1. Introduction to Python: introduction, installation, Anaconda environment, Jupyter Notebook, Spyder editor
2. Introduction to Python: basic concepts and syntax, basic data types and working with variables, control structures
3. Structured data types (data structures), NumPy and Pandas libraries for data science: basic principles, selected cases
4. Working with financial time series in Python, calculation of basic statistics and visualization
5. Mean-variance portfolio optimization
6. General efficient sets: mean-semivariance, mean-CVaR, mean-CDaR
7. Black-Litterman model
8. The uniform portfolio, the portfolio with uniform risk contributions, Hierarchical Risk Parity portfolio
9. Measuring portfolio performance and risk
10. Possible approaches to portfolio rebalancing, alarms
11. Introduction to algorithmic trading and automated trading systems
12. Algorithmic trading and automated trading systems - example of two moving averages
13. Introduction to Python packages yfinance, PyPortfolioOpt, empyrial, Zipline
14. Using MS Excel for portfolio optimization and calculating historical performance
2. Introduction to Python: basic concepts and syntax, basic data types and working with variables, control structures
3. Structured data types (data structures), NumPy and Pandas libraries for data science: basic principles, selected cases
4. Working with financial time series in Python, calculation of basic statistics and visualization
5. Mean-variance portfolio optimization
6. General efficient sets: mean-semivariance, mean-CVaR, mean-CDaR
7. Black-Litterman model
8. The uniform portfolio, the portfolio with uniform risk contributions, Hierarchical Risk Parity portfolio
9. Measuring portfolio performance and risk
10. Possible approaches to portfolio rebalancing, alarms
11. Introduction to algorithmic trading and automated trading systems
12. Algorithmic trading and automated trading systems - example of two moving averages
13. Introduction to Python packages yfinance, PyPortfolioOpt, empyrial, Zipline
14. Using MS Excel for portfolio optimization and calculating historical performance