AI-Powered Stock Market Dataset

What’s Included in the Dataset?

1. Comprehensive Historical Data

• Coverage: Includes all US stocks and ETFs listed on the NYSE, NASDAQ, and NYSE MKT.
• Time Span: Updated as of 11/10/2017, covering years of daily trading data.

2. Detailed Pricing Metrics

• Date: The trading date for each record.
• Open and Close Prices: Reflecting the starting and ending stock prices each day.
• High and Low Prices: Capturing the daily price range for each stock.
• Volume: The number of shares traded daily.
• Open Interest: Adjusted values to reflect dividends and stock splits, ensuring data accuracy.

Advantages of the Huge Stock Market Dataset

1. Wide Market Coverage:
   Gain access to data from the largest US stock exchanges, providing a holistic view of the financial market.
2. Ready-to-Use Format:
   Data is presented in clean and structured CSV format, making it easy to import and analyze in tools like Excel, Python, or R.
3. Ideal for Machine Learning and AI Applications:
   • Train predictive models for stock price forecasting.
   • Develop financial algorithms for trading strategies.
   • Perform sentiment analysis on stock trends.
4. Research and Educational Value:
   Perfect for academic studies, financial market research, or teaching the fundamentals of stock market analysis.
5. Dividend and Split Adjustments:
   Ensures the dataset is reliable for backtesting and predictive analytics by incorporating corporate actions.

Applications of This Dataset

• Conduct detailed technical analysis on stock performance.
• Build machine learning models for trend prediction and automated trading.
• Identify patterns in trading volume and price fluctuations.
• Create visualizations to uncover market insights.

Why Choose This Dataset?

The Huge Stock Market Dataset bridges the gap between expensive, inaccessible financial data and those eager to explore the market. With a vast array of metrics and reliable adjustments, it’s a goldmine for both seasoned professionals and beginners delving into financial analytics.