This repository offers a guide to key principles in contemporary investment strategies and portfolio management, using Python programming. It starts with basic concepts such as calculating arithmetic and logarithmic returns and assessing various risk and reward metrics, including annualized returns, volatility, and metrics like Sharpe and Sortino. The repository also introduces the Capital Asset Pricing Model (CAPM), essential for understanding asset pricing and risk management.
Progressing from these fundamentals, the content moves into Modern Portfolio Theory (MPT), examining a range of portfolio construction methods. This includes traditional approaches like Equally Weighted and Minimum Variance portfolios, as well as more advanced methods like Mean-Variance, Black-Litterman, and Multi-Factor Models.
There is a focused exploration of more complex strategies such as Risk-Parity and Beta-Neutral portfolios, featuring a distinctive approach to optimizing the covariance matrix.
The concluding section is dedicated to backtesting methodologies, a vital aspect of assessing investment strategy performance. It discusses techniques such as Rolling and Expanding Windows, and their practical application in real-life investment scenarios.