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)

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)

Stock price prediction

Portfolio optimization

Algorithmic trading

Market sentiment analysis

Risk management

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

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

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)

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)

Stock price prediction

Portfolio optimization

Algorithmic trading

Market sentiment analysis

Risk management

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

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

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)

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)

Stock price prediction

Portfolio optimization

Algorithmic trading

Market sentiment analysis

Risk management

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

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

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)

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)

Stock price prediction

Portfolio optimization

Algorithmic trading

Market sentiment analysis

Risk management

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

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

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)

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)

Stock price prediction

Portfolio optimization

Algorithmic trading

Market sentiment analysis

Risk management

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

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