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Portfolio Management & Optimization: Excel, R, Python, AI
Role Play
Rating: 4.5 out of 5(197 ratings)
24,287 students

Portfolio Management & Optimization: Excel, R, Python, AI

Portfolio management, modern portfolio theory, efficient frontier, Excel Solver, R fPortfolio, PyPortfolioOpt, ChatGPT
Role Play
Created byCarlos Martínez, PhD • Instructor with 20+ Best-Selling Courses, Mercedes Chávez
Last updated 1/2026
English
English [Auto],Korean [Auto],

What you'll learn

  • Optimize for the highest Sharpe ratio in a real data portfolio using Excel´s Solver Add-in and R´s fPortfolio package
  • Understand and Operationalize Markowitz´s Portfolio Theory
  • Calculate Variance and Sharpe ratio for a twenty-asset portfolio
  • Compute Covariance and Correlation of two assets
  • Calculate Value at Risk (VaR) of a Portfolio
  • Learn basic Vector Algebra (Matrix Multiplication)

Course content

4 sections • 48 lectures • 4h 50m total length

  • Welcome to the Course!2:40

    Learn to optimize portfolios using market data to maximize expected return per unit of risk with Excel and R, through hands-on tutorials and 20-asset examples.

  • PLEASE READ: A Respectful Request for Constructive Feedback and Consideration2:14
  • Improving Your Learning Experience2:27

    Adjust playback speed and sound intensity to tailor your learning, rely on auto-generated transcriptions and subtitles, and rate the course with confidence, knowing a 30-day refund is available.

  • Section Introduction0:57

    Explore the Markowitz portfolio theory and its risk-reducing role for assets in a multi-asset portfolio. Illustrate diversification and build a step-by-step asset portfolio to understand the risk-return trade-off.

  • Defining Stock Return & Risk6:04

    Explore how mean return and standard deviation quantify asset risk, illustrating how higher volatility signals greater risk and how dominated assets guide portfolio choices under Markowitz theory.

  • Retrieving Data From Yahoo! Finance5:18

    Navigate to Yahoo Finance; open historical data to view high, low, close, adjusted close, volume, and stock splits; download five-year CSVs for Tesla and Walmart to compute returns and risk.

  • Calculating Stock Return & Risk7:41

    Learn to calculate stock returns and risk using Excel, applying text-to-columns, computing baseline returns, expected returns, and standard deviation for Walmart and Tesla.

  • Calculating Stock Return & Risk [Assignment]
  • Defining Portfolio Return & Risk5:41

    Define the two-asset portfolio return as a weighted average and show how portfolio risk, via standard deviation, drops with diversification when assets are not perfectly correlated.

  • Calculating Portfolio Return and Standard Deviation6:43

    Calculate the portfolio return and risk for a 50/50 Wal-Mart and Domino's Pizza mix, using monthly returns to estimate a 1.6 percent return and a 4.4 percent portfolio standard deviation.

  • Calculating Portfolio Return & Standard Deviation [Assingment]
  • Introduction to Markowitz Portfolio Theory4:17

    Learn the Markowitz portfolio theory as a mean-variance, portfolio optimization model that creates a diversified, lower-risk portfolio by selecting uncorrelated securities along the efficient frontier and tangency portfolio.

  • Efficient Frontier4:37

    Explore the efficient frontier, the best trade-off between risk and return for portfolios of risky assets. Learn how diversification and the global minimum variance portfolio shape achievable outcomes.

  • Illustrating Efficient Frontier8:07
  • [Assignment] Efficient Frontier
  • Choosing the Best Portfolio from the Efficient Frontier4:03

    Identify the most preferred portfolio on the efficient frontier by locating the tangency with the investor's indifference curve, considering risk aversion and risk-free options.

  • Capital Market Line7:35

    Explore the capital market line, tangent to the efficient frontier at the market portfolio. It blends risk-free lending and borrowing with risky assets, with the Sharpe ratio as its slope.

  • Risk-free Rate: US Treasury Bonds1:54

    Locate the risk-free rate using the daily treasury yield curve rates from the U.S. Department of the Treasury, focusing on the 30-year yield as of February 23, 2021.

  • Sharpe Ratio (Tutorial)5:25

    Maximize the Sharpe ratio with Excel Solver to obtain the tangency portfolio from a seed 50/50 mix using monthly returns and a 0.18% monthly risk-free rate.

