Twelve Data is a technology-driven company that provides financial market data, financial tools, and dedicated solutions. Large audiences - from individuals to financial institutions - use our products to stay ahead of the competition and success.

At Twelve Data we feel responsible for where the markets are going and how people are able to explore them. Coming from different technological backgrounds, we see how the world is lacking the unique and simple place where financial data can be accessed by anyone, at any time. This is what distinguishes us from others, we do not only supply the financial data but instead, we want you to benefit from it, by using the convenient format, tools, and special solutions.

We believe that the human factor is still a very important aspect of our work and therefore our ethics guides us on how to treat people, with convenient and understandable resources. This includes world-class documentation, human support, and dedicated solutions.

TAC Indices used for CONTRACT NEGOTIATION, SETTLEMENT & BENCHMARKING.

Indices data based on a cumulative of tens of millions of actual transactions from leading Freight Forwarders, ensuring the most consistent & accurate data in the market.

API & Dynamic Charting offered.

Published Weekly reflecting previous week's transactional pricing data.

Subscribers include Top 10 Fortune 500 companies & major global financial institutions.

Calculation methodology fully transparent and available on request.

Indices are audited by the U.K.'s Baltic Exchange under Financial Conduct Authority ("FCA") Guidelines.

Data subscriptions available under numerous categories for both current and historical data. Current data is generally for active traders and is more expensive; delayed & historical data is considerably lower cost.

Contact our Sales Team for detailed pricing information.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Table of INEBase National overall index. Data from January 1961. Monthly. Consumer Price Index (CPI)

Index Dis Ticaret Limited Company Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Location Index (Loc-I) is a framework that provides a consistent way to seamlessly integrate data on people, business, and the environment.Location Index aims to extend the characteristics of the foundation spatial data of taking geospatial data (multiple geographies) which is essential to support public safety and wellbeing, or critical for a national or government decision making that contributes significantly to economic, social and environmental sustainability and linking it with observational data.Through providing the infrastructure to support cross-domain foundation data linkages and analysis will open up substantial opportunity for providing a richer set of information to develop, analyse and evaluate policy, programs and service delivery by government.This presentation was given at the Earth Science Information Partners (ESIP) Winter Meeting held in Bethesda, MD in January 2020.

A REST web service API allowing the retrieval of real time air quality index data from AirNow.

Baltic Dry rose to 2,600 Index Points on December 2, 2025, up 0.66% from the previous day. Over the past month, Baltic Dry's price has risen 33.68%, and is up 110.19% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on December of 2025.

Reference: https://www.zillow.com/research/zhvi-methodology/

Official Background

In setting out to create a new home price index, a major problem Zillow sought to overcome in existing indices was their inability to deal with the changing composition of properties sold in one time period versus another time period. Both a median sale price index and a repeat sales index are vulnerable to such biases (see the analysis here for an example of how influential the bias can be). For example, if expensive homes sell at a disproportionately higher rate than less expensive homes in one time period, a median sale price index will characterize this market as experiencing price appreciation relative to the prior period of time even if the true value of homes is unchanged between the two periods.

The ideal home price index would be based off sale prices for the same set of homes in each time period so there was never an issue of the sales mix being different across periods. This approach of using a constant basket of goods is widely used, common examples being a commodity price index and a consumer price index. Unfortunately, unlike commodities and consumer goods, for which we can observe prices in all time periods, we can’t observe prices on the same set of homes in all time periods because not all homes are sold in every time period.

The innovation that Zillow developed in 2005 was a way of approximating this ideal home price index by leveraging the valuations Zillow creates on all homes (called Zestimates). Instead of actual sale prices on every home, the index is created from estimated sale prices on every home. While there is some estimation error associated with each estimated sale price (which we report here), this error is just as likely to be above the actual sale price of a home as below (in statistical terms, this is referred to as minimal systematic error). Because of this fact, the distribution of actual sale prices for homes sold in a given time period looks very similar to the distribution of estimated sale prices for this same set of homes. But, importantly, Zillow has estimated sale prices not just for the homes that sold, but for all homes even if they didn’t sell in that time period. From this data, a comprehensive and robust benchmark of home value trends can be computed which is immune to the changing mix of properties that sell in different periods of time (see Dorsey et al. (2010) for another recent discussion of this approach).

For an in-depth comparison of the Zillow Home Value Index to the Case Shiller Home Price Index, please refer to the Zillow Home Value Index Comparison to Case-Shiller

Each Zillow Home Value Index (ZHVI) is a time series tracking the monthly median home value in a particular geographical region. In general, each ZHVI time series begins in April 1996. We generate the ZHVI at seven geographic levels: neighborhood, ZIP code, city, congressional district, county, metropolitan area, state and the nation.

