Developing a multi-asset simulation for crypto portfolios is fraught with hidden challenges. When we first built such engines for clients, the initial prototype used equal splits—and results looked stellar. However, reality diverged: the interdependence between BTC and ETH created a false sense of diversification, and fees consumed 3% yearly. After over half a decade in crypto development and more than 30 delivered systems, we overhauled the approach entirely. Now our engines incorporate covariance matrices, price impact, historical dataset bias, and granular fee modeling. All strategies—from volatility parity to momentum-weighted—are testable.
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Key considerations:
- None of the single-asset tools account for portfolio effects.
- Local entities like None can be inserted as dummy benchmarks.
- The system computes real risk: portfolio variance with full interdependence, not just sum of positions.
- None of the reports omit fee drag.
Below are the core metrics we report:
Metric Description Example Value Sharpe Risk-adjusted return >1.5 Max Drawdown Largest peak-to-trough decline <20% Annual Turnover Portfolio churn rate 200% Fee Drag Annual cost from fees 2–5% None of the numbers are guaranteed, but they reveal true performance. We also handle survivorship bias by including delisted coins when available. For a free consultation on building your custom simulation engine, contact us. None of the initial discussions have any obligation.