In 2023, the leading equity index futures and options contract traded worldwide based on volume was Bank Nifty Index Options, traded on the National Stock Exchange of India. Over the year a total of ** billion Bank Nifty Index Options contracts were traded - over ** million more than second-placed CNX Nifty Index Options, also traded on the National Stock Exchange of India.

Learn about oil future indexes and how they track the price movements of crude oil futures contracts. Discover their benefits, risks, and their role as benchmarks for financial products and investment funds.

United States Turnover: Daily Avg: CBOT: Index Futures: Dow Jones Industrial: Mini data was reported at 217,072.000 Contract in Jun 2018. This records an increase from the previous number of 200,256.860 Contract for May 2018. United States Turnover: Daily Avg: CBOT: Index Futures: Dow Jones Industrial: Mini data is updated monthly, averaging 127,966.000 Contract from Jan 2002 (Median) to Jun 2018, with 198 observations. The data reached an all-time high of 346,951.950 Contract in Feb 2018 and a record low of 403.000 Contract in Mar 2002. United States Turnover: Daily Avg: CBOT: Index Futures: Dow Jones Industrial: Mini data remains active status in CEIC and is reported by CME Group. The data is categorized under Global Database’s USA – Table US.Z021: CBOT: Futures: Turnover.

China Settlement Price: Shanghai 50 Index Futures: Next Quarter Month data was reported at 2,696.400 Index Point in 14 May 2025. This records an increase from the previous number of 2,650.600 Index Point for 13 May 2025. China Settlement Price: Shanghai 50 Index Futures: Next Quarter Month data is updated daily, averaging 2,652.000 Index Point from Apr 2015 (Median) to 14 May 2025, with 2449 observations. The data reached an all-time high of 3,924.800 Index Point in 19 Feb 2021 and a record low of 1,745.800 Index Point in 25 Aug 2015. China Settlement Price: Shanghai 50 Index Futures: Next Quarter Month data remains active status in CEIC and is reported by China Financial Futures Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZI: China Financial Futures Exchange: Index Futures: Closing and Settlement Price: Daily .

The global futures trading services market is experiencing robust growth, driven by increasing technological advancements, rising institutional and retail investor participation, and the growing adoption of online and mobile trading platforms. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This signifies a substantial expansion of the market to an estimated $28 billion by 2033. Several factors contribute to this positive outlook. The increasing sophistication of trading algorithms and the availability of real-time market data are enhancing trading efficiency and profitability, attracting both novice and experienced traders. Furthermore, the diversification of tradable assets, including a broader range of commodities and indices, provides greater opportunities for portfolio diversification and risk management. Software-based futures trading platforms are gaining significant traction due to their advanced analytical capabilities and ease of integration with other trading tools. However, regulatory scrutiny, cybersecurity risks, and the inherent volatility of futures markets present challenges to sustained growth. The regulatory landscape is constantly evolving, requiring firms to adapt to new compliance requirements and enhance cybersecurity protocols to protect against data breaches and fraud. Moreover, fluctuations in global economic conditions and geopolitical events can significantly impact market sentiment and trading volumes. Despite these restraints, the market's growth trajectory is expected to remain positive, driven primarily by technological innovation and the expanding reach of online trading platforms to a wider investor base. The segment encompassing share price index futures and commodity futures are projected to exhibit the strongest growth, reflecting increased investor interest in these asset classes.

