EntropiaTech
Overview Performance Positioning Entropy Research
NAV 522.92  ·  September 30, 2026
EntropiaTech · Global Macro

Macro alpha through
entropy-driven
multi-asset allocation

A diversified multi-asset strategy spanning equities, rates, bonds, FX and commodities — with systematic entropy signals driving dynamic allocation and active risk management across all market regimes.

+418.78%
Total Return
2.65
Sharpe Ratio
-7.55%
Max Drawdown
NAV · BASE 100 · NOV 2018 – JUL 2026

Key Metrics

Strategy Overview

As of September 30, 2026  ·  Net of fees  ·  Base 100 at inception (November 06, 2018)

518.78
Latest NAV
Base 100
+418.78%
Total Return
Since inception
+22.29%
Ann. Return
Since inception
2.65
Sharpe Ratio
Since inception
-7.55%
Max Drawdown
February 13, 2025
8.42%
Ann. Volatility
Since inception
NAV Performance vs. Flat Benchmark
Net of fees — Base 100 on November 06, 2018
Drawdown History
Underwater curve — all episodes
Rolling 12-Month Return
252 business-day rolling return, net of fees

Returns

Performance Analysis

Annual and monthly breakdown — 2026 year-to-date through June 30

Annual Net Returns
Net of fees — 2026 partial year (YTD to June 30)
Monthly Return Calendar (%)
Net of fees  ·  73/95 positive months (77%)  ·  Green = positive  ·  Red = negative
Metric Since InceptionLast 12 Months
Total Net Return+418.78%+12.44%
Annualised Return+22.29%—
Annualised Volatility8.42%6.29%
Sharpe Ratio2.651.86
Calmar Ratio2.951.55
% Positive Months76.84% (73/95)—
Latest NAV (Base 100)518.78—
Strategy InceptionNovember 06, 2018—
Report DateSeptember 30, 2026—
Maximum Drawdown — UCITS Peak-to-Valley
MDD: -7.55%  ·  Peak: February 13, 2025  ·  Trough: April 08, 2025
Drawdown duration: 39 trading days (54 calendar days)  ·  Recovery date: June 03, 2025
Time to recovery: 41 trading days (56 calendar days)  ·  Full cycle: 80 trading days (110 calendar days)
Key Ratios
Since inception vs. last 12 months

Allocation

Current Portfolio Exposure

As of September 30, 2026  ·  Notional weights by asset class  ·  Positive = long  ·  Negative = short / hedge

Asset Class Exposures
Entropy-weighted allocation + active overlays
Exposure Detail
Notional weights as of report date

Market Intelligence

Entropia Scores by Asset Class

As of September 30, 2026  ·  Score 0 = Maximum Calm  ·  Score 1 = Maximum Stress  ·  Portfolio average: 59.0%

Entropia Score Ranking
Green < 0.33 (Calm)  ·  Orange 0.33–0.66 (Neutral)  ·  Red ≥ 0.66 (Stress)

Research

Scientific Foundation: Entropy vs. Volatility

Why information-theoretic risk measures outperform standard deviation in modern multi-asset portfolios

The Limits of Volatility

Standard deviation assumes Gaussian returns and symmetric risk — both systematically violated in financial markets.

  • Non-normality: Returns exhibit excess kurtosis and negative skewness (Mandelbrot, 1963; Fama, 1965) — variance is blind to this.
  • Asymmetry blindness: Variance penalises upside and downside equally — inconsistent with investor preferences (Kahneman & Tversky, 1979).
  • Backward-looking: Realised volatility rises after a crisis. Entropy detects stress before it appears in second-moment statistics (Gradojevic & Caric, 2017).
  • Regime-blind: Vol cannot distinguish a calm trending phase from a pre-crisis accumulation with similar vol but very different risk profiles.

Shannon Entropy as Risk Measure

First formalised by Claude Shannon (1948), entropy captures the full informational complexity of any distribution:

H(X) = − Σ pᵢ · log₂(pᵢ)
  • Distribution-free: Valid under fat tails, bimodality and regime switches — no Gaussian assumption (Scrucca, 2024).
  • Tail-sensitive: Rényi entropy calibrates explicitly to tail behaviour (Lassance & Vrins, 2019).
  • Early warning: Identified every major crisis 1998–2026: Dot-com, GFC, COVID, 2025 tariff shock (Fernandez-Mejia et al., 2025).
  • Predictive power: Twice the cross-sectional return explanatory power of CAPM beta (PLOS ONE, 2015).
Feature Traditional Vol-Based EntropiaTech Entropy Approach
Risk MeasureStandard deviation (2nd moment)Entropy (full distributional complexity)
Distribution AssumptionGaussian returns requiredDistribution-free — valid under fat tails
Regime SensitivityLagging — rises after crisisLeading — detects structural stress early
Tail RiskSystematically underweightedNaturally captured via distributional shape
Allocation LogicMean-variance frontierMaximum Entropy Principle (MaxEnt)
Overlay TriggersVol target / threshold VaRPer-asset entropy score thresholds

Key Scientific References