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AdvancedPortfolio ManagementPython

Run this module

cd "Monte Carlo Portfolio Simulator"
python "simulator.py"

View source on GitHub

Monte Carlo Portfolio Simulator

This utility helps you forecast possible futures for a portfolio using random simulations—a key idea in finance, risk management, and statistics!

What is a Monte Carlo Simulation?

  • It uses repeated random sampling to simulate thousands of paths your portfolio could take.
  • Helps answer: "What might my $10,000 investment be worth in 10 years, factoring in market randomness?"

Why Is It Useful?

  • See the spread of possible outcomes (best case, worst case, average)
  • Plan for risk, not just averages
  • Core technique used by institutional and retail investors, and in exams/interviews

How to Use

  1. Choose your initial_investment, an average return (mu), volatility (sigma), number of periods, and number of simulations.
  2. Import and call monte_carlo_sim() or use the plot_monte_carlo() function to visualize paths.

Example

from simulator import monte_carlo_sim, plot_monte_carlo
init = 10000
mu = 0.07 / 252      # 7% yearly mean
sigma = 0.15 / (252**0.5)  # 15% yearly volatility
periods = 10 * 252
results = monte_carlo_sim(init, mu, sigma, periods)
plot_monte_carlo(init, mu, sigma, periods, simulations=100)

Key Ideas

  • Simulates possible (not guaranteed) futures—actual outcomes may differ
  • Shows the importance of diversification and volatility management
  • Inspires deeper learning in probability and finance

For more info, see the docstrings in simulator.py and other UTILS modules!

Continue in Portfolio Management

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  • Portfolio Optimizer

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  • Portfolio Tracker

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