The VA Drug Pricing database contains the current prices for pharmaceuticals purchased by the federal government. These listed prices are based on the Federal Supply Schedule (FSS). This database is mandated by Public Law 102-585, the Veterans Health Care Act of 1992, which sets the maximum amount that a drug may be bought for by the Veterans Health Administration (VHA). The source of this information is contained in printed contracts or data files supplied by the drug manufacturers, representing the pricing agreements between VHA and the manufacturers. Price data is input by the National Acquisition Center (NAC) into the database administered by the Pharmacy Benefits Management Strategic Health Care Group. Information from this database is published on the World Wide Web at the following site: http://www.pbm.va.gov. The users of this database include pharmaceutical manufacturers, drug wholesalers, Office of Inspector General (OIG) and those who purchase pharmaceuticals for the VHA and other government agencies.

This dataset on Datarooms.org provides a pricing plans comparison of Virtual Data Room (VDR) providers. It includes details on provider names, prices, pricing plans, targeted industries, and free trial offerings.

Registrar-Specific Domains Whois Database provides a detailed pricing list for purchasing registrar-specific whois database files. These databases are organized based on registrars and include whois information for domain names registered with each registrar. The pricing packages are tailored to each registrar's database, offering a variety of options.

This site provides guides and other resoruces to help sites archive the goal of managing and executing a construction project within the funding limit approved by Congress. This site includes links to the Manual of Preparation for Cost Estimates and Related Documents for VA Facilities.

We create tailor-made solutions for every customer, so there are no limits to how we can customize your scraper. You don't have to worry about buying and maintaining complex and expensive software, or hiring developers.

You can get the data on a one-time or recurring (based on your needs) basis.

Get the data in any format and to any destination you need: Excel, CSV, JSON, XML, S3, GCP, or any other.

The IvyDB Signed Volume dataset, available as an add-on product for IvyDB US, contains daily data on detailed option trading volume. Trades in the IvyDB US dataset are assigned as either buyer-initiated or seller-initiated based on the trade price and the bid-ask quote at the time of the trade. The total assigned daily volume is aggregated and updated nightly.

MealMe provides comprehensive grocery and retail SKU-level product data, including real-time pricing, from the top 100 retailers in the USA and Canada. Our proprietary technology ensures accurate and up-to-date insights, empowering businesses to excel in competitive intelligence, pricing strategies, and market analysis.

Retailers Covered: MealMe’s database includes detailed SKU-level data and pricing from leading grocery and retail chains such as Walmart, Target, Costco, Kroger, Safeway, Publix, Whole Foods, Aldi, ShopRite, BJ’s Wholesale Club, Sprouts Farmers Market, Albertsons, Ralphs, Pavilions, Gelson’s, Vons, Shaw’s, Metro, and many more. Our coverage spans the most influential retailers across North America, ensuring businesses have the insights needed to stay competitive in dynamic markets.

Key Features: SKU-Level Granularity: Access detailed product-level data, including product descriptions, categories, brands, and variations. Real-Time Pricing: Monitor current pricing trends across major retailers for comprehensive market comparisons. Regional Insights: Analyze geographic price variations and inventory availability to identify trends and opportunities. Customizable Solutions: Tailored data delivery options to meet the specific needs of your business or industry. Use Cases: Competitive Intelligence: Gain visibility into pricing, product availability, and assortment strategies of top retailers like Walmart, Costco, and Target. Pricing Optimization: Use real-time data to create dynamic pricing models that respond to market conditions. Market Research: Identify trends, gaps, and consumer preferences by analyzing SKU-level data across leading retailers. Inventory Management: Streamline operations with accurate, real-time inventory availability. Retail Execution: Ensure on-shelf product availability and compliance with merchandising strategies. Industries Benefiting from Our Data CPG (Consumer Packaged Goods): Optimize product positioning, pricing, and distribution strategies. E-commerce Platforms: Enhance online catalogs with precise pricing and inventory information. Market Research Firms: Conduct detailed analyses to uncover industry trends and opportunities. Retailers: Benchmark against competitors like Kroger and Aldi to refine assortments and pricing. AI & Analytics Companies: Fuel predictive models and business intelligence with reliable SKU-level data. Data Delivery and Integration MealMe offers flexible integration options, including APIs and custom data exports, for seamless access to real-time data. Whether you need large-scale analysis or continuous updates, our solutions scale with your business needs.

