Real-time commodities pricing data allows you to grasp where the market is, was and will be – from exchange data and OTC prices to specialist fundamentals.

Gold fell to 3,386.92 USD/t.oz on August 8, 2025, down 0.25% from the previous day. Over the past month, Gold's price has risen 2.21%, and is up 39.35% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on August of 2025.

Graph and download economic data for Global Price Index of All Commodities (PALLFNFINDEXQ) from Q1 2003 to Q2 2025 about World, commodities, price index, indexes, and price.

Crude Oil fell to 64.33 USD/Bbl on August 7, 2025, down 0.03% from the previous day. Over the past month, Crude Oil's price has fallen 5.86%, and is down 15.57% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on August of 2025.

China Settlement Price: Dalian Commodity Exchange: Live Hog: 3rd Month data was reported at 13,870.000 RMB/Ton in 13 May 2025. This records a decrease from the previous number of 13,885.000 RMB/Ton for 12 May 2025. China Settlement Price: Dalian Commodity Exchange: Live Hog: 3rd Month data is updated daily, averaging 17,100.000 RMB/Ton from Jan 2021 (Median) to 13 May 2025, with 1049 observations. The data reached an all-time high of 28,005.000 RMB/Ton in 02 Mar 2021 and a record low of 12,840.000 RMB/Ton in 10 Jan 2025. China Settlement Price: Dalian Commodity Exchange: Live Hog: 3rd Month data remains active status in CEIC and is reported by Dalian Commodity Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZB: Dalian Commodity Exchange: Commodity Futures: Settlement Price: Daily.

Real-Time Material Price Index API Market Outlook

According to our latest research, the global Real-Time Material Price Index API market size reached USD 1.48 billion in 2024, reflecting strong momentum driven by surging demand for dynamic pricing intelligence across industries. The market is projected to grow at a robust CAGR of 16.2% from 2025 to 2033, reaching a forecasted size of USD 5.15 billion by 2033. This accelerated expansion is primarily attributed to the increasing adoption of digital procurement, supply chain automation, and the need for real-time materials cost transparency in volatile global markets.

The growth of the Real-Time Material Price Index API market is propelled by several critical factors. The rise in globalization and the complexity of supply chains have made it imperative for organizations to access accurate, up-to-the-minute pricing data for a wide array of raw materials. As commodity prices continue to fluctuate due to geopolitical tensions, trade policies, and environmental disruptions, the reliance on real-time APIs for price tracking and forecasting has become a strategic necessity. Enterprises are leveraging these APIs to optimize procurement decisions, manage risk, and maintain competitiveness in fast-evolving markets. The integration of artificial intelligence and machine learning into these solutions further enhances their predictive capabilities, enabling organizations to anticipate price shifts and plan accordingly.

Another significant driver is the digital transformation sweeping through traditional sectors such as construction, manufacturing, and energy. These industries are increasingly deploying Real-Time Material Price Index APIs to automate their procurement processes, minimize human error, and ensure compliance with contractual obligations tied to material costs. The ability to seamlessly integrate these APIs with enterprise resource planning (ERP) and supply chain management (SCM) systems has unlocked new efficiencies and cost savings. Furthermore, the proliferation of cloud-based deployment models has democratized access to real-time pricing intelligence, making it feasible for small and medium-sized enterprises (SMEs) to harness the same tools as large corporations.

The market is also benefiting from heightened regulatory scrutiny and sustainability initiatives. Governments and regulatory bodies are mandating greater transparency in sourcing and pricing, particularly for critical and rare materials. Real-Time Material Price Index APIs are playing a pivotal role in helping organizations meet these requirements by providing auditable, real-time data feeds. Additionally, as companies strive to achieve sustainability targets, these APIs aid in evaluating the cost implications of alternative sourcing strategies and greener materials. This confluence of regulatory, operational, and strategic factors is expected to sustain the market’s growth trajectory through the forecast period.

Regionally, North America leads the Real-Time Material Price Index API market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, has witnessed widespread adoption across its construction and manufacturing sectors, driven by the rapid digitization of supply chains and robust investment in procurement technologies. Europe is experiencing a surge in demand, fueled by stringent regulatory frameworks and the push for sustainable sourcing. Meanwhile, Asia Pacific is emerging as the fastest-growing region, with countries like China and India investing heavily in digital infrastructure and industrial automation. Latin America and the Middle East & Africa are gradually catching up, propelled by modernization initiatives and the growing need for supply chain resilience.

