★ 20 Ways to Generate Stock Investment Profits with AI - 4. Algorithmic Portfolio Optimization

 

4. Algorithmic Portfolio Optimization

Introduction
Static allocations (60/40 stocks/bonds) ignore changing market dynamics. Algorithmic optimization uses AI to adjust weights for target risk–return profiles.

Core Techniques

• Covariance Estimation: Apply Ledoit–Wolf shrinkage for robust, stable covariance matrices.

• Mean-Variance Optimization: Solve for maximum expected return at a given volatility using cvxpy.

• Dynamic Rebalancing: Trigger rebalance monthly or when weights deviate >5%.

How to Build

1. Estimate Inputs: Calculate expected returns via blended factors (value, momentum) and estimate covariance from 60-day rolling returns.

2. Optimization: Formulate the problem—maximize µᵀw − λ·wᵀΣw—choose risk aversion λ based on target volatility (e.g., 10% annualized).

3. Backtest: Simulate with daily rebalancing costs; compare to static benchmark portfolios.

4. Deployment: Automate with Python scripts on AWS EC2, send orders through brokerage API.

Illustrative Result
A dynamically allocated equity/bond portfolio matched a 9% annual return with half the volatility of a static 60/40 mix from 2012–2024.

Conclusion
Algorithmic optimization allows portfolios to adapt to shifting risk and return environments—enhancing stability without sacrificing performance.