portfolio-optimization-guide

npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill portfolio-optimization-guide --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# Portfolio Optimization Guide A skill for implementing and researching portfolio optimization methods, from classical mean-variance optimization to modern robust and factor-based approaches. Covers Markowitz theory, Black-Litterman, risk parity, and machine learning-enhanced portfolio construction. ## Mean-Variance Optimization ### Classical Markowitz Portfolio ```python import numpy as np from scipy.optimize import minimize def mean_variance_optimize(expected_returns: np.ndarray, cov_matrix: np.ndarray, target_return: float = None, risk_free_rate: float = 0.02) -> dict: """ Markowitz mean-variance optimization. expected_returns: array of expected returns for each asset cov_matrix: covariance matrix of asset returns target_return: target portfolio return (None for max Sharpe) """ n_assets = len(expected_returns) def portfolio_volatility(weights): return np.sqrt(weights @ cov_matrix @ weights) def neg_sharpe(weights): ret = weights @ expected_returns vol = portfolio_volatility(weights) return -(ret - risk_free_rate) / vol # Constraints constraints = [ {"type": "eq", "fun": lambda w: np.sum(w) - 1}, # weights sum to 1 ] if target_return is not None: constraints.append( {"type": "eq"