Component Analysis

The Real-Time Material Price Index API market is segmented by component into software and services. The software segment dominates the market, driven by the proliferation of advanced API platforms that offer real-time da

Real-Time Material Price Index API Market Outlook

According to our latest research, the global Real-Time Material Price Index API market size reached USD 1.14 billion in 2024, demonstrating robust momentum as organizations increasingly prioritize dynamic pricing and supply chain optimization. The market is projected to grow at a CAGR of 12.7% from 2025 to 2033, reaching an estimated USD 3.39 billion by 2033. This growth is driven by heightened demand for real-time data integration, the proliferation of digital transformation initiatives across industries, and a growing emphasis on cost control and procurement efficiency. As per our latest research, the adoption of Real-Time Material Price Index APIs is accelerating, particularly as businesses seek to enhance agility and make data-driven decisions in volatile market environments.

One of the primary growth factors propelling the Real-Time Material Price Index API market is the increasing complexity and globalization of supply chains. Organizations across sectors such as construction, manufacturing, and energy face constant fluctuations in material costs due to geopolitical tensions, supply disruptions, and volatile commodity prices. Real-Time Material Price Index APIs empower these enterprises with instant access to up-to-date pricing data, enabling more accurate forecasting, agile procurement strategies, and optimized inventory management. This capability is especially critical in industries where material costs represent a significant portion of overall expenses, allowing businesses to maintain competitiveness and protect margins in an unpredictable economic landscape.

Another significant driver is the rapid digitalization of procurement and enterprise resource planning (ERP) systems. As companies invest in automation and digital transformation, the integration of Real-Time Material Price Index APIs into their digital ecosystems becomes essential for seamless operations. These APIs facilitate the automatic synchronization of pricing data with purchasing, finance, and inventory modules, reducing manual intervention and minimizing the risk of costly errors. The demand for cloud-based solutions, in particular, is surging, as they offer scalability, flexibility, and ease of integration with existing platforms. This trend is further supported by the proliferation of Industry 4.0 initiatives, where real-time data is the backbone of smart manufacturing and supply chain optimization.

The growing emphasis on data-driven decision-making is also fueling market expansion. Enterprises are increasingly leveraging advanced analytics and artificial intelligence to derive actionable insights from real-time material price data. This enables proactive risk management, dynamic pricing strategies, and improved supplier negotiations. The ability to access and analyze granular, real-time pricing information is becoming a competitive differentiator, particularly in sectors where margins are tight and responsiveness to market changes is critical. As organizations recognize the value of integrating Real-Time Material Price Index APIs with their business intelligence tools, the market is expected to witness sustained growth over the forecast period.

From a regional perspective, North America currently leads the Real-Time Material Price Index API market, driven by early adoption of digital technologies and the presence of major players in the technology and manufacturing sectors. However, Asia Pacific is emerging as a high-growth region, fueled by rapid industrialization, expanding construction activities, and increasing investment in digital infrastructure. Europe also holds a significant share, supported by stringent regulatory requirements and a strong focus on supply chain transparency. Meanwhile, Latin America and the Middle East & Africa are witnessing gradual adoption, with growth opportunities arising from infrastructure development and modernization initiatives. Overall, the global market is characterized by diverse regional dynamics, with each geography contributing uniquely to the overall growth trajectory.

Component Analysis

The Real-Time Material Price Index API market by component is primarily segmented into software and services. The software segment comprises API platforms, integration tools, and analytics solutions that facilitate the seamless retrieval and processing of real-time material pricing data. These software solutions are designed to be highly scalable and adaptable, cate

Commodity Services Market Outlook

In 2023, the global commodity services market size was valued at approximately USD 12 billion and is projected to reach USD 18 billion by 2032, growing at a CAGR of 4.5% during the forecast period. The market's growth can be attributed to the increasing globalization of trade, advancements in technology, and heightened demand for risk management and advisory services in volatile markets. These factors are driving the market toward a sustainable growth trajectory.

