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Finance & Accounting Investing & Trading Python

Investment Portfolio Analysis with Python

Learn investment portfolio analysis from basic to expert level through practical course with Python programming language
Rating: 4.1 out of 54.1 (86 ratings)
950 students
Created by Diego Fernandez
Last updated 3/2018
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Read or download main asset classes benchmark indexes replicating funds data to perform investment portfolio analysis operations by installing related packages and running code on Python IDE.
  • Compare main asset classes benchmark indexes replicating funds returns and risks tradeoffs for cash, bonds, stocks, commodities, real estate and currencies.
  • Estimate portfolio expected returns, historical and market participants implied volatility.
  • Approximate portfolio expected excess returns using capital asset pricing model (CAPM), Fama-French-Carhart factors model and arbitrage pricing theory model (APT).
  • Hedge portfolio systematic risk through options trading strategies benchmark indexes replicating funds.
  • Evaluate hedge fund index performance and assess portfolio returns and risks amplification through leverage.
  • Calculate portfolio performance metrics such as Sharpe, Treynor, Sortino, and Kelly ratios.
  • Estimate benchmark global portfolios returns from periodically rebalanced equal weighted asset allocations and those from well-known investment managers.
  • Optimize global portfolios asset allocation weights for mean maximization, standard deviation minimization, mean maximization and standard deviation minimization, mean maximization and value at risk minimization objectives within training range based on Markowitz portfolio theory.
  • Approximate global portfolios returns from periodically rebalanced optimized asset allocations within testing range and compare them with equal weighted and well-known investment managers benchmark portfolios.
  • Evaluate global portfolios performance through global risk factors model and estimate their expected return, expected excess return and expected return contribution from global risk factors exposure while assessing investment costs impact on portfolio performance.

Requirements

  • Python programming language is required. Downloading instructions included.
  • Python Distribution (PD) and Integrated Development Environment (IDE) are recommended. Downloading instructions included.
  • Practical example data and Python code files provided with the course.
  • Prior basic Python programming language knowledge is useful but not required.

Description

Full Course Content Last Update 03/2018

Learn investment portfolio analysis through a practical course with Python programming language using index replicating ETFs and Mutual Funds historical data for back-testing. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced investor. All of this while exploring the wisdom of Nobel Prize winners and best practitioners in the field.

Become an Investment Portfolio Analysis Expert in this Practical Course with Python

  • Read or download main asset classes benchmark indexes replicating funds data to perform investment portfolio analysis operations by installing related packages and running code on Python IDE.
  • Compare main asset classes benchmark indexes replicating funds returns and risks tradeoffs for cash, bonds, stocks, commodities, real estate and currencies.
  • Estimate portfolio expected returns, historical and market participants implied volatility.  
  • Approximate portfolio expected excess returns using capital asset pricing model (CAPM), Fama-French-Carhart factors model and arbitrage pricing theory model (APT).
  • Hedge portfolio systematic risk through options trading strategies benchmark indexes replicating funds.
  • Evaluate hedge fund index performance and assess portfolio returns and risks amplification through leverage.
  • Calculate portfolio performance metrics such as Sharpe, Treynor, Sortino, and Kelly ratios.
  • Estimate benchmark global portfolios returns from periodically rebalanced equal weighted asset allocations and those from well-known investment managers.
  • Optimize global portfolios asset allocation weights for mean maximization, standard deviation minimization, mean maximization and standard deviation minimization, mean maximization and value at risk minimization objectives within training range based on Markowitz portfolio theory.
  • Approximate global portfolios returns from periodically rebalanced optimized asset allocations within testing range and compare them with equal weighted and well-known investment managers benchmark portfolios.
  • Evaluate global portfolios performance through global risk factors model and estimate their expected return, expected excess return and expected return contribution from global risk factors exposure while assessing investment costs impact on portfolio performance.

Become an Investment Portfolio Analysis Expert and Put Your Knowledge in Practice

Learning investment portfolio analysis is indispensable for finance careers in areas such as asset management, private wealth management, and risk management within institutional investors represented by banks, insurance companies, pension funds, hedge funds, investment advisors, endowments and mutual funds. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors optimized asset allocation strategies research and development.

But as learning curve can become steep as complexity grows, this course helps by leading you step by step using index replicating funds historical data for back-testing and to achieve greater effectiveness.

Content and Overview

This practical course contains 44 lectures and 7.5 hours of content. It’s designed for all investment portfolio analysis knowledge levels and a basic understanding of Python programming language is useful but not required.

At first, you’ll learn how to read or download index replicating funds historical data to perform investment portfolio analysis operations by installing related packages and running code on Python IDE.

Then, you’ll define main asset classes by comparing their benchmark indexes replicating funds returns and risks tradeoffs. After that, you’ll segment main asset classes into traditional and alternative ones. For traditional asset classes, you’ll define cash and cash equivalents, fixed income or bonds and equities or stocks. Regarding cash and cash equivalents traditional asset class, you’ll use U.S. total money market benchmark index replicating fund. Regarding cash and cash equivalents traditional asset class, you’ll use U.S. total money market benchmark index replicating fund is used. Regarding fixed income or bonds traditional asset class, U.S. total bond market, U.S. short term bond market, U.S. long term bond market and international total bond market benchmark indexes replicating funds. Regarding equities or stocks traditional asset class, you’ll use U.S. total stock market, U.S. large cap stock market, U.S. small cap stock market, U.S. small cap growth stock market, U.S. small cap value stock market, international total stock market, international developed stock market and international emerging stock market benchmark indexes replicating funds. For alternative asset classes, you’ll define commodities, real estate and currencies or foreign exchange. Regarding commodities alternative asset class, you’ll use oil and gold prices benchmark indexes replicating funds. Regarding real estate alternative asset class, you’ll use U.S. real estate investment trust market benchmark index replicating fund. Regarding currencies or foreign exchange alternative asset class, you’ll use U.S. dollar major currencies benchmark index replicating fund.

