Quantum Oak is a systematic U.S. equity fund supported by Met Capital's proprietary AI and machine learning research. We pursue exceptional performance and the highest standards of management, research and client service.
The strategy is fully automated and long-only. It trades liquid U.S. equities through a small, fixed portfolio of equally weighted positions, each opened with a predefined profit target and stop-loss. Portfolio-level drawdown controls govern overall exposure. No shorts, options or derivatives are used.
Quantum Oak is a fund within Met Capital Ltd, the holding company for funds, managed accounts and businesses. U.S. onshore and offshore fund vehicles are supported by BNP Paribas as prime broker and custodian. Trading results and performance undergo external audit.
Dr. Sulkhan Metreveli, Met Capital's founder and principal, brings more than 15 years of work in economic modeling and information dynamics. Alongside the COO, Chief Research Officer, quant and compliance officer, he leads a team with decades of combined experience across research, markets, operations and oversight.
| Fund Name | Quantum Oak Capital |
| Manager | Quantum Oak Capital |
| Domicile | United States |
| Strategy | Systematic, long-only U.S. equities, supported by proprietary AI and machine learning research |
| Target Return | 30%+ annualized |
| Asset Universe | U.S. Stock Equities (liquid large- and mid-cap names) |
| Why U.S. Equities | Deep liquidity, tight spreads, and reliable price data going back decades — essential for the kind of statistical analysis we do |
| Research Partner | ACR Institute — Artificial Consciousness Research Institute, Zürich |
Strategies are discovered, validated and deployed through a controlled research process, then executed under fixed trading rules.
AI researchers run autonomous workstreams to discover new strategies and improve those already deployed. Discovery begins by mapping market behaviour and practical tradeability. Supervised learning tests signal filtering and position sizing; unsupervised learning identifies market regimes and asset clusters; evolutionary search explores alternative trading rules. Conventional models compete with approaches drawn from fields such as physics and biology.
Candidates are judged on weaker periods, complexity and parameter stability, using objectives and criteria fixed before experimentation. Every trial, including failures, is recorded with its parameters, outcomes and trial count. Candidates face validation, sealed test data and paper trading, with stress tests for higher costs and dependence on a single holding or year. Improvements must outperform the deployed strategy on sealed data and in paper trading.
The automated strategy seeks continuation in liquid U.S. equities moving above recent volume-based trading ranges, while excluding overextended moves. A daily pre-market cycle checks broad-market conditions before new entries and fills a small, fixed portfolio of equally weighted positions, each with a predefined profit target and stop-loss.
Real capital, real trades, since April 2025. The figures below are a static snapshot as of the report date. This document is updated quarterly. A continuously updating view of the same trading activity is available on the performance dashboard.
*As of 02 June 2026.
| Month | Quantum Oak | PSH (Ackman) | S&P 500 | Alpha vs PSH | Alpha vs SPY |
|---|---|---|---|---|---|
| Apr 2025 | +13.1% | +4.4% | +7.9% | +8.7 | +5.2 |
| May 2025 | +25.4% | +10.8% | +6.3% | +14.6 | +19.1 |
| Jun 2025 | +8.6% | +3.5% | +4.8% | +5.1 | +3.8 |
| Jul 2025 | +12.7% | -2.4% | +2.3% | +15.1 | +10.4 |
| Aug 2025 | +4.2% | +5.0% | +2.1% | -0.8 | +2.2 |
| Sep 2025 | +19.6% | +3.9% | +3.3% | +15.7 | +16.3 |
| Oct 2025 | +9.8% | -1.7% | +2.4% | +11.5 | +7.4 |
| Nov 2025 | -2.8% | +1.8% | +0.2% | -4.6 | -3.0 |
| Dec 2025 | -0.2% | -2.2% | -0.2% | +1.9 | 0.0 |
| Jan 2026 | -10.3% | -2.5% | +1.5% | -7.7 | -11.7 |
| Feb 2026 | -2.5% | -7.7% | -0.9% | +5.2 | -1.7 |
| Mar 2026 | -9.7% | -7.1% | -5.2% | -2.6 | -4.5 |
| Apr 2026 | +39.6% | +13.4% | +10.5% | +26.2 | +29.1 |
| May 2026 | +103.7% | +0.3% | +5.3% | +103.4 | +98.4 |
| Jun 2026 | +24.6% | -8.5% | -1.3% | +33.1 | +25.9 |
| Jul 2026 | -50.7% | +3.9% | 0.0% | -54.5 | -50.7 |
| Aug 2026 | +33.8% | +4.4% | +2.7% | +29.3 | +31.1 |
| Sep 2026 | +6.5% | -4.8% | -0.6% | +11.4 | +7.1 |
| Oct 2026 (MTD) | +3.2% | — | +0.9% | — | +2.3 |
| Cumulative | +367.7% | +12.2% | +49.8% | +355.4 | +317.9 |
| Current NAV (as of report date) | $4,676,703 |
| Net Profit (as of report date) | $3,676,703 |
| Starting Capital | $1,000,000 |
| Inception | 22 April 2025 |
| Report Date | 2 October 2026 |
How Quantum Oak stacks up against well-known benchmarks and actively managed funds over the same period.
