EntropiaTech
Overview Performance Strategy Entropy Research
NAV 168.20  ·  August 31, 2026
EntropiaTech · Crypto Trading Adaptive

Intraday entropy cycles
on digital assets
pondered by global market entropia score

A quantitative intraday strategy on BTC, ETH, BNB, SOL and XRP — based on the 3rd law of thermodynamics. Entropy extrema detect high-probability market turning points, combined with dynamic CCI filtering and cycle validation before any trade is executed.

+67.49%
Total Return
+35.75%
Annualised
1.98
Sharpe Ratio
NAV · BASE 100 · OCT 2024 – AUG 2026

Key Metrics

Strategy Overview

As of August 31, 2026  ·  Net of fees  ·  Base 100 at inception (October 12, 2024)

167.49
Latest NAV
Base 100
+67.49%
Total Return
Since inception
+35.75%
Ann. Return
Since inception
1.98
Sharpe Ratio
Since inception
-10.99%
Max Drawdown
January 19, 2025
18.02%
Ann. Volatility
Since inception
NAV Performance vs. Flat Benchmark
Net of fees — Base 100 on October 12, 2024
Drawdown History
Underwater curve — UCITS peak-to-valley
Rolling 12-Month Return
365-day additive return, net of fees

Returns

Performance Analysis

Annual and monthly breakdown — 2026 year-to-date through August 31

Annual Net Returns
2026 partial year (YTD to August 31)
Monthly Return Calendar (%)
17/23 positive months (74%)  ·  Green = positive  ·  Red = negative
Metric Since InceptionLast 12 Months
Total Net Return+67.49%+44.55%
Annual Return (Arithmetic, 365 Days)+35.75%+44.55%
Annualised Volatility18.02%18.45%
Sharpe Ratio1.982.44
Sortino Ratio4.14—
Calmar Ratio3.254.09
% Positive Months73.91% (17/23)—
Latest NAV (Base 100)167.49—
Strategy InceptionOctober 12, 2024—
Report DateAugust 31, 2026—
Maximum Drawdown — UCITS Peak-to-Valley
MDD : -10.99%  ·  Peak : January 19, 2025  ·  Trough : March 09, 2025
Drawdown : 35 measured days (49 cal. days)  ·  Recovery : May 07, 2025
Time to recovery : 43 measured days (59 cal. days)  ·  Full cycle : 78 measured days (108 cal. days)
Key Ratios at a Glance
Since inception vs. last 12 months

Trading Universe

Activity by Asset — 5,104 Total Trades

Medium turnover (~5 cycles/day per asset) — positions held 0.5h to 6h — loss per trade capped at 10 bps + slippage

Strategy Blueprint

How the Entropy Trading Engine Works

Six-step process from signal generation to execution — based on the 3rd law of thermodynamics (Entropy Extremum)

01
⚡ Signal Generation
Entropy Oscillator Computation
A local entropy oscillator is computed on 30-minute bars, measuring the informational complexity of the price distribution. Entropy minima indicate a directional move running out of steam; maxima indicate a trend losing coherence. Refined tick-by-tick for precision.
02
🔬 CCI Confirmation
Dynamic CCI Cross-Validation
Local dynamic CCIs (mean-reversion) are computed simultaneously. Short signal: entropy minimum + CCI maximum (overbought). Long signal: entropy maximum + CCI minimum (oversold). Both conditions must be met for a potential trade.
03
🔎 Cycle Filtering
Market Direction Validation
Trade, stop-loss and take-profit are sent simultaneously. If the market moves in the expected direction, the entropy cycle is confirmed and the trade is filled. If not, the cycle is discarded at zero cost. This high-level filtering eliminates most false positives before capital is ever deployed.
04
🎯 Profit Management
Dynamic Take-Profit Target
The profit target is recalculated dynamically each period, based on the non-linear variation of market variance. This allows the strategy to adapt to changing volatility regimes rather than relying on fixed profit levels.
05
🛡 Risk Control
Strict Stop-Loss Architecture
Stop-loss is set at exactly 10 bps + slippage on every trade, every time — no exceptions. A trailing stop dynamically follows the expected profit target to lock in gains as the trade progresses in the desired direction.
06
📡 Reporting
Real-Time JSON & Email Alerts
An instant JSON file is generated at every cycle and backed by email notification. Full daily performance reports are sent automatically. Available on BTC, ETH, BNB, SOL, XRP — with SX5E, SPX, Gold, Copper, CDS, VIX on demand.