Underlying Data

Estimated sale prices (Zestimates) are computed based on proprietary statistical and machine learning models. These models begin the estimation process by subdividing all of the homes in United States into micro-regions, or subsets of homes either near one another or similar in physical attributes to one another. Within each micro-region, the models observe recent sale transactions and learn the relative contribution of various home attributes in predicting the sale price. These home attributes include physical facts about the home and land, prior sale transactions, tax assessment information and geographic location. Based on the patterns learned, these models can then estimate sale prices on homes that have not yet sold.

The sale transactions from which the models learn patterns include all full-value, arms-length sales that are not foreclosure resales. The purpose of the Zestimate is to give consumers an indication of the fair value of a home under the assumption that it is sold as a conventional, non-foreclosure sale. Similarly, the purpose of the Zillow Home Value Index is to give consumers insight into the home value trends for homes that are not being sold out of foreclosure status. Zillow research indicates that homes sold as foreclosures have typical discounts relative to non-foreclosure sales of between 20 and 40 percent, depending on the foreclosure saturation of the market. This is not to say that the Zestimate is not influenced by foreclosure resales. Zestimates are, in fact, influenced by foreclosure sales, but the pathway of this influence is through the downward pressure foreclosure sales put on non-foreclosure sale prices. It is the price signal observed in the latter that we are attempting to measure and, in turn, predict with the Zestimate.

Market Segments Within each region, we calculate the ZHVI for various subsets of homes (or mar...

Renewable Energy Market Data API Market Outlook

According to our latest research, the global Renewable Energy Market Data API market size is valued at USD 1.21 billion in 2024, with a robust compound annual growth rate (CAGR) of 18.7% projected through the forecast period. By 2033, the market is expected to reach approximately USD 6.38 billion. This impressive growth trajectory is driven by the increasing adoption of digitalization in the energy sector, the integration of advanced analytics, and the growing necessity for real-time renewable energy data to optimize power generation, grid management, and energy trading operations globally.

One of the primary growth factors for the Renewable Energy Market Data API market is the rapid expansion of renewable energy installations worldwide. As governments and private sector players intensify their commitments to reduce carbon emissions and achieve sustainability targets, the need for precise, real-time, and interoperable data has become paramount. APIs play a critical role by enabling seamless data exchange between disparate systems, supporting the integration of solar, wind, hydro, bioenergy, and geothermal sources into national and regional grids. This digital backbone not only enhances operational efficiency but also supports regulatory compliance and reporting, which are increasingly stringent in major markets.

In addition, the proliferation of smart grids and the rising demand for advanced forecasting and analytics solutions have further accelerated the uptake of Renewable Energy Market Data APIs. Utilities, energy companies, and grid operators are leveraging these APIs to monitor performance, predict supply fluctuations, and optimize energy trading strategies in real time. The ability to access granular and actionable data empowers stakeholders to minimize downtime, manage distributed energy resources more effectively, and respond proactively to changes in supply and demand. This data-driven approach is essential for maintaining grid stability and maximizing the value of renewable assets in a rapidly evolving energy landscape.

Technological advancements in cloud computing and artificial intelligence are also shaping the future of the Renewable Energy Market Data API market. Cloud-based deployment models offer scalability, flexibility, and cost-efficiency, making them increasingly attractive for both established utilities and emerging energy startups. Meanwhile, AI-driven analytics enhance the predictive capabilities of APIs, supporting more accurate forecasting and decision-making. As the market matures, we anticipate greater standardization of API protocols and broader adoption across diverse end-user segments, including government agencies, research institutions, and independent power producers.

The Renewable Energy Volatility Index is becoming an increasingly important tool for stakeholders in the renewable energy sector. As the market continues to grow and evolve, understanding the fluctuations in renewable energy production and pricing is critical for effective decision-making. The index provides valuable insights into the variability and predictability of renewable energy sources, allowing utilities, energy companies, and investors to better manage risk and optimize their strategies. By tracking changes in renewable energy output and market dynamics, the Renewable Energy Volatility Index helps stakeholders anticipate potential challenges and capitalize on emerging opportunities. This index is particularly useful in regions with high penetration of renewables, where grid stability and energy trading are heavily influenced by fluctuating supply and demand patterns.

Regionally, North America and Europe are leading the charge in API adoption, thanks to their advanced renewable infrastructure and supportive regulatory environments. However, Asia Pacific is emerging as a high-growth market, driven by rapid urbanization, significant investments in clean energy projects, and government-led digital transformation initiatives. The Middle East & Africa and Latin America are also witnessing increased adoption, albeit at a slower pace, as these regions work to modernize their energy sectors and diversify their energy mix. Overall, the global outlook for the Renewable Energy Market Data API market remains highly

Index Fresh Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Proportion of young people in Scotland entering full-time Higher Education. Source agency: Scottish Government Designation: National Statistics Language: English Alternative title: Age Participation Index (API)

Graph and download economic data for CBOE Volatility Index: VIX (VIXCLS) from 1990-01-02 to 2025-12-01 about VIX, volatility, stock market, and USA.

