Strategy Engineering
Workbench
From research intent → reproducible experiments → paper/live deployment.
A workflow-first studio for systematic teams. Draft strategies in natural language, compile into versioned workflows, sweep systematically with OPUS (M×N), and ship with evidence—without turning your repo into a script graveyard.
View Sample Evidence PackAnalysis complete. Evidence Pack generated with walk-forward validation, sensitivity analysis, and cost impact assessment.
Note: Demo outputs are illustrative. Your results depend on data, assumptions, costs, and execution constraints.
Every run has a Run ID: dataset version, parameters, workflow version, environment.
Plugin interfaces for data, factors, signals, execution, storage.
Paper → Live separation, risk modules, approvals and audit logs.
Batch backtests, parallel sweeps, OPUS (M×N) experiment matrices.
Traditional Quant R&D Breaks at Scale
- Data sources don't agree on schema or timing
- Factor research and backtests live in different tools
- Optimization is untracked; overfitting slips in quietly
- Script sprawl becomes the "system"
The Unified Studio Workflow
- Data → Features → Signals → Execution, in one flow
- Versioned experiments and reviewable results
- Evidence Packs by default (robustness, sensitivity, cost impact)
- A clear path from research → paper → live
Four Core Modules
A practical operating system for strategy engineering.
Data Engine
Unified ingestion with cleaning, alignment, resampling, and schema standardization.
Factor Lab
Build reusable factor assets from indicators, alternative data, and composites.
Strategy Studio
Define entry/exit, position sizing, and risk rules—visually or via config.
Backtest & Optimization
One-click backtests, batch sweeps, automatic reports, and strategy asset versioning.
Systematic Process
A systematic process from idea to deployment.
All steps record data versions, parameters, metrics, and environment—so results can be reproduced and reviewed.
All steps automatically record data versions, parameters, metrics, and environment for full reproducibility.
Core Capabilities
AI-Assisted Research
- Draft workflows from intent
- Suggest parameter ranges (with constraints)
- Summarize drawdown structure and failure modes
Experiment Management
- Versioned backtest cards
- A/B comparisons and diffs
- Re-run any result from Run ID
Overfitting Controls
- Multi-split validation (WFO)
- Sensitivity checks (spike vs plateau)
- Cost impact and fragility flags
Production Readiness
- Paper → Live separation
- Pluggable risk modules
- Audit logs & rollbacks (team/enterprise)
Who It's For
Individual Quants
Ship cleaner research. Keep your work reproducible.
Small Quant Teams
Standardize R&D, reduce rework, and review results consistently.
Institutional Research
Auditable, controlled deployment with permission governance.
Crypto / Multi-source
Unify exchange feeds with on-chain and alternative data in one pipeline.
Architecture & Integrations
Modular Architecture
Decoupled components. Clear interfaces.
Plugin Interfaces
Data, factors, signals, storage, execution.
Deployment Options
Cloud · On-prem · Hybrid
Security
Encrypted storage, scoped access, secrets handling.
Observability
Logs · metrics · alerts
Boundaries: Not designed for tick-level HFT matching replication; assumptions are declared per run.
Early Access Plans
Developer
Local backtesting, basic factor + strategy build, report export
- Local backtesting
- Basic factors & strategy building
- Report export
Team
Collaboration, permissions, experiment comparison, shared asset library
- Collaboration & permissions
- Experiment comparison
- Shared asset library
- Priority support
Enterprise
Private deployment, audit/compliance, SLA options, custom connectors
- Private deployment
- Audit & compliance
- SLA guarantee
- Custom data/execution
Frequently Asked Questions
What is the relationship between CorrAI and Quant Studio AI?
CorrAI is the platform. Quant Studio AI is the strategy R&D workbench under CorrAI.
Does it support custom and third-party data?
Yes. Bring your own market/on-chain/alternative data into the Data Engine with schema/version management.
Can it achieve research-to-production consistency?
The same Strategy Spec can run across backtest, paper, and live—with declared assumptions and execution constraints.
How does it reduce overfitting?
Walk-forward validation, stability checks, sensitivity analysis, and robustness tests—solidified as evidence in strategy assets.
Build one workflow. Review the evidence. Decide with confidence.
Request early access for reproducible, auditable quantitative research.
Request Early Access