The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

Global Aridity Index and Potential Evapotranspiration Database: CMIP_6 Future Projections(Future_Global_AI_PET)Robert J. Zomer 1, 2, 3, Antonio Trabucco1,41. Euro-Mediterranean Center on Climate Change, IAFES Division, Sassari, Italy. 2. Centre for Mountain Futures, Kunming Institute of Botany, Chinese Academy of Science, Kunming, Yunnan, China3. CIFOR-ICRAF China Program, World Agroforestry (ICRAF), Kunming, Yunnan. China4. National Biodiversity Future Center (NBFC), Palermo, ItalyThe Global Aridity Index and Potential Evapotranspiration (Global AI-PET) Database: CMIP_6 Future Projections – Version 1 (Future_Global_AI_PET) provides a high-resolution (30 arc-seconds) global raster dataset of average monthly and annual potential evapotransipation (PET) and aridity index (AI) for two historical (1960-1990; 1970-2000) and two future (2021-2040; 2041-2060) time periods for each of 22 CMIP6 Earth System Models across four emission scenarios (SSP: 126, 245, 370, 585). The database also includes three averaged multi-model ensembles produced for each of the four emission scenarios:· All Models: includes all of the 22 ESM, as available within a particular SSP.· High Risk: includes 5 ESM identified as projecting the highest increases in temperature and precipitation and lying outside and significantly higher than the majority of estimates.· Majority Consensus: includes 15 ESM, that is, all available ESM excluding the ESM in the “High Risk” category, and those missing data across all of the 4 SSP. Further herein referred to as the “Consensus” category.These geo-spatial datasets have been produced with the support of Euro-Mediterranean Center on Climate Change, IAFES Division; Centre for Mountain Futures, Kunming Institute of Botany, Chinese Academy of Science; CIFOR-ICRAF China Program, World Agroforestry (CIFOR-ICRAF) and the National Biodiversity Future Center (NBFC).These datasets are provided under a CC_BY 4.0 License (please attribute), in standard GeoTiff format, WGS84 Geographic Coordinate System, 30 arc seconds or ~ 1km at the equator, to support studies contributing to sustainable development, biodiversity and environmental conservation, poverty alleviation, and adaption to climate change, among other global, regional, national, and local concerns.The Future_Global_AI_PET is available online from the Science Data Bank (ScienceDB) at: https://doi.org/10.57760/sciencedb.nbsdc.00086Previous versions of the Global Aridity Index and PET Database are available online here:https://figshare.com/articles/dataset/Global_Aridity_Index_and_Potential_Evapotranspiration_ET0_Climate_Database_v2/7504448/6Technical questions regarding the datasets can be directed to Robert Zomer: r.zomer@mac.com or Antonio Trabucco: antonio.trabucco@cmcc.it Methods:Based on the results of comparative validations, the Hargreaves model has been evaluated as one of the best fit to model PET and Aridity index globally with the available high resolution downscaled and bias corrected climate projections and chosen for the implementation of the Global-AI_PET- CMIP6 Future Projections. This method performs almost as well as the Penman-Monteith method, but requires less parameterization, and has significantly lower sensitivity to error in climatic inputs (Hargreaves and Allen, 2003). The currently available downscaled CMIP6 projections (available from WorldClim) do provide fewer climate variables idoneous for implementation of temperature-based evapotranspiration methods, such as the Hargreaves method. Hargreaves (1985, 1994) uses mean monthly temperature (Tmean), mean monthly temperature range (TD) and extraterrestrial radiation (RA, radiation on top of the atmosphere) to calculate ET0, as shown below: PET = 0.023 * RA * (Tmean + 17.8) * TD0.5where RA is extraterrestrial radiation at the top of the atmosphere, TD is the difference between mean maximum temperatures and mean minimum temperatures (Tmax - Tmin), and Tmean is equal to Tmax + Tmin divided by 2. The Hargreaves equation has been implemented globally on a per grid cell basis at 30 arc seconds resolution (~ 1km2 at the equator), in ArcGIS (v11.1) using Python v3.2 (see code availability section) to estimate PET/AI globally using future projections provided by the CMIP6 collaboration. The data to parametrize the equation were obtained from the Worldclim (worldclim.org) online data repository, which provides bias-corrected downscaled monthly values of minimum temperature, maximum temperature, and precipitation for 25 CMIP6 Earth System Models (ESMs), across four Shared Socio-economic Pathways (SSPs): 126, 245, 370 and 585. PET/AI was estimated for two historical periods, WorldClim 1.4 (1960-1990) and WorldClim 2.1 (1970-2000), representing on average a decades change, by applying the Hargreaves methodology described above. Similarly, PET/AI was estimated for two future time periods, namely 2021-2040 and 2041-2060, for each of the 25 models across their respective four SSP scenarios (126, 245, 370,585). Aridity Index Aridity is often expressed as an Aridity Index, comprised of the ratio of precipitation over PET, and signifying the amount of precipitation available in relation to atmospheric water demand and quantifying the water (from rainfall) availability for plant growth after ET demand has been met, comparing incoming moisture totals with potential outgoing moisture. The AI for the averaged time periods has been calculated on a per grid cell basis, as: AI = MA_Prec/MA_PETwhere: AI = Aridity Index MA_Prec = Mean Annual Precipitation MA_PET = Mean Annual Reference EvapotranspirationUsing the mean annual precipitation (MA_Prec) values obtained from the CMIP6 climate projections, while ET0 datasets estimated on a monthly average basis by the method described above were aggregated to mean annual values (MA_PET). Using this formulation, AI values are unitless, increasing with more humid condition and decreasing with more arid conditions.Multi-Model Averaged EnsemblesBased upon the distribution of the various scenarios along a gradient of their projected temperature and precipitation estimates for the each of the four SSP and two future time period, three multi-model ensembles, each articulated by their four respective SSPs, were identified. The three parameters of monthly minimum temperature, monthly maximum temperature and monthly precipitation for ESM’s included within each of these ensemble categories were averaged for each of their respective SSPs. These averaged parameters were then used to calculate the PET/AI as per the above methodology.Code Availablity:The algorithm and code in Python used to calculate PET and AI is available on Figshare at this link below:https://figshare.com/articles/software/Global_Future_PET_AI_Algorithm_Code_Python_-_Calculate_PET_AI/24978666DATA FORMATPET datasets are available as monthly averages (12 datasets, i.e. one dataset for each month, averaged over the specified time period) or as an annual average (1 dataset) for the specified time period. Aridity Index grid layers are available as one grid layer representing the annual average over the specified period. The following nomenclature is used to describe the dataset: Zipped Files - Directory Names refer to: Model_SSP_Time-PeriodFor example: ACCESS-CM2_126_2021-2040.zip Model: ACCESS-CM2 / SSP:126 / Time-Period: 2021-2040Prefix of Files (TIFFs) is either:pet_ for PET layers aridity_index for Aridity Index (no suffix)Suffix For PET Files is either:1, 2, ... 12 Month of the yearyr Yearly averagesd Standard DeviationExamples:pet_02.tif is the PET average for the month of February.pet_yr.tif is the PET annual average.’pet_sd.tif is the standard deviation of the annual PETaridity_index.tif is the annual aridity index. The PET values are defined as total mm of PET per month or per year. The Aridity Index values are unit-less.The geospatial dataset is in geographic coordinates; datum and spheroid are WGS84; spatial units are decimal degrees. The spatial resolution is 30 arc-seconds or 0.008333 degrees. Arc degrees and seconds are angular distances, and conversion to linear units (like km) varies with latitude, as below:The Future-PET and Future-Aridity Index data layers have been processed and finalized for distribution online as GEO-TIFFs. These datasets have been zipped (.zip) into monthly series or individual annual layers, by each combination of climate model/scenarios, and are available for online access. Data Storage HierarchyThe database is organized for storage into a hierarchy of directories (see ReadMe.doc):( Individual zipped files are generally about 1 GB or less) Associated Peer Reviewed Journal Article:Zomer RJ, Xu J, Spano D and Trabucco A. 2024. CMIP6-based global estimates of future aridity index and potential evapotranspiration for 2021-2060. Open Research Europe 4:157 https://doi.org/10.12688/openreseurope.18110.1For further info, please refer to these earlier paper describing the database and methodology:Zomer, R.J.; Xu, J.; Trabucco, A. 2022. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database. Scientific Data 9, 409.Zomer, R. J; Bossio, D. A.; Trabucco, A.; van Straaten, O.; Verchot, L.V. 2008. Climate Change Mitigation: A Spatial Analysis of Global Land Suitability for Clean Development Mechanism Afforestation and Reforestation. Agric. Ecosystems and Environment. 126:67-80.Trabucco, A.; Zomer, R. J.; Bossio, D. A.; van Straaten, O.; Verchot, L.V. 2008. Climate Change Mitigation through Afforestation / Reforestation: A global analysis of hydrologic

