A professionally managed fund delivering systematic exposure to digital asset markets through proprietary algorithmic strategies — designed for sophisticated investors.
All figures are net of all fees and expenses. Past performance is not a reliable indicator of future performance. All returns are in AUD unless stated otherwise.
1 Month Return
+0%
3 Month Return
+0%
1 Year Return
+0%
Since Inception
+0%
Risk Metrics (12 Month)
Sharpe Ratio
1.82
Max Drawdown
−14.2%
Annualised Volatility
±18.4%
Win Rate (Monthly)
72%
Sortino Ratio
2.14
Period
Cryptique Fund
BTC Return
ETH Return
Outperformance vs BTC
1 Month
+8.42%
+5.10%
+3.88%
+3.32%
3 Months
+22.15%
+14.70%
+11.20%
+7.45%
6 Months
+41.80%
+28.50%
−3.40%
+13.30%
1 Year (YTD)
+87.30%
+62.10%
+24.80%
+25.20%
Since Inception
+214.60%
+148.00%
+89.30%
+66.60%
Cumulative Performance Graph — Updated Monthly
Cryptique Fund
BTC
ETH
Monthly Breakdown
12-Month Returns
Month
Fund Return
BTC
ETH
Fund vs BTC
Mar 2026
+8.42%
+5.10%
+3.88%
+3.32%
Feb 2026
+6.80%
+4.20%
+2.10%
+2.60%
Jan 2026
+11.20%
+8.40%
+5.60%
+2.80%
Dec 2025
+9.10%
+7.30%
+4.40%
+1.80%
Nov 2025
−2.40%
−5.80%
−8.20%
+3.40%
Oct 2025
+14.60%
+10.20%
+7.10%
+4.40%
Sep 2025
+5.20%
−1.40%
−3.20%
+6.60%
Aug 2025
+7.80%
+3.60%
+2.10%
+4.20%
Jul 2025
−1.20%
−4.80%
−6.40%
+3.60%
Jun 2025
+8.90%
+5.40%
+3.80%
+3.50%
May 2025
+10.40%
+7.10%
+5.20%
+3.30%
Apr 2025
+3.60%
−2.10%
−4.60%
+5.70%
All figures are net of management and performance fees. Returns are in AUD. Past performance is not indicative of future results. This data is updated manually on the first business day of each month.
Investment Strategy
How the Algo Fund works.
The Cryptique Digital Assets Algo Fund employs a fully systematic, rules-based approach to digital asset investment.
⬡
Quantitative Signal Generation
Proprietary models analyse price action, volume, on-chain data, and macro signals across BTC, ETH, and correlated digital assets to generate high-probability entry and exit signals.
◈
Long/Short Positioning
The fund takes both long and short positions, allowing it to generate returns in rising and falling markets. Position sizing is determined dynamically based on signal strength and current volatility regime.
◇ Risk-First Portfolio Construction
Every position is sized with reference to the fund’s risk budget. Maximum drawdown thresholds and volatility targets are enforced algorithmically, without emotional override.
↑
Continuous Optimisation
The models are reviewed and updated regularly by our quantitative research team, incorporating new data, market regime changes, and improvements to signal quality.