Quantitative Research
ML architects with institutional backgrounds
VERIFIEDReal-time data. Deterministic execution. No emotion.
We build self-learning quantitative systems that trade Forex and crypto with no human input — powered by continuously retrained DeepSeek LLMs capable of reasoning through live market data.
Every model adapts dynamically, improving as conditions change, ensuring consistent precision and measurable performance.
All trades, metrics, and balance updates are streamed directly from our live engine, providing provable transparency and real-time accountability — every signal, every result, fully auditable.
We build fully autonomous quantitative systems that trade Forex and crypto with zero human intervention. Our live engines train, execute, and log every decision in real time so performance is provable, auditable, and continuously improving.
We design, train, and operate fully autonomous quantitative trading systems that trade Forex and crypto with zero human intervention.
In today’s constantly evolving markets, manual trading has become nearly impossible to sustain profitably.
Our mission is to empower retail traders by deploying adaptive AI systems that learn from live data, execute with discipline, and remove human bias from every decision.
Each model evolves dynamically, ensuring performance remains robust even as market behavior shiftsDeliver repeatable, measurable performance driven by data, robust models, and disciplined risk control — not opinions. Every decision is deterministic, every outcome is logged, and every result is verifiable.
Every statistic, trade and equity update on this site is sourced directly from our live execution engine and the raw CSV feeds stored in public. No simulations. No cherry-picking. All P&L, drawdowns and trade-level records are logged, auditable, and available for inspection so stakeholders can verify outcomes independently.
Our models operate under strict, pre-defined risk parameters, executing without emotion or hesitation.
Each strategy follows deterministic logic with auditable position sizing, stop-loss criteria, and compounding frameworks, ensuring long-term stability and trust.Autonomous execution eliminates human emotion and enforces consistent risk parameters across markets and conditions. Models are governed by strict position-sizing, stop criteria, and portfolio-level constraints — ensuring that every trade adheres to pre-defined, auditable risk rules.
Every three months, the system performs a structured retraining step:
-The newest 3 months of market data are added.
-The oldest 3 months are removed.
-The full historical dataset remains balanced, but always updated.
-Recent performance and fresh market dynamics gain more weight.
-Older data is still used for structural context, regime patterns, and long-term bias detection.
This creates a dynamic dataset where the model consistently learns from what is happening now without becoming blind to multi-year behavior.Our core team blends quantitative research, systems engineering, and low-latency execution. Led by veteran quant engineers and ML architects from institutional trading desks, every decision is code-first: models, risk rules, and execution are authored, tested, and deployed as deterministic systems that report their results live.
Alongside the rolling dataset, the model continuously evaluates its own trades:
-Winning patterns receive adaptive reinforcement
-Underperforming behaviors get deprioritized
-Volatility, regime shifts, and structural breaks trigger internal recalibration
-The model learns from its own mistakes — reducing the need for human intervention
Instead of rigid rules, the retraining loop creates a living system that evolves with the market while maintaining long-term consistency.Each principal holds advanced degrees in mathematics, computer science, or financial engineering and a track record of production-grade trading systems. Resumes and credential attestations are logged and checksumed alongside our system outputs so leadership actions remain auditable without compromising operational security.
ML architects with institutional backgrounds
VERIFIEDDeepSeek-based systems retrained continuously on live market data
AI-driven recognition of structural market shifts for dynamic bias control
VERIFIEDAll results sourced from live executions — no simulations or revisions
Production-quality code & probabilistic thinking
Upcoming XAUUSD, NZD, and JPY engines under development
Gained extensive experience trading Forex and crypto manually, developing a deep understanding of market behavior, volatility cycles, and execution timing across multiple asset classes.
Transitioned from simulated testing to live, low-latency execution. Introduced strict risk controls, on-chain and treasury monitoring, and continuous backtest-vs-live divergence reporting.
Transitioned from manual execution to semi-automated systems. Began testing algorithmic frameworks and experimenting with data-driven signal generation to reduce human error.
Expanded architecture to support 24/7 digital-asset markets with connectivity to major exchanges. Implemented real-time hedging logic and exchange-failover routing.
Discovered that static, rule-based systems fail to perform consistently across multiple years and market regimes — leading to the pursuit of adaptive, learning-based approaches.
The AI revolution accelerated. Began studying large-scale language models and reinforcement learning, exploring how reasoning-based systems could transform quantitative trading.
Applied AI research directly to trading — designing and training the first version of our proprietary model capable of reasoning through live data without fixed rule sets.