NAV 569.35 · June 30, 2026
A multi-asset strategy combining entropy-driven macro allocation with active hedging overlays across equities, rates, credit, FX and commodities — delivering consistent returns with superior drawdown control.
Key Metrics
As of June 30, 2026 · Net of fees · Base 100 at inception (November 06, 2018)
Returns
Annual and monthly breakdown — 2026 year-to-date through June 30
| Metric | Since Inception | Last 12 Months |
|---|---|---|
| Total Net Return | +469.35% | +11.54% |
| Annualised Return | +24.56% | — |
| Annualised Volatility | 8.03% | 6.52% |
| Sharpe Ratio | 3.06 | 1.28 |
| Calmar Ratio | 3.58 | — |
| % Positive Months | 77% (70/91) | — |
| Latest NAV (Base 100) | 569.35 | — |
| Strategy Inception | November 06, 2018 | — |
| Report Date | June 30, 2026 | — |
Allocation
As of June 30, 2026 · Notional weights by asset class · Positive = long · Negative = short / hedge
Market Intelligence
As of June 30, 2026 · Score 0 = Maximum Calm · Score 1 = Maximum Stress · Portfolio average: 37.4%
Research
Why information-theoretic risk measures outperform standard deviation in modern multi-asset portfolios
Standard deviation assumes Gaussian returns and symmetric risk — both systematically violated in financial markets.
First formalised by Claude Shannon (1948), entropy captures the full informational complexity of any distribution:
| Feature | Traditional Vol-Based | EntropiaTech Entropy Approach |
|---|---|---|
| Risk Measure | Standard deviation (2nd moment) | Entropy (full distributional complexity) |
| Distribution Assumption | Gaussian returns required | Distribution-free — valid under fat tails |
| Regime Sensitivity | Lagging — rises after crisis | Leading — detects structural stress early |
| Tail Risk | Systematically underweighted | Naturally captured via distributional shape |
| Allocation Logic | Mean-variance frontier | Maximum Entropy Principle (MaxEnt) |
| Overlay Triggers | Vol target / threshold VaR | Per-asset entropy score thresholds |