| Metric | Quantum Oak | ARKK (Wood) | QQQ (Nasdaq) | S&P 500 | PSH (Ackman) |
|---|---|---|---|---|---|
| Total Return | +367.7% | +102.1% | +73.1% | +49.8% | +12.2% |
| Ann. Return | 189.5% | 62.5% | 46.0% | 32.1% | 8.3% |
| Ann. Volatility | ~73% | 37.0% | 18.4% | 12.7% | 20.9% |
| Sharpe Ratio | 1.76 | 1.50 | 2.16 | 2.27 | 0.47 |
| Max Drawdown | -59.5% | -31.4% | -12.2% | -9.1% | -18.2% |
| Calmar Ratio | 2.98 | 1.99 | 3.77 | 3.52 | 0.46 |
| Return / Drawdown | 6.2× | 3.3× | 6.0× | 5.4× | 0.7× |
| Positive Months | 13 / 19 | 11 / 19 | 13 / 19 | 14 / 19 | 10 / 18 |
| Symbol | Trades | Win Rate | P&L | % of Total |
|---|---|---|---|---|
| MU | 9 | 67% | +$1,528,815 | 41.9% |
| AMD | 9 | 78% | +$1,402,700 | 38.4% |
| TSLA | 15 | 47% | +$423,809 | 11.6% |
| NVDA | 12 | 67% | +$402,953 | 11.0% |
| QCOM | 1 | 100% | +$343,117 | 9.4% |
| AAPL | 10 | 60% | +$146,043 | 4.0% |
| INTC | 1 | 100% | +$127,120 | 3.5% |
| PLTR | 5 | 40% | +$71,258 | 2.0% |
| MSFT | 10 | 40% | -$86,644 | -2.4% |
| CAR | 4 | 0% | -$993,992 | -27.2% |
Trading discipline and drawdown controls are built into the strategy and applied automatically.
Strategies are developed through Met Capital's proprietary AI and machine learning research. Every trial is recorded, candidates are tested on sealed data and in paper trading, and research is isolated from live systems. Execution is fully automated, with deployment controlled by the CEO/CIO.
Available exclusively to accredited and qualified investors. Trading results and performance undergo external audit.
Administrator: Opus Fund Management
Prime Brokerage & Custodian: BNP Paribas
Research Partner: ACR Institute
Full documentation is available on request during due diligence.
| Minimum Investment | $1,000,000 USD / EUR / CHF |
| Management Fee | 3% annually |
| Performance Fee | 30% of profits above high-water mark — you don't pay performance fees on recovered losses |
| Lock-Up Period | 18 months — we need this time horizon for the strategy to work through full market cycles |
| Redemption | Quarterly with advance notice |
| Fund Liquidity | $500,000,000 USD capacity |
| Administrator | Opus Fund Management |
| Prime Brokerage & Custodian | BNP Paribas |
| Research Partner | ACR Institute (Zürich, Switzerland) |