🎯 Retirement Planner with Monte Carlo Simulation
A comprehensive retirement planning tool built with Streamlit that combines deterministic projections with Monte Carlo risk analysis. Perfect for financial planning, portfolio optimization, and retirement strategy development.
✨ Features
📊 Portfolio Configuration
Set initial portfolio value and retirement timeline
Configure withdrawal amounts and inflation rates
Tax rate optimization for capital gains and income
📈 Scenario Analysis
Basecase Scenario : Your expected retirement pathStress Test Scenario : Crisis impact analysisCustom Crisis : Define your own market shock scenariosHistorical crisis templates (Dot-Com, 2008, COVID-19)
🎯 Asset Allocation
7 asset classes: Equities, Bonds, REITs, Precious Metals, Crypto, Real Estate, Cash
Default allocations: 50% Equities, 40% Bonds, 5% Precious Metals, 5% Cash
Expected returns, income, and volatility parameters
Mean reversion settings for advanced modeling
🎲 Monte Carlo Simulation
Thousands of simulations with random market returnsInteractive charts with confidence intervals (5th, 10th, 25th, 50th, 75th, 90th, 95th percentiles)Risk analysis with depletion probabilityPortfolio floor visualization (configurable minimum values)Logarithmic scaling for better visualization
📊 Advanced Analytics
Correlation matrix configuration for realistic asset relationshipsVolatility modeling with historical dataMean reversion parameters for sophisticated modelingMobile-responsive design for accessibility
🚀 Quick Start
Installation
Clone the repository :
1 git clone https://github.com/YOUR_USERNAME/retirement-planner.git
2 cd retirement-planner
Install dependencies :
pip install -r requirements.txt
Run the application :
streamlit run retirement_planner.py
Open your browser to http://localhost:8501
Usage Workflow
📊 Portfolio Configuration - Set your portfolio value and retirement timeline📈 Scenarios - Configure your basecase and stress test scenarios🎯 Asset Allocation - Set your portfolio weights and expected returns📊 Expected Returns - Calculate future portfolio value based on expected returns (no variation)🎲 Monte Carlo - Run simulations on thousands of potential paths into the future
Advanced Users: Experiment with parameters (correlations, volatilities, mean reversion) once familiar with the mechanics of this tool.
📱 Mobile-Friendly Design
Responsive tabs with larger fonts and iconsTouch-optimized interface for mobile devicesWord wrapping for better readabilityProfessional layout that works on all screen sizes
🎨 Visualization Features
Monte Carlo Charts
Percentile lines showing confidence intervalsColor-coded risk levels : Green (low risk) → Red (high risk)Interactive hover with detailed valuesLogarithmic scaling for exponential growth visualization
Risk Analysis
5th percentile focus : Your 1 in 20 worst-case scenarioDepletion risk : Probability of running out of moneyPortfolio floor : Configurable minimum value displayStress testing : Crisis impact analysis
⚙️ Configuration Options
Monte Carlo Parameters
Iterations : 100 to 50,000 simulationsPortfolio floor : 0.1% to 50% of initial valueCorrelation matrix : Customizable asset relationships
Asset Classes
Equities : Growth stocks and equity fundsBonds/CPF RA : Fixed income and CPF Retirement AccountREITs : Real Estate Investment TrustsPrecious Metals : Gold and other precious metalsCrypto : Cryptocurrency investmentsPhysical Real Estate : Property investmentsCash/CPF OA : Cash and CPF Ordinary Account
📊 Sample Results
The tool provides comprehensive analysis including:
Median portfolio values across time horizonsRisk percentiles (5th, 25th, 50th, 75th, 95th)Depletion probabilities for different scenariosCrisis impact analysis with historical and custom scenarios
🛠️ Technical Details
Dependencies
Streamlit : Web application frameworkNumPy : Numerical computationsPandas : Data manipulationPlotly : Interactive visualizations
Architecture
Modular design with separate functions for calculations and visualizationSession state management for user interactionsResponsive CSS for mobile optimizationError handling with graceful fallbacks
🤝 Contributing
Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request
📄 License
This project is licensed under the MIT License - see the
LICENSE file for details.
👨💻 Author
Created by Wolfgang Seidl via Claude.ai
🙏 Acknowledgments
Built with Streamlit
Visualization powered by Plotly
Financial modeling concepts from modern portfolio theory
Monte Carlo methods for risk analysis
📞 Support
For questions, issues, or feature requests, please open an issue on GitHub.
⚠️ Disclaimer : This tool is for educational and planning purposes only. It does not constitute financial advice. Always consult with a qualified financial advisor before making investment decisions.
