A comprehensive stock price analysis project using Python. Features include technical indicators (RSI, MACD, Bollinger Bands), statistical analysis, and visualizations. Implements data validation, outlier detection, and correlation analysis with interactive visualizations.
This project provides a comprehensive exploratory data analysis (EDA) of a stock price dataset, including:
- Technical indicators calculation and analysis
- Time series pattern detection
- Statistical analysis and visualization
- Data validation and quality checks
- 1857 rows, 21 columns
- Features: price (open, high, low, close, adjclose), volume, RSI, returns, lagged features, trend (target)
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Data Validation and Quality Checks
- Price logic validation
- Missing data detection
- Duplicate checks
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Technical Analysis
- RSI (Relative Strength Index)
- MACD (Moving Average Convergence Divergence)
- Bollinger Bands
- Volume analysis
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Statistical Analysis
- Outlier detection (Z-score and IQR methods)
- Correlation analysis
- Time series patterns
- High data quality with no duplicates or logic errors
- Technical indicators provide useful trend signals
- Return-based features show meaningful correlations
- Volume spikes and outliers are properly detected
All visualizations are saved in the figures/ directory:
- Bollinger Bands analysis
- Technical indicators distribution
- Correlation matrix
- Volume and price trends
- Clone the repository:
git clone https://github.com/xcan16/stock-price-eda.git- Install required packages:
pip install pandas numpy matplotlib seaborn- Run the Jupyter notebook to see the analysis
The analysis provides a robust foundation for:
- Trading strategy development
- Market pattern analysis
- Risk assessment
- Technical indicator optimization
For detailed analysis and code, please refer to the Jupyter notebook in this repository.