Markowitz MVO
Get the highest expected return for the level of risk you're willing to take.
Risk Parity
Spread risk evenly across holdings so no single asset dominates.
HRP
Group similar assets and balance risk within each group. Robust when correlations shift.
HERC
An evolution of HRP. Better behavior on portfolios with many assets.
Your portfolio is currently empty. You can import holdings, add them manually, or start with a popular portfolio.
Optimization settings
Risk Measure
How risk is defined when optimizing your portfolio
Risk Free Rate
The minimum return you'd expect without taking any risk
%
Reoptimize Frequency
See how your portfolio would have performed if you run optimization on a regular schedule
Optimization Date
Results after this date show real, out-of-sample performance
Training Window
How much historical data is used to calculate optimal weights
Correlated Assets
Automatically removes assets that move too similarly to improve diversification
Constraints
Set minimum and maximum allocation limits for each asset
%
%
Benchmark
Compare your optimized portfolio against a market index
Optimal Asset Allocation
Click Calculate to get resultsOriginal vs Optimized
Portfolio Performance
The chart shows the growth of an initial investment of $10,000 in Optimized Portfolio, comparing it to the performance of the S&P 500 index or another benchmark. All prices have been adjusted for splits and dividends.
Click Calculate to get resultsAllocation Over Time
Click Calculate to get resultsClick Calculate to get resultsPortfolio Drawdowns
Click Calculate to get resultsPortfolio Volatility
Click Calculate to get resultsAsset Correlations Table
Click Calculate to get resultsPair optimization with diversification
Optimization reweights what you already hold. Diversification Analysis finds the gaps and suggests new assets to fill them — a natural next step before locking in weights.
Open Diversification AnalysisHERC vs HRP
Both methods use hierarchical clustering, but differ in allocation:
- HRP: Weights inversely proportional to each cluster's risk (higher risk = lower weight)
- HERC: Adjusts weights so each cluster contributes equally to total portfolio risk
- Result: HERC typically produces more balanced risk distribution across distinct asset groups
When to Use HERC
HERC works best when:
- Your portfolio has distinct asset groups (tech, bonds, commodities, cash, etc)
- You want equal risk contribution at the cluster level, not just individual assets
- You're optimizing for tail risk measures (CVaR, CDaR) rather than just volatility
- You want to prevent any single group from dominating portfolio risk
Trade-offs
- More computationally intensive than HRP (requires optimization at each node)
- Performance can be less stable than HRP across different market conditions
- Like HRP, doesn't require covariance matrix inversion (more stable than traditional MVO)
- Works best with 15+ assets that form natural, distinct clusters