The yield on 10 Year TIPS Yield eased to 1.90% on August 1, 2025, marking a 0.07 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.06 points, though it remains 0.14 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. This dataset includes a chart with historical data for the United States 10 Year TIPS Yield.

The yield on US 2 Year Note Bond Yield eased to 3.70% on August 1, 2025, marking a 0.27 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.10 points and is 0.19 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. US 2 Year Treasury Bond Note Yield - values, historical data, forecasts and news - updated on August of 2025.

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.

Octane-Boosted Returns with iShares iBonds Term TIPS ETF?

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.

iShares iBonds Oct 2026 Term TIPS ETF: Inflation Protection in Sight?

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.

TIP TEAMINVEST PRIVATE GROUP LIMITED

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.

iShares iBonds ETF: Tipping the Scales of Inflation?

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

View data of the effective yield of an index of non-investment grade publically issued corporate debt in the U.S.

Prices for US 20Y including live quotes, historical charts and news. US 20Y was last updated by Trading Economics this August 1 of 2025.

The yield on US 20 Year Bond Yield eased to 4.80% on August 1, 2025, marking a 0.11 percentage point decrease from the previous session. Over the past month, the yield has fallen by 0.02 points, though it remains 0.61 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. This dataset includes a chart with historical data for US 20Y.

The yield on Brazil 10Y Bond Yield eased to 14.02% on August 1, 2025, marking a 0.09 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.51 points and is 2.19 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Brazil 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on August of 2025.

View a 10-year yield estimated from the average yields of a variety of Treasury securities with different maturities derived from the Treasury yield curve.

The yield on Germany 10Y Bond Yield eased to 2.67% on August 1, 2025, marking a 0.03 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.05 points and is 0.51 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Germany 10-Year Bond Yield - values, historical data, forecasts and news - updated on August of 2025.

Graph and download economic data for 10-Year Real Interest Rate (REAINTRATREARAT10Y) from Jan 1982 to Jul 2025 about 10-year, interest rate, interest, real, rate, and USA.

The yield on China 10Y Bond Yield eased to 1.71% on August 1, 2025, marking a 0.03 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.07 points, though it remains 0.41 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. China 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on August of 2025.

Prices for United States Stock Market Index (US30) including live quotes, historical charts and news. United States Stock Market Index (US30) was last updated by Trading Economics this August 2 of 2025.

Australia's main stock market index, the ASX200, fell to 8662 points on August 1, 2025, losing 0.92% from the previous session. Over the past month, the index has climbed 0.75% and is up 9.05% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Australia. Australia Stock Market Index - values, historical data, forecasts and news - updated on August of 2025.