Quantitative Research
ML architects with institutional backgrounds
VERIFIEDReal-time data. Deterministic execution. No emotion.
Oculus Quant is an autonomous quantitative trading initiative built with the standards of a modern hedge fund — powered by a network of AI agents responsible for research, monitoring, execution, and reporting. The objective is straightforward: disciplined, data-driven decision-making with verifiable performance. All results are presented with clear, auditable metrics and continuous reporting. We focus on building systems that are engineered for live markets: robust, adaptive, and consistent by design. Oculus Quant is a long-term infrastructure project — measurable, accountable, and built to scale.
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 shifts Deliver 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.
This approach ensures that reported metrics reflect complete market cycles, including periods of low liquidity, regime shifts, and elevated volatility.
By reporting on a full-year basis, we avoid short-term distortion and provide a realistic, long-term view of system behavior.
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.
Oculus Quant runs all strategies on a rolling basis, continuously updating and re-scoring edge as the execution window shifts forward. Decisions are evaluated with recency-weighted performance so the system adapts to current market structure, volatility, liquidity, and momentum while retaining long-term context for stability. In parallel, dedicated AI monitoring agents generate monthly, quarterly, and annual reports that quantify edge decay, regime drift, and execution quality (spreads, slippage, drawdown clustering), and explicitly flag whether operational action is required—such as tightening filters, reducing exposure, or pausing specific setups. Fine-tuning or retraining is treated as a last-resort intervention, only considered when these reports confirm a durable structural shift that the strategy’s built-in self-correction and rapid regime detection cannot absorb.
Risk is dynamically allocated based on strategy performance and regime quality.
Capital is increased toward consistently profitable patterns in stable regimes, and reduced when conditions deteriorate.
Volatility expansions, drawdown clusters, or regime shifts automatically trigger internal risk tightening.
The objective is controlled compounding through discipline, selectivity, and capital preservation. 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
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.
Real-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 shifts Deliver 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.
This approach ensures that reported metrics reflect complete market cycles, including periods of low liquidity, regime shifts, and elevated volatility.
By reporting on a full-year basis, we avoid short-term distortion and provide a realistic, long-term view of system behavior.
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.
Oculus Quant uses specialized LoRA models to continuously interpret market structure, volatility, and sentiment regimes.
Trade decisions are evaluated against a rolling window of the most recent 500+ executed trades, ensuring adaptation is driven by real performance, not static rules.
Recent data receives higher weight, while long-term history remains available for structural context.
This allows the system to adapt to changing market conditions without overfitting to short-term noise. 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.
Risk is dynamically allocated based on strategy performance and regime quality.
Capital is increased toward consistently profitable patterns in stable regimes, and reduced when conditions deteriorate.
Volatility expansions, drawdown clusters, or regime shifts automatically trigger internal risk tightening.
The objective is controlled compounding through discipline, selectivity, and capital preservation. 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
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.
Real-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 shifts Deliver 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.
This approach ensures that reported metrics reflect complete market cycles, including periods of low liquidity, regime shifts, and elevated volatility.
By reporting on a full-year basis, we avoid short-term distortion and provide a realistic, long-term view of system behavior.
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.
Oculus Quant uses specialized LoRA models to continuously interpret market structure, volatility, and sentiment regimes.
Trade decisions are evaluated against a rolling window of the most recent 500+ executed trades, ensuring adaptation is driven by real performance, not static rules.
Recent data receives higher weight, while long-term history remains available for structural context.
This allows the system to adapt to changing market conditions without overfitting to short-term noise. 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.
Risk is dynamically allocated based on strategy performance and regime quality.
Capital is increased toward consistently profitable patterns in stable regimes, and reduced when conditions deteriorate.
Volatility expansions, drawdown clusters, or regime shifts automatically trigger internal risk tightening.
The objective is controlled compounding through discipline, selectivity, and capital preservation. 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
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.