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.

Baltic Dry rose to 2,018 Index Points on August 1, 2025, up 0.75% from the previous day. Over the past month, Baltic Dry's price has risen 39.85%, and is up 20.48% 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 August of 2025.

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.

Taiwan TWSE: Equity Market Index: Trading & Consumers' Goods data was reported at 265.650 31Dec1994=100 in Oct 2018. This records a decrease from the previous number of 276.610 31Dec1994=100 for Sep 2018. Taiwan TWSE: Equity Market Index: Trading & Consumers' Goods data is updated monthly, averaging 115.695 31Dec1994=100 from Jan 1995 (Median) to Oct 2018, with 286 observations. The data reached an all-time high of 276.680 31Dec1994=100 in Jun 2018 and a record low of 49.070 31Dec1994=100 in Apr 2003. Taiwan TWSE: Equity Market Index: Trading & Consumers' Goods data remains active status in CEIC and is reported by Taiwan Stock Exchange Corporation. The data is categorized under Global Database’s Taiwan – Table TW.Z001: Taiwan Stock Exchange (TWSE): Indices.

Graph and download economic data for Trade Weighted U.S. Dollar Index: Other Important Trading Partners, Goods (DISCONTINUED) (TWEXO) from 1995-01-04 to 2020-01-01 about trade-weighted, trade, exchange rate, currency, goods, rate, indexes, and USA.

CRB Index fell to 369.61 Index Points on July 31, 2025, down 1.78% from the previous day. Over the past month, CRB Index's price has risen 1.62%, and is up 12.72% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. CRB Commodity Index - values, historical data, forecasts and news - updated on August of 2025.

Import Volume Index data was reported at 130.173 2015=100 in 2021. This records an increase from the previous number of 118.805 2015=100 for 2020. Import Volume Index data is updated yearly, averaging 95.285 2015=100 from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 130.173 2015=100 in 2021 and a record low of 51.203 2015=100 in 2000. Import Volume Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Croatia – Table HR.World Bank.WDI: Trade Index. Import volume indexes are derived from UNCTAD's volume index series and are the ratio of the import value indexes to the corresponding unit value indexes. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD’s estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates and calculates unit value indexes at the country level using the current year’s trade values as weights. For economies for which UNCTAD does not publish data, the import volume indexes (lines 73) in the IMF's International Financial Statistics are used.;United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics.;;

description: Shows index traders in selected agricultural markets. These traders are drawn from the noncommercial and commercial categories. The noncommercial category includes positions of managed funds, pension funds, and other investors that are generally seeking exposure to a broad index of commodity prices as an asset class in an unleveraged and passively-managed manner. The commercial category includes positions for entities whose trading predominantly reflects hedging of over-the-counter transactions involving commodity indices, for example, a swap dealer holding long futures positions to hedge a short commodity index exposure opposite institutional traders, such as pension funds.; abstract: Shows index traders in selected agricultural markets. These traders are drawn from the noncommercial and commercial categories. The noncommercial category includes positions of managed funds, pension funds, and other investors that are generally seeking exposure to a broad index of commodity prices as an asset class in an unleveraged and passively-managed manner. The commercial category includes positions for entities whose trading predominantly reflects hedging of over-the-counter transactions involving commodity indices, for example, a swap dealer holding long futures positions to hedge a short commodity index exposure opposite institutional traders, such as pension funds.

In general, the stock prices of the same industry have a similar trend, but those of different industries do not. When investing in stocks of different industries, one should select the optimal model from lots of trading models for each industry because any model may not be suitable for capturing the stock trends of all industries. However, the study has not been carried out at present. In this paper, firstly we select 424 S&P 500 index component stocks (SPICS) and 185 CSI 300 index component stocks (CSICS) as the research objects from 2010 to 2017, divide them into 9 industries such as finance and energy respectively. Secondly, we apply 12 widely used machine learning algorithms to generate stock trading signals in different industries and execute the back-testing based on the trading signals. Thirdly, we use a non-parametric statistical test to evaluate whether there are significant differences among the trading performance evaluation indicators (PEI) of different models in the same industry. Finally, we propose a series of rules to select the optimal models for stock investment of every industry. The analytical results on SPICS and CSICS show that we can find the optimal trading models for each industry based on the statistical tests and the rules. Most importantly, the PEI of the best algorithms can be significantly better than that of the benchmark index and “Buy and Hold” strategy. Therefore, the algorithms can be used for making profits from industry stock trading.

United States USD Trade Weighted Index: Nominal: Other Important Trading Partner data was reported at 168.237 Jan1997=100 in Nov 2018. This records an increase from the previous number of 166.528 Jan1997=100 for Oct 2018. United States USD Trade Weighted Index: Nominal: Other Important Trading Partner data is updated monthly, averaging 96.825 Jan1997=100 from Jan 1973 (Median) to Nov 2018, with 551 observations. The data reached an all-time high of 168.237 Jan1997=100 in Nov 2018 and a record low of 1.998 Jan1997=100 in Jul 1973. United States USD Trade Weighted Index: Nominal: Other Important Trading Partner data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.M016: US Dollar Trade Weighted Index.

