Recap
Introduction to Portfolio Analysis in Python
Charlotte Werger
Data Scientist
Chapter 1: Calculating risk and return
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- A portfolio as a collection of weight and assets
- Diversification
- Mean returns versus cumulative returns
- Variance, standard deviation, correlations and the covariance matrix
- Calculating portfolio variance
Chapter 2: Diving deep into risk measures
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- Annualizing returns and risk to compare over different periods
- Sharpe ratio as a measured of risk adjusted returns
- Skewness and Kurtosis: looking beyond mean and variance of a distribution
- Maximum draw-down, downside risk and the Sortino ratio
Chapter 3: Breaking down performance
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- Compare to benchmark with active weights and active returns
- Investment factors: explain returns and sources of risk
- Fama French 3 factor model to breakdown performance into explainable factors and alpha
- Pyfolio as a portfolio analysis tool
Chapter 4: Finding the optimal portfolio
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- Markowitz' portfolio optimization: efficient frontier, maximum Sharpe and minimum volatility portfolios
- Exponentially weighted risk and return, semicovariance
Continued learning
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- Datacamp course on Portfolio Risk Management in Python
- Quantopian's lecture series: https://www.quantopian.com/lectures
- Learning by doing: Pyfolio and PyPortfolioOpt
End of this course
Introduction to Portfolio Analysis in Python
