Portfolio Optimization¶

Find optimal portfolio weights using various strategies.

Basic Usage¶

from portfolio_analysis import PortfolioOptimizer

optimizer = PortfolioOptimizer(prices, risk_free_rate=0.04)

Optimization Strategies¶

Maximum Sharpe Ratio¶

Find the portfolio with the highest risk-adjusted return:

result = optimizer.optimize_max_sharpe()
print(f"Weights: {result['weights']}")
print(f"Expected Return: {result['return']:.2%}")
print(f"Volatility: {result['volatility']:.2%}")
print(f"Sharpe Ratio: {result['sharpe_ratio']:.2f}")

Minimum Volatility¶

Find the least risky portfolio:

result = optimizer.optimize_min_volatility()

Target Return¶

Find minimum volatility for a given return target:

result = optimizer.optimize_target_return(target_return=0.10)

Risk Parity¶

Equal risk contribution from each asset:

result = optimizer.optimize_risk_parity()
print(f"Risk contributions: {result['risk_contributions']}")

Constraints¶

# Long-only with max 40% per asset
result = optimizer.optimize_max_sharpe(weight_bounds=(0, 0.4))

# Allow short selling
result = optimizer.optimize_max_sharpe(weight_bounds=(-0.3, 1.0))

Efficient Frontier¶

# Generate frontier points
frontier = optimizer.generate_efficient_frontier(n_points=50)

# Visualize
optimizer.plot_efficient_frontier(show_assets=True, show_optimal=True)

Factor-Aware Optimization¶

from portfolio_analysis.factors import FactorOptimizer

factor_opt = FactorOptimizer(prices, factor_data)

# Target specific factor exposures
result = factor_opt.optimize_target_exposures(
    target_betas={'Mkt-RF': 1.0, 'SMB': 0.3, 'HML': 0.2}
)

# Neutralize factors
result = factor_opt.optimize_factor_neutral(factors=['SMB', 'HML'])

# Maximize alpha
result = factor_opt.optimize_max_alpha(model='ff3')