Simple Dynamic Stock/Bond/Gold Portfolios

Battle Count: 65

Highly relevant to quantitative portfolio management. Demonstrates that standard quantitative finance tools (volatility targeting, momentum, ridge regression forecasting, convex optimization) can substantially improve upon fixed-weight benchmarks under realistic retail investor constraints. The Markowitz portfolio achieves Sharpe ratio of 1.08 vs 0.56 for 60/40. Directly applicable to multi-asset fund management, risk-managed portfolio construction, and systematic asset allocation. The comparison against seven risk-based methods (risk parity, minimum variance, Black-Litterman, etc.) is particularly valuable for practitioners.

Very low complexity. Methods are explicitly designed to be implementable in a few lines of code. Volatility targeting is a simple scaling operation. The Markowitz optimization is a 3-variable convex problem solved in under 1ms. Ridge regression with 42 features is standard. CVXPY modeling language and Clarabel solver handle the optimization. Open-source code provided. No proprietary data or complex ML pipelines required.

Open-source software (CVXPY + Clarabel solver) implements all methods and reproduces every result. All data sources are publicly available (Yahoo Finance, FRED, Kenneth French data library). GitHub repository provided. Methods are simple, transparent, and can be implemented in a few lines of code. Detailed appendices cover accounting, forecasting, statistical significance, lag sensitivity, hyperparameter sensitivity, and benchmark comparisons.