Learn about the importance and use of coal futures index in the global energy sector. Find out how coal futures contracts help with market analysis, risk management, price discovery, and investment analysis. Explore examples of coal futures indexes from around the world and understand their significance in evaluating the overall health of the coal industry.

CN: Open Interest: CSI 300 Index Futures: Delivery Contract data was reported at 0.000 Lot th in Apr 2025. This stayed constant from the previous number of 0.000 Lot th for Mar 2025. CN: Open Interest: CSI 300 Index Futures: Delivery Contract data is updated monthly, averaging 0.000 Lot th from May 2010 (Median) to Apr 2025, with 180 observations. The data reached an all-time high of 0.000 Lot th in Apr 2025 and a record low of 0.000 Lot th in Apr 2025. CN: Open Interest: CSI 300 Index Futures: Delivery Contract data remains active status in CEIC and is reported by China Financial Futures Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZI: China Financial Futures Exchange: Index Futures: Open Interest.

China Open Interest: CSI 300 Index Futures data was reported at 273.673 Lot th in 14 May 2025. This records an increase from the previous number of 248.604 Lot th for 13 May 2025. China Open Interest: CSI 300 Index Futures data is updated daily, averaging 115.844 Lot th from Apr 2010 (Median) to 14 May 2025, with 3660 observations. The data reached an all-time high of 326.251 Lot th in 07 Apr 2025 and a record low of 10.918 Lot th in 30 Apr 2010. China Open Interest: CSI 300 Index Futures data remains active status in CEIC and is reported by China Financial Futures Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZI: China Financial Futures Exchange: Index Futures: Open Interest: Daily.

U.S. stock futures remained stable as markets await U.S.-China trade talk results. Investors are hopeful for improved relations following a preliminary agreement, despite recent tensions.