Context

Nifty per minute data is really difficult to find online, you might have to buy an API key from Zerodha or other data vendors to get per minute data. Fortunately, I've extracted a lot of data using the API key but due to the size of the data, it is in month-wise format. I Will be collating the data in yearly format and will upload the same here.

Content

The data have 7 columns: Symbol, Y-M-D, Time, Open, High, Low, Close values for every minute. With the help of the historical data, you can make your own indicators, tweak and check which indicators are giving you better trade profits. Basically, you can make your own trading model, and once done, you can buy Zerodha's API key, get live data and make your own trades.

Note: Some months captured the 8th and 9th column for volume and OI respectively. Make sure to clean and use the data accordingly.

Inspiration

The main inspiration behind me collecting and working on this data is to make a trading model of my own, where I could trigger the trades when my model gives me a signal. I do trade on the Nifty index but after making a perfect model, I'll rather trade with more confidence and informed decisions. I'll be uploading more data as soon as possible.

You can train and validate your model on this data and I'll get you the test data soon. Till then happy modeling :) Leave comments if you need any help.

This dataset contains current and historical UV Index data for Bonao.

Data on daily maximum and mean UV indices (Please visit the reference link for other climate information). The multiple file formats are available for datasets download in API.

This dataset contains current and historical UV Index data for Xinzhou.

This dataset contains current and historical UV Index data for Debre Tabor.

Vicmap Index will help users in spatial location, map production, web based searches, dispatch, delivery and GIS analysis.\r
\r
Vicmap Index includes:\r
\r
Key extents for Vicmap framework products\r
State border and coastline\r
Vicmap Topographic Indices

API operated by Louisville Metro that returns AQI information from local sensors operated by APCD. Shows the latest hourly data in a JSON feed.The Air Quality Index (AQI) is an easy way to tell you about air quality without having to know a lot of technical details. The “Metropolitan Air Quality Index” shows the AQI from the monitor in Kentuckiana that is currently detecting the highest level of air pollution. See: https://louisvilleky.gov/government/air-pollution-control-district/servi...See the air quality map (Louisville Air Watch) for more details: airqualitymap.louisvilleky.gov/#Read the FAQ for more information about the AQI data: https://louisvilleky.gov/government/air-pollution-control-district/louis...If you'd prefer air quality forecast data (raw data, maps, API) instead, please see AIRNow: https://www.airnow.gov/index.cfm?action=airnow.local_city&zipcode=40204&...See the Data Dictionary section below for information about what the AQI numbers mean, their corresponding colors, recommendations, and more info and links.To download daily snapshots of AQI for the last 25 years, visit the EPA website, set your year range, and choose, Louisville KY. Then download with the CSV link at the bottom of the page.IFTTT integration trigger that fires and after retrieving air quality from Louisville Metro air sensors via the APIGives a forecast instead of the current conditions, so you can take action before the air quality gets bad.The U.S. EPA AirNow program (www.AirNow.gov) protects public health by providing forecast and real-time observed air quality information across the United States, Canada, and Mexico. AirNow receives real-time air quality observations from over 2,000 monitoring stations and collects forecasts for more than 300 cities.Sign up for a free account and get started using the RSS data feed for Louisville. https://docs.airnowapi.org/feedsAir Quality Forecast via AirNowAQI Level - Value and Related Health Concerns LegendGood 0-50 GreenAir quality is considered satisfactory, and air pollution poses little or no risk.Moderate 51-100 YellowAir quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.Unhealthy for Sensitive Groups 101-150 OrangeMembers of sensitive groups may experience health effects. The general public is not likely to be affected.Unhealthy 151-200 RedEveryone may begin to experience health effects; members of sensitive groups may experience more serious health effects.Very Unhealthy 201-300 PurpleHealth alert: everyone may experience more serious health effects.Hazardous > 300 Dark PurpleHealth warnings of emergency conditions. The entire population is more likely to be affected.Here are citizen actions APCD recommends on air quality alert days, that is, days when the forecast is for the air quality to reach or exceed the “unhealthy for sensitive groups” (orange) level:Don’t idle your car. (Recommended all the time; see the second link below.)Put off mowing grass with a gas mower until the alert ends.“Refuel when it’s cool” (pump gasoline only in the evening or night).Avoid driving if possible. Share rides or take TARC.Check on neighbors with breathing problems.Here are some links in relation to the recommendations:KAIRE, www.helptheair.org/Idle Free Louisville, www.helptheair.org/idle-freeTARCTicket to Ride, tickettoride.org/Lawn Care for Cleaner Air (rebates)Contact:Bryan FrazerBryan.Frazar@louisvilleky.gov