Algorithmic Crypto Asset Management · Pre-Launch

Open
Performance,
Closed Alpha.

ArchitectorQuant is a systematic algorithmic strategy for crypto markets, built on professional research methodology. Every trade is verifiable. The methodology is engineered for survival across market regimes. The strategy itself stays proprietary — that's the edge.

Get Launch Updates See the Approach

Pillar I

Open Performance

Every trade is recorded with timestamps, fills, slippage, and PnL. Live verification is available via exchange API and on-chain proofs once trading goes public. No edited screenshots, no curated metrics, no missing months.

Pillar II

Closed Alpha

The strategy logic, signal weights, and feature engineering remain proprietary. Disclosing them destroys the edge. What is shared publicly: methodology class, risk framework, and verifiable results.

Why This Asymmetry

What's Open,
What's Closed

In professional asset management this division is standard. Renaissance discloses audited returns; the Medallion strategy is locked. ArchitectorQuant applies the same logic at smaller scale: investors see results, the model stays guarded.

FULLY OPEN

REMAINS PROPRIETARY

→ Trade-by-trade ledger

× Specific signal definitions

→ Realized PnL, fees, slippage

× Feature weights and thresholds

→ Drawdown curve, monthly returns

× Ensemble model structure

→ Risk framework (this page)

× Regime classification logic

→ Methodology class

× Strategy parameters

Verifiable Performance

Track
Record

The system is in pre-launch validation. Live track record begins with funded prop-firm accounts in 2026 and continues through public deployment. Stats below populate once live trading starts. Backtests will be clearly labelled as backtests, separate from live results.

System Status — Pre-Launch Validation

Live Since

—

Realized Sharpe

—

Max Drawdown

—

Total Trades

—

Past performance does not guarantee future results. All performance data, once published, will link to verifiable sources — exchange API endpoints or on-chain transaction proofs.

The Approach

Methodology,
Not Predictions

Most retail crypto strategies fail not because of bad ideas but because of bad methodology — lookahead bias, multiple-testing inflation, uncalibrated confidence, naive sizing. ArchitectorQuant is built on the methodology class used by professional quant funds.

M.01 Regime Detection

Three parallel Hidden Markov Models at 5m / 15m / 1h timeframes deliver consensus posterior probabilities. The system trades only when regime composition aligns with the strategy's edge profile.

M.02 Signal Discovery

Hundreds of candidate features run through walk-forward validation with Benjamini-Hochberg FDR control plus correlation pruning. Output: a small set of orthogonal signals that survive multiple-testing scrutiny.

M.03 Probability Calibration

Ensemble outputs pass through isotonic calibration before any sizing decision. A predicted 60% win rate must mean an actual 60% win rate, not 45%. Without this, fractional Kelly is meaningless.

M.04 Honest Validation

Sealed test sets. No re-tuning on holdout. Backtest Sharpe must survive realistic slippage and replication within ±15% — otherwise the strategy doesn't ship. The graveyard of discarded ideas is part of the discipline.

Capital Preservation

Six-Layer
Risk Framework

Returns matter, but survival matters more. Every position passes through six independent risk layers before sizing is finalized. A single layer failing the position kills it — no overrides.

LAYER 01

Empirical Kelly

Position size derived from historical win-rate and average R-multiple of the strategy in the current regime — not a theoretical optimum.

LAYER 02

Fractional Cap

Maximum exposure capped at 25% of full Kelly. Reduces volatility and protects against estimation error in win-rate inputs.

LAYER 03

Regime Confidence

Position size scales with HMM regime certainty. Mixed regimes — where multiple states have similar probability — get systematically smaller positions.

LAYER 04

Correlation Penalty

Cross-strategy correlation reduces total exposure when multiple strategies want similar trades. Prevents hidden concentration risk.

LAYER 05

Drawdown Scaling

As realized drawdown approaches risk budget, position size shrinks automatically. The system de-leverages itself.

LAYER 06

Hard Caps & Kill-Switch

Absolute exposure caps per asset and per portfolio. Automated kill-switch on drawdown breach, regime drift, or calibration failure.

Path to Public Capital

Roadmap

Q2 2026

Architecture & Contracts

Layer 0 system foundations: protocol interfaces, immutable invariants, MarketState schema, trade-record logging, research-journal framework.

Q3–Q4 2026

Data Pipeline & Regime Models

Canonical Parquet store, multi-TF HMM validated, Volume Profile zones integrated. Feature matrix expanded across all timeframes with versioned migrations.

Q1-Q2 2027

Signal Factory & Ensemble

Mass signal generation with FDR + correlation filtering. Logistic Regression + XGBoost ensemble with isotonic calibration. Sealed walk-forward validation on 2025 holdout data.

Q3–Q4 2027

Live Validation — Personal Capital

Paper trading on Binance testnet, then live trading with own capital at minimum size. Establishing structural backtest-live parity. Building first verifiable track record.

Q1 2028

Prop-Firm Funded Capital

Velotrade / HyroTrader evaluation. Funded accounts as capital scaling without external investor risk. Public track record continues to accrue.

Q2 2028

Public Vehicle Launch

Public access to the strategy through a regulated channel — exchange copy-trading program, on-chain vault, or registered investment vehicle, depending on jurisdictional review. Fully verifiable performance, transparent fee structure.

2028+

Multi-Strategy Portfolio

Adding mean-reversion → momentum → funding-arbitrage → volatility-harvesting strategies through the same eight-layer pipeline. Each strategy validated independently before joining the portfolio.

Cloud Infrastructure

Built on
Hyperscaler-Grade Cloud

Quantitative research is compute-heavy and reproducibility-critical. ArchitectorQuant runs research workloads on AWS as primary cloud, with Google Cloud as secondary research environment.

AWS EC2 AWS S3 AWS Bedrock AWS SageMaker AWS Lambda RDS PostgreSQL Google BigQuery Google Vertex AI Google Cloud Run Python 3.11 XGBoost hmmlearn scikit-learn Apache Parquet Docker Terraform

Operator

Built by
One Operator

[Bondariev Oleksii]

Founder · Quantitative Research · Engineering

Quantitative researcher and software engineer building ArchitectorQuant since 2026. Eight-layer architecture, multi-timeframe HMM regime detection, calibrated probabilistic ensembles, six-layer risk framework — all engineered as a single coherent system. ArchitectorQuant is intentionally a one-operator operation: small surface area, single point of accountability, no committee dilution of risk discipline. Every line of trading logic, every parameter, every risk threshold sits with one person.

Get Launch Updates

ArchitectorQuant goes live in stages through 2026–2027. Sign up to receive milestone updates — backtest results, prop-firm evaluation outcomes, and the public-launch announcement when verifiable track record begins.

No spam. Milestone-only updates. Unsubscribe anytime.

Important Disclaimer

ArchitectorQuant is an algorithmic strategy in pre-launch validation. The information on this page does not constitute an offer to sell securities, an offer to manage assets, or financial advice. Nothing here is a guarantee or projection of future returns. Past performance, when published, will not guarantee future results.

Cryptocurrency trading involves substantial risk of loss and is not suitable for every investor. Any future public capital arrangement will be operated only in compliance with applicable regulations of the relevant jurisdiction and will be offered through a regulated channel.

Open Performance, Closed Alpha.