The primary growth factor for the commodity services market is the growing need for risk management in the face of fluctuating commodity prices. As global markets become more interconnected, the volatility in commodity prices has escalated, necessitating advanced risk management tools and services. Companies across various sectors, including agriculture, energy, and metals, are increasingly leveraging these services to mitigate risks and ensure market stability. These risk management services cover a broad spectrum, from hedging strategies using futures and options to more complex financial instruments.

Another key driver is the technological advancements in commodity trading and brokerage services. The advent of sophisticated trading platforms and algorithms has revolutionized the commodity services market. These technologies enable faster transaction execution, enhanced data analytics, and improved market intelligence, thereby attracting more participants into the market. Furthermore, blockchain technology is being integrated for increased transparency and reduced fraud, which further boosts market confidence and participation.

The increasing demand for specialized research and advisory services also fuels the market's growth. With the complexity of global markets, businesses seek in-depth market analysis, trend forecasting, and strategic advice to make informed decisions. Research and advisory firms provide valuable insights into market dynamics, regulatory changes, and economic indicators, helping companies navigate the intricate landscape of commodity trading. This service segment is seeing robust growth as companies become more dependent on expert guidance to optimize their trading strategies.

Regionally, North America holds a significant share of the commodity services market, driven by its well-established financial markets and advanced technological infrastructure. The region's dominance is expected to continue, supported by the presence of major commodity exchanges and brokerage firms. Meanwhile, the Asia Pacific region is experiencing the fastest growth, primarily due to expanding industrial activities and increasing participation in global trade. The burgeoning economies of China and India, in particular, are key contributors to this regional growth, with their rising demand for various commodities.

Trading and Brokerage Analysis

The trading and brokerage segment is a cornerstone of the commodity services market, providing essential platforms and services for buying and selling various commodities. This segment has evolved significantly with the advent of electronic trading platforms that offer real-time market data, automated trading systems, and enhanced connectivity across global markets. These platforms have democratized access to commodity trading, allowing even small and medium-sized enterprises to participate actively.

In recent years, the role of brokerage firms has expanded beyond mere transaction facilitation to providing comprehensive market analysis, trading recommendations, and personalized investment strategies. Brokerage firms are now leveraging advanced analytics and big data to offer tailored solutions to their clients, enhancing their decision-making capabilities. This trend is particularly prominent in the energy and metals sectors, where market dynamics are highly complex and require specialized expertise.

Moreover, the integration of blockchain technology is poised to transform the trading and brokerage landscape. Blockchain offers unparalleled transparency and security, reducing the risk of fraud and ensuring the integrity of transactions. Several commodity exchanges and brokerage firms are already piloting blockchain-based platforms, which could set a new standard for the industry. This technological shift is expected to attract more institutional investors, further boosting market liquidity and stability.

The trading and brokerage segment also faces challenges, particularly in terms of regulatory compliance and cybersecurity. With increasi

Natural gas fell to 3 USD/MMBtu on August 8, 2025, down 2.33% from the previous day. Over the past month, Natural gas's price has fallen 6.80%, but it is still 39.78% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Natural gas - values, historical data, forecasts and news - updated on August of 2025.

The crude oil price chart in the commodity market is a live representation of the current and historical prices of crude oil. It provides traders and investors with valuable information about the market trend and helps them make informed decisions.

Corn fell to 383.01 USd/BU on August 8, 2025, down 0.39% from the previous day. Over the past month, Corn's price has fallen 4.07%, and is down 3.04% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Corn - values, historical data, forecasts and news - updated on August of 2025.

Learn about live grain commodity prices and how they impact the cost of production for farmers and the price consumers pay for food products. Track these prices on exchanges like CME, ICE, and MGEX to monitor broader trends in the agricultural industry.

United States PCE Inflation Nowcast: sa: Contribution: Commodity Prices: Live Cattle Futures: CME: Settlement Price: 1st Month data was reported at 0.000 % in 12 May 2025. This stayed constant from the previous number of 0.000 % for 05 May 2025. United States PCE Inflation Nowcast: sa: Contribution: Commodity Prices: Live Cattle Futures: CME: Settlement Price: 1st Month data is updated weekly, averaging 0.000 % from Apr 2019 (Median) to 12 May 2025, with 320 observations. The data reached an all-time high of 18.073 % in 25 Mar 2024 and a record low of 0.000 % in 12 May 2025. United States PCE Inflation Nowcast: sa: Contribution: Commodity Prices: Live Cattle Futures: CME: Settlement Price: 1st Month data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s United States – Table US.CEIC.NC: CEIC Nowcast: Personal Consumption Expenditure (PCE) Inflation: Headline.