Why Choose MealMe? Comprehensive Coverage: Data from the top 100 grocery and retail chains in North America, including Walmart, Target, and Costco. Real-Time Accuracy: Up-to-date pricing and product information ensures competitive edge. Customizable Insights: Tailored datasets align with your specific business objectives. Proven Expertise: Trusted by diverse industries for delivering actionable insights. MealMe empowers businesses to unlock their full potential with real-time, high-quality grocery and retail data. For more information or to schedule a demo, contact us today!

We offer three easy-to-understand equity data 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 equity pricing data, standardized financial statement data, and supplementary fundamental datasets.

When you’re ready for launch, it’s a seamless transition to our Silver package for additional data sets, 15-minute delayed equity pricing data, expanded history, and more.

• Historical EOD equity prices & technicals (10 years history)
• Security reference data
• Standardized & as-reported financial statements (5 years history)
• 7 supplementary fundamental data sets

Bronze Benefits:

• Web API access
• 300 API calls/minute limit
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Silver

The Silver package is ideal for startups that are in development, testing, or in the beta launch phase. Hit the ground running with 15-minute delayed and historical intraday and EOD equity prices, plus our standardized and as-reported financial statement data with nine supplementary data sets, including insider transactions and institutional ownership.

When you’re ready to scale, easily move up to the Gold package for our full range of data sets and full history, real-time equity pricing data, premium support options, and much more.

• 15-minute delayed & historical intraday equity prices
• Historical EOD equity prices & technicals (full history)
• Security reference data
• Standardized & as-reported financial statements (10 years history)
• 9 supplementary fundamental data sets

Silver Benefits:

• Web API access
• 2,000 API calls/minute limit
• 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

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 our complete collection of equity pricing data feeds, from real-time to historical EOD, plus standardized financial statement data and nine supplementary feeds.

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 equity prices
• Historical intraday equity prices
• Historical EOD equity prices & technicals (full history)
• Security reference data
• Standardized & as-reported financial statements (full history)
• 9 supplementary fundamental data sets

Gold Benefits:

• No exchange fees
• No user reporting or variable per-user exchange fees
• High liquidity (6%+)
• Web API & WebSocket access
• 2,000 API calls/minute limit
• 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
• Access to engineering team
• Concierge customer success team
• Comarketing & promotional initiatives

Platinum

Don’t see a package that fits your needs? Our team can design premium custom packages for institutions.

The global retail pricing software market was valued at USD 12.28 Billion in 2022 and is projected to reach USD 21.89 Billion by 2030, registering a CAGR of 7.5% for the forecast period 2023-2030. Factors Affecting Retail Pricing Software Market Growth

Benefits of Retail pricing software; 

Retail pricing software offers several benefits to businesses, helping them make more informed pricing decisions and ultimately improving their competitiveness, profitability, and customer satisfaction. For instance, retail pricing software leverages vast amounts of data, including historical sales data, customer behavior data, and market trends. In addition, pricing software uses advanced algorithms to calculate optimal prices for products or services. This optimization takes into account factors such as production costs, demand elasticity, competitor pricing, and desired profit margins.

Restraining Factor:

Cost and Complexity of software;

Retail pricing software often involves significant initial costs for implementation, customization, and training. Smaller retailers with limited budgets might find it challenging to invest in such solutions. Additionally, the complexity of these systems might deter some businesses, especially those without dedicated IT resources.

Market Opportunity:

The growing trend of omnichannel retailing;

Omnichannel retailing refers to providing a seamless and integrated shopping experience across various channels, both online and offline. This includes brick-and-mortar stores, e-commerce websites, mobile apps, social media platforms, and more. The goal is to create a consistent and convenient shopping experience for customers regardless of how they choose to interact with the retailer. The rise of omnichannel retailing presents several challenges and opportunities when it comes to pricing strategies. Retailers need to maintain consistent pricing across all channels to avoid customer confusion and frustration.