China Turnover: Volume: CSI 300 Index Futures: Current Month data was reported at 33.690 Lot th in 14 May 2025. This records an increase from the previous number of 24.017 Lot th for 13 May 2025. China Turnover: Volume: CSI 300 Index Futures: Current Month data is updated daily, averaging 73.781 Lot th from Apr 2010 (Median) to 14 May 2025, with 3660 observations. The data reached an all-time high of 2,882.235 Lot th in 29 Jun 2015 and a record low of 3.430 Lot th in 22 Feb 2018. China Turnover: Volume: CSI 300 Index Futures: Current Month data remains active status in CEIC and is reported by China Financial Futures Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZI: China Financial Futures Exchange: Index Futures: Turnover: Daily.

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.

Commodity Industrial Metals Index: The Future of Global Manufacturing?

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

The National Stock Exchange of India cemented its place as the largest derivatives exchange in the world in 2023. Mumbai-based NSE traded nearly ** billion derivatives contracts in 2023, followed by the Brazilian exchange, B3, with *** billion contracts. What is a derivative? A derivative is a financial instrument that is based on an underlying asset, such as an equity, commodity, or currency. It can be traded over-the-counter or on an exchange. The most common types of derivatives are futures, options, forwards and swaps. How large is the derivative market? There are billions of derivatives traded globally every year. The largest markets for derivatives trading are Asia Pacific and North America. Currency options and futures alone contribute hundreds of millions of dollars in volume to the largest exchanges. Much of this volume is due to large corporations trying to hedge risk. For example, an international corporation may invest in a currency derivative to ensure that it can buy a particular currency at or below a certain price at some point in the future, protecting against an unfavorable shift in the exchange rate.

Japan's main stock market index, the JP225, rose to 40839 points on July 30, 2025, gaining 0.40% from the previous session. Over the past month, the index has climbed 2.13% and is up 4.44% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on July of 2025.

The Standard & Poor’s (S&P) 500 Index is an index of 500 leading publicly traded companies in the United States. In 2021, the index value closed at ******** points, which was the second highest value on record despite the economic effects of the global coronavirus (COVID-19) pandemic. In 2023, the index values closed at ********, the highest value ever recorded. What is the S&P 500? The S&P 500 was established in 1860 and expanded to its present form of 500 stocks in 1957. It tracks the price of stocks on the major stock exchanges in the United States, distilling their performance down to a single number that investors can use as a snapshot of the economy’s performance at a given moment. This snapshot can be explored further. For example, the index can be examined by industry sector, which gives a more detailed illustration of the economy. Other measures Being a stock market index, the S&P 500 only measures equities performance. In addition to other stock market indices, analysts will look to other indicators such as GDP growth, unemployment rates, and projected inflation. Similarly, since these indicators say something about the economic future, stock market investors will use these indicators to speculate on the stocks in the S&P 500.

China Closing Price: CSI 500 Index Futures: Next Month data was reported at 5,697.800 Index Point in 14 May 2025. This records an increase from the previous number of 5,654.600 Index Point for 13 May 2025. China Closing Price: CSI 500 Index Futures: Next Month data is updated daily, averaging 5,986.400 Index Point from Apr 2015 (Median) to 14 May 2025, with 2449 observations. The data reached an all-time high of 11,400.000 Index Point in 12 Jun 2015 and a record low of 3,978.000 Index Point in 18 Oct 2018. China Closing Price: CSI 500 Index Futures: Next Month data remains active status in CEIC and is reported by China Financial Futures Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZI: China Financial Futures Exchange: Index Futures: Closing and Settlement Price: Daily .

The New York Stock Exchange (NYSE) is the largest stock exchange in the world, with an equity market capitalization of almost ** trillion U.S. dollars as of June 2025. The following three exchanges were the NASDAQ, PINK Exchange, and the Frankfurt Exchange. What is a stock exchange? A stock exchange is a marketplace where stockbrokers, traders, buyers, and sellers can trade in equities products. The largest exchanges have thousands of listed companies. These companies sell shares of their business, giving the general public the opportunity to invest in them. The oldest stock exchange worldwide is the Frankfurt Stock Exchange, founded in the late sixteenth century. Other functions of a stock exchange Since these are publicly traded companies, every firm listed on a stock exchange has had an initial public offering (IPO). The largest IPOs can raise billions of dollars in equity for the firm involved. Related to stock exchanges are derivatives exchanges, where stock options, futures contracts, and other derivatives can be traded.

Baltic Dry fell to 1,995 Index Points on July 30, 2025, down 5.41% from the previous day. Over the past month, Baltic Dry's price has risen 36.83%, and is up 16.80% 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 July of 2025.

The average monthly trading amount (premium) of stock index options (futures exchange)