A proprietary autonomous FX HFT system combining Meta-Adaptive Walk-Forward Architecture, JEPA self-supervised representation learning, and hierarchical XGBoost ensemble decision layers. Operating under a Saint Vincent & Grenadines registered company. Target vehicle: Luxembourg AIFM-regulated hedge fund.
Did you want to own a hedge fund in a prestigious jurisdiction but lacked the know-how or capital?
Give yourself Christmas 2026.
We are offering a 20% equity stake in a Luxembourg-registered hedge fund targeting USD 1 billion AUM by 2030 — including a seat on the board of directors. MYTHOS V5.0 executes 50–100 trades per day with a walk-forward win rate of 60% and net R:R ≥ 1:2 after all fees. At 70 trades/day, 0.4% risk per trade, monthly compounding multiplier = 3.39×. A €5,000 seed account reaches over €1,000,000 by Christmas 2026 — covering all Luxembourg HF founding costs and two years of operations.
- 1Sign NDA + partner agreement (MR. BROKER LTD., Saint Vincent & Grenadines, reg. 21824IBC2013 — already operational)
- 2Contribute to Vast.ai GPU compute auction: €8,000 — funds V5.0 full training run (240 JEPA encoders, multi-seed WFA validation, 7-node RTX 5090/4090 GPU cluster)
- 3Developer tooling & AI research credits (claude.ai): €2,000 — architecture iteration, code verification, scientific documentation. Development of this system to date has already cost tens of thousands of euros.
- 4Open RoboForex prime broker trading account: minimum €5,000 — segregated seed capital; negotiated 1 USD round-trip commission per lot (100,000 notional) — 5–15× cheaper than retail. Your capital, traded by MYTHOS V5.0.
- ★Result: By Christmas 2026, live HFT trading has grown your €5,000 to €1,000,000+. You hold 20% equity + board seat in a fund targeting €1 billion AUM by 2030.
Strategic partnership — not a startup pitch: The FX28 MYTHOS architecture represents tens of thousands of euros in engineering, GPU compute, AI tooling, and financial data infrastructure already invested. The technology is built. WFA-validated models exist. The GPU cluster is running. We are inviting co-investment in the commercialisation pathway only.
Live & Walk-Forward Performance — Documented Results
7 WFA folds PASSED | SL=2.5 pip / TP=5.0 pip
| Metric | Value |
|---|---|
| WFA Win Rate | 44–47% |
| R:R Ratio | 1:2 |
| OOS Folds Passed | 7 / 10 |
| Best OOS Return | +2,493% |
3 WFA folds PASSED | SL=2.0 pip / TP=4.0 pip
| Metric | Value |
|---|---|
| WFA Win Rate | 51–55% |
| R:R Ratio | 1:2 |
| OOS Folds Passed | 3 / 5 |
| Best OOS Return | +1,349% |
HFT Compounding Math — Why €5,000 Becomes €1,000,000+ in 8 Months
WFA OOS performance does not guarantee future results. Maximum drawdown in validated WFA periods: 8–14%. HFT compounding projection uses geometric (log-normal) expectation. Actual results vary with execution, slippage, and market conditions.
Equity Curve — €5,000 Seed, HFT Compounding (3.39× monthly)
MYTHOS V5.0 — Five-Level Architecture
Architecture Components — Scientific Foundations
Tick bars (Fibonacci T2–T55, Kagi K03–K20, Range R05–R50, 3-Line Break) sample on market information events — not on clocks. Each bar represents equal information content, producing near-i.i.d. returns that dramatically improve ML accuracy. JEPA predicts in latent space — not raw price values — forcing encoders to learn deep structural relationships.
Feature families: TD Sequential (24-dim), Johansen-Kalman (20-dim), Price Patterns (32-dim), Cross-Asset (8-dim).
[LeCun 2022] JEPA; [Assran et al. CVPR 2023] I-JEPA; [Bardes et al. ICLR 2022] VICReg; [Gu & Dao NeurIPS 2023] Mamba; [Easley et al. 2012] Tick bars
Classical WFA uses fixed IS/OOS window sizes — suboptimal as market regimes change. V5.0's XGBoost+LightGBM meta-learner is trained on 1.2 million historical WFA experiment outcomes. It learns what window sizes, model families, and regime filters produce the best OOS results — and applies this knowledge adaptively to every new training run.