The COVID-19 impact on Retail Pricing Software Market;

The COVID-19 pandemic had a significant impact on various industries, including the retail pricing software market. The pandemic led to changes in consumer behaviour, with increased online shopping and a shift away from physical stores. The sudden change in consumer behaviour has increased demand for pricing software that could quickly adapt to these shifts. With more consumers turning to online shopping, retailers needed pricing software that could handle the complexities of pricing across multiple online platforms, marketplaces, and channels. Many retailers had to offer promotions and discounts to attract customers during economic uncertainty. Pricing software was crucial for planning and executing these strategies effectively. Introduction of Retail Pricing Software

Retail pricing software is a specialized type of software designed to help businesses, particularly retailers, determine the optimal prices for their products or services. It leverages various data points, algorithms, and analytical tools to assist in setting prices that align with the business's goals, market conditions, customer preferences, and competitive landscape. Retail pricing software gathers a wide range of data from both internal and external sources. his can include historical sales data, customer behaviour data, competitor pricing information, economic indicators, and more. Integration with the business's point-of-sale systems, e-commerce platforms, and other relevant software helps ensure that the data is up-to-date and accurate.

The data set includes the top 25 list for costliest prescribed drugs, most frequently prescribed drugs and the prescribed drugs with the highest monthly median out-of-pocket costs. Each of these top 25 lists are given for commercial plans and are broken out by brand or generic category (i.e., Brand or Generic, Brand, and Generic). The includes National Drug Code (NDC), Drug Name, number of prescriptions, number of individuals, total costs, cost per prescription and monthly median out-of-pocket costs for each NDC in each top 25 list.

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

This dataset shows the Center for Nutrition Policy and Promotion (CNPP) Food Prices Database presents the cost of these consumed foods for 2003-2004. The information in this database should be of interest to nutrition educators and economists who wish to compare relative prices of different foods as consumed, rather than the purchase price.

Construction Bidding Cost Database Market Outlook

According to our latest research, the global Construction Bidding Cost Database market size reached USD 1.98 billion in 2024. The market is exhibiting robust momentum, propelled by the increasing digitization of construction processes and a heightened demand for cost transparency and efficiency. With a projected compound annual growth rate (CAGR) of 11.7% from 2025 to 2033, the market is forecasted to attain a value of USD 5.51 billion by 2033. This impressive growth trajectory is primarily driven by the widespread adoption of advanced software solutions in construction project management, a growing emphasis on minimizing bid errors, and the rising need for real-time data analytics to support accurate project estimation and cost control.

One of the primary growth factors for the Construction Bidding Cost Database market is the increasing complexity of construction projects globally. As projects become larger and more intricate, stakeholders demand precise cost estimation and bidding processes to ensure profitability and competitiveness. The integration of artificial intelligence and machine learning within construction bidding platforms has further enhanced accuracy and streamlined workflows. These technologies enable predictive analytics, automate repetitive tasks, and reduce human errors, thereby improving overall project outcomes. The need for robust cost databases is underscored by the rising frequency of design changes and scope modifications, which necessitate agile and responsive cost management solutions throughout the project lifecycle.

Another significant driver is the regulatory environment and the growing focus on compliance and risk management in construction. Governments and industry bodies are increasingly mandating transparent bidding processes and accurate cost reporting to mitigate risks of fraud, misallocation of funds, and project delays. The adoption of Construction Bidding Cost Database solutions enables stakeholders to maintain comprehensive audit trails, adhere to regulatory standards, and facilitate more effective communication among project participants. This is particularly evident in public infrastructure projects, where transparency and accountability are paramount. As a result, the market is witnessing accelerated adoption across both public and private sectors, further fueling its expansion.

The proliferation of cloud-based solutions is another key factor contributing to market growth. Cloud deployment offers unparalleled accessibility, scalability, and integration capabilities, allowing construction firms to manage bids and cost data across multiple locations and projects seamlessly. This has proven invaluable amidst the ongoing digital transformation of the construction industry, as organizations seek to leverage real-time data sharing and collaboration tools. Additionally, the shift towards integrated project delivery models and the use of Building Information Modeling (BIM) are reinforcing the need for comprehensive cost databases, enabling more accurate forecasting, budgeting, and resource allocation.

In the realm of construction project management, Construction Takeoff Software plays a pivotal role by automating the quantification process of materials and labor needed for a project. This software significantly reduces the time and effort required for manual takeoffs, thereby enhancing accuracy and efficiency. By integrating with construction bidding cost databases, takeoff software ensures that estimators have access to real-time data, facilitating more precise cost estimations and competitive bidding. The ability to quickly adjust to design changes and scope modifications is another advantage, as it allows construction firms to remain agile and responsive to client needs. As the industry continues to embrace digital transformation, the adoption of construction takeoff software is expected to rise, further driving the demand for comprehensive cost database solutions.