The balanced annual panel data for 32 sub-Saharan countries from 2000 to 2020 was used for this study. The countries and period of study was informed by availability of data of interest. Specifically, 11 agricultural commodity dependent countries, 7 energy commodity dependent countries and 14 mineral and metal ore dependent countries were selected (Appendix 1). The annual data comprised of agricultural commodity prices, global oil prices (GOP) and mineral and metal ore prices, export value of the dependent commodity, total export value of the country, real GDP (RGDP) and terms of trade (TOT). The data for export value of the dependent commodity, total export value of the country, real GDP and terms of trade was sourced from world bank database (World Development Indicators). Data for agricultural commodity prices, global oil prices (GOP) and mineral and metal ore prices are obtained from World Bank commodity price data portal. This study used data from global commodity prices from the World Bank's commodity price data site since the error term (endogenous) is connected with each country's commodity export price index. The pricing information covered agricultural products, world oil, minerals, and metal ores. One benefit of adopting international commodity prices, according to Deaton and Miller (1995), is that they are frequently unaffected by national activities. The utilization of studies on global commodity prices is an example (Tahar et al., 2021). The commodity dependency index of country i at time i was computed as the as the ratio of export value of the dependent commodity to the total export value of the country. The commodity price volatility is estimated using standard deviation from monthly commodity price index to incorporate monthly price variation (Aghion et al., 2009). This approach addresses challenges of within the year volatility inherent in the annual data. In footstep of Arezki et al. (2014) and Mondal & Khanam (2018), standard deviation is used in this study as a proxy of commodity price volatility. The standard deviation is used because of its simplicity and it is not conditioned on the unit of measurement.

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.

Real-time price data collected by the Boston Market News Reporter. The NOAA Fisheries' "Fishery Market News" began operations in New York City on February 14, 1938. The primary function of this joint Federal/industry program is to provide accurate and unbiased reports depicting current conditions affecting the trade in fish and fishery products. The Boston and New York Market News Reports are now hosted by the Northeast Fisheries Science Center. Please navigate to the URL below for 2014 and newer data: https://www.nefsc.noaa.gov/read/socialsci/marketNews.php

