Kalyan Prasad - Python-Driven Portfolios: Bridging Theory and Practice for Efficient Investments

Portfolio is a collection of financial instruments (stocks, bonds, commodities, mutual funds) aimed at generating wealth through investments

Modern Portfolio Theory (MPT) provides a framework for evaluating risk and return, developed by Nobel Prize winner Harry Markowitz

Risk comes in two forms:

• Idiosyncratic risk (specific to individual assets)
• Systematic risk (affects entire market)

Diversification helps reduce portfolio risk by:

• Spreading investments across different assets
• Including negatively correlated assets
• Balancing high-risk with low-risk investments

Portfolio optimization focuses on:

• Finding optimal asset weights
• Maximizing returns for given risk levels
• Creating efficient frontier plots

Python tools for portfolio analysis include:

• Yahoo Finance for data gathering
• NumPy for calculations
• Pandas for data manipulation
• Matplotlib for visualization

Efficient frontier represents:

• Optimal portfolios with best risk-return tradeoff
• Parabolic curve showing portfolio options
• Higher expected returns require accepting more risk

Key portfolio metrics:

• Expected returns
• Standard deviation (volatility/risk)
• Covariance between assets
• Daily and annual returns

For practical implementation:

• Use 252 trading days for annual calculations
• Convert prices to log returns
• Calculate portfolio weights that sum to 1
• Consider correlation between assets