[Bailey & López de Prado 2014] Deflated Sharpe; [Chen & Guestrin KDD 2016] XGBoost; [Ke et al. NeurIPS 2017] LightGBM
With K orthogonal sub-models per pair: S_portfolio = √K × S_individual. At K=16 sub-models (each Sharpe 0.8): portfolio Sharpe = 3.2 — institutional grade. Orthogonality enforced via subspace projection during joint training.
[Markowitz 1952] Portfolio theory; [Lo 2002] Sharpe ratio analysis
BurstRegimeAnalyzer: 2-state Gaussian HMM on OOS P&L. State 0 = HIGH-EDGE (trade), State 1 = low-edge (pause). Filterability gate: ΔAUC ≥ 1.0pp, τ_half ≥ 60 bars, AUC ≥ 0.62, Cohen's φ ≥ 0.40.
SPRT Champion-Challenger (Wald 1947): a challenger model is only promoted to live trading when it statistically beats the champion at α=β=5% — eliminating false model transitions caused by noise.
[Wald 1947] Sequential Analysis; [Baum & Petrie 1966] HMM; [Hamilton 1989] Regime-switching
Fixed-pip stops are suboptimal: low-vol sessions need tighter stops, high-vol needs wider. KalmanSLTPPredictor maintains state [ATR_estimate, vol_trend] updated every bar, outputting adaptive sl_pips / tp_pips per trade. Targets R:R ≥ 1:2 net after commission in all regimes.
[Kalman 1960] Optimal filtering; [Kelly 1956] Information rate; [Chan 2013] Adaptive stops
| Metric | V4.1 | V5.0 |
|---|---|---|
| WFA windows | Fixed | Meta-adaptive |
| Sub-models / pair | 1 | K=10–20 |
| Portfolio Sharpe | ~0.8 | ~3.2 (K=16) |
| SL/TP | Fixed pips | Kalman-adaptive |
| Pairs | 2 (EUR/GBP) | 28 FX pairs |
Luxembourg Hedge Fund — Structure & Costs
Luxembourg is the world's second largest fund domicile (€5.6 trillion AUM). The AIFM framework provides: full EU investor passport (30 EEA countries), institutional credibility required by pension funds and family offices, tax efficiency, and a mature fund administration ecosystem. A Luxembourg AIFM structure is the gold standard for a regulated, scalable trading vehicle.
One-Time Launch Costs
| Item | Cost Range | Notes |
|---|---|---|
| Legal structuring & AIFM application | €15,000–35,000 | PPM, fund prospectus, Articles of Incorporation |
| CSSF regulatory registration | €5,000–12,000 | Commission de Surveillance du Secteur Financier |
| Depositary / Custodian setup | €8,000–20,000 | Required under AIFMD; Luxembourg bank or specialist |
| Fund administrator setup | €5,000–15,000 | NAV calculation, investor register, reporting |
| Audit (first year) | €8,000–18,000 | Big 4 or recognised Luxembourg auditor |
| Technology / prime broker integration | €2,000–5,000 | FIX API, risk reporting, MT5/API |
| TOTAL LAUNCH (minimum) | €43,000–105,000 | Covered by trading profits by month 2 |
Annual Operating Costs
| Item | Annual Cost | Notes |
|---|---|---|
| Fund administrator | €15,000–30,000 | NAV, regulatory filings, reporting |
| Depositary / custody | €12,000–25,000 | ~0.1–0.25% of AUM with minimums |
| Audit & compliance | €8,000–18,000 | Statutory audit + ongoing AIFM compliance |
| Legal maintenance | €5,000–12,000 | Regulatory updates, investor queries |
| Technology infrastructure | €3,000–8,000 | GPU compute, APIs, data feeds |
| Annual running costs | €43,000–93,000/yr | Self-funded from management fees at €5M+ AUM |
| 2-year total (launch + ops) | €129,000–291,000 | Fully covered by trading profits before month 4 |
At 70 trades/day, 60% WR, 1:2 R:R net, 0.4% risk/trade (Kelly-conservative): monthly multiplier 3.39×. The €5,000 seed exceeds €100k (LUX minimum) by month 2, covers all 2-year operating costs by month 3, and reaches €1.85M by Christmas 2026. All from the investor's segregated RoboForex account.
Now: MR. BROKER LTD. (SVG, reg. 21824IBC2013) — operational, IP ownership, pilot trading.
2027: Luxembourg AIFM registered with auditable 12-month live track record.
2027+: EU passport, institutional investor distribution, board governance formalised.
