A comprehensive collection of Jupyter notebooks for financial analysis, including portfolio management, technical analysis, and trading strategy backtesting. This toolkit provides practical implementations of modern financial analysis techniques using Python.
Portfolio Analysis and Management
This notebook implements an interactive Streamlit application for comprehensive investment portfolio analysis. Key features include:
- Data Input Interface: User-friendly input for financial assets (e.g., AAPL, MSFT, GOOGL, BTC) and analysis date selection
- Data Acquisition: Leverages
yfinanceAPI to retrieve historical adjusted closing prices - Return Calculations:
- Daily portfolio returns computation
- Cumulative returns analysis
- Benchmark comparison against S&P 500 index
- Risk Assessment: Portfolio volatility calculation using covariance matrix methodology
- Data Visualization:
- Comparative performance charts (Portfolio vs S&P 500)
- Portfolio composition pie charts with equal weighting
- Risk Metrics: Portfolio risk analysis compared to benchmark volatility
Automated Technical Analysis with TradingView
This notebook demonstrates the integration of tradingview_ta library for automated technical analysis:
- Analysis Configuration: Connects to TradingView API for specific symbol analysis (example: 'X' symbol on NYSE)
- Technical Indicators: Comprehensive indicator suite including:
- RSI (Relative Strength Index)
- Stochastic Oscillators (Stoch.K, Stoch.D)
- CCI (Commodity Channel Index)
- ADX (Average Directional Index)
- MACD (Moving Average Convergence Divergence)
- Moving Averages (EMA, SMA across multiple timeframes)
- Bollinger Bands
- Pivot Points (Classic, Fibonacci, Camarilla, Woodie, Demark)
- Trading Recommendations: Automated buy/sell recommendations based on technical analysis
- Executive Summary: Consolidated recommendation output (BUY/SELL/NEUTRAL) with signal distribution
Trading Strategy Backtesting Framework
This notebook implements and validates trading strategies using the backtesting library:
-
Strategy Implementation: SMACross (Simple Moving Average Crossover)
- Short-term MA: 50 periods
- Long-term MA: 100 periods
- Buy signal: Short MA crosses above Long MA
- Sell signal: Long MA crosses above Short MA
-
Backtest Configuration:
- Initial capital: $10,000
- Commission structure: 0.2%
- Dataset: SPY ETF from 2018-01-01
-
Performance Metrics:
- Total and annualized returns
- Volatility and risk-adjusted ratios (Sharpe, Sortino)
- Maximum and average drawdown analysis
- Trade statistics and win rate
- Profit factor and expectancy calculations
-
Parameter Optimization: Automated optimization for optimal moving average parameters
-
Interactive Visualization: Bokeh-powered interactive charts featuring:
- Equity curve evolution
- Entry/exit point markers
- Technical indicator overlays
- Drawdown period analysis
- Python: Core programming language
- yfinance: Yahoo Finance API wrapper for financial data
- pandas: Data manipulation and analysis framework
- matplotlib: Static data visualization library
- streamlit: Web application framework for interactive dashboards
- tradingview_ta: TradingView technical analysis integration
- backtesting: Vectorized backtesting framework
- ta: Technical analysis indicator library
- bokeh: Interactive visualization library
- numpy: Numerical computing foundation
pip install yfinance pandas matplotlib streamlit tradingview_ta backtesting ta bokeh numpy-
Jupyter Notebook Execution:
jupyter lab # Navigate to desired .ipynb file and execute cells sequentially -
Streamlit Portfolio Analysis:
# Convert notebook to .py file first, then: streamlit run portfolio_app.py -
Command Line Backtesting:
# Execute backtesting notebook cells or import as module from test_backtesting import SMACross, run_backtest
- Portfolio Managers: Risk assessment and performance attribution analysis
- Quantitative Traders: Strategy development and validation framework
- Financial Analysts: Rapid technical analysis and market screening
- Academic Researchers: Financial modeling and empirical analysis foundation
- Finance Students: Practical implementation of theoretical concepts
- Retail Investors: Personal portfolio optimization and strategy testing
- Advanced Portfolio Optimization
- Modern Portfolio Theory implementation
- Efficient frontier calculation
- Risk parity and factor-based allocation models
- Enhanced Risk Management
- Value at Risk (VaR) calculations
- Expected Shortfall (ES) metrics
- Monte Carlo simulation framework
- Extended Data Sources
- Alternative data integration (sentiment, economic indicators)
- Cryptocurrency market data expansion
- Real-time data streaming capabilities
- Machine Learning Integration
- Predictive modeling for price movements
- Feature engineering for technical indicators
- Ensemble methods for signal generation
- Advanced Strategy Framework
- Multi-asset strategy support
- Options and derivatives backtesting
- High-frequency trading simulation
- Web Application Development
- Full-stack dashboard with user authentication
- Cloud deployment (AWS/GCP/Azure)
- Real-time portfolio monitoring
- Institutional Features
- Multi-portfolio management system
- Compliance and regulatory reporting
- Performance attribution analysis
- API Development
- RESTful API for strategy execution
- Webhook integration for automated trading
- Third-party platform connectivity
- Custom Indicator Development: Build proprietary technical indicators
- Pattern Recognition: Automated chart pattern detection using computer vision
- Sentiment Analysis: Social media and news sentiment integration
- Market Regime Detection: Statistical methods for market state identification
- Factor Models: Fama-French multi-factor model implementation
- ESG Integration: Environmental, Social, Governance scoring
- Alternative Assets: Real estate, commodities, and private equity analysis
- Currency Hedging: Multi-currency portfolio risk management
- Stress Testing: Historical and hypothetical scenario analysis
- Correlation Analysis: Dynamic correlation monitoring and alerts
- Liquidity Risk: Market impact and liquidity-adjusted returns
- Credit Risk: Counterparty risk assessment for fixed income
- Broker API Integration: Automated order execution (Interactive Brokers, Alpaca)
- Cloud Infrastructure: Scalable computing for large-scale backtesting
- Notification Systems: Email/SMS alerts for strategy signals
- Database Integration: PostgreSQL/MongoDB for historical data storage
- Vectorized Operations: Optimized pandas operations for large datasets
- Memory Management: Efficient data handling for extended historical periods
- Parallel Processing: Multi-core utilization for parameter optimization
- Caching Strategies: Redis integration for frequently accessed data
We welcome contributions! Please see our contributing guidelines for:
- Code style and formatting standards
- Testing requirements and coverage expectations
- Documentation standards and examples
- Pull request and issue templates
This project is provided as-is for educational and research purposes. Some code components may be derived from various open-source projects and tutorials. Please ensure compliance with original source licenses when using or redistributing any part of this code.
IMPORTANT NOTICE:
- This software is strictly for educational and research purposes only
- The code may contain reused and adapted components from various sources and tutorials
- Past performance does not guarantee future results in any financial market
- This toolkit is not intended for live trading or actual investment decisions
- No financial advice is provided - this is purely educational material
- Users are solely responsible for any use of this code and its outcomes
- Always consult with qualified financial professionals before making investment decisions
- The authors assume no liability for any financial losses incurred through use of this software
- Code is provided without warranty of any kind, express or implied
- Users should verify all calculations and logic before any practical application