From a regional perspective, North America currently dominates the Construction Bidding Cost Database market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The United States leads in terms of technology adoption and investment in digital construction solutions, while the Asia Pacific region is experiencing the fastest growth, driven by rapid urbaniza

In 2024, Tokyo was ranked as the most expensive market worldwide for data center construction. The cost per watt in the Japanese capital was reported at **** U.S. dollars, ahead of Singapore at **** U.S. dollars. A range of local factors such as land prices, labor availability, and power costs can heavily impact data center construction pricing.

InvaCost is the most up-to-date, comprehensive, standardized and robust data compilation and description of economic cost estimates associated with invasive species worldwide1. InvaCost has been constructed to provide a contemporary and freely available repository of monetary impacts that can be relevant for both research and evidence-based policy making. The ongoing work made by the InvaCost consortium2,3,4 leads to constantly improving the structure and content of the database (see sections below). The list of actual contributors to this data resource now largely exceeds the list of authors listed in this page. All details regarding the previous versions of InvaCost can be found by switching from one version to another using the “version” button above. IMPORTANT UPDATES: 1. All information, files, outcomes, updates and resources related to the InvaCost project are now available on a new website: http://invacost.fr/2. The names of the following columns have been changed between the previous and the current version: ‘Raw_cost_estimate_local_currency’ is now named ‘Raw_cost_estimate_original_currency’; ‘Min_Raw_cost_estimate_local_currency’ is now named ‘Min_Raw_cost_estimate_original_currency’; ‘Max_Raw_cost_estimate_local_currency’ is now named ‘Max_Raw_cost_estimate_original_currency’; ‘Cost_estimate_per_year_local_currency’ is now named ‘Cost_estimate_per_year_original_currency’3. The Frequently Asked Questions (FAQ) about the database and how to (1) understand it, (2) analyse it and (3) add new data are available at: https://farewe.github.io/invacost_FAQ/. There are over 60 questions (and responses), so there’s probably yours.4. Accordingly with the continuous development and updates of the database, a ‘living figure’ is now available online to display the evolving relative contributions of different taxonomic groups and regions to the overall cost estimates as the database is updated: https://borisleroy.com/invacost/invacost_livingfigure.html5. We have now added a new column called ‘InvaCost_ID’, which is now used to identify each cost entry in the current and future public versions of the database. As this new column only affects the identification of the cost entries and not their categorisation, this is not considered as a change of the structure of the whole database. Therefore, the first level of the version numbering remains ‘4’ (see VERSION NUMBERING section).

CONTENT: This page contains four files: (1) 'InvaCost_database_v4.1' which contains 13,553 cost entries depicted by 66 descriptive columns; (2) ‘Descriptors 4.1’ provides full definition and details about the descriptive columns used in the database; (3) ‘Update_Invacost_4.1’ has details about the all the changes made between previous and current versions of InvaCost; (4) ‘InvaCost_template_4.1’ (downloadable file) provides an easier way of entering data in the spreadsheet, standardizing all the terms used on it as much as possible to avoid mistakes and saving time at post-refining stages (this file should be used by any external contributor to propose new cost data).

METHODOLOGY: All the methodological details and tools used to build and populate this database are available in Diagne et al. 20201 and Angulo et al. 20215. Note that several papers used different approaches to investigate and analyse the database, and they are all available on our website http://invacost.fr/.

VERSION NUMBERING: InvaCost is regularly updated with contributions from both authors and future users in order to improve it both quantitatively (by new cost information) and qualitatively (if errors are identified). Any reader or user can propose to update InvaCost by filling the ‘InvaCost_updates_template’ file with new entries or corrections, and sending it to our email address (updates@invacost.fr). Each updated public version of InvaCost is stored in this figShare repository, with a unique version number. For this purpose, we consider the original version of InvaCost publicly released in September 2020 as ‘InvaCost_1.0’. The further updated versions are named using the subsequent numbering (e.g., ‘InvaCost_2.0’, InvaCost_2.1’) and all information on changes made are provided in a dedicated file called ‘Updates-InvaCost’ (named using the same numbering, e.g., ‘Updates-InvaCost_2.0’, ‘Updates-InvaCost_2.1’). We consider changing the first level of this numbering (e.g. ‘InvaCost_3.x’ ‘InvaCost_4.x’) only when the structure of the database changes. Every user wanting to have the most up-to-date version of the database should refer to the latest released version.