Market Intelligence

Current Entropy Readings

As of August 31, 2026  ·  0 = Maximum Calm (fully ordered)  ·  1 = Maximum Stress (maximum disorder)

Crypto Entropia Score
0.5257
NEUTRAL — Mixed signals
Entropy Product (Cross-Asset)
0.3736
NEUTRAL — Moderate cross-asset stress
Global Market Entropia Score — Dynamic Exposure Factor
Daily exposure multiplier applied to trade allocation  ·  Average: 0.3901  ·  Current: 0.3900
Entropy in Crypto Markets — The Thermodynamic Logic
Why the 3rd law of thermodynamics predicts market turning points

In thermodynamics, entropy extrema mark transitions between states. Applied to financial markets: when informational entropy reaches a local minimum, the market distribution is becoming unnaturally ordered — a directional trend is consuming its own energy and a reversal becomes statistically probable. When entropy reaches a local maximum, disorder peaks and the prevailing trend loses its structural coherence.

These entropy extrema are calculated on 30-minute windows, then refined tick-by-tick. Combined with dynamic CCI readings, the system identifies only the highest-conviction turning points — and still requires the market to confirm direction before any capital is deployed. In this Plus variant, the global market Entropia Score further modulates each trade's allocation: high cross-asset entropy (calm) amplifies exposure while stress episodes automatically reduce it.

Research

Scientific Foundation

The thermodynamic and information-theoretic basis of entropy-driven intraday trading

Shannon Entropy & the Extremum Principle

Shannon (1948) formalised entropy as a measure of informational complexity. The strategy directly applies this to price-return distributions:

H(X) = − Σ pᵢ · log₂(pᵢ)
  • Entropy minimum: The distribution is becoming maximally concentrated — directional momentum is self-exhausting. The probability of continuation drops sharply.
  • Entropy maximum: The distribution is maximally disordered — trend coherence breaks down. Reversal becomes statistically dominant.
  • Tick-by-tick refinement: After detection on 30-minute bars, entropy is recalculated at the tick level for expected profit and stop-loss precision.
  • CCI synergy: Dynamic CCI confirms the overbought/oversold state that the entropy oscillator flags — requiring both signals to align reduces false positives significantly.

Risk & Execution Architecture

The strategy operates within a three-layer risk framework that bounds losses before any execution decision is made:

  • Layer 1 — Signal filtering: Entropy extremum + CCI confirmation required. Most potential signals are eliminated at this stage.
  • Layer 2 — Cycle validation: The market must confirm the expected direction before the trade fills. Unconfirmed cycles are discarded at zero cost.
  • Layer 3 — Hard stop: Stop-loss fixed at 10 bps + slippage on every trade, without exception. Trailing stop locks in gains dynamically as profit develops.
  • Position duration: Strictly 0.5h to 6h. No overnight exposure. Capital is never left at risk across session boundaries or during low-liquidity windows.
  • Dynamic profit: Take-profit is recalculated each period from the non-linear market variance model — adapting to current regime rather than fixed targets.
Dimension Traditional Crypto Strategies EntropiaTech Entropy Engine
Signal basisPrice patterns, momentum, RSIEntropy extrema (thermodynamic)
ConfirmationSingle indicator or noneDynamic CCI + entropy oscillator
Trade validationThreshold-based entryMarket-confirmed cycle (dual-layer)
Max loss per tradeVariable / discretionaryFixed 10 bps + slippage
Profit targetFixed pip / R-multipleDynamic non-linear variance model
Overnight riskOften presentZero — max 6h position duration
ReportingDaily or T+1Real-time JSON + email per cycle

Key Scientific References