Sri Lanka CSE: Index: Trading data was reported at 12,056.720 NA in Nov 2018. This records a decrease from the previous number of 12,206.220 NA for Oct 2018. Sri Lanka CSE: Index: Trading data is updated monthly, averaging 1,312.570 NA from Jan 1987 (Median) to Nov 2018, with 383 observations. The data reached an all-time high of 33,276.330 NA in May 2011 and a record low of 155.760 NA in Nov 1988. Sri Lanka CSE: Index: Trading data remains active status in CEIC and is reported by Colombo Stock Exchange. The data is categorized under Global Database’s Sri Lanka – Table LK.Z001: Colombo Stock Exchange: Index.

Bangladesh BD: Net Barter Terms of Trade Index data was reported at 68.332 2000=100 in 2020. This records an increase from the previous number of 65.803 2000=100 for 2019. Bangladesh BD: Net Barter Terms of Trade Index data is updated yearly, averaging 103.596 2000=100 from Dec 1980 (Median) to 2020, with 41 observations. The data reached an all-time high of 162.264 2000=100 in 1985 and a record low of 57.575 2000=100 in 2011. Bangladesh BD: Net Barter Terms of Trade Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Trade Index. Net barter terms of trade index is calculated as the percentage ratio of the export unit value indexes to the import unit value indexes, measured relative to the base year 2000. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates and calculates unit value indexes at the country level using the current year's trade values as weights.;United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics.;;

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.

Net Barter Terms of Trade Index data was reported at 99.930 2015=100 in 2021. This records an increase from the previous number of 99.062 2015=100 for 2020. Net Barter Terms of Trade Index data is updated yearly, averaging 100.967 2015=100 from Dec 2000 (Median) to 2021, with 22 observations. The data reached an all-time high of 108.386 2015=100 in 2001 and a record low of 97.159 2015=100 in 2018. Net Barter Terms of Trade Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sweden – Table SE.World Bank.WDI: Trade Index. Net barter terms of trade index is calculated as the percentage ratio of the export unit value indexes to the import unit value indexes, measured relative to the base year 2000. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year’s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD’s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates and calculates unit value indexes at the country level using the current year's trade values as weights.;United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics.;;

This graph depicts the business receipts index (BRI) of Hong Kong's trading industry from 2014 to 2024, by sector. In 2024, the business receipts index for the retail trade sector in Hong Kong ranged at **** index points.

Israel Trading Volume: TASE: Avg Daily: Derivative: Option & Future: TA-35 Index data was reported at 104.000 Unit th in Oct 2018. This records an increase from the previous number of 94.000 Unit th for Sep 2018. Israel Trading Volume: TASE: Avg Daily: Derivative: Option & Future: TA-35 Index data is updated monthly, averaging 98.500 Unit th from May 2017 (Median) to Oct 2018, with 18 observations. The data reached an all-time high of 136.000 Unit th in Feb 2018 and a record low of 66.000 Unit th in Jul 2018. Israel Trading Volume: TASE: Avg Daily: Derivative: Option & Future: TA-35 Index data remains active status in CEIC and is reported by Tel Aviv Stock Exchange. The data is categorized under Global Database’s Israel – Table IL.Z005: Tel Aviv Stock Exchange: Trading Value and Trading Volume. The index was released under the name TA-25 Monthly Options and was broadened to TA-35 Monthly Options in May 2017.

At the end of February 2025, the DAX index reached ********* points, marking its highest level since January 2015. Moreover, this also reflected a strong recovery from the global coronavirus (COVID-19) pandemic, having risen from ******** points at the end of March 2020 and surpassing its pre-pandemic level of approximately ********* points at the end of December 2019. Origin and composition of the DAX Index The DAX (Deutscher Aktienindex) is the most important German stock index, showing the value trends of the 40 largest companies by market capitalization listed on the Frankfurt stock exchange. The DAX index was introduced on July 1, 1988 and is a continuation of the Börsen-Zeitung Index, established in 1959. The count among their number some of the most recognizable companies in the world, such as carmakers Volkswagen and Daimler, sportswear brand adidas, and industrial giants Siemens and BASF. After the DAX, the 50 next-largest German companies are included in the midcap MDAX index, while the 70 next-largest small and medium-sized German companies (ranked from 91 to 160) are included in the SDAX index. The Frankfurt Stock Exchange All the companies included in the DAX family of indices are traded on the Frankfurt Stock Exchange. Dating back to 1585, the Frankfurt Stock Exchange is considered to be the oldest exchange in the world. It is the twelfth largest stock exchange in the world in terms of market capitalization, and accounts for around ** percent of all equity trading in Germany. Two main trading venues comprise the Frankfurt Stock Exchange: the Börse Frankfurt is a traditional trading floor; while the Xetra is an electronic trading system which accounts for the vast majority of trading volume on Frankfurt Stock Exchange. As of December 2023, the total market capitalization of all companies listed on the Frankfurt Stock Exchange was around *** trillion euros.

Japan's main stock market index, the JP225, fell to 40800 points on August 1, 2025, losing 0.66% from the previous session. Over the past month, the index has climbed 2.61% and is up 13.62% 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 August of 2025.

Net barter terms of trade index (2000 = 100) in Greece was reported at 95.4 in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Greece - Net barter terms of trade index (2000 = 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Graph and download economic data for Nominal Other Important Trading Partners U.S. Dollar Index (Goods Only) (DISCONTINUED) (TWEXOMTH) from Jan 1973 to Dec 2019 about trade-weighted, trade, exchange rate, currency, goods, rate, indexes, and USA.