AI-native
hedge fund
Multi-agent systems that discover, validate, and backtest trading strategies autonomously. No hand-coded rules.
How it works
From raw data to validated strategies
A four-stage pipeline powered by specialized AI agents working under a meta-orchestrator.
Strategy Generation
AI agents autonomously explore market data to discover novel trading strategies across any symbol or asset class.
Risk & Bias Check
Dedicated validator and bias-checker agents stress-test every hypothesis for statistical rigor and overfitting.
Deterministic Engine
Realistic backtests with slippage, fees, and position limits. No look-ahead bias. Reproducible results.
Strategy Store
Validated strategies are cataloged, versioned, and ranked. Ready for allocation review or live deployment.
Rigor over volume
Most strategies don't make it
Our multi-agent validation pipeline rejects the vast majority of hypotheses. That's by design.
Broad autonomous exploration across asset classes
Statistical rigor and signal quality gates
Overfitting, look-ahead bias, drawdown limits
Deterministic backtest passed, versioned & cataloged
Transparency
Multi-agent validation in action
Every hypothesis must achieve unanimous approval from independent agents. This is a sample conversation showing the validation flow.
Reviewing hypothesis #2847 -- 5-day mean reversion on mid-cap equities.
Preliminary signal quality confirmed. Metrics exceed minimum thresholds for validation.
Cross-validated against existing portfolio. Low correlation confirmed -- minimal overlap. Approved.
Max drawdown within configured limit. Position sizing within bounds. Approved.
No look-ahead bias detected. Out-of-sample results consistent with in-sample. Approved.
Deterministic backtest complete. Post-cost metrics within bounds. Strategy stored and versioned.
Methodology
Every test we run, visible
Full transparency on validation criteria. No black boxes. Every strategy must clear every gate before it can be stored.
Statistical Rigor
- Walk-forward validation across multiple windows
- Out-of-sample stability requirements
- Minimum sample size thresholds enforced
- Multiple hypothesis testing correction
- Regime-conditional performance decomposition
Bias Detection
- Look-ahead bias detection in feature engineering
- Survivorship bias testing on universe construction
- Data snooping prevention via hold-out sets
- Selection bias checks on strategy filtering
- Overfitting detection via complexity penalties
Risk Constraints
- Maximum drawdown limits per strategy
- Position concentration rules by sector and name
- Cross-strategy correlation thresholds
- Liquidity constraint enforcement
- Leverage and margin requirement compliance
Backtesting Integrity
Honest backtests, realistic results
We model transaction costs, slippage, and position limits from day one. Our metrics are post-cost, not theoretical.
- Historical prices only
- No transaction costs modeled
- Perfect execution assumed
- Unlimited position sizes
- Single backtest window
- Market impact modeling
- Bid-ask spread and slippage costs
- Realistic position size constraints
- Walk-forward out-of-sample testing
- Regime-conditional decomposition
Risk Management
Three layers of control
Institutional-grade risk management, not an afterthought. Every trade passes through three independent control layers.
Strategy-Level Controls
- Max drawdown limits per strategy
- Position size constraints
- Holding period guardrails
Portfolio-Level Controls
- Cross-strategy correlation checks
- Concentration limits by sector
- Beta exposure constraints
Execution-Level Controls
- Slippage modeling & simulation
- Liquidity constraint enforcement
- Real-time order book analysis
Discipline
What we deliberately avoid
Knowing what not to do is as important as knowing what to build. Constraints create credibility.
Not our advantage. We focus on deeper, research-driven alpha.
Can't backtest honestly without reliable price data and volume.
Risk management is a priority, not a constraint to optimize around.
Every decision is traceable through the multi-agent audit log.
Instead, we commit to
Why Radius
Traditional quant vs. AI-native
Built from the ground up as an AI-native system, not a traditional quant stack with LLMs bolted on.
| Dimension | Traditional Quant | Radius Labs |
|---|---|---|
| Strategy Discovery | Human researchers, quarterly updates | Autonomous agents, continuous 24/7 |
| Hypothesis Testing | Manual backtests, single analyst review | Multi-agent consensus validation |
| Bias Detection | Periodic review, often post-hoc | Real-time bias checking on every hypothesis |
| Execution & Backtesting | Often ignores slippage and fees; manual order routing | Deterministic backtests with costs, slippage modeling, and automated execution pipelines |
| Adaptation Speed | Weeks to months for new strategies | Continuous discovery, minutes to validate |
| Scalability | Limited by team size | Scales with compute, symbol-agnostic |
Strategy Discovery
Hypothesis Testing
Bias Detection
Execution & Backtesting
Adaptation Speed
Scalability
Context
The evolution of quant finance
Four decades of quantitative innovation, each building on the last. We're at the frontier.
Statistical Arbitrage
Pairs trading and mean reversion pioneers at DE Shaw, Medallion fund inception
Factor Models
Fama-French factors, systematic momentum, first generation of quantitative strategies
Machine Learning
Gradient boosting, random forests, feature engineering applied to alpha generation
Deep Learning
Neural networks for signal extraction, NLP on earnings calls, alternative data boom
Multi-Agent AI Systems
You are hereAutonomous agent orchestration for end-to-end strategy discovery, validation, and execution
Institutional Readiness
Built for institutional capital
We understand what allocators need. Our infrastructure roadmap is designed for institutional-grade deployment.
Roadmap
From research to live execution
Validators & Backtesting
Multi-agent strategy discovery and validation with a deterministic backtest engine. Symbol-agnostic design and cost control.
Alternative Data & Signal Generation
Sourcing and integrating alternative data for signals generation: patents, FDA approvals, government contracts, satellite imagery, sentiment analysis, and more.
Live Execution
Paper trading, then live deployment with position management, real-time risk monitoring, and continuous strategy refinement.
Interested in what we're building?
We're looking for LPs, allocators, and technical partners who share our conviction in AI-native finance.