Green bond indices make it easier for investors to track the performance of green bonds and compare it with other investments. Bloomberg Barclays MSCI Global Green Bond Index was launched in 2014 with the aim provide a benchmark for the green bonds market. Between 2015 and 2020, the Bloomberg Barclays MSCI Global Green Bond Index saw an overall increase, reaching a value of 121.91 as of the end of 2020. By the end of 2022, however, the index value fell to 86.94, before increasing again to 96.09 by the end of 2023.

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

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.

Barclays (BCS) Navigating the Financial Landscape

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 ICE BofA US Corporate Index Option-Adjusted Spread (BAMLC0A0CM) from 1996-12-31 to 2025-07-10 about option-adjusted spread, corporate, and USA.

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.

Bitcoin's Barclays Adventure

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 ICE BofA CCC & Lower US High Yield Index Effective Yield (BAMLH0A3HYCEY) from 1996-12-31 to 2025-07-10 about CCC, yield, interest rate, interest, rate, and USA.

The S&P Bitcoin index is anticipated to rise with moderate risk. Potential indicators include increased investor confidence, a favorable regulatory environment, and a positive correlation with traditional financial markets. However, risks associated with the index include volatility, exchange security issues, and regulatory uncertainties, which could impact its performance and value.

Graph and download economic data for ICE BofA BBB US Corporate Index Effective Yield (BAMLC0A4CBBBEY) from 1996-12-31 to 2025-07-11 about BBB, yield, corporate, interest rate, interest, rate, and USA.

Open Banking Data - Bank and ATM locations Four csv files containing geolocations for Barclays' and HSBC's ATMs and branches in the UK. Their Open Banking API documentation is available in the following links: https://developer.hsbc.com/index.html https://developer.barclays.com/catalogue/api