  • [Assigment] Sharpe Ratio & Optimal Portfolio
  • Capital Market Line (Tutorial)6:40

    Learn to plot the capital market line and its tangent to the efficient frontier by calculating the tangent portfolio’s return and risk against the risk-free asset.

  • [Assignment] Capital Market Line

Requirements

  • Basic knowledge of Microsoft Excel: Familiarity with spreadsheet functions and formulas will be helpful for understanding and implementing portfolio optimization techniques using Excel's Solver Add-in.
  • Basic understanding of R programming (Not mandatory): Although not mandatory, having a basic knowledge of R programming will enable you to effectively utilize R's fPortfolio package for portfolio optimization.
  • Desire to learn and optimize investment portfolios: A strong interest in investment management and a willingness to learn and apply portfolio optimization techniques will enhance your experience and enable you to derive maximum benefit from the course.
  • This course is designed to cater to both beginners and experienced individuals interested in portfolio optimization. The prerequisites are kept at a minimum to ensure accessibility and provide an opportunity for beginners to grasp the concepts and techniques covered.

Description

Portfolio Management & Optimization: Excel, R, Python, AI is a practical course that teaches you how to build and optimize investment portfolios using real market data—so you can make better decisions about risk, return, diversification, and portfolio construction.

You will learn the core logic of Modern Portfolio Theory (MPT / Markowitz) and apply it step-by-step to compute portfolio metrics (expected return, volatility, correlation, covariance) and build the Efficient Frontier. The course starts with intuitive, transparent workflows in Excel, and then scales to more automated and professional toolsets in R and Python.

What you will do in this course

  • Apply portfolio management principles to structure diversified portfolios

  • Master modern portfolio theory and the intuition behind optimization

  • Build the efficient frontier and interpret optimal portfolios

  • Optimize portfolios using Excel Solver (clear and hands-on)

  • Use R (fPortfolio) to automate portfolio optimization and compare results

  • Use Python (PyPortfolioOpt) to run modern optimization workflows efficiently

  • Leverage AI / ChatGPT as a productivity tool for structure, interpretation, validation, and faster iteration

What’s included

  • Step-by-step tutorials with a learning-by-doing approach

  • Downloadable resources: Excel files, R code, and Python scripts

  • Practice tasks (with solutions) to validate your progress and confidence

Who this course is for

This course is designed for students and professionals who want to strengthen their skills in portfolio management and portfolio optimization, whether for academic work, professional finance/analytics, or to build a more rigorous investment framework.

What students say

  • Deepakraja S.: “Awesome course… best for beginners who would like to start their quant career and understand portfolio theory.”

  • Etienne R.: “Very practical… helps investors structure a portfolio efficiently and go beyond Excel using R.”

  • Omar H.: “Exceptional course. Explained everything in a concise, clear and to the point manner.”

  • Ernest A.: “The video ‘Defining Stock Return & Risk’ helped me understand the importance of standard deviation.”

  • Marcelo A.: “This course was extremely helpful for my thesis. Clear, practical, and valuable—thank you, Professor.”

If your goal is to move from theory to execution—building optimized portfolios with Excel, R, Python, and AI—this course will take you there step by step.

Who this course is for:

  • This course is designed for individuals who are interested in investment management and optimizing investment portfolios. The content is suitable for the following students:
  • Finance professionals: Financial analysts, portfolio managers, investment advisors, and professionals working in the asset management industry who want to enhance their knowledge and skills in portfolio optimization techniques.
  • Students pursuing finance or related degrees: Undergraduate and graduate students studying finance, economics, or other quantitative disciplines who want to gain a practical understanding of portfolio optimization methods and apply them in their academic or professional careers. This course has been highly beneficial for students pursuing finance master's programs at prestigious universities, as it complements their curriculum and helps them successfully complete their postgraduate studies.
  • Quantitative analysts: Individuals with a background in quantitative analysis who want to expand their expertise in portfolio optimization using Excel's Solver Add-in and R's fPortfolio package.
  • Individuals interested in personal finance: Individuals who are keen on managing their own investment portfolios and want to learn effective strategies for optimizing risk and return.
  • Investment enthusiasts: Anyone with a general interest in investment management and a desire to understand the concepts and techniques behind portfolio optimization.

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