Next, you’ll define returns and risks using U.S. large cap stocks market benchmark index replicating fund. After that, you’ll calculate expected returns through historical returns mean and media. Then, you’ll estimate risks through historical returns standard deviation, mean absolute deviation and market participants implied volatility. Later, you’ll approximate portfolio expected excess returns through capital asset pricing model (CAPM), Fama-French-Carhart factors model and arbitrage pricing theory model (APT). Next, you’ll hedge portfolio systematic risk through options trading strategies and evaluate hedge fund index performance together with the assessment of returns and risks amplification through portfolio leverage. 

After that, you’ll define portfolio optimization through global assets allocation. Next, you’ll calculate Sharpe ratio, Treynor ratio, Sortino ratio and Kelly ratio portfolio performance metrics. Then, you’ll estimate benchmark global portfolios returns from periodically rebalanced equal weighted asset allocations and those from well-known investment managers. Later, you’ll optimize global asset allocation weights within training range for mean maximization, standard deviation minimization, mean maximization and standard deviation minimization, mean maximization and value at risk minimization objectives based on Markowitz portfolio theory. After that, you’ll calculate global portfolio returns within testing range using previously optimized periodically rebalance asset allocation weights and compared with equal weighted and well-known investment managers benchmark portfolios.

Later, you’ll evaluate optimized portfolios performance through global risk factors model. After that, you’ll estimate optimized portfolios expected returns, expected excess returns and global risk factors exposure returns contribution. Finally, you’ll assess investment costs impact on portfolio performance.

Who this course is for:

  • Students at any knowledge level who want to learn about investment portfolio analysis using Python programming language.
  • Finance professionals or academic researchers who wish to deepen their knowledge in quantitative finance.
  • Experienced investors who desire to research optimized asset allocation strategies.
  • This course is NOT about “get rich quick” investment strategies or magic formulas.

Course content

5 sections • 44 lectures • 7h 34m total length

  • Preview05:07
  • Preview04:20
  • Investment Portfolio Analysis
    04:34
  • Investment Portfolio Analysis Data
    19:02
  • Course Data File
    00:03
  • Course Code Files
    00:03
  • Course Overview Slides
    00:02
  • Course Bibliography
    00:02

  • Asset Classes Slides
    00:02
  • Preview09:02
  • Cash and Cash Equivalents
    14:05
  • Fixed-Income or Bonds
    17:23
  • U.S. Equities or Stocks
    17:54
  • International Equities or Stocks
    14:06
  • Commodities
    10:41
  • Real Estate
    09:56
  • Currencies or Foreign Exchange
    10:53

  • Returns and Risks Slides
    00:02
  • Returns and Risks Overview
    06:48
  • Expected Returns
    09:50
  • Risks
    13:04
  • Returns Normality
    09:57
  • Returns and Risks Relationship
    09:11
  • Capital Asset Pricing Model
    15:15
  • Fama-French-Carhart Factors Model
    15:34
  • Arbitrage Pricing Theory Model
    10:36
  • Portfolio Hedge
    15:37
  • Hedge Funds
    09:30
  • Portfolio Leverage
    11:43

  • Portfolio Optimization Slides
    00:02
  • Portfolio Optimization Overview
    11:39
  • Portfolio Performance Metrics
    14:34
  • Portfolio Benchmarks
    14:11
  • Mean Maximization Portfolio Optimization
    16:44
  • Standard Deviation Minimization Portfolio Optimization
    15:20
  • Markowitz Portfolio Optimization
    14:28
  • Mean-VaR Portfolio Optimization
    18:36

  • Portfolio Performance Slides
    00:02
  • Portfolio Performance Overview
    07:16
  • Mean Maximization Portfolio Performance
    17:19
  • Standard Deviation Minimization Portfolio Performance
    15:30
  • Markowitz Portfolio Performance
    15:08
  • Mean-VaR Portfolio Performance
    16:45
  • Investment Costs
    11:59

Instructor

Diego Fernandez
Exfinsis
Diego Fernandez
  • 3.8 Instructor Rating
  • 2,277 Reviews
  • 11,688 Students
  • 36 Courses

Diego Fernandez is author of high-quality online courses and ebooks at Exfinsis for anyone who wants to become an expert in financial data analysis.

His main areas of expertise are financial analysis and data science. Within financial analysis he has focused on computational finance, quantitative finance and trading strategies analysis. Within data science he has concentrated on machine learning, applied statistics and econometrics. For all of this he has become proficient in Microsoft Excel®, R statistical software® and Python programming language® analysis tools. 

He has important online business development experience at fast-growing startups and blue-chip companies in several European countries. He has always exceeded expected professional objectives by starting with a comprehensive analysis of business environment and then efficiently executing formulated strategy.

He also achieved outstanding performance in his undergraduate and postgraduate degrees at world-class academic institutions. This outperformance allowed him to become teacher assistant for specialized subjects and constant student leader within study groups. 

His motivation is a lifelong passion for financial data analysis which he intends to transmit in all of the courses.

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