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Badakhshan, Badghis, Baghlan, Balkh, Bamyan, Daykundi, Farah, Faryab, Paktya, Ghazni, Ghor, Hilmand, Hirat, Nangarhar, Jawzjan, Kabul, Kandahar, Kapisa, Khost, Kunar, Kunduz, Laghman, Logar, Wardak, Nimroz, Nuristan, Paktika, Panjsher, Parwan, Samangan, Sar-e-pul, Takhar, Uruzgan, Zabul, Market Average, Armavir, Ararat, Aragatsotn, Tavush, Gegharkunik, Shirak, Kotayk, Syunik, Lori, Vayotz Dzor, Yerevan, Kayanza, Ruyigi, Bubanza, Karuzi, Bujumbura Mairie, Muramvya, Gitega, Rumonge, Bururi, Kirundo, Cankuzo, Cibitoke, Muyinga, Rutana, Bujumbura Rural, Makamba, Ngozi, Mwaro, SAHEL, CASCADES, SUD-OUEST, EST, BOUCLE DU MOUHOUN, CENTRE-NORD, PLATEAU-CENTRAL, HAUTS-BASSINS, CENTRE, NORD, CENTRE-SUD, CENTRE-OUEST, CENTRE-EST, Khulna, Chittagong, Barisal, Rajshahi, Dhaka, Rangpur, Sylhet, Mymensingh, Ouaka, Mbomou, Bangui, Nana-Mambéré, Ouham, Sangha-Mbaéré, Ombella M'Poko, Mambéré-Kadéï, Vakaga, Ouham Pendé, Lobaye, Haute-Kotto, Kémo, Nana-Gribizi, Bamingui-Bangoran, Haut-Mbomou, Nord, Extrême-Nord, Ouest, Nord-Ouest, Adamaoua, Sud-Ouest, Est, Littoral, Centre, Haut-Uele, Nord-Kivu, Ituri, Tshopo, Kwilu, Kasai, Sud-Kivu, Kongo-Central, Nord-Ubangi, Sud-Ubangi, Kasai-Central, Bas-Uele, Tanganyika, Lualaba, Kasai-Oriental, Kwango, Haut-Lomami, Haut-Katanga, Maniema, Kinshasa, Equateur, Lomami, Likouala, Brazzaville, Point-Noire, Pool, Bouenza, Cuvette, Lekoumou, Nzerekore, Boke, Kindia, Kankan, Faranah, Mamou, Labe, Kanifing Municipal Council, Central River, Upper River, West Coast, North Bank, Lower River, Bafata, Tombali, Cacheu, Sector Autonomo De Bissau, Biombo, Oio, Gabu, Bolama, Quinara, North, South, Artibonite, South-East, Grande'Anse, North-East, West, North-West, SULAWESI UTARA, SUMATERA UTARA, KALIMANTAN UTARA, JAWA BARAT, NUSA TENGGARA BARAT, NUSA TENGGARA TIMUR, SULAWESI SELATAN, JAMBI, JAWA TIMUR, KALIMANTAN SELATAN, BALI, BANTEN, JAWA TENGAH, RIAU, SUMATERA BARAT, KEPULAUAN RIAU, PAPUA, SULAWESI BARAT, BENGKULU, MALUKU UTARA, DAERAH ISTIMEWA YOGYAKARTA, KALIMANTAN BARAT, KALIMANTAN TENGAH, PAPUA BARAT, SUMATERA SELATAN, MALUKU, KEPULAUAN BANGKA BELITUNG, ACEH, DKI JAKARTA, SULAWESI TENGGARA, KALIMANTAN TIMUR, LAMPUNG, GORONTALO, SULAWESI TENGAH, Anbar, Babil, Baghdad, Basrah, Diyala, Dahuk, Erbil, Ninewa, Kerbala, Kirkuk, Missan, Muthanna, Najaf, Qadissiya, Salah al-Din, Sulaymaniyah, Thi-Qar, Wassit, Coast, North Eastern, Nairobi, Rift Valley, , Eastern, Central, Nyanza, Attapeu, Bokeo, Bolikhamxai, Champasack, Houaphan, Khammouan, Louangphabang, Louangnamtha, Oudomxai, Phongsaly, Salavan, Savannakhet, Sekong, Vientiane Capital, Vientiane, Xaignabouly, Xiengkhouang, Akkar, Mount Lebanon, Baalbek-El Hermel, Beirut, Bekaa, El Nabatieh, Nimba, Grand Kru, Grand Cape Mount, Gbarpolu, Grand Bassa, Rivercess, Montserrado, River Gee, Lofa, Bomi, Bong, Sinoe, Maryland, Margibi, Grand Gedeh, East, North Central, Uva, Western, Sabaragamuwa, Southern, Northern, North Western, Kidal, Gao, Tombouctou, Bamako, Kayes, Koulikoro, Mopti, Segou, Sikasso, Yangon, Rakhine, Shan (North), Kayin, Kachin, Shan (South), Mon, Tanintharyi, Mandalay, Kayah, Shan (East), Chin, Magway, Sagaing, Zambezia, Cabo_Delgado, Tete, Manica, Sofala, Maputo, Gaza, Niassa, Inhambane, Maputo City, Nampula, Hodh Ech Chargi, Hodh El Gharbi, Brakna, Adrar, Assaba, Guidimakha, Gorgol, Trarza, Tagant, Dakhlet-Nouadhibou, Nouakchott, Tiris-Zemmour, Central Region, Southern Region, Northern Region, Tillaberi, Tahoua, Agadez, Zinder, Dosso, Niamey, Maradi, Diffa, Abia, Borno, Yobe, Katsina, Kano, Kaduna, Gombe, Jigawa, Kebbi, Oyo, Sokoto, Zamfara, Lagos, Adamawa, Cordillera Administrative region, Region XIII, Region VI, Region V, Region III, Autonomous region in Muslim Mindanao, Region IV-A, Region VIII, Region VII, Region X, Region II, Region IV-B, Region XII, Region XI, Region I, National Capital region, Region IX, North Darfur, Blue Nile, Nile, Eastern Darfur, West Kordofan, Gedaref, West Darfur, North Kordofan, South Kordofan, Kassala, Khartoum, White Nile, South Darfur, Red Sea, Sennar, Al Gezira, Central Darfur, Tambacounda, Diourbel, Ziguinchor, Kaffrine, Dakar, Saint Louis, Fatick, Kolda, Louga, Kaolack, Kedougou, Matam, Thies, Sedhiou, Shabelle Hoose, Juba Hoose, Bay, Banadir, Shabelle Dhexe, Gedo, Hiraan, Woqooyi Galbeed, Awdal, Bari, Juba Dhexe, Togdheer, Nugaal, Galgaduud, Bakool, Sanaag, Mudug, Sool, Warrap, Unity, Jonglei, Northern Bahr el Ghazal, Upper Nile, Central Equatoria, Western Bahr el Ghazal, Eastern Equatoria, Western Equatoria, Lakes, Aleppo, Dar'a, Quneitra, Homs, Deir-ez-Zor, Damascus, Ar-Raqqa, Al-Hasakeh, Hama, As-Sweida, Rural Damascus, Tartous, Idleb, Lattakia, Ouaddai, Salamat, Wadi Fira, Sila, Ennedi Est, Batha, Tibesti, Logone Oriental, Logone Occidental, Guera, Hadjer Lamis, Lac, Mayo Kebbi Est, Chari Baguirmi, Ennedi Ouest, Borkou, Tandjile, Mandoul, Moyen Chari, Mayo Kebbi Ouest, Kanem, Barh El Gazal, Ndjaména, Al Dhale'e, Aden, Al Bayda, Al Maharah, Lahj, Al Jawf, Raymah, Al Hudaydah, Hajjah, Amran, Shabwah, Dhamar, Ibb, Sana'a, Al Mahwit, Marib, Hadramaut, Sa'ada, Amanat Al Asimah, Socotra, Taizz, Abyan