RECOMMENDATIONS: Every user should read the ‘Usage notes’ section of Diagne et al. 20201 before considering the database for analysis purposes or specific interpretation. InvaCost compiles cost data published in the literature, but does not aim to provide a ready-to-use dataset for specific analyses. While the cost data are described in a homogenized way in InvaCost, the intrinsic disparity, complexity, and heterogeneity of the cost data require specific data processing depending on the user objectives (see our FAQ). However, we provide necessary information and caveats about recorded costs, and we have now an open-source software designed to query and analyse this database6.

CAUTION: InvaCost is currently being analysed by a network of international collaborators in the frame of the InvaCost project2,3,4 (see https://invacost.fr/en/outcomes/). Interested users may contact the InvaCost team if they wish to learn more about or contribute to these current efforts. Users are in no way prevented from performing their own independent analyses and collaboration with this network is not required. Nonetheless, users and contributors are encouraged to contact the InvaCost team before using the database, as the information contained may not be directly implementable for specific analyses.

RELATED LINKS AND PUBLICATIONS:

1 Diagne, C., Leroy, B., Gozlan, R.E. et al. InvaCost, a public database of the economic costs of biological invasions worldwide. Sci Data 7, 277 (2020). https://doi.org/10.1038/s41597-020-00586-z

2 Diagne C, Catford JA, Essl F, Nuñez MA, Courchamp F (2020) What are the economic costs of biological invasions? A complex topic requiring international and interdisciplinary expertise. NeoBiota 63: 25–37. https://doi.org/10.3897/neobiota.63.55260

3 Researchgate page: https://www.researchgate.net/project/InvaCost-assessing-the-economic-costs-of-biological-invasions

4 InvaCost workshop: https://www.biodiversitydynamics.fr/invacost-workshop/

5 Angulo E, Diagne C, Ballesteros-Mejia L. et al. (2021) Non-English languages enrich scientific knowledge: the example of economic costs of biological invasions. Science of the Total Environment 775:144441. https://doi.org/10.1016/j.scitotenv.2020.144441

6Leroy B, Kramer A M, Vaissière A-C, Courchamp F and Diagne C (2020) Analysing global economic costs of invasive alien species with the invacost R package. BioRXiv. doi: https://doi.org/10.1101/2020.12.10.419432

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 214 companies listed on the Panama Stock Exchange (XPTY) in Panama. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Panama:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Panama:

Panamanian Stock Exchange Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Panamanian Stock Exchange (Bolsa de Valores de Panamá). This index provides an overview of the overall market performance in Panama.

Panamanian Stock Exchange Foreign Company Index: The index that tracks the performance of foreign companies listed on the Panamanian Stock Exchange. This index reflects the performance of international companies operating in Panama.

Company A: A prominent Panamanian company with diversified operations across various sectors, such as shipping, logistics, or finance. This company's stock is widely traded on the Panamanian Stock Exchange.

Company B: A leading financial institution in Panama, offering banking, insurance, or investment services. This company's stock is actively traded on the Panamanian Stock Exchange.

Company C: A major player in the Panamanian energy or real estate sector, involved in the production and distribution of related products. This company's stock is listed and actively traded on the Panamanian Stock Exchange.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Panama, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Panama ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Panama?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Panama exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direc...

Get the latest insights on price movement and trend analysis of Cement in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East Africa).

The Center for Research in Security Prices (CRSP) stock databases provide time-series and event data on individual stocks, augmented with market time-series. Daily and monthly time-series variables include returns, closing, low bid and high ask prices, and trading volume. Event data includes distributions, shares outstanding, names, etc.

Dataset is an external database available here for Cornell affiliates: https://johnson.library.cornell.edu/database/wharton-research-data-services-wrds/

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Unlock comprehensive Hospitality, Travel Tourism Data, including Hotel Rates & Pricing, Flight, and Restaurant Data. Dive into insights from Online Travel Agencies (OTAs) and Short-Term Rentals. Explore tourist attractions with our Tourism Data. Take advantage of APISCRAPY Data today!