Python Monte Carlo for Beginners: Investment Risk Analysis Made Easy

Learn how to harness Monte Carlo simulation in Python to evaluate complex investment decisions with this step-by-step tutorial! 📈 Discover how to model cash flow uncertainty using triangular distributions , calculate **Net Present Value (NPV)**, and assess project risk through 10,000+ simulation runs. This video walks you through:

Discounting future cash flows with Python functions
Building reproducible stochastic models using NumPy and pandas
Visualizing results with cumulative frequency distributions to determine break-even probabilities
Structuring code for scalable simulations (test different discount rates or investment horizons effortlessly)

🔥 *Includes free GitHub code* for the full Jupyter notebook, data files, and Plotly visualization tools! Perfect for data scientists, financial analysts, or anyone interested in **risk-aware decision-making**.

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👏 Credit for the investment problem goes to Prof Mike Pidd and his book Computer Simulation in Management Science.

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⏳ Timestamps ⏳
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0:00 - Monte-Carlo is awesome!
0:21 - Credit
0:33 - The investment decision problem
1:56 - Notebook setup
3:54 - Flawed deterministic analysis
5:22 - Monte Carlo Simulation
9:13 - Coding the model
19:12 - An alternative experiment
19:38 - Next steps

#MonteCarlo #Python #DataScience #FinancialModeling #InvestmentAnalysis