Finoval — Markowitz Portfolio Analyzer

Select Assets & Weights

Select up to 8 assets. Set each weight above 0% — all selected weights must total exactly 100%.

0 / 8 selected

How this works

Pick your cryptos and allocation. Using up to a year of real daily price history, we compute each asset’s historical return, volatility, and how they moved together — then map every possible mix onto the efficient frontier.

We find the minimum-risk and optimal (Max Sharpe) historical allocations, capped at 40% per asset
Your portfolio is plotted against the frontier so you can see how it compares historically
The risk and correlation insights are the most reliable takeaway — returns are one past snapshot, not a forecast

Fetching prices

First-time analysis fetches live prices — results are cached for 24h and load instantly on repeat runs.

Asset Statistics 365-day daily returns · annualized

Data period Historical window used to compute returns, volatility & correlations — longer periods include more market cycles.

Efficient Frontier 12,000 simulations · colored by Sharpe ratio

Low Sharpe

High Sharpe

Minimum Risk

Lowest historical volatility via diversification — risk & correlation are more stable insights than return rankings

≈ Optimal (Max Sharpe)

Most risk-efficient mix over the selected past period — not a forecast or recommendation

Understanding these results

Both portfolios above are derived entirely from past price data over the selected window. They show what would have been most risk-efficient historically — not a prediction of what will perform best going forward, and not investment advice.

Why the “optimal” sometimes looks counterintuitive: Markowitz optimization rewards recent past winners and penalizes recent losers — even if those losers may be sounder going forward. If an asset had a great year, the model overweights it. If it had a rough year, the model avoids it. Past returns don’t predict future returns. This is the fundamental limitation of optimizing purely on historical data.

What IS reliably useful: Volatility and correlation are more stable over time than raw return rankings. The risk breakdown, the correlation matrix, and the diversification structure are the genuinely valuable output — they show how your assets move together and where your risk is concentrated. Use those insights; treat the specific return numbers as one historical snapshot.

40% cap: We limit each asset to 40% across all 12,000 simulations to prevent the model from concentrating everything in one past winner. Unconstrained Markowitz commonly allocates 90%+ to a single asset.

Data: CoinGecko · last 1 year of daily prices · risk-free rate 4% annual · historical analysis only, not a forecast or financial advice.

Correlation Matrix Daily returns · past 365 days

In crypto, most large coins move together — correlations of 0.5–0.9 are typical. We use a 0.70 threshold adapted to this market: Green = relatively independent for crypto (< 0.70), Red = move almost identically (0.70+). Diagonal = self-correlation (always 1.00). True diversification often means mixing coins from different niches — L1s, DeFi, AI, memes — not just different names.

Your Portfolio vs. Historical Optimal Based on past data only · not a forecast or recommendation