Stock Market Dataset 2025 - Comprehensive Financial Data

Overview

This dataset contains comprehensive stock market data for June 2025, featuring daily trading information across multiple sectors. The dataset includes 14 key financial metrics and indicators, making it ideal for financial analysis, machine learning projects, and algorithmic trading research.

Dataset Features

Core Price Data

• Date: Trading date in YYYY-MM-DD format
• Ticker: Stock symbol identifier
• Open: Opening price for the trading day
• Close: Closing price for the trading day
• High: Highest price reached during the day
• Low: Lowest price reached during the day

Trading Metrics

• Volume: Number of shares traded
• Market Cap: Total market capitalization

Financial Ratios & Indicators

• PE Ratio: Price-to-Earnings ratio for valuation analysis
• Dividend Yield: Annual dividend as percentage of stock price
• EPS: Earnings Per Share
• 52 Week High: Highest price in the past 52 weeks
• 52 Week Low: Lowest price in the past 52 weeks

Classification

• Sector: Industry sector classification (Technology, Healthcare, Energy, Financials, etc.)

Use Cases

Machine Learning Applications

• Stock Price Prediction: Time series forecasting using historical OHLC data
• Volatility Modeling: Risk assessment using price ranges and volume data
• Sector Classification: Multi-class classification based on financial metrics
• Anomaly Detection: Identifying unusual trading patterns

Financial Analysis

• Portfolio Optimization: Asset allocation using sector diversification
• Risk Assessment: Volatility analysis using 52-week ranges
• Valuation Analysis: P/E ratio and dividend yield comparisons
• Technical Analysis: Support/resistance levels using OHLC data

Research Applications

• Market Trend Analysis: Sector performance comparison
• Correlation Studies: Inter-sector and stock relationships
• Backtesting Strategies: Historical performance validation
• Economic Impact Studies: Market response analysis

Data Quality

Completeness

• No missing values in core price data
• All financial ratios calculated and validated
• Consistent date formatting across all records

Accuracy

• Real-time market data sourced from reliable financial APIs
• Cross-validated against multiple data sources
• Outlier detection and correction applied

Technical Specifications

File Format

• Primary: CSV format for maximum compatibility
• Size: Optimized for efficient loading and processing
• Encoding: UTF-8 for universal compatibility

Data Types

• Numeric: Float64 for price and ratio data
• Integer: Int64 for volume data
• String: Ticker symbols and sector classifications
• DateTime: Pandas-compatible date format

Advanced Applications

• Time series analysis with ARIMA/LSTM models
• Portfolio optimization using Modern Portfolio Theory
• Risk metrics calculation (VaR, Sharpe ratio)
• Sector rotation strategies

Data Schema

Column	Type	Description	Example
Date	datetime	Trading date	2025-06-22
Ticker	string	Stock symbol	AAPL
Open	float	Opening price	150.25
Close	float	Closing price	152.30
High	float	Daily high	153.50
Low	float	Daily low	149.80
Volume	integer	Shares traded	25000000
Market_Cap	float	Market capitalization	2.5e12
PE_Ratio	float	Price-to-earnings	28.5
Dividend_Yield	float	Dividend percentage	1.2
EPS	float	Earnings per share	5.35
52_Week_High	float	52-week maximum	180.00
52_Week_Low	float	52-week minimum	120.00
Sector	string	Industry sector	Technology

License & Attribution

This dataset is provided for educational and research purposes. Please cite this dataset if used in academic research or commercial applications.

Updates & Maintenance

• Regular updates with new trading data
• Quality checks and validation procedures
• Community feedback integration
• Version control for reproducibility

Contact & Support

For questions, suggestions, or collaboration opportunities, please reach out through Kaggle messaging or create a discussion in the dataset comments.

Reaching Out Links

• Kaggle
• LinkTree
• LinkedIN

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Tags : #finance #stocks #machine-learning #time-series #trading #market-data #financial-analysis