Context

US Dollar Index is an index of the value of the US dollar relative to a basket of foreign currencies. The Index goes up when the U.S. dollar gains strength (value) when compared to other currencies.

It is a weighted geometric mean of the dollar's value relative to the following select currencies:

Euro (EUR), 57.6% weight Japanese yen (JPY) 13.6% weight Pound sterling (GBP), 11.9% weight Canadian dollar (CAD), 9.1% weight Swedish krona (SEK), 4.2% weight Swiss franc (CHF) 3.6% weight

Reference - Wikipedia

Content

This data contains historical index value since 2001 till date, which helps to analyze the strength pattern of the US dollar against its trading currencies.

Inspiration

1. Is there seasonality in this data of US dollar value?
2. How the US dollar index valued during the recession/during lockdown and later recovery phase?

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.

Dollar Index: Ascending or Descending?

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

Dataset

This dataset was created by Alejandro GalvisF

Contents