranties about the accuracy, reliability, completeness, date, or timeliness of the Material. ISO New England Inc. shall have no liability to you, your employer or any other third party based on your use of or reliance on the Material.

Data workup: LMP data were downloaded directly from the ISOs using scripts similar to the pvvm.data.download_lmps() function (see below for caveats), then repackaged into single-node single-year files using the pvvm.data.nodalize() function. These single-node single-year files were then combined into the dataframes included in this repository, using the procedure shown in the pvvm-pvtos-data.ipynb notebook for MISO. We provide these yearly dataframes, rather than the long-form data, to minimize file size and number. These dataframes can be unpacked into the single-node files used in the analysis using the pvvm.data.copylmps() function.

Code license and usage notes

Code (*.py and *.ipynb files) is provided under the MIT License, as specified in the pvvm/LICENSE file.

Updates to the code, if any, will be posted in the non-static repository at https://github.com/patrickbrown4/pvvm_pvtos. The code in the present repository has the following version-specific dependencies:

matplotlib: 3.0.3

numpy: 1.16.2

pandas: 0.24.2

pvlib: 0.6.1

scipy: 1.2.1

tqdm: 4.31.1

To use the NSRDB download functions, modify the "settings.py" file to insert a valid NSRDB API key, which can be requested from https://developer.nrel.gov/signup/. Locations can be specified by passing latitude, longitude floats to pvvm.data.downloadNSRDBfile(), or by passing a string googlemaps query to pvvm.io.queryNSRDBfile(). To use the googlemaps functionality, request a googlemaps API key (https://developers.google.com/maps/documentation/javascript/get-api-key) and insert it in the "settings.py" file.

Note that many of the ISO websites have changed in the time since the functions in the pvvm.data module were written and the LMP data used in the above papers were downloaded. As such, the pvvm.data.download_lmps() function no longer works for all ISOs and years. We provide this function to illustrate the general procedure used, and do not intend to maintain it or keep it up to date with the changing ISO websites. For up-to-date functions for accessing ISO data, the following repository (no connection to the present work) may be helpful: https://github.com/catalyst-cooperative/pudl.

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