India's main stock market index, the SENSEX, rose to 81800 points on July 21, 2025, gaining 0.05% from the previous session. Over the past month, the index has climbed 0.54% and is up 1.48% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

Euro Area's main stock market index, the EU50, fell to 5347 points on July 21, 2025, losing 0.23% from the previous session. Over the past month, the index has climbed 2.39% and is up 9.18% 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 July of 2025.

Socioeconomic dataset for analysing demand prediction of weekend markets in the city of Hamburg, Germany

In this DDLitlab funded Data Literacy student project, our goal was to predict weekend markets in the city of Hamburg and using open-source data and OpenStreetMaps in conjunction with Machine Learning Algorithms. You can find a brief article about the initial grant and our approach here : https://www.cliccs.uni-hamburg.de/about-cliccs/news/2023-news/2023-08-24-ddlitlab-event.html

Github repository: https://gitlab.rrz.uni-hamburg.de/exploring-avenues-for-the-deployment-of-machine-learning-algorithms-for-sustainable-small-agricultural-business-information-using-openstreetmap/main-project-v-3

This repository is intended to make our codes and visualisations openly available to the University of Hamburg students for further research. This is not to be used without citation under any circumstances and the University/authors deserve the right to withdraw consent at any time.

Please do not forget to cite our work in the event of fair use.

Organisation of our Github repository

Codes: contains the codes for the different methods deployed for data preparation,variable selection,visualisations showing the spatial characteristics of our variables, calculating indices such as correlation coefficients and machine learning methods in increasing order of complexity. City-district (Stadtteil) as the unit of analysis.

Data (uploaded datasets) : The open source data obtained for the project has been obtained from OpenStreetMaps (https://wiki.openstreetmap.org/wiki/Use_OpenStreetMap ) and Statistik Nord (https://www.statistik-nord.de/ ) . Each variable contains values for all stadtteils (city-districts) of Hamburg. The filenames are self explanatory.

The Hamburg shapefile has been obtained from Geofabrik https://www.geofabrik.de/de/data/shapefiles.html In addition to the original data uploaded in the section, we have also laid down the final data we have deployed with the algorithms, in the final final_data.csv

Our repository contains the following additional sections:

Results: This section contains results from the codes processed in the first section. It includes the final 10 variables selected for the study, the results from the VIF analysis, correlation matrix, and some model output statistics.

Visualisations: This section is dedicated to visualisations of the variables used for the study and the results from deployment of various methods. In case of any questions, please do not hesitate to contact us at our official student IDs : first.lastname@studium.uni-hamburg.de. We are also available on LinkedIn for professional networking in case of other queries.

Data curators /DDLitLab data literacy project team

Ferdinand Hölzl

Leidy Gicela Vergara Lopez

Shivanshi Asthana

Shuyue Qu

Sojung Oh

Juan Miguel Rodriguez Lopez

Off Within 2 Weeks: sa: Single Family: Kansas City, MO data was reported at 38.419 % in Jul 2020. This records a decrease from the previous number of 50.629 % for Jun 2020. Off Within 2 Weeks: sa: Single Family: Kansas City, MO data is updated monthly, averaging 28.826 % from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 50.629 % in Jun 2020 and a record low of 2.392 % in Jan 2014. Off Within 2 Weeks: sa: Single Family: Kansas City, MO data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB011: Off Market Within 2 Weeks: by Metropolitan Areas: Seasonally Adjusted.

United States Off Within 2 Weeks: sa: Single Family: Ottawa, KS data was reported at 36.910 % in Jul 2020. This records a decrease from the previous number of 39.796 % for Jun 2020. United States Off Within 2 Weeks: sa: Single Family: Ottawa, KS data is updated monthly, averaging 17.964 % from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 72.750 % in Jan 2020 and a record low of -5.969 % in May 2012. United States Off Within 2 Weeks: sa: Single Family: Ottawa, KS data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB011: Off Market Within 2 Weeks: by Metropolitan Areas: Seasonally Adjusted.

This dataset is about book series. It has 1 row and is filtered where the books is Bear market investing strategies. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Wake County farmers market's locations, hours, and descriptions. Data from NC Farm Fresh: http://www.ncfarmfresh.com/index.asp

Forecast: Total Hours Worked in Advertising and Market Research in Canada 2024 - 2028 Discover more data with ReportLinker!

The data record contains the tracker data of the delivery vehicle from mein-Wochenmarkt.online from 02/21. The data set contains the GPS data from several market days (02/19/20, 02/21/20, 03/21/20) and was edited in such a way that the exact start and end points are not visible in order to protect the address data of customers. mein-Wochenmarkt.online is a startup that existed between February and December 2020 and delivered goods from the weekly market in Ulm and from the weekly market on Eselsberg to your door on market days. ### Data source: Datahub City of Ulm under CCZero

United States SBP: MQ: Future Needs: Identify Potential Markets for Exporting data was reported at 2.900 % in 11 Apr 2022. This records a decrease from the previous number of 3.700 % for 14 Mar 2022. United States SBP: MQ: Future Needs: Identify Potential Markets for Exporting data is updated weekly, averaging 3.300 % from Sep 2021 (Median) to 11 Apr 2022, with 10 observations. The data reached an all-time high of 4.000 % in 27 Dec 2021 and a record low of 1.400 % in 03 Jan 2022. United States SBP: MQ: Future Needs: Identify Potential Markets for Exporting data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S045: Small Business Pulse Survey: by Sector: Weekly. Beg Monday (Discontinued).

This Roundup covers critical economic data and publications used by NSW Treasury for monitoring and analysis.

Statistical results of weekly frequency.

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.

Where Will CLPT Stock Be in 16 Weeks?

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

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.

LON:ENSI Stock Forecast: A Buy For The Next 16 Weeks

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

This Roundup covers critical economic data and publications used by NSW Treasury for monitoring and analysis.

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.

Where Will BSFC Stock Be in 16 Weeks?

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

LRM05 - Average Net Weekly Live Register Changes. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Average Net Weekly Live Register Changes...

United States Off Within 2 Weeks: sa: All Residential: Big Rapids, MI data was reported at 44.260 % in Jul 2020. This records an increase from the previous number of 38.048 % for Jun 2020. United States Off Within 2 Weeks: sa: All Residential: Big Rapids, MI data is updated monthly, averaging 18.041 % from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 44.260 % in Jul 2020 and a record low of -7.107 % in Mar 2014. United States Off Within 2 Weeks: sa: All Residential: Big Rapids, MI data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB011: Off Market Within 2 Weeks: by Metropolitan Areas: Seasonally Adjusted.

This Roundup covers critical economic data and publications used by NSW Treasury for monitoring and analysis.

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.

CBAN Stock Forecast: A Hold For The Next 4 Weeks

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