NAV 434.03 · June 30, 2026
A dynamic global equity strategy combining entropy-based regime detection with systematic hedging overlays — capturing equity growth while actively limiting drawdowns across all market cycles.
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 | +334.03% | +4.20% |
| Annualised Return | +20.36% | — |
| Annualised Volatility | 8.97% | 6.83% |
| Sharpe Ratio | 2.27 | 0.55 |
| Calmar Ratio | 2.37 | — |
| % Positive Months | 68% (61/91) | — |
| Latest NAV (Base 100) | 434.03 | — |
| 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 equity portfolios
Standard deviation assumes Gaussian returns and symmetric risk — both systematically violated in equity markets.
First formalised by Claude Shannon (1948), entropy captures the full informational complexity of any distribution:
| Feature | Traditional Equity Strategies | EntropiaTech Hedged Approach |
|---|---|---|
| Risk Measure | Beta / standard deviation | Entropy (full distributional complexity) |
| Drawdown Management | Passive — static stop-loss or none | Dynamic entropy-triggered overlays |
| Regime Detection | Lagging — reacts to realised vol | Leading — entropy flags stress early |
| Tail Risk | Systematically underweighted | Hedged via VIX & CDS overlays |
| Equity Participation | Full beta, unconstrained | Entropy-weighted, dynamically adjusted |
| Overlay Triggers | None / discretionary | Per-asset entropy score thresholds |