Sharpe Ratio Optimizer

For a long-term portfolio, Sharpe above 0.5 is acceptable, above 1.0 is good, above 2.0 is excellent but rare to sustain. Compare to benchmarks: the S&P/ASX 200 Accumulation index has a historical Sharpe around 0.3-0.5. Beating the market's Sharpe means you're adding value. Be skeptical of strategies claiming Sharpe above 2.0 over long periods - verify thoroughly.

Monthly or quarterly rebalancing is typical. More frequent creates more transaction costs; less frequent lets the portfolio drift from optimal. A good approach: Check monthly, but only rebalance if weights drift more than 5% from optimal. This balances responsiveness with cost efficiency. In Australia, also watch the 12-month CGT-discount mark and the 30 June EOFY when timing sells.

Yes, for simple portfolios. Use Excel Solver with the objective to maximize Sharpe (or minimize negative Sharpe). Set constraints for weights summing to 1 and position limits. For more complex optimization (many assets, complex constraints), Python with scipy or cvxpy is more powerful and flexible.

Highest return often comes with highest volatility. A 30% return with 50% volatility might be worse risk-adjusted than 15% return with 10% volatility. Additionally, diversification can reduce portfolio volatility below any single asset while maintaining reasonable returns. Sharpe optimization finds this optimal combination.

Use the RBA cash rate / AONIA (currently around 4.35%), or the 90-day Bank Accepted Bill / BBSW (~4.4-4.5%) as a money-market proxy. The RBA itself describes the cash rate as the near risk-free benchmark rate for the Australian dollar. This represents what you could earn with virtually zero risk. If optimizing after-tax returns, use the after-tax rate and gross up franked dividend income for franking credits. Any investment should compensate you for risk above this baseline.

Optimization fits to historical data including noise. The 'optimal' weights are partly optimal for that specific historical period, not for the future. This is overfitting. Solutions: Use robust estimation, add constraints, shrink estimates, perform walk-forward analysis, and expect 20-40% degradation from in-sample to out-of-sample Sharpe.

High-volatility assets will have small weights in Sharpe-optimized portfolios unless they have very high expected returns. This is appropriate - their risk contribution would otherwise dominate. If you want more balanced exposure, consider risk parity (equal risk contribution) or add minimum weight constraints. Position sizing should reflect the volatility difference.

Yes, especially for active strategies. Approach 1: Add turnover constraint (limit weight changes). Approach 2: Add turnover penalty to objective function. Approach 3: Subtract expected transaction costs from expected returns. Net Sharpe (after costs) is what matters, not gross Sharpe. Australia has no share transaction tax, so costs are mainly brokerage and spread - but CGT on gains realised within 12 months can be a larger drag than the trading cost itself.

Steps: (1) Get market-cap weights for your universe. (2) Estimate covariance matrix. (3) Back out equilibrium returns using pi = delta x Sigma x w_mkt. (4) Express your views as return forecasts with confidence. (5) Apply BL formula to blend equilibrium with views. (6) Optimize with blended returns. Libraries like PyPortfolioOpt and Riskfolio-Lib implement BL.

Sharpe penalizes all volatility equally. Sortino only penalizes downside volatility. For assets with positive skew (large upside potential), Sortino optimization may allocate more than Sharpe would. Most investors care more about downside than upside volatility, making Sortino often more appropriate. Implementation is similar - just replace standard deviation with downside deviation.

Methods: (1) Use rolling windows with recent data weighted more heavily. (2) Regime-switching models with different parameters per regime. (3) Conditional estimation based on observable signals (A-VIX, momentum). (4) Bayesian updating that adapts to new information. (5) Shrinkage toward stable long-term estimates. Accept that parameters change and build adaptability into the system rather than assuming stationarity.

Rule of thumb: At least 10 observations per estimated parameter. For N assets: N expected returns + N(N+1)/2 covariance terms. For 10 assets: ~65 parameters -> need ~650 observations (2.5+ years of daily data). With monthly data, this becomes impractical. Solutions: Factor models (reduce dimensionality), shrinkage estimators, or simpler allocation rules for limited data.

Methods: (1) Walk-forward analysis - optimize rolling, test forward. (2) Cross-validation appropriate for time-series. (3) Compare to simpler baselines (equal weight, risk parity). (4) Check IS vs OOS Sharpe ratio (50-80% retention is realistic). (5) Examine weight stability - very unstable weights suggest overfitting. (6) Use information criteria (AIC, BIC) to penalize complexity.

Approach 1: Pre-compute optimal weights for each regime, switch based on regime indicator. Approach 2: Regime-conditional expected returns in dynamic optimization. Approach 3: Robust optimization that performs acceptably across regimes. Challenge: Real-time regime detection is imperfect. Consider blending regime portfolios based on regime probabilities rather than hard switching.

Testing SR1 > SR2 requires accounting for correlation between the two return series. Jobson-Korkie test or bootstrap methods are appropriate. For uncorrelated series with T observations: SE(SR1 - SR2) ~= sqrt[2(1 + SR_avg^2/2) / T]. Need substantial data (3+ years) for statistical significance. High correlation between strategies makes comparison harder (reduces test power).