Investment Terms — What You Get
You Contribute (Total min. €15,000)
| Item | Amount |
|---|---|
| Vast.ai GPU compute (V5.0 training) | €8,000 |
| Claude.ai developer tooling | €2,000 |
| RoboForex seed account | min. €5,000 |
| Total | min. €15,000 |
You Receive
- ★20% equity stake in Luxembourg AIFM hedge fund — founding partner terms, locked before launch
- ★Board of Directors seat — strategic oversight of fund direction and risk
- ●20% of management + performance fees on your equity share
- ●Full transparency — real-time P&L, WFA validation reports, model dashboards
- ●Your €5,000+ seed capital remains in your segregated account throughout
AUM Growth — Your 20% Stake Value at Scale
| AUM Milestone | Mgmt Fee (2%) | Perf. Fee (20%@10%) | Your 20% / Year | Stake Value (10× multiple) |
|---|---|---|---|---|
| €5M (2027) | €100k | €100k | €40k/yr | €400k |
| €50M (2028) | €1M | €1M | €400k/yr | €4M |
| €200M (2029) | €4M | €4M | €1.6M/yr | €16M |
| €1B (2030) | €20M | €20M | €8M/yr | €80M |
Execution Roadmap
Scientific References
| # | Authors | Title / Venue | Application in MYTHOS V5.0 |
|---|---|---|---|
| [1] | LeCun, Y. (2022) | A Path Towards Autonomous Machine Intelligence | JEPA architecture — latent-space prediction |
| [2] | Assran et al. (CVPR 2023) | Self-Supervised Learning with I-JEPA | I-JEPA implementation for 240 encoder streams |
| [3] | Bardes, Ponce, LeCun (ICLR 2022) | VICReg | Anti-collapse regularisation |
| [4] | Gu & Dao (NeurIPS 2023) | Mamba: Linear-Time Sequence Modeling | O(n) tick-stream encoder backbone |
| [5] | Chen & Guestrin (KDD 2016) | XGBoost | Multi-head decision layer; meta-learner |
| [6] | Ke et al. (NeurIPS 2017) | LightGBM | Meta-WFA learner joint ensemble |
| [7] | Easley, López de Prado, O'Hara (2012) | The Volume Clock | Tick bar information-theoretic foundation |
| [8] | Kalman (1960) | A New Approach to Linear Filtering | KalmanSLTPPredictor; hedge ratio estimation |
| [9] | Johansen (1988) | Statistical Analysis of Cointegration Vectors | Cross-pair cointegration alpha (JH confluence) |
| [10] | Wald (1947) | Sequential Analysis | SPRT Champion-Challenger model promotion |
| [11] | Baum & Petrie (1966) | HMM Statistical Inference | BurstRegimeAnalyzer 2-state HMM |
| [12] | Bailey & López de Prado (2014) | Deflated Sharpe Ratio | WFA bias correction; meta-learner objective |
| [13] | DeMark (1994) | The New Science of Technical Analysis | TD Sequential 24-dim feature family |
| [14] | Kelly (1956) | A New Interpretation of Information Rate | Position sizing; optimal fraction calculation |
| [15] | Markowitz (1952) | Portfolio Selection | Orthogonal sub-model portfolio construction |
| [16] | Hamilton (1989) | Nonstationary Time Series | Markov regime-switching; BurstRegimeAnalyzer |
| [17] | Gatev, Goetzmann, Rouwenhorst (2006) | Pairs Trading | Cross-asset (CA) confluence feature design |
Richmond Hill Road, Kingstown
Saint Vincent and the Grenadines, VC0100
To proceed: contact us to sign NDA, review partner agreement, and confirm allocation. Minimum commitment: €15,000 (€8k GPU + €2k tooling + €5k seed account). Founding partner slots are limited. Board seats are available to first-round participants only.
Disclaimer: This document is a private placement memorandum distributed to qualified investors only. It does not constitute a public offer of securities. Past walk-forward OOS performance does not guarantee future trading results. All projected returns are based on documented WFA backtests and mathematical compounding — they are illustrative, not guaranteed. FX trading involves substantial risk of loss. The Luxembourg hedge fund has not yet been registered with CSSF — this describes the intended pathway. Recipients should seek independent legal and financial advice before committing capital.
Confidentiality: Strictly confidential. Do not reproduce or distribute without written consent. © 2026 MR. BROKER LTD. | [email protected]
