Multi-Asset Monte Carlo Simulator

A sophisticated financial simulator for analyzing and projecting returns across multiple asset classes, with advanced mathematical models and visualization tools.

Features

• Multi-Asset Support: Simulate equity indices, individual stocks, cryptocurrencies, and bonds
• Advanced Mathematical Models:
  • Standard Monte Carlo
  • Geometric Brownian Motion (GBM)
  • GARCH(1,1) for volatility clustering
  • Markov Chain for regime-switching
  • Feynman Path Integral for complex dynamics
• Kelly Criterion Analysis: Calculate optimal leverage for long-term growth
• Comprehensive Statistics:
  • Return distributions
  • Maximum drawdown analysis
  • Sharpe ratio comparisons
  • Confidence intervals
• Interactive Visualizations:
  • Dynamic price charts
  • Distribution plots
  • Sharpe ratio comparisons

Installation

# Clone the repository
git clone https://github.com/yourusername/monte-carlo-simulator.git
cd monte-carlo-simulator

# Install dependencies
pip install -r requirements.txt

# Optional: Install GARCH dependencies
pip install arch

Usage

# Run the Streamlit app
streamlit run app.py

Then open your browser to http://localhost:8501

How to Use

1. Select Asset & Parameters in the sidebar

   • Choose an asset type and specific asset
   • Set the historical data period
   • Configure investment amount and time horizon

2. Choose a Mathematical Model

   • Different models have different assumptions and use cases

3. Set Leverage Method

   • Manual: Set leverage directly
   • Kelly Criterion: Optimal leverage for long-term growth
   • Fractional Kelly: More conservative approach
   • Numerical Optimization: Finds best leverage through simulation

4. Run the Simulation

   • Generate thousands of possible future scenarios
   • Analyze the results across different tabs

Mathematical Background

The simulator implements various stochastic processes to model asset price movements:

• Standard Monte Carlo: Samples returns from a normal distribution
• Geometric Brownian Motion: Uses the SDE dS = μS dt + σS dW
• GARCH(1,1): Models volatility clustering with σ²ₜ = ω + α·r²ₜ₋₁ + β·σ²ₜ₋₁
• Markov Chain: Captures regime-switching behavior
• Feynman Path Integral: Quantum-inspired approach for complex dynamics

Requirements

• Python 3.7+
• Streamlit
• Pandas
• NumPy
• Matplotlib
• yfinance
• scikit-learn
• Optional: arch (for GARCH models)

Deployment

This application can be deployed using:

• Streamlit Cloud
• Docker containers
• Firebase with Cloud Run
• Heroku or other PaaS providers

License

MIT License

Disclaimer

This application is for educational purposes only and does not constitute investment advice. Past performance is not indicative of future results. Leveraged investing involves significant risks.