Food price increases hit the egg category the hardest between December 2021 and December 2024 in the United States. The price of eggs increased by **** percent in 2024.

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.

Ventyx's Rise: (VTYX) Stock Forecast

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

In early April, claiming to boost the country's domestic economy, President Trump made an executive order to implement new, widespread tariffs. In addition to the 10 percent baseline tariff imposed on all U.S. imports, Trump also announced specific tariffs on a number of important trading partners, such as the European Union, China, and Vietnam, which account for over 40 percent of all U.S. imports. According to a survey taken just after the announcement, roughly 20 percent of surveyed Americans were planning to make purchases because they expected prices to increase as a result of the tariffs.

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.

Rise and Fall of SRBK: A Tale of Two Banks?

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.

Telecom Plus (TEP): The Rise of the Challenger?

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

Key information about House Prices Growth

• Japan house prices grew 4.3% YoY in Nov 2024, following an increase of 2.0% YoY in the previous month.
• YoY growth data is updated monthly, available from Apr 2009 to Nov 2024, with an average growth rate of 1.9%.
• House price data reached an all-time high of 10.2% in Apr 2022 and a record low of -9.4% in Apr 2009.

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.

UMB Future: Ready to Rise? (UMB)

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

Key information about House Prices Growth

• Thailand house prices grew 8.5% YoY in Sep 2021, following an increase of 4.9% YoY in the previous month.
• YoY growth data is updated monthly, available from Mar 2009 to Sep 2021, with an average growth rate of 5.7%.
• House price data reached an all-time high of 20.2% in Dec 2009 and a record low of -6.1% in Dec 2020.

Gasoline fell to 2.16 USD/Gal on July 14, 2025, down 1.30% from the previous day. Over the past month, Gasoline's price has fallen 3.40%, and is down 13.22% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gasoline - values, historical data, forecasts and news - updated on July of 2025.

Coffee rose to 305.70 USd/Lbs on July 14, 2025, up 5.93% from the previous day. Over the past month, Coffee's price has fallen 11.39%, but it is still 26.62% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coffee - values, historical data, forecasts and news - updated on July of 2025.

House Price Index YoY in the United States decreased to 3 percent in April from 3.90 percent in March of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

Housing Inventory: Price Increased Count Month-Over-Month in Maine was 27.27% in May of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Price Increased Count Month-Over-Month in Maine reached a record high of 300.00 in September of 2023 and a record low of -70.00 in April of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Price Increased Count Month-Over-Month in Maine - last updated from the United States Federal Reserve on July of 2025.

📈 Daily Historical Stock Price Data for Ag Growth International Inc. (2004–2025)

A clean, ready-to-use dataset containing daily stock prices for Ag Growth International Inc. from 2004-05-18 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Ag Growth International Inc. Ticker Symbol: AFN.TO Date Range: 2004-05-18 to 2025-05-28 Frequency: Daily Total Records:… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-ag-growth-international-inc-20042025.

Key information about House Prices Growth

• Canada house prices grew 0.1% YoY in Jan 2025, following an increase of 0.1% YoY in the previous month.
• YoY growth data is updated monthly, available from Jan 1982 to Jan 2025, with an average growth rate of 1.8%.
• House price data reached an all-time high of 16.5% in Mar 1989 and a record low of -9.7% in Apr 1991.

Key information about House Prices Growth

• Taiwan house prices grew 12.5% YoY in Sep 2024, following an increase of 11.9% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2002 to Sep 2024, with an average growth rate of 7.3%.
• House price data reached an all-time high of 20.9% in Mar 2010 and a record low of -6.0% in Mar 2016.

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.

Interactive Brokers on the Rise? (IBKR)

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.

BlackSky's (BKSY) Shares Anticipated to Rise Based on Recent Analyst Forecasts

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

Key information about House Prices Growth

• Denmark house prices grew 12.3% YoY in Jan 2022, following an increase of 9.2% YoY in the previous month.
• YoY growth data is updated monthly, available from Jan 2007 to Jan 2022, with an average growth rate of 3.0%.
• House price data reached an all-time high of 16.9% in Mar 2021 and a record low of -16.0% in May 2009.

In the fourth quarter of 2024, the Bulgaria, Spain, and Portugal registered the highest house price increase in real terms (adjusted for inflation). In Bulgaria, house prices outgrew inflation by nearly ** percent. When comparing the nominal price change, which does not take inflation into consideration, the average house price growth was even higher.

Meanwhile, many countries experienced declining prices, with Turkey recording the biggest decline, at ** percent. That has to do with a broader trend of a slowing global housing market.

Key information about House Prices Growth

• Morocco house prices grew 0.8% YoY in Dec 2024, following a decrease of 0.4% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2007 to Dec 2024, with an average growth rate of 0.3%.
• House price data reached an all-time high of 7.6% in Mar 2017 and a record low of -6.0% in Dec 2021.