A comprehensive Streamlit web application for analyzing portfolio risk using advanced clustering techniques and visualizing asset relationships with modern data science methodologies.

• Intelligent Asset Clustering: K-means clustering with optimal cluster determination
• Interactive Date Range Selection: Flexible historical data analysis
• Advanced Risk Metrics: Sharpe ratio, maximum drawdown, skewness, kurtosis
• Efficient Frontier Visualization: Monte Carlo simulation for portfolio optimization
• Rolling Volatility Analysis: Multiple time windows (7, 30, 90 days)

• Enhanced Correlation Heatmaps: Interactive correlation analysis with insights
• Risk Distribution Charts: Comprehensive risk profiling across clusters
• Elbow Curve Analysis: Automated optimal cluster detection
• Efficient Frontier Plots: Risk-return optimization visualization

• Financial Terms Chatbot: Interactive assistant for portfolio concepts
• Contextual Help: Real-time explanations of financial metrics
• Smart Recommendations: Data-driven insights and suggestions

• Comprehensive Error Handling: Graceful handling of data issues
• Data Quality Validation: Automatic data cleaning and validation
• Performance Optimization: Cached data fetching and efficient processing
• Type Safety: Full type hints for better code reliability

git clone https://github.com/yourusername/risk-analyser.git
cd risk-analyser
pip install -r requirements.txt

streamlit run risk-app.py

Your CSV file should contain an 'Assets' column with stock ticker symbols:

Assets
FSR.JO
MTN.JO
KIO.JO
TSLA
NVDA

The application includes configurable parameters:

• Maximum clusters: Adjust analysis granularity (2-40 clusters)
• Date ranges: Flexible historical period selection
• Rolling windows: Customizable volatility analysis periods

• Portfolio volatility and returns
• Sharpe ratio and risk-adjusted returns
• Maximum drawdown analysis
• Distribution characteristics (skewness, kurtosis)

• Optimal cluster number determination
• Risk contribution by cluster
• Asset composition breakdown
• Correlation pattern analysis

• Efficient frontier generation
• Optimal portfolio identification
• Risk reduction opportunities
• Performance benchmarking

• Streamlit: Web application framework
• Plotly: Interactive visualization
• scikit-learn: Machine learning algorithms
• yfinance: Financial data API
• pandas/numpy: Data processing

• ✅ Type hints throughout codebase
• ✅ Comprehensive error handling
• ✅ Enhanced user interface
• ✅ Performance optimizations
• ✅ Expanded financial metrics
• ✅ Interactive visualizations
• ✅ Data quality validation
• ✅ Modular code structure

1. Portfolio Construction: Build diversified portfolios using clustering insights
2. Risk Management: Identify and mitigate concentration risks
3. Asset Allocation: Optimize weights based on efficient frontier analysis
4. Performance Analysis: Track rolling metrics and correlation changes
5. Educational Tool: Learn about modern portfolio theory concepts

Contributions are welcome! Please feel free to submit pull requests or open issues for improvements.

This project is licensed under the MIT License - see the LICENSE file for details.

Created by Mahlatse Ndala | Quant Analyst