Euro Area's main stock market index, the EU50, fell to 5446 points on August 21, 2025, losing 0.48% from the previous session. Over the past month, the index has climbed 2.95% and is up 11.49% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on August of 2025.

Stock market index in Mexico, June, 2025 The most recent value is 130.44 points as of June 2025, a decline compared to the previous value of 131.33 points. Historically, the average for Mexico from January 1970 to June 2025 is 35.98 points. The minimum of 0 points was recorded in January 1970, while the maximum of 131.33 points was reached in May 2025. | TheGlobalEconomy.com

Prices for Vietnam Ho Chi Minh Stock Index including live quotes, historical charts and news. Vietnam Ho Chi Minh Stock Index was last updated by Trading Economics this August 20 of 2025.

United States Index: NYSE Financial data was reported at 7,713.770 31Dec2002=5000 in Nov 2018. This records an increase from the previous number of 7,543.040 31Dec2002=5000 for Oct 2018. United States Index: NYSE Financial data is updated monthly, averaging 6,396.895 31Dec2002=5000 from Dec 2002 (Median) to Nov 2018, with 192 observations. The data reached an all-time high of 9,933.900 31Dec2002=5000 in May 2007 and a record low of 2,518.780 31Dec2002=5000 in Feb 2009. United States Index: NYSE Financial data remains active status in CEIC and is reported by New York Stock Exchange. The data is categorized under Global Database’s United States – Table US.Z001: NYSE: Indexes.

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.

Cotton Index: The Future of Textile Trade?

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

Graph and download economic data for Current Unfilled Orders; Diffusion Index for New York (UOCDISA066MSFRBNY) from Jul 2001 to Aug 2025 about unfilled orders, diffusion, orders, NY, manufacturing, and indexes.

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.

United States - Indexes of Aggregate Weekly Hours of All Employees, Wholesale Trade was 107.00000 Index 2007=100 in April of 2025, according to the United States Federal Reserve. Historically, United States - Indexes of Aggregate Weekly Hours of All Employees, Wholesale Trade reached a record high of 108.10000 in December of 2024 and a record low of 88.10000 in February of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Indexes of Aggregate Weekly Hours of All Employees, Wholesale Trade - last updated from the United States Federal Reserve on July of 2025.

Graph and download economic data for Current Employment; Diffusion Index for Federal Reserve District 3: Philadelphia (NECDFNA066MNFRBPHI) from May 1968 to Jul 2025 about FRB PHI District, diffusion, employment, indexes, and USA.

United States - Indexes of Aggregate Weekly Hours of All Employees, Transportation and Warehousing was 147.10000 Index 2007=100 in April of 2025, according to the United States Federal Reserve. Historically, United States - Indexes of Aggregate Weekly Hours of All Employees, Transportation and Warehousing reached a record high of 160.40000 in December of 2024 and a record low of 86.60000 in February of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Indexes of Aggregate Weekly Hours of All Employees, Transportation and Warehousing - last updated from the United States Federal Reserve on August of 2025.

Graph and download economic data for Current Work Hours; Diffusion Index for Federal Reserve District 3: Philadelphia (AWCDFSA066MSFRBPHI) from May 1968 to Jul 2025 about FRB PHI District, diffusion, hours, indexes, and USA.

United States Diffusion Index: sa: Mfg: 3 Months Span data was reported at 67.100 Unit in Oct 2018. This records an increase from the previous number of 63.200 Unit for Sep 2018. United States Diffusion Index: sa: Mfg: 3 Months Span data is updated monthly, averaging 49.000 Unit from Jan 1991 (Median) to Oct 2018, with 334 observations. The data reached an all-time high of 82.200 Unit in Nov 1997 and a record low of 2.600 Unit in Mar 2009. United States Diffusion Index: sa: Mfg: 3 Months Span data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G041: Current Employment Statistics Survey: Diffusion Index.

Graph and download economic data for CBOE NASDAQ 100 Volatility Index (VXNCLS) from 2001-02-02 to 2025-08-19 about VIX, volatility, stock market, and USA.

Graph and download economic data for S&P CoreLogic Case-Shiller 20-City Home Price Sales Pair Counts (SPCS20RPSNSA) from Jan 2000 to May 2025 about sales, HPI, housing, price index, indexes, price, and USA.

Graph and download economic data for Industrial Capacity: Total Index (CAPB50001SQ) from Q1 1967 to Q2 2025 about capacity, industry, indexes, and USA.

United States Index: Standard & Poors: S&P 500 Consumer Staples data was reported at 566.680 1941-1943=10 in Oct 2018. This records an increase from the previous number of 554.910 1941-1943=10 for Sep 2018. United States Index: Standard & Poors: S&P 500 Consumer Staples data is updated monthly, averaging 291.220 1941-1943=10 from Dec 2001 (Median) to Oct 2018, with 203 observations. The data reached an all-time high of 595.650 1941-1943=10 in Jan 2018 and a record low of 190.250 1941-1943=10 in Mar 2003. United States Index: Standard & Poors: S&P 500 Consumer Staples data remains active status in CEIC and is reported by Standard & Poor's. The data is categorized under Global Database’s United States – Table US.Z016: Standard & Poors: US Indexes.

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.

What happens to gold if CPI increases?

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

Iran IR: Import Value Index data was reported at 287.811 2000=100 in 2016. This records a decrease from the previous number of 300.763 2000=100 for 2015. Iran IR: Import Value Index data is updated yearly, averaging 120.537 2000=100 from Dec 1980 (Median) to 2016, with 37 observations. The data reached an all-time high of 470.600 2000=100 in 2010 and a record low of 69.606 2000=100 in 1986. Iran IR: Import Value Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank.WDI: Trade Index. Import value indexes are the current value of imports (c.i.f.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2000). UNCTAD's import value indexes are reported for most economies. For selected economies for which UNCTAD does not publish data, the import value indexes are derived from import volume indexes (line 73) and corresponding unit value indexes of imports (line 75) in the IMF's International Financial Statistics.; ; United Nations Conference on Trade and Development, Handbook of Statistics and data files, and International Monetary Fund, International Financial Statistics.; ;

Index: TSE: 1st Section: MA: Real Estate data was reported at 1,520.779 04Jan1968=100 in Jun 2018. This records a decrease from the previous number of 1,559.857 04Jan1968=100 for May 2018. Index: TSE: 1st Section: MA: Real Estate data is updated monthly, averaging 925.960 04Jan1968=100 from Dec 1987 (Median) to Jun 2018, with 367 observations. The data reached an all-time high of 2,363.700 04Jan1968=100 in Dec 1989 and a record low of 402.363 04Jan1968=100 in Apr 2003. Index: TSE: 1st Section: MA: Real Estate data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.