Description

This dataset, titled "Cryptocurrency Market Sentiment & Prediction," is a synthetic collection of real-time crypto market data designed for advanced analysis and predictive modeling. It captures a comprehensive range of features including price movements, social sentiment, news impact, and trading patterns for 10 major cryptocurrencies. Tailored for data scientists and analysts, this dataset is ideal for exploring market volatility, sentiment analysis, and price prediction, particularly in the context of significant events like the Bitcoin halving in 2024 and increasing institutional adoption.

Key Features Overview: - Price Movements: Tracks current prices and 24-hour price change percentages to reflect market dynamics. - Social Sentiment: Measures sentiment scores from social media platforms, ranging from -1 (negative) to 1 (positive), to gauge public perception. - News Sentiment and Impact: Evaluates sentiment from news sources and quantifies their potential impact on market behavior. - Trading Patterns: Includes data on 24-hour trading volumes and market capitalization, crucial for understanding market activity. - Technical Indicators: Features metrics like the Relative Strength Index (RSI), volatility index, and fear/greed index for in-depth technical analysis. - Prediction Confidence: Provides a confidence score for predictive models, aiding in assessing forecast reliability.

Purpose and Applications: - Perfect for machine learning tasks such as price prediction, sentiment-price correlation studies, and volatility classification. - Supports time series analysis for forecasting price movements and identifying volatility clusters. - Valuable for research into the influence of social media and news on cryptocurrency markets, especially during high-impact events.

Dataset Scope: - Covers a simulated 30-day period, offering a snapshot of market behavior under varying conditions. - Focuses on major cryptocurrencies including Bitcoin, Ethereum, Cardano, Solana, and others, ensuring relevance to current market trends.

Dataset Structure Table:

Dataset Statistics Table:

This dataset is a powerful resource for machine learning projects, sentiment analysis, and crypto market research, providing a robust foundation for AI/ML model development and testing.

General commodity price information is data that provides market price information for major general commodities provided by the Korea Exchange. This information includes real-time prices, daily rate of increase and decrease, and trading volume for products listed on the Petroleum Electronic Commerce Market, KRX Gold Market, and Carbon Emissions Market. This data consists of three operations. Each operation is as follows. ① Petroleum Price: Searches for price information for petroleum products listed on the Petroleum Electronic Commerce Market. ② Gold Price: Provides price information for gold products listed on the KRX Gold Market. ③ Emissions Price: Searches for price information for emission products listed on the Carbon Emissions Market.

Summary of Commodity Price Relationships Across Data Sources.