EntropiaTech
Overview Performance Positioning Entropy Research
NAV 219.61  ·  June 30, 2026
EntropiaTech · Absolute Return Plus

Consistent returns
in every regime via
entropy intelligence

A capital-preservation focused multi-asset strategy delivering steady absolute returns across all market environments — with entropy-driven allocation and strict drawdown control.

+119.61%
Total Return
2.73
Sharpe Ratio
-4.89%
Max Drawdown
NAV · BASE 100 · NOV 2018 – JUN 2026

Key Metrics

Strategy Overview

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

219.61
Latest NAV
Base 100
+119.61%
Total Return
Since inception
+10.44%
Ann. Return
Since inception
2.73
Sharpe Ratio
Since inception
-4.89%
Max Drawdown
April 09, 2020
3.82%
Ann. Volatility
Since inception
NAV Performance vs. Flat Benchmark
Net of fees — Base 100 on November 06, 2018
Drawdown History
Underwater curve — all episodes (UCITS peak-to-valley)
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  ·  74/91 positive months (81%)  ·  Green = positive  ·  Red = negative
Metric Since InceptionLast 12 Months
Total Net Return+119.61%+11.17%
Annualised Return+10.44%—
Annualised Volatility3.82%3.67%
Sharpe Ratio2.732.64
Calmar Ratio2.13—
% Positive Months81% (74/91)—
Latest NAV (Base 100)219.61—
Strategy InceptionNovember 06, 2018—
Report DateJune 30, 2026—
Maximum Drawdown — UCITS Peak-to-Valley
MDD: -4.89%  ·  Peak: April 09, 2020  ·  Trough: April 21, 2020
Drawdown duration: 8 business days (12 cal. days)  ·  Recovery date: May 19, 2020
Time to recovery: 20 business days (28 cal. days)  ·  Full cycle: 28 business days (40 cal. days)
Key Ratios
Since inception vs. last 12 months

Allocation

Current Portfolio Exposure

As of June 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 June 30, 2026  ·  Score 0 = Maximum Stress  ·  Score 1 = Maximum Calm  ·  Portfolio average: 37.4%

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