Optimizing Stock Portfolios with Machine Learning

Investors aim to maximize returns while minimizing risks. Traditional methods like Modern Portfolio Theory (MPT) rely on historical data and simple assumptions about asset behavior. However, real-world markets are dynamic and often non-linear. Machine learning techniques enhance portfolio optimization by learning complex relationships between stocks, predicting returns, modeling volatility, and adjusting portfolio allocations dynamically for optimal risk-adjusted performance.

Using historical stock prices, returns, volatility metrics, and macroeconomic indicators, machine learning models can predict asset behavior and optimize portfolio composition. Techniques like Mean-Variance Optimization, Reinforcement Learning, Genetic Algorithms, and Deep Learning Forecasting are applied. Models dynamically adjust stock weight allocations to optimize Sharpe ratio, minimize volatility, or maximize returns under given risk constraints.

Historical stock returns, volatility (standard deviation), and correlation matrices are computed. Optimization algorithms maximize return while minimizing risk, often using an objective like maximizing the Sharpe Ratio. Machine learning models can forecast expected returns or volatility, assisting in smarter allocation decisions. Techniques like Reinforcement Learning allow dynamic rebalancing in changing market conditions, adapting to new information quickly.

• Collect historical stock price data from sources like Yahoo Finance, Alpha Vantage, or Quandl.
• Calculate daily/weekly returns, volatility metrics, correlation matrices, and Sharpe Ratios for different stocks.
• Apply Mean-Variance Optimization, Genetic Algorithms, or ML predictions (regression models) for future returns forecasting.
• Use optimization solvers (e.g., SciPy Optimize, PyPortfolioOpt) to compute optimal portfolio weights.
• Simulate portfolio performance over time using backtesting to validate optimization strategy effectiveness.

• PyPortfolioOpt Library for Portfolio Optimization
• Modern Portfolio Theory Explained Simply
• Financial Data API – Alpha Vantage