# Investment Portfolio Analysis with Python

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- 7.5 hours on-demand video
- 8 articles
- 15 downloadable resources
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Try Udemy for Business- 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.

- 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.

**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.

- 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.

In this lecture you will view course disclaimer and learn which are its objectives, how you will benefit from it, its previous requirements and my profile as instructor.

In this lecture you will learn that it is recommended to view course in an ascendant manner as each section builds on last one and also does its complexity. You will also study course structure and main sections (course overview, asset classes, returns and risks, portfolio optimization and portfolio performance).

In this lecture you will learn investment portfolio analysis data reading or downloading into Python PyCharm Integrated Development Environment (IDE), data sources, code files originally in .TXT format that need to be converted in .PY format, Python packages Miniconda Distribution for Python 3.6 64-bit (PD) installation (numpy, pandas, pandas-datareader, scipy, statsmodels and matplotlib) and related code (import <package> as <name>, read_csv(), DataReader() functions).

In this lecture you will learn section lectures’ details and main themes to be covered related to asset classes (cash and cash equivalents, fixed-income or bonds, equities or stocks, commodities, real estate and currencies or foreign exchange).

In this lecture you will learn section lectures’ details and main themes to be covered related to returns and risks (expected returns, risks, returns normality, returns and risks relationship, capital asset pricing model CAPM, Fama-French-Carhart factors model, arbitrage pricing theory model APT, portfolio hedge, hedge funds and portfolio leverage).

In this lecture you will learn section lectures’ details and main themes to be covered related to portfolio optimization (portfolio performance metrics, portfolio benchmarks, mean maximization portfolio optimization, standard deviation minimization portfolio optimization, Markowitz portfolio optimization, mean maximization and VaR minimization portfolio optimization).

In this lecture you will learn portfolio mean maximization portfolio optimization definition and main calculations (shift(), mean(), lambda, minimize(), round(), cumprod(), len(), std(), sqrt(), plot(), title(), legend(), show(), DataFrame(), print() functions).

In this lecture you will learn standard deviation minimization portfolio optimization definition and main calculations (shift(), std(), lambda, minimize(), round(), cumprod(), len(), sqrt(), plot(), title(), legend(), show(), DataFrame(), print() functions).

In this lecture you will learn mean maximization portfolio performance definition and main calculations (shift(), mean(), lambda, minimize(), round(), add_constant(), OLS.fit(), summary(), print(), mean(), subplots(), bar(), set_title(), get_height(), get_x(), get_width(), show() functions, for loops).

In this lecture you will learn standard deviation minimization portfolio performance definition and main calculations (shift(), std(), lambda, minimize(), round(), add_constant(), OLS.fit(), summary(), print(), mean(), subplots(), bar(), set_title(), get_height(), get_x(), get_width(), show() functions, for loops).

In this lecture you will learn Markowitz portfolio performance definition and main calculations (shift(), mean(), std(), lambda, minimize(), round(), add_constant(), OLS.fit(), summary(), print(), mean(), subplots(), bar(), set_title(), get_height(), get_x(), get_width(), show() functions, for loops).

In this lecture you will learn Mean-VaR portfolio performance definition and main calculations (shift(), mean(), std(), norm.ppf(), lambda, minimize(), round(), add_constant(), OLS.fit(), summary(), print(), mean(), subplots(), bar(), set_title(), get_height(), get_x(), get_width(), show() functions, for loops).