Python Quantitative Finance & Stock Analysis Course (Pandas, NumPy, SciPy) 🐍 - Basic Monte Carlo Simulation of a Stock Portfolio || Python Programming

@MattMacarty **Take your financial modeling beyond spreadsheets!** This tutorial introduces how to use **Python** for a basic **Monte Carlo Simulation** to project the potential future value of a stock portfolio, accounting for market volatility and uncertainty. We move beyond a static savings calculator to characterize the entire *distribution* of possible outcomes over a 30-year investment period, including annual additions. ### 🐍 **Key Python Libraries Used:** * **NumPy:** For statistical calculations (mean, standard deviation, percentiles) and generating random returns. * **Pandas:** For managing and iterating through the 5,000 simulation runs. * **Matplotlib:** For visualizing the distribution with a histogram. --- ### ⏱️ Video Chapters / Timestamps 0:00 - Introduction: The Goal of Monte Carlo Portfolio Simulation 0:47 - Setup: Recommended Anaconda Platform and Importing Libraries (NumPy, Pandas) 1:18 - **The Static Model:** Calculating a future value with a constant return 2:58 - The Problem with Static Models: Portfolio returns are not reliable 3:20 - **The Monte Carlo Approach:** Introducing Expected Return and Volatility 4:07 - Running a **Single Simulation** to see a variable outcome 5:16 - The Need for **5,000 Simulations** to define the distribution 5:37 - Setting up the **Pandas DataFrame** for 5,000 iterations 6:08 - Running the 5,000 simulations 7:26 - Visualization: Plotting the first five volatile outcome paths 8:15 - Generating **Summary Statistics** (Mean, Max, Min) with NumPy 9:39 - Using the **Pandas `.describe()`** function to see quartiles 10:45 - Visualization: Plotting the **Histogram (Distribution)** of Ending Values 12:05 - **Calculating Probability:** Finding the chance of having less than $1 Million 13:02 - Analyzing **Percentiles** (e.g., 95% chance of having more than $400k) --- ### 🔎 Summary of Skills Learned * **Monte Carlo Logic:** Transition from static modeling to distribution-based forecasting. * **Python for Finance:** Use **NumPy** to generate normally distributed random returns and **Pandas** to structure simulation data. * **Data Analysis:** Calculate the probability and percentile of a financial outcome, providing a risk-adjusted view of investment goals. --- ### 🏷️ Recommended Keywords ```csv Monte Carlo Simulation, Stock Portfolio Projection, Python for Finance, NumPy, Pandas, Volatility Modeling, Portfolio Probability, Investment Forecasting, Retirement Simulation, Jupyter Notebook, Random Returns, Financial Modeling